Evidence mapPaperPMID 42597803Full record

SynthesisFrontiers in public health2026

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.

Wei Sun, Dong Xie, Wenhui Hou, Menglin Zhang, Chendi Wang, Ziru Wang, He Yang, Guodong Xu, Haochen Dai

Abstract readScoping ReviewSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wei Sun *School of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Dong Xie *School of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Wenhui HouSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Menglin ZhangSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Chendi WangSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Ziru WangSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
He YangSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Guodong XuSchool of Nursing, Changchun University of Chinese Medicine, Jilin, China.
Haochen DaiSchool of Public Health, Jilin University, Jilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Data MiningDiabetes Mellitus, Type 2ExerciseHealth BehaviorMachine LearningSelf-ManagementWearable Electronic DevicesDigital HealthHumansdigital wearable devicesexercise self-managementmachine learningtext miningtype 2 diabetes mellitus

Identifiers

PMID42597803
PMCPMC13469597

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.