Personal activity intelligence e-health program in people with type 2 diabetes: a pilot randomized controlled trial
Open accessAbstract
INTRODUCTION: Innovative strategies are needed to enable people with type 2 diabetes (T2D) to self-manage physical activity (PA). Personal Activity Intelligence (PAI) is a new metric that uses the heart rate response to PA to inform the user as to whether they are doing enough PA to reduce the risk of premature mortality. The PAI score reflects PA over the previous 7 d with the goal to maintain a score ≥100. The aim of this study was to investigate the feasibility, acceptability, and efficacy of the PAI e-Health Program in people with T2D. METHODS: Thirty participants with T2D who were not meeting PA guidelines were randomly assigned to 12 wk of either 1) PAI e-Health Program or 2) PA attention control. The PAI e-Health Program consisted of receiving a wrist-worn heart rate monitor and an app with the PAI metric, and attending 4 × 2 h·wk-1 sessions of exercise and counseling. Feasibility and acceptability of the program were evaluated by achievement of a PAI score ≥100 and participant feedback. Efficacy was determined from changes in glycemic control, cardiorespiratory fitness, exercise capacity (time-on-test), body composition, sleep time, and health-related quality of life. RESULTS: Program participants in the PAI e-Health Program had a mean ± SD PAI score of 119.7 ± 60.6 and achieved ≥100 PAI on 56.4% of the days. The majority of participants (80%) intended to continue to use PAI monitoring. Compared with control, the PAI group significantly improved their exercise capacity (mean difference, 95% confidence interval) (63 s, 17.9-108.0 s), sleep time (67.2 min, 7.2-127.1 min), total percent body fat (-1.3%, -2.6% to -0.1%), and gynoid fat percent (-1.5%, -2.6 to -0.5). CONCLUSIONS: The PAI e-Health Program is feasible, acceptable, and efficacious in people with T2D.
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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).”
From 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 (2026) · cites this paper as [18]
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)
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References 35 · Cited by 32
- Authors
- 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.
- Affiliations
- The University of Queensland (AU)
- Centre for Research on Exercise, Physical Activity and Health, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, Queensland, AUSTRALIA
- Griffith University (AU)
- ORCID
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2021-08-02
- Volume
- 54
- Issue
- 1
- Pages
- 18-27
- ISSN
- 0195-9131 (ISSN-L)
- 1530-0315
- OpenAlex
- W3189705390
- Cited by
- 32
- References
- 35
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 6 · 2021: 3
- FWCI
- 6.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion)
- Funders
- Diabetes Australia
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- type 2 diabetes, physical activity, heart rate monitoring, self-management, exercise capacity, sleep duration, body composition, glycemic control, cardiorespiratory fitness, health-related quality of life, feasibility and acceptability
- MeSH terms
- Aged, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Exercise Therapy, Feasibility Studies, Female, Health Promotion, Humans, Male, Middle Aged, Monitoring, Physiologic, Pilot Projects, Quality of Life, Exercise, Telemedicine, Accelerometry
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