Download Advancing the Science of Perceptual Accuracy

Advancing the Science of Perceptual Accuracy in Pediatric
Asthma and Diabetes
Mariella M. Lane, PHD
Department of Psychology, Texas A&M University
Objectives To review research on perceptual accuracy in pediatric asthma and diabetes and to
provide recommendations for future research efforts and clinical applications of the construct in these
populations. Methods A literature search was conducted using Medline and PsychInfo databases as
well as the bibliographies of relevant articles. Results Children and adolescents with asthma or diabetes evidence considerable variability in perceptual accuracy and frequently make clinically relevant
errors that have the potential to affect self-management behavior. Conclusions Recommendations
for future research include studying distinct types of perceptual errors, empirically supporting the relationship between perceptual accuracy and relevant outcomes, identifying factors related to perceptual
inaccuracy, and conducting longitudinal research and intervention studies. Recommendations for
applying the construct in clinical practice include adopting an individualized approach to symptoms to
guide patient education and management, identifying patients prone to making clinically relevant
errors, and developing and implementing interventions to improve accuracy.
Key words
adolescents; asthma; children; diabetes; estimation accuracy; perceptual accuracy;
sympton perception.
The primary purpose of this article is to provide recommendations to advance the field of perceptual accuracy research
and to apply the construct meaningfully in clinical practice
with children and adolescents with asthma or diabetes. To
provide the reader with the essential context for these recommendations, this article offers a review of the concept of
perceptual accuracy, why it is an important construct to
measure, how it is measured, results of literature evaluating
accuracy in asthma and diabetes, variables associated with
accuracy and error, and existing interventions designed to
improve accuracy in these populations. Following this
review, recommendations to further the methodological
rigor of perceptual accuracy research and for applying the
construct in pediatric clinical practice are offered.
The Concept of Symptom Perception
Conceptual Definition of Symptom Perception
Researchers have defined symptom perception as the perceptual and cognitive processes underlying conscious
awareness of a physical symptom and associated sensations
(Rietveld, 1998; Rietveld & Brosschot, 1999; Rietveld,
Kolk, Prins, & Colland, 1997). It has also been defined
as “the patient’s consciously appreciated sensation of a
physiologic problem” (Banzett, Dempsey, O’Donnell, &
Wamboldt, 2000; p. 1178). Simply put, symptom perception refers to detection of symptoms of a physical problem,
perceptual accuracy (i.e., congruence between subjective
assessment and objective state; Fritz, Yeung, et al., 1996)
being the characteristic of interest. Both under-perception
(i.e., failure to detect symptoms of a physical problem) and
over-perception (i.e., detection of symptoms in the absence
of a physical problem) can occur.
Symptom Perception Defined in Diabetes
In line with the conceptual definition above, one can
define symptom perception in diabetes as conscious
awareness of or ability to detect physical sensations associated with hypo- and hyperglycemia (e.g., pounding heart,
shakiness) that result from blood glucose fluctuations.
All correspondence concerning this article should be addressed to Mariella M. Lane, Psychiatry & Psychology Service, Texas
Children’s Hospital, 6621 Fannin St. CC 1740.01, Houston, TX, 77030-2399. E-mail: [email protected]
Journal of Pediatric Psychology 31(3) pp. 233–245, 2006
doi:10.1093/jpepsy/jsj008
Advance Access publication April 13, 2005
Journal of Pediatric Psychology vol. 31 no. 3 © The Author 2005. Published by Oxford University Press on behalf of the Society of Pediatric Psychology.
All rights reserved. For permissions, please e-mail: [email protected]
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Literature on perceptual inaccuracy in diabetes typically
focuses on under-perception of symptoms of low blood
glucose and associated failure to detect hypoglycemia,
often termed hypoglycemia unawareness (Clarke et al.,
1995). However, Kovatchev, Cox, Gonder-Frederick,
Schlundt, and Clarke (1998) indicated that although
some patients detect no symptoms of hypoglycemia (i.e.,
under-perception), others experience symptoms at normal blood glucose levels (i.e., over-perception). Although
less often discussed, similar perceptual errors can occur
with respect to hyperglycemia. It is important to note that
symptoms in diabetes are idiosyncratic, the methodological implications of which will be discussed later.
Symptom Perception Defined in Asthma
Symptom perception in asthma is the conscious awareness of or ability to detect physical symptoms of asthma
(e.g., wheezing, chest tightness) and associated sensations
such as breathlessness that result from airway obstruction
(Rietveld, 1998; Rietveld & Brosschot, 1999; Rietveld
et al., 1997). Inaccurate symptom perception can be
described as discordance between actual airway obstruction and patient awareness of this obstruction. It can
manifest as airway obstruction without symptoms
(under-perception) or symptoms without airway obstruction (over-perception; Rietveld, 1998). Symptom perception is relevant to both detection of prodromal symptoms
and recognition of an acute asthma exacerbation.
Importance of Perceptual Accuracy
Recognition and interpretation of physical symptoms
influences illness behaviors such as self-diagnosis,
medical help-seeking, health care decision-making,
and self-treatment processes. Perceptual accuracy is
important with regard to medical management, selfmanagement, adherence, morbidity, mortality, and is a
potential target for treatment. For both asthma and diabetes, health care providers frequently instruct patients
to monitor symptoms to gauge current functioning and
guide management decisions. Both over- and underperception of symptoms could result in serious medical
consequences and self-management errors. Pediatric
and adult asthma research has supported the role of
perceptual inaccuracy in asthma management, overutilization of medical resources, morbidity, and mortality
(Davenport, Cruz, Stecenko, & Kifle, 2000; Fritz,
McQuaid, Spirito, & Klein, 1996; Kifle, Seng, &
Davenport, 1997; Kikuchi, Okabe, & Tamura, 1994;
Magdele, Berar-Yanay, & Weiner 2002; Rubenfeld &
Pain, 1976; Strunk, 1987; Yoos & McMullen, 1999;
Zach & Karner, 1989).
Some intervention studies in diabetes have supported a link between improving accuracy and relevant
outcomes. Driving performance can be significantly
impaired by relatively mild hypoglycemia (Cox,
Gonder-Frederick, Kovatchev, Julian, & Clarke, 2000),
and long-term follow up of adults who underwent an
intervention to improve accuracy reflected fewer automobile crashes than the control group (Cox, GonderFrederick, Julian, & Clarke, 1994). Some studies have
suggested that perceptual accuracy positively correlates
with glycosylated hemoglobin, and intensive intervention may improve both (Cox et al., 1991, 1994). Intervention can also improve detection of hypoglycemic
episodes, and may preserve the counterregulatory
responses associated with improved glycemic control
(Kinsley et al., 1999).
Methods for Quantifying Perceptual Accuracy
Measuring Perceptual Accuracy in Diabetes
Symptom perception research often compares the subjective self-report of a patient’s perceived symptoms
(e.g., shakiness) to an objective measure of functioning
(e.g., observed blood glucose; Cox, Gonder-Frederick,
Antoun, Cryer, & Clarke, 1993). However, use of the
symptom intensity method in diabetes is limited
because the pattern of symptoms an individual experiences in relation to blood glucose level is highly idiosyncratic. Both hypo- and hyperglycemia can be
symptomatic, but no specific set of symptoms reliably
covaries with blood glucose across patients (Freund,
Bennett-Johnson, Rosenbloom, Alexander, & Hansen,
1986; Gonder-Frederick & Cox, 1991). However, individual symptom-blood glucose relationships can be identified
and may be reliable over time (Cox, Gonder-Frederick,
Pohl, & Pennebaker, 1983; Pennebaker et al., 1981;
Pennebaker, Gonder-Frederick, Cox, & Hoover, 1985).
Assessment of perceptual accuracy in diabetes can also
compare an observed measure of functioning (e.g., blood
glucose) to estimated functioning predicted by the patient
(e.g., patient prediction of blood glucose), termed blood
glucose estimation accuracy instead of symptom perception.
Initially, data analysis consisted of examining the mean correlation between a patient’s estimated and observed functioning over successive trials. Then Cox and colleagues
introduced error grid analysis as an approach to quantifying
perceptual accuracy that accounts for the relative clinical
importance of perceptual errors (Cox et al., 1989).
Error grid analysis, which involves plotting an individual’s estimated and observed blood glucose over successive trials and observing the clinically meaningful
Perceptual Accuracy in Asthma and Diabetes
zone into which plots fall, provides the frequency of clinically accurate estimates as well as benign errors and clinically
important errors (i.e., failure to detect hypo- and hyperglycemia, estimation of these states when one is euglycemic, and
mistaking hypo- for hyperglycemia and vice versa; Figure
1). A zones include clinically accurate estimates of blood
glucose, and B zones reflect clinically benign estimation
errors. C zones include clinically significant errors likely to
lead to overcorrective treatment whereas D zones include
failures to detect extreme levels of blood glucose. E zones
reflect mistaking hypo- for hyperglycemia and vice versa.
An accuracy index (AI) is computed by subtracting the
summed percentage of clinically significant errors from the
summed percentage of accurate estimates and ranges from
–100 to 100. Positive AI scores reflect higher frequency of
clinically accurate estimates compared with clinically serious errors, whereas negative AI scores indicate more of
such errors than accurate estimates (Cox et al., 1989). An
AI of zero reflects equal numbers of accurate and clinically
relevant, inaccurate estimates.
Figure 1. Error grid analysis for evaluating an individual’s accuracy of
blood glucose estimation. Diagonal line, perfect estimated–observed
blood glucose agreement; above diagonal, overestimates; below
diagonal, underestimates; A zones, clinically accurate estimates of
blood glucose; B zones, clinically benign errors; Upper C zone,
overestimation of a normal blood glucose likely to lead to
overcorrective treatment; Lower C zone , underestimation of a normal
blood glucose likely to lead to overcorrective treatment; Upper
D zone, failure to detect hypoglycemia; Lower D zone, failure to
detect hyperglycemia; Upper E zone, estimating as hyperglycemic
when hypoglycemic; Lower E zone, estimating as hypoglycemic
when hyperglycemic; Accuracy Index, % A zone – (%C +%D +%E).
(From Blood Glucose Estimations in Adolescents with Type I Diabetes:
Predictors of Accuracy and Error, by L. J. Meltzer, S. Bennett-Johnson,
S. Pappachan, and J. Silverstein, 2003, Journal of Pediatric
Psychology, 28, p. 205. Copyright 2003 by the Society of Pediatric
Psychology. Reprinted with permission.)
Measuring Perceptual Accuracy in Asthma
In asthma, symptom perception has typically been assessed
in one of three ways: (a) comparing the subjective selfreport of a patient’s perceived symptoms (e.g., reported
breathlessness) to an objective measure of pulmonary
functioning (e.g., Peak Expiratory Flow Rate or PEFR),
(b) comparing an observed measure of functioning (e.g.,
PEFR) to estimated lung functioning predicted by the
patient (e.g., patient prediction of PEFR), or (c) assessing
the perception of externally applied resistive loads by
experimentally manipulating airflow and asking patients
to rate symptoms or report noticed changes (Fritz,
McQuaid, Nassau, Klein, & Mansell, 1999; Fritz, Yeung, et
al., 1996; Rietveld, 1998; Rietveld & Brosschot, 1999;
Rietveld, Prins, & Kolk, 1996). A correlational approach is
a common data analytic strategy, but researchers are
beginning to abandon it because of a variety of limitations,
including inability to capture information regarding type
of perceptual error (Fritz, Yeung, et al., 1996).
Fritz and Wamboldt (1998) adapted Cox and colleagues’ error grid analysis for application to asthma.
Klein et al. (2004) recently modified this grid to promote clinical utility. Similar to the diabetes error grid,
Klein and colleagues’ asthma risk grid consists of plotting estimated PEFR against observed PEFR (both converted into percent of personal best units for
interindividual comparison) over successive trials and
observing the clinically meaningful zone into which
plots fall (Figure 2). Vertical lines mark 50%, 80%, and
100% of the child’s best value (corresponding to the
National Heart, Lung, and Blood Institute “Green,”
“Yellow,” and “Red” zone guidelines; National Institutes of Health, 1997). Plots fall into one of three clinically meaningful zones and percentage of plots in each
zone is calculated, summing to 100%. The Accurate
zone includes estimates within ±10% of the actual
value, detection of compromised functioning, and correct recognition of adequate functioning. The Danger
zone includes failure to recognize clinically significant
compromised functioning, and the Symptom Magnification zone reflects underestimation of PEFR that may
lead to over-utilization of medical resources.
Research Assessing Perceptual Accuracy
Perceptual Accuracy in Healthy Individuals
Considerable research has investigated healthy individual’s
ability to perceive a variety of autonomic symptoms
(e.g., blood pressure, heart rate, skin temperature; Fritz,
Yeung, & Taitel, 1994; Pennebaker et al., 1985). A review
of these data is beyond the scope of this article, but in
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Figure 2. Error grid analysis for evaluating an individual’s accuracy of
peak flow estimation. Accurate zone, “subjective assessment
corresponds with clinical status”; the ‘wedge’ (estimate within
±10%) and boxes 1, 5, and 9”; Danger zone, clinically compromised
functioning is not recognized; boxes 4, 7, and 8”; Symptom
Magnification zone, oversensitivity to or exaggeration of symptoms;
boxes 2, 3, and 6.” (From “The Asthma Risk Grid: Clinical
Interpretation of Symptom Perception,” by R. B. Klein, N. Walders, E. L.
McQuaid, S. Adams, D. Yaros, and G. K. Fritz, 2004, Asthma and
Allergy Proceedings, 25, p. 3. Copyright 2004 by Oceanside
Publications, Inc. Reprinted with permission.)
general, healthy participants’ evaluation of autonomic
symptoms are often inaccurate, with correlation coefficients between subjective and objective measures typically ranging from .15 to .25 (Pennebaker, 1987).
However, the ecological validity of these paradigms is
questionable, and one might speculate that patients
whose symptoms are relevant indicators of physical
functioning and have adaptive utility may be more accurate (Pennebaker et al., 1985). Some research has compared the ability of healthy children and those with
asthma to detect resistive loads on breathing, with some
results suggesting that asthmatic children are more sensitive to/accurate at detecting loads (Fritz et al., 1999;
McQuaid, Fritz, Yeung, Biros, & Mansell, 1996), while
other results suggest they are less so (Dahme, Richter, &
Mass, 1996; Rietveld, Prins, et al., 1996). No research
was found investigating the ability of healthy individuals
to estimate blood glucose.
Perceptual Accuracy in Pediatric Diabetes
Ability to accurately estimate blood glucose varies greatly
across adult diabetes patients, with clinically accurate
estimates from error grid analysis typically ranging from
40 to 90% (Cox et al., 1985, 1989; Fritsche, Stumvoll,
Renn, & Schmulling, 1998). Error grid analyses indicate
that adults make clinically serious errors up to 20% of
the time (Cox et al., 1985).
Adolescent patients also vary considerably in their
ability to estimate blood glucose level, but they are significantly less accurate in blood glucose estimation than
are adults. Mean accuracy indices for adolescents have
ranged from 7 to 38 (Freund et al., 1986; Lane & Heffer,
2005; Meltzer, Bennett-Johnson, Pappachan, & Silverstein
2003; Nurick & Johnson, 1991; Ruggiero, Kairys, Fritz, &
Wood, 1991; Wiebe, Alderfer, Palmer, Lindsay, &
Jarrett, 1994). Some adolescents have negative AI, indicating they make more clinically relevant estimation
errors than accurate estimates (Wiebe et al., 1994).
Overall, children and adolescents tend to make accurate
estimates, clinically benign errors, and clinically relevant errors each approximately 1/3 of the time (GonderFrederick, Snyder, & Clarke, 1991; Meltzer et al., 2003;
Ruggiero et al., 1991). Mean correlations between estimated and observed blood glucose have ranged from .29
to .51 with a wide range in individual correlations (Freund
et al., 1986; Ruggiero et al., 1991).
Accuracy indices generally are higher in older adolescents than in younger ones (Freund et al., 1986; Lane &
Heffer, 2005; Meltzer et al., 2003; Nurick & Johnson,
1991; Ruggiero et al., 1991), and younger children are
even poorer at estimating blood glucose than adolescents. Gonder-Frederick et al. (1991) reported a mean
AI of –0.68 in their small sample of children 6–12 years
of age. As a group the children made clinically significant errors at a similar frequency to accurate estimates,
and over half of the individual AIs were negative
(Gonder-Frederick et al., 1991). Gonder-Frederick et al.
(2004) reported a mean AI of 4 in another sample of
children 6–11 years, with a mean of 23% for accurate
estimates and 19% for clinically significant errors.
Across child and adolescent studies, failure to detect
extreme levels of blood glucose tends to be the most
common clinically serious error (Gonder-Frederick
et al., 2004; Kovatchev et al., 1998; Lane & Heffer, 2005;
Ruggiero et al., 1991; Ryan, Suprasongsin, Dulay, &
Becker, 2002), with the exception of Meltzer et al.’s
(2003) study in which the most common error was misestimation of normal blood glucose likely to lead to
overcorrective treatment. Individuals tend to normalize
their estimate of blood glucose, overestimating blood
glucose when it is low and underestimating it when it
is high (Moses & Bradley, 1985). Overall, research
reflects that diabetic patients of all ages make potentially
serious errors in perception of blood glucose levels but
that children and adolescents are especially at risk
(Gonder-Frederick & Cox, 1991).
Perceptual Accuracy in Asthma and Diabetes
Perceptual Accuracy in Pediatric Asthma
Studies investigating the perceptual accuracy of pediatric asthma patients have cited a wide range of perceptual
accuracy with at least a significant minority of children
classified as inaccurate perceivers (Cabral, Conceicao,
Saldiva, & Martins, 2002; Creer, Harm, & Marion, 1988;
Fritz, Klein, & Overholser, 1990; Fritz, McQuaid, et al.,
1996; Rietveld et al., 1997; Sly, Landeau, & Weymouth,
1985; Yoos & McMullen, 1999). Perceptual inaccuracy
in children with asthma includes both under- and overperception of symptoms.
A factor complicating summarization of research
using the symptom intensity method is the fact that the
meaning of a strong negative or strong positive correlation varies across studies based on methodology. Among
pediatric studies in which a strong negative correlation
reflects accuracy, Chai Purcell, Brady, & Falliers (1968,
as cited in Taplin & Creer, 1978) reported individual
mean correlations ranging from –.25 to –.70, and Yoos
and McMullen (1999) reported a sample mean correlation of –.55. In Fritz et al.’s (1990) study a strong positive correlation reflected accuracy; individual mean
correlations between children’s subjective report of
symptoms and observed PEFR ranged from –.16 to .86,
with a sample mean correlation of .38. Further, research
has indicated that many children have no significant
correlation between symptom scores and lung functioning (Cabral et al., 2002; Rietveld et al., 1997; Sly et al.,
1985; Tsanakas, 1986).
Using the patient prediction method, Fritz, McQuaid,
et al. (1996) examined patient estimated and observed pulmonary functioning in children with asthma. The FEF25-75
(forced expiratory flow in the midportion of a breath) accuracy correlations ranged from –.54 to .82 with a mean of
.17, and the PEFR accuracy correlations ranged from –.39
to .88 with a mean of .29. Overall, wide variation exists in
the perceptual accuracy of pediatric asthma patients, a sizeable minority exhibiting significant inaccuracy. No published research has applied the asthma risk grid to the
measurement of PEFR estimation accuracy in children, and
a correlational approach to data analysis has notable limitations (Fritz, Yeung, et al., 1996).
Accuracy of Parents and Parent-Child
Concordance
Diabetes
Using error grid analysis, Gonder-Frederick et al.,
(1991) reported that both parents and children demonstrated poor blood glucose estimation accuracy, making
clinically relevant errors as frequently as accurate estimates, with no significant difference between parents
and children for mean accuracy or error scores. Accuracy scores of parents and children were correlated at
r = 0.30, and parents were more accurate for younger
children. Both parents and children tended to underestimate blood glucose overall, but some differences in error
type emerged between parents and children. Children
made C, D, and E zone errors about equally, whereas
parents made D zone errors most often. Children were
more likely than parents estimate their blood glucose as
hypoglycemic when it was in the hyperglycemic range
(i.e., Lower E zone).
Asthma
Because management of asthma is a joint undertaking
between parent and child, evaluating the accuracy of
parent’s assessments of their children’s functioning, as
well as concordance between parent and child assessments is important. Some researchers have found parents’ perceptions of children’s airway obstruction to be
quite inaccurate and unrelated to physical functioning
(Horak, Grassi, Skladal, & Ulmer, 2003; Panditi &
Silverman, 2003; Sly et al., 1985; Yoos & McMullen
1999). Yoos, Kitzman, McMllen, and Sidora (2003)
reported similar accuracy between parents and children
and consistency in the direction of errors, whereas Panditi
and Silverman (2003) reported poor agreement between
parent and child report of symptoms.
One could hypothesize that parental symptom
reporting patterns may relate to accuracy in evaluating a
child’s physiological functioning. Fritz, McQuaid, et al.
(1996) found that children of parents with more extreme
tendencies to report their own physical symptoms may
be more accurate than children of parents with moderate
symptom reporting patterns. Unfortunately, these data
are extremely difficult to interpret because the group of
parents with extreme symptom reporting includes both
those who over- and under-report.
Variables Related to Perceptual Accuracy
and Error
Diabetes
A small body of research has investigated the relationship of demographic, illness-related, and psychological
variables to accuracy and/or error in children and adolescents with diabetes. Results frequently but not exclusively suggest that older children are more accurate
estimators (Gonder-Frederick et al., 1991; Meltzer et al.,
2003; Lane & Heffer, 2005; Ruggiero et al., 1991; Ryan
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et al., 2002). Results on gender differences are equivocal
(Lane & Heffer, 2005; Meltzer et al., 2003; Ruggiero et al.,
1991). Regarding illness-related variables, duration of diabetes and experience with self-monitoring of blood glucose
have not been related to AI in children and adolescents
(Gonder-Frederick et al., 1991; Lane & Heffer, 2005;
Ruggiero et al., 1991). Mean blood glucose level, blood
glucose variability, and experience of extreme blood glucose episodes have been negatively related to AI and may
predict estimation errors (Gonder-Frederick et al., 1991;
Lane & Heffer, 2005; Meltzer et al., 2003; Wiebe et al.,
1994). Awareness of glycemia symptom patterns, accuracy of symptom beliefs, and confidence in ability to
detect hypo- and hyperglycemia have also been unrelated to accuracy (Freund et al., 1986; Gonder-Frederick
& Cox, 1986, 1991; Gonder-Frederick et al., 1991; Wiebe
et al., 1994). Even fewer studies have investigated the relationship between psychological variables and accuracy;
those that exist focus on anxiety and yield equivocal
results (Ryan et al., 2002; Wiebe et al., 1994).
Asthma
Regarding demographic variables, results are equivocal
concerning the relationship between age and perceptual
accuracy (Cabral et al., 2002; Fritz et al., 1990; Fritz,
McQuaid, et al., 1996; Yoos & McMullen, 1999; Yoos
et al., 2003). Gender has not been related to accuracy
(Cabral et al. 2002; Fritz et al., 1990; Yoos et al., 2003).
Yoos et al. (2003) reported that White children and
those of upper socioeconomic status (SES) families were
most accurate, but not significantly so after accounting
for illness severity.
Illness-related variables that do not appear relevant
to accuracy include age at diagnosis, duration of illness,
and use of preventive medication (Cabral et al., 2002;
Fritz et al., 1990; Fritz, McQuaid, et al., 1996; Rietveld,
Prins, & Colland, 2001). Some studies suggest that
asthma severity is negatively related to accuracy (Julius,
Davenport, & Davenport, 2002; Yoos et al., 2003),
whereas others have cited no relationship between the
two (Cabral et al., 2002; Fritz et al., 1990). Slow decline
in pulmonary functioning and habituation to prolonged
symptoms may be related to decreased accuracy;
patients with prolonged airway obstruction perceive
symptoms less well than patients with acute onset airway obstruction (Rietveld & Everaerd, 2000). Although
Fritz et al. (1990) reported that accuracy was unrelated
to both PEFR variability and current PEFR, some
research suggests that patients with lower lung functioning at the point of accuracy assessment tend to be less
accurate (Apter et al., 1997; Yoos et al., 2003), perhaps
because of hypoxia or hypercapnia and associated cognitive impairment (Eckert, Catcheside, Smith, Frith, &
McEvoy, 2004).
Several studies have indicated that patient-reported
symptoms such as dyspnea are independent of lung
functioning, suggesting that symptoms are a subjective
experience more related to psychological factors than to
airway pathophysiology (Rietveld, Everaerd, & van Beest,
2000; Rietveld et al., 2001). Some psychological variables proposed to impact accuracy include anxiety/negative mood, defensive or repressive style, cognitive
abilities, and familial teaching/modeling (Fritz et al.,
1994; Fritz, Yeung, et al., 1996). Some research has
demonstrated that negative mood influences the subjective experience of breathlessness (not objective lung
functioning) and is associated with increased symptom
reporting and diminished perceptual accuracy (Lehrer,
Feldman, Giardino, Song, & Schmaling, 2002; Main,
Moss-Morris, Booth, Kaptein, & Kolbe, 2003; Rietveld,
Everaerd, & van Beest, 1999; Rietveld & Prins, 1998).
However, Fritz, McQuaid, et al. (1996) reported that
trait anxiety was not related to perceptual accuracy in
children. Research is equivocal about the impact of a
defensive or repressive style on accuracy (Fritz,
McQuaid, et al., 1996; Isenberg, Lehrer, & Hochron,
1997; Lehrer et al., 2002; Steiner, Higgs, Fritz, Laszlo, &
Harvey 1987). Cognitive developmental level may also
be related to perceptual accuracy (Fritz, Yeung, et al.,
1996; Yoos & McMullen, 1999), and Fritz, McQuaid,
et al. (1996) reported that an intelligence estimate was
related to increased perceptual accuracy in children.
Interventions for Improving Accuracy
Interventions in Diabetes
Though some adults demonstrate improved estimation
accuracy following blood glucose feedback interventions, larger studies have suggested that practice with
blood glucose self-monitoring and provision of feedback
does not yield substantial improvement in accuracy
(Gross, Levin, Mulvill, Richardson, & Davidson, 1984;
Gross et al., 1983; Malerbi & Matos, 2001; Meltzer et al.,
2003; Schandry & Leopold, 1996; Schandry, Leopold, &
Vogt, 1996). A structured inpatient program that did not
target improvement in estimation accuracy also was not
sufficient to exert an effect (Fritsche et al., 1998).
Cox and colleagues have developed and modified
Blood Glucose Awareness Training (BGAT), a weekly
group training program designed to improve blood glucose
estimation accuracy (Cox, Carter, Gonder-Frederick, Clarke,
& Pohl, 1988; Cox et al., 1989, 1995). The intervention
Perceptual Accuracy in Asthma and Diabetes
is designed to increase: (a) awareness of one’s idiosyncratic
symptoms that reliably covary with blood glucose, (b)
awareness of inaccurate symptom beliefs and other factors
causing errors, and (c) knowledge of effects of external factors on blood glucose. Patients are provided with psychoeducational material and learn to plot and interpret their
own error grid. Cox et al. (1991) have also developed an
intensive version of BGAT. Multiple studies have supported the efficacy of BGAT in improving blood glucose
estimation accuracy and the detection of low blood glucose
levels (Cox et al., 1989, 1991, 1994, 1995).
Interventions in Asthma
Interventions seeking to improve perceptual accuracy
typically employ patient practice of PEFR self-monitoring
and either symptom intensity rating or PEFR prediction.
Results have been mixed but yield predominantly no
effect on accuracy (Fritz, McQuaid, et al., 1996; Reeder,
Dolce, Duke, Raczynski, & Bailey, 1990; Rietveld, Kolk, &
Prins, 1996; Schandry et al., 1996; Silverman et al., 1987;
Sly et al., 1985; Wagner & Ruggiero, 2004). No comprehensive psychoeducational intervention has been developed to improve perceptual accuracy in asthma.
Recommendations for Advancing Perceptual
Accuracy Research
Recommendations for Both Pediatric Asthma
and Diabetes Research
Examine Distinct Types of Perceptual Errors
Most research using error grid analysis has focused on
the AI and variables related to global perceptual accuracy. Future research should examine each type of specific perceptual error instead of or in addition to a global
AI. Because calculation of the AI (i.e., subtracting summed
percentages of different types of errors from the percentage of clinically accurate estimates) includes multiple
perceptual errors in the formula, two individuals with
the same AI may have arrived at their respective values
due to very different errors (e.g., failure to detect
hypoglycemia versus failure to detect hyperglycemia
versus underestimating or overestimating normal blood
glucose). Further, differential relationships may exist
between specific errors and relevant constructs (e.g., anxiety), and use of the global AI may mask these relationships.
For example, a recent study in a sample of children and
adolescents with diabetes found differential relationships between age and specific type of perceptual error;
failure to detect hypoglycemia was positively related to
age, whereas failure to detect hyperglycemia was negatively related to age (Lane & Heffer, 2005).
Support the Relationship Between Perceptual
Accuracy and Self-management
Although the relationship between perceptual accuracy
and self-management behavior seems intuitive, it needs
to be empirically demonstrated. Examination of specific
perceptual errors and specific self-management behaviors is needed. Concurrent assessment of both perceptual accuracy and self-management behaviors would be
ideal. This may be feasible through the use of hand-held
computerized assessment in which a participant rates
current symptoms, predicts current blood glucose, and
indicates self-management behaviors in which he or she
plans to engage given that predicted blood glucose
before actually testing blood glucose level.
Admittedly, research on the relationship between
perceptual accuracy and self-management behavior is
complicated by the typical reliance of a child or adolescent on a parent or other adult to monitor self-management
(Banzett et al., 2000), and this should be taken into
account. One could hypothesize that parental modeling
may be related to child report of symptoms or child
accuracy in evaluating physiological functioning. Parents potentially play a key role in modeling or teaching
children about how to attend to, ignore, interpret, and
respond to symptoms—a fruitful area of research.
Nonadherence may be inadvertent or purposeful
and links to perceptual inaccuracy should be studied.
Wamboldt (1998) suggests that failure to perceive aversive symptoms may inhibit self-management because of
the absence of distress that often initiates behavior and
the absence of negative reinforcement that occurs when
medication use reduces symptom distress. Alternatively,
patients who believe they are accurate may not take
medications when asymptomatic. Wamboldt (1998)
describes a pilot study in which adolescents with asthma
who were more accurate were less likely to comply with
oral steroids and more likely to comply with theophylline,
suggesting that accuracy may be differentially predictive
of medication adherence.
Conduct Longitudinal Studies of Perceptual Accuracy
Longitudinal research is needed to further assess reliability of relationships between symptoms and physical
functioning, as well as to evaluate developmental
changes in accuracy. Such research is also integral to
determining whether accuracy is more of a trait-like or
state-like phenomenon, an important question impacting the interpretation of perceptual accuracy research.
Longitudinal research is also important to determine
whether subgroups of consistently accurate/inaccurate
children can be identified.
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Identify Predictors of Inaccuracy and Develop Models
of Accuracy and Error
Although research examining a variety of potential predictors of accuracy has begun, its development is insufficient
to reliably predict which individuals are most at risk for
clinically serious errors. Learning how to identify such
children is an important research effort with clear clinical implications. Evaluating mechanisms by which accuracy may improve with increasing age (e.g., cognitive
development, illness knowledge, social learning) would
also be of interest.
Few studies evaluate potential predictors of perceptual
accuracy in diabetes, and those that do have primarily
focused on demographic and illness-related variables.
Examining other constructs, such as inaccurate symptom beliefs, attentional style, or anxiety, may prove
fruitful and inform intervention efforts (Wiebe et al.,
1994). Kovatchev et al.’s (1998) stochastic model of self
regulation (internal condition→symptom perception
→appraisal→self-regulation decision) or Cox et al.’s
(1993) biopsychosocial model for detection of hypoglycemic symptoms (physical reaction→physical consequences→symptom detection→symptom interpretation)
may be applied to conceptualize and study “breakdowns” in the path from impaired functioning to detection of the problem to intervention.
A variety of mechanisms for perceptual inaccuracy
in asthma have been proposed and may guide research
(Creer, 1983; Rietveld, 1998; Rietveld & Brosschot,
1999; Rietveld & Prins, 1998). Research examining proposed mechanisms should incorporate PEFR estimation
and asthma risk grid analysis. Some research confuses
constructs of perceptual accuracy with symptom
appraisal and decision to initiate treatment (Hardie,
Gold, Janson, Carrieri-Kohlman, & Boushey, 2002), and
applying a more standard methodology for assessing
accuracy using the asthma risk grid will facilitate interpretation and comparison of results.
linking interventions to outcomes is key, intervention
studies should seek to demonstrate the effect of improved
accuracy on clinical outcomes such as morbidity, healthcare utilization, and health-related quality of life.
Cox and colleague’s BGAT intervention has demonstrated efficacy in improving blood glucose estimation
accuracy in both adults and in adolescents, primarily
reducing failure to detect hypoglycemia. Identifying key
components of this intervention and examining mechanisms of efficacy for BGAT, particularly internal versus.
external cue training, may be of use, as well as identifying
factors predicting treatment success and maintenance of
gains. Most Cox and colleague’s samples have mean ages
in the 30s or 40s (Cox et al., 1991, 1993, 1994, 1995);
the program should be further evaluated in adolescent
samples and modified for developmentally appropriate
use with younger children and their parents. Further
investigation of intensive BGAT versus the standard program and independent evaluations of BGAT efficacy are
also needed.
An intervention for improving PEFR estimation
accuracy in asthma is currently underway and can be
modeled after key concepts of the BGAT program by targeting: (a) awareness of idiosyncratic symptoms that
reliably covary with peak flow, (b) awareness of inaccurate symptom beliefs and other factors causing errors,
and (c) knowledge of effects of external factors on pulmonary functioning. Children and adolescents can be
trained in use and interpretation of both the peak flow
meter and the asthma risk grid. Examining the relationships between individual symptom ratings from a symptom
checklist and PEFR can identify idiosyncratic symptoms
that predict compromised pulmonary functioning for a
given patient. Interventions could also perhaps be individually tailored in accordance with predominant type
of error (i.e., Danger zone vs. Symptom Magnification
zone).
Conduct Intervention Studies for Children
and Adolescents
Self-monitoring or practice with estimation generally
does not yield improvements in perceptual accuracy in
either asthma or diabetes; more complex interventions
are required. For both pediatric asthma and diabetes,
research needs to develop or amend current interventions
to be developmentally appropriate and evaluate their efficacy. Research efforts may aim to identify necessary components for improvement to make interventions more
parsimonious, and interventions may benefit from individual tailoring to predominant type(s) of error. Because
Consider the Ramifications of Incorporating B Zones
with A Zones
One of Klein et al.’s (2004) modifications to the asthma
risk grid from the grid originally modified for asthma by
Fritzand Wamboldt (1998) is that the “Benign Overestimation” and “Benign Underestimation” zones were incorporated with the accurate zone. Similarly in the diabetes
error grid, because B zone estimates do not have clinically relevant implications for self-management behavior, perhaps clinically irrelevant B zone estimates that
are not within 20% of observed blood glucose should be
considered essentially the same as A zone estimates. A
Recommendations Specific to Diabetes Research
Perceptual Accuracy in Asthma and Diabetes
zone and B zone estimates have the same implications
for self-management (i.e., do nothing), making the
requirement that an estimate be within 20% of the
observed value arbitrary and clinically irrelevant.
Recommendations Specific to Asthma Research
Employ an Individualized Approach to Symptoms
For symptom perception in asthma, an implicit assumption has been that symptoms have the same meaning
about pulmonary functioning for different people, or
that they reliably covary with pulmonary functioning
across patients. This is an empirical question, and one
not supported in diabetes research. Yoos and McMullen
(1999) reported that when children were asked to
describe a bad breathing day, cough was the most common symptom reported (53%), followed by wheeze
(46%), and 19% of children did not mention cough,
wheeze, or shortness of breath. Asthma research
employing the symptom intensity method should assess
numerous symptoms, not just breathlessness, and
should compare their relative importance as each may
not be equally valuable in predicting lung functioning,
especially across patients. The finding that symptom
descriptors may vary according to ethnicity (Hardie
et al., 2000) further supports the use of the PEFR prediction method to evaluate accuracy instead of the
symptom intensity method. But, both research and clinical interventions should employ an individualized
approach to determine which specific symptoms are
related to impaired pulmonary functioning for a specific
person.
Examine Perceptual Accuracy for Slow Decline
in Pulmonary Functioning Versus Acute Onset
Individuals with slow decline in pulmonary functioning
may be less likely to perceive symptoms or changes in
functioning than those with acute onset airway obstruction and may therefore experience increased risk for
morbidity and mortality (Rietveld & Everaerd, 2000).
Should perceptual accuracy be poorer for those who
experience slow declines in functioning and are likely to
habituate to this level of functioning, this may be a target for intervention. In fact, the most effective intervention for those with incremental decline in pulmonary
functioning may be different than for those who experience more acute declines. The relative importance of
symptoms to which one attends may differ for incremental versus acute decline (e.g., fatigue versus breathlessness). Training individuals to detect slow decline in
functioning may prove more difficult, and increased reliance on PEFR monitoring may be indicated for such
patients.
Evaluate the Asthma Risk Grid
Research should seek to validate the asthma risk grid proposed and illustrated by Klein et al. (2004), demonstrating
its association with relevant outcomes. Following its validation, employing the grid in research paradigms to
assess accuracy in asthma and evaluating its utility in
clinical practice are promising directions.
Recommendations for Applying Perceptual
Accuracy in Clinical Practice
Physicians should be educated about the propensity of
patients to make perceptual errors and the implications
such errors have for self-management. The key application of perceptual accuracy to clinical practice is the
identification of inaccurate patients and subsequent
intervention to improve their accuracy. Assessment of
perceptual accuracy in clinical practice may also inform
and facilitate medical management. For this to occur,
assessment and interventions need to be of minimal burden to both physician and patient.
In endocrinology, physicians and diabetes educators
should reconsider the practice of teaching classical signs
of hypo- and hyperglycemia and instead adopt an individualized approach to symptoms predictive of hypoand hyperglycemia. Patients with disorders make critical
daily self-management decisions based on their subjective estimates of their blood glucose levels, and even
those who use self-monitoring of blood glucose tend to
treat themselves for hypoglycemia based on perceived
symptoms without first verifying the low blood glucose
level using objective self-measurement (Cox et al., 1989,
1991; Gonder-Frederick & Cox, 1986). This underscores the importance of assessing accuracy in clinical
practice and intervening as needed; broadening the
implementation of BGAT by diabetes educators may
increase its application to more patients and assist in
reducing errors.
In pulmonology, ability to identify accurate patients
and those prone to clinically relevant errors may also
facilitate an individualized approach to medical management. As Klein et al. (2004) suggest, those prone to estimates in the Danger zone can be educated about their
individually predictive symptoms, the danger of relying
on their subjective sense of pulmonary functioning, and
the need to rely on regular peak flow monitoring regardless of symptoms. Those making symptom magnification errors can be informed of which symptoms actually
predict their functioning and which do not, and they can
be assisted in differentiating asthma-related physiological
sensations from those that are unrelated (Klein et al., 2004).
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Based on Wamboldt’s (1998) pilot data, accurate
perceivers may need rationale for the importance of prophylactic medications such as inhaled steroids. Further,
for accurate individuals, symptom-based action plans
may be appropriate. In one sample of adults with asthma
who were not poor perceivers of bronchoconstriction,
peak flow monitoring action plans were equivalent to
symptom-based action plans with respect to appropriate
use of the plan and morbidity variables (Adams, Boath,
Homan, Campbell, & Ruffin, 2001). Although the National
Heart, Lung, and Blood Institute (NHLBI) recommends
designing pediatric self-management plans on the basis
of peak flow measurement, patient adherence to meter
use is often low and measurement may only occur at
best 2–3 times per day (Reeder et al., 1990; Sly, 1996).
Therefore, if accurate patients can be identified and
trained to use symptom-based plans, they could be considerably more practical.
Conclusions
Children and adolescents with asthma or diabetes evidence considerable variability in perceptual accuracy and
frequently make clinically relevant perceptual errors that
have the potential to affect self-management behavior.
The science of perceptual accuracy can be promoted by
studying distinct types of perceptual errors in addition to
global perceptual accuracy, empirically supporting the
relationship between symptom perception and selfmanagement behavior, identifying factors related to perceptual inaccuracy, and conducting longitudinal research
and intervention studies. Application of the construct in
clinical practice may identify patients in need of intervention and facilitate both self- and medical management.
Received September 14, 2004; revision received November
10, 2004; accepted January 31, 2005
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