The Effectiveness of a Web-Based Computer-Tailored Physical Activity Intervention Using Fitbit Activity Trackers: Randomized Trial
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Abstract
BACKGROUND: Web-based interventions that provide personalized physical activity advice have demonstrated good effectiveness but rely on self-reported measures of physical activity, which are prone to overreporting, potentially reducing the accuracy and effectiveness of the advice provided. OBJECTIVE: This study aimed to examine whether the effectiveness of a Web-based computer-tailored intervention could be improved by integrating Fitbit activity trackers. METHODS: Participants received the 3-month TaylorActive intervention, which included 8 modules of theory-based, personally tailored physical activity advice and action planning. Participants were randomized to receive the same intervention either with or without Fitbit tracker integration. All intervention materials were delivered on the Web, and there was no face-to-face contact at any time point. Changes in physical activity (Active Australia Survey), sitting time (Workforce Sitting Questionnaire), and body mass index (BMI) were assessed 1 and 3 months post baseline. Advice acceptability, website usability, and module completion were also assessed. RESULTS: A total of 243 Australian adults participated. Linear mixed model analyses showed a significant increase in total weekly physical activity (adjusted mean increase=163.2; 95% CI 52.0-274.5; P=.004) and moderate-to-vigorous physical activity (adjusted mean increase=78.6; 95% CI 24.4-131.9; P=.004) in the Fitbit group compared with the non-Fitbit group at the 3-month follow-up. The sitting time and BMI decreased more in the Fitbit group, but no significant group × time interaction effects were found. The physical activity advice acceptability and the website usability were consistently rated higher by participants in the Fitbit group. Non-Fitbit group participants completed 2.9 (SD 2.5) modules, and Fitbit group participants completed 4.4 (SD 3.1) modules. CONCLUSIONS: Integrating physical activity trackers into a Web-based computer-tailored intervention significantly increased intervention effectiveness. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616001555448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371793 (Archived by WebCite at http://www.webcitation.org/73ioTxQX2).
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The magnitude of change in physical activity was similar to or larger than that achieved in previous computer-tailoring interventions.
“The magnitude of change in physical activity was similar to [36,37] or larger than [38,39] that achieved in previous computer-tailoring interventions.”
From A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study (2020) · cites this paper as [36]
Blinding of outcome assessment was assessed as high risk of bias in two studies because of subjective measures and because the authors clearly mentioned that it was an unblinded clinical trial.
“This was assessed as being at high risk of bias in two studies because of the use of subjective measures [104] and because the authors clearly mentioned that it was an unblinded clinical trial [90].”
From Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis (2020) · cites this paper as [104]
Sedentary behavior was measured objectively, except in two studies that utilized a self-reported questionnaire to obtain daily sitting time or sedentary behavior.
“Sedentary behavior was measured objectively, except in two studies [82,104] that utilized a self-reported questionnaire to obtain daily sitting time or sedentary behavior.”
From Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis (2020) · cites this paper as [104]
Previous studies have shown higher usability of Fitbit use compared with this study.
“Previous studies have also shown higher usability of Fitbit use [38-40] compared with this study.”
From Feasibility, Usability, and Effectiveness of a Machine Learning–Based Physical Activity Chatbot: Quasi-Experimental Study (2021) · cites this paper as [38]
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- Authors
- Vandelanotte, C., Duncan, M. J., Maher, C. A., Schoeppe, S., Rebar, A. L., Power, D. A., Short, C. E., Doran, C. M., Hayman, M. J., & Alley, S. J.
- Affiliations
- Central Queensland University (AU)
- University of Newcastle Australia (AU)
- University of South Australia (AU)
- Freemasons Foundation Centre for Men's Health (AU)
- Corresponding author
- Vandelanotte, Corneel
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-12-18
- Volume
- 20
- Issue
- 12
- Pages
- e11321
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6315269
- OpenAlex
- W2889992659
- Cited by
- 68
- References
- 42
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 10 · 2023: 6 · 2022: 7 · 2021: 14 · 2020: 18 · 2019: 5
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Acquire (CQUniversity) (submittedVersion, cc-by)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion, cc-by)
- Funders
- National Heart Foundation of Australia (ID: 100029, ID100427, ID101240, 100427, 101240, 1105926, 100029, 1090517)
- Central Queensland University
- Medical Research Council
- National Health and Medical Research Council (100029, 100427, 1105926, 1090517, 1125586, 1125913, 101240)
- 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
- computer-tailored intervention, online intervention, Fitbit, wearable activity trackers, physical activity promotion, action planning, sitting time, body mass index, moderate-to-vigorous physical activity, website usability
- MeSH terms
- Fitness Trackers, Australia, Female, Humans, Male, Middle Aged, Exercise, Internet
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