Technology Use for Diabetes Problem Solving in Adolescents with Type 1 Diabetes: Relationship to Glycemic Control.

BACKGROUND: This study examines technology use for problem solving in diabetes and its relationship to hemoglobin A1C (A1C). SUBJECTS AND METHODS: A sample of 112 adolescents with type 1 diabetes completed measures assessing use of technologies for diabetes problem solving, including mobile applications, social technologies, and glucose software. Hierarchical regression was performed to identify the contribution of a new nine-item Technology Use for Problem Solving in Type 1 Diabetes (TUPS) scale to A1C, considering known clinical contributors to A1C. RESULTS: Mean age for the sample was 14.5 (SD 1.7) years, mean A1C was 8.9% (SD 1.8%), 50% were female, and diabetes duration was 5.5 (SD 3.5) years. Cronbach's α reliability for TUPS was 0.78. In regression analyses, variables significantly associated with A1C were the socioeconomic status (ß = -0.26, P < 0.01), Diabetes Adolescent Problem Solving Questionnaire (ß = -0.26, P = 0.01), and TUPS (ß = 0.26, P = 0.01). Aside from the Diabetes Self-Care Inventory--Revised, each block added significantly to the model R(2). The final model R(2) was 0.22 for modeling A1C (P < 0.001). CONCLUSIONS: Results indicate a counterintuitive relationship between higher use of technologies for problem solving and higher A1C. Adolescents with poorer glycemic control may use technology in a reactive, as opposed to preventive, manner. Better understanding of the nature of technology use for self-management over time is needed to guide the development of technology-mediated problem solving tools for youth with type 1 diabetes.

Features of online health communities for adolescents with type 1 diabetes.

The aim of this exploratory study was to examine diabetes online health communities (OHCs) available to adolescents with type 1 diabetes (T1D). We sought to identify and classify site features and relate them to evidence-based processes for improving self-management. We reviewed 18 OHCs and identified the following five feature categories: social learning and networking, information, guidance, engagement, and personal health data sharing. While features that have been associated with improved self-management were present, such as social learning, results suggest that more guidance or structure would be helpful to ensure that those processes were focused on promoting positive beliefs and behaviors. Enhancing guidance-related features and structure to existing OHCs could provide greater opportunity for effective diabetes self-management support. To support clinical recommendations, more research is needed to quantitatively relate features and participation in OHCs to patient outcomes.