Download The bidirectionality of the relationship between insomnia, anxiety

The bidirectionality of the relationship between
insomnia, anxiety and depression in adolescents: A
longitudinal study
Pasquale K Alvaro
Bachelor of Psychology (Hons)
Thesis submitted for the degree of Doctor of Philosophy
School of Psychology
Faculty of Health Sciences
University of Adelaide
August 2014
Table of contents
List of Tables ................................................................................................................... vii
List of Figures .................................................................................................................. vii
Declaration ........................................................................................................................ i
Acknowledgements ........................................................................................................... ii
Abstract ............................................................................................................................ 1
Chapter 1: Introduction ..................................................................................................... 4
1.1.
Brief overview of thesis and chapter ......................................................................................4
1.2. Adolescence ..................................................................................................................................4
1.3. Sleep .............................................................................................................................................6
1.3.1. Sleep regulation: a two-process model ................................................................................................ 7
1.4. Adolescent sleep ...........................................................................................................................9
1.5. Insomnia .....................................................................................................................................10
1.5.1. Risk-factors for insomnia disorder and secondary insomnia ............................................................. 10
1.5.2. Epidemiology of insomnia .................................................................................................................. 14
1.5.2.1. Epidemiology of insomnia and gender ....................................................................................... 15
1.5.2.2. Epidemiology of insomnia during adolescence and chronicity .................................................. 17
1.5.3. Concurrent and longitudinal associations with insomnia during adolescence .................................. 18
1.6. Depression ..................................................................................................................................20
1.6.1. Risk-factors for depression ................................................................................................................. 21
1.6.2. Epidemiology of depression ............................................................................................................... 25
1.6.2.1. Epidemiology of depression and gender .................................................................................... 26
1.6.2.2. Epidemiology of depression and age .......................................................................................... 28
1.6.3. Concurrent and longitudinal associations with depression during adolescence ............................... 28
1.7. Anxiety ........................................................................................................................................30
1.7.1. Risk-factors of anxiety ........................................................................................................................ 31
1.7.2. Epidemiology of anxiety ..................................................................................................................... 33
1.7.2.1. Epidemiology of anxiety and gender .......................................................................................... 34
1.7.2.2. Epidemiology of anxiety and age ................................................................................................ 34
1.7.3. Concurrent and longitudinal associations with anxiety during adolescence ..................................... 35
1.8. The relationship between insomnia, depression and anxiety ....................................................36
1.8.1. Potential mechanisms that underlie the relationship between insomnia, depression and anxiety .. 38
1.8.1.1. Common risk-factors .................................................................................................................. 38
1.8.1.2. Cause-effect relationship............................................................................................................ 40
1.9. The bidirectionality of the relationship between insomnia, depression and anxiety ................41
1.9.1. Methodological variations across studies .......................................................................................... 45
1.9.2. Overlapping methodological issues within the literature .................................................................. 46
1.9.2.1. Chronotype ................................................................................................................................. 47
1.10. Aims of the thesis .....................................................................................................................49
1.11. Significance/Contribution to the discipline ..............................................................................50
Chapter 2: Exegesis ......................................................................................................... 53
2.1. The thesis ....................................................................................................................................53
2.2. Study 1 - A Systematic Review Assessing Bidirectionality between Sleep Disturbances, Anxiety,
and Depression ..................................................................................................................................56
2.3. Study 2 – The Independent Relationships between Insomnia, Depression, Subtypes of Anxiety,
and Chronotype during Adolescence ................................................................................................57
2.4. Study 3 – Bidirectional relationships between insomnia and depression, and insomnia and
subtypes of anxiety during adolescence: does chronotype effect these relationships? ..................59
2.5. Sample size rationale for studies 2 and 3 ...................................................................................62
2.6. Summary .....................................................................................................................................63
Chapter 3: Study 1 ........................................................................................................... 64
3.1. Abstract.......................................................................................................................................66
3.1.1. Study Objectives ................................................................................................................................. 66
3.1.2. Design ................................................................................................................................................. 66
3.1.3. Measurements and Results ................................................................................................................ 66
3.1.4. Conclusions......................................................................................................................................... 67
3.2. Introduction ................................................................................................................................67
3.3. Methods ......................................................................................................................................69
3.3.1. Data sources and study selection ....................................................................................................... 69
3.3.2. Quality assessment............................................................................................................................. 70
3.3.3. Data extraction and synthesis ............................................................................................................ 71
3.4. Results.........................................................................................................................................72
3.4.1. Included and excluded studies ........................................................................................................... 72
3.4.2. Quality assessment............................................................................................................................. 76
3.4.3. Overall synthesis ................................................................................................................................ 78
3.4.4. Synthesis of sleep variables ................................................................................................................ 84
3.5. Discussion ...................................................................................................................................87
Acknowledgments .............................................................................................................................93
Chapter 4: Study 2 ........................................................................................................... 94
4.1. Abstract.......................................................................................................................................96
4.1.1. Objectives ........................................................................................................................................... 96
4.1.2. Methods ............................................................................................................................................. 96
4.1.3. Results ................................................................................................................................................ 96
4.1.4. Conclusions......................................................................................................................................... 96
4.2. Introduction ................................................................................................................................97
4.3. Method .......................................................................................................................................99
4.3.1. Participants......................................................................................................................................... 99
4.3.2. Measurements .................................................................................................................................100
4.3.3. Procedure .........................................................................................................................................103
4.3.4. Statistical analyses............................................................................................................................104
4.4. Results.......................................................................................................................................106
4.4.1. Descriptive statistics .........................................................................................................................106
4.4.2. Depression and Insomnia .................................................................................................................109
4.4.3. GAD and Insomnia ............................................................................................................................109
4.4.4. OCD and Insomnia ............................................................................................................................110
4.4.5. Panic Disorder and Insomnia ............................................................................................................110
4.4.6. Separation Anxiety and Insomnia.....................................................................................................110
4.4.7. Social Phobia and Insomnia ..............................................................................................................111
4.5. Discussion .................................................................................................................................111
4.5.1. Limitations ........................................................................................................................................115
4.5.2. Conclusions.......................................................................................................................................117
Acknowledgements .........................................................................................................................118
Chapter 5: Study 3 ......................................................................................................... 119
5.1. Abstract.....................................................................................................................................122
5.1.1. Study Objectives ...............................................................................................................................122
5.1.2. Design ...............................................................................................................................................122
5.1.3. Settings .............................................................................................................................................122
5.1.4. Participants.......................................................................................................................................122
5.1.5. Measurement and Results ...............................................................................................................122
5.1.6. Conclusion ........................................................................................................................................123
5.2. Introduction ..............................................................................................................................124
5.3. Methods ....................................................................................................................................127
5.3.1. Participants.......................................................................................................................................127
5.3.2. Measures ..........................................................................................................................................128
5.3.2.1. Insomnia ...................................................................................................................................128
5.3.2.2. Chronotype ...............................................................................................................................129
5.3.2.3. Depression and subtypes of anxiety .........................................................................................130
5.3.3. Procedure .........................................................................................................................................131
5.3.4. Statistical Analyses ...........................................................................................................................132
5.4. Results.......................................................................................................................................136
5.4.1. Step-wise regression analyses ..........................................................................................................142
5.5. Discussion .................................................................................................................................145
5.5.1. Clinical implications ..........................................................................................................................148
5.5.2. Limitations ........................................................................................................................................151
5.5.3. Conclusions.......................................................................................................................................153
5.5. Appendix ...................................................................................................................................154
Chapter 6: Discussion .................................................................................................... 158
6.1. Summary of the results from Studies 1, 2 and 3 ......................................................................159
6.2. The Bidirectionality of the relationships between insomnia and depression, and insomnia and
subtypes of anxiety ..........................................................................................................................161
6.2.1. Bidirectionality across various age groups and cultures ..................................................................161
6.2.2. Bidirectionality across different methods of assessment ................................................................163
6.2.3. Bidirectionality and follow-up time-periods ....................................................................................163
6.2.4. Bidirectionality and time-period across subtypes of anxiety ...........................................................164
6.2.5. Potential mechanisms of the relationship between insomnia, depression and GAD ......................165
6.2.5.1. Common risk-factors ................................................................................................................166
6.2.5.2. Cause-effect relationship..........................................................................................................168
6.3. The effects of chronotype on the relationship between insomnia, depression and subtypes of
anxiety .............................................................................................................................................169
6.3.1. The prediction and confounding effects of chronotype ...................................................................170
6.3.2. Chronotype, insomnia, depression, and subtypes of anxiety across a 6 month period ..................171
6.3.3. Mechanisms .....................................................................................................................................172
6.4. Pathways between insomnia, depression, subtypes of anxiety and chronotype ....................173
6.5. Clinical implications ..................................................................................................................178
6.5.1. Prevention ........................................................................................................................................178
6.5.2. Treatment .........................................................................................................................................180
6.6. Limitations and future research ...............................................................................................184
6.6.1. Insomnia subtypes............................................................................................................................184
6.6.2. Sleep deprivation .............................................................................................................................185
6.6.3. Insomnia, depression, anxiety and stressful life events ...................................................................186
6.6.4. Insomnia, depression, anxiety and physical activity ........................................................................187
6.6.5. Technology, sleep, and chronotype .................................................................................................188
6.7. Conclusions ...............................................................................................................................190
7. Appendix – bootstrapped regression analyses when controlling for outcome variables at
baseline ........................................................................................................................ 191
References .................................................................................................................... 193
List of Tables
Table 1 ................................................................................................................................................................... 71
Table 2 ................................................................................................................................................................... 74
Table 3 ................................................................................................................................................................... 77
Table 4 ................................................................................................................................................................... 79
Table 5 ................................................................................................................................................................... 86
Table 6 .................................................................................................................................................................107
Table 7 .................................................................................................................................................................108
Table 8 .................................................................................................................................................................108
Table 9 .................................................................................................................................................................108
Table 10 ...............................................................................................................................................................138
Table 11 ...............................................................................................................................................................139
Table 12 ...............................................................................................................................................................139
Table 13 ...............................................................................................................................................................140
Table 14 ...............................................................................................................................................................141
Table 15 ...............................................................................................................................................................143
Table 16 ...............................................................................................................................................................144
Table 17 ...............................................................................................................................................................191
Table 18 ...............................................................................................................................................................192
List of Figures
Figure 1. Aetiologically exclusive relationships between insomnia, anxiety and depression. ............................... 43
Figure 2. A bidirectional relationship between insomnia, anxiety and depression. .............................................. 44
Figure 3. Summary of the reasons for exclusion.................................................................................................... 73
Figure 4. Summary of the total number of studies (analyses) that supported each type of relationship between
sleep disturbances, anxiety and depression. ......................................................................................................... 83
Figure 5. Steps for analyses. ................................................................................................................................105
Figure 6. Steps for regression analyses ................................................................................................................135
Figure 7. Pathways between insomnia and depression. .....................................................................................174
Figure 8. Pathways between insomnia and GAD. ................................................................................................174
Figure 9. Indirect pathways between insomnia and GAD. ...................................................................................175
Figure 10. Indirect pathways between insomnia and depression. ......................................................................175
Figure 11. Pathways between insomnia and OCD, PD, SAD and SP. ...................................................................176
Figure 12. Pathways between eveningness and insomnia. .................................................................................177
Figure 13. Pathways between eveningness, insomnia, and depression or GAD. ................................................177
Figure 14. Pathways between eveningness and OCD, PD, SAD or SP. .................................................................177
Declaration
I hereby declare this submission is my own work and that, to the best of my knowledge and
belief, it contains no material that has been accepted for the award of any other degree or
diploma of a university or other institute of higher learning, except where due
acknowledgement is made in the body of the text. All work contained in the submission was
initiated, undertaken, and prepared within the period of candidature. In addition, I certify
that no part of this work will, in the future, be used in a submission in my name for any other
degree or diploma in any university of other tertiary institutions without the prior approval
of the University of Adelaide and where applicable, any partner institution responsible for
the joint award of this degree. I give consent to this copy of my thesis when deposited in the
University Library, being made available for loan and photocopying, subject to the provisions
of the Copyright Act 1968. The author acknowledges that copyright of published works
contained within this thesis (as listed below) resides with the copyright holder(s) of those
works.
Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing
bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7),
1059-1068.
Alvaro, P. K., Roberts, R. M., & Harris, J. K. (In Press). The independent relationships between
insomnia, depression, subtypes of anxiety, and chronotype during adolescence. Sleep
Medicine
X
Pasquale Alvaro
PhD Candidate
i
Acknowledgements
At the end of this long and eventful journey, there are many people who I wish to
acknowledge. Firstly, I would like to acknowledge all the staff from The University of
Adelaide, particularly in the School of Psychology. All the office staff were always nice and
easy to work with, and often made things run much quicker and smoother. I would like to
acknowledge in particular Mrs Maureen Bell, who helped me develop the search terms for
my systematic review; Dr Stewart Howell, who helped with the statistical analysis for the
second manuscript; and Associate Professor Paul DelFabbro, who help with the statistical
analyses for the third manuscript.
Secondly, to Dr Catherine Wiseman-Hakes. Meeting you at the World Sleep
Conference in Valencia was very important for two reasons. One, it has given me the chance
to keep in touch with a passionate sleep researcher, who loves her work, and is always
willing to help people. Two, I made a lifelong friend who has already given me a lot. Thank
you for everyone, it means so much to me.
Professor Oliviero Bruni. My five month stay in Rome was rewarding for so many
reasons, not least because of your support. It was a real pleasure to work with such a giant
figure in the European and world sleep research community. Grazie di tutto. Non
dimentichero’ mai tutto quello che hai fatto per me, e che mi hai insegnato. Anche, Forza
Juve per sempre!!
To my supervisors Drs Rachel and Jodie. Through your guidance, I have become a much
better researcher, writer and professional, and learned the importance of attention to
detail. I wouldn’t have made it without your help. Thank you both so much for all the time
ii
you have given me, for everything you have taught, and for the sense of humour you both
brought to our meetings. I was lucky enough to have such a great pair of supervisors. You
both definitely made things much easier for me.
To all my friends and extended family. You have all been great to me, and lent a hand
when I needed it. Of my friends, I would particularly like to acknowledge Paulo, Storm, Matt
Rudd, Ben JRS, Chanto, Chan and Laura. Of my extended family, I would like to acknowledge
Gianluca, Nino il donkey, Domenico, Sara, Ciccio Frisina, Antonella Sacca’, Carmine Sacca’,
Grazia Rugolo and Uncle Tony. You have all been extremely important in keeping me sane,
and gave me so much. I will not forget the importance of all of your support during this
critical period in my life. Per gli italiani, Il period che ho passato in Italia e’ stato speciale
grazie a tutti voi e il support che ho ricevuto mi ha aiutato a stare bene. Senza il Vostro auito,
sarebbe stato tutto piu’ difficile.
To my little brother Philip. The time we spent playing video games and watching TV
were very important in helping me get through this PhD. Keeping my mind off work when I
needed to relax were critical in recharging my batteries. Also, although you’re younger than
me, the advice you give can be mature beyond your years. Thanks for your support.
To my dearest Emily. You’re right behind me doing your assignment on the effects of
globalisation on the Australian labour force with a paper bag around your feet as I sit here
typing. Meeting you almost a year ago, during a particularly difficult period of my life, was an
absolute God send that can only be grateful for. You’ve taught me so much about life, and
have helped me mature and become a much better person. No matter what happens, you
will always be special to me. Thank you so much for putting up with my craziness, thank you
for helping me to become a better person, and thank you for being you.
iii
To my father. Your words of advice during critical periods really helped me when I
needed it most. You were always honest with me, no matter the situation, and even told me
the hard truth when you thought I needed it. You have given me so much in my life, and
literally none of this would be possible without you. After all you’ve been through, you still
continue to work, live life, and be happy. I want to follow the example you set, and repay all
the things you’ve sacrificed for us. Thank you for everything. I love you.
To my mum. Words cannot describe the amount of sentiment I hold for you. You are
supermum. You can do everything and anything. You do so much for me, maybe too much. I
love you so much. You are one of the most important reasons why I’m where I am today.
Thank you so much for being supermum, and for dedicating your life to your family. I want
to repay everything you’ve ever done for dad, Philip and I. You are the absolute best mum in
the world. Thank you.
To my Nannu. It’s fitting that the last words I write in my thesis document are about
you, because not a day has gone by that I haven’t thought about you. Even now I can still
hear your voice as clearly as I hear my fingers hit the keyboard. I was and continue to be
inspired by your character, and you will always be my hero. The love you showed me was
unrivalled, and the influence you’ve had on me will last forever. I still feel overjoyed when
people say I act or look like you, but I will always know that I could never be as handsome as
you were! More importantly, if I could be half the man you were, I know I’d be a great man.
Nanna and mum miss you. I miss you. We all miss you. Save a seat for me next to you, and
make sure Uncle Pat and Nonna are there too. Until we meet again.
iv
v
Abstract
Bidirectionality refers to whether variable x predicts and/or is predicted by variable
y. This thesis identified and accounted for gaps within the literature on the bidirectionality of
the relationship between insomnia, depression, and anxiety during adolescence. Namely,
bidirectionality was assessed across different subtypes of anxiety, using continuous
variables. The independent effect of chronotype on the bidirectionality of the relationship
between insomnia and depression, and insomnia and subtypes of anxiety were also
considered.
Study one systematically reviewed the literature of the bidirectional associations
between sleep disturbances, anxiety and depression across all age groups. In total, the
systematic review contained nine independent studies. Best available evidence indicates
that insomnia is bidirectionally related to anxiety and depression. The limited data available
suggests that bidirectionality may extend beyond insomnia to other sleep disturbances,
although additional research is needed to further clarify this notion.
Study two assessed the cross-sectional independent relationships between
insomnia and depression, and insomnia and various subtypes of anxiety during adolescence.
The predictive effect of chronotype on insomnia, depression, and subtypes of anxiety was
also assessed. Baseline data from 318 South Australian high school students in grades 7 to 11
(age range 12-18, mean 14.96 ± 1.34) were collected. Insomnia, depression, subtypes of
anxiety and chronotype were assessed by validated self-report questionnaires. Insomnia
predicted depression and panic disorder (PD) after controlling for confounders, although the
latter was not considered clinically significant. Depression and generalised anxiety disorder
1
(GAD) predicted insomnia after confounders were controlled. Insomnia was not significantly
associated with other subtypes of anxiety once depression was controlled. Eveningness
uniquely predicted insomnia and depression, but was not associated with any anxiety
subtype.
Study three investigated the bidirectionality of the relationship between insomnia
and various subtypes of anxiety, and insomnia and depression; and the independent
predictive effects of chronotype on insomnia, depression, and each subtype of anxiety
during adolescence. The study was longitudinal, with a 6-month follow-up. Two-hundred and
fifty-five high school students completed self-report questionnaires at baseline and followup. Once depression was controlled, insomnia predicted depression and GAD after
controlling for other variables, and vice-versa, but was not related to other anxiety subtypes
in either direction. An evening chronotype predicted insomnia once other variables were
controlled, but did not predict depression or subtypes of anxiety once insomnia was
controlled.
Together, the results suggest that insomnia is bidirectionally related to depression
and GAD, and related to other subtypes of anxiety through a common factor, depression.
Furthermore, chronotype predicts the development of insomnia, and is related to
depression and anxiety subtypes through the common factor of insomnia. Chronotype, then,
may be a risk-factor for the development of insomnia, which may subsequently contribute to
depression or GAD, which in turn may create a vulnerability to other anxiety disorders.
Ultimately, these findings may significantly enhance prevention and treatment. Chronotype,
depression and GAD should be considered while implementing insomnia interventions,
insomnia may be important to address in the treatment of depression and GAD, and
2
depression should be assessed and considered for all sleep and anxiety disorder
presentations.
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Chapter 1: Introduction
1.1. Brief overview of thesis and chapter
This thesis is focussed on the bidirectionality of the relationship between insomnia and
depression, and insomnia and different subtypes of anxiety during adolescence, as well as
the effects of chronotype on these relationships. Although this thesis concerns adolescents,
discussions about literature based on adult samples are included when there was insufficient
evidence in the adolescent literature. Chapter 1 introduces key concepts, such as
adolescence, sleep, adolescent sleep, insomnia, depression, and anxiety. Chapter 1 then
discusses the existing literature on the relationship between insomnia, depression and
anxiety during adolescence, including the bidirectionality of these relationships.
Methodological problems and the significance of chronotype to this topic are then
discussed, followed by the aims, significance and contribution of this thesis. Chapter 1 is a
precursor to studies 1, 2 and 3. Therefore, an attempt was made to avoid as much overlap
between chapter 1 and the introductions of the studies as possible. The author sincerely
apologises in advance where overlap is present.
1.2. Adolescence
Adolescence refers to an age range from 12 to 18, and is characterised by physical and
sexual maturation (Malina & Bouchard, 1991); psychosocial (Blakemore, 2008), emotional
(Larson, Moneta, Richards, & Wilson, 2002), and behavioural (Spear, 2000) change; and
substantial development in brain structures and functional organisation (Casey, Jones, &
Hare, 2008; Ernst, Pine, & Hardin, 2006; Paus, Keshavan, & Giedd, 2008). Such changes, both
individually and when interacting, have been found to increase the vulnerability to mental
4
health disorders following puberty. For example, Brand and Kirov (2011) note that various
psychological functions such as emotional processing and executive control undertake
considerable and rapid change during adolescence, and these changes have been associated
with maladaptive decision making and actions; significant distress; and increased incidence
of unintentional injuries, homicide, suicide attempts, and substance abuse. Furthermore,
Teunissen and colleagues’ (2011) well-designed longitudinal study found that an interaction
between early pubertal development and low levels of popularity (rather than the former
alone) predicted future depressive symptoms. Unsurprisingly, then, a rapid increase of
prevalence rates in mental health disorders from childhood to adolescence has been widely
reported (Green, 2005; Kessler, Avenevoli, & Merikangas, 2001; Saluja et al., 2004), and the
median of lifetime risk for first onset of mental health disorders is age 14 (Kessler et al.,
2005). Together, the evidence portrays a clear picture that adolescence is a vulnerable
period for the development of mental health disorders, although prospective and
longitudinal research identifying the age of initial onset would further strengthen this
argument.
The physical, neuronal, psychological and behavioural markers of adolescence are
associated with changes to the sleep-wake cycle, sleep timing, sleep duration, and sleep
architecture (Brand & Kirov, 2011; Crabtree & Williams, 2009; Laberge et al., 2001; O'Brien &
Mindell, 2005; Roffwarg, Muzio, & Dement, 1966; Tarokh & Carskadon, 2010). Such changes
may lead to sleep patterns that eventually resemble those observed in adults, and result
from the maturation of sleep regulatory mechanisms within the brain during adolescence;
synaptic reorganisation typical of adolescence (Roffwarg, et al., 1966; Tarokh & Carskadon,
2010; Taylor, Jenni, Acebo, & Carskadon, 2005); and/or the adaptation of the sleep-wake
5
cycle to increased social, academic, familial or environmental pressures (Kaneita et al., 2009;
Laberge, et al., 2001; O'Brien & Mindell, 2005; Petersen & Leffert, 1995; Peterson, 2004).
Adolescence is therefore an extremely vulnerable period for the development of sleep
disturbances and mental disorders (Graber, Brook-Gunn, & Petersen, 1996; Kessler, et al.,
2005). This thesis, then, focusses specifically on adolescents.
1.3. Sleep
Sleep is a reversible, recurring and active state of reduced consciousness that
consolidates learning and memory, and promotes growth, repair and restorative processes
throughout the brain and body (Benington, 2000; Diekelmann & Born, 2010; Krueger & Obal
Jr, 2003; Peigneux, Laureys, Delbeuck, & Maquet, 2001; J. M. Siegel, 2009; Tononi & Cirelli,
2006). Normal sleep contains two distinct phases, non-rapid eye movement (NREM) and
rapid eye movement (REM). NREM sleep can be divided into three stages, NREM stage 1 – 3
(Kryger, Roth, & Dement, 2005).
Sleep usually begins with a brief period of stage 1 NREM sleep, where alpha waves that
are present during wakefulness fade (Colten & Altevogt, 2006). Stage 2 NREM sleep then
follows (Colten & Altevogt, 2006), which is characterised by sleep spindles (spurts of
rhythmic activity that last for 1-2 seconds) and k-complexes (short negative high-voltage
complex, followed by slower positive peaks of rhythmic activity and a final negative
complex) (Rechtschaffen & Kales, 1968). Sleep then progresses into stage 3 NREM (formerly
classified as stages 3 and 4), where deeper phases or slow-wave-sleep (SWS) occurs, blood
pressure and body temperature decline and electroencephalograms (EEGs) commonly
display slow, high-voltage delta waves (Colten & Altevogt, 2006). SWS is typically proceeded
by a short period of stage 2 NREM sleep, after which a short phase of REM occurs (Brand &
6
Kirov, 2011). REM sleep is characterised by the sudden movement of eyes under closed
eyelids (Colten & Altevogt, 2006), EEG activation akin to that seen during wakefulness,
muscle atonia (inhibition) and irregular heart rate and respiration (Aserinsky & Kleitman,
1953; Kryger, et al., 2005; Rechtschaffen & Kales, 1968).
The above process is equivalent to one sleep cycle, and usually takes approximately 90
minutes to complete (Brand & Kirov, 2011). Five or more sleep cycles typically occur during
normal human sleep, depending on sleep need (Rechtschaffen & Kales, 1968; Sinton &
McCarley, 2000). The first half of the night contains an increased proportion of SWS,
whereas the second half predominately contains REM and stage 2 NREM sleep (Brand &
Kirov, 2011).
The NREM and REM stages describe the physiological mechanisms that underlie sleep,
but ignore the processes that regulate sleep and wakefulness. The following subsection
describes a model of sleep regulation, then discusses how this model can explain the
initiation, maintenance and termination of sleep, along with the maintenance of
wakefulness.
1.3.1. Sleep regulation: a two-process model
Sleep is believed to be regulated via an interaction of two independent processes;
process S and process C (Achermann & Borbély, 1994). Process S, the homeostatic regulation
of sleep, represents the propensity of an individual to fall asleep. Although its neurobiology
is unclear (Carskadon, Acebo, & Jenni, 2004), process S is known to accumulate during
wakefulness and diminish during sleep (Borbély, 1982). Accordingly, the propensity for sleep
is determined by the amount of prior wakefulness and sleep (Carskadon, et al., 2004).
7
Therefore, the peak of homeostatic sleep propensity occurs just before sleep onset, and the
nadir at sleep termination following adequate sleep.
Process C is the circadian timing system of sleep propensity (Carskadon, 2011) that
changes over the course of 24 hours (Tononi & Cirelli, 2006). The circadian sleep propensity
is regulated by the suprachiasmatic nucleus (SCN) (Klein, Moore, & Reppert, 1991), and is
hence independent from prior waking and sleep (Dijk & Czeisler, 1995). The SCN neuronal
firing rate decreases in late day/early evening, stimulating sympathetic activity and resulting
in the production of melatonin by the pineal gland (Moore, 2007). Melatonin chemically
induces drowsiness and heat loss (Kräuchi, Cajochen, Pache, Flammer, & Wirz-Justice, 2006),
lowers core body temperature (Cagnacci, Kräuchi, Wirz-Justice, & Volpe, 1997), further
inhibits the firing of SCN neurons, and in turn promotes sleep propensity and reduces the
circadian drive for arousal (Moore, 2007). Melatonin production, along with sleep
propensity, rises until it peaks at approximately 2am to 4am (Brzezinski, 1997; Dijk &
Czeisler, 1995). Melatonin levels and sleep propensity then drop rapidly during the waking
day, which is likely to be at least partially due to the inhibiting effects of bright light on the
production of melatonin (Brainard et al., 2001). Therefore, the nadir of melatonin
production and circadian sleep propensity occurs in the late afternoon/early evening,
rendering sleep during this period unlikely (Dijk & Czeisler, 1995).
Homeostatic and circadian processes seem to function as corresponding systems to
promote sleep initiation and termination. The sleep systems simultaneously contribute to
increased likelihood of sleep nearing the typical bedtime, and promote termination of sleep
nearing the typical rise time. In contrast, the homeostatic and circadian processes function
as opposing systems to maintain sleep and wakefulness. The decline in homeostatic sleep
8
propensity coincides with an increase in circadian sleep propensity during the night to
maintain sleep, whereas the rising homeostatic propensity for sleep throughout the waking
day coincides with the decline of circadian sleep propensity to maintain wakefulness
(Carskadon, et al., 2004).
1.4. Adolescent sleep
Adolescent sleep is characterised by declines in the amplitude and proportion of
Delta waves and a decrease in NREM sleep from childhood (Baker, Turlington, & Colrain,
2012; Tarokh & Carskadon, 2010). In particular, SWS decreases by 40% during adolescence.
This finding may reflect a loss of cortical synaptic density that occurs with age (Feinberg,
1983). REM sleep occupies approximately 20 to 25% of adolescent sleep (Carskadon &
Dement, 2011). Ultimately, the adolescent sleep architecture closely resembles that
described in section 1.3.
A delayed sleep phase is also a feature of adolescent sleep (Giannotti, Cortesi,
Sebastiani, & Ottaviano, 2002). Adolescents become more evening-orientated with age due
to circadian rhythm and environmental changes (Crowley, Acebo, & Carskadon, 2007),
suggesting that older adolescents prefer later bed and rise times. The delayed sleep phase is
more apparent on non-school nights (weekends and vacations) than school nights
(Carskadon, 1990; Giannotti, et al., 2002; Wolfson & Carskadon, 1998) (see Carskadon,
Acebo & Jenni, 2004, for a review), although evidence suggests that adolescents often prefer
later bedtimes even on school nights (Carskadon, Wolfson, Acebo, Tzischinsky, & Seifer,
1998). These findings, paired with the enforced early starts for school, often result in sleep
deprivation during the week, and therefore highlight the importance of effective sleep
management strategies in adolescents.
9
1.5. Insomnia
Insomnia refers to sleeplessness that results from troubles with sleep initiation (falling
asleep), sleep maintenance (remaining asleep without enduring nocturnal awakenings),
sleep duration (awaking early in the morning), and/or non-restorative sleep (waking up
feeling unrefreshed despite adequate sleep opportunity) (Johnson, 2006; Poceta & Mitler,
1998). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)
(American Psychiatric Association, 2013), insomnia symptoms meet the criteria for insomnia
disorder when the aetiology of sleeplessness is not attributable to other disorders, presents
independently from other causal factors for at least three nights a week for three months
despite adequate opportunity for sleep, and causes impairment in social, occupational,
educational, academic, behavioural or other important areas of functioning. Insomnia
symptoms and disorders have historically been referred to as secondary insomnia when the
aetiology of sleeplessness is attributable to other disorders or causal factors (Lipton, Becker,
& Kothare, 2008). Accordingly, insomnia disorder and secondary insomnia have different
risk-factors. The following section describes the risk-factors that are related to insomnia
disorder and secondary insomnia. Note that insomnia disorder was labelled ‘primary
insomnia’ in the previous DSM (American Psychiatric Association, 2000), and therefore was
used extensively in previous literature. The following section will retain the original
terminology when used in the studies presented, and hence will use ‘primary insomnia’ and
‘insomnia disorder’ interchangeably.
1.5.1. Risk-factors for insomnia disorder and secondary insomnia
The risk-factors for insomnia disorder are best categorised as biological factors,
physiological traits, and psychosocial stressors. Regarding biological risk-factors, work from
10
Gehrman et al. (2011), Gregory, Rijsdijk, Lau, Dahl, and Eley (2009), and Barclay, Gregory,
Eaves, and Silberg (In Press) suggests that insomnia is at least moderately heritable. In
particular, Barclay and colleagues’ (In Press) longitudinal twin study reported that genetic
vulnerability at age 8 significantly predicted the development of clinically significant
insomnia disorder at ages 10, 14 and 15. Furthermore, Gregory and colleagues’ (2009)
longitudinal study reported a genetic predisposition for chronic paediatric sleep problems.
Together, these studies suggest that genetic factors may contribute to the development and
maintenance of insomnia disorder and other sleep problems. However, as noted by Ohayon
(2002) and Roberts, Roberts, and Chan (2008), the genetics of primary insomnia and other
sleep disorders are still largely unknown, and, as Tafti, Maret, and Dauvilliers (2005) stated,
genetic factors of primary insomnia are highly likely to be complex. Future research could
aim to identify the genes that interact with environmental factors and in turn contribute to
the development of insomnia disorder.
Regarding the physiological risk-factors, trait hyperarousal and hyperactivity have been
associated with the development of insomnia across various studies. Riemann et al. (2010)
reviewed a wide range of autonomic, neuroendocrine, neuroimmunological, neuroimaging,
and electrophysiological studies that demonstrate increased day and night time arousal in
those with primary insomnia. In addition, Bonnet and Arand’s (2010) extensive literature
review demonstrated a large amount of research depicting elevated whole body and brain
metabolic activation, and increased heart rate and sympathetic nervous system activation
during sleep in patients with primary insomnia. Indeed, both Riemann, et al. (2010) and
Bonnet and Arand (2010) argue that the evidence strongly suggests high physiological
arousal and activity levels across the 24-hour sleep cycle can predispose individuals to the
11
development of primary insomnia, which seems accurate given their rigorous and systematic
review of the literature.
Interestingly, Riemann, et al. (2010) state that primary insomnia is likely to result from
an interaction between predisposing factors such as genetic and physiological traits, and
psychosocial/medical stressors. This theory, labelled the stress-diathesis model of insomnia
(Spielman, Caruso, & Glovinsky, 1987), is widely endorsed (Bonnet & Arand, 2003; Perlis,
Giles, Mendelson, Bootzin, & Wyatt, 1997), and argues that along with genetics and trait
hyperactivity and hyperarousal, psychological predisposing factors such as a tendency for
stress, worry or rumination may interact with precipitating factors such as psychosocial
stressors (i.e., negative life events), and in turn trigger acute episodes of primary insomnia
symptoms and eventually lead to chronic primary insomnia . Most recently, a longitudinal
study of over 2,300 adults demonstrated that premorbid trait sleep reactivity interacts with
stressful life events and stress-induced cognitive intrusion to further increase the risk of
developing insomnia disorder two years later (Drake, Pillai, & Roth, 2014). The authors
suggest that trait sleep reactivity interacts with stressful life events and stress-induced
cognitive intrusion to trigger the development of insomnia (Drake, et al., 2014). This
statement seems probable given the amount of endorsement the stress-diathesis model for
primary insomnia has received from respected scholars (Bonnet & Arand, 2003; Perlis, et al.,
1997; Riemann, et al., 2010; Spielman, et al., 1987), but empirical evidence is limited, and
therefore definitive conclusions cannot be made.
Perhaps the most common theory of the development of chronic primary insomnia
from acute symptoms stems from the International Classification of Sleep Disorders’ [Second
Edition (ICSD-2)] notion of psychophysiological insomnia (American Academy of Sleep
12
Medicine, 2005). Acute episodes of primary insomnia are theorised to become sub-chronic
when reinforced by maladaptive coping strategies, such as excessive time spent in bed, and
remaining awake while in bed (Perlis, et al., 1997; Spielman, et al., 1987). Both maladaptive
strategies may reinforce one another: individuals with insomnia often retire before sleepy to
increase the opportunity for sleep, yet remain awake while in bed (Perlis, et al., 1997;
Spielman, et al., 1987); and wakefulness in bed often leads to increased time in bed to
increase sleep opportunity (Spielman, et al., 1987). The consequence is wakefulness in bed
for an excessive period of time, and in turn an experience of physiological, cognitive and
somatic arousal (e.g., excessive worry and rumination about sleeplessness, and
physiologically hyperarousal before sleep onset). Sub-chronic insomnia is thought to become
chronic when physiological, cognitive and somatic arousal are conditioned to the sleep
environment (Bonnet & Arand, 2010; Perlis, et al., 1997) rather than pre-sleep de-arousal
that is seen in good sleepers (Robertson, Broomfield, & Espie, 2007), hence, obstructing the
sleep onset process. However, psychophysiological insomnia has been removed from the
newest edition of the International Classification of Sleep Disorders (ICSD-3) diagnostic
manual, because it was not considered to be reliably reproducible in clinical practice
(American Academy of Sleep Medicine, 2014). Nevertheless, the mechanisms of
psychophysiological insomnia have been supported in the literature (Bastien, St-Jean, Morin,
Turcotte, & Carrier, 2008; Broman & Hetta, 1994; Perlis, et al., 1997; Robertson, et al., 2007),
and therefore may still have important implications for theories of primary insomnia.
Early explanations for insomnia considered it primarily as a symptom or consequence
of other disorders, particularly depression (Morin & Benca, 2012), but such a distinction is
now thought to oversimplify the notion of insomnia. The term secondary insomnia, then,
13
was adopted to differentiate primary insomnia from sleeplessness that is attributed to riskfactors, such as social factors, other sleep disorders, medication, medical conditions (e.g.,
asthma), neurological disorders, psychiatric disorders and substance abuse (Lipton, et al.,
2008). Research is moving towards an understanding of the possible development of primary
insomnia processes even in the presence of contributing or triggering comorbidities
(Jansson-Fröjmark & Lindblom, 2008). Secondary insomnia, therefore, is now often referred
to as comorbid insomnia.
1.5.2. Epidemiology of insomnia
Insomnia is the most common sleep problem reported by or diagnosed within the
general population (Bixler, Kales, Soldatos, Kales, & Healy, 1979). However, prevalence rates
in adolescents have ranged widely, from 2 to 38% (Abdel-Khalek, 2004; Johnson & Breslau,
2001; Roberts, Ramsay Roberts, & Ger Chen, 2002), which likely results from the differing
methodologies, definitions, symptom severity, and symptom duration of insomnia used
across studies (Johnson, 2006). However, there is also a wide range of frequencies reported
within studies. For example, the prevalence of any insomnia symptom during adolescence
using a 30 day period without a severity criteria is approximately 30% (range from 23 to
40%), and 11% (range from 7.2 to 40%) for those using a severity criteria (Johnson, 2006).
Furthermore, the only four studies that have used diagnostic criteria in adolescents report
rates of 4% (Ohayon, Roberts, Zulley, Smirne, & Priest, 2000), 2% (Ohayon & Roberts, 2001)
10.9% (Dohnt, Gradisar, & Short, 2012) and 11% (Johnson, Roth, Schultz, & Breslau, 2006).
Therefore, regardless of the criteria used to identify insomnia, an accurate single estimate of
prevalence rates seems difficult to achieve. Range estimates may provide a more reliable
source of information.
14
In a review of over 50 epidemiological studies of children, adolescents and adults,
Ohayon (2002) investigated the prevalence of insomnia according to the following four
categories; (1) insomnia symptoms; (2) insomnia symptoms accompanied by daytime
consequences; (3) dissatisfaction with sleep quantity or quality; and (4) insomnia diagnosed
according to the DSM-IV or International Classification of Sleep Disorders (ICSD) criteria. The
prevalence rates for insomnia symptoms alone ranged from 30 to 48%, compared to 12 to
16% when frequency criteria were added (e.g., at least 3 nights per week, or often/always).
Prevalence rates ranged from 10 to 28% when a severity criterion was added (e.g., moderate
to extreme levels of insomnia). The prevalence rates of insomnia symptoms accompanied
with daytime consequences was approximately 10% and ranged from 9 to 15%. Eight to 18%
of the general population were identified with insomnia based on dissatisfaction with sleep
quality and quantity. Finally, the prevalence of insomnia based on the DSM-IV or ICSD
criteria was approximately 6%, ranging from 4.4 to 6.4% (Ohayon, 2002). Collectively, the
evidence above suggests that insomnia is highly prevalent, but the frequency rates vary
greatly with definition and severity, and therefore can be difficult to estimate.
1.5.2.1. Epidemiology of insomnia and gender
Most studies have found higher rates of insomnia in females than males (L. Hale et al.,
2009; Kirmil-Gray, Eagleston, Gibson, & Thoresen, 1984; Mellinger, Blater, & Uhlenhuth,
1985; Ohayon, 2002; Roth, 2005; Sivertsen, Krokstad, Øverland, & Mykletun, 2009; Yang,
Zuo, & Eaton, 1987). Ohayon (2002) demonstrated that the gender effect remains
irrespective of the severity and definitions (e.g., symptoms to disorder continuum) that are
used to assess insomnia, with a female/male ratio for insomnia symptoms of approximately
1.4:1 and a ratio for insomnia diagnosis of 2:1. Furthermore, Manni and colleagues (1997)
15
found a significant association between chronic poor sleep (symptoms of insomnia) and the
female gender. A recent meta-analysis found a female:male risk ratio of 1.41 (95%
confidence interval: 1.28-1.55), which progressively increased across age (Zhang & Wing,
2006).
Evidence suggests the gender effect may partially result from biological factors.
Johnson, Roth, Schultz and colleagues (2006) reported a 2.75-fold increased risk for females
of insomnia diagnosed by the DSM-IV criteria following onset of menses in adolescents, but
no association between insomnia and maturational development in boys. The retrospective
nature of the variables that were used for the analyses of these findings provide a good
platform to suggest that a biological aspect of the gender effect may be the onset of
puberty, but prospective and longitudinal studies are necessary to provide more definitive
conclusions.
Alternatively, but not necessarily independently, Stattin and Magnusson (1990) argued
that increased social pressures following the onset of puberty are more prevalent for girls
than boys, and hence may contribute to the gender effect. However, this argument was
made regarding the gender effect of depression in adolescents and thought potentially
relevant to insomnia by Johnson and colleagues (2006), because few (if any) studies have
been conducted in this area concerning insomnia (see section 1.6.2.1. for the discussion
about depression and the gender effect in adolescents). Future studies should assess the
different social pressures faced by prepubescent boys and girls and adolescent males and
females, along with the association between each type of social pressure and insomnia.
16
1.5.2.2. Epidemiology of insomnia during adolescence and chronicity
Even after the onset of puberty, studies that assess the effects of age on the
prevalence of insomnia have suggested that prevalence rates increase during adolescence.
For example, Siomos et al., (2010) found that 8% of 14 year olds reported insomnia
symptoms compared to 21.1% of 15 year olds, and Liu and colleagues (2002) reported a
higher overall prevalence rate of insomnia for adolescents aged 18 (23.3%) than those aged
12 (10.2%). The increase in prevalence of insomnia across adolescence is likely to at least be
partially attributable to the decreased amount of sleep that often occurs during adolescence
(Liu & Zhou, 2002; Siomos, et al., 2010). Indeed, a recent meta-analysis of 41 adolescent
studies reported a strong correlation between age and total sleep times worldwide (r = 0.66) (Gradisar, Gardner, & Dohnt, 2011).
Insomnia during adolescence is also likely to be of a chronic nature. Roberts, Roberts,
and Duong (2008) found that 46% of adolescents who met the criteria for primary insomnia
at baseline maintained their diagnostic status after one year. Moreover, Johnson, Roth, and
Breslau (2006) reported that 88% of adolescents with a history of insomnia show ongoing
chronic insomnia symptoms (i.e. not naturally remitting). Finally, two studies found that 50%
of adolescents who report at least one symptom of insomnia at baseline report the same
symptom(s) two and four year later (Morrison, McGee, & Stanton, 1992; Patten, Choi, Gillin,
& Pierce, 2000). Therefore, it seems that disorders and even symptoms of insomnia during
adolescence can persist for years after onset without intervention.
The evidence presented in this section suggests that insomnia symptoms and disorders
frequently develop during adolescence, and are likely to persist across time. Efforts towards
insomnia prevention and treatment may most effectively target adolescents, and prevent
17
future negative outcomes that are associated with insomnia. The following section discusses
the concurrent and longitudinal associations of adolescent insomnia.
1.5.3. Concurrent and longitudinal associations with insomnia during
adolescence
Studies have reported a relationship between insomnia and neurocognitive
impairment. Fernández-Mendoza and colleagues (2009) reported that perceived
concentration and memory was significantly poorer in adolescents and young adults with
insomnia complaints than those without (24.2% vs 14.9% and 32.2% vs 22.9%, respectively).
The same study reported a significant association between insomnia complaints, and poorer
perceived concentration and memory after controlling for Delayed Sleep Phase Syndrome
(DSPS), suggesting that insomnia symptoms are related to subjective concentration and
memory beyond DSPS in adolescents and young adults (Fernández-Mendoza, et al., 2009).
Insomnia has also been associated with adult objective memory performance in overnight
sleep laboratory studies that used polysomnography to formally assess sleep, specifically
declarative and procedural memory (Backhaus et al., 2006; Nissen et al., 2006). Finally, poor
sleep and sleep deprivation, both of which have been related to insomnia (according to
Johnson et al., 2006), are risk-factors for impaired declarative and procedural memory
consolidation (Walker & Stickgold, 2004) during adolescence (Curcio, Ferrara, & De Gennaro,
2006), which Curcio and colleagues (2006) argues in a review of over 100 research articles
(including experimental sleep manipulation studies) can affect learning. Therefore, although
causal inferences cannot be made with certainty, the methodological rigour of the above
studies allows for a strong suggestion that sleeplessness may directly impact neurocognitive
performance. Indeed, Curcio et al., (2006) suggest that the neurocognitive dysfunction
18
associated with sleeplessness may be explained by the vulnerability of the prefrontal cortex
to sleep loss.
Insomnia has also been related to compromised academic achievement. Paavonen et
al. (2000) found an association between children who reported sleep complaints (mainly
problems with sleep onset and night awakenings) and poorer academic grades as reported
by teachers. Furthermore, a non-experimental study of Greek adolescents reported a
significant association between academic performance and insomnia (multiple linear
regression beta = -0.390) that was higher in females than males (Lazaratou, Dikeos,
Anagnostopoulos, Sbokou, & Soldatos, 2005). Studies have also inferred an association
between poorer academic achievement and other sleep problems [for a detailed review see
Curcio et al., (2006)]. The association between insomnia and poorer academic achievement
is likely to at least partially be explained by the fact that insomnia is considered a risk-factor
for neurocognitive deficits, as discussed above.
Insomnia also precipitates various behavioural and psychological problems during
adolescence. A recent longitudinal study found that adolescents with symptoms or
diagnoses of chronic insomnia were more likely to report problems at home, school and with
peers, poorer life satisfaction and perceived mental health, depressed mood, and
alcohol/drug use at 12-month follow up than those without insomnia (Roberts, Roberts, &
Duong, 2008). Similarly, a longitudinal study by Johnson and Breslau (2001) found that
insomnia at baseline predicts the use of cigarettes, alcohol and any illicit drug after adjusting
for age, gender, ethnicity, family income, and internalising (depression and anxiety) and
externalising (deviance and aggression) problems. These findings suggest that insomnia
predicts substance use independently from demographic factors and psychiatric problems
19
(Johnson & Breslau, 2001). Indeed, Bootzin and Stevens (2005) found that treatment leading
to improved sleep also lead to a reduction of substance abuse problems in adolescents at
12-month follow-up, suggesting a potential causal factor between insomnia and substance
abuse. Regarding other behavioural and psychological problems, Kirmil-Gray, et al. (1984)
reported an association between poor sleep and depression, low energy levels, tenseness,
mood alternations and irritability. Insomnia is also a risk-factor for conduct problems,
delinquent and aggressive behaviour, anger, irritability, fearfulness, tenseness, social
problems, risk-taking behaviour, withdrawal, emotional instability, and suicide attempts and
ideation (Choquet, Kovess, & Poutignat, 1993; Gau, 2000; Liu & Zhou, 2002; Morrison, et al.,
1992; O'Brien & Mindell, 2005; Tynjälä, Kannas, & Valimaa, 1993; Vignau et al., 1997;
Wolfson & Carskadon, 1998).
Of all the associations with mental health problems, perhaps the strongest are
between insomnia and depression, and insomnia and anxiety (Liu & Zhou, 2002). The
following two sections discuss depression and anxiety in more detail.
1.6. Depression
A depressed mood is characterised by sadness, low mood, or reduced pleasure in
previously enjoyable activities; with or without feelings of guilt, hopelessness, low selfesteem or low self-regard (Parker, 1979). It is considered as pathological when symptoms
are sustained and cause clinically significant distress or impaired functioning in social,
academic, occupational or other important areas of life (American Psychiatric Association,
2013). Depressive symptoms may be attributable to a major depressive episode/disorder
when a depressive or irritable mood, or loss of interest is experienced most days for at least
two weeks and accompanied by at least four additional symptoms. Such symptoms include:
20
significant changes in appetite or weight (a change of more than 5% of body weight in a
month), sleep (insomnia or hypersomnia), or psychomotor activity; fatigue or decreased
energy most days; feelings of worthlessness or excessive, inappropriate guilt most days;
difficulty thinking, concentrating, or making decisions; and recurrent thoughts of death,
suicidal ideation, plans, or attempts (American Psychiatric Association, 2013).
According to the diathesis-stress model, depression develops as a consequence of an
interaction between stressful life events, such as child neglect or the death of a loved one,
and various risk-factors (Monroe & Simons, 1991). An increased amount of risk-factors
reduces the stressors required to trigger the onset of depression, and vice-versa.
Consequently, the degree of predisposition for the development of depression varies
according to the amount of risk-factors and stressors. The following section describes
neurological, biological and psychosocial risk-factors of adolescent depression, and makes
references to studies that assess the interaction between risk-factors and stressful/traumatic
life events.
1.6.1. Risk-factors for depression
Various neurological and biological factors have been associated with the development
of depression during adolescence. M. J. Owens and Nemeroff (1998) proposed a chemical
imbalance of neurotransmitters such as dopamine, and serotonin as a risk-factor for
adolescent depression, a theory that has been both supported and contradicted across
adolescent and adult studies (Angold & Costello, 2006; Dunlop & Nemeroff, 2007; Eley et al.,
2004; Risch et al., 2009). Furthermore, abnormal brain structures may also serve as riskfactors for the development of depression during adolescence as suggested by well-designed
and widely cited research. Studies have reported increased pituitary gland and frontal grey
21
matter volume, decreased brain and frontal white matter volumes, and a significant trend
towards increased cerebrospinal fluid in adolescents with major depressive disorder relative
to healthy controls (MacMaster & Kusumakar, 2004; Steingard et al., 2002). A recent metaanalysis reported the same abnormalities in adult brains, along with lateral ventricle
enlargement, increased cerebrospinal fluid volume and decreased volumes of the
hippocampus, frontal lobe and basal ganglia in patients with depression relative to health
controls (Kempton et al., 2011). Depression is also proposed to be at least partially heritable,
as Thapar and Rice (2006) reported in a comprehensive review a 30 to 50% genetic risk for
the development of depression during adolescence.
Various psychosocial factors have also been associated with the development of
depression during adolescence, including low socio-economic status (Goodman, Huang,
Wade, & Kahn, 2003), childhood poverty, high maternal anxiety and depression, distressed
parental relationships, divorce/separation (Spence, Najman, Bor, O'Callaghan, & Williams,
2002), and negative inferential styles (Alloy et al., 2006). Poorer coping styles, less social
support, and ongoing stressful life events have been proposed as some underlying riskfactors for depression in those with lower socio-economic status (Turner & Lloyd, 1999).
Furthermore, a recent study found that childhood maladaptive schemas is related to
depression during early, middle and late adolescence, but only reported an interaction with
stressful life events in older adolescents (Braet, Vlierberghe, Vandevivere, Theuwis, &
Bosmans, 2013). Braet, et al.’s finding suggests that the diathesis-stress model in its current
state may be insufficient to explain all aspects of the development of adolescent depression,
although replications of such findings is necessary for more definitive conclusions.
22
Interestingly, stressful life events that are related to the onset of adolescent
depression may differ across age. Braet, et al. (2013) found that stress induced by paternal
and particularly maternal rejection strongly predicts depressive symptoms in younger
adolescents, weakly during middle adolescence, and not at all in older adolescence, whereas
peer rejection becomes a more important predictor with age. Furthermore, studies have
shown that stressful life events more strongly predict initial onset rather than relapse of
depression in adolescents, whereas dysphoric mood and dysfunctional thinking are stronger
predictors of the onset of recurrent rather than initial depressive episodes (Daley, Hammen,
& Rao, 2000; Lewinsohn, Allen, Seeley, & Gotlib, 1999; Post, 1992). Lewinsohn, et al. (1999)
proposed that the more depressive episodes experienced by an adolescent, the more the
episode becomes independent of the stressful event, and hence the less a psychosocial
stressor is required for the reoccurrence of an episode. Indeed, studies have provided
support for this theory (Ma & Teasdale, 2004), although additional evidence is required for
further consolidation.
Thapar, Collishaw, Pine, and Thapar (2012) argued that an interaction between genetic
factors and psychosocial events can increase the risk of depression during adolescence.
Indeed, Caspi et al. (2003) reported findings that suggested people with the S allele gene are
more likely to suffer from depression if (and only if) they also experienced stressful or
traumatic life events such as childhood maltreatment. Subsequent studies have reported
inconsistent findings, with two meta-analyses suggesting that the serotonin transporter
genotype does not increase the risk of depression when interacting with stressful life events
(Munafò, Durrant, Lewis, & Flint, 2009; Risch, et al., 2009). However, Karg, Burmeister,
Shedden, and Sen (2011) note that both meta-analyses have received widespread criticism
23
for the limited number of studies that were included in the analyses (n= 5 in Munafò, et al.,
2009; 14 in Risch, et al., 2009), excluding studies that assessed an interaction with other
psychosocial variables (e.g., childhood maltreatment), and the lack of relevant data that was
obtained from the original studies or follow-up emails [for example, Munafò & associates
(2009) excluded ten studies that met the inclusion criteria due to an inability to obtain
primary data]. Taking such criticisms into account, Karg and colleagues’ (2011) meta-analysis
of 54 studies found that the S allele gene strongly moderates the relationships between
depression and childhood maltreatment and depression and specific medical conditions, and
marginally moderates the relationship between depression and stressful life events.
Therefore, it seems that the S allele gene predisposes an individual to the development of
depression via an interaction with a range of psychosocial factors, but the magnitude of the
interaction ranges across psychosocial factors. In any case, the sensitivity to stressful and
traumatic life events has been argued to intensify as a result of the hormonal and
maturational changes during adolescence, hence further predisposing individuals to
depression (Thapar, et al., 2012).
Finally, depression may be precipitated by other health-related factors, such as a lack
of exercise (Brand et al., 2010), quality of diet (Jacka et al., 2010), disabilities and chronic
illnesses (Camhi, Morgan, Pernisco, & Quan, 2000), drug and alcohol use (Bootzin & Stevens,
2005), and sleep disturbances such as insomnia (Fergusson & Woodward, 2002). Regarding
sleep disturbances, a recent meta-analysis of adults has indicated that insomnia at baseline
is related to the development of depression at follow-up (Baglioni et al., 2011). A discussion
about the mechanisms that underlie the relationship between insomnia and depression can
be found in studies 1, 2 and 3.
24
1.6.2. Epidemiology of depression
Like those for insomnia, the findings from epidemiological studies of adolescent
depression have varied as a function of the definitions used by researchers. In general, point
prevalence estimates for major depressive disorder during adolescence range from 1%
(Fergusson, Horwood, & Lynskey, 1993) to 7% (Garrison et al., 1997), whereas 6 and 12month estimates range from 2% (McGee et al., 1990; Velez, Johnson, & Cohen, 1989) to 13%
(Feehan, McGee, Raja, & Williams, 1994). Lifetime prevalence estimates are higher still,
ranging from 4% (Whitaker et al., 1990) to 24% (Lewinsohn, Hops, Roberts, Seeley, &
Andrews, 1993). A meta-analysis found that after controlling for the effects of time-frame,
taxonomy [(e.g., DSM vs International Classification of Disease ICD)], and measurement
instrument, the prevalence rate for a depressive disorder during adolescence is
approximately 5.6% (Costello, Erkanli, & Angold, 2006).
The prevalence rates of pathological depressive symptoms also vary considerably.
Myers and Troutman (1993) reported a range from 10 to 40% in a review of youth
depression. More recently, large epidemiological studies of adolescent depression
symptoms have found prevalence rates from approximately 18 to 28% (Rothon et al., 2010;
Rushton, Forcier, & Schectman, 2002; Saluja, et al., 2004). Furthermore, in an
epidemiological study of 13, 568 adolescents, Rushton, et al. (2002) found that 19% of
adolescents reported mild symptoms of depression, while 9% reported moderate or severe
symptoms. Therefore, depressive disorders, along with clinically significant symptoms of
depression at different spectrums of a severity continuum, are common during adolescence.
25
1.6.2.1. Epidemiology of depression and gender
Studies have consistently depicted higher rates of depression in females than males
following puberty (Hankin et al., 1998; Hyde, Mezulis, & Abramson, 2008; Nolen-Hoeksema
& Girgus, 1994). This phenomenon is unlikely the result of differences in help-seeking or
symptom reporting, as the gender effect has been found in healthy and clinical samples
(Thapar, et al., 2012), across different grades (Saluja, et al., 2004) and methods of
assessment (Thapar, et al., 2012). Adolescent depression was thought to be mainly
associated with post-pubertal changes to hormones and brain structures, and more closely
related to female hormonal development than chronological age (Thapar, et al., 2012).
Indeed, Brooks-Gunn and Warren (1989) found an association between depressive affect
and the fastest rise of oestradiol levels in girls aged 10 to 14, yet no association between
depressive affect and age.
Nevertheless, evidence suggesting that oestrogen and other gonadal hormones are the
main individual predictors of the development of depression is limited. The involvement of
neuroendocrine changes in the development of depression was mainly presumed because of
the sharp increase in depression that occurs over the five years following puberty onset
(Weller, Kloos, Kang, & Weller, 2006). Furthermore, some studies have found that
psychosocial factors such as undesirable life events explain more variance of depressive
affect than hormonal factors [8% vs 4% (Brooks-Gunn & Warren, 1989)]. Together, this
evidence suggests a lack of empirical evidence for the notion that gonadal hormones are the
main individual predictor of the development of depression following puberty, and that
psychosocial factors are stronger predictors of post-pubertal depression. Therefore,
oestrogen and gonadal hormones are unlikely to be the main mechanism that underlies the
26
gender effect. Indeed, various psychosocial factors have been associated or theorised to be
associated with the gender effect, including emotion-focused coping strategies [which is
associated with femininity (Washburn-Ormachea, Hillman, & Sawilowsky, 2004)] and large
changes to social roles, school environments, and parental, peer and romantic relationships
(Cyranowski, Frank, Young, & Shear, 2000).
However, it seems that an interaction between genetic and psychosocial factors have a
greater association with the gender effect in adolescents than either individually. BrooksGunn and Warren’s (1989) study found that an interaction between negative life events and
oestrogen change accounted for 17% of the variance in depressive affect, as opposed to the
8% and 4% of variance that the individual constructs accounted for respectively. More
recently, Natsuaki et al. (2009) found that an interaction between physical development and
severe responses to interpersonal stressors were associated with more internalising
problems in young girls than either construct individually. Indeed, various researchers have
suggested that hormonal changes are more likely to indirectly predict the onset of
depression through sensitising the brain to the effects of stress or other psychosocial factors
than directly contribute to the depression of depression in teenagers (Angold & Costello,
2006; Goodyer, Tamplin, Herbert, & Altham, 2000; Hyde, et al., 2008), although further
research may help solidify such an argument. Nevertheless, the current evidence suggests
that biological and psychosocial factors that change following puberty, both individually and
when interacting, are likely to explain the gender effect found in adolescent depression.
Future research could identify more specific biological and psychosocial factors that interact
to further consolidate this notion.
27
1.6.2.2. Epidemiology of depression and age
The prevalence rate of depression tends to rise substantially from childhood to
adolescence (Green, 2005; Kessler, et al., 2001). Saluja, et al. (2004) reported prevalence
rates of 10% in grade 6, 20.4% in grade 8 and 24.5% in grade 10. Such a substantial increase
in frequency may result from the maturation of the social information processing network
(brain structures include the amygdala and prefrontal cortex; associated with reward and
danger) and psychosocial changes (e.g., increased subjective stress levels) that occur during
adolescence (Nelson, Leibenluft, McClure, & Pine, 2005; Thapar, et al., 2012). Abnormal
development of the social information processing network may render adolescents more
susceptible to a depressive episode in reaction to a social stressor. Indeed, Thomas et al.
(2001) reported functional abnormalities in amygdala responsiveness to particular social
stimuli in depressed and anxious adolescents. It could also be a mismatch in the maturation
of particular brain structures (Blakemore, 2008). Nelson, et al. (2005) proposed that the
slower development of the prefrontal cortex (involved in impulse inhibition and goal-setting
behaviour, i.e., executive functioning) relative to brain structures involved in detecting social
stimuli and emotional-awareness leads to the onset of depression during adolescence,
depending on psychosocial stimuli (e.g., rejection by romantic partners). In any case, it
seems that an interaction between biological and psychosocial risk-factors also explains the
heightened prevalence rates of depression in adolescents relative to children.
1.6.3. Concurrent and longitudinal associations with depression during
adolescence
Depression during adolescence is related to present and future morbidity. A crosssectional study found that adolescent depression was associated with issues such as
28
absenteeism, smoking, and suicidal ideation after controlling for socio-demographic
variables, life events, sexual abuse, physical abuse and exposure to violence (Glied & Pine,
2002). Regarding future morbidity, adolescent depression has been found to predict adult
psychiatric problems, such as suicidal ideation (2.3:1 odds ratio) (Fergusson, Horwood,
Ridder, & Beautrais, 2005), and agoraphobia without panic (4.3:1 odds ratio) (W. E.
Copeland, Shanahan, Costello, & Angold, 2009), all of which were found after controlling for
comorbid adolescent mental health problems and other factors. Adolescent depression also
predicts other future health related outcomes, such as health status (β = .16), self-health
perception (β = 1.10), medical care use (β = 1.26), physical impairment at work (β = .38)
(Keenan-Miller, Hammen, & Brennan, 2007), and the presence of disease and functional
impairment (Lewinsohn, Seeley, Hibbard, Rohde, & Sack, 1996). Together, these studies
suggest that adolescent depression is a risk-factor for current and future psychological,
behavioural, and physical health problems.
The extent to which adolescent depression predicts negative outcomes in adults is
particularly evident in females. A study found that adolescent depression in girls predicted
tobacco dependence and more medical problems at age 21 above and beyond prior health
status issues (Bardone et al., 1998). Adolescent girls with depression are more likely to
smoke, attain lower levels of education, report lower self-worth, and higher levels of body
dissatisfaction and eating problems during young adulthood than adolescents without
depression (Franko et al., 2005). Another study of adolescent girls found that depression
predicted higher rates of marriage during young adulthood and subsequent marital
dissatisfaction above and beyond the presence of adolescent non-affective psychiatric
disorders (Gotlib, Lewinsohn, & Seeley, 1998). Therefore, prevention and treatment efforts
29
for various negative psychosocial outcomes in adult women should target depression in
adolescent females.
Depression during adolescence is also a risk-factor for future depression (Thapar, et al.,
2012). Fergusson, et al. (2005) found that depression at 17 and 18 years of age predicts
depression at 25 years of age (2.4:1 odds ratio), and G. Fava, Ruini and Belaise’s (2007)
review article suggested that adults often fail to completely recover between depressive
episodes. Furthermore, although longitudinal community and clinical studies have found
that 60-90% of depressive episodes during adolescence remit within a year (V. Dunn &
Goodyer, 2006; March, 2004), follow-up studies suggest that 50-70% of these adolescents
suffer from further depressive episodes within five years (V. Dunn & Goodyer, 2006;
Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2000).. Together, the evidence strongly suggests
that depression during adolescence is often chronic and/or relapsing, and can persist into
adulthood.
1.7. Anxiety
Anxiety is a natural reaction to a stressor (i.e., safety threat) involving the sympathetic
nervous system, that is characterised by cognitive, affective and physiological symptoms
such as acute stress, tension, discomfort, worry, an increased heart rate, and excessive fears
about future negative events (Evans, Foa, & Gur, 2005; Kaplan & Sadock, 1998). An anxiety
response is classified as clinically significant when symptoms arise in the absence or in excess
of objectively-threatening stimuli (Evans, et al., 2005), and is diagnosed as a disorder when
symptoms become intrusive, are experienced amid tension and excess apprehension, and
cause substantial distress or functional impairment more often than not for at least 6
months (American Psychiatric Association, 2013).
30
According to the DSM-5 (American Psychiatric Association, 2013), anxiety disorders
can manifest in various ways, including excessive worry over separation from home or loved
ones [separation anxiety disorder (SAD)]; excessive and persistent fear of embarrassment or
negative evaluation during social situations or performances [social phobia (SP)]; distinct
episodes of unexpected intense fear, distress, or physiological reactions such as palpitations,
sweating, a sense of imminent danger, an urge to escape, or a fear of dying [panic disorder
(PD)], that is sometimes accompanied by an avoidance of situations that may trigger panic
(agoraphobia); excessive and persistent fear of objectively non-threatening stimuli such as
animals, the natural environment or medical procedures (phobias); and excessive and often
uncontrollable worry accompanied by physiological anxiety [generalised anxiety disorder
(GAD)]. Obsessive-compulsive disorder (OCD), defined as severe recurrent time-consuming
obsessions or compulsions, was originally classified as an anxiety disorder in the DSM-IV, but
has a separate category in the DSM-5.
Like depression, the diathesis-stress model has been used to explain the development
of anxiety during adolescence (Mineka & Zinbarg, 2006; Rapee, Schniering, & Hudson,
2009).The following section describes various risk-factors that have been associated with the
development of anxiety problems.
1.7.1. Risk-factors of anxiety
It seems reasonable to assume that the development of anxiety disorders contains a
genetic component. In a study of 1,162 twin pairs and 426 siblings, Ehringer, Rhee, Young,
Corley, and Hewitt (2006) found evidence indicating genetic influences for attention deficit
hyperactivity disorder, GAD, SAD, and major depressive disorder. Various other studies have
reported similar findings across different anxiety disorders (Stevenson, Batten, & Cherner,
31
1992; Thapar & McGuffin, 1995; Topolski et al., 1997), along with and an overlapping genetic
component with depression (Eley, 1997; Gregory & Eley, 2007).
Environmental factors are also known vulnerabilities for the development of anxiety
disorders during adolescence. Ehringer, et al. (2006) suggested that environmental factors
are stronger predictors of lifetime GAD and major depressive disorder than genetic factors,
although this may have been the result of insufficient statistical power. They also note that
environmental influences shared between siblings (twins and singletons) are associated with
GAD, but not SAD, whereas non-shared environmental factors were associated with both
(Ehringer, et al., 2006). Shared environmental influences predicting the development of
anxiety disorders include negative or overcritical and particularly overprotective or overcontrolling parenting styles (Bögels & Brechman-Toussaint, 2006; McLeod, Wood, & Weisz,
2007; Rapee, 1997; Wood, McLeod, Sigman, Hwang, & Chu, 2003), and non-shared
environmental factors include peers, hobbies, parental treatment, and social support (J.
Dunn & Plomin, 1990; La Greca & Lopez, 1998).
Personality factors also increase the risk of anxiety during adolescence. Meeus, Van de
Schoot, Klimstra, and Branje (2011) found that an over-controlling personality style was
associated with future anxiety, whereas resilience appeared to have a protective effect
against future anxiety. Furthermore, Prior, Smart, Sanson, and Oberklaid (2000) reported an
association between shy-inhibited temperament persisting from childhood to adolescence
and adolescent anxiety. Prinzie, van Harten, Deković, van den Akker, and Shiner (2014)
reported a similar finding, with shyness, irritability and altruism during childhood predicting
more problematic adolescent anxiety and depression. The same study also found that
energy, optimism, compliance and trait anxiety during childhood predicted adolescent
32
anxiety symptoms, but shyness, irritability and compliance were moderated by over-reactive
parenting (Prinzie, et al., 2014). Therefore, it seems that an interaction between certain
personality and environmental factors during childhood may further increase the risk of
developing anxiety during adolescence.
Finally, various disorders, such as insomnia, have been associated with the
development of anxiety (Jansson-Fröjmark & Lindblom, 2008). A discussion about the
mechanisms that underlie the relationship between insomnia and anxiety can be found in
studies 1, 2 and 3.
1.7.2. Epidemiology of anxiety
Anxiety disorders are the most prevalent mental health problems experienced during
adolescence (Beesdo, Knappe, & Pine, 2009). Beesdo, et al. (2009) argues that estimates of
prevalence rates vary because of differences in assessment instruments (e.g., Composite
International Diagnostic Interview, and Kiddie-Schedule for Affective Disorders), information
source (e.g., self-report, parent/teacher report), method of data aggregation (from multiple
information sources or multiple assessment waves), diagnostic criterion (e.g., DSM-III-R,
DSM-IV, and ICD-10), definitions (e.g., any anxiety disorder), and strictness of symptom
severity (e.g., impairment required or not) used across studies. Nevertheless, a recent metaanalysis found that 11% of adolescents aged 13 – 18 suffer from any anxiety disorder, 6.7%
suffer from specific phobias, 5.0% from SP, 2.3% from SAD, 1.9% from GAD, and 1.1% from
PD (Costello, Egger, Copeland, Erkanli, & Angold, 2011).
33
1.7.2.1. Epidemiology of anxiety and gender
Similarly to insomnia and depression, females report higher rates of anxiety than
males across all anxiety disorders (Beesdo, et al., 2009). Gender differences tend to increase
during adolescence to approximately 2:1 – 3:1 (Pine, Cohen, Gurley, Brook, & Ma, 1998;
Wittchen, Nelson, & Lachner, 1998). As with insomnia and depression, the gender effect is
likely to be associated with biological and psychosocial factors, namely post-pubertal
changes to hormones and brain structures, female hormonal development, increased social
issues for girls relative to boys, and a combination of all. Nevertheless, more research is
needed to further identify the risk-factors that are relevant to adolescent girls for the
development of anxiety problems.
1.7.2.2. Epidemiology of anxiety and age
Prevalence rates of anxiety disorders remain constant after puberty for some disorders
but increase for others. In their review, Beesdo, et al. (2009) note that the prevalence rates
for SAD, and specific and social phobia are similar in children and adolescents, but are higher
in adolescents for PD. The meta-analysis conducted by Costello et al. (2011) found similar
results. The causes of the increase in prevalence rate in PD are likely to be similar to those
for insomnia and depression, such as post-pubertal changes to hormones and brain
structures, and increased social, academic and familial pressures from childhood to
adolescence.
34
1.7.3. Concurrent and longitudinal associations with anxiety during
adolescence
Anxiety can impact the school life of adolescents. Various studies have suggested that
anxiety disorders during childhood and adolescence are associated with premature
withdrawal from school and difficulties with concentration and motivation, both of which
can compromise school performance and impact the quality of relationships with peers and
family (Stein & Kean, 2000; Van Ameringen, Mancini, & Farvolden, 2003; Varley & Smith,
2003). Because anxiety is often chronic in nature (see below), the transitional process from
childhood to adolescence can be impaired by the consequent feelings of worthlessness and
low self-esteem (Varley & Smith, 2003). Indeed, SAD has been associated with victimisation
at school (R. S. Siegel, La Greca, & Harrison, 2009), which could further disrupt the
transitional process and hence impact on academic achievement and quality of life at school.
Longitudinal data suggests that anxiety symptoms and disorders consistently present
during adolescence. Adolescent anxiety disorders have been found to predict anxiety
disorders in the short and long term (Lewinsohn, Zinbarg, Seeley, Lewinsohn, & Sack, 1997;
Woodward & Fergusson, 2001). Furthermore, various studies have reported that GAD, OCD,
SP, and specific phobias during adolescence predict the same disorders 1.3 to 15 years later
(Berg et al., 1989; Bittner et al., 2004; W. Hale, Raaijmakers, Muris, Van Hoof, & Meeus,
2008; Merikangas, Avenevoli, Acharyya, Zhang, & Angst, 2002; Pine, et al., 1998; Stein et al.,
2001). Therefore, like insomnia and depression, anxiety disorders are likely to be chronic,
and/or relapsing conditions that persist well into adulthood.
Anxiety during adolescence is considered a risk-factor for other health and
psychological outcomes. Berg, et al. (1989) showed that 56% of adolescents with OCD
35
developed other anxiety and mood disorders at 2-year follow-up. Studies have also shown
an association between anxiety at ages 14 – 16 and other subtypes of anxiety, depression,
illicit drug dependence, and failure to attend university at ages 16 – 21 after controlling for
various covariates (Essau, Conradt, & Petermann, 2002; Woodward & Fergusson, 2001).
Furthermore, anxiety disorders in adolescent females have been associated with more
medical problems in young adulthood, including anaemia, arthritis, cancer, hepatitis,
diabetes, serious back trouble, heart trouble, kidney and bladder infections, epilepsy, acne,
colitis, menstrual problems, and migraines (Bardone, et al., 1998). Finally, Johnson, Roth,
and Breslau (2006) found that prior anxiety predicts insomnia during adolescence. Together,
the evidence suggests that anxiety problems during adolescence are risk-factors for a
number of psychological, behavioural, and medical problems, including insomnia and
depression. The following section discusses the association between insomnia, anxiety and
depression in adolescents.
1.8. The relationship between insomnia, depression and
anxiety
Studies investigating the relationship between insomnia, depression and anxiety have
defined the latter two as either a single variable (Gregory & O'Connor, 2002; Kaneita, et al.,
2009), or two separated constructs (Liu & Zhou, 2002). Regarding the former, Gregory and
O'Connor (2002) found a correlation between sleep problems and depression/anxiety at age
15 (r= 0.52). Moreover, Kaneita, et al. (2009) reported an association between the Japanese
version of the Pittsburgh Sleep Quality Index (PSQI) overall score, which is highly sensitive in
detecting primary insomnia in Japanese adolescents (Doi et al., 2000), and mental health
status as measured by the Japanese version of the 12-item General Health Questionnaire
36
(GHQ-12), which contains a depression/anxiety subscale. The same authors subsequently
validated the GHQ-12 in Japanese adolescents and found that current subjective insomnia
symptoms were associated with both the depression/anxiety and loss of positive emotions
subscales (Suzuki et al., 2011). In addition, Liu and Zhou (2002) found that Chinese
adolescents who reported experiencing insomnia symptoms ‘often’ within the past month
also reported higher levels of anxiety/depressive symptoms than those who did not report
experiencing insomnia symptoms (25.4% vs 9.9% respectively). Given the above studies have
been conducted in different countries, the association between insomnia and
depression/anxiety seems to be present irrespective of culture.
Interestingly, the magnitude of the association between sleep problems and
depression/anxiety may be affected by developmental changes overtime (Gregory & Sadeh,
2011). Gregory and O’Connor (2002) found a positive correlation between sleep problems
and depression/anxiety, which significantly increased from r = .39 at age 4 to r = .52 at age
15. Furthermore, Johnson, Chilcoat, and Breslau (2000) reported a greater association
between troubles sleeping and depression/anxiety at age 11 (OR= 9.7) than at age 6 (OR=
4.7). These findings may be explained by the physical and social changes experienced during
adolescence that can impact on the development of insomnia, anxiety and depression, such
as increased autonomy and psychosocial (e.g., peer groups), familial, and educational
demands (Kaneita, et al., 2009; Lipton, et al., 2008).
Various studies have also reported an association between insomnia and depression,
and insomnia and anxiety. Kirmil-Gray and associates (1984) found that 66.7% of adolescents
who reported insomnia symptoms at least once a week also reported pathological
depressive symptoms, whereas 36% of those who slept well reported depressive symptoms
37
(p < 0.05). Also, Ohayon et al., (2000) demonstrated that 76.5% of adolescents aged 15 – 18
with an anxiety disorder and 67.6% with a depressive disorder reported at least one current
symptom of insomnia. Furthermore, Roane and Taylor (2008) found that adolescents aged
12 – 18 with clinically significant insomnia symptoms were 3.27 times more likely to display
clinically significant depression symptoms than those without clinically significant insomnia
symptoms. Regarding anxiety subtypes, Alfano, Zakem, Costa, Taylor, and Weems (2009)
reported that sleep disturbances are associated with clinically significant symptoms of GAD,
PD/agoraphobia, and SP, but not OCD or SAD in adolescents. Considering the evidence, it
seems that a relationship between insomnia and depression, and insomnia and anxiety
(assessed as an overall construct) during adolescence is consistently reported across
definitions and severity criteria, but not subtypes of anxiety.
1.8.1. Potential mechanisms that underlie the relationship between
insomnia, depression and anxiety
There are various mechanisms that may underlie the consistently reported
associations between insomnia and depression, and insomnia and anxiety. These disorders
may arise due to common risk-factors, or there may be a causal relationship. Both
mechanisms are likely to underlie the relationship between insomnia, depression and
anxiety, although to what extent is unclear. The following section describes common riskfactors and causality in more detail.
1.8.1.1. Common risk-factors
Common risk-factors have been theorised to explain the relationship between
disorders in two ways. Neale and Kendler (1995) proposed that common risk-factors, all
independent and of small effect, are alternative forms of the same disorder, suggesting that
38
a disorder may manifest in different ways (e.g., via insomnia or depression). In contrast,
Staner (2010) notes that such risk-factors may result in two distinctive yet associated
disorders if risk-factors are common to both conditions.
Although both theories have merit, the evidence seems to contradict the prior theory
and support the latter theory when considering the relationships between insomnia and
depression. Various studies have reported a predictive relationship between insomnia and
depression after controlling for covariates (i.e., common risk-factors) (Baglioni, et al., 2011;
Jansson-Fröjmark & Lindblom, 2008; Meijer, Reitz, Deković, van den Wittenboer, & Stoel,
2010), suggesting that the disorders are separate constructs even when considering
common risk-factors. Furthermore, cross-sectional and longitudinal studies have reported
that anxiety and other covariates (e.g., age and gender) partially account for the relationship
between insomnia and depression, suggesting that although insomnia and depression are
separate disorders, comorbid insomnia and depression is at least partially due to common
risk-factors (i.e., anxiety, age and gender) (Alfano, et al., 2009; Gregory, et al., 2009; JanssonFröjmark & Lindblom, 2008). Therefore, the current evidence indicates that common riskfactors are a mechanism that underlies the relationship between insomnia and depression,
but other factors are also likely to explain this relationship.
The relationship between insomnia and anxiety, however, is less clear. Cross-sectional
and longitudinal studies have reported mixed findings, where covariates (e.g., depression,
gender and age) have been found to completely account for the relationship between
insomnia and anxiety in some studies (Alfano, et al., 2009), and partially account for this
relationship in other studies (Jansson-Fröjmark & Lindblom, 2008; Meijer, et al., 2010). One
reason for the contrast in results could be that anxiety is often assessed as an overall
39
variable (i.e., the different anxiety disorders are grouped as one), and studies have reported
that the relationship between insomnia and anxiety differs across anxiety subtypes (Alfano,
Ginsburg, & Kingery, 2007). Even so, it seems safe to assume that insomnia and anxiety
disorders are separate constructs given the different symptoms, diagnostic criteria and
recommended treatments for each disorder (although some do overlap) (American
Psychiatric Association, 2013). Therefore, the above evidence likely indicates that the
mechanisms underlying the relationship between insomnia and anxiety are either partially
or entirely explained by common risk-factors.
1.8.1.2. Cause-effect relationship
Insomnia, depression and anxiety may also have a causal relationship, where the
development of an insomnia disorder results in the development of a depressive or anxiety
disorder, or vice-versa. Although the notion of causality is difficult to ascertain, Hill (1965)
proposed a widely used criteria that focusses on nine aspects of a relationship by which
causal inferences can be made. Such aspects of the relationship include the magnitudes of
the effect-sizes that are reported in studies, the consistency of a significant effect across
different studies and cultures, the specificity of the observed association (is it limited to a
particular population or circumstance?), the bidirectionality of the relationship, the doseresponse linear relationship of two variables, the biological plausibility (are there biological
mechanisms that can explain the relationship?), coherence (correspond with the known
facts of the disorder), the experimental evidence available, and analogy (can a judgment be
made in similar circumstances?) (Hill, 1965).
Hill (1965) notes that these criteria do not bring irrefutable evidence for a cause-effect
relationship. In fact, the specificity, dose-response linear relationship, biological plausibility
40
and analogy criteria are features he argues cannot be demanded. Rather, such criteria aim to
assist in determining whether or not there is a more logical explanation than a cause-effect
relationship. With this in mind, the findings of the relationships between insomnia and
depression and insomnia and anxiety largely satisfy Hill’s (1965) criteria. Studies across many
cultures have consistently reported a wide range of dose-linear associations, with varying
effect-sizes, and have not seriously conflicted with the known facts about insomnia,
depression or anxiety (Gregory & O'Connor, 2002; Gregory, et al., 2009; Jansson-Fröjmark &
Lindblom, 2008; Kaneita, et al., 2009; J. M. Kim et al., 2009). Such studies have often control
for potential covariates, and hence further strengthen the notion of a cause-effect
relationship. There has also been experimental evidence based on treatment studies (Alfano,
et al., 2007; Manber et al., 2008), and various biologically plausible explanations (see
Chapters 3, 4 and 5).
The bidirectionality of the relationships between insomnia and depression, and
insomnia and anxiety has also been assessed (Gregory & O'Connor, 2002; Jansson-Fröjmark
& Lindblom, 2008). Bidirectionality refers to whether insomnia predicts or is predicted by
anxiety and depression. The following section introduces the topic of bidirectionality,
describes the research within this area of study, and highlights the methodological issues
within this literature.
1.9. The bidirectionality of the relationship between
insomnia, depression and anxiety
Research that assesses the bidirectionality of the relationship between insomnia,
depression and anxiety aims to explore the potential overlapping developmental courses of
these problems. Anxiety may be a risk factor for insomnia, and insomnia may be a risk factor
41
for depression (Johnson, Roth, & Breslau, 2006), or each may be associated with the
development of the other (Jansson-Fröjmark & Lindblom, 2008). Such research can help
identify key aetiological factors for insomnia, depression and anxiety, establish whether the
onset of one is a risk-factor for the others, and inform public health campaigns and clinical
interventions for each disorder (Jansson-Fröjmark & Lindblom, 2008; Johnson, Roth, &
Breslau, 2006; Kaneita, et al., 2009). Bidirectionality can only be established by considering
whether insomnia is associated with the development of depression or anxiety, and whether
depression or anxiety is associated with the development of insomnia, in subsequent
analyses. A recent meta-analysis of longitudinal studies found that baseline insomnia
predicts follow up depression, but did not establish directionality because the association
between baseline depression and follow-up insomnia was not assessed simultaneously
(Baglioni, et al., 2011).
Recent evidence on bidirectionality has been limited and contentious. After controlling
for gender, race, and prior disorders of insomnia, anxiety or major depressive disorder,
Johnson, Roth, and Breslau (2006) reported significant associations between prior anxiety
disorders and onset of insomnia disorder [Hazard Ratio=3.5; the hazard in an exposed group
divided by the hazard in an unexposed group (Hernán, 2010)], and prior insomnia disorder
and onset of major depressive disorder (Hazard Ratio=3.8). However, a significant
relationship was not found between prior insomnia disorder and onset of an anxiety
disorder, or prior major depressive disorder and onset of insomnia disorder. Similarly,
Ohayon and Roth (2003) found that adults with current but no previous history of any
anxiety disorder presented with insomnia symptoms simultaneously and after the anxiety
disorder more often than before (38.6% , 43.5% and 18% respectively), whereas those
42
without a history of mood disorders presented with insomnia symptoms more often before
mood disorders than simultaneously or after (41%, 29.4% and 28.9% respectively). These
studies suggest the possibility of two separate cause-effect relationships (Figure 1), where
anxiety leads to insomnia (Figure 1a) and insomnia leads to depression (Figure 1b) (Johnson,
Roth, & Breslau, 2006), but not vice-versa. Consequently, insomnia, depression, and anxiety
are thought to have distinct natural courses of development during adolescence, from
anxiety to insomnia and insomnia to depression (Johnson, Roth, & Breslau, 2006).
Therefore, the relationship between insomnia, depression, and anxiety may predominately
follow a pathway from anxiety to insomnia to depression (Figure 1c) (Johnson, Roth, &
Breslau, 2006).
Anxiety
Insomnia
Insomnia
Depression
(1a)
(1b)
Anxiety
Insomnia
Depression
(1c)
Figure 1. Aetiologically exclusive relationships between insomnia, anxiety and depression.
(1a) Anxiety leads to insomnia (1b) Insomnia leads to depression (1c) Anxiety leads to insomnia, which
leads to depression
In contrast, Jansson-Frojmark and Lindblom (2008) demonstrated an association
between symptoms of anxiety and depression, and new episodes of insomnia at 1-year
follow-up (OR=4.27 and 2.28, respectively). They also reported an association between
symptoms of insomnia, and new cases of anxiety (OR=2.30) and depression (OR=3.51) at 1year follow-up. Similarly, after controlling for predictor variables at baseline, Morphy and
43
colleagues (2007) reported a relationship between symptoms of anxiety and depression at
baseline, and symptoms of insomnia at 12-month follow-up , and vice-versa. Among
adolescents, Kaneita and associates (2009) found a significant relationship between poor
sleep quality present at baseline and follow-up, and mental health status
(depression/anxiety symptoms) at follow-up (OR=5.81) in participants who did not display
poor mental health at baseline. Poor mental health status present at baseline and follow-up
also predicted poor sleep quality at follow-up (OR=6.9) in participants who did not display
poor sleep quality at baseline. Gender, baseline sleep quality or mental health status, and
baseline lifestyle and contentment with daily life were used as covariates in each analysis
(Kaneita, et al., 2009). These studies suggest a bidirectional relationship between insomnia,
depression, and anxiety, where each contribute to the development of one another
(Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; Morphy, et al., 2007) (Figure 2).
Consequently, insomnia, depression and anxiety are thought to have overlapping courses of
development rather than two distinct associations, which may be facilitated by familial,
social and environmental stressors (Kaneita, et al., 2009) or abnormalities to
neurotransmitters associated with the sleep wake cycle, anxiety and depression such as
dopamine, hypocretin-1 and serotonin (Holmes, 2003; Nestler & Carlezon, 2006; Peroutka,
1998; Tseng & O'Donnell, 2007; Yoshioka, Matsumoto, Togashi, & Saito, 1996).
Insomnia
Anxiety
Insomnia
Depression
Figure 2. A bidirectional relationship between insomnia, anxiety and depression.
44
1.9.1. Methodological variations across studies
The inconsistent nature of the evidence may relate to variations in the type of
methodologies employed to date. Studies that support the model depicted in Figure 1 have
used cross-sectional designs and retrospective reports (Johnson, Roth, & Breslau, 2006;
Ohayon & Roth, 2003). Cross-sectional designs assess variables at a single time-point rather
than over a period of time, and retrospective reports often display recall bias (poor recall of
past events), rounding (participants excessively report ages ending with 0 or 5) and
telescoping effects (onset of disorders are often reported more recently or less recently than
in reality), thus compromising the accuracy of memory (Golub, Johnson, & Labouvie, 2000).
Cross-sectional designs and retrospective reports cannot separate cause-effect relationships
from associations and hence are unsuitable for accurate investigations of bidirectionality.
Conversely, studies supporting the bidirectional theory have used longitudinal designs and
prospective reports (Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; Morphy, et
al., 2007), resulting in more power to suggest (but not assume) causality and reliability to
assess the bidirectionality of the relationship between insomnia, anxiety and depression
over time. They have also accounted for the baseline covariates, which is essential in
establishing bidirectionality (Chilcoat & Breslau, 1998). However, the study that investigated
adolescents used a combined depression/anxiety variable, which could mask the different
relationships between insomnia and depression, and insomnia and anxiety. Therefore,
inferences cannot be suggested about the developmental pathways of insomnia, depression
and anxiety during adolescence.
The inconsistent populations and variables that have been investigated in the
literature is another important variation. Some studies have assessed sleep quality in
45
adolescents (Kaneita, et al., 2009), while others have assessed insomnia in adults (JanssonFröjmark & Lindblom, 2008). The related yet slightly inconsistent variables used across
studies may contribute to the contradictory findings.
1.9.2. Overlapping methodological issues within the literature
The supporting studies of both theories also contain overlapping methodological issues
(Jansson-Fröjmark & Lindblom, 2008; Johnson, Roth, & Breslau, 2006; Kaneita, et al., 2009;
Morphy, et al., 2007; Ohayon & Roth, 2003). Firstly, the effects of different types of anxiety
subtypes on the bidirectionality of the relationship between insomnia, depression and
anxiety have not been considered. Johnson et al., (2006) suggested that insomnia may
present more often in adolescents with OCD and less with simple phobias than with other
forms of anxiety disorders and major depressive disorder and depression during
adolescence, although this was not tested specifically tested by the authors. Also, Alfano,
Ginsburg, and Kingery (2007) found that youths aged 6 to 17 who suffered from GAD or SAD
were more likely to experience insomnia than youths who did not, but did not find a
significant difference between youths who suffered from SP and youths who did not.
Therefore, it may be that bidirectionality may differ across anxiety subtypes.
Secondly, the bidirectionality of the relationship between insomnia, depression, and
anxiety has only been assessed using categorical variables, assuming that each disorder as
present or absent. However, symptoms of insomnia, depression, and anxiety can differ in
severity, and the relationship between these symptoms likely in part depends on severity
levels. That is, the more severe the insomnia symptom, the more severe the depression or
anxiety symptom. Indeed, youth studies have reported positive correlations between sleep
problems and depression (Alfano, et al., 2009), and insomnia and anxiety (Alfano, et al.,
46
2007). Furthermore, a study that performed two meta-analyses reported the reliability and
validity of measures of psychopathology increase by 15% and 37% respectively when
assessed as continuous variables compared to categorical variables (Markon, Chmielewski, &
Miller, 2011). Therefore, assessing bidirectionality using continuous variables will allow for
the consideration of symptom severity, and is potentially a more reliable and valid method
to assess insomnia, depression and anxiety.
1.9.2.1. Chronotype
Finally, the impact of chronotype on the relationships between insomnia and
depression, and insomnia and anxiety has not been considered in previous studies.
Chronotype refers to a circadian rhythm position indicator that categorises individuals
according to both their body clock position relative to the 24 hour day (Roenneberg et al.,
2004) and the time he/she prefers to engage in cognitively and physically demanding
activities (Ferraz, Borges, & Vianna, 2008). People who wake and sleep earlier are classified
as morning types, whereas those who wake and sleep later are classified as evening types.
As noted in section 1.4., adolescents become more evening orientated with age during
adolescence (Crowley, et al., 2007). Such a phenomenon may be problematic, as studies
have suggested that a preference for evenings is associated with insomnia, depression and
anxiety during adolescence. Giannotti, et al. (2002) found that an evening chronotype at age
14 – 16 predicted anxiety/depression at age 16.1 – 18.6. Eveningness was also associated
with insomnia at ages 14 – 16 and 16.1 – 18.6. Furthermore, Randler, Bilger, and DíazMorales (2009) reported that adolescents with an evening chronotype have longer sleep
onset latencies that are indicative of sleep onset insomnia, and Gau et al. (2007) found that
eveningness is more predictive of anxious/depressive symptoms than morningness.
47
Therefore, the developmental relationship between insomnia, depression and anxiety may
be affected by chronotype rather than exclusively unidirectional or bidirectional. Indeed,
people with sleep-onset insomnia exhibit a phase delay pattern in body temperature rhythm
(Morris, Lack, & Dawson, 1990) that, coupled with retirement before sleepiness, could lead
to prolonged wakefulness in bed and hence present an opportunity for hyperarousal and
rumination as per psychophysiological insomnia. Regarding depression and anxiety,
homeostatic, circadian and personality factors (particularly low self-control) have been
hypothesised to underlie the association between an eveningness chronotype and poor
mental and physical health (Saxvig, Pallesen, Wilhelmsen-Langeland, Molde, & Bjorvatn,
2012). Furthermore, Gaspar-Barba et al. (2009) argue that the association between
depression, anxiety disorders and eveningness may be related to corticotropin-releasing
factor (Arborelius, Owens, Plotsky, & Nemeroff, 1999).
Nevertheless, the effects of chronotype on the bidirectionality of the relationship
between insomnia, depression and anxiety during adolescence is unclear. That is, whether
eveningness predicts insomnia after accounting for depression and anxiety; depression after
accounting for insomnia and anxiety; and anxiety subtypes after controlling for insomnia and
depression; or accounts for the relationship between these variables is still unknown. The
evidence above suggests that eveningness predicts insomnia, depression and anxiety.
However, depression and anxiety were not controlled in the previous studies when the
relationship between chronotype and insomnia was assessed, nor was insomnia controlled
when the relationships between chronotype and depression, and chronotype and anxiety
were assessed. Furthermore, the analyses that assessed the relationship between insomnia
and chronotype were cross-sectional, meaning an educated guess about the effects of
48
eveningness on the development of insomnia is difficult to make based on the current
literature. Eveningness would be a risk factor for the development of insomnia should it
predict insomnia at follow-up after controlling for depression and anxiety at baseline, and
therefore appear chronologically before insomnia. Similarly, eveningness would be
considered as a risk factor for the development of depression and/or anxiety should it
predict these variables at follow-up after controlling for insomnia, and depression or anxiety
at baseline, and therefore appear chronologically before depression and/or anxiety. In
contrast, eveningness would account for the relationships between insomnia and
depression, or insomnia and anxiety, should these associations no longer be significant in
any direction once chronotype was controlled.
1.10. Aims of the thesis
There were two aims of the thesis. Firstly, this thesis investigated the bidirectionality
of the relationship between insomnia, depression and different subtypes of anxiety during
adolescence, and secondly, the independent effect of chronotype on these relationships.
Three studies were conducted that contributed to the investigation of these aims.
Study 1 intended to identify the inferences that can be made about the bidirectionality of
the relationship between insomnia, depression and anxiety based on previous studies. A
systematic review was conducted that focused on the differences in results according to the
sleep variables that were assessed, as various studies have discussed and compared the
results of different sleep variables. Study 1, therefore, primarily assessed the first aim.
Study 2 aimed to investigate the independent cross-sectional relationships between
insomnia and depression, and insomnia and subtypes of anxiety during adolescence, and the
49
independent effect of chronotype on these relationships. The anxiety subtypes that were
assessed in study 2 included GAD, OCD, PD, SAD, and SP. Continuous variables were used to
indicate the severity of symptoms for insomnia, depression and subtypes of anxiety. The
results of this study contributed to the research of both aims, by providing a base to predict
the findings of study 3.
Study 3 assessed both aims, and hence was the main study of this thesis. The
methodology was longitudinal and prospective, with two data collection points (one baseline
and one follow-up assessment six months apart). This study investigated the bidirectionality
of the relationship between insomnia and depression, and insomnia and subtypes of anxiety
during adolescence after chronotype and other covariates were controlled, along with the
independent predictive power of chronotype on insomnia, depression and anxiety subtypes
after controlling for baseline anxiety subtypes, depression, and/or insomnia. Continuous
variables were also used to indicate the severity of symptoms for insomnia, depression and
subtypes of anxiety.
1.11. Significance/Contribution to the discipline
This thesis will contribute to the understanding of the aetiological relationship
between and hence prevention of insomnia, depression and anxiety during adolescence.
Unidirectionality would suggest that subtypes of anxiety are related to the development of
insomnia, and insomnia is related to the development of depression, but not vice-versa. The
treatment of anxiety subtypes or insomnia may then prevent the development of
subsequent insomnia or depression, respectively (Johnson, Roth, & Breslau, 2006).
Bidirectionality would suggest that insomnia is a risk-factor for the development of
depression and subtypes of anxiety, and vice-versa. The presence of insomnia, anxiety or
50
depression that is preceded by key contributing symptoms may indicate targets for most
effective/efficient therapy (e.g. target prior insomnia rather than current depression).
Therefore, the successful treatment of primary insomnia may prevent the onset or
exacerbation of subsequent depression or anxiety disorders, and vice-versa (Kaneita, et al.,
2009).
An examination of the cross-sectional effects and longitudinal predictive power of
chronotype on the relationships between insomnia and depression, and insomnia and
subtypes of anxiety would identify potential underlying mechanisms of these relationships.
Chronotype (most likely eveningness) would be considered a risk-factor for the development
of insomnia, depression or subtypes of anxiety should eveningness (or morningness) predict
either variable after controlling for baseline covariates (insomnia, depression or subtypes of
anxiety). In constrast, chronotype would be considered a risk factor for the interaction
between insomnia, subtypes of anxiety and depression during adolescence should
chronotype partly account for these relationships. Consequently, prevention and treatment
strategies could target chronotype to limit the development or exacerbation of these
problems. Similarly, insomnia, subtypes of anxiety and/or depression would not be
considered as aetiologically related should the association between these variables no
longer be significant once chronotype has been controlled. Such a finding would suggest that
insomnia, depression and subtypes of anxiety do not directly impact on the development of
each other. Instead, the relationship between these variables would mainly result from a
certain chronotype, which, if targeted by public health campaigns and treatment efforts,
could reduce the relationship between insomnia, depression and subtypes of anxiety.
51
This research project offers the possibility for an improvement in the identification of
symptoms of insomnia, depression and subtypes of anxiety, along with an enhanced
understanding of the relationship between these symptoms in the adolescent population.
The consequent treatment of insomnia, depression and subtypes of anxiety may also
prevent the development or reduce the risk of unhealthy behaviours during adolescence. For
instance, Johnson and Breslau’s (2001) longitudinal study found an association between the
use of cigarettes, alcohol and any illicit drug, and adolescents’ report of sleep disturbances
after adjusting for age, sex, race and family income. Controlling for internalising (depression
and anxiety) and externalising (deviance and aggression) problems reduced this association,
suggesting that the relationship between insomnia and unhealthy behaviours is partially
attributable to psychological problems.
52
Chapter 2: Exegesis
Chapter 1 presented the definitions of major concepts such as adolescence, sleep,
adolescent sleep, insomnia, depression, anxiety and chronotype. An examination of the
epidemiology and consequences of insomnia, anxiety and depression during adolescence,
past research of the bidirectionality of the relationship between insomnia, depression and
anxiety, and methodological issues within the bidirectionality research were also presented.
Finally, chapter 1 discussed the significance and contribution of the thesis and studies in
detail.
Chapter 2 presents an exegesis aimed to explain additional information about the
thesis and each study that goes beyond the scope of the manuscripts or that was excluded
from the manuscripts because of the journal word limit. In particular, this exegesis outlines
the reasons why each study was conducted, the methodological overlap and differences
between each paper, and provides explanations about important decisions that were made.
2.1. The thesis
The concept of this thesis was conceived from a desire to understand potentially
important theories of and relationships between mental health problems, sleep
disturbances, and other factors that are not common knowledge. Assessing the birectionality
of the relationship between insomnia, depression and anxiety was considered after reading
a paper (Johnson, Roth, & Breslau, 2006) that was recommended by Dr Jodie Harris (cosupervisor). Following several meetings with Dr Rachel Roberts (primary supervisor) and Dr
Jodie Harris, the topic of this thesis was developed.
53
I decided to use self-report methods due to funding restrictions. Standardised
questionnaires were chosen that assessed insomnia, depression, generalised anxiety
disorder (GAD), obsessive compulsive disorder (OCD), panic disorder (PD), separation anxiety
disorder (SAD), social phobia (SP) and chronotype. Various potential covariates were also
assessed, such as chronotype, total sleep time, total time in bed, academic achievement,
exercise, caffeine intake, amount of time spent on homework, amount of time spent
working (occupation) outside of school hours, and alcohol and drug use. The decision to
include chronotype was based on previous research (Giannotti, et al., 2002; Randler, et al.,
2009). The decision to include the other variables was also based on previous research,
which showed an association between each construct and insomnia, anxiety and depression
(Benvegnú, Fassa, Facchini, Wegman, & Dall'Agnol, 2005; Bootzin & Stevens, 2005; Brand, et
al., 2010; Curcio, et al., 2006; Fergusson & Woodward, 2002; Kovacs & Devlin, 1998).
Although chronotype was assessed, other potential covariates were excluded from
the analyses for studies 2 and 3. Total sleep time and time in bed were used to tabulate data
on delayed sleep phase syndrome (see study 2). Academic achievement was not deemed to
have been measured reliably, as only two participants at baseline and one at follow-up
reported lower than average grades. The distributions were extremely skewed and the
amount of outliers exceeded 25% for exercise, caffeine intake, amount of time spent on
homework, amount of time spent working (occupation) outside of school hours, and alcohol
and drug use. These results are likely due to the decision to develop only one or two items
for the assessment of each variable that were not based on empirically validated
questionnaires. The decision to use few and non-validated items rather than empirically
validated questionnaires was based on a request from the Department for Education and
54
Child Development to limit the amount of questions that were asked to the adolescents.
Furthermore, the above variables were not the main focus of the thesis.
There was approximately 11% of data missing for the drug and alcohol use
variables, as the Department for Education and Child Development would only grant ethics
committee approval if students from public schools received a copy of the questionnaire
without the drug and alcohol items. Of those who answered the questions, approximately
90% and 98% stated they have never consumed alcohol or illicit drugs, respectively. The lack
of identification of alcohol and illicit drug consumption may have resulted from a caveat that
was placed on the information sheets stating the following: “the information regarding drug
and alcohol use collected in this study could in principle be obtained by court order: that is, it
could be required to be handed over to the police and used as evidence in a court of law
against your child. However, this is unlikely, and the researchers will make every effort to
ensure that any information gathered will remain confidential”. The Human Research Ethics
Committee of the University of Adelaide would only grant ethics approval if this caveat was
on the information sheets.
The above caveat was a rollover effect of a request from the Human Research Ethics
Committee and the requirement of Australian psychologists who conduct research with atrisk populations to identify and follow-up participants with potential mental health disorder.
Drs Rachel Roberts and Jodie Harris are both registered clinical psychologists, and
adolescents are considered an ‘at-risk’ population. The participants were required to write
their names on the top of the questionnaire, and were informed (along with their parents) of
the reasons for this. Once data was collected, each questionnaire was locked in a secure
location, and data was de-identified as soon as possible.
55
Adolescents were identified as at risk and requiring identification of follow-up if
they reported clinically significant scores in the questionnaire that assessed depression and
subtypes of anxiety AND reported thinking about death often or always (the RCADS, see
methods section of studies 2 and 3 for specific details regarding how the scores were
tabulated). Parents were contacted when students were identified as at risk. They were
informed of the findings, and sent a resources sheet containing various treatment options
and information sources via email or mail. The treatment options on each resources sheet
were based on the sector of Adelaide/South Australia the adolescent resided (e.g., north,
south, east, west or central).
2.2. Study 1 - A Systematic Review Assessing
Bidirectionality between Sleep Disturbances, Anxiety,
and Depression
Study 1 was conducted to give an overview of the current literature, along with the
shortcomings and potential future directions of research. Such a study was deemed a useful
introductory paper for this thesis, and lead into assessing the bidirectionality of the
relationship between insomnia, depression, and anxiety.
Participants of any age and variables of any sleep disturbance were included in study 1,
as only one study assessed bidirectionality between insomnia, anxiety and depression during
adolescence (Johnson, Roth, & Breslau, 2006), and five independent studies assessed
insomnia in various age groups (Buysse et al., 2008; Hasler et al., 2005; Jansson-Fröjmark &
Lindblom, 2008; Johnson, Roth, & Breslau, 2006; J. M. Kim, et al., 2009; Morphy, et al.,
2007). Another study was originally included in the systematic review that assessed the
place of chronic insomnia in the course of development of anxiety and depressive disorders,
56
and vice-versa (Ohayon & Roth, 2003). However, this study did not use significance testing,
and, after a discussion between all authors, was deemed not to adequately assess
bidirectionality.
The terms sleep disturbance encompassed any variable that was indicative of disrupted
sleep, including clinically significant and diagnosed disorders. The terms sleep problems
portrayed an overall variable consisting of items representing more than one sleep
disturbance that was assessed by a study included in the systematic review. For example, a
questionnaire that contained items about insomnia and nightmares would be considered as
sleep problems. Sleep problems was consistently referred to as childhood sleep problems, as
each sleep problem variable was assessed in children. Sleep quality was reflected by
variables that assessed how well an individual slept, and often contained insomnia items.
Insomnia was defined as troubles with sleep initiation, sleep maintenance, night-time
awakenings, and sleep latency following night-time awakenings. Some articles that assessed
insomnia also assessed daytime functioning. Although not specifically a sleep disturbance
variable, time in bed was included in the systematic review, as lower amounts of time in bed
suggests sleep loss.
2.3. Study 2 – The Independent Relationships between
Insomnia, Depression, Subtypes of Anxiety, and
Chronotype during Adolescence
Study 2 was a cross-sectional study of 318 high school students that used self-report
measures. There were two main aims; firstly, to assess the relationship between the main
variables of the thesis at baseline; and secondly, to discover whether or not chronotype
predicts insomnia, depression and subtypes of anxiety once covariates are controlled.
57
Chronotype was deemed to potentially predict insomnia, depression and subtypes of
anxiety, as previous studies have found that chronotype was correlated to each variable
during adolescence (Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009; Russo, Bruni,
Lucidi, Ferri, & Violani, 2007). Study 2 originally assessed the mediation effects of
chronotype on the relationship between insomnia and depression above and beyond
anxiety, and insomnia and each subtype of anxiety above and beyond depression. However,
a reviewer for study 2 suggested that such an investigation lacked theoretical support, and
hence suggested that moderation or prediction be assessed. The authors decided on
prediction to be consistent with study 3 and the overall aim of the thesis.
The decision to use generalised linear equations to analyse the aims of study 2 was
based on the recommendation of a statistician at the University of Adelaide (statistical
justifications can be found in the methods section of the study). Furthermore, although
outliers were found, the 5% trimmed mean for insomnia, depression, subtypes of anxiety
and chronotype subscales were very similar to the actual means (differences ranged from
.09 to .26), suggesting the outliers would have a minimal effect on the results. Indeed,
sensitivity analyses showed this to be the case. Therefore, these data were retained (Pallant,
2011).
Less than 2% of the data from study 2 was missing for the insomnia, depression,
subtypes of anxiety and chronotype scales. Although some evidence suggests that
missingness may have been non-random, the sample size was large and the missing values
were very small. Therefore, on the recommendation of Tabachnick and Fidell (2007),
participants with missing data were only excluded for the analyses that assessed the
58
variables containing their missing data and retained for the analyses where all data was
present.
2.4. Study 3 – Bidirectional relationships between
insomnia and depression, and insomnia and subtypes of
anxiety during adolescence: does chronotype effect
these relationships?
Study 3, a longitudinal report of 255 high school students, was the main paper for this
thesis. The aims were to investigate the bidirectionality of the association between
insomnia, depression and subtypes of anxiety, and the effects of chronotype on these
relationships. Chronotype was used as a predictor for study 3.
Bootstrap step-wise regression analyses were used rather than generalised linear
equations in study 3 after consulting a statistician. Different analyses were needed, as study
3 was longitudinal and study 2 was cross-sectional. The statistician noted that because
mediation analyses were not conducted for study 3, deciding on the statistical analysis to
use should be based on whether cluster variables (schools in this case) have a large enough
effect on the outcome variables. He recommended not accounting for clustering if schools
explain less than 5% of the outcome variables, but if they did, further tests were to be
conducted. The amount of variance explained by schools for each outcome variable was
below 4%, with most below 2%, therefore suggesting minimal clustering.
The amount of missing data for study 3 was a little higher than that of study 2, but still
below 3% for depression, each anxiety subtype and chronotype, and below 7% for insomnia
(approximately 6.6%). The evidence suggested that missing data was random, while the
59
sample size was large and the amount of missing values was very low. Therefore,
participants with missing data were only excluded from the analyses when they had missing
data for all major variables (i.e., insomnia, anxiety subtypes, depression and chronotype)
(Tabachnick & Fidell, 2007).
Outliers were also found in study 3, and the 5% trimmed means for the outcome
variables were slightly higher than those found in study 2. However, as suggested by Pallant
(2011), the outliers were retained, as the differences between the 5% trimmed means and
original means were still very low (ranging from 0.12 to 0.45). These findings, paired with the
sensitivity analyses, suggested that the outliers had a negligible impact on the results.
Originally, there was a debate regarding the length of time between baseline and
follow-up for study 3. Bidirectionality studies have typically used 12 or 24 month intervals
between baseline and follow-up (Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; J.
M. Kim, et al., 2009; Meijer, et al., 2010), but the current study used a six month interval.
This makes for a more reliable study, as potential confounders such as schools, classrooms,
friends and lifestyle (e.g., driving and dating) is more likely to remain constant. It could be
argued that seasonal affective disorders are more likely to bias the results when using a six
month follow-up, as baseline and follow-up would occur at opposing seasons. However,
studies have found low prevalence rates of winter seasonal affective disorders within
Australia [3.65% (Kasof, 2009), 1.7% (Morrissey, Raggatt, James, & Rogers, 1996), 0.7% and
0.5% (Murray, 2004)], suggesting that seasonal affective disorders are not expected to
greatly impact the results. Furthermore, such prevalence rates are likely to be exaggerated;
the Seasonal Pattern Assessment Questionnaire was used by each study to assess seasonal
affective disorders, which has been found to overestimate the prevalence rate of seasonal
60
affective disorders compared to clinical assessments (for a discussion see Mersch,
Middendorp, Bouhuys, Beersma, & Van Den Hoofdakker, 1999). Finally, summer seasonal
affective disorders are likely to occur in tropical climates (Morrissey, et al., 1996) and
therefore should not affect the study population, because South Australian weather
resembles a Mediterranean climate.
The choice of a 6-month follow-up had a repercussion on the statistical analyses.
Although study 1 argued that outcome variables must be controlled at baseline to accurately
assess bidirectional relationships, it was deemed inappropriate to control for baseline
outcome variables due to the 6-month follow-up. Firstly, symptoms and disorders of
insomnia, depression and subtypes of anxiety are often chronic (see chapter 1 for a review
of the chronicity of each disorder). Secondly, 6 months is unlikely to be enough time for
many new cases of clinically significant insomnia and mental health problems to develop
either independently or as a result of one another, although some new cases may arise.
Indeed, Pearson’s correlation analyses between baseline and follow-up variables showed r
values that ranged between .66 and .78 for insomnia, depression, generalised anxiety
disorder, separation anxiety disorder, and chronotype, and an r value of .61 for obsessive
compulsive disorder and .59 for panic disorder. Furthermore, paired samples t-tests did not
find a significant difference between baseline and follow-up insomnia, depression or anxiety
subtypes, except for PD. However, the Cohen’s d statistic was small in magnitude (d= 0.12)
(Cohen, 1992), suggesting the change was unlikely to be meaningful. Moreover, Baglioni, et
al. (2011) stated that a long-term evaluation of the relationship between insomnia and
depression should have a follow-up of no less than a 12 month. Thirdly, a minimum criterion
of GAD is the presentation of symptoms more often than not for at least 6 months. The
61
current thesis did assess clinically significant symptoms of GAD, but this assessment was
based on, and has been validated against (see chapters 4 and 5 for a review of the validation
literature) the DSM-IV diagnostic criteria (American Psychiatric Association, 2000).
Therefore, the development of clinically significant GAD symptoms is likely to reflect the
development of GAD that is diagnosed according to the DSM-IV diagnostic criteria. Indeed,
Mathyssek et al. (2013) psychometric evaluation of the Revised Children’s Anxiety and
Depression Scale (RCADS, the inventory that was used to assessed symptoms of anxiety
subtypes in this research project) suggests that measured stability or changes of anxiety
symptoms over time are highly indicative of true stability or changes in anxiety levels over
time. Nevertheless, the authors recognise the validity of the argument for controlling
baseline outcome variables, and have therefore presented such the results in an appendix in
chapter 7.
2.5. Sample size rationale for studies 2 and 3
Tabachnick and Fidell (2007) recommended the following equation to attain a sample
size that would yield adequate power (α = .05, β= .20), as dependent variables were
expected to be skewed (which was the case) and possibly small in magnitude:
N ≥ (8/f2) + (m – 1)
Where f2= R2/(1 – R2), and m= number of predictors.
An examination of the recent literature found that effect sizes (f2) generated by studies
that assessed the bidirectionality of the relationship between anxiety, depression and sleep
disturbances ranged from 0.02 - 0.33 (Gregory & O'Connor, 2002; Gregory, et al., 2009;
Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; J. M. Kim, et al., 2009), with the
62
majority between .04 and .08. An f2 statistic of .03, considered to be a small effect size
(Cohen, 1992), was used to calculate the target sample size to be conservative.
N ≥ (8/.03) + (4 – 1)
N ≥ 228.57 + 3
N ≥ 231.57
Therefore, approximately 232 participants were needed at follow up to achieve
adequate power. A further 25% was added to the target sample size at baseline to account
for dropouts. Therefore, the target sample size was 290 at baseline.
2.6. Summary
The current chapter described information that was important to but beyond the
scope of the manuscripts due to journal word limits. The reasons for conducting each study
were discussed, along with the methodological overlap and differences between the
manuscripts, and reasons for important decisions such as sample size.
The next chapter presents the first study entitled ‘A systematic review assessing
bidirectionality between sleep disturbances, anxiety and depression’. It follows a normal
structure for a systematic review, which begins with an abstract, followed by an
introduction, methods, results and discussion. The main difference between the structure of
a systematic review and an original article is in the methods section, where a systematic
review outlines the assessment of quality for each study that is included in the analyses.
63
Chapter 3: Study 1
A Systematic Review assessing Bidirectionality
between Sleep Disturbances, Anxiety and Depression.
Alvaro, P.K., Roberts, R.M., & Harris, J.K.
School of Psychology, University of Adelaide
Published: 2013, Sleep, 36 (7), 1059 – 1068.
Pasquale Alvaro (PhD Candidate)
I collected data, performed each synthesis, interpreted data, wrote the manuscript and
acted as the corresponding author. I also made the decisions on what data and arguments to
present in this paper. I later made the revisions to the paper based on the reviewers’
comments.
X
Date: 25/08/14
Pasquale Alvaro
PhD Candidate
64
Rachel Roberts (Co-Author)
I was the primary supervisor for the research project that led to this paper, so was involved
in the design of the study described in the paper and discussions of results, particularly in
the case of adolescent literature. Mr Alvaro was responsible for writing the paper, and I was
responsible for providing editorial comments and advice on making changes following
review. I hereby give my permission for this paper to be included in Mr Alvaro’s submission
for the degree of PhD at the University of Adelaide.
X
Date: 25/08/14
Rachel Roberts
Co-author
Jodie Harris (Co-Author)
I was the co-supervisor for the research project that led to this paper. I was involved in the
design of the study described in the paper and discussions of results, particularly where the
sleep variables were concerned. I also provided editorial comments and advice on making
changes following review. I hereby give my permission for this paper to be included in Mr
Alvaro’s submission for the degree of PhD at the University of Adelaide.
X
Date: 25/08/14
Jodie Harris
Co-author
65
A
Alvaro, P.K., Roberts, R.M. & Harris, J.K. (2013) A systematic review assessing bidirectionality
between sleep disturbances, anxiety and depression.
Sleep, v. 36(7), pp. 1059-1068
NOTE:
This publication is included on pages 66-92 in the print copy
of the thesis held in the University of Adelaide Library.
It is also available online to authorised users at:
http://doi.org/10.5665/sleep.2810
66
Chapter 4: Study 2
The independent relationships between insomnia,
depression, subtypes of anxiety, and chronotype
during adolescence.
Alvaro, P.K., Roberts, R.M., & Harris, J.K.
School of Psychology, University of Adelaide
Published: 2014, Sleep Medicine, 15 (8), 934 – 941.
Pasquale Alvaro (PhD Candidate)
I collected data, performed each analysis, interpreted data, wrote the manuscript and acted
as the corresponding author. I also made the decisions on what data and arguments to
present in this paper. I later made the revisions to the paper based on the reviewers’
comments.
X
Date: 25/08/14
Pasquale Alvaro
PhD Candidate
94
Rachel Roberts (Co-Author)
I was the primary supervisor for the research project that led to this paper, so was involved
in the design of the study described in the paper and discussions of results, particularly in
the case of adolescent literature. Mr Alvaro was responsible for writing the paper, and I was
responsible for providing editorial comments and advice on making changes following
review. I hereby give my permission for this paper to be included in Mr Alvaro’s submission
for the degree of PhD at the University of Adelaide.
X
Date: 25/08/14
Rachel Roberts
Co-author
Jodie Harris (Co-Author)
I was the co-supervisor for the research project that led to this paper. I was involved in the
design of the study described in the paper and discussions of results, particularly where the
sleep variables were concerned. I also provided editorial comments and advice on making
changes following review. I hereby give my permission for this paper to be included in Mr
Alvaro’s submission for the degree of PhD at the University of Adelaide.
X
Date: 25/08/14
Jodie Harris
Co-author
95
4.1. Abstract
4.1.1. Objectives
This study investigated the independent effects of depression and subtypes of anxiety
on insomnia, and vice-versa, and the independent effect of chronotype on insomnia,
depression and subtypes of anxiety.
4.1.2. Methods
Three-hundred and eighteen South Australian high school students from grades 7 to
11 (age range 12-18, mean 14.97 ± 1.34) participated in this cross-sectional study. Validated
self-report questionnaires were used to assess insomnia, depression, subtypes of anxiety
and chronotype.
4.1.3. Results
After confounder variables were controlled, insomnia predicted depression and panic
disorder PD, whereas insomnia was predicted by depression and generalised anxiety
disorder (GAD). Obsessive compulsive disorder (OCD), separation anxiety (SAD) and social
phobia (SP) were not significantly related to insomnia. Eveningness predicted the models
where depression and PD predicted insomnia and vice-versa. Eveningness also predicted the
models when insomnia was predicted by OCD, SAD and SP.
4.1.4. Conclusions
Insomnia independently predicts depression and is predicted by depression and GAD,
but not other forms of anxiety. The independent prediction of insomnia on panic disorder
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(PD), is unlikely to be clinically significant. Chronotype independently predicts insomnia and
depression, but not subtypes of anxiety. Theoretical and clinical implications are discussed.
4.2. Introduction
Insomnia is a very common sleep problem in the general population, with a lifetime
prevalence rate of approximately 11% in adolescents aged 13 to 16 (Johnson, Roth, Schultz,
et al., 2006). Recent studies have consistently found an association between insomnia and
depression, and insomnia and anxiety disorders in adolescent populations (Gregory &
O'Connor, 2002; Johnson, Roth, & Breslau, 2006), which is expected given that the presence
of insomnia is a possible criteria for the diagnosis of depression and various anxiety
disorders, and these disorders contain overlapping neurobiological, psychological and social
risk-factors (Casey, et al., 2008; Holmes, 2003; Kaneita, et al., 2009; Lipton, et al., 2008;
Nestler & Carlezon, 2006; Peroutka, 1998; Tseng & O'Donnell, 2007; Yoshioka, et al., 1996).
Insomnia comorbid with anxiety or depression can further intensify the problematic
outcomes associated with each disorder, such as alcohol and drug abuse during adolescence
(Johnson & Breslau, 2001).
Despite their well-documented association, various aspects and mechanisms of the
relationships between insomnia and anxiety, and insomnia and depression are unclear.
Firstly, the significance of the relationship between insomnia and anxiety may differ across
anxiety disorders. A recent study found an association between insomnia and separation
anxiety disorder, and insomnia and generalised anxiety disorder, but not between insomnia
and social anxiety disorder (Alfano, et al., 2007). Indeed, some associations between sleep
difficulties and anxiety disorders (generalised anxiety disorder) are expected given the
symptom overlap for diagnosis. Another study found that youths with generalised anxiety
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disorder reported higher rates of trouble sleeping overall than youths with separation
anxiety disorder, social phobia, and obsessive compulsive disorder (Alfano, Pina, Zerr, &
Villalta, 2010). Secondly, the mediated and independent effects of insomnia on anxiety or
depression, and vice-versa, remain unclear. Anxiety may mediate the relationship between
insomnia and depression, while depression may mediate the relationship between insomnia
and anxiety due to the common underlying neurobiological, psychological and social factors
mentioned above. Finally, chronotype, defined as an individual’s natural inclination for
mornings (morningness) or evenings (eveningness), could uniquely predict insomnia, anxiety
and depression. Recent studies suggest that an eveningness chronotype predicts higher
levels of insomnia, anxiety and depression during adolescence (Ferber, 1990; Giannotti, et
al., 2002; Randler, et al., 2009; Russo, et al., 2007). These studies, however, have yet to
demonstrate whether eveningness predicts insomnia beyond depression and anxiety,
anxiety beyond insomnia and depression, or depression beyond insomnia and anxiety. Such
findings would suggest that eveningness is an independent risk factor for insomnia, anxiety
and depression. These findings would be particularly important, because adolescents tend to
develop a preference for evenings due to circadian rhythm and environmental changes
(Crowley, et al., 2007), which sometimes develops into and is the main symptom of delayed
sleep phase syndrome (American Academy of Sleep Medicine, 2005). This syndrome affects
approximately 8% of adolescents (Saxvig, et al., 2012), and has been associated with
insomnia, anxiety and depression (Reid et al., 2012).
This study, therefore, adds to the previous literature by investigating in one study the
independent effect of depression and various types of anxiety on insomnia, and vice-versa,
and the effect of chronotype on insomnia, depression and subtypes of anxiety that is
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independent from other predictors. The anxiety subtypes assessed in the current study
include Generalised Anxiety Disorder (GAD), Obsessive Compulsive Disorder (OCD), Panic
Disorder (PD), Separation Anxiety Disorder (SAD) and Social Phobia (SP). It was hypothesised
that insomnia will be independently related to some but not other mental health problems,
in particular, to depression, GAD and SAD, but not SP. It was also hypothesised that
chronotype will independently predict insomnia, depression, and subtypes of anxiety above
and beyond other predictors. The independent effect was defined as the amount of variance
of a dependent variable (Y) that is explained by an independent variable (X) after controlling
for confounder variables (Cx). Insomnia, anxiety subtypes and depression were used as
predictor and outcome variables, as recent studies have suggested that insomnia, anxiety
and depression are bidirectionally related (Alvaro, Roberts, & Harris, 2013). Understanding
the pathways of the relationship between insomnia and anxiety subtypes, and insomnia and
depression can inform public health campaigns and clinical interventions for each disorder,
and also enhance the understanding of the interaction between these disorders.
4.3. Method
4.3.1. Participants
Three-hundred and eighteen South Australian secondary school students aged 12–18
(M= 14.96, SD= 1.34) participated in the study. One-hundred and sixty-four (51.6%) students
were male and 154 (48.4%) were female. Nine students were in grade 7, 102 were in grade
8, 66 in grade 9, 52 in grade 10 and 89 in grade 11. The study was voluntary, and required
student and parental consent for participation. Participants were eligible for this study if
their parents consented, were in grades 7 to 11, and were fluent in English. This study was
approved by the Human Research Ethics Committee (HREC) from the University of Adelaide,
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the Department for Education and Child Development (DECD), and Catholic Education South
Australia (CESA).
4.3.2. Measurements
A questionnaire composed of several inventories was used to assess adolescent sleep
and mental health. Demographic questions include date-of-birth, gender, and
socioeconomic status as assessed by postcode (Australian Bureau of Statistics, 2008b).
Personal questions such as previous sleep or mental-health problems, previous therapy for
sleep or mental health problems, previous or current disabilities/chronic illnesses (e.g.,
asthma, diabetes, deafness, etc), current medications that may affect sleep or mental health
and the frequency of drug and alcohol consumption were adaptations from the School Sleep
Habits Survey (SSHS) (Wolfson & Carskadon, 1998). Total Sleep time, bed time and rise time
on weekdays and weekends were also reported, the latter two of which were used to
calculate total bed time. These questions were asked to give an account of the general
characteristics of the sample.
Insomnia was measured by the Insomnia Severity Index (ISI) (Morin, 1993), a 7-item
inventory that assesses the severity of subjective symptoms and consequences of insomnia
based on the DSM-IV (American Psychiatric Association, 2000). Each item was scored on a 0
to 4 Likert scale. Total scores were calculated by the sum of each item and ranged from 0 to
28. Higher scores indicate more severe insomnia. Morin (1993) provides a scoring guideline:
0–7 no clinically significant insomnia, 8–14 subthreshold insomnia, 15–21 moderate clinical
insomnia, and 22–28 severe clinical insomnia. The symptoms assessed included difficulty
falling sleep, difficulty staying asleep, and problems waking up too early, while the
consequences assessed included impaired quality of life, worry/distress about current sleep
100
patterns, and perceived interference of sleep problems with daily life. Although designed for
adults, the ISI has been widely used in the adolescent population (Brand et al., 2011; Luo,
Zhang, & Pan, 2012; Short, Gradisar, Lack, & Wright, 2013). Furthermore, a recent study has
validated the ISI in an adolescent population, reporting significant correlations with clinical
ratings of insomnia, the Sleep-Wake Habits Questionnaire, General Mental-Health
Questionnaire, Epworth Sleepiness Scale, smoking habits, alcohol use, number of naps per
week, and academic performance (Chung, Kan, & Yeung, 2011). A Cronbach’s alpha of 0.83,
and a 2-week test-retest reliability of 0.79 for the ISI in an adolescent population (Chung, et
al., 2011) were also reported.
The Morningness-Eveningness Scale for Children (MES) (Carskadon, Vieira, & Acebo,
1993), a 10-item adaptation of the Composite Scale of Morningness (Smith, Reilly, & Midkiff,
1989), was used to assess adolescents’ orientation towards Morning and Evening
chronotypes. Seven items are scored on a Likert scale from 1 to 4, while 3 items are scored
on a Likert scale from 1 to 5. Total scores are calculated by the sum of each item and range
from 10 to 42. Lower scores indicate a tendency towards eveningness. The MES successfully
discriminates between morningness and eveningness in adolescents (Díaz-Morales, De León,
& Sorroche, 2007). In accordance with previous research (Giannotti, et al., 2002; Russo, et
al., 2007), evening and morning-types were defined as below the 10th and above the 90th
percentile, respectively, and scores in between were identified as neither types (cut-off
scores were below 20 for eveningness and above 33 for morningness). Previous studies have
reported good internal consistency, with Cronbach’s α of 0.73 (Giannotti, et al., 2002), 0.82
(Díaz-Morales, et al., 2007), and 0.82 (Warner, Murray, & Meyer, 2008) in Italian, Spanish,
and Australian adolescents respectively. Good test-retest reliability (0.78) (S. Kim, Dueker,
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Hasher, & Goldstein, 2002) and external validity have been reported (Díaz-Morales, et al.,
2007), and the MESC has been shown to predict daytime functioning, academic achievement
and various behavioural outcomes in adolescents (Giannotti, et al., 2002; Warner, et al.,
2008). Two items were altered to keep the language consistent with the Australian
population and the age of the sample. First, the phrase “gym class” was changed to “Sports
class”. Second, the item “Your parents have decided to let you set your own bed time. What
time would you pick?” was changed to “What time would you prefer to go to bed?”
Adolescents who met the following criteria were deemed to likely suffer from delayed
sleep phase syndrome (Johnson, Roth, Schultz, et al., 2006; Sivertsen et al., 2013); a
minimum of 1-hour shift in bed and rise times from weekdays to the weekend; moderate,
severe or very severe complaints of difficulty falling asleep; no or mild complaint of difficulty
maintaining sleep; AND not at all easy to wake up in the morning.
Subtypes of anxiety and depression were assessed by the Revised Child Anxiety
Depression Scale (RCADS) (Chorpita, Yim, Moffitt, Umemoto, & Francis, 2000), a 47-item selfreport questionnaire that is an adaption of the Spence Children’s Anxiety Scale (Spence,
1997) and corresponds to diagnostic categories of DSM-IV. Each item is scored on a 0 to 3
Likert scale, with higher scores corresponding to more severe depression or anxiety.
Subscales are provided for Generalised Anxiety Disorder (GAD, 6 items, scores ranging from
0 to 18), Panic Disorder (PD, 9 items, scores ranging from 0 to 27), Obsessive Compulsive
Disorder (OCD, 6 items, scores ranging from 0 to 18), Separation Anxiety Disorder (SAD, 7
items, scores ranging from 0 to 21), Social Phobia (SP, 9 items, scores ranging from 0 to 27),
and Major Depressive Disorder (MDD, 10 items, scores ranging from 0 to 30). An overall
scale for anxiety (37 items, scores ranging from 0 to 111) is also provided. Scales are
102
calculated by the sum of each item. The RCADS users guide [available on the University of
California, Los Angeles website (Chorpita, 2011)] converted raw scores into standardised T
scores, where T scores between 65 and 69 indicate borderline clinical threshold and T scores
≥ 70 indicate above clinical threshold. A recent study reported the following Cronbach’s α in
Hawaiian adolescents: SP, α=0.81; PD, α=0.85; GAD, α=0.80; MDD, α=0.76; SAD, α=0.78; and
OCD, α=0.71 (Chorpita, et al., 2000). The same study provided strong support for the
structural, convergent and discriminant validity of the RCADS. The RCADS has also been
validated in Australian adolescents; De Ross and colleagues (2002) provided support for the
internal consistency for MDD, and anxiety overall, and anxiety subscales. Good convergent
validity was also demonstrated (2002) with moderate to strong correlations between the
subscales of RCADS with scores on the Revised Manifest Anxiety Scale (RCMAS) (Reynolds &
Richmond, 1985) and the Children’s Depression Inventory (CDI) (Kovacs, 1981). Indeed, the
RCADS has been extensively used in the adolescent population (Austin & Chorpita, 2004;
Weems & Costa, 2005), including in studies that have assessed the relationship between
sleep problems, anxiety and depression (Alfano, et al., 2009).
4.3.3. Procedure
Every secondary school within a 40km radius of the Adelaide CBD that was listed by
the Department for Education and Child Development (DECD) in South Australia or in the
2010 Annual Report of the Advisory Committee on Non-Government Schools in South
Australia (n= 71 within catchment area) was approached to participate in the study. Each
principal was sent a letter that invited the school to participate and outlined the study
details, along with the relevant documents including the questionnaire, information letters
to students and parents, and consent forms. Principals who did not respond to the letter
103
received a follow-up phone call or email. Once principals agreed to participate, information
letters and consent forms for the students and parents were given to each student in
selected grades. Students were asked to return consent forms to their home-group teachers,
and those who did not were unable to participate. Questionnaires were completed in class
time under the supervision of teachers. Eight secondary schools in South Australia
participated in the current study.
4.3.4. Statistical analyses
Descriptive statistics were reported using means and standard deviations, or numbers
and percentages. Spearman’s rho (Spearman, 1910) was used to calculate correlations to
account for the slightly skewed data. Direct effects were assessed when a predictor variable
was correlated with an outcome variable.
Two analyses were conducted to assess the independent effect of insomnia on
depression or subtype of anxiety, and vice-versa, and the unique effect of chronotype on
insomnia, depression and subtypes of anxiety. One analysis used insomnia, chronotype, and
depression, an anxiety subtype or anxiety overall as the predictor variables, and an anxiety
subtype or depression as the outcome variable (Figure 5). The other analysis used insomnia
as the outcome variable, and depression or an anxiety subtype, chronotype and anxiety
overall or depression as predictor variables (Figure 5). When anxiety subtypes were
analysed, chronotype and depression were assessed as predictors. When depression was
analysed, chronotype and an anxiety overall variable were used as predictors. The overall
anxiety variable was used to control for all anxiety symptoms.
104
Depression
Insomnia
Chronotype
Chronotype
Insomnia
Depression
Anxiety
overall
Anxiety
overall
Anxiety
Subtype
Insomnia
Chronotype
Chronotype
Insomnia
Depression
Anxiety
subtype
Depression
Figure 5. Steps for analyses.
105
Generalised Estimating Equations (GEE) were used to assess the above analyses. GEE
accounts for clustered and skewed data (Galbraith, Daniel, & Vissel, 2010) that commonly
occur when data are collected from schools. GEE also produces regression coefficients (β)
and confidence intervals (CI). β represents the magnitude of the effects of X on Y, and is
used to generate the coefficients of each unique independent effect. The independent effect
of X on Y is represented by the β that is calculated after each predictor is accounted for CIs
are used for significance testing, where CIs that contain 0 fail to reject the null hypothesis.
Sample size analysis for the GEE was based on multiple regression, as both apply to the
class of generalised linear models and produce β coefficients. The difference between GEE
and multiple regression is that GEE generates robust standard errors that account for
clustered data (Galbraith, et al., 2010), and hence should have more power to detect an
effect when analysing clustered data.
4.4. Results
4.4.1. Descriptive statistics
Tables 6, 7 and 8 report descriptive statistics for demographic information, sleep and
mental health variables, and frequencies of clinically significant and sub-threshold mental
health and insomnia cases. Approximately 25% of adolescents reported suffering from past
sleep or mental health problems, while approximately 17% reported previous treatment for
sleep or mental health problems. Approximately 20% reported other medical issues, while
14% reported previous or ongoing treatment for such issues. Insomnia was the most
frequently identified clinically significant problem (11.19%), whereas MDD was the most
common mental health problem (8.39%).
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Table 6
Demographic and personal history
Variable
Self-report past mental health symptoms
Group
Number
Percent
Depression
Anxiety
Sleep problems
Depression, Anxiety
Depression, Anxiety and Insomnia
Other
No
22
21
25
4
5
8
233
6.9
6.6
7.9
1.3
1.6
2.5
73.3
Psychologist
Counsellor
GP
Other
None
11
24
5
20
263
3.5
7.5
1.6
6.2
82.7
Asthma
Chronic pain
Other
None
40
3
19
255
12.6
0.9
5.8
80.2
No
Asthma medication e.g., Ventolin puffer
ADHD medication
Sleep medication
Prescribed antidepressants
Non-prescribed antidepressants or sleep
medication
Other
272
32
1
1
0
85.5
10.1
0.3
0.3
0
1
12
0.3
3.7
Yes
No
Missing data
30
255
33
80.2
9.4
10.4
Yes
No
Missing data
5
279
34
1.6
87.7
10.7
Too little sleep
Enough sleep
Too much sleep
143
170
5
44.9
53.4
1.6
Never
Rarely
Sometimes
Usually
Always
7
50
112
133
16
2.2
15.7
35.2
41.8
5.0
Eveningness
Neither
Morningness
Missing
37
252
26
3
11.64
79.25
8.18
0.94
Self-report past treatment for symptoms of
depression, anxiety, sleep problems
Permanent disabilities or illnesses
Medication
Alcohol use
Drug use
Subjective sleep sufficiency
Frequency of sufficient sleep
Chronotype
107
Table 7
Descriptive statistics for sleep and mental health variables
Variable
N
Missing data
Insomnia
312
6
Major Depressive Disorder
314
4
Generalised Anxiety Disorder
314
4
Obsessive Compulsive Disorder
313
5
Panic Disorder
314
4
Separation Anxiety
314
4
Social Phobia
314
4
Morningness-Eveningness
315
3
Total Sleep Time weekday
313
5
Total Sleep Time weekend
314
4
Total Time in Bed weekday
316
2
Total Time in Bed weekend
311
7
Mean
7.63
7.62
5.63
4.36
6.55
3.68
6.69
27.17
8.31
9.43
8.7
9.67
SD
5.28
4.69
3.2
3.01
4.37
3.15
4.4
5.41
1.27
1.72
1.1
1.48
Minimum
0
0
0
0
0
0
0
10
4
4.5
4
3.5
Maximum
25
28
16
17
24
14
25
40
11.5
15
11.5
13.5
Table 8
Number and frequency of clinically significant cases
Depression
GAD
OCD
PD
SAD
SP
Insomnia
Clinical case frequency
29
21
7
14
17
15
39
Total case frequency
314
314
313
314
314
314
312
Clinical frequency
9.24%
6.69% 2.24%
4.46%
5.41%
4.78%
12.50%
GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation
Anxiety Disorder, SP= Social Phobia.
Correlations are reported in Table 9. ISI was moderately to highly correlated with each
subscale from the RCADS. The results also depicted small to medium correlations between
chronotype and each subscale from the RCADS, except for GAD, where no significant
correlation was found. The difference between total sleep time on weekends and weekdays
was significantly correlated with an eveningness chronotype, insomnia, depression, PD, OCD,
and SP.
Table 9
Spearman's rho correlations between anxiety, subtypes of anxiety, depression, insomnia and chronotype
Anxiety
overall
Depression
GAD
PD
OCD
SAD
SP
Insomnia
CP
Anxiety
overall
1
0.744**
0.790**
0.800** 0.797** 0.843** 0.866**
0.580**
-0.207**
Depression
1
0.484**
0.683** 0.693** 0.578** 0.659**
0.672**
-0.369**
GAD
1
0.537** 0.573** 0.649** 0.590**
0.414**
0.030
PD
1
0.637** 0.643** 0.593**
0.503**
-0.218**
OCD
1
0.619** 0.597**
0.457**
-0.193**
SAD
1
0.659**
0.447**
-0.178**
SP
1
0.496**
-0.222**
Insomnia
1
-0.438**
Chronotype
1
**. Correlation is significant at the 0.01 level (2-tailed).
GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation Anxiety
Disorder, SP= Social Phobia.
108
Generalised estimation equations were run with and without participants who were
identified with delayed sleep phase syndrome tendencies (complete data available for 300
participants, n= 18, percentage= 6%). No differences were found, and therefore all available
data were retained.
4.4.2. Depression and Insomnia
The depression scale contained an item similar to insomnia, which was thought to
possibly inflate the regression coefficients of the relationship between insomnia and
depression. The overall pattern of results remained the same when analyses were
conducted with and without the insomnia-based MDD item. Therefore, the original MDD
scale was retained.
The analyses of depression and insomnia included 302 participants. Depression had a
significant independent effect on insomnia (β= 0.526, 95% CI= 0.406 - 0.645), and vice-versa
(β= 0.3767, 95% CI= 0.276 - 0.477). Chronotype and anxiety uniquely predicted insomnia
(chronotype β= -0.210, 95% CI = -0.306 – -0.113; anxiety β= 0.040, 95% CI = 0.0017 – 0.063)
and depression (chronotype β = -0.103, 95% CI = -0.169 – -0.036; anxiety β= 0.167, 95% CI=
0.137 – 0.196).
4.4.3. GAD and Insomnia
Three-hundred and seven participants were included in the analyses for the
relationship between GAD and insomnia. Chronotype and GAD were not associated before
potential confounders were controlled. Therefore, chronotype was not used to predict GAD.
Significant independent effects were found when GAD predicted insomnia (β= 0.258,
95% CI= 0.025 - 0.336) but not when insomnia predicted GAD (β= 0.116, 95% CI= -0.005 -
109
0.236). Chronotype and depression predicted insomnia (chronotype β= -0.227, 95% CI= 00.330 - -0.124; depression β= 0.522, 95% CI= 0.385 – 0.659), and depression predicted GAD
(depression β= 0.245, 95% CI= 0.158 – 0.345).
4.4.4. OCD and Insomnia
The analyses for the relationship between OCD and insomnia contained 303
participants. A significant independent effect was not found when OCD predicted insomnia
(95% CI= -0.117 – 0.080) nor when insomnia predicted OCD (95% CI= -0.050 – 0.034).
Depression and chronotype predicted insomnia (depression β= 0.618, 95% CI= 0.499 – 0.738;
chronotype β= -0.194, 95% CI= -0.286 – -0.103), whereas depression but not chronotype
uniquely predicted OCD (depression β= 0.400, 95% CI= 0.351 – 0.449; chronotype 95% CI= 0.041 – 0.096).
4.4.5. Panic Disorder and Insomnia
The sample size for the analyses of the relationship between PD and insomnia
contained was 304. An independent significant effect was found when insomnia predicted
PD (independent β = 0.064, 95% CI= 0.007 – 0.121) but not when PD predicted insomnia
(95% CI= -0.003 – 0.201). Depression and chronotype predicted insomnia (depression β=
0.562, 95% CI= 0.463 – 0.661; chronotype β= -0.199, 95% CI= -0.290 – -0.108), but
depression and not chronotype predicted PD (depression β = 0.464, 95% CI= 0.395 – 0.532;
chronotype 95% CI= -0.010 – -0.081).
4.4.6. Separation Anxiety and Insomnia
Three hundred and three participants were used for the analyses of the relationship
between SAD and insomnia. A significant independent effect was not found when SAD
110
predicted insomnia (95% CI= -0.028 – 0.276), nor when insomnia predicted SAD (95% CI= 0.016 – 0.113). Depression and chronotype predicted insomnia (depression β= 0.581, 95%
CI= 0.479 – 0.684; chronotype β= -0.200, 95% CI= -0.294 – -0.106), whereas depression but
not chronotype uniquely predicted SAD (depression β= 0.239, 95% CI= 0.161 – 0.318;
chronotype 95% CI= -0.058 – 0.061).
4.4.7. Social Phobia and Insomnia
The sample size for the analyses of the relationship between SP and insomnia was 304.
The results failed to show a significant independent effect when SP predicted insomnia (95%
CI= -0.048 – 0.143), and vice-versa (95% CI -0.065 – 0.191). Depression and chronotype
uniquely predicted insomnia (depression β= 0.578, 95% CI= 0.481 – 0.676; chronotype β= 0.197, 95% CI= -0.285 – -0.109), whereas depression but not chronotype predicted SP
(depression β= 0.663, 95% CI= 0.522 – 0.804; chronotype 95% CI= -0.051 – 0.168).
4.5. Discussion
The first aim of the study was to investigate the independent relationship between
insomnia and depression, and insomnia and different subtypes of anxiety. The results were
consistent with previous adolescent studies that reported a positive association between
insomnia and depression (Gregory & O'Connor, 2002; Johnson, Roth, & Breslau, 2006),
insomnia and GAD, but not between insomnia and SP (Alfano, et al., 2007). The current
study, however, adds to these findings by showing that insomnia is related to depression
and GAD after chronotype and anxiety or depression (respectively) are controlled for. Higher
levels of insomnia were significantly predicted by higher levels of depression, and,
111
conversely, higher levels of insomnia significantly predicted higher levels of depression and
GAD.
The relationships observed between insomnia, GAD and depression may be at least
partially explained by abnormalities to neurotransmitters and brain structures such as
dopamine, hypocretin-1, serotonin, the brainstem and thalamus, which are associated with
the sleep-wake cycle, anxiety and depression (Casey, et al., 2008; Holmes, 2003; Nestler &
Carlezon, 2006; Peroutka, 1998; Tseng & O'Donnell, 2007; Yoshioka, et al., 1996).
Consequently, insomnia, anxiety and depression may have overlapping courses of
development, and hence contribute to the development of and result from one another
(Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; Morphy, et al., 2007).
Psychological and social factors that are common during adolescence such as increased
autonomy, and psychosocial (e.g., peer groups), familial and educational stressors (Kaneita,
et al., 2009; Lipton, et al., 2008) may also predispose adolescents to the development of
insomnia, anxiety and depression.
This study also showed that while associations between insomnia, OCD, SAD and SP
were evident, the relationships were no longer significant when depressive symptoms were
controlled for. In contrast, a previous study reported an association between insomnia and
SP (Alfano, et al., 2007), and suggested that depression has a large yet partial mediation
effect when social phobia predicts insomnia (Buckner, Bernert, Cromer, Joiner, & Schmidt,
2008). These studies used different methodologies; the items used in the current study, as
opposed to the other (Buckner, et al., 2008), are based on DSM-IV (American Psychiatric
Association, 1994) criteria and hence may better represent the symptoms that are assessed
for a clinical diagnosis of insomnia, depression and subtypes of anxiety. Furthermore, the
112
previous study used an overall sleep construct that assessed insomnia via one item (Alfano,
et al., 2007), whereas the current study used a validated 7-item instrument that is specific to
insomnia. An explanation for the current study’s findings could be that symptoms of
depression and GAD may lead to significant and persistent distress and cognitive arousal at
night, whereas symptoms of PD, SAD and SP are triggered by particular stimuli that may not
be present nocturnally (American Psychiatric Association, 1994).
The findings from this study also suggest that depression has a stronger role in either
the development or maintenance of sleep and anxiety symptoms than insomnia. Depression
and not insomnia (or chronotype) predicted each anxiety variable. Furthermore, insomnia
predicted each anxiety subtype before but not after depression was entered into the
models, suggesting that depression may explain the relationship between insomnia and
anxiety subtypes. Nevertheless, this study improves upon current clinical theories by
indicating that insomnia has a stronger relationship with depression than anxiety subtypes,
insomnia is independently related to some but not other anxiety subtypes, and depression
may be a mediating factor between insomnia and subtypes of anxiety. Such findings, paired
with the high correlations between insomnia and all anxiety subtypes before covariates were
controlled further consolidate the notion of a complex and intertwined relationship between
insomnia, anxiety and depression (Jansson-Fröjmark & Lindblom, 2008).
The second aim of this study was to investigate the effect of chronotype on insomnia,
depression, and subtypes of anxiety that is unique from other predictors. The current study
found that an evening chronotype predicted insomnia and depression after controlling for
other predictors, which replicates the findings from previous studies that reported an
association between chronotype and sleep problems, and chronotype and depression
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(Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009; Russo, et al., 2007). However, in
contrast to previous research (Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009;
Russo, et al., 2007), chronotype did not predicted OCD, PD, SAD and SP. This result is likely
due to the effect of insomnia, as chronotype was significantly correlated with OCD, PD, SAD
and SP, but did not predict either anxiety subtype after insomnia was controlled for. Also,
the results regarding PD may better reflect the large sample size, as the effect size was small
and confidence intervals approached zero. Together, these results suggest that chronotype
may have a more direct association with insomnia and depression than with anxiety
subtypes, and that the relationship between chronotype and anxiety subtypes may be
largely due to the presence of insomnia.
The independent effect of chronotype on insomnia and depression may be explained
by symptoms that are common to depression, insomnia and an evening preference during
adolescence such as persistent sleep deprivation, sleep displacement, difficulty adjusting to
social constraints and an alternating lifestyle are also related to poor sleep and mental
health (Giannotti, et al., 2002). Carskadon (Carskadon, et al., 2004) and Kaneita (Kaneita, et
al., 2009) suggested that daily pressures of life accumulate during adolescence and hence
promote later bedtimes, poorer sleep and more mental health problems in older
adolescents. Furthermore, Russo and colleagues (Russo, et al., 2007) found that later bed
and rise times occur with older age on the weekends, indicating that rise times are dictated
by school schedule. Consequently, total sleep time on school nights decreases with age but
remains constant on the weekend, suggesting that sleep deprivation may occur during the
week (Russo, et al., 2007). The results of this study showed a similar pattern.
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The current study has public health and clinical implications. Given that depression and
GAD independently predict insomnia, prevention and treatment plans for insomnia may also
focus more broadly on depression and GAD. Similarly, given that insomnia independently
predicts depression, prevention and treatment plans for depression might also consistently
focus on improving sleep. Eaton, Badawi and Melton(Eaton, Badawi, & Melton, 1995)
estimated that 47% of cases of depression could have been prevented had sleep problems
been successfully treated one year prior. Furthermore, Ohayon and Roth found that
insomnia was a precursor for relapse of anxiety and depression (Ohayon & Roth, 2003). Also,
given that chronotype and anxiety predicted insomnia and depression, prevention and
treatment plans for depression and insomnia could simultaneously focus on anxiety and the
eveningness chronotype. Finally, given the importance of depression, prevention and
treatment plans for various subtypes of anxiety disorders should also consider depression.
Therefore, interventions that focus on mental health, sleep and circadian rhythms could
prevent the development or help alleviate symptoms of insomnia, depression and the
subtypes of anxiety.
4.5.1. Limitations
The current study contained some limitations. First, directionality could not be inferred
due to the cross-sectional methodology used, and knowledge about the aetiological aspect
of the relationship between these problems remains unclear. This is particularly relevant to
the finding that GAD predicted insomnia, but insomnia did not predict depression. Such a
finding can only infer a relationship, and longitudinal studies are needed for a clearer
understanding of the direction of this relationship. Future studies, then, could assess the
longitudinal relationships between insomnia and subtypes of anxiety, and insomnia and
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depression after accounting for potential covariates, thereby expanding the current
knowledge base of the direction and hence aetiological relationship between these
problems.
Second, this study is based on self-reported sleep and mental health symptoms rather
than methods that allow a clinical diagnosis. Regarding mental health problems,
comprehensive interviews are required to detect the presence of other disorders that may
confound the results, such as adjustment or conduct disorder. Nevertheless, the RCADS has
been extensively used in the adolescent population (Austin & Chorpita, 2004; Weems &
Costa, 2005), including in studies that have assessed the relationship between sleep
problems, anxiety and depression (Alfano, et al., 2009). Furthermore, recent studies have
shown inconsistencies between subjective and objective reports of poor sleep in paediatric
populations diagnosed with Major Depressive Disorder (Bertocci et al., 2005; Forbes et al.,
2008) and anxiety disorders (Forbes, et al., 2008). The same studies also showed that
objective and subjective reports of sleep were more similar in the general population than in
youths diagnosed with anxiety or depression (Bertocci, et al., 2005; Forbes, et al., 2008).
Indeed, the current study assessed the general population, and prevalence rates of clinically
significant insomnia, depression and SP were similar to those found in other studies using
the general population (Australian Bureau of Statistics, 2008a; Costello, et al., 2011;
Johnson, Roth, & Breslau, 2006). The RCADS and ISI are also measures of mental health
disorders and insomnia that are based on the DSM-IV diagnostic criteria (American
Psychiatric Association, 1994). Future studies could use objective measures and/or clinical
methods of assessment for insomnia, depression and subtypes of anxiety.
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Moreover, other sleep problems were not assessed. However, disorders such as
obstructive sleep apnoea are unlikely to confound the results due to the relatively low
prevalence rates [range from 0.4% (Johnson & Roth, 2006) - 2.9% (Sánchez-Armengol et al.,
2001)] in paediatric populations. In any case, future studies could use polysomnography and
sleep diaries to detect other sleep disorders, and even assess biological measures that are
directly relevant to chronotype and therefore could better detect delayed sleep phase
syndrome such as core body temperature and dim light melatonin onset.
4.5.2. Conclusions
In conclusion, this study adds to the current literature by assessing the independent
relationship between insomnia and depression, and insomnia and anxiety across subtypes of
anxiety, while investigating the effects of chronotype on insomnia, anxiety and depression
above and beyond potential confounders. The general adolescent population was the
targeted sample, and psychometrically sound measures based on the DSM-IV (American
Psychiatric Association, 1994) criteria for insomnia, subtypes of anxiety and depression were
used. The results suggested that insomnia is independently related to symptoms of
depression and GAD, but not the other subtypes of anxiety. Furthermore, an evening
preference uniquely predicted insomnia and depression, but not GAD, PD, OCD, SAD or SP.
Prevention and treatment efforts for insomnia and depression should potentially consider
and concurrently focus on mental health, sleep and the eveningness chronotype, whereas
prevention and treatment efforts for anxiety subtypes may consider also focussing on
insomnia and depression.
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Acknowledgements
Dr Stewart Howell contributed to the statistical analyses.
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Chapter 5: Study 3
Bidirectional relationships between insomnia and
depression, and insomnia and subtypes of anxiety
during adolescence: does chronotype affect these
relationships?
Alvaro, P.K., Roberts, R.M., Harris, J.K., & Bruni, O.
School of Psychology, University of Adelaide
Prepared for submission
Pasquale Alvaro (PhD Candidate)
I collected data, performed each analysis, interpreted data, wrote the manuscript and acted
as the corresponding author. I also made the decisions on what data and arguments to
present in this paper.
X
Date: 25/08/14
Pasquale Alvaro
PhD Candidate
119
Rachel Roberts (Co-Author)
I was the primary supervisor for the research project that led to this paper, so was involved
in the design of the study described in the paper and discussions of results, particularly in
the case of adolescent literature. Mr Alvaro was responsible for writing the paper, and I was
responsible for providing editorial comments. I hereby give my permission for this paper to
be included in Mr Alvaro’s submission for the degree of PhD at the University of Adelaide.
X
Date: 25/08/14
Rachel Roberts
Co-author
Jodie Harris (Co-Author)
I was the co-supervisor for the research project that led to this paper. I was involved in the
design of the study described in the paper, the write up of the introduction, and discussions
of results, particularly where the sleep variables were concerned. I hereby give my
permission for this paper to be included in Mr Alvaro’s submission for the degree of PhD at
the University of Adelaide.
X
Date: 25/08/14
Jodie Harris
Co-author
120
Oliviero Bruni (Co-Author)
I was responsible for Pasquale Alvaro during his academic visit to La Sapienza Unverity of
Rome. As a co-author of this paper, I was heavily involved in editing the write-up, and
statistical analyses. I also contributed to the design of the study, the introduction, and the
discussions of results, particularly where the adolescent sleep research was concerned. I
hereby give my permission for this paper to be included in Mr Alvaro’s submission for the
degree of PhD at the University of Adelaide.
X
Date: 20/08/14
Oliviero Bruni
Co-author
121
5.1. Abstract
5.1.1. Study Objectives
To assess (1) the bidirectionality of the relationship between insomnia and various
subtypes of anxiety, and insomnia and depression after controlling for confounders; (2) the
independent predictive effects of chronotype on insomnia, depression, and each subtype of
anxiety during adolescence.
5.1.2. Design
Prospective, longitudinal study with a 6-month follow-up. Assessment of insomnia,
subtypes of anxiety, depression and chronotype was made via self-report questionnaires.
5.1.3. Settings
Community sample from eight high schools in Adelaide, South Australia.
5.1.4. Participants
The study was completed at baseline and follow-up by 255 high-school students aged
12 – 18 (M= 14.96, SD= 1.34), attending school grades 7 to 11 at baseline.
5.1.5. Measurement and Results
Participants completed the Insomnia Severity Index, the Revised Child Anxiety and
Depression Scale to assess subtypes of anxiety and depression, and the MorningnessEveningness Scale to assess chronotype. After accounting for covariates, insomnia was
bidirectionally related to depression (covariates were baseline age, gender, chronotype and
anxiety overall) and Generalised Anxiety Disorder (GAD, covariates were baseline age,
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gender, chronotype and depression), but was not related to other subtypes of anxiety
(covariates were baseline age, gender, chronotype and depression). Chronotype predicted
insomnia after accounting for covariates (baseline age, gender, depression and anxiety), but
not depression (covariates were baseline age, gender, insomnia and anxiety overall) or any
anxiety subtypes (covariates were baseline age, gender, insomnia and depression).
5.1.6. Conclusion
Evidence suggests that insomnia is related to depression and GAD above and beyond
other factors, and vice-versa, but not to other anxiety subtypes. An eveningness chronotype
independently predicts the development of insomnia above and beyond other factors, but
not depression or anxiety subtypes. Eveningness, then, predisposes an individual to the
development of insomnia, which is a risk-factor for depression or GAD.
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5.2. Introduction
Insomnia, anxiety and depression are common disorders during adolescence, with
prevalence rates of 10.7% (Johnson, Roth, & Breslau, 2006), 17% (Johnson, Roth, & Breslau,
2006), and 5.6% (Costello, et al., 2006) respectively. In adolescents, symptoms of insomnia,
anxiety and depression have been associated with various problematic outcomes, such as
reduced satisfaction with life (Roberts, Roberts, & Duong, 2008), poor academic
achievement (Paavonen, et al., 2000; Varley & Smith, 2003), suicidal ideation (Choquet, et
al., 1993; Glied & Pine, 2002), excessive use of tobacco, alcohol and drugs (Johnson &
Breslau, 2001), and various medical and psychiatric problems in adulthood (Bardone, et al.,
1998; Franko, et al., 2005). Insomnia is also often comorbid with anxiety and depression,
perhaps partly attributable to insomnia being a possible criterion for anxiety and depressive
disorders, but also to the overlapping neurobiological, psychological and social risk-factors
(Casey, et al., 2008; Holmes, 2003; Kaneita, et al., 2009; Lipton, et al., 2008; Nestler &
Carlezon, 2006; Peroutka, 1998; Tseng & O'Donnell, 2007; Yoshioka, et al., 1996). Such
comorbidity can exacerbate the consequences associated with each disorder, particularly
the use of tobacco, alcohol and illicit drugs (Johnson & Breslau, 2001). These findings, paired
with the increased risk of developing insomnia, anxiety and depression following puberty
(Beesdo, et al., 2009; Johnson, Roth, & Breslau, 2006; Thapar, et al., 2012), highlight the
importance of understanding the relationship between these disorders during adolescence.
A recent systematic review has suggested that insomnia and anxiety, and insomnia and
depression are bidirectionally related (Alvaro, et al., 2013). That is, insomnia is related to the
development of anxiety and depression, and anxiety and depression are related to the
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development of insomnia (Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009;
Morphy, et al., 2007).
However, various uncertainties remain about the nature of these bidirectional
relationships. One study in teenagers aged 13 to 15 found that prior anxiety predicted
insomnia and prior insomnia predicted depression, but prior depression did not predict
insomnia and prior insomnia did not predict anxiety (Johnson, Roth, & Breslau, 2006).
However, this study was retrospective, and longitudinal adolescent studies have depicted
bidirectional relationships between other sleep and mental health problems (Kaneita, et al.,
2009; Meijer, et al., 2010).
Moreover, a recent adolescent study found significant associations between insomnia
and Separation Anxiety Disorder (SAD), and insomnia and Generalised Anxiety Disorder
(GAD), but not between insomnia and Social Anxiety Disorder (Alfano, et al., 2007). Although
such findings are expected given the symptom overlap for diagnosis, the bidirectionality of
the relationship between insomnia, anxiety and depression during adolescence, particularly
for different anxiety disorders, remains unclear.
Finally, recent studies have reported an association between an evening chronotype
(chronotype refers to a circadian rhythm position indicator that categorises individuals
according to both their body clock position relative to the 24 hour day (Roenneberg, et al.,
2004) and the time he/she prefers to engage in cognitively and physically demanding
activities (Ferraz, et al., 2008)) and insomnia, anxiety and depression during adolescence
(Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009; Russo, et al., 2007). Such
relationships are important, because adolescents tend to become more evening-orientated
(i.e., become evening stimulus seeking and exhibit delayed bedtimes) with age due to
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circadian rhythm and environmental changes that are experienced during adolescence
(Crowley, et al., 2007). In some cases, eveningness tendencies predispose adolescents to the
development of delayed sleep phase syndrome (American Academy of Sleep Medicine,
2005), which affects approximately 8% of adolescents (Saxvig, et al., 2012), and has been
associated with insomnia (Reid, et al., 2012), anxiety (Reid, et al., 2012; Saxvig, et al., 2012),
depression (Saxvig, et al., 2012), smoking (Saxvig, et al., 2012), alcohol use (Saxvig, et al.,
2012), and poor academic achievement (Saxvig, et al., 2012) during adolescence.
Nevertheless, the independent predictive effect of an evening chronotype on insomnia after
accounting for depression and anxiety; on depression after accounting for insomnia and
anxiety; and on anxiety subtypes after controlling for insomnia and depression are still
unknown. An evening chronotype may be a predisposing, precipitating or perpetuating
factor in depressive, anxiety and/or insomnia symptoms during adolescence.
The current study, therefore, investigated the bidirectionality of the longitudinal
relationships between insomnia and subtypes of anxiety [Generalised Anxiety Disorder
(GAD), Obsessive Compulsive Disorder (OCD), Panic Disorder (PD), Separation Anxiety
Disorder (SAD) and Social Phobia (SP)], and insomnia and depression during adolescence.
Note that although OCD was removed from the anxiety disorders category in the current
Diagnostic and Statistical Manual of Mental Disorders (DSM 5) (American Psychiatric
Association, 2013), it was included in the current study because of the strong associations
reported with insomnia, depression and other subtypes of anxiety in adolescents (Johnson,
Roth, & Breslau, 2006).
This study also considered the predictive effect of chronotype on insomnia once
depression and anxiety were controlled, on depression once insomnia and anxiety were
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controlled, and on anxiety subtypes once insomnia and depression were controlled. It was
hypothesised that insomnia would be bidirectionally related to depression and some anxiety
subtypes, but not others, and that an evening chronotype will predict insomnia, depression
and subtypes of anxiety. Such an investigation will add to the current knowledge of the
potential pathways of insomnia, depression and subtypes of anxiety, inform public health
campaigns and clinical interventions for each problem, and enhance the understanding of
the interaction between each problem.
5.3. Methods
5.3.1. Participants
Three-hundred and eighteen South Australian secondary school students aged 12 – 18
(M= 14.96, SD= 1.34) volunteered to participate in the study at baseline. Of these, 255
completed the study by returning the questionnaire at follow up (approximately 20%
attrition rate; M age = 15.49, SD= 1.32). One-hundred and forty (54.90%) were male and 115
(45.10%) were female. At follow up, 68 students were in grade 8, 39 in grade 9, 76 in grade
10, 29 in grade 11 and 42 in grade 12. Participants were eligible if their parents consented,
they completed the questionnaires at baseline and follow-up, were in grades 7 to 11 at
baseline, and were fluent in English.
Attrition was not found to be related to baseline insomnia, depression, GAD, OCD,
SAD, SP or chronotype. However, there was a significant difference in mean severity of panic
disorder symptoms [t(313)= 3.21, p<.01] between adolescents who dropped out (M= 5.20,
SD= 5.36) and those who completed the study (M= 3.89, SD= 3.61). Nevertheless, Cohen’s
effect size was of small magnitude (d= 0.35) (Cohen, 1992), suggesting that the symptoms of
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PD in those who dropped out were only marginally more severe than those who completed
the study.
5.3.2. Measures
The demographic questionnaire contained items about personal history of mental
health and sleep problems, medical history, treatment for mental health, sleep or medical
problems, chronic illnesses, alcohol use and drug use; sleep duration, variability and
sufficiency (defined as how often the adolescent thought they had sufficient sleep); and total
sleep time on weeknights and weekends. The questionnaire also contained measures of
insomnia, chronotype, depression and subtypes of anxiety.
5.3.2.1. Insomnia
The Insomnia Severity Index (ISI) (Bastien, Vallières, & Morin, 2001) was used to assess
insomnia. The ISI is a 7-item self-report inventory that assesses the severity of subjective
symptoms and consequences of insomnia based on the DSM-IV (American Psychiatric
Association, 2000). Each item is scored on a 0 to 4 Likert scale, and total scores are
calculated by summing each item (total score range 0 to 28). Higher total scores denote
more severe insomnia. Scores between 0–7 indicate no clinically significant insomnia, 8–14
indicates subthreshold insomnia, 15–21 indicates moderate clinical insomnia, and 22–28
indicates severe clinical insomnia (Morin, 1993). Items include questions assessing difficulty
falling sleep, difficulty staying asleep, problems waking up too early, satisfaction with current
sleep patterns, worry/distress about current sleep patterns, and perceived interference of
sleep problems with daily life. The ISI has been widely used (Brand, et al., 2011; Luo, et al.,
2012; Short, et al., 2013) and also validated in the adolescent population (Chung, et al.,
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2011). A recent study reported significant correlations between the ISI and clinical diagnosis
of insomnia, the Sleep-Wake Habits Questionnaire, General Mental-Health Questionnaire,
Epworth Sleepiness Scale, smoking habits, alcohol use, number of naps per week, and
academic performance during adolescence (Chung, et al., 2011) . Furthermore, a Cronbach’s
alpha of 0.83, and a 2-week test-rest reliability of 0.79 for the ISI in an adolescent population
(Chung, et al., 2011) were reported. In the current study, a Cronbach’s alpha of 0.86 and
0.84 were obtained for baseline and follow-up, respectively.
5.3.2.2. Chronotype
The Morningness-Eveningness Scale (MES) (Carskadon, et al., 1993) was used to assess
adolescents’ chronotype. It is a 10-item adaptation of the Composite Scale of Morningness
(Smith, et al., 1989). Seven items are scored on a 1 to 4 Likert scale, and 3 items are scored
on a 1 to 5 Likert scale. Total scores range from 10 to 42 and are calculated by summing each
item. Higher scores indicate a tendency towards morningness. The MES can differentiate
morningness from eveningness in adolescents (Díaz-Morales, et al., 2007). Extreme evening
and morning-types were defined as below the 10th and above the 90th percentile,
respectively, and scores in between were identified as neither types (cut-off scores were 20
and below for eveningness and 34 and above for morningness) (Giannotti, et al., 2002;
Russo, et al., 2007). Recent studies have reported Cronbach’s α of 0.73 (Giannotti, et al.,
2002), 0.82 (Díaz-Morales, et al., 2007), and 0.82 (Warner, et al., 2008) in Italian, Spanish,
and Australian adolescents. Good external validity and high test-retest reliability (0.78) (S.
Kim, et al., 2002) have been reported (Díaz-Morales, et al., 2007). The MES can also predict
daytime functioning, academic achievement and various behavioural outcomes in
adolescents (Giannotti, et al., 2002; Warner, et al., 2008), and successfully discriminate
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morningness from eveningness in adolescents (Díaz-Morales, et al., 2007). Two items were
altered. First, the phrase “gym class” was changed to “sports class” to keep the language
relevant to the Australian population. Second, the item “Your parents have decided to let
you set your own bed time. What time would you pick?” was changed to “What time would
you prefer to go to bed?” to be more age-appropriate for the sample. Cronbach’s alpha for
the MES in the current study was 0.80 and 0.82 for baseline and follow-up, respectively.
5.3.2.3. Depression and subtypes of anxiety
Depression and subtypes of anxiety were assessed by the Revised Child Anxiety and
Depression Scale (RCADS) (Chorpita, et al., 2000). The RCADS is a 47-item adaption of the
Spence Children’s Anxiety Scale (Spence, 1997) that corresponds to the DSM-IV(American
Psychiatric Association, 1994) diagnostic criteria for Major Depressive Disorder (MDD; 10
items), Generalised Anxiety Disorder (GAD; 6 items), Panic Disorder (PD; 9 items), Separation
Anxiety Disorder (SAD; 7 items), Obsessive Compulsive Disorder (OCD; 6 items) and Social
Phobia (SP; 9 items). Each item is scored on a 0 to 3 Likert scale with higher scores indicating
more severe depression or subtype of anxiety. Total scores are calculated by the sum of each
item for each scale. MDD scores range from 0 to 30, GAD ranges from 0 to 18, PD ranges
from 0 to 27, SAD ranges from 0 to 21, OCD ranges from 0 to 18, and SP ranges from 0 to 27.
An overall 37-item scale for anxiety is also provided, with scores ranging from 0 to 111. A
recent study reported Cronbach’s alphas in Hawaiian adolescents for each subscale: SP,
alpha=0.81; PD, alpha=0.85; GAD, alpha=0.80; MDD, alpha=0.76; SAD, alpha=0.78; and OCD,
alpha=0.71 (Chorpita, et al., 2000). The majority of Cronbach’s alphas for each subscale at
baseline and follow-up in the current study ranged from 0.80 to 0.90, with OCD at baseline
(alpha= 0.76), and SAD at baseline (0.68) and follow-up (0.73) falling below this range. This
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study also reported good support for the structural, convergent and discriminant validity of
the RCADS. Furthermore, internal consistency for MDD, anxiety subscales and overall anxiety
has been found in Australian adolescents (De Ross, et al., 2002). Good convergent validity
was also demonstrated (De Ross, et al., 2002) via moderate to strong correlations between
the subscales of RCADS with scores on the Revised Manifest Anxiety Scale [RCMAS (Reynolds
& Richmond, 1985)] and the Children’s Depression Inventory [CDI (Kovacs, 1981)].
Standardised T scores converted from raw scores are used to indicate the clinical significance
of each variable. Scores between 65 and 69 indicate borderline clinical threshold and T
scores ≥ 70 indicate above clinical threshold (Chorpita, 2011).
5.3.3. Procedure
All principals of high schools within a 40km radius of the Adelaide CBD that were listed
with the Department for Education and Child Development in South Australia (Department
for Education and Child Develpoment, 2012) or in the 2010 Annual Report of the Advisory
Committee on Non-Government Schools in South Australia (Government of South Australia,
2011) (n= 71 within catchment area) were approached. Each school principal received a
package via mail that contained an invitation to participate in the study, a copy of the
questionnaire, information letters for students and parents, and consent forms. The schools
that did not respond to the letter received a follow-up phone call or email. Eight schools
agreed to participate in the study. Information letters and consent forms for the students
and parents were then given to each student in grades that were selected by the school.
Questionnaires were completed in class time under the supervision of teachers at baseline
and six months later. Baseline data was collected from May 2012 until August 2012, while
follow-up data was collected from October 2012 to February 2013 follow-up range 5 – 7
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months. This study was approved by the Human Research Ethics Committee from the
University of Adelaide, the Department for Education and Child Development, and Catholic
Education South Australia.
5.3.4. Statistical Analyses
The analysis of missing data was based on the recommendations of Tabachnick and
Fidell (2007). Less than 2% of the baseline data was missing for the insomnia, depression,
subtypes of anxiety and chronotype scales. Some evidence suggests that missingness at
baseline may have been non-random, as the missing data group had a significantly higher
SAD symptom severity score than those without missing data. However, the missing data
group for this analysis only contained one participant, which is not enough to adequately
represent the remaining participants with missing data. Furthermore, the evidence
suggested that missing data at follow-up was random, as no significant differences were
found in predictor or outcome variables between those with and without missing data. In
any case, a large the sample size was retained after attrition and the amount of missing
values was very low (below 3% for measures of depression, anxiety subtype and chronotype,
and 6.6% for the insomnia measure). Therefore, on the recommendation of Tabachnick and
Fidell (2007), participants with missing data at baseline and/or follow-up were only excluded
for the analyses that assessed the variables containing their missing data.
Means, standard deviations, and frequencies were used to report descriptive statistics.
Paired samples t-tests were used to assess significant differences between baseline and
follow-up mean symptom severity. Statistics of frequencies for chronotype groups (eveningtypes, morning-types and neither-types) according to those who were and were not
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identified with sub-threshold and clinically significant insomnia were also reported.
Pearson’s r was used to calculate correlations.
Two sets of step-wise regression analyses (Pallant, 2011) were conducted to
investigate the bidirectionality of the relationship between insomnia and subtypes of
anxiety, and insomnia and depression during adolescence, along with the effect of
chronotype on these relationships. Insomnia and chronotype at baseline were used to
predict depression or anxiety subtypes at follow-up for the first analysis, while depression or
anxiety subtypes and chronotype at baseline was used to predict insomnia at follow-up for
the second analysis.
Four steps were used to assess the hypotheses. Firstly, the predictor variable was
regressed on the outcome variable at follow-up. Secondly, chronotype at baseline was
added as a predictor when correlated with the predictor and outcome variable. Thirdly,
anxiety overall or depression was added as a predictor to statistically control these variables.
Finally, age at baseline and gender were added as a predictor and hence statistically
controlled for to impart control for physical development of adolescents (see Figure 6).
Bidirectionality is indicated if insomnia predicted depression or an anxiety subtype at the
fourth step, and vice-versa. Chronotype is suggested to independently predict insomnia,
depression, or an anxiety subtype should it predict an outcome variable at step 4.
Bootstrapping, a technique that is used when outcome variables are skewed, produces
confidence intervals based on resampling with replacement. Bootstrapping was used to
calculate correlations and hierarchical regression analyses, as the skewed coefficients and an
inspection of the histograms for each variable suggested that the data may be slightly
skewed. Based on Preacher and Hayes’ (Preacher & Hayes, 2008) recommendations, 5000
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resamples were used for final reporting, and bias-corrected and accelerated (BCa) 95%
confidence intervals were calculated.
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Step 1, 2, 3, 4
Depression
Step 1, 2, 3, 4
Step 2, 3, 4
Chronotype
Step 2, 3, 4
Insomnia
Chronotype
Insomnia
Step 3, 4
Step 4
Anxiety overall
Depression
Step 3, 4
Age
Step 4
Gender
Step 1, 2, 3, 4
Step 2, 3, 4
Anxiety overall
Age
Gender
Anxiety
subtype
Step 1, 2, 3, 4
Chronotype
Step 2, 3, 4
Insomnia
Chronotype
Anxiety
subtype
Insomnia
Step 3, 4
Step 4
Depression
Age
Step 3, 4
Step 4
Gender
Depression
Age
Gender
Figure 6. Steps for regression analyses
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5.4. Results
Demographic information, personal history and subjective sleep variables are reported
in Table 10. Descriptive statistics for symptoms of insomnia, depression and anxiety
subtypes at baseline and follow-up were reported in Table 11. A significant difference was
not found between baseline and follow-up mean scores for insomnia, depression and each
anxiety subtype, except PD. However, Cohen’s d was of small magnitude (d= 0.12),
suggesting a small increase in PD symptom severity across the two waves. Adolescents slept
longer on the weekends than weekdays at baseline (9 hours and 25 minutes vs. 8 hours and
17 minutes respectively) and follow-up (9 hours and 19 minutes vs. 8 hours and 10 minutes).
Frequencies for sub-threshold and clinically significant mental health and insomnia,
and circadian preferences were reported in Table 12. Insomnia was the most common
clinically significant problem at baseline and follow-up (11.60% and 10.08%), whereas OCD
at baseline (2.79%) and OCD and GAD (4% each) at follow-up were the least common
clinically significant problems. Furthermore, at baseline and follow-up, respectively,
approximately 12.60% and 7.87% of participants reported one clinically significant problem,
3.94% and 6.69% reported two clinically significant problems, and 7.09% and 4.72% reported
three or more clinically significant problems. The distribution of chronotypes in participants
with sub-threshold and clinically significant insomnia showed a higher prevalence of evening
type at baseline and follow-up (Table 13).
Table 14 shows the bootstrapped correlations involving baseline and follow-up
variables. Correlations were found between all variables at baseline. Insomnia, depression,
and subtypes of anxiety at baseline and follow-up were correlated with insomnia,
depression, subtypes of anxiety at follow-up. Chronotype at baseline was associated with
136
insomnia, depression, GAD and PD at follow-up, but not with OCD, SAD or SP. Therefore,
chronotype at baseline was not entered into the models that predicted OCD, SAD or SP.
137
Table 10
Demographic and personal history
Variable
Group
Baseline
Mean (SD)
14.94 (1.30)
Number (%)
Follow-up
Mean (SD)
15.49 (1.32)
Number (%)
Male
Female
140 (55.12)
114 (44.88)
140 (55.12)
114 (44.88)
7
8
9
10
11
12
8 (3.15)
77 (30.31)
57 (22.44)
48 (18.90)
64 (25.20)
0 (0)
0 (0)
68 (26.77)
39 (15.35)
76 (29.92)
29 (11.42)
42 (16.54)
Depression
Anxiety
Sleep problems
Depression, Anxiety
Sleep problems,
Depression
Sleep problems,
Anxiety
Sleep problems,
Depression, Anxiety
Other
No
16 (6.30)
14 (5.51)
23 (9.06)
4 (1.57)
11 (4.33)
17 (6.69)
18 (7.10)
5 (1.97)
1 (0.39)
2 (0.79)
3 (1.18)
1 (0.40)
1 (0.39)
6 (2.36)
186 (73.23)
2 (0.79)
8 (3.15)
190 (74.80)
Age
Gender
Grade
Past mental health and sleep problems
Past treatment depression, anxiety,
insomnia
Psychologist
Counsellor
GP
Psychologist and GP
Psychologist, GP and
Counsellor
GP and other
Counsellor and other
Other
None
10 (3.94)
16 (6.30)
3 (1.18)
1 (0.39)
7 (2.76)
10 (3.94)
5 (2.0)
0 (0)
2 (0.79)
1 (0.39)
1 (0.39)
6 (2.36)
214 (84.25)
1 (0.39)
1 (0.39)
0 (0)
8 (3.14)
216 (85.04)
Asthma
Chronic pain
Asthma, others
Other
None
31 (12.20)
3 (1.18)
3 (1.18)
16 (6.30)
201 (79.13)
29 (11.48)
3 (1.18)
4 (1.57)
13 (5.19)
207 (81.50)
Asthma medication
e.g., Ventolin puffer
Sleep medication
Valerian
Other
None
24 (9.45)
1 (0.39)
1 (0.39)
7 (2.75)
221 (87.01)
21 (8.27)
0 (0)
0 (0)
8 (3.15)
225 (88.58)
Yes
No
Missing data
24 (9.45)
201 (79.13)
29 (11.42)
31 (12.20)
192 (75.59)
31 (12.20)
Yes
No
Missing data
3 (1.18)
221 (87.01)
30 (11.81)
3 (1.18)
220 (86.61)
31 (12.20)
Too little sleep
Enough sleep
Too much sleep
Missing data
113 (44.5)
137 (53.9)
4 (1.6)
0 (0)
105 (41.3)
146 (57.5)
2 (0.79)
1 (0.39)
Chronic illnesses
Medication
Alcohol consumption during the last two
weeks
Drug use during the last two weeks
Subjective opinion on amount of sleep
138
Table 10 cont…
Variable
Subjective opinion of sufficient sleep
Never
Rarely
Sometimes
Usually
Always
Missing data
Baseline
Follow-up
4 (1.57)
40 (15.74)
87 (34.25)
110 (43.30)
13 (5.12)
0 (0)
4 (1.57)
36 (14.17)
92 (36.22)
103 (40.55)
18 (7.09)
1 (0.39)
Table 11
Descriptive statistics for sleep and mental health symptoms at baseline and follow-up
Variable
N
Baseline
Range
(min/max)
Mean (SD)
N
Follow-up
Range
(min/max)
Significance
test
Mean (SD)
p
Insomnia
250
0-25
7.45 (5.22)
238
0-26
7.50 (5.01)
.63
Depression
252
0-24
6.68 (3.25)
247
0-24
6.80 (5.37)
.47
GAD
253
1-19
5.63 (3.20)
250
0-18
6.06 (3.78)
.47
OCD
251
0-17
3.92 (3.13)
250
0-18
3.87 (3.33)
.82
PD
252
0-18
3.42 (3.64)
250
0-26
3.80 (4.08)
.05
DAD
251
0-14
3.00 (2.72)
248
0-14
2.97 (2.87)
.95
SP
252
0-25
9.65 (5.44)
249
0-26
10.44 (5.64)
.74
Chronotype
252
10-40
27.19 (5.29)
250
13-39
27.50 (5.36)
.15
TST WD
250
4-11.50
8.28 (1.23)
241
3-13.17
8.16 (1.38)
.18
TST WE
251
4.5-15
9.41 (1.73)
244
4-13.00
9.32 (1.56)
GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation Anxiety
Disorder, SP= Social Phobia, TST WD= Total sleep time weekday, TST WE= Total sleep time weekend
Table 12
Amount of cases of clinically significant and sub-threshold insomnia, subtypes of anxiety and depression, and
morningness, neutral and eveningness chronotype at baseline and follow-up
Baseline
Follow-up
SubClinically
Clinically
threshold n
significant
Sub-threshold n
significant n
Variable
(%)
n (%)
Total n (%)
(%)
(%)
Total n (%)
Insomnia
80 (32.00)
29 (11.6)
109 (43.60)
84 (35.29)
24 (10.08)
108 (45.38)
Depression
11 (4.37)
23 (9.16)
34 (13.49)
12 (4.86)
20 (8.10)
22 (8.91)
GAD
16 (6.32)
17 (6.72)
33 (13.04)
9 (3.60)
10 (4.00)
19 (7.60)
OCD
12 (4.78)
7 (2.79)
19 (7.57)
8 (3.20)
10 (4.00)
18 (7.20)
PD
12 (4.76)
11 (4.37)
23 (9.13)
10 (4.00)
16 (6.40)
26 (10.40)
SAD
20 (7.97)
17 (6.77)
37 (14.74)
16 (6.45)
24 (9.68)
40 (16.13)
10 (3.97)
12 (4.76)
22 (8.73)
10 (4.00)
14 (5.60)
24 (9.60)
SP
N/A
N/A
26 (10.24)
N/A
N/A
25 (9.84)
Morningness
N/A
N/A
200 (78.74)
N/A
N/A
201 (79.13)
Neither
N/A
N/A
28 (11.02)
N/A
N/A
28 (11.02)
Eveningness
GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation
Anxiety Disorder, SP= Social Phobia, subthreshold insomnia= Insomnia Severity Index scores between 8 – 14,
clinically significant insomnia= Insomnia Severity Index scores ≥15, subthreshold mental health variables= Revised
Children’s Anxiety and Depression Scale standardised T scores between 65 and 69, clinically significant mental health
variables= Revised Children’s Anxiety and Depression Scale standardised T scores ≥70, Eveningness= MorningnessEveningness Questionnaire scores ≤20, Neither= Morningness-Eveningness Questionnaire scores between 21 – 33,
Morningness= Morningness-Eveningness Questionnaire scores ≥34.
139
.42
Table 13
Amount (%) of chronotypes according to insomnia status at baseline and follow-up
Eveningness n (%)
Morningness n (%)
Baseline
Without clinically significant insomnia
14 (10.07)
25 (17.99)
Subthreshold insomnia
12 (15.19)
8 (10.13)
Clinically significant insomnia
14 (50.00)
1 (3.57)
Follow-up
Without clinically significant insomnia
9 (6.92)
33 (25.39)
Subthreshold insomnia
20 (24.10)
7 (8.43)
Clinically significant insomnia
12 (50.00)
2 (3.57)
Without clinically significant insomnia= Insomnia Severity Index scores between 0 – 7.
Subthreshold insomnia= Insomnia Severity Index scores between 8 – 14
Clinically significant insomnia= Insomnia Severity Index scores ≥15
Eveningness= Morningness-Eveningness Questionnaire scores ≤20
Morningness= Morningness-Eveningness Questionnaire scores ≥34
Neither= Morningness-Eveningness Questionnaire scores between 21 – 33
140
Neither n (%)
100 (71.94)
59 (74.68)
13 (46.43)
88 (67.69)
56 (67.47)
10 (46.43)
Table 14
Correlations involving baseline and follow-up depression, subtypes of anxiety, insomnia and chronotype
Baseline
Follow-up
Depression
GAD
OCD
PD
SAD
SP
Insomnia
Chronotype
Depression
GAD
OCD
PD
SAD
SP
Insomnia
Chronotype
Baseline
Depression
1
GAD
.56
**
1
OCD
.60**
.65**
1
.65
**
.61
**
.62
**
1
.51
**
.65
**
.60
**
.62
**
1
SP
.60
**
.61
**
.59
**
.56
**
**
1
Insomnia
.69**
.52**
.40**
.49**
.36**
.43**
1
**
**
**
**
**
**
**
1
PD
SAD
Chronotype
-.36
-.19
-.15
-.18
.65
-.14
-.17
-.40
Follow-up
Depression
GAD
.72**
.52**
.46**
.48**
.42**
.50**
.59**
-.30**
1
**
**
**
**
**
**
**
**
**
1
.444
.67
.52
.49
.53
.46
.41
-.15
.62
.39**
.53**
.61**
.42**
.4**
.45**
.33**
-.10
.66**
.69**
1
.48
**
.48
**
.41
**
.59
**
.43
**
.39
**
.40
**
**
.62
**
.58
**
.64
**
1
.42
**
.58
**
.47
**
.47
**
.66
**
.51
**
.37
**
.61
**
.66
**
.62
**
.65
**
1
SP
.43
**
.51
**
.43
**
.42
**
.49
**
.71
**
.35
**
-.10
.66
**
.62
**
.68
**
.53
**
**
1
Insomnia
.67**
.46**
.41**
.40**
.46**
.71**
-.38**
.71**
.50**
.47**
.45**
.43**
.53**
**
**
**
**
**
**
**
**
**
**
**
OCD
PD
SAD
.31**
-.14
-.13
.63
1
**
Chronotype
-.32
-.21
-.19
-.140
-.11
-.17
-.35
.79
-.33
.19
.16
.16
-.12
-.15
-.43
Note. Bootstrapped correlations between baseline data (n= 245 – 252) are presented in the top left-hand corner, bootstrapped correlations between baseline and follow-up (n= 234 – 249) are presented in the
bottom left hand corner, and bootstrapped correlations between follow-up data (n= 232 – 250) are presented in the bottom right hand corner. GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD=
Obsessive Compulsive Disorder, SAD= Separation Anxiety Disorder, SP= Social Phobia, **= Significant correlation according to the confidence intervals
141
1
5.4.1. Step-wise regression analyses
Insomnia at baseline predicted depression and GAD in steps 1, 2, 3, and 4, and
depression and GAD predicted insomnia across each step. Insomnia did not predict other
subtypes of anxiety once depression was entered into the model (i.e., step 3). Similarly,
other subtypes of anxiety did not predict insomnia once depression was entered into the
model (i.e., step 2 or 3, depending if chronotype was assessed as a predictor). Chronotype
predicted insomnia in steps 1, 2, 3, and 4 across each analysis, but did not predict depression
or subtypes of anxiety. Age at baseline predicted follow-up depression, but no other
outcome variables. Gender did not predict any outcome variables. Table 15 reports the final
step of the regression analyses where insomnia was used as the outcome variable, while
Table 16 reports the final step of the regression analyses where insomnia was used as a
predictor variable. The full step-wise regression analyses can be found in the appendix1.
1
Note there are two separate appendices, one in section 5.5 and one in section 7. The former is a part of the third study that will be
submitted for publication, so we decided to keep it in the same section. The latter adds information that is beyond the scope of study 3.
Therefore, we decided to separate the two appendices.
142
Table 15
Regression analyses with insomnia as the outcome variable
Model
Predictor
β (Bootstrap)
1
Depression
0.45
Chronotype
-0.15
Anxiety
0.05
Age
0.42
Gender
-0.48
CI Lower
0.25
-0.25
-0.01
0.02
-1.17
CI Upper
0.60
-0.06
0.11
0.83
0.69
R2adjusted
0.42
2
GAD
Chronotype
Depression
Age
Gender
0.22
-0.14
0.45
0.38
-0.34
0.04
-0.23
0.29
-0.04
-1.40
0.43
-0.05
0.60
0.82
0.74
0.42
3
OCD
Chronotype
Depression
Age
Gender
0.15
-0.15
0.48
0.38
-0.28
-0.11
-0.25
0.32
-0.02
-1.32
0.41
-0.05
0.65
0.79
0.82
0.40
4
PD
Chronotype
Depression
Age
Gender
0.05
-0.14
0.52
0.35
-0.31
-0.17
-0.23
0.36
-0.06
-1.34
0.26
-0.04
0.68
0.76
0.74
0.40
5
SAD
Chronotype
Depression
Age
Gender
-0.01
-0.13
0.55
0.37
-0.25
-0.27
-0.23
0.40
-0.03
-1.35
0.27
-0.03
0.71
0.78
0.80
0.40
6
SP
0.14
-0.02
0.25
Chronotype
-0.14
-0.14
-0.04
Depression
0.45
0.31
0.60
Age
0.36
-0.05
0.75
Gender
-0.60
-1.65
0.42
a. Dependent Variable: Insomnia
* β= regression co-efficient, CI= Bca 95% Confidence Interval, GAD= Generalised anxiety disorder, OCD= Obsessive compulsive
disorder, PD= Panic disorder SAD= Separation anxiety disorder, SP= Social phobia
143
0.41
Table 16
Regression analyses with insomnia as the predictor variable
R2
adjusted
0.486
Outcome Variable
Depression
Predictor
Insomnia
Chronotype
Anxiety
Age
Gender
β (Bootstrap)
0.39
-0.04
0.13
0.42
0.42
Lower
0.24
-0.16
0.08
0.03
-0.67
Upper
0.54
0.07
0.17
0.83
1.48
GAD
Insomnia
Chronotype
Depression
Age
Gender
0.16
0.02
0.22
-0.06
0.57
0.04
-0.08
0.09
-0.39
-0.34
0.28
0.13
0.37
0.27
1.45
0.22
OCD
Insomnia
Depression
Age
Gender
0.08
0.19
0.16
0.13
-0.04
0.07
-0.15
-0.71
0.19
0.33
0.46
0.10
0.16
PD
Insomnia
Chronotype
Depression
Age
Gender
0.13
0.06
0.29
0.36
-0.02
-0.03
0.11
0.01
0.27
0.16
0.49
0.70
0.25
SAD
Insomnia
Depression
Age
Gender
0.10
0.13
0.06
0.59
-0.03
0.02
-0.19
-0.92
0.20
0.24
0.31
1.28
0.25
SP
Insomnia
0.13
-0.04
0.30
Depression
0.32
0.14
0.51
Age
0.16
-0.31
0.70
Gender
1.28
-0.98
2.55
*GAD= Generalised anxiety disorder, OCD= Obsessive compulsive disorder, PD= Panic Disorder, SAD=
Separation anxiety disorder, SP= Social phobia, CI= Bca 95% Confidence Interval
144
0.22
5.5. Discussion
This study supported previous research that has found a bidirectional relationship
between insomnia and depression, (Alvaro, et al., 2013; Jansson-Fröjmark & Lindblom, 2008)
and extended upon this research by depicting a bidirectional relationship after controlling
for chronotype, anxiety and age in adolescents. Whereas previous studies have reported a
bidirectional relationship between insomnia and anxiety as an overall construct (JanssonFröjmark & Lindblom, 2008; Morphy, et al., 2007), the current study clarified a bidirectional
relationship between insomnia and GAD during adolescence. In contrast, a previous
adolescent study found that anxiety predicted the development of insomnia and insomnia
predicted the development of depression, but not vice-versa (Johnson, Roth, & Breslau,
2006). This study, however, was retrospective, and other longitudinal adolescent studies
have depicted bidirectional relationships between sleep and mental health problems
(Kaneita, et al., 2009; Meijer, et al., 2010). Therefore, it seems likely that insomnia and
depression, and insomnia and GAD are bidirectionally related during adolescence.
Several factors may explain the bidirectional associations between insomnia,
depression and GAD during adolescence, all of which may coexist. Firstly, these disorders
may involve overlapping neurobiological underpinnings such as abnormal levels of
dopamine, hypocretin-1 and serotonin (Holmes, 2003; Nestler & Carlezon, 2006; Peroutka,
1998; Tseng & O'Donnell, 2007; Yoshioka, et al., 1996). Secondly, as Batterham and
colleagues (2012) note, biological factors such as increased inflammatory dysregulation in
response to sleep disturbances (Patel, et al., 2009) are also associated with anxiety (Maes, et
al., 1998) and depression (Müller, et al., 2011). Also, various psychosocial changes in an
adolescent’s life are triggers for the onset of insomnia, depression and GAD, such as
increased autonomy, and peer group, familial and educational stressors (Kaneita, et al.,
145
2009; Lipton, et al., 2008). Indeed, insomnia, depression and GAD may be related, where
only the order of appearance of symptoms may alter, or be independent but mutually
influencing disorders (Kaneita, et al., 2009).
Interestingly, the results failed to depict a significant relationship between insomnia
and OCD, PD, SAD or SP once controlling for chronotype (in the case of PD), depression and
age. These findings are unlikely to be the result of a bias towards more severe PD symptoms
in those who dropped out of the study, as the effect-size was small and therefore unlikely to
greatly affect the results. Rather, it seems that depression completely accounted for the
relationships between insomnia and OCD, PD, SAD and SP, as these relationships were no
longer significant when depression was the sole variable that was controlled. Furthermore,
the largest r2 increase occurred after depression was entered into each model of anxiety
subtype from both directions. Together, these results suggest that the relationship between
insomnia and OCD, PD, SAD and SP may be better explained by a common association with
depression. Therefore, the course of development for insomnia may be bidirectionally
related to depression and GAD but not to OCD, PD, SAD or SP, and the significance of the
relationship between insomnia and anxiety differs across subtypes of anxiety. Symptoms of
depression and GAD (American Psychiatric Association, 1994) may result in substantial
nocturnal distress and cognitive arousal, contributing to insomnia, whereas symptoms of PD,
SAD and SP are specific to certain objectively non-threatening stimuli that are likely to be
absent at night (American Psychiatric Association, 1994).
Previous studies have reported an association between an eveningness chronotype
and insomnia, anxiety and depression during adolescence (Ferber, 1990; Giannotti, et al.,
2002; Randler, et al., 2009; Russo, et al., 2007). In particular, eveningness significantly
predicted depression/anxiety after controlling for insomnia and various other variables
146
(Giannotti, et al., 2002). However, this finding may be a function of the sample size from the
former study (742 evening-types, 1005 morning-types) given that eveningness only
explained 1% of the variance after all other variables were controlled (Giannotti, et al.,
2002). Indeed, the current study found that once age, depression, and anxiety were
controlled, eveningness significantly predicted insomnia, but once insomnia was controlled,
eveningness did not depression or anxiety subtypes. These findings suggest that insomnia
accounts for the relationships between chronotype and depression, chronotype and
subtypes of anxiety. Therefore, eveningness is not a direct predisposing factor for future
depression or anxiety subtypes during adolescence, but rather a risk-factor for the
development of insomnia, which predisposes adolescents to the development of depression
or GAD. Should depression present following insomnia, it may in turn contribute to the
development of OCD, PD, SAD or SP. Nevertheless, further research is needed for more
definitive conclusions about this possible pathway.
Various studies have also reported an association between eveningness and insomnia
during adolescence (Adan, Fabbri, Natale, & Prat, 2006; Fernández-Mendoza, et al., 2009;
Ong, Huang, Kuo, & Manber, 2007). In particular, one study of adolescents and young adults
found that eveningness predicted insomnia complaints after controlling for age, gender and
various sleep variables (Fernández-Mendoza, et al., 2009). These results, paired with those
found in the current study suggest that a preference for evenings may contribute to the
development of insomnia during adolescence and early adulthood above and beyond
depression, anxiety, and other sleep parameters.
Various mechanisms may explain the independent predictive effect of eveningness on
insomnia. Firstly, factors associated with an evening preference such as sleep-wake
irregularity may contribute to the development and maintenance of insomnia (Adan, et al.,
147
2006; Fernández-Mendoza, et al., 2009; Ong, et al., 2007). Similarly, adolescents may
experience symptoms of sleeplessness and sleep deprivation induced by an evening
preference and an enforced school schedule. Teenagers often become more eveningorientated with age due to various circadian rhythmic and environmental changes, yet often
rise early during the week for school and sleep later on weekends (Crowley, et al., 2007).
Consequently, total sleep time on school nights decreases with age but remains constant on
weekends (Russo, et al., 2007). The results of this study showed a similar pattern at baseline
and follow-up, in that total sleep time on school nights was negatively correlated with age
but remained constant on the weekend.
5.5.1. Clinical implications
The results of this study have prevention and treatment implications for symptoms and
disorders of insomnia, depression and anxiety. The bidirectional relationship between
symptoms of insomnia and depression, and insomnia and GAD suggests prevention and
treatment plans for insomnia disorder that also consider symptoms of depression and/or
GAD may prevent or alleviate the primary insomnia disorder, and vice-versa. Alternatively,
prevention and treatment programs may focus on symptom overlap and processes that
mutually contribute to the development of clinically significant (or even disordered)
insomnia, depression and GAD. For example, excessive worry is common to GAD,
depression, and insomnia disorder (American Psychiatric Association, 2013; Harvey &
Greenall, 2003; Jansson-Fröjmark & Linton, 2006a), and hence may be one of many
underlying processes that is key to efficiently and effectively address bidirectionality and
comorbidity between these disorders. Indeed, Danielsson, Harvey, MacDonald, JanssonFröjmark, and Linton (2013) found that catastrophic worry partially mediates the
relationship between sleep disturbances and depression in adolescents, and a study
148
reported symptom improvements in adolescents diagnosed with GAD after cognitivebehavioural therapy that focussed on excessive worry (Leger, Ladouceur, Dugas, & Freeston,
2003). Future research is needed to further consolidate this notion, and could investigate
the mediation effects of excessive worry on the relationship between symptoms of insomnia
disorder and GAD, or the efficacy of treatment for insomnia disorder and depression in
adolescents that focusses on excessive worry.
The chronotype construct may be useful for identifying a cohort of adolescents who
are at increased risk for developing an insomnia disorder. At baseline and follow-up, half of
the adolescents who were identified with clinically significant insomnia reported an evening
chronotype. Similarly, Giannotti, et al. (2002) found higher prevalence rates of clinically
significant sleep onset insomnia symptoms in evening-type adolescents relative to morningtypes. Together, the results suggest that clinically significant insomnia and an evening
chronotype are likely to co-present, and therefore, a large cohort of adolescents with
insomnia will also report a delayed sleep phase. Early clinical assessment, then, could aim to
detect delayed sleep phases in adolescents by assessing chronotype preference, and in turn
identify at risk cohorts or individuals before more severe insomnia symptoms (and perhaps
insomnia disorder) develop. Future research could consolidate this notion by aiming to
further improve the identification processes of insomnia disorder. In particular, research
could also randomly assign adolescents with an evening circadian preference who are not
diagnosed with an insomnia (or sleep) disorder to a control group or
treatment/prevention/education group that aims to stabilise circadian rhythms, and
compare the longitudinal outcomes across treatment groups. A larger prevalence rate of
insomnia disorder in the control group would suggest that early assessment of insomnia
disorder should aim to identify adolescents with a delayed sleep phase.
149
Given the research discussed above and the predictive effect of baseline eveningness
on insomnia severity at six month follow-up, prevention and treatment plans for insomnia
that also consider evening tendencies could lead to improved outcomes in the cohort of
adolescents with a delayed sleep phase. Furthermore, such prevention and treatment plans
for this adolescent cohort could also improve clinically significant symptoms of depression,
OCD, PD, SAD and SP, given that baseline insomnia predicted follow-up depression, and
baseline depression completely accounted for the relationships between insomnia and OCD,
PD, SAD and SP. Indeed, Golden and colleagues’ (2005) meta-analysis found high efficacy for
bright light therapy when treating seasonal affective disorder and nonseasonal depression,
although the included studies used adult samples.
Nevertheless, the small magnitude of the predictive effect of baseline eveningness on
follow-up insomnia suggests that for the majority of adolescents with clinically significant
insomnia (or an insomnia disorder), alternative treatments are likely to be more efficacious
than treatments that only focus on circadian rhythms for the majority of adolescents. An
integrated approach that focusses on the various potential underlying processes of the
relationship between insomnia, depression, anxiety subtypes and chronotype may be the
best form of intervention. For example, early intervention for maintaining school attendance
(i.e., addressing school refusal) is likely to maximise social and educational functioning, and
in turn improve sleep, mood and anxiety symptoms (Egger, Costello, & Angold, 2003).
Furthermore, stress management programs may reduce stress levels that are associated
with disorders of insomnia, depression and anxiety (Hammen, 2005; McEwen, Eiland,
Hunter, & Miller, 2012; Morin, Rodrigue, & Ivers, 2003). Moreover, emotional literacy
programs could aim to improve social support, a known preventative barrier for insomnia,
depression, and anxiety disorders (Dumont & Provost, 1999; La Greca & Harrison, 2005;
150
Roberts, et al., 2002), by creating an environment that allows for open and honest
discussions about mental health symptoms. Finally, large scale and individual based
interventions (e.g., bright light therapy, behavioural sleep scheduling, pharmacotherapy);
and education for adolescents, parents and teachers about delayed sleep phases, circadian
rhythm disorders, sleep, mental health, and the importance of understanding (and hence
not pathologising) symptoms that are likely to be a normal part of the transition to
adulthood may play an important role in improving the awareness of clinically significant
symptoms and disorders of insomnia, depression, subtypes of anxiety; circadian rhythms;
and their impact on emotional well-being. Such an integrated approach could help reduce
the social and educational impact of DSM disorders, and also prevent the onset of later
insomnia, depression and anxiety disorders.
5.5.2. Limitations
Self-report questionnaires were used to assess symptoms of insomnia, depression and
anxiety subtypes, which arguably does not accurately represent DSM diagnoses of insomnia,
depression and anxiety disorders. Indeed, some studies have reported inconsistencies
between data that derives from self-report paediatric measures of symptoms and data that
derives from measures considered the gold standard of paediatric sleep and mental health
disorders, namely clinical assessments of DSM disorders and objective methods of sleep
parameters (Bertocci, et al., 2005; Forbes, et al., 2008). Nevertheless, while a perfect
reflection may be impossible, standardised and well validated psychometric inventories can
be very good indicators DSM disorders. Wolpert, Cheng, and Deighton’s (In Press) review
article of four patient reported outcome measures found that the RCADS has the most
empirical support for sensitivity to change over time in paediatric populations, and
Mathyssek, et al. (2013) found that changes to symptoms of anxiety disorders over time as
151
assessed by the RCADS most likely reflects true changes to anxiety disorders. Furthermore,
studies have shown that the RCADS is a valid screening tool for depression and anxiety
disorders in paediatric populations (Chorpita, Moffitt, & Gray, 2005; Esbjørn, Sømhovd,
Turnstedt, & Reinholdt-Dunne, 2012; Mathyssek, et al., 2013), which is expected given that
psychometric measures of depression, anxiety and even insomnia symptoms yield similar
results to objectively or clinically assessed diagnoses in the general adolescent population
(Bertocci, et al., 2005; Forbes, et al., 2008). Indeed, the prevalence rates of clinically
significant insomnia, depression and SP at baseline and follow-up in this study were similar
to those found in the general population (Australian Bureau of Statistics, 2008a; Costello, et
al., 2011; Johnson, Roth, & Breslau, 2006). Together, the evidence suggests that wellvalidated psychometric measures of symptoms can accurately reflect DSM disorders, and
therefore, studies assessing symptoms are applicable to diagnoses.
Other psychiatric, medical and sleep disorders, as well as potential genetic or
neurobiological vulnerabilities that may confound the results were not assessed in the
current study. For example, delayed sleep phase syndrome is common in adolescents and
may contribute to insomnia, depressive and anxiety symptoms (Reid, et al., 2012).
Nevertheless, although approximately 11% of students were identified as having an evening
chronotype, the insomnia construct in the current study is likely to be a valid measure of
primary insomnia given the results remained the same when those with clinical indicators of
delayed sleep phase syndrome were excluded from the analyses [see (Alvaro, Roberts, &
Harris, 2014) for details of the procedure]. Regarding genetic or neurobiological
vulnerabilities, an increased sensitivity to natural or experimental alterations of the
serotonergic system has been associated with and hence may contribute to the
development of depression and sleep (Jans, Riedel, Markus, & Blokland, 2006). Future
152
studies could replicate the current study but use objective measures and/or clinical
assessments for insomnia, depression and subtypes of anxiety, assess other sleep and
psychiatric disorders, and consider genetic and neurobiological vulnerabilities.
5.5.3. Conclusions
This study added to the literature by assessing the bidirectional relationship between
insomnia and subtypes of anxiety, and insomnia and depression during adolescence after
circadian preference is controlled for, and the independent predictive effect of chronotype
on insomnia, depression and different subtypes of anxiety. Insomnia was bidirectionally
related to depression and GAD, but not to OCD, PD, SAD, nor SP. An evening chronotype
predicted insomnia, but not any mental health problem once insomnia was statistically
controlled. Eveningness during adolescence may lead to insomnia, which may result in
depression or GAD, and depression could in turn contribute to the development of other
anxiety subtypes. Prevention and treatment efforts for insomnia should potentially consider
and concurrently focus on depression, GAD and circadian rhythm dysregulation, whereas
prevention and treatment efforts for depression and GAD may consider also focussing on
insomnia. Finally, prevention and treatment plans for OCD, PD, SAD and SP should
concurrently focus on depression.
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5.5. Appendix
Appendix a
Stepwise regression analyses with insomnia as the outcome variable
Model
1
Step
Step 1
Predictor
β (Bootstrap)
CI lower
CI upper
Depression
0.61
0.49
0.75
Depression
Chronotype
0.55
-0.15
0.42
-0.24
0.70
-0.05
Depression
Chronotype
Anxiety
0.45
-0.16
0.05
0.27
-0.26
-0.01
0.63
-0.07
0.10
Depression
Chronotype
Anxiety
Age
Gender
0.43
-0.15
0.05
0.42
-0.48
0.25
-0.25
-0.01
0.02
-1.17
0.61
-0.06
0.11
0.83
0.69
GAD
0.55
0.36
0.75
GAD
Chronotype
0.66
-0.37
0.39
-0.47
0.73
-0.27
GAD
Chronotype
Depression
0.24
-0.18
0.47
0.06
-0.27
0.33
0.43
-0.08
0.62
GAD
Chronotype
Depression
Age
Gender
0.22
-0.14
0.45
0.38
-0.34
0.04
-0.23
0.29
-0.04
-1.40
0.43
-0.05
0.60
0.82
0.74
OCD
0.66
0.45
0.88
OCD
Chronotype
0.58
-0.32
0.39
-0.42
0.78
-0.22
OCD
Chronotype
Depression
0.14
-0.16
0.49
-0.10
-0.25
0.33
0.39
-0.06
0.67
OCD
Chronotype
Depression
Age
Gender
0.15
-0.15
0.48
0.38
-0.28
-0.11
-0.25
0.32
-0.02
-1.32
0.41
-0.05
0.65
0.79
0.82
Step 2
0.40
Step 3
0.41
Step 4
2
0.42
0.13
Step 1
0.27
Step 2
0.41
Step 3
0.42
Step 4
3
R2
adjusted
0.38
Step 1
0.16
Step 2
0.27
Step 3
0.40
Step 4
0.40
154
Appendix a cont…
Model
4
Step
Step 1
Predictor
β (Bootstrap)
CI lower
CI upper
PD
0.55
0.38
0.73
PD
Chronotype
0.45
-0.30
0.30
-0.40
0.63
-0.20
PD
Chronotype
Depression
0.04
-0.14
0.54
-0.18
-0.24
0.37
0.24
-0.04
0.71
PD
Chronotype
Depression
Age
Gender
0.05
-0.14
0.52
0.35
-0.31
-0.17
-0.23
0.36
-0.06
-1.34
0.26
-0.04
0.68
0.76
0.74
SAD
0.59
0.32
0.87
SAD
Chronotype
0.49
-0.32
0.26
-0.44
0.74
-0.21
SAD
Chronotype
Depression
-0.01
-0.14
0.56
-0.24
-0.24
0.41
0.23
-0.04
0.72
SAD
Chronotype
Depression
Age
Gender
-0.01
-0.13
0.55
0.37
-0.25
-0.27
-0.23
0.40
-0.035
-1.35
0.27
-0.03
0.71
0.78
0.80
SP
0.43
0.31
0.56
SP
Chronotype
0.38
-0.28
0.26
-0.38
0.51
-0.18
SP
Chronotype
Depression
0.12
-0.15
0.47
-0.01
-0.25
0.32
0.25
-0.05
0.63
SP
Chronotype
Depression
Age
Gender
0.14
-0.14
0.45
0.36
-0.60
-0.02
-0.14
0.31
-0.05
-1.65
0.25
-0.04
0.60
0.75
0.42
R2
adjusted
0.16
Step 2
0.25
Step 3
0.39
Step 4
5
0.40
Step 1
0.09
Step 2
0.20
Step 3
0.40
Step 4
6
0.40
Step 1
0.22
Step 2
0.30
Step 3
0.41
Step 4
0.41
a. Dependent Variable: Insomnia
* CI = Bias-corrected and accelerated confidence interval, GAD= Generalised anxiety disorder, OCD= Obsessive compulsive
disorder, PD= panic disorder, SAD= separation anxiety disorder, SP= social phobia
155
Appendix b
Baseline insomnia, chronotype, anxiety and age regressed on follow-up depression
Outcome Variable
Step
Predictor
β (Bootstrap)
Depression
Step 1
Insomnia
0.63
Step 2
Insomnia
0.62
Chronotype
-0.03
Step 3
Insomnia
0.40
Chronotype
-0.06
Anxiety
0.14
Step 4
Insomnia
0.39
Chronotype
-0.04
Anxiety
0.13
Age
0.42
Gender
0.42
GAD
Step 1
Insomnia
0.31
Step 2
Insomnia
0.31
Chronotype
0.01
Step 3
Insomnia
0.16
Chronotype
0.03
Depression
0.24
Step 4
Insomnia
0.16
Chronotype
0.02
Depression
0.22
Age
-0.06
Gender
0.57
OCD
Step 1
Insomnia
0.22
Step 2
Insomnia
0.08
Depression
0.20
Step 3
Insomnia
0.08
Depression
0.2
Age
0.16
CI lower
CI upper
0.52
0.74
0.50
-0.15
0.73
0.08
0.25
-0.18
0.09
0.55
0.05
0.19
0.24
-0.16
0.08
0.03
-0.67
0.54
0.07
0.17
0.83
1.48
0.23
0.39
0.23
-0.10
0.40
0.10
0.03
-0.07
0.10
0.28
0.13
0.38
0.04
-0.08
0.09
-0.39
-0.33
0.28
0.13
0.37
0.27
1.45
0.14
0.30
-0.04
0.08
0.20
0.32
-0.04
0.08
-0.13
0.20
0.32
0.45
R2
0.37
0.37
0.47
0.47
.18
.18
.22
.22
0.12
0.16
0.16
PD
Step 1
0.16
Insomnia
0.34
0.22
0.42
Insomnia
Chronotype
0.34
0.03
0.22
-0.06
0.45
0.12
Insomnia
Chronotype
Depression
0.13
0.06
0.31
-0.02
-0.03
0.13
0.28
0.16
0.51
Insomnia
Chronotype
Depression
Age
0.12
0.07
0.31
0.36
-0.02
-0.03
0.13
-0.01
0.27
0.16
0.50
0.74
Step 2
0.17
Step 3
0.24
Step 4
0.25
156
Appendix b cont…
Outcome Variable
SAD
Step
Predictor
β (Bootstrap)
CI lower
CI upper
Insomnia
0.21
0.14
0.28
Insomnia
Depression
0.09
0.17
-0.01
0.07
0.19
0.27
Insomnia
Depression
Age
Gender
0.10
0.13
0.06
0.59
-0.01
0.02
-0.19
-0.92
0.20
0.24
0.31
1.28
Insomnia
0.39
0.25
0.52
Insomnia
Depression
0.13
0.37
-0.04
0.18
0.29
0.57
Step 1
R2
0.14
Step 2
0.19
Step 3
0.25
SP
Step 1
0.13
Step 2
0.19
Step 3
0.22
Insomnia
0.13
-0.04
0.30
Depression
0.32
0.14
0.51
Age
0.16
-0.31
0.70
Gender
1.28
-0.98
2.55
* CI= Bias-corrected and accelerated confidence interval, GAD= Generalised anxiety disorder, OCD= Obsessive compulsive
disorder, PD= panic disorder, SAD= Separation anxiety disorder, SP= social phobia
157
Chapter 6: Discussion
Chapter 1 defined and reviewed the literature of the major concepts of the thesis.
Chapter 1 also presented a literature review about the bidirectionality of the relationship
between insomnia, depression and anxiety. The literature review depicted inconsistent
findings from the studies that have assessed the direction of the relationship between
insomnia, depression and anxiety, with one-way and bidirectional models as the two most
supported hypotheses. It was argued that these inconsistencies are the result of different
methodologies employed by previous studies, and overlapping methodological issues within
the literature should be addressed. In particular, chronotype had not been considered in
previous research. These arguments formed the basis of the two main research aims
addressed in this thesis, which were to investigate:
(1) The bidirectionality of the relationship between insomnia, depression and different subtypes
of anxiety;
(2) The independent effect of chronotype on these relationships.
Aim 1 was assessed by studies 1 and 3, whereas aim 2 was assessed by study 3, and
study 2 contributed to the research base of both aims. Study 1 systematically reviewed the
current literature on the bidirectionality of the relationship between insomnia, depression
and anxiety. Study 2 investigated the cross-sectional independent relationships between
insomnia and depression, and insomnia and subtypes of anxiety, along with the independent
effects of chronotype on insomnia, depression, and subtypes of anxiety. Study 3 assessed
the bidirectionality of the relationship between insomnia and depression over time, and
insomnia and subtypes of anxiety during adolescence after chronotype and other covariates
were controlled. This study also assessed the independent predictive power of chronotype
158
on insomnia, depression and anxiety subtypes after controlling for baseline anxiety
subtypes, depression, and/or insomnia.
The current chapter summarises the results of each paper, then discusses these results
according to the aims stated above, with an attempt to avoid as much overlap with the three
studies as possible. Some overlap was unavoidable, and the author again apologises for the
instances where overlap is present.
This chapter also expands on the discussions provided in the above studies with
information that could be not included because of word restrictions. Section 6.2 focuses on
the bidirectionality of the relationships between insomnia and depression, and insomnia and
subtypes of anxiety, and section 6.3 discusses the independent effect of chronotype on
these problems. Additional inferences about the aims are made from considering the results
of studies 1, 2 and 3. Section 6.4 discusses the theoretical implications of the results, while
section 6.5 focusses on the clinical implications of the results. Section 6.6 discusses the
limitations and future research that are directly relevant to the thesis but beyond the scope
of the studies.
6.1. Summary of the results from Studies 1, 2 and 3
Study 1 found that the bidirectionality of the relationship between sleep disturbances
and depression, and sleep disturbances and anxiety differs across sleep variables. Childhood
sleep problems predicted higher levels of depression and a combined depression/anxiety
variable, but not vice-versa. A one-way relationship was also found where anxiety predicted
excessive daytime sleepiness, but excessive daytime sleepiness was not associated with
depression. Insomnia and sleep quality were bidirectionality related to anxiety and
depression, and depression/anxiety, respectively. Definitive conclusions about
159
bidirectionality were not made in study 1 for excessive daytime sleepiness, childhood sleep
problems and sleep quality due to the small number studies, the heterogeneity of samples
across studies, and the overall nature of the sleep and mental health variables used that may
mask potentially differences between different sleep and mental health variables.
Nevertheless, best available evidence supports the bidirectional theory of the relationship
between insomnia, depression and anxiety.
Study 2 reported prevalence rates of 12.50%, 9.24%, 6.69%, 2.24%, 4.46%, 5.41% and
4.78% for clinically significant insomnia, depression, GAD, OCD, PD, SAD and SP, respectively,
which were similar to prevalence rates reported in previous studies (Myers & Troutman,
1993; Ohayon, 2002). Correlations between insomnia and each mental health variable
before controlling for covariates were also reported. After covariates were controlled,
insomnia predicted depression, whereas insomnia was predicted by depression and GAD.
Interestingly, OCD, SAD and SP were not significantly related to insomnia once depression
was entered into the model, suggesting depression may be the factor underlying these
relationships. Eveningness uniquely predicted insomnia and depression once all covariates
were controlled. Eveningness was correlated with each anxiety subtype (except GAD) before
covariates before but not after insomnia was controlled, suggesting insomnia may account
for these relationships. Although insomnia independently predicted panic disorder (PD), the
effect was not likely to be clinically significant.
Prevalence rates for study 3 at follow-up were similar to those reported at baseline in
study 2, except SAD was more prevalent in study 3 than study 2 (9.68% vs 6.77%). In any
case, the prevalence rates reinforce the frequency and importance of sleep and mental
health problems in adolescents. Study 3 also reported correlations between insomnia and
each mental health variable before covariates were controlled. After accounting for
160
covariates, insomnia was bidirectionally associated with depression and GAD. However, after
controlling for baseline depression, insomnia did not significantly predict other subtypes of
anxiety, and vice-versa. Baseline chronotype predicted insomnia at follow-up after
accounting for covariates, but not depression or any anxiety subtype after controlling for
baseline insomnia. Insomnia was interpreted as associated with the development of
depression and GAD above and beyond other factors, and vice-versa. The relationship
between insomnia and other anxiety subtypes was suggested to be the result of a
confounding effect of depression. An eveningness chronotype was interpreted to
independently predict the development of insomnia above and beyond other factors, but
not depression or anxiety subtypes. The relationship between chronotype and mental health
problems was suggested to be the result of a common association with insomnia.
6.2. The Bidirectionality of the relationships between
insomnia and depression, and insomnia and subtypes of
anxiety
The additional inferences discussed in section 6.2 include the consistency of
bidirectionality across age groups, cultures, methods of assessment, follow-up time-periods,
and anxiety subtypes over a period of time. Underlying mechanisms of the relationships
between insomnia, depression and GAD are also discussed.
6.2.1. Bidirectionality across various age groups and cultures
The evidence from studies 1 and 3 support the theory of a bidirectional relationship
between insomnia, depression and anxiety. Whereas study 3 assessed adolescents from
Australia, study 1 identified large population studies that found bidirectional relationships in
young adults (Buysse, et al., 2008; Hasler, et al., 2005), adults (Jansson-Fröjmark & Lindblom,
161
2008; Morphy, et al., 2007), and the elderly (J. M. Kim, et al., 2009) across European (Buysse,
et al., 2008; Hasler, et al., 2005; Morphy, et al., 2007) and Asian cultures (J. M. Kim, et al.,
2009). Studies that have assessed the longitudinal associations between insomnia and
depression, or insomnia and anxiety from one direction (e.g., longitudinal association
between baseline insomnia and follow-up depression, or baseline anxiety and follow-up
insomnia) have also found significant relationships across various ages and cultures
(Baglioni, et al., 2011; Jansson-Fröjmark & Linton, 2006b). Indeed, an association between
insomnia and depression, and insomnia and anxiety (overall and subtypes) have been
reported across age and cultures in cross-sectional studies (Alfano, et al., 2009; Cheung &
Wong, 2011), including study 2.
Together, the studies above can be interpreted in various ways. Firstly, the relationship
between insomnia and depression remains bidirectional from adolescence to older
adulthood. Neurobiological and psychological factors that are likely to maintain this
relationship, such as abnormal dopamine secretion and familial stressors, may be constant
throughout life, particularly without treatment. Secondly, the relationship between insomnia
and depression remains bidirectional across cultures. This explanation may support the
notion of a genetic component of the relationship between insomnia and depression, or
similar neurobiological factors and psychological stressors may be experienced across
cultures. Indeed, studies have reported overlapping genetic components of insomnia,
depression and anxiety in youths (Gehrman, et al., 2011). Thirdly, insomnia is likely to be
bidirectionally related to at least some anxiety problems across age and culture, for the
same reasons stated above. Fourthly, insomnia and GAD are more likely to be directly
associated than other subtypes of anxiety during adolescence (an explanation of this was
given in study 3). Finally, the relationships between insomnia and depression, insomnia and
162
an overall construct of anxiety, and insomnia and GAD are also stable across age and
cultures, both longitudinally and cross-sectionally.
6.2.2. Bidirectionality across different methods of assessment
Bidirectionality has also been reported across different methods of assessment. Of the
studies identified in the systematic review that reported bidirectional relationships between
insomnia and depression, and/or insomnia and anxiety, two used self-report questionnaires
(Jansson-Fröjmark & Lindblom, 2008; Morphy, et al., 2007), two studies that assessed the
same sample used semi-structured interviews (Buysse, et al., 2008; Hasler, et al., 2005), and
one used structured interviews (J. M. Kim, et al., 2009). The study that found a one-way
relationship was conducted using structured interviews, but was retrospective. This
consistency is in contrast to previous non-bidirectional insomnia and mental health studies
that have reported different results across different methods of assessment (Bertocci, et al.,
2005; Forbes, et al., 2008). Therefore, although a limitation, the use of self-report
questionnaires to investigate bidirectionality may be less problematic than when used in
non-bidirectional studies. The findings that the reliability and validity of measures of
psychopathology increase by 15% and 37% respectively when assessed as continuous
variables compared to categorical variables (Markon, et al., 2011) further support the use of
self-report questionnaires.
6.2.3. Bidirectionality and follow-up time-periods
The studies that have reported bidirectional relationships between insomnia and
depression, and insomnia and anxiety have used different time-periods between baseline
and follow-up. Study 3 used a six-month follow-up period, whereas two studies included in
the systematic review used a one-year follow-up (Jansson-Fröjmark & Lindblom, 2008;
163
Morphy, et al., 2007), one study used a two-year follow-up (J. M. Kim, et al., 2009), and the
two studies that assessed the same sample used two-year, five-year and six-year follow-up
periods (Buysse, et al., 2008; Hasler, et al., 2005). These findings indicate a rapid
development (i.e. within six months) and chronic nature (i.e., at least 6 years) of the
bidirectional relationship between insomnia and depression, and insomnia and anxiety
problems across the life-span.
Such a relationship can be both rapidly developing and chronic in nature even during
adolescence. The adolescent studies in the systematic review that assessed sleep quality (a
variable that overlaps greatly with insomnia) found bidirectional relationships with
depression/anxiety variables after one (Meijer, et al., 2010) and two-year follow-ups
(Kaneita, et al., 2009). Therefore, a bidirectional relationship between insomnia and
depression, and insomnia and certain subtypes of anxiety may develop within six-months (as
suggested by study 3) and last for at least 2 years during adolescence.
Together, the above evidence suggests the rapid development of subsequent sleep or
mental health issues following insomnia, depression or GAD, which in turn highlights the
importance of prompt identification and treatment of each problem. A discussion about
prevention and treatment for insomnia, depression and subtypes of anxiety can be found in
section 6.5. below.
6.2.4. Bidirectionality and time-period across subtypes of anxiety
Study 3 reported a bidirectional relationship between insomnia and GAD after
controlling for chronotype and other covariates. The results of study 2 were similar to the
results of study 3 in that both reported relationships between insomnia and GAD once
chronotype and other covariates were statistically controlled. Furthermore, insomnia was
164
related to OCD, SAD and SP before but not after depression was controlled. Together, these
results suggest that the nature of the relationship between insomnia and GAD, OCD, SAD
and SP remains constant over a 6 month period.
The results regarding anxiety subtypes support Alfano, et al.’s (2009) finding of a crosssectional association between sleep problems and GAD, but not the findings of crosssectional relationships between sleep problems and PD, and sleep problems and SP (Alfano,
et al., 2009). Alfano, et al. (2009) used the RCADS to assess the anxiety subtypes, but
assessed depression using the Child Depression Scale, which may account for the differences
in results. Also, Alfano, et al. (2009) assessed sleep problems as an overall construct,
whereas insomnia was assessed as a single variable in the current thesis. The natures of the
relationships are likely to differ between subtypes of anxiety and sleep problems, and even
depression and various sleep problems. Such a premise is further supported by the findings
of study 1. Previous studies did not assess the longitudinal associations between insomnia
and specific subtypes of anxiety.
It must be noted that GAD predicted insomnia in study 2, but not vice-versa, whereas a
bidirectional relationship was found between insomnia and GAD in study 3. The longitudinal
methodology of study 3 allows for reliable directional inferences, whereas the crosssectional methodology of study 2 allows only for associational inferences.
6.2.5. Potential mechanisms of the relationship between insomnia,
depression and GAD
Studies 1, 2 and 3 suggested various common mechanisms, risk-factors or processes
that may underlie the bidirectionality of the relationships between insomnia and depression,
and insomnia and GAD, such as neurobiological (Holmes, 2003; Nestler & Carlezon, 2006;
165
Peroutka, 1998; Tseng & O'Donnell, 2007; Yoshioka, et al., 1996), psychological and social
factors (Kaneita, et al., 2009; Lipton, et al., 2008). In addition, the diathesis-stress model
(Monroe & Simons, 1991) proposes that stressors may trigger the development of disorders
(e.g., insomnia, depression or GAD) in genetically predisposed or otherwise vulnerable
individuals. Taylor, Lichstein, et al. (2005) infer that insomnia induced stress may precipitate
the development, or trigger the relapse of depression or anxiety, and vice-versa. Those with
sleep initiation problems often spend nocturnal awakenings ruminating about past failures
or worry about future issues, thus resulting in depression or anxiety (Taylor, Lichstein, et al.,
2005). Conversely, daytime stress associated with ruminations regarding past failures or
worry about future issues usually extend to bedtime, increasing psychological and
physiological arousal, and therefore causing insomnia (Taylor, Lichstein, et al., 2005).
Nevertheless, such theories are mostly speculation, and more research is needed to support
or refute such notions. This section will discuss common risk-factors and a potential causeeffect relationship between symptoms and disorders of insomnia, depression, GAD and
other subtypes of anxiety.
6.2.5.1. Common risk-factors
Neale and Kendler (1995) propose that common risk-factors, all independent and of
small effect, actually reflect alternative forms of the same disorder, suggesting that the same
disorder may manifest in different ways (e.g., via insomnia or depression). In contrast,
Staner (2010) notes that such risk-factors may result in two distinctive yet associated
disorders if risk-factors are common to both conditions. The results of studies 2 and 3
support the latter theory regarding the relationships between insomnia and OCD, PD, SAD
and SP, where depression was found to be the common link. Previous studies have
suggested that hopelessness, a maladaptive core belief and symptom of depression, is
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strongly correlated with insomnia in young adults (Ribeiro et al., 2012) and anxiety during
adolescence (Thompson, Mazza, Herting, Randell, & Eggert, 2005). An adolescent with
depression may contain a hopelessness core belief that may trigger or contribute to the
development of concurrent episodes of insomnia and anxiety. In any case, together, the
results from this thesis and the studies above suggest that depressive symptoms, such as
hopelessness, are the common link between insomnia and various subtypes of anxiety.
Common maladaptive coping strategies may also underlie the developmental overlap
between insomnia, depression and anxiety subtypes. Emotional coping strategies have been
associated with insomnia, depression and anxiety (Morin, et al., 2003; Plancherel &
Bolognini, 1995). Examples of emotional coping strategies include seeking comfort and
support from others, avoiding stress triggers, writing about deep emotions, relaxation
techniques, emotional suppression, self-criticism, and wishing the problem will go away
(Compas, Connor-Smith, Saltzman, Thomsen, & Wadsworth, 2001; Folkman, 1984). For
example, an examination period may trigger physiological anxiety and excessive,
uncontrollable worry in adolescents who fear failure, leading to the avoidance of studying,
which can precipitate the development of depressive symptoms (e.g., hopelessness) and
further physiological arousal, and thus result in sleep initiation problems. Alternatively,
nocturnal insomnia episodes may provide opportunities for excessive rumination about
problems or worry, all of which are likely to exacerbate poor sleep, depressed mood, and
anxiety. Substance abuse has been associated with insomnia, depression and anxiety during
adolescence (Johnson & Breslau, 2001; Kaneita et al., 2006), and hence may represent
another common mechanism that underlies the relationship between these variables.
Depressive or anxious symptoms may precipitate substance use (adolescents often report
alcohol or marijuana use as a coping mechanism for depression), which can precipitate the
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development of insomnia and anxiety (Shibley, Malcolm, & Veatch, 2008). Conversely, an
adolescent with sleep initiation problems may consume excessive amounts of alcohol or
marijuana to assist with sleep, which may precipitate or exacerbate symptoms of depression
and anxiety.
Attentional biases towards threatening stimuli may also underlie the relationship
between insomnia, depression and anxiety. Hankin, Gibb, Abela, and Flory (2010) reported
that adolescents with depression are more likely to have an attentional bias towards sad
facial expressions rather than happy or angry, whereas youths with anxiety are more likely to
focus on angry facial expressions. Interestingly, youths with comorbid anxiety and
depression are more likely to have an attentional bias towards angry and sad facial
expressions, and avoided happy faces. These findings suggest an attentional bias towards
cues that resemble adolescents’ internalising problems. Moreover, Mor and Winquist’s
(2002) meta-analysis of children, adolescents and adults reported an association between
self-focused rumination and negative affect (depression, anxiety and negative mood). The
same study found that private self-focus (self-motivated goals that do not require the
consideration of others) was more strongly related to depression and GAD, whereas public
self-focus (goals that require the consideration of others) was more strongly associated with
SAD (Mor & Winquist, 2002). Therefore, an adolescent may have a self-focussed attentional
bias towards, which may lead to rumination about a threat or hopelessness, and in turn
contribute to sleeplessness or depression.
6.2.5.2. Cause-effect relationship
Finally, Insomnia, depression and GAD may be bidirectionally and causally related,
where the development of an insomnia disorder results in the development of depression
and GAD, and vice-versa. According to Hill’s (1965) widely cited criteria, a causal relationship
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can be inferred (but not confirmed) when: previous studies have consistently reported
significant effects (even across cultures), large effect-sizes and a dose-response linear
relationship; there are clear directions of inference (e.g., insomnia causes depression); the
relationship is biologically plausible and corresponds with the known facts of the disorders;
experimental evidence is available; and a judgment be made in similar circumstances.
Indeed, previous findings, along with the results from the current thesis, largely satisfy
Hill’s (1965) criteria. Studies across many cultures have consistently reported a wide range of
dose-linear associations, with varying effect-sizes, and have not seriously conflicted with the
known facts about insomnia, depression or anxiety (Gregory & O'Connor, 2002; Gregory, et
al., 2009; Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; J. M. Kim, et al., 2009).
Such studies have often control for potential covariates, and hence further strengthen the
notion of a cause-effect relationship. There has also been experimental evidence based on
treatment studies (Alfano, et al., 2007; Manber, et al., 2008), and various biologically
plausible explanations that have been discussed in chapters 3, 4 and 5. Furthermore, the
current thesis, in accordance with previous research, supports the notion that insomnia is
bidirectionally related to depression and GAD. Considering the evidence, it seems plausible
to infer a bidirectional and at least partial cause-effect relationship between insomnia and
depression, and insomnia and GAD.
6.3. The effects of chronotype on the relationship
between insomnia, depression and subtypes of anxiety
Section 6.3. discusses the effects of chronotype on the relationship between insomnia,
depression and subtypes of anxiety. Specifically, the prediction and confounding effects of
chronotype will be considered, along with the relationship between chronotype and
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insomnia, depression and anxiety across a 6-month period, and the mechanisms that
underlie these relationships.
6.3.1. The prediction and confounding effects of chronotype
Studies 2 and 3 found that an eveninging chronotype independently predicts insomnia
at 6-month follow-up, but not depression or subtypes of anxiety. Furthermore, chronotype
was not found to account for the relationships between insomnia and depression, or
insomnia and subtypes of anxiety, as each relationship remained significant when
chronotype was the only controlled variable. Together, these results suggest that
chronotype effects the relationships between insomnia, depression and GAD by initiating a
pathway for and hence contributing to the development of insomnia [which may then
contribute to the development of depression or GAD (see section 6.4.1. for further details
about potential pathways between chronotype, insomnia, depression and subtypes of
anxiety)], rather than accounting for these relationships. Such results support the notion of
the reviewer for study 2, who suggested a lack of empirical evidence for assessing the
mediation effect of chronotype on the relationship between insomnia, depression and
subtypes of anxiety.
In study 3, it was noted that eveningness is a risk-factor for the development of
delayed sleep phase syndrome in adolescents. Eveningness is also a risk-factor for the
development of sleep onset insomnia (Weitzman et al., 1981), which is likely reflected in the
findings discussed above. Those with an evening chronotype may retire at a time that is
earlier than, and does not align with their circadian preference, and hence experience
problems with initiating sleep. This process can lead to maladaptive sleeping strategies, such
as spending an excessive amount of time in bed and remaining awake while in bed (Perlis, et
al., 1997; Spielman, et al., 1987), and presents an opportunity for physiological, cognitive
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and somatic arousal. Such arousal can become conditioned to the sleep environment, and
hence can develop into chronic, psychophysiological insomnia (Bonnet & Arand, 2010; Perlis,
et al., 1997; Riemann, et al., 2010). Therefore, an evening chronotype is likely to be a riskfactor for the development of chronic psychophysiological insomnia.
6.3.2. Chronotype, insomnia, depression, and subtypes of anxiety
across a 6 month period
The results from studies 2 and 3 found that eveningness was no longer associated with
PD once insomnia was controlled, suggesting that the relationships between eveningness
and PD is dependent on insomnia from baseline until at least a 6-month period. However,
the relationships between eveningness and other subtypes of anxiety before controlling for
potential confounders were inconsistent across studies 2 and 3. Eveningness may predict PD
above and beyond insomnia, and chronotype may be correlated to other subtypes of anxiety
given a longer period of time between assessments, but research has yet to be done in this
area. Future studies could replicate the current methodology using longer follow-up time
periods (one or two years).
Interestingly, study 2 found a relationship between depression and eveningness after
insomnia and anxiety overall were controlled, whereas study 3 failed to report a relationship
between chronotype at baseline and depression at follow-up once insomnia at baseline was
controlled. These results suggest an inconsistent nature of the relationship between
eveningness and depression across a 6-month period, in that eveningness is related to
depression above and beyond insomnia and anxiety at a given point in time, but not to
depression six months later above and beyond insomnia at baseline. A theoretical
explanation is difficult to propose, as previous studies have not assessed the longitudinal
associations between chronotype and depression during adolescence. Furthermore, the
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disparity between study 2 and 3 is surprising given that, firstly, the daily pressures of life are
suggested to accumulate during adolescence and hence promote later bedtimes, poorer
sleep and more mental health problems (Carskadon, et al., 2004; Kaneita, et al., 2009), and
secondly, various factors typical of those with an evening preference are also symptoms of
depression and insomnia, such as persistent sleep deprivation, sleep displacement, difficulty
adjusting to social constraints and a changing lifestyle (Giannotti, et al., 2002). The difference
in results cannot be attributed to the differences in statistical analyses, as bootstrapped
regression analyses at baseline yielded identical results as generalised estimating equations.
Additional research is needed to further explore the effects of baseline insomnia on the
longitudinal relationship between depression and chronotype. Biological markers of
chronotype, objective measures of sleep and clinically assessed mental health variables
could be used.
6.3.3. Mechanisms
The finding that baseline eveningness predicted follow-up insomnia may be explained
by the notion that people with sleep-onset insomnia exhibit a phase delay pattern in body
temperature rhythm (Morris, et al., 1990), which, paired with retirement before sleepy,
could lead to prolonged wakefulness in bed and hence present an opportunity for
hyperarousal and rumination as per psychophysiological insomnia. The indirect association
between chronotype and the mental health variables contradicts the notion that personality
factors, particularly low self-control, may explain the association between an eveningness
chronotype, depression and anxiety (Saxvig, et al., 2012). Instead, a shift in preference for
later bed and rise times during adolescence (Russo, et al., 2007) coupled with an enforced
early rise time for school may result in sleep deprivation or insomnia symptoms (particularly
sleep initiation problems), which could lead to depression or GAD. Sleep deprivation and
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insomnia could also result in poorer stress management and coping skills (Killgore et al.,
2008) that would otherwise act as preventative barriers for stress and mental health
problems during adolescence (Dumont & Provost, 1999). Furthermore, the daily life
pressures mentioned above may lead to later bedtimes, which may result in sleep
deprivation during adolescence, and in turn more mental health problems (Carskadon, et al.,
2004; Kaneita, et al., 2009).
These results also add to Gaspar-Barba et al.’s (2009) notion that the association
between depression, anxiety disorders and eveningness may be related to corticotropinreleasing factor (Arborelius, et al., 1999). This hormone could be a biological factor that
contributes to the relationship between eveningness and insomnia rather than eveningness
and mental health, considering it is hypothesised to be related to the aetiology of insomnia
(Roth, Roehrs, & Pies, 2007). The common association with corticotropin-releasing factors
may also at least partially explain why the relationship between eveningness and depression,
and eveningness and subtypes of anxiety is dependent on insomnia. Conversely, circadian
abnormalities such as mutations to molecular clock genes have been reported to accelerate
or delay circadian cycles (Bunney & Bunney, 2000), which may predispose an adolescent to
sleep initiation problems or delayed sleep phase syndrome, and in turn contribute to the
development of depression.
6.4. Pathways between insomnia, depression, subtypes
of anxiety and chronotype
The results from this thesis suggest that insomnia is likely be developmentally related
to depression and GAD, but not to other subtypes of anxiety during adolescence.
Furthermore, eveningness is likely to be an independent risk-factor for the development of
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insomnia, but not depression or subtypes of anxiety within a 6-month period. Such findings
have theoretical implications on particular developmental pathways between insomnia,
depression, subtypes of anxiety and chronotype. The following section discusses these
pathways in further detail.
Studies 2 and 3 reported independent bidirectional relationships between insomnia
and depression, and insomnia and GAD above and beyond chronotype, anxiety overall or
depression, and age. Such evidence suggests but cannot infer direct (causal) bidirectional
relationships alone, as treatment or induced sleep deprivation studies are needed to infer
causality. Indeed, studies have shown that chronic partial sleep deprivation induced in
caregivers of children with chronic illnesses mediates the severity of depressive symptoms in
the caregivers (Meltzer & Mindell, 2006). Furthermore, an enforced six days of sleep
restriction resulted in sleep EGG abnormalities and endocrine disturbances akin to those in
depression (Spiegel, Leproult, & Van Cauter, 1999). Together, this evidence suggests that the
developmental relationship between insomnia and depression is bidirectional and at least
partially direct. Therefore, a pathway may follow from insomnia to depression or GAD, and
vice-versa (Figures 7 and 8).
Insomnia
Depression
Depression
Insomnia
Figure 7. Pathways between insomnia and depression.
Insomnia
GAD
GAD
Insomnia
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Figure 8. Pathways between insomnia and GAD.
Study 3 also reported partial indirect effects when insomnia predicted depression and
GAD, and when GAD predicted insomnia. In particular, the magnitude of the predictive
effect of insomnia on depression and GAD decreased once anxiety (as an overall construct)
and depression, respectively, were statistically controlled. Similarly, the magnitude of the
predictive effect of GAD on insomnia decreased once depression was controlled. These
findings suggest that the relationship between insomnia and GAD may be at least partially
attributable to depression, and that the predictive effect of insomnia on depression may be
at least partially attributable to anxiety. Therefore, a complex interaction between insomnia,
depression and GAD seems to occur during adolescence, where depression may partially
account for the bidirectional relationship between insomnia and GAD, and anxiety may
partially account for the predictive effect of insomnia on depression (Figures 9, and 10).
Depression
Insomnia
Depression
GAD
GAD
Insomnia
Figure 9. Indirect pathways between insomnia and GAD.
Anxiety
Insomnia
Depression
Figure 10. Indirect pathways between insomnia and depression.
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The relationships between insomnia and OCD, PD, SAD and SP were found to be
completely accounted for by depression. These findings suggest that the development of
OCD, PD, SAD or SP may involve insomnia, and vice-versa, despite the fact that insomnia did
not predict OCD, PD, SAD or SP once depression was controlled, and vice-versa.
Consequently, insomnia resulting in depression may in turn result in OCD, PD, SAD or SP; and
OCD, PD, SAD or SP that leads to depression may in turn lead to insomnia (Figure 11). Those
with sleep initiation problems may lie in bed awake in the dark, which might intensify
depressive or anxious feelings about life, and in turn lead to anxiety problems or depression
(Staner, 2010). Similarly, depressive symptoms such as feelings of worthlessness may result
in heightened anxiety about life, which in turn result in problems with sleep initiation.
Alternatively, Staner (2010) suggested that insomnia may reduce an individual’s capacity to
deal with personal and social problems, thereby increasing the likelihood of stressful life
events or poor responses to such events that could result in depression or anxiety.
Insomnia
Depression
OCD, PD,
SAD or SP
OCD, PD,
SAD or SP
Depression
Insomnia
Figure 11. Pathways between insomnia and OCD, PD, SAD and SP.
The results also suggest that eveningness is an independent risk-factor for the
development of insomnia (Figure 12). However, eveningness was found to predict
depression and GAD before but not after insomnia was statistically accounted for, suggesting
that eveningness may to lead to insomnia, which then may lead to depression and GAD
(Figure 13). Moreover, eveningness that leads to insomnia, which leads to depression may in
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turn lead to OCD, PD, SAD and SP (Figure 14), as eveningness predicted OCD, PD, SAD and SP
before but not after insomnia and depression were controlled. These findings suggest that
pathways leading to insomnia, depression and each anxiety subtype may involve an evening
preference in some capacity.
Insomnia
Eveningness
Figure 12. Pathways between eveningness and insomnia.
Eveningness
Insomnia
Depression
or GAD
Figure 13. Pathways between eveningness, insomnia, and depression or GAD.
Eveningness
Insomnia
Depression
Figure 14. Pathways between eveningness and OCD, PD, SAD or SP.
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OCD, PD,
SAD or SP
6.5. Clinical implications
The results from the current thesis suggest that prevention and treatment efforts for
insomnia should consider depression and GAD during adolescence, and vice-versa. Public
health interventions and treatment plans that also focus on circadian preferences during
adolescence may prevent and alleviate insomnia, and hence consequent depression and
GAD. Finally, prevention and treatment plans for OCD, PD, SAD and SP should also focus on
depression during adolescence, given that depression was associated with these anxiety
subtypes beyond other covariates. Previous research suggesting that the treatment of sleep
problems can alleviate or prevent depression and anxiety was considered in the discussion
section of study 3. The following section discusses in further detail some prevention and
treatment options for insomnia, depression and anxiety during adolescence.
6.5.1. Prevention
As argued in study 3, adolescents develop a stronger preference for evenings with age
because of various circadian rhythm and environmental changes, but often rise earlier than
desired due to an enforced school schedule (Crowley, et al., 2007). Consequently,
adolescents may experience symptoms of sleeplessness and sleep deprivation, which may
then contribute to mental health issues during adolescence (Liu & Zhou, 2002). Preventative
approaches for insomnia, depression and subtypes of anxiety, then, may benefit from
considering sleep deprivation, and therefore delaying school start time. A recent study found
significant improvements in measures of satisfaction with sleep, motivation, alertness and
class attendance, and reductions in fatigue, depressed mood, and daytime sleepiness
following a 30 minute delay in school start time (J. A. Owens, Belon, & Moss, 2010).
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Therefore, later school starts seem to improve sleep, emotional well-being, and other
elements of adolescent life such as neurocognitive performance.
Recent studies have also suggested that a delay in school start time may result in
improved academic performance (Carrell, Maghakian, & West, 2011), which is expected
given the improvement in measures of motivation, alertness and class attendance described
above (J. A. Owens, et al., 2010), and the association between insomnia, sleep deprivation,
neurocognitive impairment and academic achievement (Curcio, et al., 2006). Furthermore,
Danner and Phillips (2008) reported an increase in total sleep time, a decrease in recovery
sleep on weekends, and a reduction in average motor vehicle collisions by 16.5% two years
after baseline, following an one hour shift in school start times. In contrast, the average rate
of motor vehicle collisions for those who started school at normal time increased by 7.8%
over the two-year period (Danner & Phillips, 2008). Therefore, a delay in school start time
may result not only in improved sleep, mental health and neurocognitive performance, but
also everyday functioning, safety of adolescent (and other) drivers, and less sleep
deprivation relative to adolescents who start school at normal time. Policy managers and
public health strategists should consider postponing school start time in Australia to prevent
the development of insomnia, depression and anxiety disorders, which in turn may improve
academic performance, neurocognitive performance, and overall quality of life.
Alternatively, early regulation of circadian rhythms by, for example, increasing
environmental bright light exposure in mornings and regulating weekend catch-up
opportunities may prevent the onset of clinically significant problems.
As noted in study 3, education is important to preventative efforts for insomnia,
depression and anxiety subtypes. In particular, psychological and emotional literacy can be
taught to students and teachers. Educational sessions or programs can inform students
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about the importance of sleep and mental health, aim to promote emotional well-being, and
teach coping mechanisms that may be effective in enhancing mental health (and thereby
preventing or treating sleep or psychological problems). Such sessions or courses could also
aim to up-skill teachers in identifying the symptoms of insomnia, depression, and anxiety
disorders, along with the potential mechanisms behind disruptive and difficult behaviour
(e.g., school refusal, drug and alcohol use). Indeed, a systematic review of 17 studies
reported that a universal approach to mental health promotion in schools was effective
when implemented continuously for more than a year, and aimed to promote emotional
well-being rather than prevent mental illness (Wells, Barlow, & Stewart-Brown, 2003).
6.5.2. Treatment
Treatment efforts for insomnia, depression and anxiety can be effective when
targeting adolescents. Psychological treatment, in particular, a multifaceted cognitive
behavioural therapeutic approach, emerged as the treatment of choice for insomnia during
adolescence (Edinger & Means, 2005). Cognitive behavioural therapy (CBT) results in
subjective and objective improvements in sleep that are stable over time and effective in
clinical and primary care settings (Edinger & Means, 2005). Schlarb, Liddle, and Hautzinger
(2011) found a reduction in problems with sleep initiation, sleep efficacy, sleep duration,
lethargy, rumination, and mental health following a short-term multifaceted group cognitive
behavioural therapy for adolescents and parents. Such evidence provides support for, and
therefore highlights the importance of using interventions that focus on the psychological
aspects of insomnia.
Recent studies have also provided support for the use of CBT and other psychological
approaches to treat adolescent depression and anxiety (A. James, Soler, & Weatherall, 2005;
Reinecke, Ryan, & DuBois, 1998). In their meta-analysis of six depression treatment studies
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using CBT, Reinecke, et al. (1998) found a large effect of 1.02 (Cohen’s d) from baseline
(before treatment) to follow-up (after treatment). Another systematic review suggested that
CBT is an effective method of treatment for childhood and adolescent anxiety disorders in
comparison to those on a waiting list who did not receive treatment, and those who
received attention only (i.e., diary or support, but not cognitive behavioural therapy) (A.
James, et al., 2005). Furthermore, a meta-analysis by Weisz, McCarty, and Valeri (2006)
found that psychological therapies such as cognitive behavioural therapy are effective,
although not as much as suggested by A. James, et al. (2005) when used to treat adolescent
mental health disorders. In any case, a CBT program may help to increase the adolescents’
knowledge about sleep and mental health topics, such as sleep hygiene and helpful coping
strategies, and in turn assist in cognitive restructuring, behavioural change (Schlarb, et al.,
2011), and hence symptom change.
Pharmacotherapeutic treatment for insomnia, depression and anxiety during
adolescence, on the other hand, may be useful in some settings but not others. A metaanalysis suggested that tricyclic antidepressants are no more effective than a placebo when
treating depression in children and adolescents (Hazell, O'Connell, Heathcote, Robertson, &
Henry, 1995). Moreover, in a review about the use of melatonin in paediatric insomnia,
Armour and Paton (2004) found that melatonin reduced sleep latency, but was not shown to
consistently improve other types of sleep disturbance. Furthermore, short-term concerns
with regard to melatonin use in paediatric populations included the exacerbation of epilepsy
and asthma (Armour & Paton, 2004). Other commonly used medication for mental health
disorders have yielded dangerous side-effects. For example, selective serotonin reuptake
inhibitors are widely used to treat adolescent depression (Safer, 1997; Weisz & Jensen,
1999), but adverse effects such as suicidal ideation and attempts have been reported
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(Vitiello & Swedo, 2004; Whittington et al., 2004). Also, despite the fact that antidepressants
such as fluoxetine have been reported to produce significantly greater improvements in
adolescent depression than placebo or cognitive behavioural therapy (Treatment for
Adolescents with Depression Study Team, 2004), Weisz, et al. (2006) notes that the U.S.
Food and Drug Administration had issued a warning on all antidepressants in 2004 to
emphasise the possible risk of suicidality. Therefore, the use of medication as a form of
treatment for depression and anxiety needs to be considered carefully during adolescence.
Indeed, data from both published and unpublished studies indicate that the risks of
pharmacotherapeutic treatment in children and young people out-way the benefits
(Whittington, et al., 2004).
Recent studies have suggested that a combination of medication and CBT may be a
superior approach to the use of either forms of treatment alone for adolescent mental
health disorders. Firstly, studies that have assessed the combination effect of medication
and CBT on mental health report fewer or no cases of suicidal ideation or attempts (March,
2004; Treatment for Adolescents with Depression Study Team, 2004; Walkup et al., 2008).
Also, less suicidal ideation in adolescents treated with fluoxetine and CBT have been
reported than in those treated solely with fluoxetine (Treatment for Adolescents with
Depression Study Team, 2004), suggesting that a combination treatment program may yield
less aversive effects and suicidality than medication alone. Finally, recent paediatric studies
have also reported greater improvement in mental health following the combination of CBT
and medication relative to CBT or medication alone (Treatment for Adolescents with
Depression Study Team, 2004; Walkup, et al., 2008). Together, these studies suggest that a
multifaceted treatment approach that considers biological and psychosocial aspects of
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depression and anxiety disorders are safer, more efficacious, and therefore should be the
preferred method of intervention when treating adolescents (Whittington, et al., 2004).
Studies have also suggested that bright light therapy (BLT), a treatment method for
adolescent delayed sleep phase syndrome (of which eveningness is an essential symptom),
may be useful for treating adolescent insomnia and depression when used in conjunction
with CBT and other therapies (Bootzin & Stevens, 2005; Gradisar et al., 2011). Bootzin and
Stevens (2005) note that BLT is used to alter the sleep/wake circadian rhythm, particularly in
those who are phase delayed, cannot initiate sleep until the early morning, and struggle with
morning awakenings. A shift from a delayed sleep phase to a neutral sleep preference may
reduce sleeplessness, depression and anxiety symptoms. Indeed, Gradisar, Dohnt, et al.
(2011) found that, when compared to a waitlist, CBT plus BLT leads to significantly reduced
sleep onset latency, time of sleep onset, wake after sleep onset, sleeplessness and fatigue;
earlier sleep onset and rise times; increased total sleep times on school nights; and less
depressive symptoms. Furthermore, Bootzin and Stevens (2005) reported that adolescents
who completed at least four sessions of a six-session group treatment for sleep disturbances
that included stimulus control instructions, BLT, sleep hygiene education, cognitive therapy
and mindfulness-based stress reduction showed improved sleep onset latency, number of
awakenings, total sleep time, and self-rated quality of sleep; and reduced sleepiness, worry,
mental health distress, and substance abuse problems at 12-month follow-up. Therefore,
not only is a multifaceted program that considers biological and psychosocial factors the
preferred method of treating adolescent insomnia, anxiety and depression, but also delayed
sleep phase syndrome and other psychiatric disorders.
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6.6. Limitations and future research
The limitations discussed in studies 1, 2 and 3 include the use of self-report measures;
insomnia assessed as an overall variable rather than as different symptoms; the lack of
control over potential confounding variables such as other sleep disorders, other psychiatric
disorders, and genetic/neurobiological vulnerabilities. The future research that was
suggested includes bidirectionality studies that assess different insomnia symptoms; and
replication of study 3 with objective measures and/or clinical assessments for insomnia,
depression and subtypes of anxiety, the assessment of other sleep and psychiatric disorders,
and the consideration of genetic and neurobiological vulnerabilities. The current section
discusses the limitations and future research that is directly relevant to the thesis topic, but
beyond the scope of studies 1, 2 and 3. The topics to be discussed include bidirectionality
across insomnia symptoms and sleep deprivation; stressful life events; physical exercise; and
sleep, chronotype and technology. Insomnia symptoms and physical exercise were included
in the current section, because the discussion provided in study 1 was deemed insufficient
considering different anxiety subtypes had been assessed.
6.6.1. Insomnia subtypes
The current study assessed insomnia as an overall variable rather than specific
symptoms. Ohayon (2005) found people who reported non-restorative sleep were 3.64 and
4.02 times more likely to suffer from anxiety and mood disorders respectively than those
who reported other insomnia symptoms (troubles initiating and maintaining sleep).
Similarly, Hartz et al.’s (2007) investigation of risk factors for insomnia in a rural population
found that restless sleep (considered similar to non-restorative sleep) (Hartz, et al., 2007, p.
941) yielded the highest association with low energy levels and depression, whereas anxiety
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symptoms were more often associated with difficulty maintaining sleep. Together, these
results suggest that different aspects of insomnia have different risk-factors and therefore
possibly different aetiologies. Each insomnia subtype (e.g., problems with sleep initiation,
sleep maintenance, early morning awakenings, and non-restorative sleep) would then
require different preventative and treatment strategies. Given the findings of study 3,
depression and certain anxiety subtypes may be risk-factors and aetiologically related to
different types of insomnia.
The current study did not assess bidirectionality across insomnia subtypes, as the
authors could not locate an inventory that assessed different subtypes of insomnia and that
was validated in adolescent populations. Nevertheless, future studies could assess insomnia
subtypes via subjective measures such as sleep diary, objective measures (e.g.,
polysomnography, actigraphy) or clinical interviews. Longitudinal methodology could be
used to investigate bidirectionality, and treatment studies could be used to investigate
aetiology. Large samples would be needed in both cases, which would likely be obtained
through the general population.
6.6.2. Sleep deprivation
Previous research has reported an association between sleep deprivation, depression
and anxiety during adolescence (Fredriksen, Rhodes, Reddy, & Way, 2004; Talbot,
McGlinchey, Kaplan, Dahl, & Harvey, 2010). Fredriksen, et al. (2004) found that increased
sleep deprivation over time independently predicted increased depressive symptoms and
reduced self-esteem over time. Furthermore, Talbot, et al. (2010) reported that younger
adolescents considered their main worry as more threatening when sleep deprived
compared to when well rested. The same study also reported a greater increase in anxiety
during a catastrophic event in younger and middle adolescents when sleep deprived relative
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to when well rested (Talbot, et al., 2010). Considering the research, sleep deprivation may
be a risk-factor for the development of depression and anxiety during adolescence.
Future research could assess the bidirectionality of the relationship between sleep
deprivation, depression and anxiety subtypes. Sleep deprivation could be assessed by
objective measures (e.g., polysomnography, actigraphy), while depression and anxiety could
be assessed by clinical interviews. Longitudinal methodology and treatment studies could be
used to investigate directionality and aetiology.
6.6.3. Insomnia, depression, anxiety and stressful life events
The current study did not assess stressful life events. Bernert, Merrill, Braithwaite, Van
Orden, and Joiner Jr (2007) found that family life stress predicted insomnia at three week
follow-up in older adolescents and young adults after controlling for depression.
Furthermore, Braet, et al. (2013) found that stress induced by different factors strongly
predicts depressive symptoms in younger, middle, and older adolescence. Moreover,
Moksnes, Espnes, and Haugan (2014) reported that stress related to academic performance
significantly predicted symptoms of depression and anxiety, whereas stress related to peer
pressure significantly predicted depression. Finally, stressful life events are risk-factors for
clinically significant symptoms and a diagnosis of post-traumatic stress disorder (PTSD) in
adolescents (Trickey, Siddaway, Meiser-Stedman, Serpell, & Field, 2012), which has been
associated with sleep disturbances (Garrison, Weinrich, Hardin, Weinrich, & Wang, 1993),
depression (Luo, et al., 2012) and anxiety (Giannopoulou et al., 2006). Stressful life events
and PTSD, then, may be a confounding factor for and hence affect the bidirectionality of the
relationship between insomnia, depression and anxiety in adolescents.
186
Future research could assess whether stressful life events and PTSD are separate riskfactors for the development of insomnia, depression and anxiety; mediate the relationships
between these variables; or moderate the relationship between these variables. A
replication of the current study that includes measures of stressful life events may be the
simplest method, but studies using objective measures and clinical assessment of insomnia,
depression and anxiety would be the preferred option. In the case of self-report measures,
inventories based on the DSM-5 could be developed and used.
6.6.4. Insomnia, depression, anxiety and physical activity
Physical activity has also been associated with insomnia, depression, and anxiety.
Brand, et al. (2010) found that adolescent athletes report better sleep and less depressive
and anxiety symptoms. Furthermore, there is emerging evidence that physical activity can be
protective against developing depression and reduce anxiety symptoms that are common in
depression (Martinsen, 2008). This evidence suggests that physical activity may be a riskfactor for the development of depression and symptoms of anxiety, but it is currently
unknown whether physical activity is a separate risk-factor for the development of insomnia.
Furthermore, physical activity may mediate or moderate the relationships between insomnia
and depression or insomnia and anxiety. The current study included physical activity
variables, but did not include such variables in the analyses due to extremely skewed data.
Future research could assess the effects of a treatment program based on physical
activity to prevent or improve rates of insomnia, depression and anxiety. For example, a
group of adolescents with diagnosed insomnia, depression and/or an anxiety disorder could
be subjected to 45 minutes (the approximate length of period for a class in South Australian
schools) of moderately intensive exercise over a determined time period (e.g., everyday for
one week), and compared to adolescents who have a physically inactive class or a placebo
187
intervention. Indeed, group exercise has been found to improve depressive symptoms
compared to usual activity, with improved hormonal responses to stress and physical fitness
in young females (Nabkasorn et al., 2006). Perhaps more interestingly, a study could
assessed the preventative and treatment effects of physical activity and bright light therapy
on insomnia, depression and anxiety. For example, a physical education class could be
moved to the first morning lesson under bright lights (outdoors or indoor gymnasium), and
compared to bright light therapy alone, morning physical education classes under dimmer
light, and no change to school class schedule. Such a study would be inexpensive, effective,
and possible for most schools.
6.6.5. Technology, sleep, and chronotype
Previous research has reported associations between sleep disturbances and night
time technology use (Calamaro, et al., 2009; King, Delfabbro, Zwaans, & Kaptsis, 2013).
Calamaro, et al. (2009) found that high school students who slept between 8 – 10 hours per
night had a 1.5 to two-fold decrease in night time technology use than those with 6 – 8
hours and 3 – 5 hours, while those who fell asleep during school were much more likely to
use technology at night than those who remained awake (OR 69.9). Furthermore, a South
Australian adolescent study reported associations between technology use and sleep
duration on weekdays and weekends, bedtime delays, and sleep interruptions (King, et al.,
2013). The association between sleep disturbances and night time technology use may at
least partially result from a disruption to the circadian rhythm cycle caused by bright light
emitted by the device. Perhaps depending on the timing of duration of exposure (Heath et
al., 2014), bright light suppresses melatonin production (Lewy, Wehr, Goodwin, Newsome, &
Markey, 1980), inducing wakefulness and inhibiting sleep (Cajochen, Kräuchi, & Wirz‐Justice,
2003). Bright lights at night from electronic devices, then, may delay melatonin secretion,
188
and consequently result in later bedtimes, and induce sleep onset insomnia and delayed
sleep phases during adolescence. These findings, paired with the notion that technology use
at night increases cognitive, emotional, and physiological arousal at bedtime, and in turn
sleep onset latency while reducing total sleep time (King, et al., 2013), suggests that night
time technology use may be a risk-factor of and hence potentially contribute to the aetiology
of sleep deprivation and insomnia.
Future research could investigate the impact of technology on chronotype and the
development of insomnia during adolescence. A controlled experimental design that
assesses the effects of induced night time technology use on sleep and the circadian rhythm
would be ideal, particularly where technology use is controlled and recorded, and sleep and
the circadian rhythm are assessed in a sleep laboratory via objective means. Such a study
could be run overnight or across several nights. Alternatively, a longitudinal study that
assessed night time technology use via questionnaires, and sleep and circadian rhythm
variables via objective measures or self-report measures would also advance current
research. Variables that could be assessed include melatonin levels, core body temperature,
wakefulness at night, bedtime, duration of sleep onset, time of sleep onset, wake-time, risetime, time between wake and rise-time, difficulty falling asleep and rising, sleep efficiency,
sleep quality, and number of awakenings. A significant relationship between technology use
and insomnia from either study design would suggest that the former may be related to the
aetiology of the latter. Lower melatonin levels; progressively later bedtimes, time of sleep
onset, wake-times, and rise-time; and shorter time between wake and rise-time during the
week due to light from induced technology use would suggest a progressively later circadian
preference. Preventative and treatment efforts for insomnia or delayed sleep phase
syndrome during adolescence may then consider night time technology use, which, given
189
the results of this thesis, could impact the development or maintenance of depression and
GAD.
6.7. Conclusions
Best available evidence suggests a bidirectional longitudinal relationship between
insomnia and depression, and insomnia and anxiety. After controlling for covariates and
assessing different anxiety subtypes, insomnia seems to be bidirectionally related to
depression and GAD, but not longitudinally related to OCD, PD, SAD or SP. Furthermore,
eveningness at baseline seems to predict insomnia 6 months later after controlling for
covariates, but not depression, GAD, OCD, PD, SAD or SP. Prevention and treatment efforts
for insomnia may also consider depression, GAD and an eveningness preference, whereas
prevention and treatment efforts for depression and GAD may consider insomnia. Finally,
given the strong effect of depression on anxiety subtypes, prevention and treatment plans
for OCD, PD, SAD and SP should concurrently focus on depression.
190
7. Appendix – bootstrapped regression analyses when controlling for
outcome variables at baseline
Table 17
Bootstrap regression analysis when predicting insomnia and outcome variable is controlled at baseline.
Model
Predictor
β (Bootstrap)
CI Lower
CI Upper
1
Depression
0.178
0.018
0.334
Chronotype
-0.055
-0.146
0.031
Anxiety
0.024
-0.024
0.071
Age
0.254
-0.092
0.615
Gender
-0.240
-1.165
0.685
Insomnia
0.498
0.374
0.635
2
GAD
Chronotype
Anxiety
Age
Gender
Insomnia
3
R2adjusted
0.538
0.069
-0.048
0.209
0.233
-0.143
0.495
0.069
-0.096
-0.132
0.069
-0.122
-1.062
0.364
-0.096
0.261
0.034
0.344
0.588
0.781
0.627
0.261
0.538
OCD
Chronotype
Anxiety
Age
Gender
Insomnia
0.136
-0.057
0.172
0.248
-0.117
-0.088
-0.143
0.014
-0.091
-1.062
0.351
0.025
0.329
0.600
0.829
0.540
4
PD
Chronotype
Anxiety
Age
Gender
Insomnia
-0.009
-0.043
0.233
0.218
-0.115
0.512
-0.201
-0.129
0.085
-0.112
-0.997
0.386
0.162
0.039
0.383
0.560
0.778
0.638
0.536
5
SAD
Chronotype
Anxiety
Age
Gender
Insomnia
-0.067
-0.041
0.244
0.209
-0.067
0.515
-0.285
-0.127
0.103
-0.140
-0.974
0.390
0.148
0.046
0.388
0.581
0.845
0.644
0.536
6
SP
0.092
-0.011
0.204
0.542
Chronotype
-0.052
-0.142
0.033
Anxiety
0.174
0.035
0.315
Age
0.227
-0.128
0.596
Gender
-0.332
-1.276
0.619
Insomnia
0.499
0.376
0.624
Note. Bootstrapped regression analyses for when depression and subtypes of anxiety at baseline were used to predict insomnia at
follow-up after controlling for insomnia at baseline. 5000 bootstrapped were computed. β= regression co-efficient, CI= Bca 95%
Confidence Interval, GAD= Generalised anxiety disorder, OCD= Obsessive compulsive disorder, PD= Panic disorder SAD= Separation
anxiety disorder, SP= Social phobia.
191
Table 18
Regression analyses with insomnia as the predictor variable
R2
adjusted
0.538
Outcome Variable
Depression
Predictor
Insomnia
Chronotype
Anxiety
Age
Gender
Depression
β (Bootstrap)
0.184
0.012
0.049
0.382
0.200
Lower
0.051
-0.088
0.000
-0.024
-0.804
Upper
0.318
0.111
0.099
0.764
1.181
GAD
Insomnia
Chronotype
Depression
Age
Gender
GAD
0.029
-0.014
0.054
0.023
0.356
0.604
-0.068
-0.103
-0.066
-0.257
-0.389
0.438
0.121
0.079
0.184
0.296
1.140
0.759
0.462
OCD
Insomnia
Depression
Age
Gender
OCD
0.086
-0.039
0.172
0.275
0.648
-0.001
-0.154
-0.101
-0.427
0.457
0.172
0.077
0.453
0.988
0.827
0.382
PD
Insomnia
Chronotype
Depression
Age
Gender
PD
0.076
0.034
0.071
0.419
0.378
0.570
-0.051
-0.045
-0.093
0.100
-0.477
0.350
0.205
0.117
0.246
0.727
1.219
0.767
0.398
SAD
Insomnia
Depression
Age
Gender
SAD
0.083
-0.015
0.101
0.985
0.646
0.006
-0.093
-0.104
0.432
0.511
0.162
0.061
0.306
1.558
0.792
0.509
SP
Insomnia
0.087
-0.036
0.206
0.503
Depression
-0.059
-0.217
0.100
Age
0.163
-0.283
0.609
Gender
0.698
-0.388
1.756
SP
0.691
0.567
0.815
Note. Bootstrapped regression analyses for when insomnia at baseline was used to predict depression and
subtypes of anxiety at follow-up after controlling predicted variable at baseline. 5000 bootstrapped were
computed. GAD= Generalised anxiety disorder, OCD= Obsessive compulsive disorder, PD= Panic Disorder,
SAD= Separation anxiety disorder, SP= Social phobia, CI= Bca 95% Confidence Interval
192
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