Cited by 400Mobile Health and mHealth ApplicationsPhysical Activity and HealthContext-Aware Activity Recognition Systems

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Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression

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Laranjo, Liliana, Ding, Ding, Heleno, Bruno, Kocaballi, Baki, Quiroz, Juan C, Tong, Huong Ly, Chahwan, Bahia, Neves, Ana Luisa, Gabarron, Elia, Dao, Kim Phuong, Rodrigues, David, Neves, Gisela Costa, Antunes, Maria L, Coiera, Enrico, Bates, David W·2021·British Journal of Sports Medicine

DOI 10.1136/bjsports-2020-102892

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Abstract

Objective: To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback. Design: Systematic review and meta-analysis. Data sources: PubMed and seven additional databases, from 2007 to 2020. Study selection: Randomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted. Data extraction and synthesis: We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses. Main outcome measures: Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents. Results: Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I 2 =69%, T 2 =0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression. Conclusion: Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.

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Authors: Laranjo, L., Ding, D., Heleno, B., Kocaballi, B., Quiroz, J. C., Tong, H. L., Chahwan, B., Neves, A. L., Gabarron, E., Dao, K. P., Rodrigues, D., Neves, G. C., Antunes, M. L., Coiera, E., & Bates, D. W.
Affiliations: The University of Sydney (AU)
Universidade Nova de Lisboa (PT)
UNSW Sydney (AU)
Macquarie University (AU)
Brigham and Women's Hospital (US)
Corresponding author: Laranjo, Liliana
ORCID: Laranjo, Liliana
Ding, Ding
Heleno, Bruno
Quiroz, Juan C
Tong, Huong Ly
Dao, Kim Phuong
Coiera, Enrico
Bates, David W
Venue: British Journal of Sports Medicine
Publisher: BMJ
Published: 2020-12-21
Volume: 55
Issue: 8
Pages: 422-432
ISSN: 0306-3674 (ISSN-L)
1473-0480
DOI: 10.1136/bjsports-2020-102892
OpenAlex: W3113845499
Cited by: 400
References: 91
Citations by year: 2026: 55 · 2025: 80 · 2024: 102 · 2023: 54 · 2022: 75 · 2021: 34
FWCI: 7.77
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: British Journal of Sports Medicine (publishedVersion)
PubMed (publishedVersion)
Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
Repository of Digital Objects for Teaching Research and Culture (University of Valencia) (submittedVersion, other-oa)
Zenodo (CERN European Organization for Nuclear Research) (cc-by)
Funders: National Health and Medical Research Council (APP1134919)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: mobile health apps, wearable activity trackers, physical activity promotion, self-monitoring, feedback, step count, moderate-to-vigorous physical activity, text messaging, personalization, adults
MeSH terms: Smartphone, Fitness Trackers, Adult, Feedback, Humans, Regression Analysis, Health Behavior, Exercise, Mobile Applications

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Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression

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