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Journal of
Oral Rehabilitation
Journal of Oral Rehabilitation 2015 42; 275--281
The unique contribution of elements of smile aesthetics to
psychosocial well-being
A. LUKEZ*, A. PAVLIC†, M. TRINAJSTIC ZRINSKI† & S. SPALJ†
*School of Medicine, Uni-
versity of Rijeka, Rijeka, and †Department of Orthodontics, School of Medicine, University of Rijeka, Rijeka, Croatia
SUMMARY Pleasant smile aesthetics is an important
contributory factor to psychosocial well-being. The
aim of this study was to determine the
psychosocial influence of smile aesthetics. The
study was cross-sectional on a convenient sample
that included patients, pupils, students and faculty
staff. A total of 155 subjects (36% male) aged 12–39
(mean age 21, interquartile range 19–23) were
included. Occlusal characteristics were recorded by
the Index of Complexity, Outcome and Need, and
smiling frontal view photographs were obtained.
Fourteen
variables
were
measured
using
photogrammetric analysis: smile width, visibility
of buccal corridors, maximum teeth exposure,
total gingival display, lip thickness, degree of
occlusal cant and deviation from golden
proportion of the teeth in maxillary intercanine
sector. Psychometric instruments included the
Psychosocial
Impact
of
Dental
Aesthetics
Questionnaire and the Rosenberg Self-Esteem
Scale. Statistical analysis comprised multiple linear
Introduction
Improving facial appearance is one of the primary
goals of modern dentistry and related head and face
medical disciplines as well as craftspeople such as cosmeticians and hairdressers (1). A major motivational
determinant for orthodontic treatment is the perception of altered aesthetics and it has been estimated
that seventy-five percentage of patients who seek
orthodontic treatment do so for aesthetic reasons (2).
Pleasant facial aesthetics are an important contributory factor to psychosocial well-being (2). Emotional
stability, dominance, anxiety and self-esteem seem to
© 2014 John Wiley & Sons Ltd
regressions. Malocclusion severity is the most
important predictor of psychosocial influence of
smile aesthetics and self-esteem, the unique
contribution of which accounts for a total of 4–
27% of variability. Female gender is associated
with higher psychological influence of dental
aesthetics while male gender and older age with
self-esteem.
Malocclusions
have
higher
psychosocial impact than parameters of mini- and
micro-aesthetics of smile related to visibility of
buccal corridors, amount of teeth exposure,
gingival display, lip thickness, presence of occlusal
cant and deviation from golden proportion of the
teeth. It appears that people are not as focused on
details of their smile as they are on distinctive
malposition of teeth.
KEYWORDS: smile aesthetics, psychosocial influence,
golden proportion, malocclusion
Accepted for publication 3 October 2014
be developed in interaction with facial and dental aesthetics (3). Studies have shown that more attractive
people are regarded as being more intelligent and successful (4). It is widely known that the mouth and
eyes are the main personal features our attention is
directed towards during conversation (5), while the
smile is a powerful communication tool (6).
The smile is one of the facial characteristics that
positively influences facial attractiveness (1), containing different components such as teeth and soft tissues, which should form a harmonious symmetrical
whole (4, 7). It appears that some elements of
smile such as broadness, less visible buccal corridors,
doi: 10.1111/joor.12250
276
A . L U K E Z et al.
moderate gingival visibility and the absence of occlusal cant have a positive effect on smile and consequently facial aesthetics (8, 9). Children, teenagers
and adolescents with aligned teeth are considered as
more popular, intelligent and friendly, whereas those
with crowded dentition are thought to be less attractive and less intelligent (2).
The aim of this study was to explore the unique
contribution of elements of smile aesthetics to psychosocial well-being in adolescents and young adults.
Materials and methods
The study was cross-sectional on a convenient sample
that included patients seeking dental and orthodontic
treatment at the University Dental Clinic Rijeka,
pupils, students and Medical Faculty staff in Rijeka,
Croatia. A total of 155 subjects (36% male) without
intellectual disabilities, in permanent dentition with
all teeth present (at least first molar to first molar),
aged 12–39 (mean age 21, interquartile range 19–23)
were included. Occlusal characteristics were recorded
by the Index of Complexity, Outcome and Need
(ICON) (10) and central incisor height was measured
with a sliding caliper. Smiling frontal view photographs were obtained (photographed in natural head
position with calibration device). Fourteen variables
were measured using photogrammetric analysis in
AudaxCeph software*: smile width, visibility of buccal
corridors, maximum teeth exposure, total gingival display, lip thickness, degree of occlusal cant and deviation from golden proportion of teeth in the maxillary
intercanine sector (Supplementary data Table S1)
(11). Three examiners performed all analyses. Interand intra-examiner reproducibilities were assessed by
intra-class correlation coefficient on 40 subjects. Intraexaminer reproducibility was assessed by repeated
measurements within a 1-week period. Intra- and interexaminer agreements were good (073–096 for interexaminer and 071–099 for intra-examiner), being
highest for smile index, smile height index and smile
width and lowest for buccal corridors.
To measure the psychosocial well-being, the validated Croatian version of the Psychosocial Impact of
Dental Aesthetics Questionnaire (PIDAQ) (12) and
the Rosenberg Self-Esteem Scale (13) were used. The
*Audax, Ljubljana, Slovenia.
PIDAQ covers four dimensions: social impact (SI),
psychological impact (PI), dental self-confidence
(DSC) and aesthetic concern (AC). Pearson correlations and multiple linear regression were used for statistical analysis in SPSS 10.0 software.† Correlation
coefficients <025 were considered poor and were not
interpreted. The problem of multicollinearity was controlled by variance inflation factor and tolerance statistics. It was hypothesised that elements of miniaesthetics influence the psychosocial well-being more
than those of micro-aesthetics, most in the DSC
domain and least in AC. The hypothesis of stronger
correlation with DSC and least with AC is based on a
previous study (12).
Results
Descriptive statistics are available in supplementary
data (Table S2). Among the elements of smile aesthetics, increase in malocclusion severity was linearly
related only to increased buccal corridors (r = 029)
and decreased smile width (r = 028), but the correlation was weak (Table 1).
Among components of psychosocial well-being,
increased malocclusion severity was mostly related to
decreased DSC (r = 060), similarly in SI, PI and AC
(r = 049–052) and least related to decreased selfesteem (SE) (r = 023) in univariate analyses
(Table 1). Other elements of smile aesthetics were not
significantly related to psychosocial well-being.
While controlling for other predictors in the model
of multiple regression, malocclusion severity remains
the most important predictor of psychosocial wellbeing, the unique contribution of which accounts for
a total of 4–27% of variability (Tables 2–6). Female
gender, less incisor exposure and decreased visibility
of lateral incisor width than golden proportion standard are additional predictors of higher level of AC,
each accounting for additional 2% of variability above
182% accounted for by malocclusion (Table 2).
Female gender is associated with higher PI of dental
aesthetics (24% in addition to 164% accounted for
by malocclusion) (Table 3). ICON is the only significant predictor of DSC and SI, accounting for 27% and
17% of variability (Tables 4 and 5). Male gender and
older age are associated with SE, each accounting for
†
SPSS Inc., Chicago, IL, USA.
© 2014 John Wiley & Sons Ltd
ELEMENTS OF SMILE AESTHETICS AND PSYCHOSOCIAL WELL-BEING
Table 1. Pearson correlation between psychosocial well-being dimensions and elements of smile aesthetics
Variable
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Lip thickness proportion
Maximum incisor exposure
Total gingival display
Gingival display
Smile width
Smile height index
Lateral step proportion
Central incisor width-to-length ratio
Smile index
Deviation of lateral incisors from golden proportion
Deviation of canine from golden proportion
Occlusal plane cant
Gender
Age
ICON score
ICON
SE
029*
028*
005
005
013
011
002
001
016
008
008
017*
014
011
003
018*
100
DSC
0
002
0
005
0
002
002
021*
007
004
001
015
004
001
023*
024*
023*
SI
024*
025*
002
015
016*
012
006
008
023*
013
003
02*
011
001
005
014
060*
PI
013
014
001
002
003
002
006
01
008
012
001
021*
005
006
011
023*
049*
021*
018*
006
012
013
008
01
009
024*
013
003
024
008
012
018*
017*
051*
AC
024*
021*
0
012
01
008
005
01
015
005
001
024
008
003
014
017*
052*
*P < 005.
Table 2. Multiple linear regression models for prediction of AC
Unstandardised
coefficient B
Intercept
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Maximum incisor exposure
Total gingival display
Lateral step proportion
Smile index
Deviation of canine from golden proportion
Deviation of lateral incisor from golden proportion
Occlusal plane cant
Age
ICON score
Gender (1 = M, 2 = F)
7624
0047
0015
0072
0027
0016
0036
0058
0001
0020
0030
0071
0835
SE
5449
0061
0049
0034
0025
0024
0175
0028
0012
0149
0043
0011
0410
Standardised
coefficient ß
0074
0029
0310
0216
0062
0025
0162
0004
0009
0053
0475
0148
P
0164
0439
0757
0038
0275
0501
0835
0040
0960
0896
0484
<0001
0044
Semipartial
correlation
0053
0021
0143
0075
0046
0014
0141
0003
0009
0048
0427
0139
R = 0585; R2 = 0342; Adjusted R2 = 0286; P < 0001.
additional 45% and 37% above the 42% accounted
for by malocclusion (Table 6). Other parameters of
mini- and micro-aesthetics are not significant predictors of psychosocial well-being.
Discussion
This study demonstrated that only a few of microand mini-aesthetic smile traits influence psychosocial
well-being, demonstrating a small portion of unique
© 2014 John Wiley & Sons Ltd
contribution to the explanation of total variability.
Malocclusion severity measured by ICON exhibiting
the highest influence on psychosocial well-being. It
is somehow expected as this index is mostly
contributed to by a visual scale of dental aesthetic
impairment.
Weak correlation between ICON and other photogrametrically measured features of smile aesthetics indicates that these could be the additional elements
influencing aesthetics perception.
277
278
A . L U K E Z et al.
Table 3. Multiple linear regression models for prediction of PI
Unstandardised
coefficient B
Intercept
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Maximum incisor exposure
Total gingival display
Lateral step proportion
Smile index
Deviation of lateral incisor from golden proportion
Deviation of canine from golden proportion
Occlusal plane cant
Age
ICON score
Gender (1 = M, 2 = F)
7119
0037
0001
0079
0027
0029
0031
0079
0007
0387
0045
0113
1574
SE
9173
0102
0082
0058
0042
0040
0294
0047
0019
0252
0073
0019
0691
Standardised
coefficient ß
0035
0001
0202
0129
0064
0013
0132
0028
0108
0046
0450
0166
P
0439
0718
0991
0174
0514
0481
0916
0094
0717
0127
0537
<0001
0024
Semipartial
correlation
0025
0001
0093
0045
0048
0007
0115
0025
0105
0042
0405
0155
R = 0586; R2 = 0343; Adjusted R2 = 0287; P < 0001.
Table 4. Multiple linear regression models for prediction of SI
Unstandardised
coefficient B
Intercept
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Maximum incisor exposure
Total gingival display
Lateral step proportion
Smile index
Deviation of lateral incisor from golden proportion
Deviation of canine from golden proportion
Occlusal plane cant
Age
ICON score
Gender (1 = M, 2 = F)
5855
0016
0017
0053
0032
0033
0114
0091
0011
0164
0141
0116
0949
SE
9633
0107
0086
0061
0044
0042
0309
0049
0020
0264
0077
0020
0725
Standardised
coefficient ß
0015
0019
0134
0148
0075
0047
0150
0042
0046
0144
0457
0099
P
0544
0879
0848
0385
0473
0433
0713
0068
0596
0537
0067
<0001
0193
Semipartial
correlation
0011
0014
0062
0051
0056
0026
0131
0038
0044
0131
0411
0093
R = 0536; R2 = 0287; Adjusted R2 = 0226; P < 0001.
Higher self-esteem seems to be mostly influenced
by lower malocclusion severity, male gender and
increase in age. None of the measured smile aesthetics
features demonstrated significant impact on selfesteem. Apparently, minor impairments in one’s smile
aesthetics do not influence self-esteem, instead selfesteem is probably more determined by other factors
such as general physical appearance, success and popularity in school, financial status, health, or trends
imposed by mass media (2, 14, 15).
Dental self-confidence decreases while social
impact increases with higher malocclusion severity.
Females and those with pronounced malocclusions
demonstrated higher psychological impact. Those
were the only significant predictors. Although we
had expected that at least mini-aesthetic smile
parameters (buccal corridors, smile width, gingival
display, incisor exposure and occlusal cant) would
impact the psychosocial well-being, it is obvious that
adolescents and young adults are more focused on
visible malposition of teeth than on details of their
smile. Occlusal cant of 3 mm and gingival display of
4 mm are perceived as aesthetically unpleasant by
laypersons (16). Moreover, excessive gingival display
negatively affects judgments on the attractiveness of
one’s smile as well as the estimate of one’s self-confidence, friendliness, trustworthiness and intelligence
(17).
© 2014 John Wiley & Sons Ltd
ELEMENTS OF SMILE AESTHETICS AND PSYCHOSOCIAL WELL-BEING
Table 5. Multiple linear regression models for prediction of DSC
Unstandardised
coefficient B
Intercept
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Maximum incisor exposure
Total gingival display
Lateral step proportion
Smile index
Deviation of lateral incisor from golden proportion
Deviation of canine from golden proportion
Occlusal plane cant
Age
ICON score
Gender (1 = M, 2 = F)
5638
0036
0093
0081
0001
0029
0372
0034
0011
0154
0028
0169
0769
SE
10172
0113
0091
0064
0046
0045
0326
0052
0022
0279
0081
0021
0766
Standardised
coefficient ß
0029
0090
0176
0005
0056
0132
0049
0037
0037
0025
0575
0069
P
0580
0753
0307
0211
0978
0520
0257
0514
0612
0581
0730
<0001
0317
Semipartial
correlation
0020
0066
0081
0002
0042
0074
0042
0033
0036
0022
0518
0065
R = 0641; R2 = 0360; Adjusted R2 = 0360; P < 0001.
Table 6. Multiple linear regression models for prediction of SE
Intercept
Total buccal corridor percentage
Visible posterior teeth width ratio during smile
Maximum incisor exposure
Total gingival display
Lateral step proportion
Smile index
Deviation of lateral incisor from golden proportion
Deviation of canine from golden proportion
Occlusal cant
Age
ICON score
Gender (1 = M, 2 = F)
Unstandardised
coefficient B
SE
55836
0009
0116
0032
0001
0012
0228
0076
0016
0200
0213
0059
2213
10575
0118
0094
0067
0048
0047
0339
0054
0022
0290
0084
0022
0796
Standardised
coefficient ß
0008
0127
0078
0006
0025
0091
0122
0061
0054
0211
0226
0225
P
<0001
0941
0222
0637
0977
0803
0502
0164
0471
0491
0013
0008
0006
Semipartial
correlation
0006
0093
0036
0002
0019
0051
0106
0055
0052
0192
0204
0211
R = 0432. R2 = 0187. Adjusted R2 = 0117; P = 0003.
Buccal corridors are regarded as an important smile
feature, although some authors reported their correlation with aesthetic smile (18) while others rejected
the relation (19). Buccal corridors are detected as
unpleasant if their visibility exceeds 10–15% of smile
width (20). Still, some studies reported that having
minimal buccal corridors is a preferred aesthetic feature in both men and women (21).
AC is probably substantially moderated by details
of smile aesthetic, as in addition to female gender
and pronounced malocclusion, reduced incisor exposure and decreased visibility of the lateral incisor
compared with golden proportion standards enhance
© 2014 John Wiley & Sons Ltd
AC. It has been previously reported that female
gender produces a twofold higher odd of being
dissatisfied with general dental appearance (22),
which may be a consequence of the fact that teeth
appearance is more important to women than to
men. It appears that large gingival exposure does
not necessary affect aesthetic appearance of the
smile while insufficiently visualised maxillary incisors are perceived hardly attractive (23). It is suggested that golden proportion standards can rarely
be found in dentitions, even in the ones with most
pleasant aesthetics (24). That is why alteration of
golden proportion does not endow a large unique
279
280
A . L U K E Z et al.
contribution to the explanation of variability of
psychosocial well-being. Perhaps some other parameters of micro-aesthetics that were not taken into
account in the present study, such as tooth colour
intensity and shade, discolorations, infractions, tooth
shape, quality of restorations and gingival architecture may provoke additional concern (22). Still, psychological elements and female gender, and not
clinically assessed dental status, are the main predictors of seeking dental therapy for improvement of
dental aesthetics (25).
There is apparently a great variability in perception
of psychosocial well-being and its relation to smile
aesthetics. It might be that the relation between components of smile and well-being is not linear, and
there are some cut-off points of aesthetically pleasant
features. A previous study has shown that personality
traits affect the expression of smile (10). Along these
lines, personality traits may mediate or moderate selfjudgments, or some people may not pone much significance on dental aesthetics.
Many aforementioned studies considered the perception of smile details in subjects with normocclusion (16, 18–21, 23, 24), which resulted in an
accentuation of these details. In contrast, the ICON
score of the subjects of the present research ranged
from 7 (easy) up to 95 (very difficult malocclusion
complexity) and, as such, allowed for a broader and
more comprehensive evaluation of various elements
of smile. Apparently, people are more affected by
severe malocclusions rather than minor disproportions
in smile aesthetics. People seem to be concerned
about altered smile aesthetics but not in a way that it
could affect them psychologically or cause problems
in their social lives.
Previous research has shown that professionals are
more sensitive to minor smile aesthetic impairments
than laypersons, and our findings confirm that a large
number of mini- and micro-aesthetic features remain
unnoticed and do not produce a significant effect on
psychosocial well-being (16). Clinicians must consider
each factor of aesthetics individually, but be aware
that the unique contribution of each component is
very low. Thus, many elements act together to create
a harmonic entity producing the final aesthetic effect
that satisfies the patient. Obviously, there is a large
number of other variables that can affect one’s psychosocial well-being but it appears that numerous
mini- and microparameters of smile aesthetics are not
among them.
Conclusion
Malocclusions have higher psychosocial impact than
parameters of mini- and micro-aesthetics of smile
related to visibility of buccal corridors, gingival and
teeth display, lip thickness, presence of occlusal cant
and deviation from golden proportion of the teeth. It
appears that people are not as focused on details of
their smile as they are on distinctive malposition of
teeth.
Acknowledgment
This work was supported by the research grants of
University of Rijeka No. 13.06.2.1.53. to principal
investigator Stjepan Spalj. It was presented as an eoral presentation at the 4th Virtual World Congress of
Dental Students in Zagreb, Croatia, 14 May 2014 and
was awarded the 1st prize.
Conflicts of interest
No conflict of interests declared.
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Correspondence: Magda Trinajstic Zrinski, Department of Orthodontics, School of Medicine, University of Rijeka, Kresimirova 40, Rijeka
51000, Croatia. E-mail: [email protected]
Supporting Information
Additional Supporting Information may be found in
the online version of this article:
Table S1 Definition of photogrammetric variables
of smile esthetics.
Table S2 Descriptive statistics.
281