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Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression
Open accessAbstract
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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“Several systematic reviews and meta-analyses [15–19] have shown that these digital technology-based programs resulted in increased PA and reduced body weight, at least for a short duration.”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [19]
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“ulatory BCTs is growing [70, 74, 75], our results suggest that such interventions may be of little benefit to partici-”
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“[85] linked four BCTs or BCT clusters to intervention effectiveness; text messaging, personalization, graded tasks, and goals and planning.”
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“nonindividualized interventions has been repeatedly demonstrated in various populations [30,38,39], especially in adults [40], but not yet in children or adolescents, although several randomized controlled trials address this matter.”
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“[4], 25 of the 28 included studies found an increase in physical activity following an intervention carried out on an application on a mobile phone.”
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“Digital interventions such as activity monitors, smartphone and tablet applications (apps), automated Short Message Service (SMS), chatbots, and computer-tailored advice are effective in the general population [8, 9] and show promise in older adults [10, 11].”
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“Other previous reviews have focused more broadly on wearable devices [16,17] and their use in specific diseases such as cardiovascular disease [18], diabetes [19], depression [20], or ordinary trackers for physical activity promotion [21,22].”
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Bizzozero‐Peroni, B., Goñi, V. D., Jiménez‐López, E., Rodríguez‐Gutiérrez, E., Sequí‐Domínguez, I., de Arenas‐Arroyo, S. N., López‐Gil, J. F., Martínez‐Vizcaíno, V., & Mesas, A. E. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.51208 · Cited by 62
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“The BCTs included in these categories are consistent with reviews in eHealth interventions in CVD [13] and non-CVD populations [9, 52].”
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“20 It also aligns with findings from other trials evaluating various PA interventions across a range of populations and settings, including patients with chronic conditions 23 as well as healthy 21 and older 22 adults in community settings.”
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“Moreover, existing research has not examined the use of fitness trackers, which have been known to have a positive impact on health behaviors [18,20,21].”
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“Several studies [34,71,72] confirmed that hybrid intervention modes were better than single intervention modes.”
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“Equipped with built-in sensors that can monitor and encourage PA in real time, they can facilitate sending personalized reminders and motivational messages [7-10], which have been proven to significantly increase PA [10-12].”
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“Similarly, the meta-analysis by Laranjo et al [91] found that mobile apps and WATs caused small to moderate increases in PA (equivalent to 1850 steps per day) among healthy adult populations.”
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“Several systematic reviews with meta-analysis have established that WAT-use is effective to increase PA [22, 23, 26] which in turn might improve cardiometabolic health and mobility [24, 25].”
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“progress and set goals, and have been shown to increase physical activity.(62,63) However, although technology has the potential to increase accessibility to physical activity support, inequities remain.”
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“A further meta-analysis showed that app-based interventions to increase physical activity are more effective when they include personal components such as face-to-face sessions, phone calls or text messages from real coaches or therapists [31].”
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“Although some systematic reviews have reported a positive effect of mobile technologies on behavioral outcomes [18,20-31], none of these studies have focused on evidence from the MENA region.”
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“…meaningful behaviour change, supported by self-monitoring of diet, physical activity, and/or weight via digital health, being associated with improved diet and physical activity or weight loss in adults with overweight or obesity (Teasdale et al. , 2018; Patel et al. , 2021; Laranjo et al. , 2021).”
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“However, as many factors vary between people, and even within the same person over time,(25) research has suggested that a one-size-fits-all approach is insufficient, and personalized mobile apps can lead to greater physical activity increases, compared to non-personalized apps.(17,26,27) Several meta-analyses suggest…”
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Smartphone Applications to Prevent Type 2 Diabetes: A Systematic Review and Meta-Analysis
Jahan, E., Almansour, R., Ijaz, K., Baptista, S. N., Giordan, L. B., Ronto, R., Zaman, S. J., O’Hagan, E. T., & Laranjo, L. (2024) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2024.01.008 · Cited by 27
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“WATs have been used in interventions to promote PA and systematic reviews have shown that they can be effective in increasing PA levels in healthy adults [25], older adults [19], individuals with rheumatic and musculoskeletal diseases [26], and other chronic diseases [27].”
MethodDelobelle, J., Lebuf, E., Dyck, D. V., Compernolle, S., Janek, M., Backere, F. D., & Větrovský, T. (2024) · Digital Health · doi:10.1177/20552076241262710 · Cited by 24
“61 However, frequent syncing is of utmost importance for adequately gathering real-time data and for preventing data loss.”
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Zhang, N., Zhou, M., Li, M., & Ma, G. (2023) · Journal of Medical Internet Research · doi:10.2196/41926 · Cited by 21
“A study conducted by Laranjo et al [19] found that interventions using a combination of mobile apps and physical activity trackers could effectively promote physical activity.”
Wearable activity trackers and artificial intelligence in the management of rheumatic diseases
Davergne, T., Kedra, J., & Gossec, L. (2021) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-021-01100-5 · Cited by 20
Exercise Recommendations and Practical Considerations for Asthma Management—An EAACI Position Paper
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“…suggests that exercise interventions involving mobile apps or trackers with automated and continuous self-monitoring and feedback (particularly those including text-messaging and personalised features) lead to a significant improvement in step-based physical activity in healthy adults [116].”
König, L., & Suhr, R. (2023) · Journal of Medical Internet Research · doi:10.2196/46336 · Cited by 16
“Various studies have shown that health apps can promote both the physical [8] and mental health [9] of their users.”
Kamada, M., HAYASHI, H., Shiba, K., TAGURI, M., KONDO, N., Lee, I.-M. M., & Kawachi, I. I. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002770 · Cited by 22
Tabira, K., Oguma, Y., Yoshihara, S., Shibuya, M., Nakamura, M., Doihara, N., Hirata, A., & Manabe, T. (2024) · JMIR Aging · doi:10.2196/56184 · Cited by 18
“Recently, mobile apps have been used successfully to increase physical activity levels [12,13]. eHealth encompasses health care services and information delivered with the aid of information and communication technology (ICT), including computers, mobile phones, and satellite communications.”
BackgroundWons, O. B., Lampe, E. W., Patarinski, A. G. G., Schaumberg, K., Butryn, M. L., & Juarascio, A. S. (2022) · Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · doi:10.1007/s40519-022-01466-8 · Cited by 17
Moccia, S., Solbiati, S., Khornegah, M., Bossi, F., & Caiani, E. G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106753 · Cited by 17
Anderberg, I., Kemps, E., & Prichard, I. (2025) · Body Image · doi:10.1016/j.bodyim.2024.101836 · Cited by 16
Oliveira, J. S., Sherrington, C., Rissel, C. E., Howard, K., Tong, A., Merom, D., Wickham, J. B., Bauman, A. E., Lord, S. R., Lindley, R. I., et al. (2024) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-107027 · Cited by 19
Kardan, M., Jung, A., Iqbal, M., Keshtkar, S., Geidl, W., & Pfeifer, K. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00097-6 · Cited by 19
“This finding is consistent with previous reviews in this field that have focused on adults [91] and on specific populations, such as individuals with CVDs [50] and COPD [34].”
ResultSaiz-González, P., Hassan, M. A. S., & Gao, Z. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11164798 · Cited by 16
“Additionally, PA interventions comprising wearables and smartphone applications seem to be effective to promote PA [18,19].”
BackgroundResultCasanova‐Lizón, A., Manresa‐Rocamora, A., Sarabia, J. M., Pastor, D., Javaloyes, A., Peña‐González, I., & Moya‐Ramón, M. (2025) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2025.1578478 · Cited by 18
“Furthermore, a systematic review with meta-analysis suggests that the impact on VO 2 peak is greater when mobile apps include features such as continuous monitoring (65) and automated feedback (66).”
Brierley, M. L., Chater, A., Edwardson, C. L., Castle, E. M., Hunt, E. R., Biddle, S., Sisodia, R., & Bailey, D. P. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01336-6 · Cited by 17
“Tailoring interventions to the individual (e.g., through personalised smartphone app features and health coaching with a trained professional) are more likely to lead to improved physical activity and HbA1c outcomes [25, 26].”
BackgroundCross, T. J., Isautier, J., Morris, S. J., Johnson, B. D., Wheatley, C. M., & Taylor, B. J. (2021) · JMIR Public Health and Surveillance · doi:10.2196/31278 · Cited by 15
Truong, L. K., Mosewich, A. D., Miciak, M., Pajkic, A., Silvester-Lee, T., LI, L. C., & Whittaker, J. L. (2022) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2022.100333 · Cited by 16
Mohan, S., & Jenkins, J. (2025) · Oxford Open Digital Health · doi:10.1093/oodh/oqaf011 · Cited by 17
“The participants emphasized the importance of customizing the app’s content to align with the users’ goals, as it helps to create a more comfortable user experience [64].”
Zhang, D., Huang, J., Zhang, Y. H., Wei, Z., Long, T., Guo, X., & Li, M. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112907 · Cited by 15
Soulard, J., Carlin, T., Knitza, J., & Vuillerme, N. (2022) · JMIR mhealth and uhealth · doi:10.2196/34734 · Cited by 15
“Indeed, trackers can also be used as an intervention to motivate users to increase physical activity and decrease sedentary behavior [17,64,83-85] and could further prevent inactivity- or sedentary-related diseases (eg, cardiovascular diseases) [12].”
BackgroundNovák, J., Jurková, K., Lojkaskova, A., Jaklova, A., Kühnová, J., Pfeiferová, M., Král, N., Janek, M., Omcirk, D., Malisova, K., et al. (2024) · BMC Public Health · doi:10.1186/s12889-024-18384-2 · Cited by 15
“To broaden accessibility, including for those with limited technology literacy, the mHealth intervention is delivered in the form of text messages [16, 71].”
BackgroundInfluentialBello, B., Hartley, S. E., & Yeowell, G. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266765 · Cited by 15
“Evidence has also highlighted the benefits of technology for enhancing physical activity promotion including smartphone apps and activity trackers [53, 54].”
BackgroundSwift, D. L., Ross, L. M., Laddu, D. R., Conroy, M. B., German, C. A., Evangelista, L. S., Marvel, F. A., Jerome, G. J., on behalf the American Heart Association Council on Lifestyle and Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, et al. (2026) · Circulation · doi:10.1161/cir.0000000000001441 · Cited by 13
Vos, A. L., de Bruijn, G., Klein, M. C. A., Lakerveld, J., Boerman, S. C., & Smit, E. G. (2023) · JMIR Formative Research · doi:10.2196/40851 · Cited by 13
“Nevertheless, meta-analyses have indicated that app-based PA interventions can significantly increase the daily step count in adult populations [74-76].”
BackgroundDigital technologies for step counting: between promises of reliability and risks of reductionism
Angelucci, A., Canali, S., & Aliverti, A. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1330189 · Cited by 13
Technology-Supported Behavior Change—Applying Design Thinking to mHealth Application Development
Schweitzer, R., Schlögl, S., & Schweitzer, M. (2024) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe14030039 · Cited by 19
“However, most of these positive effects have only been observed in the short-term, after which, high dropout rates are reported [18,23,24].”
InfluentialValidity of mHealth devices for counting steps in individuals with Parkinson's disease
Lana, R. D. C., de Paula, A. R., Silva, A. F. S., Costa, P. H. V., & Polese, J. C. (2021) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2021.06.018 · Cited by 12
Hodkinson, A., Kontopantelis, E., Zghebi, S. S., Grigoroglou, C., McMillan, B., van Marwijk, H., Bower, P., Tsimpida, D., Emery, C. F., Burge, M. R., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/36337 · Cited by 14
“Wearable physical activity trackers, such as accelerometers, pedometers, and the Fitbit (Fitbit Inc), are portable electrical or electromechanical devices that count each step a person takes by detecting the motion of the person along the body’s long axis [7,8].”
Yoshimura, E., Tajiri, E., Michiwaki, R., Matsumoto, N., Hatamoto, Y., & Tanaka, S. (2022) · JMIR mhealth and uhealth · doi:10.2196/35628 · Cited by 14
Technology obsolescence across the adult lifespan in a USA internet sample
Gray, N., & Charness, N. H. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1005822 · Cited by 13
“Mobile devices can be used to monitor health in ways that can increase effectiveness of health interventions (18).”
BackgroundAng, G., Edney, S. M., Tan, C. S., Lim, N., Tan, J., Müller‐Riemenschneider, F., & Chen, C. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac193 · Cited by 13
“However, to translate these improvements to population health, effective interventions need to be scaled up and delivered to large numbers of people within real-world contexts (6).”
BackgroundMateo‐Orcajada, A., Vaquero‐Cristóbal, R., & Abenza‐Cano, L. (2023) · Child Care Health and Development · doi:10.1111/cch.13146 · Cited by 14
Smartphone Apps and Wearables for Health Parameters in Young Adulthood: Cross-Sectional Study
Leuzzi, G., Job, M., Scafoglieri, A., & Testa, M. (2025) · JMIR Human Factors · doi:10.2196/64629 · Cited by 14
“…apps and wearables, along with their functions, is crucial for creating preventive and educational interventions [16] that assist young people in monitoring and improving their health [17-19], well-being, and quality of life through familiar technology tailored to their needs and expectations.”
Chen, W., Yao, J., Wang, H., Jia, S., Zhu, X., & Mao, L. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1711514 · Cited by 12
Hu, E., Wang, H., Yao, J., Liao, M., & Chen, W. (2026) · Frontiers in Aging · doi:10.3389/fragi.2025.1737277 · Cited by 12
Zhou, W., Shang, S., & Cho, Y. (2024) · Journal of Medical Internet Research · doi:10.2196/51291 · Cited by 12
“This is consistent with observational studies of general adults [12] and older adults [13] and meta-analyses of clinical trials [8,10,18].”
Chen, X., Wang, F., Zhang, H., Yang, L., Zhu, S., & Yang, Y. (2025) · BMC Public Health · doi:10.1186/s12889-025-22170-z · Cited by 19
“The literature reveals that researchers and practitioners are increasingly interested in using WATs to promote healthy behavior among adolescents [28], based in part on promising examples of PA promotion among adults using wearables [29, 30].”
Remskar, M., Ainsworth, B., Maynard, O., Malkowski, O. S., Birch, A., Burd, A., Caretu, T.-C., Assaad, L. E., Christodoulou-Tsiaoukkas, A., Menon, A., et al. (2025) · Mental health and physical activity · doi:10.1016/j.mhpa.2025.100680 · Cited by 13
Social Media Chatbot for Increasing Physical Activity: Usability Study
Larbi, D., Gabarrón, E., & Denecke, K. (2021) · Studies in health technology and informatics · doi:10.3233/shti210604 · Cited by 13
“Digital technology interventions can successfully increase physical activity among adults [3-6].”
BackgroundConroy, D. E., Wu, J.-C., Lee, A. M., Brunke-Reese, D., & Lagoa, C. M. (2023) · Health Psychology · doi:10.1037/hea0001271 · Cited by 14
Pierro, F. D., Sisti, D., Rocchi, M., Belli, A., Bertuccioli, A., Cazzaniga, M., Palazzi, C. M., Tanda, M. L., & Zerbinati, N. (2024) · Nutrients · doi:10.3390/nu16234199 · Cited by 13
Wai, J. P. M., Wen, C. P., Tsai, M. K., Chen, C. H., Lee, J.-H., Chu, T.-W. D., Chiou, H. Y., Wen, C., Tari, A. R., Wisløff, U., et al. (2025) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2025.01.004 · Cited by 12
Metadata
- 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
- 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
- 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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