The Impact of Cognitive Distortions, Stress, and Adherence on Metabolic Control in Youths With Type 1 Diabetes

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1 JOURNAL OF ADOLESCENT HEALTH 2004;34: ORIGINAL ARTICLE The Impact of Cognitive Distortions, Stress, and Adherence on Metabolic Control in Youths With Type 1 Diabetes STEPHANIE P. FARRELL, Ph.D., ANTHONY A. HAINS, Ph.D., W. HOBART DAVIES, Ph.D., PHILIP SMITH, Ph.D., AND ELAINE PARTON, R.N., M.A., C.P.N.P. Purpose: To investigate the role of cognitive distortions in the relationship between adherence behavior, diabetes-specific stress, general stress, and metabolic control. Methods: Obtained questionnaire data, glucometer readings, and glycosylated hemoglobin (HbgA 1c ) assays from 143 youths (11 18 years old) with type 1 diabetes. Examined path model of relationships between cognitive distortions, stress, adherence behavior, and metabolic control. Data were analyzed using path analysis. Results: Higher levels of negative cognitive distortions were associated with more stress (both diabetes-specific and general). Higher levels of general stress then led to less adherent behavior and subsequently poorer metabolic control (higher HbgA 1c ). More diabetes-specific stress also led to poorer metabolic control, as well as general stress. Conclusions: The findings indicate an indirect role of negative cognitive distortions in metabolic control. The current findings suggest that instead of the proposed direct link between cognitive distortions and adherence behavior, an indirect relationship may exist through stress. Society for Adolescent Medicine, 2004 KEY WORDS: Adherence Adolescents Cognitive distortions From the University of Wisconsin Children s Hospital, Madison, Wisconsin (S.P.F.); University of Wisconsin, Milwaukee, Wisconsin (A.A.H., W.H.D., P.S.); Children s Hospital of Wisconsin, Milwaukee, Wisconsin (W.H.D.); and Medical College of Wisconsin, Milwaukee, Wisconsin (E.P.). Address correspondence to: Stephanie P. Farrell, Ph.D., University of Wisconsin Children s Hospital, 600 Highland Ave (mail code 2424), Madison, WI, sp.farrell@hosp.wisc.edu Manuscript accepted March 10, Diabetes-specific stress Insulin-dependent diabetes Type 1 diabetes Effective blood glucose control in youths with type 1 diabetes minimizes the risk of serious later complications, including renal failure, cardiovascular disease, and neuropathy. Research regarding the relationship of life stress to metabolic control has yielded inconsistent results [1,2]. Stress could directly affect glycemic control through physiological mechanisms and could affect control indirectly through adherence [1]. The management of this disease entails a complex treatment regimen of diet, exercise, blood glucose monitoring, and insulin injections, as well as adjustment of the regimen with respect to exercise, stress, and infections [3]. Although adherence is presumed to be linked to metabolic control, measures of a youth s metabolic control may reflect the influence of other variables such as taking too much insulin or hormonal changes owing to puberty [4]. Methodological challenges have contributed to the lack of consistent findings regarding the relationship between adherence and metabolic control. There is an emerging consensus that adherence is multidimensional and appropriate assessment requires a combination of methods to measure the distinct behaviors involved in the regimen [1]. There is some evidence to suggest that cognitive processes may mediate the relationship between stress, adherence, and metabolic control. With increasing disease duration, youths view their type 1 Society for Adolescent Medicine, X/04/$ see front matter Published by Elsevier Inc., 360 Park Avenue South, New York, NY doi: /s x(03)

2 462 FARRELL ET AL JOURNAL OF ADOLESCENT HEALTH Vol. 34, No. 6 diabetes as more upsetting and the regimen, harder [5]. The role that cognitive appraisal processes play in adolescent adherence behavior has been examined [6]. Adolescents who have a negative perception of their bodies, perceive little internal control over their health, and have an external attributional style for negative events are at the greatest risk for poor adherence. Also, when their blood sugars are out of control, youths who perceive little internal control are more likely to respond by avoidance rather than developing a cognitive orientation toward mastery and using the information from meter readings to solve adherence problems [6]. Other research has suggested that youths who worry more about their diabetes tend to show poorer metabolic control [7]. In addition, youths who attribute the occurrence of negative events to something inherently stable about themselves revealed better glycemic control [8]. This research contrasts with findings that individuals with diabetes who attribute negative events to stable and internal causes may be susceptible to learned helplessness, which was significantly associated with poor metabolic control [9]. Cognitive-behavioral theory proposes that individuals respond primarily to cognitive representations of a situation, rather than to the situation itself [10]. Therefore, how one thinks about a situation affects one s emotions and behavior. If one s thinking is negative and distorted (e.g., overly maladaptive appraisals or interpretation of events), the feelings will likely also be negative, and the subsequent behavior will be affected accordingly. For instance, adolescents with type 1 diabetes become increasingly influenced by what they perceive and anticipate would be negative reactions or disapproval from peers in social situations that would require adherence behaviors [11]. The purpose of this study was to investigate the role of cognitive distortions in the relationship between adherence behavior, diabetes-specific stress, general stress, and metabolic control. The theoretical underpinnings of cognitive-behavioral theory suggest areas for exploratory analyses where path analysis is appropriate, and also suggest a likely temporal order of variables. Cognitive behavioral theory suggests that how individuals think (e.g., cognitive distortions) about events in their lives (e.g., diabetesspecific stress and general stress) may affect their behavioral choices (e.g., adherence). These changes in adherence could lead to changes in metabolic control. To our knowledge, there has been no identification of specific cognitive distortions that are Figure 1. Completely Identified Path Model. related to adherence problems in youths with diabetes, nor are we aware of any measures that examine cognitive distortions with this group. The application of the theory for the population of adolescents with type 1 diabetes is not yet well developed, suggesting only which variables to examine and a likely temporal order of these variables. However, we do not know how and to what extent the preceding variables in the model affect the following variables. There are no empirical data to justify eliminating any of the recursive paths, so all possible paths between the variables in the hypothesized direction were examined. Figure 1 displays the completely identified path model. Methods Participants After IRB approval, eligible participants were identified from the diabetes outpatient clinic schedule at a children s hospital in a large midwestern city. One hundred and fifty youths with type 1 diabetes of at least 1-year duration were recruited. One participant was excluded from analyses when it was discovered that her length of illness was less than a year. Six other youths did not return their completed instruments by mail. Therefore, the final sample was 143 youths. The participants ranged in age from years to years (mean 14.5 years; SD 1.67 years). There were 75 males (52.4%) and 68 females (47.6%). Eighty-seven percent of the sample was European American (n 125), with the remaining 13% composed of African-American (n 9), Latino (n 6), Asian American (n 2), and other (n 1).

3 June 2004 DIABETES 463 The length of time since diagnosis ranged from 1 year, 0 months to 16 years, 8 months (mean 5 years, 8 months; SD 3 years, 5 months). Measures Demographic information. The following demographic information was obtained from each youth s medical record: gender, age, ethnicity, and duration and age of onset of type 1 diabetes. Cognitive distortions. The Children s Negative Cognitive Error Questionnaire (CNCEQ; [12]) is a 24-item self-report questionnaire that assesses various cognitive distortions as represented by selective abstraction, personalization, overgeneralization, and catastrophizing. The child is presented with vignettes that illustrate the distortions across athletic, social, and academic domains. Each situation is followed by a thought that the youth is instructed to rate on a 5-point Likert scale on how similar the thought is to the way he or she might think in the described situation. A total score is computed and ranges from 24 to 120, with higher scores indicative of more cognitive distortions. Although various types of cognitive distortions are represented in the instrument, efforts to find support of a multidimensional structure have not been found, suggesting a single, global construct of cognitive errors [13]. This total score was used in this study. A total score internal consistency of.89 and test-retest reliability of.65 have been reported [12]. Stress. Diabetes-specific stress was examined using the Diabetes Stress Questionnaire (DSQ; [14]). This instrument was designed to assess the daily stressors for adolescents that are related to diabetes. The DSQ is a self-report measure that requires the respondent to rate how stressful or how much of a hassle 65 situations are on a 4-point Likert scale. Scoring yields a composite scaled score, ranging from 0 to 195, with higher scores indicative of higher levels of stress. Internal consistency has been reported to be excellent with Cronbach alpha The measure has also been shown to have good concurrent validity [14]. The youth form of the Life Stressors and Social Resources Inventory (LISRES-Y; [15] was used to identify the level of current general stressors and their sources. The LISRES-Y is appropriate for youths between ages 12 and 18 years. However, one participant who was almost 12 was allowed to complete the instrument and participate in the study, given that norms were not used in the analyses. The respondent answers 208 items covering eight major areas of life experiences. This study included the nine scales that measure life stressors (Physical Health, School, Home and Money, Parents, Siblings, Extended Family, Boyfriend/Girlfriend, Friends, and Negative Life Events). Raw scores are converted to T scores with higher scores representing greater stress. Internal consistency reliabilities range from 0.66 to 0.92 for the Stressor scales. The mean of the 9 life stressor scales T scores was computed for each youth to obtain an average stressor score. For those participants who indicated Not Applicable on one or more of the scales (e.g., a youth without siblings or a boyfriend/girlfriend), the mean was computed using the applicable scales. Adherence behavior. A self-report measure of adherence was completed by the participants. The Diabetes Compliance Questionnaire (DCQ; [16]) requires respondents to rate 13 aspects of adherence to the diabetes self-care regimen on a 5-point Likert scale, anchored in the frequency of each behavior ranging from 1 (poor adherence) to 5 (excellent adherence). A total score is computed and ranges from 14 to 70, with higher scores representing better adherence. The internal consistency has been reported as Cronbach alpha 0.80 [16]. Frequency of checking one s blood sugar over the past week was used as an indicator of adherence as well. Youths are instructed to check their blood glucose levels several times a day. Both the blood glucose values and the number of checks are recorded. For this study, the number of checks in the previous 7 days was downloaded from the glucometer memory chip. Metabolic control. Metabolic control was measured by the percentage of glycosylated hemoglobin (HbgA 1c ). HbgA 1c levels reflect the average level of blood glucose over a 2- to 3-month period. HbgA 1c levels above 9.0% are indicative of poorer metabolic control. Procedure Once consent was obtained, the youths were asked to complete the four instruments at one session. The length of completion time was approximately 1 hour and 15 min. Each youth received $20 for participating.

4 464 FARRELL ET AL JOURNAL OF ADOLESCENT HEALTH Vol. 34, No. 6 Analyses Given the complexity of the relationships of variables examined, path analysis was used in this study. Cognitive behavioral theory guided the selection of variables examined, as well as the temporal order. That is, how youths think (i.e., cognitive distortions) about events in their lives (i.e., diabetes-specific stress and general stress) may affect their behavior (e.g., adherence), and subsequent metabolic control. Given the inconsistent and incomplete existing literature base on the roles of stress, adherence, and cognitive errors on metabolic control, path analysis will identify the viable causal relationship that exists in the data when all recursive paths among the variables in the hypothesized direction are examined. Thus, completely identified recursive models, with all possible paths between the variables, were run with the data for both measures of adherence (frequency of checking glucometer and total score on DCQ) for the entire sample (n 143). Standardized regression coefficients for each path (arrow) were used to estimate the strengths of relationships specified in the model. For a two-tailed test at the.05 level of significance, the critical value associated with the t-distribution with 60 degrees of freedom is 2.00 [17]. Those paths that were not significant (i.e., critical region, CR 2.00) were eliminated systematically. Beginning with the least significant path, one by one the nonsignificant paths were removed from the initial completely identified models. Each subsequent model was re-run and evaluated for goodness of fit using several criteria (Chi-squared/degree of freedom ratio, probability level, GFI, and AGFI). To determine whether gender should be a covariate in the path analyses, males and females were compared on HbgA 1c, cognitions, adherence, age, date of diagnosis, diabetes stress, general stress, and glucometer checks. To control for family-wise error a Bonferroni correction was made on the p value. Results Preliminary Analyses The means and standard deviations of the participant variables across gender and for the total sample are summarized in Table 1. HbgA 1c values ranged from 6.1 to 16.1 with a typical clinical cutoff of 9 and higher values indicative of poorer metabolic control. The average duration of diabetes was 5 years and 9 months (SD months). Higher scores on the CNCEQ, DSQ, and LISRES-Y are indicative of more cognitive distortions, diabetes-specific stress, and Table 1. Gender and Total Sample Means and Standard Deviations Across Variables Males Females Total sample Variable M SD M SD M SD Age a HbgA 1C CNCEQ c Disease duration a Glucometer freq. b DCQ d DSQ e Stress f a in months. b frequency of blood glucose checks per week. c Children s Negative Cognitive Error Questionnaire. d Diabetes Compliance Questionnaire. e Diabetes Stress Questionnaire. f mean life stressor T scores from Life Stressors and Social Resources Inventory (LISRES-Y). general stress, respectively; the means for the total sample across these instruments were not clinically significant. Higher scores on glucometer frequency and DCQ are indicative of better adherence. The mean number of blood glucose checks was approximately 21 times in a week or three times a day (SD 10.43). Most youths are encouraged to monitor their glucose levels between 3 and 5 times a day. Both an objective (frequency of glucometer use in the previous week) and a subjective (score on DCQ) measure of adherence were used in this study. These variables were significantly correlated (r.44, p.01), but the magnitude of correlation suggested they were not redundant, with less than 20% common variance. Although the objective glucometer reading is more reliable than the self-reported DCQ, it only taps one aspect of adherence, whereas the DCQ assesses 13 areas of adherence behavior. It was therefore deemed appropriate to run the model twice, using each measure of adherence to compare their explanatory power. Path Analyses The only significant gender difference was that females self-reported more diabetes-specific stress than males (F 16.59, p.001). Given this significant gender difference, comparative statistics were calculated for males and females for the models presented in Figures 2 and 3 for both frequency of glucometer checks as the measure of adherence ( , df 8, p.218) and total score on DCQ ( , df 16, and p.213). Owing to the lack of statistically significant differences between males

5 June 2004 DIABETES 465 Table 2. Chi-square/Degree of Freedom Ratios, Probability Levels, Goodness of Fit Indices, and Adjusted Goodness of Fit Indices Across the Modifications with Frequency of Glucometer Readings as Measure of Adherence in Model Modifications Chi-Square/df Probability GFI AGFI Step Step Step Step Step Step Step Step Note: GFI Goodness of Fit; AGFI Adjusted Goodness of Fit. Figure 2. Parsimony Model With Frequency of Glucometer Checks as the Measure of Adherence. and females, only the collapsed data (total sample) are presented here. A completely identified recursive model with frequency of glucometer checks as the measure of adherence was run. Figure 2 depicts the resulting parsimony model, which is comprised of the pathways that were significant and retained in the final model, along with their path coefficients. In the final model, cognitive distortions affected both diabetesspecific (C.R ) and general stress (C.R ), but not frequency of glucometer checks. The more cognitive distortions a youth had, the higher the level of reported stress. Diabetes stress then affected HbgA 1c (C.R ), and general stress Figure 3. Parsimony Model With Total Score on DCQ as the Measure of Adherence. affected frequency of glucometer checks (C.R ), which subsequently affected HbgA 1c levels (C.R ). Diabetes-specific stress appears to adversely affect metabolic control directly, whereas general stress may have an indirect influence on HbgA 1c levels, through its adverse effect on one aspect of adherence behavior. The more general stress reported, the less frequently youths checked their blood glucose levels and consequently the poorer their metabolic control. Lastly, a significant path between diabetes-specific stress and general stress was maintained in the final model (C.R ). The more diabetes stress reported, the more general stress was noted. Table 2 presents the Chisquare/degree of freedom ratios, probability levels, Goodness of Fit Indices, and Adjusted Goodness of Fit Indices across these modifications. The Chisquare, GFI, and AGFI values suggest a good datamodel fit. The parsimony model with total score on DCQ as the measure of adherence is depicted in Figure 3. The two parsimony models with different measures of adherence (one with frequency of blood glucose checks and the other with self-reported adherence) were identical with the exception of a significant path between date of diagnosis and adherence in the second model. Cognitive distortions affected both diabetes specific (C.R ) and general stress (C.R ), but not adherence. Specifically, the more cognitive distortions a youth had, the higher the level of reported stress. Diabetes stress then affected HbgA 1c (C.R ), and general stress affected adherence (C.R ), which subsequently affected HbgA 1c (C.R ). Diabetesspecific stress adversely affected metabolic control directly, whereas general stress had an indirect in-

6 466 FARRELL ET AL JOURNAL OF ADOLESCENT HEALTH Vol. 34, No. 6 Table 3. Chi-square/Degree of Freedom Ratios, Probability Levels, Goodness of Fit Indices, and Adjusted Goodness of Fit Indices Across the Modifications with Total Score on the DCQ as Measure of Adherence in Model Modifications Chi-Square/df Probability GFI AGFI Step Step Step Step Step Step Step Note: GFI Goodness of Fit; AGFI Adjusted Goodness of Fit. fluence on HbgA 1c levels, through its adverse effect on self-reported adherence behavior. The more general stress reported, the less adherent behavior was reported, and consequently, the poorer the metabolic control (higher HbgA 1c ). A significant path between diabetes-specific stress and general stress (C.R ) was also found with the more diabetes stress associated with more general stress. Unlike the model in Figure 2 however, the parsimony model presented in Figure 3 had a significant path between date of diagnosis and adherence, with longer duration of type 1 diabetes being associated with less adherent behavior. Table 3 presents the Chi-square/ degree of freedom ratios, probability levels, Goodness of Fit Indices, and Adjusted Goodness of Fit Indices across the modifications. The Chi-square, GFI, and AGFI values suggest a good data-model fit. Discussion The two models were identical, with the exception of a significant path between date of diagnosis and adherence in the model with DCQ as the measure of adherence. Cognitive distortions affected both diabetes-specific and general stress. The more cognitive distortions a youth had, the higher the level of reported stress. Diabetes stress directly affected a youth s metabolic control, with greater diabetes stress causing increases in HbgA 1c. General stress on the other hand, indirectly affected HbgA 1c through its effect on adherence behavior (both self-reported and the objective measure of glucometer checks). The more general stress reported, the less adherent youths were and consequently, the poorer their metabolic control. An additional finding was a significant path between diabetes-specific stress and general stress. It is noteworthy that general stress affected adherence behavior, whereas diabetes-specific stress did not. Perhaps, although rated as upsetting and a hassle, the diabetes-specific stressors do not interfere with a youth s regimen. Just because a youth is distressed by specific situations that are illness related, does not necessarily mean that he or she will relinquish responsibility of self-care, nor does it mean that a youth will become hypersensitive to the sequelae of the illness and increase adherence. It is possible that general life stress, which may affect several areas of one s life, could be more intrusive. An elevated score on the general stress measure might indicate stress across health, finances, living situation, parents, siblings, school, and friends. Cognitive behavioral theory posits that how one thinks influences how one behaves. The lack of a significant finding in the path between cognitive distortions and adherence behavior is, thus, surprising. Although previous researchers have not directly examined the impact of cognitive distortions on adherence behavior in youths with type 1 diabetes, several related studies suggested a possible link [3,6]. The current findings suggest that instead of the proposed direct link between cognitive distortions and adherence behavior, an indirect relationship may exist through stress. Although exploratory, such analyses may be extremely beneficial for future researchers, as well as suggesting clinical pathways to guide intervention. Although conceptual frameworks are needed, the complexity of the disease requirements and the various demands faced by youths and their families at different developmental stages creates a challenge for any one model to encompass all the relevant variables [18]. The complex interplay between variables exceeds our modeling capability barring longitudinal data. Other variables to consider in future investigations include the level of responsibility assumed by the youth for the various aspects of his or her medical care, whether the youth has an accurate understanding of the regimen tasks required, the ability of the youth to execute these tasks and the level of competence exhibited by the youth to make adjustments when problems arise. Variables in the family system and treatment system may also make important contributions to these outcomes. Several limitations should be noted in interpreting these findings. First, results are based on a single point data collection, and not on repeated observations over a longer period of time. Data were collected during the summer, which presents the possibility of a seasonal bias. Perhaps youths experience

7 June 2004 DIABETES 467 less stress in the summer because they are not in school. In addition, activity levels may be increased in the summer, potentially affecting metabolic control. Generalizability may be limited by the differences in those who have regular visits compared with those who only seek care when there is an emergent problem such as diabetic ketoacidosis (DKA). In addition, this sample was not ethnically diverse. Also, the age range spanned several years of adolescence, and although this increases the range of generalizability, differences likely exist between adolescents at the extremes of this range with respect to development. The use of self-report of diabetes-specific stress, general stress, cognitive distortions, and adherence behavior may have caused participants to respond in a socially desirable way, overestimating their adherence behavior. In addition, the DCQ required participants to recall adherence behavior over the past week, and its reliability may have been compromised by faulty memory. Also, the length of time needed to complete the instruments may have produced fatigue and influenced responding in some way. A multi-method assessment of adherence would be preferable. Finally, a global measure of cognitive distortions was used in this study. An examination of diabetes-specific cognitive distortions and their relationship to adherence and metabolic control would be of interest for future research. Health care practitioners within diabetes treatment teams should be aware of the cognitive style, diabetes-specific stress, and life stressors of youths with diabetes, in addition to the typically obtained information on adherence and metabolic control. Examining the interconnections among these variables provides important clues for treatment decisions. For example, the finding that cognitive distortions play an indirect role in metabolic control has important implications for interventions. Coupled with the significant relationship revealed between adherence behavior and HbgA 1c levels, this indicates the potential usefulness of cognitive behavioral interventions in improving adherence and reducing stress among youths with type 1 diabetes. Such findings invite clinicians and researchers to examine the application of cognitive restructuring in decreasing cognitive distortions and potentially affecting metabolic control. This article is based on the first author s dissertation. This study was supported in part by a grant from the Children s Hospital of Wisconsin Foundation. References 1. Johnson SB. Insulin-dependent diabetes mellitus in childhood. In: Roberts M (ed). Handbook of Pediatric Psychology, 2nd edition. New York: The Guilford Press, 1995: Surwit R, Schneider M, Feinglos M. Stress and diabetes mellitus. Diabetes Care 1992;15: Christie MJ, French D, Weatherstone L, West A. The patients perceptions of chronic disease and its management: Psychosomatics, holism and quality of life in contemporary management of childhood asthma. Psychother Psychosom 1991;56: Reid P, Appleton P. Insulin dependent diabetes mellitus: Regimen adherence in children and young people. Ir J Psychol 1991;12: Kovacs M, Iyengar S, Goldston D, et al. Psychological functioning of children with insulin-dependent diabetes mellitus: A longitudinal study. J Pediatr Psychol 1990;15: Bennett-Murphy LM, Thompson RJ, Morris MA. Adherence behavior among adolescents with type 1 insulin-dependent diabetes mellitus: The role of cognitive appraisal processes. J Pediatr Psychol 1997;22: Liakopoulou M, Korvessi M, Dacou-Voutetakis C. Personality characteristics, environmental factors and glycemic control in adolescents with diabetes. Eur Child Adolesc Psychiatry 1992; 1: Brown RT, Kaslow NJ, Sansbury L, et al. Internalizing and externalizing symptoms and attributional style in youth with diabetes. J Am Acad Child Adolesc Psychiatry 1991;30: Kuttner MJ, Delamater AM, Santiago JV. Learned helplessness in diabetic youths. J Pediatr Psychol 1990;15: Kendall PC, Chansky TE, Kane MT, et al. Anxiety Disorders in Youth: Cognitive-Behavioral Interventions. New York: Allyn & Bacon, Thomas AM, Peterson L, Goldstein D. Problem solving and diabetes regimen adherence by children and adolescents with type 1 Diabetes in social pressure situations: A reflection of normal development. J Pediatr Psychol 1997;22: Leitenberg H, Yost LW, Carroll-Wilson M. Negative cognitive errors in children: Questionnaire development, normative data, and comparisons between children with and without self-reported symptoms of depression, low self-esteem, and evaluation anxiety. J Consult Clin Psychol 1986;54: Messer SC, Kempton T, Van Hasselt VB, et al. Cognitive distortions and adolescent affective disorder. Behav Modif 1994;18: Boardway R, Delamater A, Tomakowsky J, Gutai J. Stress management training for adolescents with diabetes. J Pediatr Psychol 1993;18: Moos R. Development and applications of new measures of life stressors, social resources, and coping responses. Eur J Psychol Assess 1995;11: Brownlee-Duffeck M, Peterson L, Simonds JF, et al. The role of health beliefs in the regimen adherence and metabolic control of adolescents and adults with diabetes mellitus. J Consult Clin Psychol 1987;55: Mueller RO. Basic Principles of Structural Equation Modeling: An Introduction to LISREL and EQS. New York: Springer- Verlag, La Greca AM, Schuman WB. Adherence to prescribed medical regimens. In: Roberts M (ed). Handbook of Pediatric Psychology, 2nd edition. New York: The Guilford Press, 1995:55 83.