Increasing Physical Activity With Mobile Devices: A Meta-Analysis
Open accessAbstract
BACKGROUND: Regular physical activity has established physical and mental health benefits; however, merely one quarter of the U.S. adult population meets national physical activity recommendations. In an effort to engage individuals who do not meet these guidelines, researchers have utilized popular emerging technologies, including mobile devices (ie, personal digital assistants [PDAs], mobile phones). This study is the first to synthesize current research focused on the use of mobile devices for increasing physical activity. OBJECTIVE: To conduct a meta-analysis of research utilizing mobile devices to influence physical activity behavior. The aims of this review were to: (1) examine the efficacy of mobile devices in the physical activity setting, (2) explore and discuss implementation of device features across studies, and (3) make recommendations for future intervention development. METHODS: We searched electronic databases (PubMed, PsychINFO, SCOPUS) and identified publications through reference lists and requests to experts in the field of mobile health. Studies were included that provided original data and aimed to influence physical activity through dissemination or collection of intervention materials with a mobile device. Data were extracted to calculate effect sizes for individual studies, as were study descriptives. A random effects meta-analysis was conducted using the Comprehensive Meta-Analysis software suite. Study quality was assessed using the quality of execution portion of the Guide to Community Preventative Services data extraction form. RESULTS: Four studies were of "good" quality and seven of "fair" quality. In total, 1351 individuals participated in 11 unique studies from which 18 effects were extracted and synthesized, yielding an overall weight mean effect size of g = 0.54 (95% CI = 0.17 to 0.91, P = .01). CONCLUSIONS: Research utilizing mobile devices is gaining in popularity, and this study suggests that this platform is an effective means for influencing physical activity behavior. Our focus must be on the best possible use of these tools to measure and understand behavior. Therefore, theoretically grounded behavior change interventions that recognize and act on the potential of smartphone technology could provide investigators with an effective tool for increasing physical activity.
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mHealth interventions have been significantly more effective than comparison conditions for physical activity (Hedge g=0.54; 95% CI 0.17, 0.91).
“In particular, mHealth interventions have been significantly more effective than comparison conditions for physical activity (Hedge g=0.54; 95% CIs 0.17, 0.91] [9] and led to significant improvement in diabetes management (mean 0.5% reduction in HbA1c) [104].”
From The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review (2019) · cites this paper as [9]
Previous syntheses either focused on a specific health issue or were constrained by small sample sizes and a lack of moderator analyses, leaving what factors account for variance in effect sizes unanswered.
“However, previous syntheses either focused on a specific health issue [9,10] or were constrained by small sample sizes and a lack of moderator analyses [3,4], leaving what factors account for variance in ESs unanswered.”
From The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review (2019) · cites this paper as [9]
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- Authors
- Fanning, J., Mullen, S. P., & McAuley, E.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Fanning, Jason
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2012-11-21
- Volume
- 14
- Issue
- 6
- Pages
- e161
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3514847
- OpenAlex
- W1994463929
- Cited by
- 457
- References
- 38
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 27 · 2023: 20 · 2022: 22 · 2021: 39 · 2020: 35 · 2019: 45 · 2018: 58 · 2017: 50 · 2016: 54 · 2015: 36 · 2014: 28 · 2013: 18
- FWCI
- 3.88
- 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)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (5R01AG020118-6, R01AG020118)
- 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
- physical activity, mobile devices, mobile health, smartphones, behaviour change interventions, physical activity promotion, device characteristics
- MeSH terms
- Humans, Motor Activity, Internet, Computers, Handheld, Cell Phone
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