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Coping with everyday stress and links to medical and psychosocial adaptation in diabetic adolescents
JOURNAL OF ADOLESCENT HEALTH 2003;33:180 –188
INTERNATIONAL ARTICLE
Coping With Everyday Stress and Links to Medical and Psychosocial Adaptation in Diabetic Adolescents INGE SEIFFGE-KRENKE, Ph.D. AND MARK STEMMLER, Ph.D.
Purpose: To study coping with everyday stressors in a longitudinal sample of 98 adolescents with insulin-dependent mellitus (type 1) diabetes. Methods: The adolescents with type 1 diabetes were classified into three homogeneous groups of metabolic control by latent class analysis, based on annual tests of hemoglobin A1 values. Questionnaires assessing frequent minor stressors as well as ways of coping with these stressors were given annually over the course of 4 years. Latent class analysis revealed three distinctive groups of metabolic control over time. Adolescents who exhibited continuously poor, satisfactory, and good metabolic control. Eighty percent of the adolescents stayed in the group assigned to them over the 4-year period. Results: Adolescents with stable good metabolic control were characterized by lower levels of minor stressors that decreased over time, but those with stable satisfactory and poor metabolic control experienced continuously higher stress levels. Adolescents with stably good metabolic control also employed less avoidant coping in dealing with minor stressors, compared with the two other groups. Conclusions: Because of the danger of long-term complications, it is important to discriminate among different groups of metabolic control over time. Further, the impact of non-illness-related minor stressors on metabolic control should be considered for prevention purposes. © Society for Adolescent Medicine, 2003 KEY WORDS:
Adolescents Chronic illness Coping From the University of Mainz, Mainz, Germany (I.S.-K.); and University of Erlangen-Nuremberg, Erlangen, Germany (M.S.). Address correspondence to: Inge Seiffge-Krenke, Ph.D., Department of Psychology, University of Mainz, Staudingerweg 9, 55099 Mainz Germany. E-mail: [email protected] Manuscript accepted October 15, 2002. 1054-139X/03/$–see front matter doi:10.1016/S1054-139X(02)00707-3
Diabetes mellitus Latent class analysis Longitudinal study Metabolic control Minor stressors Germany
Developmental factors have been largely neglected in research on coping with chronic illness in adolescence. In adolescent patients with insulin-dependent diabetes mellitus (IDDM), medical adaptation can be clearly ascertained through metabolic control, the fraction of glycosylated hemoglobin in the blood, determined via hemoglobin HbA1 and HbA1c values. The quality of metabolic control is directly related to short-term complications such as hyperglycemia or diabetic coma. Moreover, continuous poor metabolic control is associated with a greater likelihood of long-term medical complications such as slowed growth, kidney disease, blindness, and reduced life expectancy [1]. Consequently, the decline in metabolic control seen in many adolescents [2,3] is an area of great concern, both for clinicians and parents. Although this decline in metabolic control is partly attributable to physiological aspects of puberty [4,5], other factors may contribute to this outcome. Considerable research is showing that several types of stressors are disturbing enough to affect physiological functioning [6,7]. Lazarus and Launier [8] understood stress to be created by any event in which an environment or internal demand (or both) tax or exceed an individual’s adaptive sources. Two different types of stress were analyzed, which differ in frequency, predictability, control, and negative impact on adolescents’ health: critical life events and © Society for Adolescent Medicine, 2003 Published by Elsevier Inc., 360 Park Avenue South, New York, NY 10010
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minor events such as everyday hassles [9 –11]. Although critical life events are relatively infrequent, hardly predictable, and extremely burdensome, everyday stressors are highly predictable, comparably frequent, and perceived as mildly stressful and controllable [12]. The incidence of a severe chronic illness such as diabetes can be considered a critical life event. Because it is hardly predictable and can seldom be controlled or influenced by the adolescents, it is perceived as highly stressful. Consequently, research has concentrated on the perceived stressfulness of diabetes [13] or focused on illnessspecific stressors such as disease-related pain, medical procedures (such as injections), or the stress associated with hospital admission [14,15] but neglected the role of everyday stressors or minor events. However, there are a number of reasons to explore the impact of everyday stressors, which may add to, or interact with, illness-specific stressors. The demands of diabetes management require that the adolescent constantly exercise extreme self-control (e.g., he or she must remember to eat and administer insulin at predetermined times). Compliance with the medical regimen may cause disturbances at school and work as well as in interactions with friends and romantic partners. Research suggests that adolescents with diabetes feel strained by the responsibility for self-care [16] and restricted in their daily activities [17]. Although the disruptive effect of diabetes management on everyday life is quite obvious, hardly any study to date has analyzed the longitudinal links between such everyday stressors and metabolic control in adolescent samples. A growing number of studies, however, are demonstrating the negative impact of everyday stressors on adolescents’ health, influencing the immune and circulatory system [7,13]. There is good reason to expect that everyday stress may influence metabolic control and lead to elevated HbA1 levels, which are difficult to control. No study to date has analyzed the impact of everyday stressors on the course of diabetes by analyzing metabolic control longitudinally. Lazarus and Launier [8] defined coping as a process of managing stressors (e.g., internal and external demands). Although a lot of research exists on coping with critical life events and everyday hassles in normative samples [11,12], little is known about the specific coping strategies diabetic adolescents use to cope with stressful aspects of their life. The majority of studies about coping of adolescents with chronic conditions have focused on coping with the illness (e.g., the critical life event) [14,18] or
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during invasive medical procedures [19]. Diabetic adolescents are, at best, included in mixed samples of diverse chronic illnesses. However, the specific conditions of diabetes (e.g., the strong impact on selfmanagement, invisibility of the illness, and comparably “normal” course of life) highlight the necessity to analyze coping with everyday stress in adolescents with diabetes. Considering the number of minor stressors that are associated with having diabetes, the adaptive way of coping with these stressors may be critically important for medical adaptation (e.g., achieving a satisfactory or good metabolic control). In fact, there is evidence that minor stressors may have direct physiological effects resulting in raised blood sugar levels [13,20]. Therefore, instability in metabolic control or deteriorating of metabolic control during adolescence may be the result of increased levels of minor stress experienced during this developmental phase and/or an increasing inability to deal with these stressors competently. Although with age, healthy adolescents become more competent in coping with minor stressors [12], the cumulative effect of dealing with illness-specific stressors and non-illness-specific minor stressors may result in a less adaptive coping style in diabetic adolescents, which subsequently might influences their level of metabolic control. The purpose of this study was to extend our knowledge about the relationships between medical and psychosocial adaptation in adolescents with diabetes. The overall approach is exploratory. It is expected that diabetics with continuously poor metabolic control from early to late adolescence would experience higher levels of everyday stress and employ at the same time a less functional style of coping. In sum, medical and psychosocial adaptation is expected to be worse in this group, compared with adolescents with stable satisfactory, or good, metabolic control.
Method Sample Subjects for the present study were participants in the German Longitudinal Study on Juvenile Diabetes [5]. The adolescents with IDDM were recruited from their regular source of pediatric health care services. The study received full Institutional Review Board approval. Eighty-eight percent of the families approached agreed to participate. All physical examinations were done in full accordance with the declaration of Helsinki [21]. Ninety-eight 14-year-old
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adolescents with diabetes (46 females and 52 males) and their 91 mothers participated in four annual waves of data collection (T1 through T4). The mean illness duration was 5.4 years. At the beginning of the study, more than 90% of the adolescents already injected themselves with insulin and were responsible for adhering to their diet as well as performing their daily blood sugar and urine tests. The families of adolescents with diabetes were selected to match German comparison families of adolescents with respect to sociodemographic criteria. Eighty-four percent of the subjects were from two-parent families; all fathers and 65% of the mothers were employed. The families came from broad socioeconomic strata (SES); 52% of the families were middle class. Eighty-nine percent of the sample were of German descent, and the remaining 11% did not have a German citizenship. From those families approached, 90% continued their participation; no significant differences were found between the characteristics of those families who participated in all phases of the study and those who withdrew, with respect to marital status of the parents, number of children per family, employment status of the parents, and SES. Measures Adolescent stress. Stress perception was measured with the Problem Questionnaire [12], which assesses minor stressors in diverse problem areas. The instrument consisted of 64 items that had been frequently named as typical and salient everyday stressors in earlier studies. The adolescents were asked to indicate the stressfulness of a specific problem, ranging from “1” ⫽ “not stressful at all” to “5” ⫽ “highly stressful.” Factor analysis revealed seven domains including problems with self, parents, peers, opposite sex, school, leisure time, and future. Cronbach alpha for the subscales ranged between .70 and .84. Adolescent coping. Coping style was measured with the Coping Across Situations Questionnaire [12], which encompasses 20 coping strategies across eight possible problem domains: self, parents, peers, opposite sex, school, leisure time, vocational goals, and future. The adolescents were allowed to mark multiple coping strategies for each area. Factor analysis revealed three factors [12] representing the following coping styles: active coping (e.g., “I discuss the problem with my parents”), internal coping (e.g., “I think about the problem and try to find different solutions”), and withdrawal, a form of avoidant coping (e.g., “I withdraw because I cannot change
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anything anyway”). The Cronbach alpha for the internal consistency of these dimensions was .88, .77, and .73, respectively. Procedure The longitudinal study on adolescents with IDDM consisted of a total of four annual surveys following receipt of informed consent. Each summer, members of the research team visited the families at home and administered several questionnaires. The questionnaires assessed all important areas in the life of an adolescent like school, peers, romantic relationships, leisure, family climate, body image, pubertal status, developmental tasks, minor and major stressors, coping, and a self-report of symptoms of psychopathology. Cartoons were added to entertain the adolescents while filling out the pages. It took about 30 – 45 minutes for checking and answering all questions. Annual tests of glycosylated hemoglobin (HbA1 values) served as a criterion for metabolic control. Statistical analyses. First, for each adolescent patient, the level of medical adaptation over 4 years was determined. Based on the results of a latent class analysis (LCA) [22], diabetic adolescents were grouped according to their longitudinal quality of metabolic control [23] into three different classes of metabolic control: continuously good, continuously satisfactory, and continuously poor metabolic control. Then links between medical adaptation and psychosocial adaptation were determined over time. Repeated-measures analysis of variance (MANOVA) analyzed whether diabetic adolescents with varying medical adaptation (e.g., adolescents belonging to different classes of metabolic control) differed in their stress perception and coping style over time. Latent class analysis to determine longitudinal medical adaptation. Although the assessment of metabolic control is based on an interval level measure, its medical meaning is qualitative: HbA1 scores below 7.6 are considered as good metabolic control, scores between 7.6 and 9.6 are considered as satisfactory, and scores above 9.6 are considered as poor metabolic control [23]. These qualitative labels have a serious effect with respect to the probability of suffering from long-term damage. Therefore, as a measure of medical adaptation, it is important to determine the stability or change in metabolic control over time. The study of interindividual differences in stability or change in metabolic control needs to reflect the ordinal ranking of the data. A longitudinal
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Table 1. Results of the Three Class Latent Class Analysis (LCA) Classes I
II III
Class Labels
Portion (Size)
LR
Chi-Quadrat
Read Cressie
Continuously good metabolic control
0.30 (n ⫽ 27)
53.51 df ⫽ 54 p ⫽ 60 (p ⫽ 53)
52.12 df ⫽ 54 p ⫽ 54 (p ⫽ 45)
Continuously satisfactory metabolic control Continuously poor metabolic control
0.52 (n ⫽ 52) 0.18 (n ⫽ 19)
60.03 df ⫽ 54 p ⫽ 26 (p ⫽ .29)
The p values in parentheses were based on a bootstrap with 500 samples; the procedure sampling from cells within the program PANMARK 3.07 [27] was used.
version of LCA [24] was chosen to detect the number of qualitatively distinct groups or classes of metabolic control over time in the present sample. Latent class analysis is a statistical method to divide a sample of individuals into homogeneous groups or classes with respect to one or more categorical outcome variables. LCA provides goodness-of-fit statistics to determine the number of all possible groups represented in the model. In addition, the size of each group can be assessed. Within each class all individuals are considered locally independent with respect to the outcome variables [25]. In addition to LCA statistics like latent class probabilities and conditional probabilities, a transition matrix in which the proportion of class members can be identified that stay within their class and switch classes from t to t⫹1 was obtained. A longitudinal LCA [22] was applied to detect the number of qualitatively distinct groups or classes of metabolic control over the course of 4 years. Because LCA uses a closed system of data in which missing data are a problem, the data set was scrutinized with respect to missing values. In the analyses all adolescents with complete data at two of four data assessments were included (61% of the adolescents had no data missing; 22% had one data point missing, and 8% had two data points missing). Missing data were estimated according to a procedure suggested by Bingham et al. [26]. This procedure uses as an estimator the sample mean plus the individual mean deviation from the sample mean at time points with complete data. The program PANMARK version 3.07 by van de Pol et al. [27] was used for all latent class analyses. A three-class model on the quality of metabolic control over time fitted the data most adequately (Table 1). Of the adolescents with diabetes, 30% (n ⫽ 27) showed a continuously good metabolic control over time and were assigned to class I, 52% (n ⫽ 52) showed a continuously satisfactory metabolic control over time (class II), and 18% (n ⫽ 19) exhibited a continuously poor metabolic control over time (class III).
Results Of the 81 possible cell combinations (i.e., 3 ⫻ 3 ⫻ 3 ⫻ 3), only 41 were realized; the remaining cells were empty. Because of the relatively sparse data, a bootstrap on 500 samples was conducted to generate meaningful support for the fit statistics [28]. All three fit statistics (i.e., likelihood ratio (LR), Chi-square, and Read Cressie) were significant (see Table 1). The excellent fit of the three-class latent class model suggested that there was no significant movement or transition among the classes. Further analyses of latent variable Markov models [29] were conducted to estimate possible changes among classes over time. These latent Markov analyses indeed revealed that a three-class latent Markov model fitted the data: LR ⫽ 68.14, df ⫽ 66, p ⫽ .40 (bootstrap p ⫽ .72); Chi-square ⫽ 64.04, df ⫽ 66, p ⫽ .55 (bootstrap p ⫽ .86); Read Cressie ⫽ 61.43, df ⫽ 66, p ⫽ .64 (bootstrap p ⫽ .83). The analysis of the transition matrix revealed that about 80% of all members stayed in their class over time. All of the adolescents (except one) who switched their class of medical adaptation moved to a lower quality of metabolic control: 14 diabetics moved from good to satisfactory metabolic control, five diabetics switched from good to poor metabolic control, and 11 diabetics moved from satisfactory to poor metabolic control. Taken together, these analyses evidenced three qualitative distinct classes of medical adaptation and, further, that adolescents with diabetes rather tend to stay than to switch their qualitatively distinct classes of metabolic control over a time of 4 years.
Changes in Stress Perception and Coping Style Over Time Depending on Metabolic Control In the next step, adolescents belonging to different classes of metabolic control over time were compared with respect to changes in stress perception and coping style over time. The class membership was used as a three-level between-subject factor in
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repeated-measures ANOVA and, additionally, in cross-sectional one-way ANOVAs. Stress perception. Adolescents from class I experienced the lowest stress level in the domain “self” across all four time points (class effect: F (2,95) ⫽ 3.51, p ⬍ .05; Table 2). Adolescents with satisfactory levels of metabolic control throughout the study reported the highest problems with self at three time points and poorly adapted adolescents at one time point. No significant time effect or time by class interaction emerged. Regarding stress with “peers,” a significant class effect emerged (F(2,95) ⫽ 3.84, p ⬍ .05); continuously good adapted adolescents reported significantly less peer-related stressors than their medically less well-adapted agemates. Poorly adapted patients showed the highest stress with peers. A significant time-by-class interaction (F(6,188) ⫽ 2.86, p ⬍ .05) indicated that although the stress level with peers continued to remain high for poorly and satisfactory adapted diabetics, the low peer-related stress perceived by medically good adapted adolescents even decreased over time. In addition, a significant class effect (F(2,95) ⫽ 3.36, p ⬍ .05) with regard to stress experienced in the domain “leisure” evolved. The continuously good adapted diabetics described the lowest stress scores across the four time points. No significant time effect or time by class interaction emerged. For all adolescents with diabetes, a significant decrease in stress perceived in the domain “opposite sex” (F(3,93) ⫽ 3.93, p ⬍ .05) and “future” (F(3,93) ⫽ 5.15, p ⬍ .05) as well as in the total score of stress was found. No systematic variation among the three classes or time effects emerged for the problem areas “parents” and “school.” Coping style. Active coping and internal coping increased for all adolescents with diabetes over time (active coping: F(3,93) ⫽ 17.01, p ⬍ .05; internal coping: (F(3,93) ⫽ 24.44, p ⬍ .05; Table 3). No significant class or time-by-class interaction emerged. Regarding avoidant coping, a significant class effect emerged (F(2,95) ⫽ 7.15, p ⬍ .05) with poorly adapted adolescents showing the lowest scores for this dysfunctional coping style at T1 and T4 and adolescents with good metabolic control exhibiting the lowest scores at time T2 and T3. The significant time effect (F(3,93) ⫽ 2.98, p ⬍ .05) was based on a U-shaped change across time, with the lowest scores at T1 and T4 and the highest scores at T2. There was no significant time-by-class interaction in avoidant coping.
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Discussion This study explores the links between medical adaptation and psychosocial adaptation in adolescents with diabetes with a focus on coping with nonillness-specific everyday stress. Up to 30% of those diabetic adolescents whose medical adaptation, measured via metabolic control, is poor over an extended period of time develop health damages such as retinopathy and nephropathy [30]. A first aim of this study was, therefore, to analyze whether adolescents with diabetes can be assigned to stable groups according to their level of metabolic control over an extended time span, starting at age 14 years. The adapted approach was exploratory, not theorydriven. Adolescents in this study were assigned to classes on the basis of largest probability indicated by the results of a longitudinal LCA [22]. Latent class analyses revealed three distinctive groups with stable but different levels of metabolic control over 4 years, those with continuously good, satisfactory, and poor metabolic control. The assignment of adolescents to these groups was stable (e.g., 80% of the adolescents stayed in the classes of medical adaptation to which they were assigned). Longitudinal data on metabolic control are necessary to determine the quality of medical adaptation as well as the stability or change in the level of metabolic control over time. A second aim of the study was to analyze stress perception and coping style of adolescents with diabetes belonging to these three groups with stable levels of good, satisfactory, and poor metabolic control. Hauser et al [13] listed several studies that have considered the role of psychological stress in metabolic control of diabetic patients. Our study suggested close links between stressful everyday experiences and diabetic metabolic states. Diabetic adolescents assigned to the three latent classes differed significantly in their perception of non-illnessspecific minor stressors, particularly in the domains self, peers, and leisure, in which the lowest stress levels were reported by the stable medically good adapted adolescents. The results of this longitudinal study thus highlights that impairments in developing an identity [31] and maintaining close friendships [32,33] may not be characteristic for all diabetics but rather for those with satisfactory or poor metabolic control. One area of concern for adolescents with pediatric conditions is how their peer relations may be affected [17]. Indeed, stress with peers obtained the highest mean values of all sources of stress in our study of adolescents with diabetes, irrespective of their level of metabolic control. Close
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Table 2. Scale Scores for Minor Stressors Perceived in Different Problem Areas and Results of the Repeated Measures Analysis of Variance for the Three Latent Classes Time of Assessment Problem Areas Self
Peers
Opposite sex
Leisure
Parents
School
Future
Total score
Class
T1 M (SD)
T2 M (SD)
T3 M (SD)
T4 M (SD)
I II III I II III I II III I II III I II III I II III I II III I II III
1.921 (0.51) 2.071 (0.70) 1.961 (0.75) 2.241 (0.71) 2.201 (0.71) 2.251 (0.72) 2.191 (0.74) 2.191 (0.80) 2.301 (0.93) 2.001 (0.56) 2.231 (0.66) 2.261 (0.73) 1.961 (0.71) 1.961 (0.71) 2.161 (0.91) 2.131 (0.59) 2.211 (0.69) 2.381 (0.73) 2.841 (0.68) 2.731 (0.77) 2.731 (0.90) 15.281 (3.45) 15.591 (4.15) 16.041 (4.48)
1.591 (0.48) 2.052 (0.68) 1.961 (0.59) 1.811 (0.61) 2.272 (0.76) 2.382 (0.52) 1.711 (0.60) 2.122 (0.77) 2.352 (0.97) 1.821 (0.60) 2.192 (0.72) 2.292 (0.75) 1.771 (0.58) 2.001 (0.69) 2.091 (0.78) 1.921 (0.69) 2.231 (0.79) 2.261 (0.69) 2.421 (0.67) 2.641,2 (0.70) 2.431,2 (0.76) 13.021 (3.58) 15.392 (4.33) 15.862 (3.40)
1.611 (0.48) 1.982 (0.73) 2.002 (0.49) 1.871 (0.57) 2.201,2 (0.79) 2.442 (0.58) 1.721 (0.64) 2.082 (0.79) 2.282 (0.76) 1.821 (0.60) 2.192 (0.77) 2.171,2 (0.56) 1.881 (0.70) 2.011 (0.72) 2.081 (0.74) 1.981 (0.68) 2.271 (0.70) 2.251 (0.71) 2.351 (0.61) 2.651,2 (0.66) 2.631,2 (0.69) 13.221 (3.58) 15.392 (4.33) 15.862 (3.40)
1.641 (0.49) 1.982 (0.63) 1.821 (0.57) 1.771 (0.47) 2.262 (0.70) 2.202 (0.50) 1.781 (0.57) 2.001,2 (0.69) 1.901,2 (0.71) 1.791 (0.58) 2.132 (0.74) 2.001 (0.64) 1.821 (0.64) 2.071 (0.66) 1.941 (0.76) 1.951 (0.68) 2.251 (0.72) 2.231 (0.65) 2.361 (0.66) 2.651,2 (0.70) 2.491,2 (0.57) 13.111 (3.26) 15.352 (3.90) 14.591 (3.43)
Time ⫻ Class
Class Effect
Time Effect
3.51* (2,95)
2.25† (3,93)
1.55 (6,188)
3.84* (2,95)
1.42 (3,93)
2.86* (6,188)
2.32 (2,95)
4.37*** (3,93)
2.09† (6,188)
3.36* (2,95)
2.49† (3,93)
0.64 (6,188)
0.84 (2,95)
0.37 (3,93)
0.84 (6,188)
2.12 (2,95)
1.29 (3,93)
0.39 (6,188)
0.99 (2,95)
4.49** (3,93)
1.14 (6,188)
2.85† (2.95)
5.15** (3,93)
1.37 (6,188)
† p ⬍ .10; * p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001. Mean scores with different indices are significantly different (post hoc comparisons). For the post hoc comparisons, the least significant difference was applied. Class I ⫽ continuously good metabolic control (n ⫽ 27); class II ⫽ continuously satisfactory metabolic control (n ⫽ 52); class III ⫽ continuously poor metabolic control (n ⫽ 19).
friends and peers provide a significant source of support for adolescents who struggle with the management of a chronic disease, such as diabetes [34,35]. We found that adolescents with stable poor and stable satisfactory metabolic control reported the highest stress level in the domain peers; this result was consistent over time. Although problems with peers decreased over time for the adolescents with good metabolic control, problems with peers remained consistently high for the less well-adjusted diabetics. This may suggest that adolescents with good metabolic control finally get to manage their peer relations after a while, whereas the poorly adapted patients continuously lack an important source of social support when dealing with illness and nonillness-specific stress, a result that may have influenced their medical adaptation. Similar pattern can be observed with respect to self-related problems and problems with leisure. An effect of diabetes on self-concept and identity has been already shown in earlier research [36].
Only a few differences were found for adolescents differing in medical adaptation in coping with everyday stressors. Adolescents with IDDM in stable poor metabolic control revealed the highest scores in avoidant coping at age 14 years and at the end of our survey, at age 17 years, whereas adolescents with stable good metabolic control had the lowest scores in avoidant coping at ages 14 and 15 years. No differences, however, emerged with respect to the functional coping styles (i.e., active and internal coping) among the three groups varying in medical adaptation. Taken together, adolescents with poor medical adaptation employed functional coping modes as frequently as the other groups. This is an important finding because dysfunctional coping styles (like deficits in active coping or higher levels of avoidant coping) were found in different clinical groups such as depressed, delinquent, or conduct-disordered adolescents [7,37,38]. Our findings highlight that a maladaptive coping style is not a characteristic of adolescents with stable poor medical adaptation.
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Table 3. Scale Scores for Coping Style and Results of the Repeated-Measures Analysis of Variance for the Three Latent Classes Time of Assessment
CASQ Scales Active coping
Class I II III
Internal coping
I II III
Avoidant coping
I II III
T1 M (SD)
T2 M (SD)
T3 M (SD)
T4 M (SD)
19.041 (11.02) 14.791,2 (8.96) 12.472 (7.56) 14.301 (8.57) 14.251 (9.87) 12.901 (11.35) 6.851 (7.65) 7.851 (6.22) 4.581 (4.18)
25.301 (9.79) 22.571 (8.10) 23.791 (9.21) 24.481 (7.79) 24.041 (7.38) 24.041 (8.59) 5.521 (6.22) 11.292 (7.75) 6.941,3 (4.77)
24.131 (13.45) 18.711 (11.20) 20.501 (11.20) 23.001 (13.87) 19.481 (11.98) 21.211 (10.32) 4.111 (7.14) 8.112 (7.81) 5.211,3 (6.07)
23.791 (12.27) 20.571 (11.08) 20.381 (13.23) 21.441,2 (11.93) 20.691 (10.50) 20.501,2 (10.70) 4.921 (6.00) 9.942 (7.95) 4.661 (4.14)
Class Effect
Time Effect
Time ⫻ Class
2.29 (2,95)
17.01*** (3,93)
0.66 (6,188)
0.23 (2,95)
24.44*** (3,93)
0.39 (6,188)
7.15** (2,95)
2.98* (3,93)
1.63 (6,188)
† p ⬍ .10; * p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001. Mean scores with different indices are significantly different (post hoc comparisons). For the post hoc comparisons, the least significant difference was applied. Class I ⫽ continuously good metabolic control (n ⫽ 27); class II ⫽ continuously satisfactory metabolic control (n ⫽ 52); class III ⫽ continuously poor metabolic control (n ⫽ 19).
The results provide strong evidence that psychosocial stress is a significant risk factor for medical adjustment in adolescents with diabetes. Active and internal coping was not significantly different among the three groups, whereas avoidant coping was. Noteworthy are changes in the stress level over time and their link to metabolic control. Although the stress level with respect to problems with peers remained the same over time for the satisfactory and poorly adapted adolescents, it decreased for the continuously medically well-adapted diabetics. This study highlights the importance of grouping adolescents according to their medical adaptation. Adolescents with stable poor metabolic control can be considered as a risk group in the medical, as well as in the psychosocial sense. They have a higher risk for serious health problems and long-term complications of diabetes [30]. Moreover, their continuous high amount of stress, particularly in the domains of self, leisure, and peers, put them at risk for psychosocial adaptation in the long run. One can suspect that metabolic control may further deteriorate in this group because continuously high stress may have direct physiological effects and raise blood sugar levels [20,39]. Although adolescents with stable sat-
isfactory metabolic control cannot be considered a risk group with respect to long-term damage, their continuous high stress levels in diverse problem areas put them at risk psychologically, and in the long run also medically because of the direct physiological effects of stress on blood sugar levels. In contrast, adolescents with stable good metabolic control can be considered as well-adapted, both in the medical and psychosocial sense. They were able to exhibit good metabolic control over the course of 4 years, experience low amounts of minor stressors, and were able to cope actively with these stressful events. Taken together, this study revealed that medical adaptation is indeed linked to psychosocial adaptation. Clinicians may benefit from routine screening of coping with minor stressors in chronically ill adolescents. These results are only a first step in figuring out the complicated mechanisms between coping with a chronic disease and everyday stress. No clear-cut statements on directionality can be done as yet. Further analyses should take the underlying causal directionality into consideration and try, for instance, to predict the quality of metabolic control in diabetic adolescents based on their coping style and
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the stressors they face over time. Further limitations concern the reliance on self-report measures in the assessment of stress, coping, and symptomatology. A major strength of the study, however, is the inclusion of physicians, who reported about medical adaptation. The research was supported by the German Bundesministerium fu¨ r Forschung und Technologie (No. 0706567). Our thanks to Anton K. Formann for his helpful comments on the latent class analyses.
References 1. Ahmed PI, Akmed N. Coping With Juvenile Diabetes. Springfield, IL: Thomas, 1985. 2. Allen C, Zaccaro DJ, Palta M, et al. Glycemic control in early IDDM. The Wisconsin Diabetes Registry. Diabetes Care 1992; 15:980 –7. 3. Palta M, Shen G, Allen C, et al. Longitudinal patterns of glycemic control and diabetes care from diagnosis in a population-based cohort with type 1 diabetes. The Wisconsin Diabetes Registry. Am J Epidemiol 1996;144:954 –61. 4. Amiel SA, Sherwin RS, Simonson DC, et al. Impaired insulin action in puberty: A contributing factor to poor glycemic control in adolescents with diabetes. N Engl J Med 1986;315: 215–9. 5. Seiffge-Krenke I. Diabetic Adolescents and Their Families: Stress, Coping, and Adaptation. New York: Cambridge University Press, 2001. 6. Lewi L. Society, Stress, and Disease. New York: Oxford University Press, 1981. 7. Seiffge-Krenke I. Adolescents’ Health: A Developmental Perspective. Mahwah, NJ: Lawrence Erlbaum, 1998. 8. Lazarus RS, Launier R. Stress-related transactions between person and environment. In: Pervin L, Lewis M (eds). Perspectives in Interactional Psychology. New York: Plenum, 1978: 287–327. 9. Lazarus RS, Folkman S. Stress, Appraisal and Coping. New York: Springer Verlag, 1991. 10. Compas BE, Malcarne VI, Fondacaro KM. Coping with stressful events in older children and young adolescents. J Consult Clin Psychol 1988;56:405–11. 11. Compas BE, Connor-Smith JK, Saltzman H, et al. Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research. Psychol Bull 2001;127: 87–127. 12. Seiffge-Krenke I. Stress, Coping, and Relationships in Adolescence. Hillsdale, NJ: Lawrence Erlbaum Associates, 1995. 13. Hauser ST, Jacobson AM, Benes KA, et al. Psychological aspects of diabetes mellitus in children and adolescents: Implications and interventions. In: Alessi NE (ed). Handbook of Child and Adolescent Psychiatry, Vol. 4. New York: Wiley, 1997:340 –54. 14. Spirito A, Stark LJ, Gil KM, et al. Coping with everyday and disease-related stressors by chronically ill children and adolescents. J Acad Child Adolesc Psychiatry 1995;34:283–90. 15. Murphy LMB, Thompson RJ, Jr, Morris MA. Adherence behavior among adolescents with type I insulin-dependent diabetes mellitus: The role of cognitive appraisal processes. J Pediatr Psychol 1997;22:811–25.
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16. Anderson BA, Auslander WF, Jung KC, et al. Assessing family sharing of diabetes responsibilities. J Pediatr Psychol 1990;15: 477–92. 17. La Greca AM, Bearman KJ. Children with pediatric conditions: can peers’ impressions be managed? And what about their friends? [Commentary]. J Pediatr Psychol 2000;25:147–9. 18. Wallander JL, Varni JW. Appraisal, coping, and adjustment in adolescents with a physical disability. In: Wallander JL, Siegel LJ (eds). Adolescent Health Problems. New York: Guilford Press, 1995:209 –31. 19. Gil KM, Williams DA, Thompson RJ, et al. Sickle cell disease in children and adolescents: The relation of child and parent pain coping strategies to adjustment. J Pediatr Psychol 1991; 16:643–63. 20. Stabler B, Surwit RS, Lane JD, et al. Type A behavior patterns and blood glucose control in diabetic children. Psychosom Med 1987;49:313–6. 21. Deklaration von Helsinki [Declaration of Helsinki]. ¨ rzte, die in der biomedizinischen Forschung Empfehlung fu¨ r A am Menschen ta¨ tig sind [Recommendations for physicians who work with humans in the field of biomedical research]. Deutsch Arztbel 1991;50:2927– 8. 22. Formann AK. Measuring change using latent class analysis. In: von Eye A, Clogg CC (eds). Analysis of Latent Variables in Developmental Research. Newbury Park: Sage, 1994:294 –312. 23. Blanz B, Rensch-Riemann B, Fritz-Sigmund D, et al. Zur Rolle erkrankungsbezogener kognitiv-emotionaler Faktoren als Determinanten der Compliance bei Jugendlichen mit Diabetes mellitus [Regarding the role of disease related cognitiveemotional factors as determinants for the compliance of adolescents suffering from diabetes mellitus]. Z Klin Psychol 1993;22:264 –75. 24. von Eye A, Clogg CC (eds). Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks, CA: Sage Publications, 1994. 25. McCutcheon AL. Latent Class Analysis. Newbury Park: Sage Publications, 1987. 26. Bingham CR, Stemmler M, Petersen AC, et al. Imputing missing data values in repeated measurement within-subjects designs. Methods Psychol Res 1998;3:131–55. 27. van de Pol F, Langeheine R, de Jong W. PANMARK User Manual. PANel Analyis Using MARKov Chains. Voorburg, The Netherlands: Central Bureau of Statistics, 1994. 28. Langeheine R, van de Pol F. Discrete-time mixed Markov latent class models. In: Dale A, Davies RB (eds). Analyzing Social and Political Change. London: Sage Publications, 1994: 167–97. 29. Langeheine R. Latent variables Markov models. In: von Eye A, Clogg CC (eds). Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks, CA: Sage Publications, 1994:373–95. 30. Schernthaner G. Sekunda¨ re pra¨ vention [Secondary prevention]. Paper presented at the 22nd Annual Meeting of the Austrian Society for Diabetes, Graz, Austria, 1994. 31. Hanson TL, Henggeler SW, Burghen GA. Social competence and parental support as mediators of the link between stress and metabolic control in adolescents with insulindependent diabetes mellitus. J Consult Clin Psychol 1987; 55:529 –33. 32. Sullivan BJ. Adjustment in diabetic adolescent girls: I. Development of the Diabetic Adjustment Scale. Psychosom Med 1979;41:119 –26. 33. Wysocki T. Associations among teenager-parent relationships, metabolic control, and adjustment to diabetes in adolescents. J Pediatr Psychol 1993;18:441–52.
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34. La Greca AM, Auslander WV, Greco P, et al. I get by with a little help from my family and friends: Adolescents’ support for diabetes care. J Pediatr Psychol 1995;20:449 –76. 35. Skinner TC, John M, Hampson SE. Social support and personal models of diabetes as predictors of self-care and wellbeing: A longitudinal study of adolescents with diabetes. J Pediatr Psychol 2000;25:257–67. 36. Sinnema G. The development of independence in chronically ill adolescents. Int J Adolesc Med Health 1986;2:1– 14.
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37. Seiffge-Krenke I, Klessinger N. Long-term effects of avoidant coping on adolescents’ depressive symptoms. J Youth Adolesc 2000;29:617–30. 38. Hastings TL, Anderson SJ, Kelley ML. Gender differences in coping and daily stress in conduct-disordered and non-conduct-disordered adolescents. J Psychopathol Behav Assess 1996;18:213–26. 39. Delamater AM, Kurtz SM, Bubb J, et al. Stress and coping in relation to metabolic control of adolescents with type I-diabetes. J Dev Behav Pediatr 1987;8:136 –40.
INTERNATIONAL ARTICLE
Coping With Everyday Stress and Links to Medical and Psychosocial Adaptation in Diabetic Adolescents INGE SEIFFGE-KRENKE, Ph.D. AND MARK STEMMLER, Ph.D.
Purpose: To study coping with everyday stressors in a longitudinal sample of 98 adolescents with insulin-dependent mellitus (type 1) diabetes. Methods: The adolescents with type 1 diabetes were classified into three homogeneous groups of metabolic control by latent class analysis, based on annual tests of hemoglobin A1 values. Questionnaires assessing frequent minor stressors as well as ways of coping with these stressors were given annually over the course of 4 years. Latent class analysis revealed three distinctive groups of metabolic control over time. Adolescents who exhibited continuously poor, satisfactory, and good metabolic control. Eighty percent of the adolescents stayed in the group assigned to them over the 4-year period. Results: Adolescents with stable good metabolic control were characterized by lower levels of minor stressors that decreased over time, but those with stable satisfactory and poor metabolic control experienced continuously higher stress levels. Adolescents with stably good metabolic control also employed less avoidant coping in dealing with minor stressors, compared with the two other groups. Conclusions: Because of the danger of long-term complications, it is important to discriminate among different groups of metabolic control over time. Further, the impact of non-illness-related minor stressors on metabolic control should be considered for prevention purposes. © Society for Adolescent Medicine, 2003 KEY WORDS:
Adolescents Chronic illness Coping From the University of Mainz, Mainz, Germany (I.S.-K.); and University of Erlangen-Nuremberg, Erlangen, Germany (M.S.). Address correspondence to: Inge Seiffge-Krenke, Ph.D., Department of Psychology, University of Mainz, Staudingerweg 9, 55099 Mainz Germany. E-mail: [email protected] Manuscript accepted October 15, 2002. 1054-139X/03/$–see front matter doi:10.1016/S1054-139X(02)00707-3
Diabetes mellitus Latent class analysis Longitudinal study Metabolic control Minor stressors Germany
Developmental factors have been largely neglected in research on coping with chronic illness in adolescence. In adolescent patients with insulin-dependent diabetes mellitus (IDDM), medical adaptation can be clearly ascertained through metabolic control, the fraction of glycosylated hemoglobin in the blood, determined via hemoglobin HbA1 and HbA1c values. The quality of metabolic control is directly related to short-term complications such as hyperglycemia or diabetic coma. Moreover, continuous poor metabolic control is associated with a greater likelihood of long-term medical complications such as slowed growth, kidney disease, blindness, and reduced life expectancy [1]. Consequently, the decline in metabolic control seen in many adolescents [2,3] is an area of great concern, both for clinicians and parents. Although this decline in metabolic control is partly attributable to physiological aspects of puberty [4,5], other factors may contribute to this outcome. Considerable research is showing that several types of stressors are disturbing enough to affect physiological functioning [6,7]. Lazarus and Launier [8] understood stress to be created by any event in which an environment or internal demand (or both) tax or exceed an individual’s adaptive sources. Two different types of stress were analyzed, which differ in frequency, predictability, control, and negative impact on adolescents’ health: critical life events and © Society for Adolescent Medicine, 2003 Published by Elsevier Inc., 360 Park Avenue South, New York, NY 10010
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minor events such as everyday hassles [9 –11]. Although critical life events are relatively infrequent, hardly predictable, and extremely burdensome, everyday stressors are highly predictable, comparably frequent, and perceived as mildly stressful and controllable [12]. The incidence of a severe chronic illness such as diabetes can be considered a critical life event. Because it is hardly predictable and can seldom be controlled or influenced by the adolescents, it is perceived as highly stressful. Consequently, research has concentrated on the perceived stressfulness of diabetes [13] or focused on illnessspecific stressors such as disease-related pain, medical procedures (such as injections), or the stress associated with hospital admission [14,15] but neglected the role of everyday stressors or minor events. However, there are a number of reasons to explore the impact of everyday stressors, which may add to, or interact with, illness-specific stressors. The demands of diabetes management require that the adolescent constantly exercise extreme self-control (e.g., he or she must remember to eat and administer insulin at predetermined times). Compliance with the medical regimen may cause disturbances at school and work as well as in interactions with friends and romantic partners. Research suggests that adolescents with diabetes feel strained by the responsibility for self-care [16] and restricted in their daily activities [17]. Although the disruptive effect of diabetes management on everyday life is quite obvious, hardly any study to date has analyzed the longitudinal links between such everyday stressors and metabolic control in adolescent samples. A growing number of studies, however, are demonstrating the negative impact of everyday stressors on adolescents’ health, influencing the immune and circulatory system [7,13]. There is good reason to expect that everyday stress may influence metabolic control and lead to elevated HbA1 levels, which are difficult to control. No study to date has analyzed the impact of everyday stressors on the course of diabetes by analyzing metabolic control longitudinally. Lazarus and Launier [8] defined coping as a process of managing stressors (e.g., internal and external demands). Although a lot of research exists on coping with critical life events and everyday hassles in normative samples [11,12], little is known about the specific coping strategies diabetic adolescents use to cope with stressful aspects of their life. The majority of studies about coping of adolescents with chronic conditions have focused on coping with the illness (e.g., the critical life event) [14,18] or
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during invasive medical procedures [19]. Diabetic adolescents are, at best, included in mixed samples of diverse chronic illnesses. However, the specific conditions of diabetes (e.g., the strong impact on selfmanagement, invisibility of the illness, and comparably “normal” course of life) highlight the necessity to analyze coping with everyday stress in adolescents with diabetes. Considering the number of minor stressors that are associated with having diabetes, the adaptive way of coping with these stressors may be critically important for medical adaptation (e.g., achieving a satisfactory or good metabolic control). In fact, there is evidence that minor stressors may have direct physiological effects resulting in raised blood sugar levels [13,20]. Therefore, instability in metabolic control or deteriorating of metabolic control during adolescence may be the result of increased levels of minor stress experienced during this developmental phase and/or an increasing inability to deal with these stressors competently. Although with age, healthy adolescents become more competent in coping with minor stressors [12], the cumulative effect of dealing with illness-specific stressors and non-illness-specific minor stressors may result in a less adaptive coping style in diabetic adolescents, which subsequently might influences their level of metabolic control. The purpose of this study was to extend our knowledge about the relationships between medical and psychosocial adaptation in adolescents with diabetes. The overall approach is exploratory. It is expected that diabetics with continuously poor metabolic control from early to late adolescence would experience higher levels of everyday stress and employ at the same time a less functional style of coping. In sum, medical and psychosocial adaptation is expected to be worse in this group, compared with adolescents with stable satisfactory, or good, metabolic control.
Method Sample Subjects for the present study were participants in the German Longitudinal Study on Juvenile Diabetes [5]. The adolescents with IDDM were recruited from their regular source of pediatric health care services. The study received full Institutional Review Board approval. Eighty-eight percent of the families approached agreed to participate. All physical examinations were done in full accordance with the declaration of Helsinki [21]. Ninety-eight 14-year-old
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adolescents with diabetes (46 females and 52 males) and their 91 mothers participated in four annual waves of data collection (T1 through T4). The mean illness duration was 5.4 years. At the beginning of the study, more than 90% of the adolescents already injected themselves with insulin and were responsible for adhering to their diet as well as performing their daily blood sugar and urine tests. The families of adolescents with diabetes were selected to match German comparison families of adolescents with respect to sociodemographic criteria. Eighty-four percent of the subjects were from two-parent families; all fathers and 65% of the mothers were employed. The families came from broad socioeconomic strata (SES); 52% of the families were middle class. Eighty-nine percent of the sample were of German descent, and the remaining 11% did not have a German citizenship. From those families approached, 90% continued their participation; no significant differences were found between the characteristics of those families who participated in all phases of the study and those who withdrew, with respect to marital status of the parents, number of children per family, employment status of the parents, and SES. Measures Adolescent stress. Stress perception was measured with the Problem Questionnaire [12], which assesses minor stressors in diverse problem areas. The instrument consisted of 64 items that had been frequently named as typical and salient everyday stressors in earlier studies. The adolescents were asked to indicate the stressfulness of a specific problem, ranging from “1” ⫽ “not stressful at all” to “5” ⫽ “highly stressful.” Factor analysis revealed seven domains including problems with self, parents, peers, opposite sex, school, leisure time, and future. Cronbach alpha for the subscales ranged between .70 and .84. Adolescent coping. Coping style was measured with the Coping Across Situations Questionnaire [12], which encompasses 20 coping strategies across eight possible problem domains: self, parents, peers, opposite sex, school, leisure time, vocational goals, and future. The adolescents were allowed to mark multiple coping strategies for each area. Factor analysis revealed three factors [12] representing the following coping styles: active coping (e.g., “I discuss the problem with my parents”), internal coping (e.g., “I think about the problem and try to find different solutions”), and withdrawal, a form of avoidant coping (e.g., “I withdraw because I cannot change
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anything anyway”). The Cronbach alpha for the internal consistency of these dimensions was .88, .77, and .73, respectively. Procedure The longitudinal study on adolescents with IDDM consisted of a total of four annual surveys following receipt of informed consent. Each summer, members of the research team visited the families at home and administered several questionnaires. The questionnaires assessed all important areas in the life of an adolescent like school, peers, romantic relationships, leisure, family climate, body image, pubertal status, developmental tasks, minor and major stressors, coping, and a self-report of symptoms of psychopathology. Cartoons were added to entertain the adolescents while filling out the pages. It took about 30 – 45 minutes for checking and answering all questions. Annual tests of glycosylated hemoglobin (HbA1 values) served as a criterion for metabolic control. Statistical analyses. First, for each adolescent patient, the level of medical adaptation over 4 years was determined. Based on the results of a latent class analysis (LCA) [22], diabetic adolescents were grouped according to their longitudinal quality of metabolic control [23] into three different classes of metabolic control: continuously good, continuously satisfactory, and continuously poor metabolic control. Then links between medical adaptation and psychosocial adaptation were determined over time. Repeated-measures analysis of variance (MANOVA) analyzed whether diabetic adolescents with varying medical adaptation (e.g., adolescents belonging to different classes of metabolic control) differed in their stress perception and coping style over time. Latent class analysis to determine longitudinal medical adaptation. Although the assessment of metabolic control is based on an interval level measure, its medical meaning is qualitative: HbA1 scores below 7.6 are considered as good metabolic control, scores between 7.6 and 9.6 are considered as satisfactory, and scores above 9.6 are considered as poor metabolic control [23]. These qualitative labels have a serious effect with respect to the probability of suffering from long-term damage. Therefore, as a measure of medical adaptation, it is important to determine the stability or change in metabolic control over time. The study of interindividual differences in stability or change in metabolic control needs to reflect the ordinal ranking of the data. A longitudinal
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Table 1. Results of the Three Class Latent Class Analysis (LCA) Classes I
II III
Class Labels
Portion (Size)
LR
Chi-Quadrat
Read Cressie
Continuously good metabolic control
0.30 (n ⫽ 27)
53.51 df ⫽ 54 p ⫽ 60 (p ⫽ 53)
52.12 df ⫽ 54 p ⫽ 54 (p ⫽ 45)
Continuously satisfactory metabolic control Continuously poor metabolic control
0.52 (n ⫽ 52) 0.18 (n ⫽ 19)
60.03 df ⫽ 54 p ⫽ 26 (p ⫽ .29)
The p values in parentheses were based on a bootstrap with 500 samples; the procedure sampling from cells within the program PANMARK 3.07 [27] was used.
version of LCA [24] was chosen to detect the number of qualitatively distinct groups or classes of metabolic control over time in the present sample. Latent class analysis is a statistical method to divide a sample of individuals into homogeneous groups or classes with respect to one or more categorical outcome variables. LCA provides goodness-of-fit statistics to determine the number of all possible groups represented in the model. In addition, the size of each group can be assessed. Within each class all individuals are considered locally independent with respect to the outcome variables [25]. In addition to LCA statistics like latent class probabilities and conditional probabilities, a transition matrix in which the proportion of class members can be identified that stay within their class and switch classes from t to t⫹1 was obtained. A longitudinal LCA [22] was applied to detect the number of qualitatively distinct groups or classes of metabolic control over the course of 4 years. Because LCA uses a closed system of data in which missing data are a problem, the data set was scrutinized with respect to missing values. In the analyses all adolescents with complete data at two of four data assessments were included (61% of the adolescents had no data missing; 22% had one data point missing, and 8% had two data points missing). Missing data were estimated according to a procedure suggested by Bingham et al. [26]. This procedure uses as an estimator the sample mean plus the individual mean deviation from the sample mean at time points with complete data. The program PANMARK version 3.07 by van de Pol et al. [27] was used for all latent class analyses. A three-class model on the quality of metabolic control over time fitted the data most adequately (Table 1). Of the adolescents with diabetes, 30% (n ⫽ 27) showed a continuously good metabolic control over time and were assigned to class I, 52% (n ⫽ 52) showed a continuously satisfactory metabolic control over time (class II), and 18% (n ⫽ 19) exhibited a continuously poor metabolic control over time (class III).
Results Of the 81 possible cell combinations (i.e., 3 ⫻ 3 ⫻ 3 ⫻ 3), only 41 were realized; the remaining cells were empty. Because of the relatively sparse data, a bootstrap on 500 samples was conducted to generate meaningful support for the fit statistics [28]. All three fit statistics (i.e., likelihood ratio (LR), Chi-square, and Read Cressie) were significant (see Table 1). The excellent fit of the three-class latent class model suggested that there was no significant movement or transition among the classes. Further analyses of latent variable Markov models [29] were conducted to estimate possible changes among classes over time. These latent Markov analyses indeed revealed that a three-class latent Markov model fitted the data: LR ⫽ 68.14, df ⫽ 66, p ⫽ .40 (bootstrap p ⫽ .72); Chi-square ⫽ 64.04, df ⫽ 66, p ⫽ .55 (bootstrap p ⫽ .86); Read Cressie ⫽ 61.43, df ⫽ 66, p ⫽ .64 (bootstrap p ⫽ .83). The analysis of the transition matrix revealed that about 80% of all members stayed in their class over time. All of the adolescents (except one) who switched their class of medical adaptation moved to a lower quality of metabolic control: 14 diabetics moved from good to satisfactory metabolic control, five diabetics switched from good to poor metabolic control, and 11 diabetics moved from satisfactory to poor metabolic control. Taken together, these analyses evidenced three qualitative distinct classes of medical adaptation and, further, that adolescents with diabetes rather tend to stay than to switch their qualitatively distinct classes of metabolic control over a time of 4 years.
Changes in Stress Perception and Coping Style Over Time Depending on Metabolic Control In the next step, adolescents belonging to different classes of metabolic control over time were compared with respect to changes in stress perception and coping style over time. The class membership was used as a three-level between-subject factor in
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repeated-measures ANOVA and, additionally, in cross-sectional one-way ANOVAs. Stress perception. Adolescents from class I experienced the lowest stress level in the domain “self” across all four time points (class effect: F (2,95) ⫽ 3.51, p ⬍ .05; Table 2). Adolescents with satisfactory levels of metabolic control throughout the study reported the highest problems with self at three time points and poorly adapted adolescents at one time point. No significant time effect or time by class interaction emerged. Regarding stress with “peers,” a significant class effect emerged (F(2,95) ⫽ 3.84, p ⬍ .05); continuously good adapted adolescents reported significantly less peer-related stressors than their medically less well-adapted agemates. Poorly adapted patients showed the highest stress with peers. A significant time-by-class interaction (F(6,188) ⫽ 2.86, p ⬍ .05) indicated that although the stress level with peers continued to remain high for poorly and satisfactory adapted diabetics, the low peer-related stress perceived by medically good adapted adolescents even decreased over time. In addition, a significant class effect (F(2,95) ⫽ 3.36, p ⬍ .05) with regard to stress experienced in the domain “leisure” evolved. The continuously good adapted diabetics described the lowest stress scores across the four time points. No significant time effect or time by class interaction emerged. For all adolescents with diabetes, a significant decrease in stress perceived in the domain “opposite sex” (F(3,93) ⫽ 3.93, p ⬍ .05) and “future” (F(3,93) ⫽ 5.15, p ⬍ .05) as well as in the total score of stress was found. No systematic variation among the three classes or time effects emerged for the problem areas “parents” and “school.” Coping style. Active coping and internal coping increased for all adolescents with diabetes over time (active coping: F(3,93) ⫽ 17.01, p ⬍ .05; internal coping: (F(3,93) ⫽ 24.44, p ⬍ .05; Table 3). No significant class or time-by-class interaction emerged. Regarding avoidant coping, a significant class effect emerged (F(2,95) ⫽ 7.15, p ⬍ .05) with poorly adapted adolescents showing the lowest scores for this dysfunctional coping style at T1 and T4 and adolescents with good metabolic control exhibiting the lowest scores at time T2 and T3. The significant time effect (F(3,93) ⫽ 2.98, p ⬍ .05) was based on a U-shaped change across time, with the lowest scores at T1 and T4 and the highest scores at T2. There was no significant time-by-class interaction in avoidant coping.
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Discussion This study explores the links between medical adaptation and psychosocial adaptation in adolescents with diabetes with a focus on coping with nonillness-specific everyday stress. Up to 30% of those diabetic adolescents whose medical adaptation, measured via metabolic control, is poor over an extended period of time develop health damages such as retinopathy and nephropathy [30]. A first aim of this study was, therefore, to analyze whether adolescents with diabetes can be assigned to stable groups according to their level of metabolic control over an extended time span, starting at age 14 years. The adapted approach was exploratory, not theorydriven. Adolescents in this study were assigned to classes on the basis of largest probability indicated by the results of a longitudinal LCA [22]. Latent class analyses revealed three distinctive groups with stable but different levels of metabolic control over 4 years, those with continuously good, satisfactory, and poor metabolic control. The assignment of adolescents to these groups was stable (e.g., 80% of the adolescents stayed in the classes of medical adaptation to which they were assigned). Longitudinal data on metabolic control are necessary to determine the quality of medical adaptation as well as the stability or change in the level of metabolic control over time. A second aim of the study was to analyze stress perception and coping style of adolescents with diabetes belonging to these three groups with stable levels of good, satisfactory, and poor metabolic control. Hauser et al [13] listed several studies that have considered the role of psychological stress in metabolic control of diabetic patients. Our study suggested close links between stressful everyday experiences and diabetic metabolic states. Diabetic adolescents assigned to the three latent classes differed significantly in their perception of non-illnessspecific minor stressors, particularly in the domains self, peers, and leisure, in which the lowest stress levels were reported by the stable medically good adapted adolescents. The results of this longitudinal study thus highlights that impairments in developing an identity [31] and maintaining close friendships [32,33] may not be characteristic for all diabetics but rather for those with satisfactory or poor metabolic control. One area of concern for adolescents with pediatric conditions is how their peer relations may be affected [17]. Indeed, stress with peers obtained the highest mean values of all sources of stress in our study of adolescents with diabetes, irrespective of their level of metabolic control. Close
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Table 2. Scale Scores for Minor Stressors Perceived in Different Problem Areas and Results of the Repeated Measures Analysis of Variance for the Three Latent Classes Time of Assessment Problem Areas Self
Peers
Opposite sex
Leisure
Parents
School
Future
Total score
Class
T1 M (SD)
T2 M (SD)
T3 M (SD)
T4 M (SD)
I II III I II III I II III I II III I II III I II III I II III I II III
1.921 (0.51) 2.071 (0.70) 1.961 (0.75) 2.241 (0.71) 2.201 (0.71) 2.251 (0.72) 2.191 (0.74) 2.191 (0.80) 2.301 (0.93) 2.001 (0.56) 2.231 (0.66) 2.261 (0.73) 1.961 (0.71) 1.961 (0.71) 2.161 (0.91) 2.131 (0.59) 2.211 (0.69) 2.381 (0.73) 2.841 (0.68) 2.731 (0.77) 2.731 (0.90) 15.281 (3.45) 15.591 (4.15) 16.041 (4.48)
1.591 (0.48) 2.052 (0.68) 1.961 (0.59) 1.811 (0.61) 2.272 (0.76) 2.382 (0.52) 1.711 (0.60) 2.122 (0.77) 2.352 (0.97) 1.821 (0.60) 2.192 (0.72) 2.292 (0.75) 1.771 (0.58) 2.001 (0.69) 2.091 (0.78) 1.921 (0.69) 2.231 (0.79) 2.261 (0.69) 2.421 (0.67) 2.641,2 (0.70) 2.431,2 (0.76) 13.021 (3.58) 15.392 (4.33) 15.862 (3.40)
1.611 (0.48) 1.982 (0.73) 2.002 (0.49) 1.871 (0.57) 2.201,2 (0.79) 2.442 (0.58) 1.721 (0.64) 2.082 (0.79) 2.282 (0.76) 1.821 (0.60) 2.192 (0.77) 2.171,2 (0.56) 1.881 (0.70) 2.011 (0.72) 2.081 (0.74) 1.981 (0.68) 2.271 (0.70) 2.251 (0.71) 2.351 (0.61) 2.651,2 (0.66) 2.631,2 (0.69) 13.221 (3.58) 15.392 (4.33) 15.862 (3.40)
1.641 (0.49) 1.982 (0.63) 1.821 (0.57) 1.771 (0.47) 2.262 (0.70) 2.202 (0.50) 1.781 (0.57) 2.001,2 (0.69) 1.901,2 (0.71) 1.791 (0.58) 2.132 (0.74) 2.001 (0.64) 1.821 (0.64) 2.071 (0.66) 1.941 (0.76) 1.951 (0.68) 2.251 (0.72) 2.231 (0.65) 2.361 (0.66) 2.651,2 (0.70) 2.491,2 (0.57) 13.111 (3.26) 15.352 (3.90) 14.591 (3.43)
Time ⫻ Class
Class Effect
Time Effect
3.51* (2,95)
2.25† (3,93)
1.55 (6,188)
3.84* (2,95)
1.42 (3,93)
2.86* (6,188)
2.32 (2,95)
4.37*** (3,93)
2.09† (6,188)
3.36* (2,95)
2.49† (3,93)
0.64 (6,188)
0.84 (2,95)
0.37 (3,93)
0.84 (6,188)
2.12 (2,95)
1.29 (3,93)
0.39 (6,188)
0.99 (2,95)
4.49** (3,93)
1.14 (6,188)
2.85† (2.95)
5.15** (3,93)
1.37 (6,188)
† p ⬍ .10; * p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001. Mean scores with different indices are significantly different (post hoc comparisons). For the post hoc comparisons, the least significant difference was applied. Class I ⫽ continuously good metabolic control (n ⫽ 27); class II ⫽ continuously satisfactory metabolic control (n ⫽ 52); class III ⫽ continuously poor metabolic control (n ⫽ 19).
friends and peers provide a significant source of support for adolescents who struggle with the management of a chronic disease, such as diabetes [34,35]. We found that adolescents with stable poor and stable satisfactory metabolic control reported the highest stress level in the domain peers; this result was consistent over time. Although problems with peers decreased over time for the adolescents with good metabolic control, problems with peers remained consistently high for the less well-adjusted diabetics. This may suggest that adolescents with good metabolic control finally get to manage their peer relations after a while, whereas the poorly adapted patients continuously lack an important source of social support when dealing with illness and nonillness-specific stress, a result that may have influenced their medical adaptation. Similar pattern can be observed with respect to self-related problems and problems with leisure. An effect of diabetes on self-concept and identity has been already shown in earlier research [36].
Only a few differences were found for adolescents differing in medical adaptation in coping with everyday stressors. Adolescents with IDDM in stable poor metabolic control revealed the highest scores in avoidant coping at age 14 years and at the end of our survey, at age 17 years, whereas adolescents with stable good metabolic control had the lowest scores in avoidant coping at ages 14 and 15 years. No differences, however, emerged with respect to the functional coping styles (i.e., active and internal coping) among the three groups varying in medical adaptation. Taken together, adolescents with poor medical adaptation employed functional coping modes as frequently as the other groups. This is an important finding because dysfunctional coping styles (like deficits in active coping or higher levels of avoidant coping) were found in different clinical groups such as depressed, delinquent, or conduct-disordered adolescents [7,37,38]. Our findings highlight that a maladaptive coping style is not a characteristic of adolescents with stable poor medical adaptation.
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Table 3. Scale Scores for Coping Style and Results of the Repeated-Measures Analysis of Variance for the Three Latent Classes Time of Assessment
CASQ Scales Active coping
Class I II III
Internal coping
I II III
Avoidant coping
I II III
T1 M (SD)
T2 M (SD)
T3 M (SD)
T4 M (SD)
19.041 (11.02) 14.791,2 (8.96) 12.472 (7.56) 14.301 (8.57) 14.251 (9.87) 12.901 (11.35) 6.851 (7.65) 7.851 (6.22) 4.581 (4.18)
25.301 (9.79) 22.571 (8.10) 23.791 (9.21) 24.481 (7.79) 24.041 (7.38) 24.041 (8.59) 5.521 (6.22) 11.292 (7.75) 6.941,3 (4.77)
24.131 (13.45) 18.711 (11.20) 20.501 (11.20) 23.001 (13.87) 19.481 (11.98) 21.211 (10.32) 4.111 (7.14) 8.112 (7.81) 5.211,3 (6.07)
23.791 (12.27) 20.571 (11.08) 20.381 (13.23) 21.441,2 (11.93) 20.691 (10.50) 20.501,2 (10.70) 4.921 (6.00) 9.942 (7.95) 4.661 (4.14)
Class Effect
Time Effect
Time ⫻ Class
2.29 (2,95)
17.01*** (3,93)
0.66 (6,188)
0.23 (2,95)
24.44*** (3,93)
0.39 (6,188)
7.15** (2,95)
2.98* (3,93)
1.63 (6,188)
† p ⬍ .10; * p ⬍ .05; ** p ⬍ .01; *** p ⬍ .001. Mean scores with different indices are significantly different (post hoc comparisons). For the post hoc comparisons, the least significant difference was applied. Class I ⫽ continuously good metabolic control (n ⫽ 27); class II ⫽ continuously satisfactory metabolic control (n ⫽ 52); class III ⫽ continuously poor metabolic control (n ⫽ 19).
The results provide strong evidence that psychosocial stress is a significant risk factor for medical adjustment in adolescents with diabetes. Active and internal coping was not significantly different among the three groups, whereas avoidant coping was. Noteworthy are changes in the stress level over time and their link to metabolic control. Although the stress level with respect to problems with peers remained the same over time for the satisfactory and poorly adapted adolescents, it decreased for the continuously medically well-adapted diabetics. This study highlights the importance of grouping adolescents according to their medical adaptation. Adolescents with stable poor metabolic control can be considered as a risk group in the medical, as well as in the psychosocial sense. They have a higher risk for serious health problems and long-term complications of diabetes [30]. Moreover, their continuous high amount of stress, particularly in the domains of self, leisure, and peers, put them at risk for psychosocial adaptation in the long run. One can suspect that metabolic control may further deteriorate in this group because continuously high stress may have direct physiological effects and raise blood sugar levels [20,39]. Although adolescents with stable sat-
isfactory metabolic control cannot be considered a risk group with respect to long-term damage, their continuous high stress levels in diverse problem areas put them at risk psychologically, and in the long run also medically because of the direct physiological effects of stress on blood sugar levels. In contrast, adolescents with stable good metabolic control can be considered as well-adapted, both in the medical and psychosocial sense. They were able to exhibit good metabolic control over the course of 4 years, experience low amounts of minor stressors, and were able to cope actively with these stressful events. Taken together, this study revealed that medical adaptation is indeed linked to psychosocial adaptation. Clinicians may benefit from routine screening of coping with minor stressors in chronically ill adolescents. These results are only a first step in figuring out the complicated mechanisms between coping with a chronic disease and everyday stress. No clear-cut statements on directionality can be done as yet. Further analyses should take the underlying causal directionality into consideration and try, for instance, to predict the quality of metabolic control in diabetic adolescents based on their coping style and
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COPING WITH EVERYDAY STRESS IN DIABETIC ADOLESCENTS
the stressors they face over time. Further limitations concern the reliance on self-report measures in the assessment of stress, coping, and symptomatology. A major strength of the study, however, is the inclusion of physicians, who reported about medical adaptation. The research was supported by the German Bundesministerium fu¨ r Forschung und Technologie (No. 0706567). Our thanks to Anton K. Formann for his helpful comments on the latent class analyses.
References 1. Ahmed PI, Akmed N. Coping With Juvenile Diabetes. Springfield, IL: Thomas, 1985. 2. Allen C, Zaccaro DJ, Palta M, et al. Glycemic control in early IDDM. The Wisconsin Diabetes Registry. Diabetes Care 1992; 15:980 –7. 3. Palta M, Shen G, Allen C, et al. Longitudinal patterns of glycemic control and diabetes care from diagnosis in a population-based cohort with type 1 diabetes. The Wisconsin Diabetes Registry. Am J Epidemiol 1996;144:954 –61. 4. Amiel SA, Sherwin RS, Simonson DC, et al. Impaired insulin action in puberty: A contributing factor to poor glycemic control in adolescents with diabetes. N Engl J Med 1986;315: 215–9. 5. Seiffge-Krenke I. Diabetic Adolescents and Their Families: Stress, Coping, and Adaptation. New York: Cambridge University Press, 2001. 6. Lewi L. Society, Stress, and Disease. New York: Oxford University Press, 1981. 7. Seiffge-Krenke I. Adolescents’ Health: A Developmental Perspective. Mahwah, NJ: Lawrence Erlbaum, 1998. 8. Lazarus RS, Launier R. Stress-related transactions between person and environment. In: Pervin L, Lewis M (eds). Perspectives in Interactional Psychology. New York: Plenum, 1978: 287–327. 9. Lazarus RS, Folkman S. Stress, Appraisal and Coping. New York: Springer Verlag, 1991. 10. Compas BE, Malcarne VI, Fondacaro KM. Coping with stressful events in older children and young adolescents. J Consult Clin Psychol 1988;56:405–11. 11. Compas BE, Connor-Smith JK, Saltzman H, et al. Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research. Psychol Bull 2001;127: 87–127. 12. Seiffge-Krenke I. Stress, Coping, and Relationships in Adolescence. Hillsdale, NJ: Lawrence Erlbaum Associates, 1995. 13. Hauser ST, Jacobson AM, Benes KA, et al. Psychological aspects of diabetes mellitus in children and adolescents: Implications and interventions. In: Alessi NE (ed). Handbook of Child and Adolescent Psychiatry, Vol. 4. New York: Wiley, 1997:340 –54. 14. Spirito A, Stark LJ, Gil KM, et al. Coping with everyday and disease-related stressors by chronically ill children and adolescents. J Acad Child Adolesc Psychiatry 1995;34:283–90. 15. Murphy LMB, Thompson RJ, Jr, Morris MA. Adherence behavior among adolescents with type I insulin-dependent diabetes mellitus: The role of cognitive appraisal processes. J Pediatr Psychol 1997;22:811–25.
187
16. Anderson BA, Auslander WF, Jung KC, et al. Assessing family sharing of diabetes responsibilities. J Pediatr Psychol 1990;15: 477–92. 17. La Greca AM, Bearman KJ. Children with pediatric conditions: can peers’ impressions be managed? And what about their friends? [Commentary]. J Pediatr Psychol 2000;25:147–9. 18. Wallander JL, Varni JW. Appraisal, coping, and adjustment in adolescents with a physical disability. In: Wallander JL, Siegel LJ (eds). Adolescent Health Problems. New York: Guilford Press, 1995:209 –31. 19. Gil KM, Williams DA, Thompson RJ, et al. Sickle cell disease in children and adolescents: The relation of child and parent pain coping strategies to adjustment. J Pediatr Psychol 1991; 16:643–63. 20. Stabler B, Surwit RS, Lane JD, et al. Type A behavior patterns and blood glucose control in diabetic children. Psychosom Med 1987;49:313–6. 21. Deklaration von Helsinki [Declaration of Helsinki]. ¨ rzte, die in der biomedizinischen Forschung Empfehlung fu¨ r A am Menschen ta¨ tig sind [Recommendations for physicians who work with humans in the field of biomedical research]. Deutsch Arztbel 1991;50:2927– 8. 22. Formann AK. Measuring change using latent class analysis. In: von Eye A, Clogg CC (eds). Analysis of Latent Variables in Developmental Research. Newbury Park: Sage, 1994:294 –312. 23. Blanz B, Rensch-Riemann B, Fritz-Sigmund D, et al. Zur Rolle erkrankungsbezogener kognitiv-emotionaler Faktoren als Determinanten der Compliance bei Jugendlichen mit Diabetes mellitus [Regarding the role of disease related cognitiveemotional factors as determinants for the compliance of adolescents suffering from diabetes mellitus]. Z Klin Psychol 1993;22:264 –75. 24. von Eye A, Clogg CC (eds). Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks, CA: Sage Publications, 1994. 25. McCutcheon AL. Latent Class Analysis. Newbury Park: Sage Publications, 1987. 26. Bingham CR, Stemmler M, Petersen AC, et al. Imputing missing data values in repeated measurement within-subjects designs. Methods Psychol Res 1998;3:131–55. 27. van de Pol F, Langeheine R, de Jong W. PANMARK User Manual. PANel Analyis Using MARKov Chains. Voorburg, The Netherlands: Central Bureau of Statistics, 1994. 28. Langeheine R, van de Pol F. Discrete-time mixed Markov latent class models. In: Dale A, Davies RB (eds). Analyzing Social and Political Change. London: Sage Publications, 1994: 167–97. 29. Langeheine R. Latent variables Markov models. In: von Eye A, Clogg CC (eds). Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks, CA: Sage Publications, 1994:373–95. 30. Schernthaner G. Sekunda¨ re pra¨ vention [Secondary prevention]. Paper presented at the 22nd Annual Meeting of the Austrian Society for Diabetes, Graz, Austria, 1994. 31. Hanson TL, Henggeler SW, Burghen GA. Social competence and parental support as mediators of the link between stress and metabolic control in adolescents with insulindependent diabetes mellitus. J Consult Clin Psychol 1987; 55:529 –33. 32. Sullivan BJ. Adjustment in diabetic adolescent girls: I. Development of the Diabetic Adjustment Scale. Psychosom Med 1979;41:119 –26. 33. Wysocki T. Associations among teenager-parent relationships, metabolic control, and adjustment to diabetes in adolescents. J Pediatr Psychol 1993;18:441–52.
188
SEIFFGE-KRENKE AND STEMMLER
34. La Greca AM, Auslander WV, Greco P, et al. I get by with a little help from my family and friends: Adolescents’ support for diabetes care. J Pediatr Psychol 1995;20:449 –76. 35. Skinner TC, John M, Hampson SE. Social support and personal models of diabetes as predictors of self-care and wellbeing: A longitudinal study of adolescents with diabetes. J Pediatr Psychol 2000;25:257–67. 36. Sinnema G. The development of independence in chronically ill adolescents. Int J Adolesc Med Health 1986;2:1– 14.
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37. Seiffge-Krenke I, Klessinger N. Long-term effects of avoidant coping on adolescents’ depressive symptoms. J Youth Adolesc 2000;29:617–30. 38. Hastings TL, Anderson SJ, Kelley ML. Gender differences in coping and daily stress in conduct-disordered and non-conduct-disordered adolescents. J Psychopathol Behav Assess 1996;18:213–26. 39. Delamater AM, Kurtz SM, Bubb J, et al. Stress and coping in relation to metabolic control of adolescents with type I-diabetes. J Dev Behav Pediatr 1987;8:136 –40.