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Cited by 15Physical Activity and HealthMobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial

Open access
Pelletier, Cynthia, Gagnon, Marie-Pierre, Alméras, Natalie, Després, Jean-Pierre, Poirier, Paul, Tremblay, Angelo, Chabot, Christian, Rhéaume, Caroline·2021·mHealth
DOI 10.21037/mhealth-20-154PMC PMC8572757
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Abstract

BACKGROUND: Adopting healthy lifestyle habits reduces the risk of type 2 diabetes (T2D) and its complications. The use of an activity tracker to monitor physical activity (PA) could favor behavior changes in patients with chronic diseases such as diabetes. The aims of this study were: (I) to evaluate the impact of an activity tracker on PA and cardiometabolic risk variables in patients with T2D; (II) to assess the feasibility of its implantation in a primary care setting. METHODS: This 3-month study was a pilot randomized controlled trial of 30 patients with T2D followed at a university-affiliated Family Medicine Group. Patients were randomly assigned to either: (I) control group, including a PA promotion intervention supported by a kinesiologist or (II) intervention group, including a PA promotion intervention supported by a kinesiologist with the addition of an activity tracker (Fitbit). Cardiometabolic risk variables, PA and motivation were assessed at baseline and after three months. Satisfaction and acceptability of wearing the activity tracker were measured in the intervention group. RESULTS: PA assessed by questionnaires increased in both groups, change being greater in the intervention group (P<0.05). Autonomous motivation in both groups was higher than controlled motivation (P<0.001). Eighty-six percent of the participants in the intervention group were satisfied with their activity tracker use and the compliance remained high. High-density lipoprotein cholesterol increased in the intervention group and decreased in the control group (P=0.014). Resting systolic and diastolic blood pressure decreased over time in both groups (P<0.05) whereas glycated hemoglobin tended to decrease in both groups (P=0.080). Significant correlations were observed between average steps per day and changes in waist circumference (pre: -0.721, P=0.044; post: -0.736, P=0.038), body mass index (pre: -0.764, P=0.010; post: -0.771, P=0.009) and fat percentage (pre: -0.654, P=0.040; post: -0.686, P=0.028) in the intervention group. CONCLUSIONS: Our pilot study shows that the use of an activity tracker improves cardiometabolic risk variables in patients with T2D and could potentially be a motivation tool to increase PA in primary care setting.

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  • 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).”

    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 [21]

    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

  • Reward mechanisms, progress tracking, and achievement feedback may help sustain engagement.

    “Reward mechanisms, progress tracking, and achievement feedback may help sustain engagement (15, 21–24)”

    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 [21]

    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

  • 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).”

    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 [21]

    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

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Authors
Pelletier, C., Gagnon, M.-P., Alméras, N., Després, J.-P., Poirier, P., Tremblay, A., Chabot, C., & Rhéaume, C.
Affiliations
  • Université Laval (CA)
  • Institut universitaire de cardiologie et de pneumologie de Québec (CA)
Corresponding author
Rhéaume, Caroline
ORCID
Venue
mHealth
Publisher
AME Publishing Company
Published
2021-10
Volume
7
Pages
59-59
ISSN
2306-9740 (ISSN-L)
DOI
10.21037/mhealth-20-154
PMC
PMC8572757
OpenAlex
W3162846250
Cited by
15
References
52
Citations by year
2026: 4 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 2
FWCI
1.26
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • mHealth (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval
Topics
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
type 2 diabetes, wearable activity trackers, Fitbit, physical activity promotion, autonomous motivation, cardiometabolic risk, primary care, HDL cholesterol, blood pressure, HbA1c, body composition, feasibility, wearable devices, lifestyle intervention, step count, waist circumference, body mass index, body fat percentage, patient satisfaction, acceptability, adherence, general practice

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