Digital Engagement in Diabetes Care: A Multi-Domain Analysis of Psychosocial and Clinical Determinants

TL;DR

This study explores how people with diabetes use digital health tools, finding that type 1 diabetes patients are more engaged, while type 2 diabetes engagement depends on treatment and stress.

Contribution

The study identifies distinct behavioral mechanisms of digital health engagement in type 1 and type 2 diabetes, suggesting the need for tailored implementation strategies.

Findings

High digital engagement is more common in type 1 diabetes (90%) compared to type 2 diabetes (4.1%).

In type 2 diabetes, insulin treatment and diabetes-related stress are strong predictors of digital engagement.

Higher self-efficacy in type 2 diabetes is linked to reduced reliance on digital tools.

Abstract

Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with the use of digital health technologies is crucial for developing targeted implementation strategies. Objectives: The aim of this study was to assess the use of digital health technologies among adults with diabetes and to explore their relationship with psychosocial factors—especially technology acceptance and self-efficacy—as well as certain clinical characteristics, including diabetes-related stress, age, and disease duration. Methods: We conducted a cross-sectional study involving 304 adults with diabetes. Digital engagement was measured using the Digital Adherence and Use Questionnaire (DAUQ), a 7-item self-report instrument (Cronbach’s α = 0.89), from which a composite Digital Engagement Score was calculated (range 1–5) to indicate the level of technology-related self-management behaviors. Participants were descriptively categorized into low- and high-engagement groups. Engagement patterns were also analyzed by diabetes type to understand structural differences in technology exposure. Relationships between psychosocial variables and the outcome were examined using correlation analyses. Since engagement among participants with type 1 diabetes (T1D) showed limited variability, multivariable regression analyses were performed on participants with type 2 diabetes (T2D) using beta regression, with linear regression as a sensitivity analysis. An exploratory beta regression was also conducted for T1D. Results: Overall, 35.5% of participants were classified as having high digital engagement. High engagement was observed in more than 90% of participants with T1D, compared to 4.1% of those with T2D. Median engagement scores differed significantly between low- and high-engagement groups (median [Q1–Q3]: 1.71 [1.71–2.39] vs. 3.86 [3.86–4.43]). Highly engaged participants reported much higher levels of openness to technology (median [Q1–Q3]: 5.00 [1.00–5.00] vs. 1.00 [1.00–1.00], p < 0.001) and self-efficacy (median [Q1–Q3]: 3.00 [3.00–3.00] vs. 5.00 [5.00–5.00], p < 0.001). In T1D, multivariable beta regression analyses showed that age was independently associated with digital engagement, with each 10-year increase corresponding to a decrease in engagement (β = −0.147, 95% CI −0.219 to −0.075, p < 0.001). Diabetes duration and psychosocial variables were not independently associated with engagement in the multivariable model. In contrast, among participants with T2D, insulin treatment emerged as the strongest independent predictor of engagement (β = 0.996, 95% CI 0.859–1.134, p < 0.001), and diabetes-related stress emerged as an independent predictor of engagement (β = 0.069, 95% CI 0.006–0.132, p = 0.033). Technology acceptance was positively associated with engagement (β = 0.694, 95% CI 0.350–1.037, p < 0.001), whereas higher self-efficacy was independently associated with lower engagement intensity (β = −0.366, 95% CI −0.608 to −0.124, p = 0.003). Age and diabetes duration were not independently associated with engagement after adjustment. Conclusions: Digital engagement appears to function as a structurally embedded component of self-management in T1D, with limited variability and largely independent of psychosocial modulation. In T2D, engagement is predominantly driven by treatment characteristics (insulin treatment), psychosocial dynamics (stress, technology acceptance), with higher self-efficacy associated with reduced reliance on digital tools. These findings suggest distinct behavioral mechanisms underlying digital health utilization across diabetes types and support the need for tailored implementation strategies.

Figures