Self-monitoring using continuous glucose monitors with real-time feedback improves exercise adherence in individuals with impaired blood glucose: a pilot study
Abstract
BACKGROUND: Exercise helps individuals with prediabetes or type 2 diabetes (T2D) manage their blood glucose (BG); however, exercise adherence in this population is dismal. In this pilot study we tested the efficacy of a self-monitoring group-based intervention using continuous glucose monitors (CGMs) at increasing exercise adherence in individuals with impaired BG. MATERIALS AND METHODS: Thirteen participants with prediabetes or T2D were randomized to an 8-week standard care exercise program (CON condition) (n = 7) or self-monitoring exercise intervention (SM condition) (n = 6). Participants in the SM condition were taught how to self-monitor their exercise and BG, to goal set, and to use CGM to observe how exercise influences BG. We hypothesized that compared with the CON condition, using a real-time CGM would facilitate self-monitoring behavior, resulting in increased exercise adherence. RESULTS: Repeated-measures analysis of variance revealed significant Condition × Time interactions for self-monitoring (P < 0.01), goal setting (P = 0.01), and self-efficacy to self-monitor (P = 0.01), such that the SM condition showed greater increases in these outcomes immediately after the program and at the 1-month follow-up compared with the CON condition. The SM condition had higher program attendance rates (P = 0.03), and a greater proportion of participants reregistered for additional exercise programs (P = 0.048) compared with the CON condition. Participants in both conditions experienced improvements in health-related quality of life, waist circumference, and fitness (P values <0.05). CONCLUSIONS: These findings provide promising initial support for the use of a real-time CGM to foster self-monitoring and exercise behavior in individuals living with prediabetes or T2D.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Goal 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).”
Such 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).”
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.
“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).”
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.
“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).”
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).”
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).”
The citation lists haven’t been fetched yet
References 62 · Cited by 70
- Authors
- Bailey, K. J., Little, J. P., & Jung, M. E.
- Affiliations
- University of British Columbia (CA)
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2016-03
- Volume
- 18
- Issue
- 3
- Pages
- 185-193
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2278254263
- Cited by
- 70
- References
- 62
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 7 · 2023: 7 · 2022: 2 · 2021: 4 · 2020: 4 · 2019: 7 · 2018: 6 · 2017: 1 · 2016: 2
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- continuous glucose monitoring, exercise adherence, prediabetes, type 2 diabetes, self-monitoring, real-time feedback, goal setting, self-efficacy, blood glucose, health-related quality of life, waist circumference, physical fitness
- MeSH terms
- Aged, Blood Glucose, British Columbia, Diabetes Mellitus, Type 2, Feasibility Studies, Follow-Up Studies, Goals, Group Processes, Humans, Male, Middle Aged, Patient Compliance, Patient Education as Topic, Physical Fitness, Pilot Projects, Prediabetic State, Quality of Life, Exercise, Monitoring, Ambulatory, Self Efficacy, Feedback, Psychological, Waist Circumference
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.
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
