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Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review
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Abstract
BACKGROUND: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users' interaction and usage behavior) and acceptability (ie, users' subjective perceptions and experiences) to help explain how and why apps and wearables work. OBJECTIVES: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. METHOD: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. RESULTS: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). CONCLUSIONS: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines.
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“However, knowledge on these apps’ potential is lacking, particularly in regard to testing apps in practical settings and using objective data, and health apps generally are not evidence-based [63].”
From Advantages and disadvantages of mobile applications for workplace health promotion: A scoping review (2024) · cites this paper as [63]
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“However, knowledge on these apps’ potential is lacking, particularly in regard to testing apps in practical settings and using objective data, and health apps generally are not evidence-based [63].”
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BackgroundPatel, M. S., Asch, D. A., Rosin, R. M., Small, D. S., Bellamy, S. L., Eberbach, K., Walters, K. J., Haff, N., Lee, S. M., Wesby, L., et al. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3627-0 · Cited by 148
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Sample size calculations for micro‐randomized trials in mHealth
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Mobile Diary for Wellness Management—Results on Usage and Usability in Two User Studies
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BackgroundDesigning motivation using persuasive ambient mirrors
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Flowie: A persuasive virtual coach to motivate elderly individuals to walk
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Understanding User Behavior Through Log Data and Analysis
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Early recovery from alcohol dependence: factors that promote or impede abstinence.
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Power in Bayesian Mediation Analysis for Small Sample Research
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“Go4Life” exercise counseling, accelerometer feedback, and activity levels in older people
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What Ever Happened to N‐of‐1 Trials? Insiders' Perspectives and a Look to the Future
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Telemetry Sampling in Game Development
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Introduction: Changing the Game
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A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial
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Technology- and Phone-Based Weight Loss Intervention
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BackgroundAssessment of a Mobile Game (“MobileKids Monster Manor”) to Promote Physical Activity Among Children
Garde, A., Umedaly, A., Abulnaga, S. M., Robertson, L., Junker, A., Chanoine, J., Ansermino, J. M., & Dumont, G. A. (2015) · Games for Health Journal · doi:10.1089/g4h.2014.0095 · Cited by 85
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Increasing physical activity through mobile device interventions: A systematic review
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Motivational cues as real-time feedback for changing daily activity behavior of patients with COPD
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Examining Factors of Engagement With Digital Interventions for Weight Management: Rapid Review
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Harnessing social dynamics through persuasive technology to promote healthier lifestyle
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Context awareness in a handheld exercise agent
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Evaluation of a Novel Mobile Exergame in a School-Based Environment
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Walking in the Wild – Using an Always-On Smartphone Application to Increase Physical Activity
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Impact of a Digital Activity Tracker-Based Workplace Activity Program on Health and Wellbeing
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Stairstep recognition and counting in a serious Game for increasing users’ physical activity
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Supporting Users in Setting Effective Goals in Activity Tracking
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Design Considerations for Social Fitness Applications
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Using Degraded Music Quality to Encourage a Health Improving Walking Pace: BeatClearWalker
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Feasibility and Acceptability of Remotely Monitored Pedometer-Guided Physical Activity
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Metadata
- Authors
- McCallum, C., Rooksby, J., & Gray, C. M.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- McCallum, Claire
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-03-23
- Volume
- 6
- Issue
- 3
- Pages
- e58
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5889496
- OpenAlex
- W2789449717
- Cited by
- 126
- References
- 207
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 9 · 2023: 18 · 2022: 22 · 2021: 22 · 2020: 18 · 2019: 14 · 2018: 9
- FWCI
- 2.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Northumbria Research Link (Northumbria University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- physical activity apps, wearable devices, mobile health, single-case experimental design, multiphase optimization strategy, user engagement, acceptability, effectiveness, accelerometer, research design evaluation, physical activity monitoring
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.
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