Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a...

Area of Science:

• Digital Health
• Behavioral Science
• Endocrinology

Background:

• Long-term self-management is crucial for type 2 diabetes mellitus (T2DM) patients.
• Exercise management is a significant challenge in T2DM self-care.
• Digital wearable devices offer potential solutions for diabetes management, but their role in exercise behavior change needs clarification.

Purpose of the Study:

• To systematically review evidence on digital wearable devices for exercise self-management in T2DM.
• To identify behavior change pathways facilitated by these devices.
• To explore research themes using machine learning for targeted intervention development.

Main Methods:

• A comprehensive scoping review was conducted across seven major databases.
• Search terms included T2DM, digital wearables, exercise self-management, physical activity, and behavior change.
• Machine learning-assisted text mining analyzed thematic patterns in included studies, following PRISMA-ScR guidelines.

Main Results:

• Eleven studies were included, showing devices support exercise self-management via self-monitoring, feedback, goal setting, motivation, social support, and self-efficacy.
• Machine learning identified key research themes: intervention design, behavioral regulation, glycemic monitoring, and physical activity tracking.
• Devices facilitate exercise self-management through interconnected pathways, not isolated functions.

Conclusions:

• Digital wearable devices support exercise self-management in T2DM patients through multiple, interacting behavior-support pathways.
• Findings inform the development of personalized digital interventions grounded in behavior change theory.
• This review highlights recurring patterns to guide future research and clinical applications.