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
Open accessAbstract
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 .
AI snapshot
Written from the paper’s full text.
TL;DR
This scoping review mapped 11 studies on digital wearable devices for exercise self-management in type 2 diabetes and used machine learning–assisted text mining to identify recurring behavior change pathways. It reports six interrelated pathways—self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-efficacy enhancement—while cautioning that the evidence is early-stage and not causal.
Key findings
- 11 studies were included, conducted in seven countries; designs were seven randomized controlled trials, one user-centered design study, one pilot study, one two-arm non-randomized controlled study, and one stratified pragmatic controlled trial. Sample sizes ranged from 13 to 84 participants, with a median of 31; five of 11 studies were explicitly described as pilot or preliminary.
- Six core behavior change pathways were identified: self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-efficacy enhancement.
- Machine learning–assisted text mining found four LDA topics: Topic 1 (25.1%) on exercise/interventions/behavior change/trial design/adherence; Topic 2 (23.2%) on behavioral self-regulation and motivation; Topic 3 (21.8%) on glycemic monitoring and clinical outcomes; Topic 4 (30.0%) on physical activity tracking and wearable applications. BERT-based clustering produced six semantic clusters.
- Co-occurrence frequencies between digital wearable devices and pathway categories were: exercise behavior 415, behavior change theory 246, self-monitoring 201, goal setting 164, glycemic indicators 162, real-time feedback 108, and motivation/adherence 87. High-frequency terms included exercise (521, 1.79%), activity (517, 1.77%), diabetes (337, 1.16%), physical activity (335, 1.15%), and intervention (300, 1.03%).
Methods
- Scoping review following PRISMA-ScR, registered on OSF; searched PubMed, Scopus, Web of Science, Embase, CINAHL, PsycINFO, and the Cochrane Library from database inception to March 13, 2026, restricted to English or Chinese. Inclusion required adults aged 18+ with T2DM and original empirical studies examining digital wearable devices and exercise self-management or related behavior change pathways. From 952 records, 11 studies were included.
- Text mining used a final corpus of 2,487 valid sentences after removing 181 DOI/reference-related sentences and 332 author-list sentences; corpus contained 29,142 words, 6,886 unique vocabulary items, and a mean of 11.7 words per sentence.
- Analyses included LDA topic modeling with K compared from 2 to 10, BERT-based semantic clustering using bert-base-uncased with 768-dimensional embeddings, keyword co-occurrence network analysis across eight predefined conceptual categories, and sentence-level co-occurrence pattern analysis. Two researchers manually validated topics, clusters, and pathway classification.
Limitations
- Stated in the paper: only 11 studies met eligibility, many were pilot, preliminary, feasibility, or developmental studies with small samples; heterogeneity in device types, intervention components, duration, comparators, theoretical foundations, and outcomes limited direct comparison.
- Stated in the paper: no formal risk-of-bias or methodological quality assessment was conducted; causal relationships cannot be established; publication bias could not be formally assessed; English/Chinese restriction may have introduced language-related selection bias.
- Stated in the paper: machine learning results were affected by corpus size and composition, preprocessing decisions, keyword dictionary construction, model selection, and parameter settings; LDA may not fully capture contextual meaning, BERT clustering may be influenced by the pretrained model and clustering procedure, and co-occurrence frequencies do not establish direction, strength, or causality. Evident from the text: the median sample size was 31 and the field remains at a relatively early stage of development, so findings are a descriptive mapping rather than comparative effectiveness evidence.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
33 of 53 cited sources checked · 10 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Exercise self-management is often one of the most difficult self-care behaviors for patients with T2DM to initiate and maintain over time, compared with dietary adherence, medication taking, and blood glucose monitoring.
“However, compared with dietary adherence, medication taking, and blood glucose monitoring, exercise self-management is often one of the most difficult self-care behaviors for patients with T2DM to initiate and maintain over time (4).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [4] Han, 2025, Influencing factors of exercise behavior in patients with type 2 diabetes:a systematic review of qualitative studies, PPA. source
The Introduction (page not available) says patients with T2DM show poor willingness to begin exercise and difficulty adhering to it over time. The paper does not establish that exercise is more difficult to initiate or maintain than dietary adherence, medication taking, or blood glucose monitoring. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Interpretationcitation
Real-time feedback enhances the intervention potential of digital wearable devices by transforming data into actionable information through reminders, notifications, trend displays, and progress summaries.
“Real-time feedback further enhances the intervention potential of digital wearable devices by transforming data into actionable information through reminders, notifications, trend displays, and progress summaries (15, 17, 21, 22).”
Quote in the paperSubstantial claim1 of 4 sources checked- cites [15] Timurtas, 2022, Technology-based and supervised exercise interventions for individuals with type 2 diabetes: randomized controlled trial, Prim Care Diabetes. source
- cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [21] Pelletier, 2021, Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial, Mhealth. source
The Material section (page not provided) says tracker data could be viewed daily, with weekly and monthly reports and adjustable goals. The Discussion and satisfaction results (pages not provided) describe step counts as helpful for tracking and motivation. The paper does not establish that real-time feedback enhances intervention effects or describe notifications and trend displays as mechanisms; it mentions reminders while discussing another study. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [22] Tanaka, 2022, The effect of real-time monitoring of physical activity intensity in diabetic patients, Jpn J Nurs Sci. source
- Interpretationcitation
Goal-setting functions are consistent with Goal-Setting Theory, because digital wearable devices translate broad recommendations into specific and actionable targets such as daily step goals or activity thresholds.
“Goal-setting functions are also consistent with Goal-Setting Theory, because digital wearable devices translate broad recommendations into specific and actionable targets such as daily step goals or activity thresholds (28).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [28] Garstang, 2024, What effect do goal setting interventions have on physical activity and psychological outcomes in insufficiently active adults? a systematic review and meta-analysis, J Phys Act Health. source
The Physical activity outcomes section (page unavailable) discusses specific daily step goals and suggests incorporating step goals into future physical-activity guidelines. It does not say that digital wearable devices translate existing recommendations into targets or establish that this device function is consistent with Goal-Setting Theory. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Interpretationcitation
Through visualized progress trajectories and stage-based summaries of achievement, digital wearable devices may help patients accumulate successful experiences and gradually internalize exercise behavior over time.
“Through visualized progress trajectories and stage-based summaries of achievement, digital wearable devices may help patients accumulate successful experiences and gradually internalize exercise behavior over time (37).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [37] Bohnert, 2023, Remote research: resources, intervention needs, and methods in people with diabetes and peripheral neuropathy, J Diabetes Sci Technol. source
The Methods describe a wrist-worn activity monitor used to collect seven days of activity data, and the Results report participants’ steps and activity levels. Neither section addresses visualized progress, achievement summaries, accumulated success, or the internalization of exercise behavior; searches for those concepts and for wearable feedback found no supporting passage. No page numbers are available in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measured
- Interpretationcitation
Self-monitoring, real-time feedback, and goal setting align closely with Social Cognitive Theory and self-regulation perspectives, as digital wearable devices support a closed-loop process of self-observation, comparison with goals, and behavioral adjustment.
“Self-monitoring, real-time feedback, and goal setting align closely with Social Cognitive Theory and self-regulation perspectives, as digital wearable devices support a closed-loop process of self-observation, comparison with goals, and behavioral adjustment (14, 17, 20, 27).”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [20] Allen, 2011, A continuous glucose monitoring and problem-solving intervention to change physical activity behavior in women with type 2 diabetes: a pilot study, Diabetes Technol Ther. source
- cites [27] Egele, 2025, How valid is bandura's social cognitive theory to explain physical activity behavior?, EJIHPE. source
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
- Findingown data
Based on the elbow point from the log perplexity curve, K = 4 was selected as the inflection point for the LDA topic model.
“The topic structure analysis showed that, based on the elbow point identified from the log perplexity curve, K = 4 was selected as the inflection point, at which the rate of decrease in perplexity markedly slowed (from −7.10 at K = 4 to −7.12 at K = 5).”
Quote in the paperSubstantial claim - Findingown data
Considerable heterogeneity was observed across the included studies in device types, intervention components, intervention duration, comparator conditions, and outcome measures.
“Considerable heterogeneity was also observed in device types, intervention components, intervention duration, comparator conditions, and outcome measures.”
Quote in the paperSubstantial claim - Findingown data
Machine learning-assisted text mining showed that the literature in this field is mainly centered on exercise intervention design, behavioral regulation mechanisms, glycemic monitoring outcomes, and physical activity tracking.
“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.”
Quote in the paperNot a substantial claim - Findingown data
The keyword co-occurrence network showed a clear modular organizational structure, within which several concept clusters could be identified.
“The keyword co-occurrence network showed a clear modular organizational structure, within which several concept clusters could be identified.”
Quote in the paperNot a substantial claim - Findingown data
Digital wearable devices facilitate exercise self-management in patients with T2DM primarily through self-monitoring, real-time feedback, goal setting, motivational activation, social support, and enhanced self-efficacy.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Most previous studies have focused primarily on outcome measures and paid limited attention to the pathways through which digital wearable devices facilitate exercise self-management.
- Rests on an assumption: Evidence synthesis remains lacking on the behavior change pathways through which digital wearable devices support exercise self-management in people with T2DM from a theory-informed perspective.
- Findingown data
High-frequency keyword analysis showed that the most frequently occurring terms were exercise (521 occurrences, 1.79%), activity (517, 1.77%), diabetes (337, 1.16%), physical activity (335, 1.15%), and intervention (300, 1.03%).
“High-frequency keyword analysis showed that the most frequently occurring terms were exercise (521 occurrences, 1.79%), activity (517, 1.77%), diabetes (337, 1.16%), physical activity (335, 1.15%), and intervention (300, 1.03%).”
Quote in the paperNot a substantial claim - Findingown data
The BERT-based semantic clustering analysis divided the entire corpus into six semantic clusters.
“The BERT-based semantic clustering analysis divided the entire corpus into six semantic clusters.”
Quote in the paperSubstantial claim - Findingown data
The core region of the network consisted of highly connected nodes such as exercise, activity, body, intervention, and diabetes.
“The core region of the network consisted of highly connected nodes such as exercise, activity, body, intervention, and diabetes.”
Quote in the paperNot a substantial claim - Findingown data
The included studies used a variety of devices, including continuous glucose monitoring systems, accelerometers, activity trackers, smartwatches, or multicomponent mobile health systems.
“Some studies used continuous glucose monitoring systems, whereas others used accelerometers, activity trackers, smartwatches, or multicomponent mobile health systems.”
Quote in the paperNot a substantial claim - Findingown data
Within Topic 3, continuous glucose monitoring (CGM; 0.0137) and blood testing (0.0136) were notable indicators of clinical application value.
“Within this topic, continuous glucose monitoring (CGM; 0.0137) and blood testing (0.0136) were notable indicators of clinical application value.”
Quote in the paperNot a substantial claim - Findingown data
The co-occurrence relationships between digital wearable devices and each category of behavior change pathways demonstrated a clear gradient of association strength.
“The co-occurrence relationships between digital wearable devices and each category of behavior change pathways demonstrated a clear gradient of association strength.”
Quote in the paperNot a substantial claim - Findingown data
Nodes related to behavior change theory-including self-efficacy, cognitive mechanisms, behavioral patterns, mastery, and social factors-were located in the more peripheral regions of the network while maintaining connections with both technological and behavioral nodes.
“Nodes related to behavior change theory—including self-efficacy, cognitive mechanisms, behavioral patterns, mastery, and social factors—were located in the more peripheral regions of the network while maintaining connections with both technological and behavioral nodes.”
Quote in the paperSubstantial claim - Findingown data
Co-occurrence analysis demonstrated that motivation and adherence were discussed less frequently in association with digital wearable devices than exercise behavior, self-monitoring, or goal setting.
“Co-occurrence analysis further demonstrated that motivation and adherence were discussed less frequently in association with digital wearable devices than exercise behavior, self-monitoring, or goal setting.”
Quote in the paperSubstantial claim - Findingnone given
Considerable heterogeneity existed across digital wearable device types, intervention components, intervention duration, comparator conditions, theoretical foundations, and outcome measures, which limited direct comparison across studies.
“In addition, considerable heterogeneity existed across digital wearable device types, intervention components, intervention duration, comparator conditions, theoretical foundations, and outcome measures, which limited direct comparison across studies.”
Quote in the paperSubstantial claim - Findingown data
BERT-based clustering identified semantic relationships among passages that were expressed using different terminology, thereby linking technological, behavioral, and clinical content across studies.
“BERT-based clustering identified semantic relationships among passages that were expressed using different terminology, thereby linking technological, behavioral, and clinical content across studies.”
Quote in the paperSubstantial claim - Interpretationreasoning
The computational analyses supplemented the manual synthesis by revealing corpus-wide patterns, underrepresented themes, and cross-study semantic relationships that might be difficult to identify consistently through narrative review alone.
“Rather, they supplemented the manual synthesis by revealing corpus-wide patterns, underrepresented themes, and cross-study semantic relationships that might be difficult to identify consistently through narrative review alone.”
Quote in the paperSubstantial claim - Interpretationown data
The existing literature has consistently focused on diabetes management interventions centered on physical activity and exercise.
“These findings indicate that the existing literature has consistently focused on diabetes management interventions centered on physical activity and exercise.”
Quote in the paperNot a substantial claim - Interpretationown data
Compared with LDA results, BERT-based clustering was better able to capture deeper semantic relationships across different research themes.
“Compared with the LDA results, BERT-based clustering was better able to capture the deeper semantic relationships across different research themes”
Quote in the paperSubstantial claim - Study design and conductreasoning
K = 4 was ultimately selected as the optimal number of topics, taking into account statistical parsimony and interpretability of the topic content.
“Taking into account statistical parsimony and interpretability of the topic content, K = 4 was ultimately selected as the optimal number of topics (Figure 3).”
Quote in the paperSubstantial claim - Interpretationown data
Topic 3 reflects a domain focused on glycemic monitoring and clinical outcome assessment.
“Topic 3 (21.8%) was characterized by intervention, glucose levels, diabetes management, time-related dimensions, and control mechanisms, reflecting a domain focused on glycemic monitoring and clinical outcome assessment.”
Quote in the paperNot a substantial claim - Interpretationreasoning
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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Most previous studies have focused primarily on outcome measures and paid limited attention to the pathways through which digital wearable devices facilitate exercise self-management.
- Rests on an assumption: Evidence synthesis remains lacking on the behavior change pathways through which digital wearable devices support exercise self-management in people with T2DM from a theory-informed perspective.
- Interpretationreasoning
The core functions of digital wearable devices-such as self-monitoring, real-time feedback, prompts and reminders, and goal management-closely align with key constructs embedded in behavior change theories.
“Notably, the core functions of digital wearable devices—such as self-monitoring, real-time feedback, prompts and reminders, and goal management—closely align with key constructs embedded in these theories.”
Quote in the paperSubstantial claim - Interpretationreasoning
Exercise self-management supported by digital wearable devices is characterized by multidimensional interactions among technological, behavioral, and clinical components.
“suggesting that exercise self-management supported by digital wearable devices is characterized by multidimensional interactions among technological, behavioral, and clinical components”
Quote in the paperSubstantial claim - Interpretationown data
The field remains at a relatively early stage of development, as indicated by the methodological characteristics of the included evidence.
“The methodological characteristics of the included evidence indicated that the field remains at a relatively early stage of development.”
Quote in the paperSubstantial claim - Interpretationown data
These nodes appeared to function as bridging concepts, linking the technological domain with the behavioral domain, as well as the clinical outcome domain.
“These nodes appeared to function as bridging concepts, linking the technological domain (e.g., monitoring devices, trackers, sensors, mobile applications, and feedback mechanisms) with the behavioral domain (e.g., goal setting, motivational drivers, adherence, self-monitoring, and counseling support), as well as the clinical outcome domain (e.g., glycemic indicators, blood glucose levels, blood-”
Quote in the paperNot a substantial claim - Interpretationown data
A real-time feedback pathway exists whereby push notifications, vibration reminders, and visualized data displays facilitate immediate behavioral adjustment.
“(2) a real-time feedback pathway, whereby push notifications, vibration reminders, and visualized data displays facilitate immediate behavioral adjustment”
Quote in the paperSubstantial claim - Interpretationown data
This pattern suggests that these constructs may play an intermediary role in the pathway linking digital wearable device functions to behavior change processes.
“This pattern suggests that these constructs may play an intermediary role in the pathway linking digital wearable device functions to behavior change processes”
Quote in the paperNot a substantial claim - Interpretationown data
A motivation activation pathway exists in which reward mechanisms, progress tracking, and achievement feedback help sustain intrinsic motivation.
“(4) a motivation activation pathway, in which reward mechanisms, progress tracking, and achievement feedback help sustain intrinsic motivation”
Quote in the paperSubstantial claim - Interpretationown data
The relatively lower co-occurrence frequency for motivation/adherence (87) indicates that the current literature may have paid comparatively less attention to how digital wearable device use influences motivational processes and adherence-related outcomes.
“The relatively lower co-occurrence frequency for motivation/adherence (87) indicates that the current literature may have paid comparatively less attention to how digital wearable device use influences motivational processes and adherence-related outcomes”
Quote in the paperNot a substantial claim - Interpretationreasoning
Self-monitoring appears to be a foundational mechanism within this framework.
“Self-monitoring appears to be a foundational mechanism within this framework.”
Quote in the paperSubstantial claim - Interpretationown data
Six core behavior change pathways were identified through which digital wearable devices may support exercise self-management in patients with T2DM.
“Based on the multidimensional evidence derived from topic modeling, semantic clustering, keyword network analysis, and co-occurrence pattern analysis, six core behavior change pathways were identified through which digital wearable devices may support exercise self-management in patients with T2DM”
Quote in the paperSubstantial claim - Interpretationreasoning
Taken together, these pathways suggest that digital wearable devices may offer a more continuous, interactive, and individualized approach to exercise management in T2DM.
“Taken together, these pathways suggest that digital wearable devices may offer a more continuous, interactive, and individualized approach to exercise management in T2DM.”
Quote in the paperSubstantial claim - Interpretationown data
A social support pathway exists through which family support, clinician interaction, and peer comparison functions reinforce exercise maintenance.
“(5) a social support pathway, through which family support, clinician interaction, and peer comparison functions reinforce exercise maintenance”
Quote in the paperSubstantial claim - Interpretationown data
A self-monitoring pathway exists through which devices track step counts, activity duration, heart rate, and energy expenditure to enhance behavioral awareness.
“(1) a self-monitoring pathway, through which devices track step counts, activity duration, heart rate, and energy expenditure to enhance behavioral awareness”
Quote in the paperSubstantial claim - Study design and conductown data
The six pathways were derived through triangulation of LDA topics, BERT-based semantic clusters, keyword co-occurrence patterns, and manual examination of the original studies.
“The six pathways were not generated solely from the frequency of individual terms or from a single machine learning model. Instead, they were derived through triangulation of LDA topics, BERT-based semantic clusters, keyword co-occurrence patterns, and manual examination of the original studies.”
Quote in the paperSubstantial claim - Interpretationreasoning
The behavior change pathways identified in this review are broadly consistent with several established behavior change theories.
“The behavior change pathways identified in this review are broadly consistent with several established behavior change theories.”
Quote in the paperNot a substantial claim - Interpretationown data
A self-efficacy enhancement pathway exists in which visualized progress and mastery experiences strengthen patients' confidence in exercise management.
“(6) a self-efficacy enhancement pathway, in which visualized progress and mastery experiences strengthen patients' confidence in exercise management”
Quote in the paperSubstantial claim - Interpretationown data
A goal-setting pathway exists in which daily step goals, exercise duration targets, and activity thresholds help establish a structured behavioral target system.
“(3) a goal-setting pathway, in which daily step goals, exercise duration targets, and activity thresholds help establish a structured behavioral target system”
Quote in the paperSubstantial claim - Premisereasoning
Previous reviews have placed comparatively less emphasis on how specific digital wearable device functions may activate or support behavior change processes related to exercise self-management in people with T2DM.
“However, they have placed comparatively less emphasis on how specific digital wearable device functions may activate or support behavior change processes related to exercise self-management in people with T2DM.”
Quote in the paperSubstantial claim - Interpretationown data
These findings identify an imbalance in the existing literature.
“These findings identify an imbalance in the existing literature”
Quote in the paperSubstantial claim - Study design and conductreasoning
Because of the small and heterogeneous evidence base, publication bias could not be formally assessed.
“Because of the small and heterogeneous evidence base, publication bias could not be formally assessed.”
Quote in the paperNot a substantial claim - Study design and conductown data
The limited number and early-stage nature of the included studies reduce the certainty and generalizability of the identified patterns.
“The limited number and early-stage nature of the included studies reduce the certainty and generalizability of the identified patterns.”
Quote in the paperSubstantial claim - Study design and conductreasoning
The mixture of randomized, non-randomized, pilot, and developmental designs means that causal relationships between digital wearable device functions, behavior change processes, and exercise-related outcomes cannot be established.
“The mixture of randomized, non-randomized, pilot, and developmental designs also means that causal relationships between digital wearable device functions, behavior change processes, and exercise-related outcomes cannot be established.”
Quote in the paperSubstantial claim - Study design and conductown data
The machine learning-assisted component provided information beyond that obtained through conventional narrative synthesis.
“The machine learning–assisted component also provided information beyond that obtained through conventional narrative synthesis.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 41
- May combine two claims. 40
Classified as probably not a substantial claim.
Study design and conductcitationA comprehensive literature search was conducted in accordance with PRISMA-ScR and the Arksey and O'Malley framework.
“In accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines and the Arksey and O'Malley framework (12, 13), a comprehensive literature search was conducted to identify studies examining behavior change related to the use of digital wearable devices to support exercise self-management in patients with T2DM.”
Quote in the paperSubstantial claim1 of 2 sources checkedMay combine two claims.
InterpretationreasoningDigital wearable devices may influence exercise self-management in T2DM through pathways including enhanced self-efficacy, motivational activation, improved self-regulation, and support for sustained behavior.
“This suggests that such devices may influence exercise self-management in T2DM through pathways including enhanced self-efficacy, motivational activation, improved self-regulation, and support for sustained behavior (10, 11).”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [10] Bilgin, 2022, The effect of motivational interviewing on metabolic control and psychosocial variables in individuals diagnosed with diabetes: systematic review and meta-analysis, Patient Educ Couns. source
- cites [11] Kang, 2021, Is self-determined motivation a useful agent to overcome perceived exercise barriers in patients with type 2 diabetes mellitus?, Front Psychol. source
The Introduction links self-efficacy and self-determined motivation to exercise participation in people with T2DM, and the Conclusion describes motivation as a predictor of exercise behavior. The paper does not study digital wearable devices or establish that devices enhance these factors, improve self-regulation, or sustain behavior. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Rests on an assumption: Most previous studies have focused primarily on outcome measures and paid limited attention to the pathways through which digital wearable devices facilitate exercise self-management.
- Rests on an assumption: Evidence synthesis remains lacking on the behavior change pathways through which digital wearable devices support exercise self-management in people with T2DM from a theory-informed perspective.
May combine two claims.
PremisecitationDigital wearable devices may enhance patients' awareness of their own behaviors and support their engagement in physical activity and behavioral adjustment by objectively capturing exercise-related indicators.
“By objectively capturing exercise-related indicators, they may enhance patients' awareness of their own behaviors and support their engagement in physical activity and behavioral adjustment (5, 6).”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [5] Mattison, 2022, The influence of wearables on health care outcomes in chronic disease: systematic review, J Med Internet Res. source
- cites [6] Diez Alvarez, 2024, The role of smartwatch technology in the provision of care for type 1 or 2 diabetes mellitus or gestational diabetes: systematic review, JMIR Mhealth Uhealth. source
May combine two claims.
InterpretationcitationOver time, repeated goal attainment and visible progress may strengthen patients' sense of mastery and self-efficacy, thereby facilitating a shift from externally prompted behavior to proactive self-management.
“Over time, repeated goal attainment and visible progress may strengthen patients' sense of mastery and self-efficacy, thereby facilitating a shift from externally prompted behavior to proactive self-management (14, 17, 20, 25, 26).”
Quote in the paperSubstantial claim3 of 5 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
The Discussion (Principal Results; page not provided) describes a tool designed to increase self-efficacy and support self-management, with feedback based on goal attainment. The Results (Stage 3: Tool (Re-)Design; page not provided) describe visible progress and rewards. The paper does not say that repeated success builds mastery over time or shifts patients from external prompts to proactive self-management. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [20] Allen, 2011, A continuous glucose monitoring and problem-solving intervention to change physical activity behavior in women with type 2 diabetes: a pilot study, Diabetes Technol Ther. source
- cites [25] Alòs, 2025, Impact of a primary care-based mobile health intervention to ‘sit less and move more' on HbA1c, blood pressure, and other clinical outcomes in office employees with type 2 diabetes: a randomized controlled trial, Prim Care Diabetes. source
The discussion (section 4.2; page unavailable) says that tracking and feedback let participants visualize their achievements, increasing motivation to keep working toward their goals. The intervention description (section 2.6; page unavailable) also documents goal-setting and progress feedback. The paper does not say that repeated attainment strengthens mastery or self-efficacy, or that patients shift from external prompts to proactive self-management. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [26] Chang, 2025, Using continuous glucose monitoring to prescribe an exercise time: a randomised controlled trial in adults with type 2 diabetes, Diabetes Res Clin Pract. source
The Discussion (section 4; page not provided) says visual feedback from an unblinded glucose monitor may improve self-efficacy, motivation, and adherence over time. It does not describe repeated goal attainment, a growing sense of mastery, or a shift from externally prompted behavior to proactive self-management; it says whether this approach empowers patients to improve self-management needs further study. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationGoal setting seems central, as devices can translate broad recommendations into specific and feasible targets, such as daily step goals or activity thresholds.
“Goal setting also seems central, as devices can translate broad recommendations into specific and feasible targets, such as daily step goals or activity thresholds (14, 17, 21).”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
In Results, Stage 3: Tool (Re-)Design (page not supplied), the paper describes using monitored activity to set realistic, personalized daily goals in minutes of moderate-to-vigorous activity, and discusses individualized activity-intensity thresholds. It does not describe setting daily step goals: the Introduction discusses step counts as pedometer feedback, not as targets. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [21] Pelletier, 2021, Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial, Mhealth. source
The Discussion (page unavailable) describes 7,500 steps/day as a recommendation and a realistic goal, and says participants found step counts helpful for tracking and motivation. The Intervention section (page unavailable) says a kinesiologist—not the device—proposed the personalized activity program. The paper does not establish that devices translate broad recommendations into specific, feasible goals or that goal setting is central. (LLM, conf=0.83)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationSuch data may strengthen patients' awareness of their behavioral status and provide a basis for behavioral adjustment.
“Such data may strengthen patients' awareness of their behavioral status and provide a basis for behavioral adjustment (14, 17, 19, 20).”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
- cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [19] Allen, 2008, Continuous glucose monitoring counseling improves physical activity behaviors of individuals with type 2 diabetes: a randomized clinical trial, Diabetes Res Clin Pract. source
- cites [20] Allen, 2011, A continuous glucose monitoring and problem-solving intervention to change physical activity behavior in women with type 2 diabetes: a pilot study, Diabetes Technol Ther. source
May combine two claims.
PremisecitationCompared with traditional approaches that rely on recall or paper-based records, digital wearable devices provide continuous and objective tracking of step counts, activity duration, heart rate, and energy expenditure, thereby converting exercise behavior into visible and quantifiable information.
“Compared with traditional approaches that rely on recall or paper-based records, digital wearable devices provide continuous and objective tracking of step counts, activity duration, heart rate, and energy expenditure, thereby converting exercise behavior into visible and quantifiable information (14, 16–18).”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured - cites [16] Paschali, 2005, Accelerometer feedback to promote physical activity in adults with type 2 diabetes: a pilot study, Percept Mot Skills. source
- cites [17] Bailey, 2016, Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study, Diabetes Technol Ther. source
- cites [18] Coombes, 2022, Personal activity intelligence e-health program in people with type 2 diabetes: a pilot randomized controlled trial, Med Sci Sports Exerc. source
The Methods describe a wrist-worn monitor that measures heart rate and an app that provides numerical PAI-score feedback; the accelerometer measures time spent at different activity intensities (Methods, page unavailable). The paper does not establish that the wearable tracks step counts or energy expenditure, or compare its tracking with recall or paper-based records. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationFamily involvement, peer comparison, clinician interaction, and remote feedback may broaden the social context of exercise behavior.
“while family involvement, peer comparison, clinician interaction, and remote feedback may broaden the social context of exercise behavior (14, 15, 19).”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
Results, Stage 3: Tool (Re-)Design (page unavailable), says relatives’ involvement would be encouraged and describes feedback messages and practice-nurse interaction. But it says activity data would be shared with the nurse, not peers; it does not support peer comparison as part of the tool. The Discussion, Principal Results (page unavailable), likewise specifies sharing with a care professional rather than peers. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [15] Timurtas, 2022, Technology-based and supervised exercise interventions for individuals with type 2 diabetes: randomized controlled trial, Prim Care Diabetes. source
- cites [19] Allen, 2008, Continuous glucose monitoring counseling improves physical activity behaviors of individuals with type 2 diabetes: a randomized clinical trial, Diabetes Res Clin Pract. source
The Methods section describes individualized counseling, role-model glucose graphs, and a follow-up phone call, supporting clinician interaction and some comparison with others. It does not describe family involvement or peer comparison as an activity, and its CGMS feedback was reviewed during counseling rather than delivered remotely (4.3 and 4.5; page numbers unavailable). (LLM, conf=0.85)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationDigital wearable devices contribute not only by recording or prompting exercise, but also by embedding behavior regulation into everyday life and supporting the initiation, adjustment, and maintenance of exercise behaviors.
“In this sense, digital wearable devices contribute not only by recording or prompting exercise, but also by embedding behavior regulation into everyday life and supporting the initiation, adjustment, and maintenance of exercise behaviors (14, 15).”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
In Results, Stage 2 (Concept Development), the paper identifies self-monitoring, tailored feedback and goal setting as strategies relevant to initiating and maintaining physical activity. Results, Stage 3 (Tool Re-Design), describes daily activity planning, adjustable goals and ongoing feedback in its proposed tool. But the claim generalizes from a tool designed for people with COPD or type 2 diabetes to wearable devices broadly, and the Introduction says whether activity changes are sustainable remains unknown. The indexed passages have no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [15] Timurtas, 2022, Technology-based and supervised exercise interventions for individuals with type 2 diabetes: randomized controlled trial, Prim Care Diabetes. source
May combine two claims.
InterpretationcitationReward mechanisms, progress tracking, and achievement feedback may help sustain engagement.
“Reward mechanisms, progress tracking, and achievement feedback may help sustain engagement (15, 21–24)”
Quote in the paperSubstantial claim3 of 5 sources checked- cites [15] Timurtas, 2022, Technology-based and supervised exercise interventions for individuals with type 2 diabetes: randomized controlled trial, Prim Care Diabetes. source
- cites [21] Pelletier, 2021, Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial, Mhealth. source
The Material section (page unavailable) says the tracker let participants view and track activity daily, generate reports, and adjust goals. The Discussion (page unavailable) says participants found step counts helpful for motivation and that 75% thought the tracker would help them stick to their activity program. The paper does not establish that reward mechanisms or achievement feedback sustain engagement. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [22] Tanaka, 2022, The effect of real-time monitoring of physical activity intensity in diabetic patients, Jpn J Nurs Sci. source
- cites [23] Hesketh, 2025, Mobile health biometrics to enhance exercise and physical activity adherence in type 2 diabetes (MOTIVATE-T2D): a decentralised feasibility randomised controlled trial delivered across the UK and Canada, BMJ Open. source
The Results (Acceptability of the MOTIVATE-T2D intervention; page unavailable) describe progress tracking as an enabler, and the Discussion (page unavailable) suggests that extending text-message feedback may help maintain behaviour change. The paper does not address reward mechanisms or specifically identify achievement feedback as a way to sustain engagement. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [24] Abdul Latif El Ejel, 2025, Digital diabetes management technologies for type 2 diabetes: a systematic review of home-based care interventions, Cureus. source
The Review section (page unavailable) says self-monitoring is an effective behavior-change technique and that real-time feedback may enhance patient engagement. It does not discuss reward mechanisms or achievement feedback, and it says sustaining long-term engagement remains challenging rather than showing that these features sustain it. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationMotivation activation and social support may be interpreted through Self-Determination Theory, which emphasizes the importance of autonomy, competence, and relatedness in sustaining behavior.
“Motivation activation and social support may be interpreted through Self-Determination Theory, which emphasizes the importance of autonomy, competence, and relatedness in sustaining behavior (29).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [29] Ntoumanis, 2021, A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health, Health Psychol Rev. source
May combine two claims.
InterpretationcitationFeatures such as individualized goals, progress visualization, reward feedback, family involvement, peer comparison, and clinician interaction may help address these psychological needs.
“Features such as individualized goals, progress visualization, reward feedback, family involvement, peer comparison, and clinician interaction may help address these psychological needs (14, 19, 20).”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [14] Van der Weegen, 2013, The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design, JMIR Mhealth Uhealth. source
The Results (Stage 3: Tool (Re-)Design; page unavailable) describe personalized goals, progress displays, reward feedback, encouragement of relatives’ involvement, and feedback from a practice nurse. They do not support peer comparison: the Data Sharing subsection says activity data would be shared with the nurse, not peers, and few respondents wanted to share results with peers. The paper discusses motivation but does not establish that the full set of features addresses the particular psychological needs invoked by the citing sentence. (LLM, conf=0.90)
Experimental — accuracy not yet measured - cites [19] Allen, 2008, Continuous glucose monitoring counseling improves physical activity behaviors of individuals with type 2 diabetes: a randomized clinical trial, Diabetes Res Clin Pract. source
The Methods (section 4.5; page unavailable) describe individualized physical-activity goals, review of participants’ glucose graphs, feedback, role-model examples, and interaction with an interventionist to address self-efficacy. The paper does not describe reward feedback or family involvement; its role-model examples are not clearly peer comparison. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [20] Allen, 2011, A continuous glucose monitoring and problem-solving intervention to change physical activity behavior in women with type 2 diabetes: a pilot study, Diabetes Technol Ther. source
May combine two claims.
PremisecitationPrevious reviews have generally examined the effects of wearable technologies on broad healthcare outcomes in chronic diseases, the clinical application of smartwatch technologies in diabetes, home-based digital diabetes management, or the effects of wearable technologies on physical activity and metabolic outcomes.
“Previous reviews have generally examined the effects of wearable technologies on broad healthcare outcomes in chronic diseases, the clinical application of smartwatch technologies in diabetes, home-based digital diabetes management, or the effects of wearable technologies on physical activity and metabolic outcomes (5, 6, 24, 31).”
Quote in the paperSubstantial claim4 of 4 sources checked- cites [5] Mattison, 2022, The influence of wearables on health care outcomes in chronic disease: systematic review, J Med Internet Res. source
- cites [6] Diez Alvarez, 2024, The role of smartwatch technology in the provision of care for type 1 or 2 diabetes mellitus or gestational diabetes: systematic review, JMIR Mhealth Uhealth. source
- cites [24] Abdul Latif El Ejel, 2025, Digital diabetes management technologies for type 2 diabetes: a systematic review of home-based care interventions, Cureus. source
- cites [31] Laffi, 2025, Effectiveness of wearable technologies in supporting physical activity and metabolic health in adults with type 2 diabetes: a systematic-narrative hybrid review, Healthcare. source
May combine two claims.
InterpretationcitationBy enabling repeated goal attainment and visible progress, digital wearable devices may help patients build confidence in their ability to manage exercise, which may in turn support the long-term maintenance of exercise behavior.
“By enabling repeated goal attainment and visible progress, digital wearable devices may help patients build confidence in their ability to manage exercise, which may in turn support the long-term maintenance of exercise behavior (30).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [30] Asmat, 2024, Effect of patient-centered self-management intervention on glycemic control, self-efficacy, and self-care behaviors in South Asian adults with type 2 diabetes mellitus: a multicenter randomized controlled trial, J Diabetes. source
The Methods (Procedures; page unavailable) describe behavioral goals, feedback and reinforcement intended to build self-efficacy in self-care behaviors including physical activity. The Discussion (page unavailable) links improved self-efficacy to sustained lifestyle changes, but the intervention used sessions, visits, calls and SMS—not digital wearable devices—and the paper says its three-month follow-up cannot establish long-term maintenance. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
Findingown dataThe coherence score increased gradually from 0.3212 at K = 2 to a peak of 0.3879 at K = 9; beyond K = 4 (coherence = 0.3592), the marginal improvement was limited.
“The coherence score increased gradually from 0.3212 at K = 2 to a peak of 0.3879 at K = 9; however, beyond K = 4 (coherence = 0.3592), the marginal improvement was limited.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataA total of 952 records were identified through the initial database search, and 11 studies were ultimately included in the scoping review.
“A total of 952 records were identified through the initial database search. Following screening based on the predefined inclusion and exclusion criteria, 11 studies were ultimately included in this scoping review.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe 11 included studies were conducted in seven countries: the United States (n=3), Canada (n=2), Australia (n=2), the Netherlands (n=1), Japan (n=1), Turkey (n=1), and Spain (n=1).
“The 11 included studies were conducted in seven countries: the United States (n = 3), Canada (n = 2), Australia (n = 2), the Netherlands (n = 1), Japan (n = 1), Turkey (n = 1), and Spain (n = 1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataTopic 1 (25.1%) was centered on exercise, interventions, behavior change, trial design, and adherence, with exercise showing the highest individual word weight across all topics (0.0463).
“Topic 1 (25.1%) was centered on exercise, interventions, behavior change, trial design, and adherence, with exercise showing the highest individual word weight across all topics (0.0463).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataA 768-dimensional embedding vector was generated for each of the 2,487 sentences.
“A 768-dimensional embedding vector was generated for each of the 2,487 sentences.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe clustering results indicated that research in this field mainly focused on the design and implementation of exercise interventions, device-based monitoring and feedback functions, goal setting and behavior maintenance, glycemic control-related outcomes, patient adherence and motivational support, and content related to behavior change theories.
“The clustering results indicated that research in this field mainly focused on the design and implementation of exercise interventions, device-based monitoring and feedback functions, goal setting and behavior maintenance, glycemic control-related outcomes, patient adherence and motivational support, and content related to behavior change theories.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFeedback appeared 127 times (0.44%) and glucose monitoring appeared 124 times (0.43%) in the corpus, reflecting a dual emphasis on real-time biofeedback mechanisms and glycemic monitoring in the included studies.
“Notably, feedback appeared 127 times (0.44%) and glucose monitoring appeared 124 times (0.43%), reflecting a dual emphasis on real-time biofeedback mechanisms and glycemic monitoring in the included studies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe distribution of the top 20 keywords across the four LDA topics illustrated distinct thematic patterns.
“The distribution of the top 20 keywords across the four LDA topics further illustrated distinct thematic patterns.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSample sizes of the included studies ranged from 13 to 84 participants, with a median sample size of 31.
“Sample sizes ranged from 13 to 84 participants, with a median sample size of 31.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataTopic 2 (23.2%) included terms related to weeks, diabetes, behavioral regulation, motivational factors, and problem-solving capacity.
“Topic 2 (23.2%) included terms related to weeks, diabetes, behavioral regulation, motivational factors, and problem-solving capacity, representing a behavioral self-regulation dimension characterized by weekly monitoring cycles and motivational strategies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataAfter preprocessing, the final corpus consisted of 2,487 valid sentences, 29,142 words, and 6,886 unique vocabulary items, with a mean of 11.7 words per sentence.
“After rigorous preprocessing, including the removal of 181 DOI/reference-related sentences and 332 author-list sentences, a final corpus of 2,487 valid sentences was obtained. The processed corpus contained 29,142 words and 6,886 unique vocabulary items, with a mean of 11.7 words per sentence.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataStudy designs among the 11 included studies were seven randomized controlled trials, one user-centered design study, one pilot study, one two-arm non-randomized controlled study, and one stratified pragmatic controlled trial.
“Study designs included seven randomized controlled trials, one user-centered design study, one pilot study, one two-arm non-randomized controlled study, and one stratified pragmatic controlled trial.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFive of the 11 included studies were explicitly described as pilot or preliminary studies, and one was a user-centered design study primarily focused on intervention development and usability rather than effectiveness.
“Five of the 11 studies were explicitly described as pilot or preliminary studies, and one was a user-centered design study primarily focused on intervention development and usability rather than effectiveness.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTopic 4 (30.0%) focused on activity volume, exercise intensity, physical activity, time management, and activity trackers, with activity volume showing the highest keyword-pair weight across all topics (0.0545).
“Topic 4 (30.0%) focused on activity volume, exercise intensity, physical activity, time management, and activity trackers, with activity volume showing the highest keyword-pair weight across all topics (0.0545).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataTopic 3 (21.8%) was characterized by intervention, glucose levels, diabetes management, time-related dimensions, and control mechanisms.
“Topic 3 (21.8%) was characterized by intervention, glucose levels, diabetes management, time-related dimensions, and control mechanisms, reflecting a domain focused on glycemic monitoring and clinical outcome assessment.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataExercise behavior showed the highest co-occurrence frequency with digital wearable devices (415 occurrences), followed by behavior change theory (246), self-monitoring (201), goal setting (164), glycemic indicators (162), real-time feedback (108), and motivation/adherence (87).
“Exercise behavior showed the highest co-occurrence frequency with digital wearable devices (415 occurrences), followed by behavior change theory (246), self-monitoring (201), goal setting (164), glycemic indicators (162), real-time feedback (108), and motivation/adherence (87).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOnly 11 studies met the eligibility criteria, and a substantial proportion of the evidence consisted of pilot, preliminary, feasibility, or developmental studies with relatively small sample sizes.
“First, only 11 studies met the eligibility criteria, and a substantial proportion of the evidence consisted of pilot, preliminary, feasibility, or developmental studies with relatively small sample sizes.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLDA quantified the relative prominence of four major thematic domains, showing that physical activity tracking and digital wearable device applications constituted the largest topic, whereas behavioral self-regulation and motivation represented a smaller proportion of the literature.
“LDA quantified the relative prominence of four major thematic domains, showing that physical activity tracking and digital wearable device applications constituted the largest topic, whereas behavioral self-regulation and motivation represented a smaller proportion of the literature.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe review protocol was registered and made publicly available on the Open Science Framework.
“The review protocol was registered and made publicly available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/PBG8W).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThis review identifies recurring patterns in the literature rather than making causal inferences.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningIrrelevant information and anomalous terms were manually reviewed and removed during text preprocessing to improve the reliability of the analytical results.
“To improve the reliability of the analytical results, irrelevant information and anomalous terms were manually reviewed and removed during the text preprocessing stage.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe findings should be interpreted as a descriptive mapping of an emerging and heterogeneous evidence base rather than as comparative estimates of intervention effectiveness.
“Therefore, the findings should be interpreted as a descriptive mapping of an emerging and heterogeneous evidence base rather than as comparative estimates of intervention effectiveness.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningLDA and BERT were selected because they provide complementary perspectives on the textual corpus.
“LDA and BERT were selected because they provide complementary perspectives on the textual corpus.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationown dataTerms such as behavioral intervention (110, 0.38%) and continuous glucose monitoring (CGM; 101, 0.35%) highlight the behavior-oriented nature of the field and the prominent role of CGM technology.
“In addition, terms such as behavioral intervention (110, 0.38%) and continuous glucose monitoring (CGM; 101, 0.35%) further highlight the behavior-oriented nature of the field and the prominent role of CGM technology.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenA formal risk-of-bias assessment was not planned for this scoping review; descriptive indicators of methodological strength were extracted to contextualize the evidence.
“Because a formal risk-of-bias assessment was not planned for this scoping review, descriptive indicators of methodological strength were also extracted to contextualize the evidence.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll extracted data were systematically organized using a standardized data extraction form to ensure consistency and facilitate subsequent analysis and tabular presentation.
“All extracted data were systematically organized using a standardized data extraction form to ensure consistency and facilitate subsequent analysis and tabular presentation.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe computational analyses were not used to estimate intervention effects or establish causal relationships.
“These analyses were not used to estimate intervention effects or establish causal relationships.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe optimal number of clusters was identified through joint evaluation of the sum of squared errors (SSE) and silhouette scores.
“The optimal number of clusters was identified through joint evaluation of the sum of squared errors (SSE) and silhouette scores.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenAll algorithm-derived themes and clusters were independently reviewed by two researchers against the source articles before being interpreted and incorporated into the final behavior change pathway framework.
“All algorithm-derived themes and clusters were independently reviewed by two researchers against the source articles before they were interpreted and incorporated into the final behavior change pathway framework.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe optimal number of topics was determined by systematically comparing coherence scores and perplexity values across models with K ranging from 2 to 10.
“The optimal number of topics was determined by systematically comparing coherence scores and perplexity values across models with K ranging from 2 to 10.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataTwo researchers reviewed topic modeling and clustering results against original study content and performed semantic refinement to ensure accuracy of topic classification and pathway identification.
“After topic modeling and clustering analysis, two researchers reviewed the results in conjunction with the original study content and performed semantic refinement where necessary to ensure the accuracy of topic classification and pathway identification.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductcitationThe study was conducted as a scoping review, with its design and reporting guided by the PRISMA Extension for Scoping Reviews (PRISMA-ScR).
“This study was conducted as a scoping review, with its design and reporting guided by the PRISMA Extension for Scoping Reviews (PRISMA-ScR) (12).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] McGowan, 2020, Reporting scoping reviews—PRISMA ScR extension, J Clin Epidemiol. source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningTo ensure reproducibility, the raw data, cleaned text, analysis scripts, and result files were systematically stored and managed under version control.
“To ensure reproducibility, the raw data, cleaned text, analysis scripts, and result files were systematically stored and managed under version control.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataTopic 2 represents a behavioral self-regulation dimension characterized by weekly monitoring cycles and motivational strategies.
“Topic 2 (23.2%) included terms related to weeks, diabetes, behavioral regulation, motivational factors, and problem-solving capacity, representing a behavioral self-regulation dimension characterized by weekly monitoring cycles and motivational strategies.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThis combined procedure reduced reliance on any single algorithm and helped distinguish repeatedly documented behavior-support processes from isolated or study-specific observations.
“This combined procedure reduced reliance on any single algorithm and helped distinguish repeatedly documented behavior-support processes from isolated or study-specific observations.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataDigital wearable devices do not act solely through isolated technical functions; their value appears to lie in the combined operation of self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-efficacy enhancement.
“Evidence derived from topic modeling, semantic clustering, keyword co-occurrence analysis, and the original content of the included studies suggests that digital wearable devices do not act solely through isolated technical functions. Instead, their value appears to lie in the combined operation of self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe computational analyses identified recurring thematic and semantic structures across the corpus, whereas the final naming, conceptual interpretation, and theoretical classification of the pathways were completed by the research team.
“The computational analyses identified recurring thematic and semantic structures across the corpus, whereas the final naming, conceptual interpretation, and theoretical classification of the pathways were completed by the research team.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningMotivation activation, social support, and self-efficacy enhancement appear to be important for long-term maintenance.
“In addition, motivation activation, social support, and self-efficacy enhancement appear to be important for long-term maintenance.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThis gradient suggests that, although the association between digital wearable devices and exercise behavior itself was the most directly discussed in the literature, digital wearable devices were also consistently linked to foundational mechanisms such as self-monitoring and goal setting.
“This gradient suggests that, although the association between digital wearable devices and exercise behavior itself was the most directly discussed in the literature, digital wearable devices were also consistently linked to foundational mechanisms such as self-monitoring and goal setting.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningTopic weights, cluster membership, and co-occurrence frequencies reflect patterns of discussion within the included literature rather than the magnitude of clinical effects.
“Topic weights, cluster membership, and co-occurrence frequencies reflect patterns of discussion within the included literature rather than the magnitude of clinical effects.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe machine learning-assisted analyses were affected by the size and composition of the corpus, text preprocessing decisions, keyword dictionary construction, model selection, and parameter settings.
“Third, the machine learning–assisted analyses were affected by the size and composition of the corpus, text preprocessing decisions, keyword dictionary construction, model selection, and parameter settings.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningLDA relies primarily on patterns of word distribution and may not fully capture contextual meaning, whereas BERT-based clustering may be influenced by the characteristics of the pretrained language model and the selected clustering procedure.
“LDA relies primarily on patterns of word distribution and may not fully capture contextual meaning, whereas BERT-based clustering may be influenced by the characteristics of the pretrained language model and the selected clustering procedure.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningMachine learning-assisted text mining helped identify and quantify recurring thematic and semantic patterns across the literature, but these patterns should not be interpreted as evidence of causal mechanisms or comparative intervention effectiveness.
“Machine learning–assisted text mining helped identify and quantify recurring thematic and semantic patterns across the literature, but these patterns should not be interpreted as evidence of causal mechanisms or comparative intervention effectiveness.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe self-efficacy enhancement pathway is closely linked to the concept of self-efficacy within Social Cognitive Theory.
“In addition, the self-efficacy enhancement pathway is closely linked to the concept of self-efficacy within Social Cognitive Theory.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe principal value of the machine learning analyses was to enhance the transparency, consistency, and breadth of evidence mapping, while the interpretation of the identified pathways remained grounded in manual examination of the included studies and relevant behavior change theories.
“The principal value of the machine learning analyses was therefore to enhance the transparency, consistency, and breadth of evidence mapping, while the interpretation of the identified pathways remained grounded in manual examination of the included studies and relevant behavior change theories.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningCo-occurrence frequencies indicate that concepts appeared within the same textual units but do not establish the direction, strength, or causal nature of their relationships.
“Co-occurrence frequencies indicate that concepts appeared within the same textual units but do not establish the direction, strength, or causal nature of their relationships.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe frequency with which a pathway appeared in the literature should not be interpreted as evidence that the pathway is effective.
“Although descriptive indicators of methodological strength were extracted to contextualize the evidence, the frequency with which a pathway appeared in the literature should not be interpreted as evidence that the pathway is effective.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe functional design of digital wearable devices is not only technologically relevant, but also theoretically congruent with the psychological processes that underpin sustained behavior change.
“Taken together, these findings suggest that the functional design of digital wearable devices is not only technologically relevant, but also theoretically congruent with the psychological processes that underpin sustained behavior change.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe computational findings should not be interpreted as independent evidence of effectiveness or causality.
“Nevertheless, the computational findings should not be interpreted as independent evidence of effectiveness or causality.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningGiven the small number of included studies, generally modest sample sizes, and substantial heterogeneity in devices and intervention designs, the findings should be regarded as a preliminary conceptual mapping of an emerging evidence base.
“Given the small number of included studies, generally modest sample sizes, and substantial heterogeneity in devices and intervention designs, the findings should be regarded as a preliminary conceptual mapping of an emerging evidence base.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningAlthough manual validation by two researchers was used to improve interpretability, some degree of researcher judgment remained unavoidable in topic labeling and pathway classification.
“Although manual validation by two researchers was used to improve interpretability, some degree of researcher judgment remained unavoidable in topic labeling and pathway classification.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe available evidence suggests that digital wearable devices may support exercise self-management in people with T2DM through several interrelated behavior change pathways, including self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-efficacy enhancement.
“In conclusion, the available evidence suggests that digital wearable devices may support exercise self-management in people with T2DM through several interrelated behavior change pathways, including self-monitoring, real-time feedback, goal setting, motivation activation, social support, and self-efficacy enhancement.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenRestricting inclusion to studies published in English or Chinese may have resulted in language-related selection bias.
“Furthermore, restricting inclusion to studies published in English or Chinese may have resulted in language-related selection bias.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningNo formal risk-of-bias or methodological quality assessment was conducted.
“Second, consistent with the purpose of a scoping review, no formal risk-of-bias or methodological quality assessment was conducted.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Most previous studies have focused primarily on outcome measures and paid limited attention to the pathways through which digital wearable devices facilitate exercise self-management.
“Although previous studies have demonstrated the potential benefits of such devices, most have focused primarily on outcome measures, with limited attention paid to the pathways through which these technologies facilitate exercise self-management.”
The paper relies on this without support.
Evidence synthesis remains lacking on the behavior change pathways through which digital wearable devices support exercise self-management in people with T2DM from a theory-informed perspective.
“Nevertheless, evidence synthesis remains lacking on the behavior change pathways through which digital wearable devices support exercise self-management in people with T2DM from a theory-informed perspective.”
The paper relies on this without support.
The pathways by which digital wearable devices facilitate exercise behavior change remain insufficiently understood.
“However, the pathways by which these devices facilitate exercise behavior change remain insufficiently understood.”
The paper relies on this without support.
Digital wearable devices have increasingly been used to support diabetes management.
“With the advancement of digital health technologies, digital wearable devices have increasingly been used to support diabetes management.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Included study has a population excluded by the eligibility criteria
MinorTable 1 includes a study of adults with prediabetes or T2DM, although the exclusion criteria rule out studies involving populations other than patients with T2DM.
“Bailey K J, et al. (17) | 2016 | Canada | Pilot study | 13 | Adults with prediabetes or T2DM; aged 18–75 years”
“Studies were excluded if they met any of the following criteria: (1) animal studies, case reports, reviews, commentaries, or other non-original articles; (2) studies that did not focus on the effects of digital wearable devices on exercise self-management behaviors in patients with T2DM; (3) duplicate publications or studies with incomplete data; (4) studies for which the full text was unavailable; or (5) studies involving populations other than patients with T2DM.”
Cites
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742 · see citing works
Ntoumanis, N., Ng, J. Y. Y., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Lonsdale, C., & Williams, G. C. (2021) · Health Psychology Review · doi:10.1080/17437199.2020.1718529 · Cited by 1,154 · cited 1× · see citing works
“Motivation activation and social support may be interpreted through Self-Determination Theory, which emphasizes the importance of autonomy, competence, and relatedness in sustaining behavior (29).”
Genitsaridi, I., Salpea, P., Salim, A., Sajjadi, S. F., Tomic, D., James, S., Thirunavukkarasu, S., Issaka, A., Chen, L., Basit, A., et al. (2025) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(25)00299-2 · Cited by 717 · see citing works
Reporting scoping reviews-PRISMA ScR extension
McGowan, J., Straus, S., Moher, D., Langlois, E. V., O'Brien, K. K., Horsley, T., Aldcroft, A., Zarin, W., Garitty, C. M., Hempel, S., et al. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.016 · Cited by 494 · see citing works
Allen, N. A., Fain, J. A., Braun, B., & Chipkin, S. R. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.01.006 · Cited by 182 · cited 3× · see citing works
“Features such as individualized goals, progress visualization, reward feedback, family involvement, peer comparison, and clinician interaction may help address these psychological needs (14, 19, 20).”
The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review
Mattison, G., Canfell, O., Forrester, D., Dobbins, C., Smith, D., Töyräs, J., & Sullivan, C. (2022) · Journal of Medical Internet Research · doi:10.2196/36690 · Cited by 171 · see citing works
van der Weegen, S., Verwey, R., Spreeuwenberg, M., Tange, H., van der Weijden, T., & de Witte, L. (2013) · JMIR mhealth and uhealth · doi:10.2196/mhealth.2526 · Cited by 128 · see citing works
Asmat, K., Dhamani, K. A., Gul, R. B., & Froelicher, E. S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.994766 · Cited by 89 · see citing works
Bailey, K. J., Little, J. P., & Jung, M. E. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0285 · Cited by 70 · see citing works
Allen, N., Whittemore, R., & Melkus, G. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0088 · Cited by 59 · see citing works
Bilgin, A., Muz, G., & Yuce, G. E. (2022) · Patient Education and Counseling · doi:10.1016/j.pec.2022.04.008 · Cited by 50 · see citing works
Accelerometer feedback to promote physical activity in adults with type 2 diabetes: a pilot study
Paschali, A. A., Kalantzi-Azizi, A., Goodrick, G. K., Papadatou, D., & Balasubramanyam, A. (2005) · Perceptual and Motor Skills · doi:10.2466/pms.100.1.61-68 · Cited by 40 · see citing works
Fraser, R., Walker, R. J., Campbell, J., Ekwunife, O. I., & Egede, L. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02036-9 · Cited by 40 · see citing works
Asmat, K., Sivarajan Froelicher, E., Dhamani, K. A., Gul, R., & Khan, N. (2024) · Journal of Diabetes · doi:10.1111/1753-0407.13611 · Cited by 38 · see citing works
COOMBES, J. S., KEATING, S. E., MIELKE, G. I., FASSETT, R. G., COOMBES, B. K., O’LEARY, K. P., COX, E. R., & BURTON, N. W. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002768 · Cited by 32 · see citing works
Timurtas, E., Inceer, M., Mayo, N., Karabacak, N., Sertbas, Y., & Polat, M. G. (2022) · Primary care diabetes · doi:10.1016/j.pcd.2021.12.005 · Cited by 30 · see citing works
How valid is bandura's social cognitive theory to explain physical activity behavior?
Egele, V. S., Klopp, E., & Stark, R. (2025) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe15020020 · Cited by 30 · see citing works
Anisha, S. A., Sen, A., Ahmad, B., & Bain, C. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02166-3 · Cited by 25 · see citing works
Garstang, K. R., Jackman, P. C., Healy, L. C., Cooper, S. B., & Magistro, D. (2024) · Journal of Physical Activity and Health · doi:10.1123/jpah.2023-0340 · Cited by 20 · see citing works
Zhang, A., Wang, J., Wan, X., Zhang, Z., Zhao, S., Bai, S., Miao, Y., Yang, S., & Jiang, X. (2025) · JMIR mhealth and uhealth · doi:10.2196/60227 · Cited by 19 · see citing works
Pelletier, C., Gagnon, M.-P., Alméras, N., Després, J.-P., Poirier, P., Tremblay, A., Chabot, C., & Rhéaume, C. (2021) · mHealth · doi:10.21037/mhealth-20-154 · Cited by 15 · see citing works
Diez Alvarez, S., Fellas, A., Wynne, K., Santos, D., Sculley, D., Acharya, S., Navathe, P., Gironès, X., & Coda, A. (2024) · JMIR mhealth and uhealth · doi:10.2196/54826 · Cited by 15 · see citing works
Novák, J., Jurková, K., Lojkaskova, A., Jaklova, A., Kühnová, J., Pfeiferová, M., Král, N., Janek, M., Omcirk, D., Malisova, K., et al. (2024) · BMC Public Health · doi:10.1186/s12889-024-18384-2 · Cited by 14 · see citing works
Hohberg, V., Lichtenstein, E., Kreppke, J.-N., Zanitti, C., Streckmann, F., Gerber, M., & Faude, O. (2025) · Sports Medicine · doi:10.1007/s40279-025-02184-8 · Cited by 14 · see citing works
Liu, D., Zhang, Y., Wu, Q., Han, R., Cheng, D., Wu, L., Guo, J., Yu, X., Ge, W., Ni, J., et al. (2024) · Journal of Diabetes · doi:10.1111/1753-0407.13549 · Cited by 13 · see citing works
Kang, H. J., Wang, J. C. K., Burns, S. F., & Leow, M. K.-S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.627815 · Cited by 12 · see citing works
Chang, C. R., Roach, L. A., Russell, B. M., & Francois, M. E. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112072 · Cited by 11 · see citing works
Alòs, F., Colomer, M. À., Bort-Roig, J., Martin-Cantera, C., Minaya, A., Saigí, I., Sitjar-Suñer, M., & Puig-Ribera, A. (2025) · Primary Care Diabetes · doi:10.1016/j.pcd.2025.05.010 · Cited by 11 · see citing works
Bohnert, K. L., Zellers, J. A., Jeong, H. J., Chen, L., York, A., & Hastings, M. K. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221103610 · Cited by 10 · see citing works
Takano, K., Oba, T., Katahira, K., & Kimura, K. (2024) · JMIR mhealth and uhealth · doi:10.2196/51216 · Cited by 10 · see citing works
Hesketh, K., Low, J., Andrews, R., Blitz, S., Buckley, B., Falkenhain, K., Job, J., Jones, C. A., Jones, H., Jung, M. E., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-092260 · Cited by 9 · see citing works
Laffi, A., Persiani, M., Piras, A., Meoni, A., & Raffi, M. (2025) · Healthcare · doi:10.3390/healthcare13192422 · Cited by 9 · see citing works
Corrêa, R. S., Froner, M. B., & Tabak, B. M. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21070837 · Cited by 7 · see citing works
Gawlik, A., Nacak, Y., Kleinert, J., Konerding, U., & Vitinius, F. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20136271 · Cited by 6 · see citing works
Bridging UTAUT and HBM: determinants of wearable device adoption among chronic disease patients
Guo, Z., & Chen, S. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1687887 · Cited by 5 · see citing works
Abdul Latif el Ejel, B., Sattar, S., Fatima, S. B., Khan, H. N., Ali, H., Iftikhar, A., Sarwer, M. A., & Mushtaq, M. (2025) · Cureus · doi:10.7759/cureus.84177 · Cited by 4 · see citing works
The effect of real-time monitoring of physical activity intensity in diabetic patients
Tanaka, R., Fujita, K., Maeno, S., Yakushiji, K., Tanaka, S., Ohnaka, K., Ashida, K., Sakamoto, S., & Nomura, M. (2022) · Japan Journal of Nursing Science · doi:10.1111/jjns.12445 · Cited by 3 · see citing works
Peng, P., Shen, Y., & Xiong, H. (2024) · Postgraduate Medicine · doi:10.1080/00325481.2024.2366156 · Cited by 3 · see citing works
Behnke, A., Lucas, K. S., Parkin, C. G., Reddy, V., Orlowski, M., & Edwards, A. (2025) · Diabetes Spectrum · doi:10.2337/ds24-0085 · Cited by 3 · see citing works
Han, Z., & Wang, L. (2025) · Patient Preference and Adherence · doi:10.2147/ppa.s552727 · Cited by 2 · see citing works
Li, J.-Y., Azlan, A. A., Hassim, N., Wang, Y., & Guo, R. (2025) · Journal of Medical Internet Research · doi:10.2196/77788 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Still being checked.
- Authors
- Sun, W., Xie, D., Hou, W., Zhang, M., Wang, C., Wang, Z., Yang, H., Xu, G., & Dai, H.
- Affiliations
- Changchun University of Chinese Medicine (CN)
- School of Nursing, Changchun University of Chinese Medicine
- Jilin University (CN)
- Corresponding author
- Xie, Dong
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2026-07-30
- Volume
- 14
- Pages
- 1871682-1871682
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC13469597
- OpenAlex
- W7171840422
- Semantic Scholar
- 4f5e9af9108143311d58efd229b97f69839e9d16
- Cited by
- 0
- References
- 41
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- PubMed Central (submittedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 2 diabetes, physical activity, self-monitoring, real-time feedback, goal setting, social support, self-efficacy, blood glucose monitoring, physical activity monitoring, digital health interventions, behaviour change theory
- MeSH terms
- Machine Learning, Self-Management, Wearable Electronic Devices, Digital Health, Diabetes Mellitus, Type 2, Humans, Health Behavior, Exercise, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
