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Effortful control and automaticity in adolescent type 1 diabetes engagement
Tori Humiston1, Sara Adams2, Amy Hughes Lansing1
1Department of Psychological Sciences, University of Vermont, Burlington, VT, United States.
Objective:
Adolescents with type 1 diabetes face substantial daily self-regulation demands to maintain treatment engagement (e.g., complete diabetes tasks) and achieve optimal glycemic control. Effortful control (cognitive self-regulation) and automaticity (habit strength) are both theorized to support these processes, yet their independent and joint contributions to diabetes outcomes remain unclear.
Methods:
Adolescents with type 1 diabetes (N = 79; Mage = 15.1 years; 61% female) completed self-report measures of effortful control, automaticity for diabetes tasks, and treatment engagement. Average blood glucose levels were collected over a 7-day period (M = 184.4 mg/dL). Pearson correlations and path analyses were conducted to examine direct and indirect associations among study variables.
Results:
Greater effortful control was associated with higher automaticity and lower blood glucose (p's < .01). Automaticity was associated with greater treatment engagement (p < .001). In the path model, effortful control remained associated with automaticity and lower blood glucose, and automaticity remained associated with treatment engagement. Continuous glucose monitor use was associated with greater engagement and lower blood glucose (p's ≤ .04). A significant indirect effect of effortful control on engagement via automaticity emerged (p = .001).
Conclusion:
Findings suggest that effortful control may support both initiation and sustained management of diabetes behaviors, contributing to improved glycemic outcomes. In contrast, automaticity may facilitate consistent engagement in routine tasks without directly influencing glycemic control. These results highlight the importance of tailoring diabetes interventions to adolescents' self-regulatory capacities and suggest that combining strategies targeting both cognitive control and automaticity may optimize disease management.
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