Coping in Adolescents with Type 1 Diabetes

The broader literature on coping indicates that the ways in which children and adolescents cope with stress are important mediators and moderators of the emotional and behavioral outcomes of stressful situations [7]. Coping in youth with type 1 diabetes has been studied over the past 25 years, using a variety of measures and approaches. Findings from these studies consistently demonstrate relationships between coping and self-management, quality of life, and metabolic control. Greater use of avoidant (or emotion-focused) coping strategies, such as wishful thinking, has been shown to be related to poorer metabolic control [8–10] and poorer psychosocial outcomes, including lower QOL and more depressive symptoms [8, 11, 12]. In contrast, greater use of approach (or problem-focused) coping strategies has been related to better metabolic control and better psychosocial adjustment in youth with type 1 diabetes [11, 13, 14]. Similarly, greater use of avoidant coping and less use of approach coping has been related to poorer self-management [13, 15]. To our knowledge, however, no studies have examined self-management as a mediator of the relationship between coping and the outcomes of metabolic control and QOL. Coping may directly influence metabolic control and QOL by decreasing the negative impact of stress; however, coping may also indirectly influence metabolic control and QOL through improved self-management. Self-management in adolescents with type 1 diabetes has been associated with both metabolic control and QOL [16, 17]. Therefore, self-management may be an important mediating link between coping and metabolic control and QOL.

Studies have consistently shown that lower income and minority youth with type 1 diabetes are at greater risk for poor metabolic control, hospitalizations for ketoacidosis, and long-term complications [18, 19]. Differences in coping styles may help to explain some of these demographic differences in diabetes-related outcomes. For example, the high levels of chronic stress experienced by adolescents from lower income families may interfere with their ability to use complex coping strategies, such as problem solving and cognitive restructuring [20]. Previous studies of coping in adolescents with type 1 diabetes have been limited by a lack of diversity. Thus, it is important to examine demographic differences in coping with diabetes, which may be affected by the higher levels of stress experienced by lower income and minority youth.

Research on coping in youth with other chronic conditions supports a developmentally appropriate model of stress and coping that differentiates between controlled/voluntary and automatic/involuntary responses to stressful events or circumstances [7, 21]. This model, supported by factor analysis, expands previous conceptualizations of coping to include three coping factors: primary control engagement coping, secondary control engagement coping, disengagement coping, as well as involuntary responses to stress, or stress reactivity (e.g., physiological arousal, rumination) [22]. Primary control engagement coping includes attempts to change the problem or one’s response to it (e.g., problem solving, emotional expression); secondary control engagement coping includes attempts to adapt to the stressor (e.g., acceptance, distraction); and disengagement coping includes avoidance, denial, and wishful thinking. This model extends previous conceptualizations of coping, in which some strategies do not clearly fit into the emotion-focused/problem-focused categories. A coping strategy such as forming a plan, for example, may help to solve a problem and also relieve negative emotions. Research with the current model has shown that higher levels of secondary control engagement coping and lower levels of disengagement coping were more effective for coping with pain in a pediatric population [22]. This model of automatic and voluntary responses to stress provides the conceptual framework for this analysis.

Participants and Procedure

Adolescents with type 1 diabetes were eligible for the study if they were between the ages of 11 and 14, had no other serious health problems, and had been diagnosed for at least 6 months. A total of 541 adolescents with type 1 diabetes were identified as eligible from outpatient diabetes clinics at four sites across the USA, selected to provide an oversample of racial/ethnic minorities. Of these, 90 refused participation (most common reasons were time commitment, lack of interest), 22 asked to be contacted later, and another 79 failed to complete baseline data. After providing consent (assent) in line with university IRB requirements, 327 adolescents completed baseline questionnaires on a password-protected, study-specific website. At least 10 subjects (and ideally 20) for each parameter in the model are sufficient for structural equation modeling [23], and our models contain 25 parameters. Demographic information was provided by parents during the consent process. Sample characteristics are presented in Table 1.

Correlational Analyses

Bivariate correlations indicate the associations between coping and stress reactivity with self-management, QOL, and metabolic control (i.e., A1C) (see Table 3).

Table 3

Correlations between coping, self-management, quality of life, and metabolic control

	1	2	3	4	5	6	7	8	9
Primary Control	–
M = 0.24 (0.04)
Secondary Control	0.52***	–
M = 0.33 (0.06)
Disengagement	−0.59***	−0.50***	–
M = 0.19 (0.04)
Stress Reactivity	−0.57***	−0.77***	0.27***	–
M = 0.24 (0.05)
SMOD Activities	0.37***	0.30***	−0.28***	−0.29***	–
M = 32.1 (5.9)
SMOD Collaboration	0.08	0.06	−0.11	0.00	0.22***	–
M = 21.8 (6.7)
SMOD Goals	0.32***	0.24***	−0.19***	−0.28***	0.35***	−0.06	–
M = 15.6 (2.8)
PedsQL	0.43***	0.53***	−0.31***	−0.50***	0.37***	−0.01	0.30***	–
M = 80.3 (12.3)
A1C	−0.14**	−0.19***	0.06	0.16*	−0.24***	−0.02	−0.21***	−0.07	–
M = 8.3 (1.5)

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SMOD self-management of diabetes, PedsQL pediatric quality of life

^*p<0.05;

^**p<0.01;

^***p<0.001

Mediation Analyses

The original structural model based on the hypothesized relationships between coping and stress reactivity, self-management, QOL, and A1C was a good fit for the data (CFI = 1.00, TLI = 1.044, RMSEA<0.001). As seen in Fig. 1, greater use of primary control engagement coping was related to higher levels of self-management, while greater stress reactivity was related to lower levels of self-management. Furthermore, the self-management factor, made up of the activities and goals subscales, was positively related to QOL. Support for mediation was found; there was a significant indirect effect of primary control engagement coping on QOL through self-management (β = 0.51±0.17, p = 0.002). There was also a significant indirect effect of stress reactivity on QOL through self-management (β = −0.25±0.11, p = 0.018). There was not a significant direct effect for any responses to stress on QOL.

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Fig. 1

Mediation model for the effect of self-management on the relationships between coping and stress reactivity on A1C and Quality of Life (QOL). The numbers provided for each path are standardized coefficients and error terms. Solid lines indicate indirect effects; dashed lines indicate direct effects

Greater self-management was also related to lower A1C. Support for mediation from responses to stress to A1C through self-management was also found; there was a significant indirect effect of primary control engagement coping on A1C through self-management (β = −0.62±0.02, p = 0.004). There was also a significant indirect effect of stress reactivity on A1C through self-management (β = 0.03±0.01, p = 0.024). Finally, there was a significant direct effect of primary control coping on A1C.

Limitations

This study is limited by the cross-sectional design, which does not allow us to test for direction of effects. As such, the mediation analyses must be considered exploratory, since tests of mediation may be biased in cross-sectional samples. In addition, the current study used baseline data from an intervention study, which required participants to commit to a study lasting 18 months. Therefore, our sample may be biased by self-selection, as the demands of the intervention study may have excluded adolescents from more stressed families. Finally, our sample was in relatively good metabolic control; the mean A1C was 8.0%, which is the target for this age group [2]. It is possible that adolescents who have poorer metabolic control use different coping strategies.

Implications

Findings from this study highlight the youth who may be at higher risk for poor adaptation. Previous studies have shown that lower socioeconomic status and minority race/ethnicity have been correlated with poorer metabolic control, more hospitalizations for ketoacidosis, higher rates of long-term complications, and increased age-adjusted mortality rates [18, 19]. Our findings may help to explain how coping contributes to poorer outcomes in these populations. In our sample, minority and lower income youth were less likely to use adaptive coping strategies (i.e., primary and secondary control coping), and their coping efforts had less of an impact on self-management. This suggests that low income and minority youth may need additional support in developing and using coping skills to deal with diabetes-related stress, and that the ways they cope with stress related to poverty and discrimination may also be important to assess.

Furthermore, results from this study highlight the potential importance of coping to self-management, QOL, and metabolic control, warranting further attention to assessment of stress and coping in adolescents and interventions to promote adaptive coping. Specifically, these results suggest that interventions that promote or teach primary control coping strategies (e.g., problem solving, emotional expression), and secondary control coping strategies (e.g., acceptance, positive thinking), may help adolescents deal with diabetes-related stress in adolescents, particularly in minority groups. Finally, our results suggest that adolescents with type 1 diabetes may benefit from learning strategies to manage stress (e.g., relaxation), reducing stress reactivity.

This research was supported by grant number R01-NR04009 from the National Institute of Nursing Research (PIs: Grey and Whittemore). Author Sarah S. Jaser was supported by awards from the National Institute of Diabetes and Digestive and Kidney Diseases (K23 NK088454) and the National Center for Research Resources (UL1 RR024139). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of NIH. Clinical Trial Registry number: NCT00684658