Frontiers in public health · 2026
Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning-assisted text mining.
СтатьяScoping ReviewНазвание и аннотация: Перевод пока не подготовлен
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Sun Wei, Xie Dong, Hou Wenhui, Zhang Menglin, Wang Chendi, Wang Ziru, Yang He, Xu Guodong, Dai Haochen
Аннотация
BACKGROUND: Long-term self-management is essential for patients with type 2 diabetes mellitus, yet exercise management remains one of the weakest components of self-care. With the advancement of digital health technologies, digital wearable devices have increasingly been used to support diabetes management. However, the pathways by which these devices facilitate exercise behavior change remain insufficiently understood.
OBJECTIVE: This scoping review aimed to systematically map the existing evidence on digital wearable devices supporting exercise self-management in patients with type 2 diabetes mellitus, identify their behavior change pathways, and use machine learning-assisted text mining to examine major research themes and hotspots, thereby informing the development of targeted interventions.
METHODS: A scoping review was conducted across PubMed, Scopus, Web of Science, Embase, CINAHL, PsycINFO, and the Cochrane Library, covering studies published from database inception to March 2026. The search strategy was constructed using terms related to type 2 diabetes mellitus, digital wearable devices, exercise self-management, physical activity, and behavior change. Machine learning-assisted literature mining was used to identify thematic patterns in the included studies. The study selection process and overall workflow were conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR).
RESULTS: 11 studies were included in the review. The evidence indicates that digital wearable devices facilitate exercise self-management in patients with type 2 diabetes mellitus primarily through self-monitoring, real-time feedback, goal setting, motivational activation, social support, and enhanced self-efficacy. Machine learning-assisted text mining further showed that the literature in this field is mainly centered on exercise intervention design, behavioral regulation mechanisms, glycemic monitoring outcomes, and physical activity tracking.
CONCLUSION: Digital wearable devices appear to support exercise self-management in people with type 2 diabetes through multiple, interacting behavior-support pathways rather than through any single function alone. This review identifies recurring patterns in the literature rather than making causal inferences, and may inform the development of personalized digital interventions for exercise self-management based on behavior change theory.
SYSTEMATIC REVIEW REGISTRATION: https://doi.org/10.17605/OSF.IO/PBG8W.
OBJECTIVE: This scoping review aimed to systematically map the existing evidence on digital wearable devices supporting exercise self-management in patients with type 2 diabetes mellitus, identify their behavior change pathways, and use machine learning-assisted text mining to examine major research themes and hotspots, thereby informing the development of targeted interventions.
METHODS: A scoping review was conducted across PubMed, Scopus, Web of Science, Embase, CINAHL, PsycINFO, and the Cochrane Library, covering studies published from database inception to March 2026. The search strategy was constructed using terms related to type 2 diabetes mellitus, digital wearable devices, exercise self-management, physical activity, and behavior change. Machine learning-assisted literature mining was used to identify thematic patterns in the included studies. The study selection process and overall workflow were conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR).
RESULTS: 11 studies were included in the review. The evidence indicates that digital wearable devices facilitate exercise self-management in patients with type 2 diabetes mellitus primarily through self-monitoring, real-time feedback, goal setting, motivational activation, social support, and enhanced self-efficacy. Machine learning-assisted text mining further showed that the literature in this field is mainly centered on exercise intervention design, behavioral regulation mechanisms, glycemic monitoring outcomes, and physical activity tracking.
CONCLUSION: Digital wearable devices appear to support exercise self-management in people with type 2 diabetes through multiple, interacting behavior-support pathways rather than through any single function alone. This review identifies recurring patterns in the literature rather than making causal inferences, and may inform the development of personalized digital interventions for exercise self-management based on behavior change theory.
SYSTEMATIC REVIEW REGISTRATION: https://doi.org/10.17605/OSF.IO/PBG8W.