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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RETRACTED: Accelerometer- and Pedometer-Based Physical Activity Interventions Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis
Hodkinson, A., Kontopantelis, E., Adeniji, C., van Marwijk, H., McMillan, B., Bower, P., & Panagioti, M. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.12895 · Cited by 55
Retracted
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,417
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., et al. (2011) · BMJ · doi:10.1136/bmj.d5928 · Cited by 33,817
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
GRADE: an emerging consensus on rating quality of evidence and strength of recommendations
Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008) · BMJ · doi:10.1136/bmj.39489.470347.ad · Cited by 23,305
The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Introduction to Meta‐Analysis
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Introduction to Meta‐Analysis
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799

Show all 91 references

Research works printed but not linked: 4

Higgins J , Green S . Cochrane Handbook for systematic reviews of interventions. Chichester: John Wiley & Sons, 2011.
10.1136/bmj.39489.470347.AD · DOI · Paper page
10.1136/bmj.l4570 · DOI · Paper page
Global mobile consumer survey: US edition | a new era in mobile continues. Deloitte 2018.

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Mantena, S., Johnson, A., Oppezzo, M., Schütz, N., Tolas, A., Doijad, R., Mattsson, C. M., Lawrie, A., Ramírez‐Posada, M., Linos, E., et al. (2025) · medRxiv · doi:10.1101/2025.02.19.25322559 · Cited by 7
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Chukwuemeka, U. M., Nnalue, A. D., Obiekwe, S. J., Maruf, F. A., Anakor, A. C., Moses, M. O., Amaechi, C., Okonkwo, U. P., & Amaechi, I. A. (2025) · BMC Digital Health · doi:10.1186/s44247-025-00159-3 · Cited by 5
Trajectories of Physical Activity During a 6‐Month Mobile App‐Based Lifestyle Modification Intervention in Physically Inactive Adults With Cardiovascular Risk Factors
Adachi, T., Miki, T., Shimizu, K., Kanai, M., & Hagiwara, Y. (2025) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.70111 · Cited by 4
“A previous meta-analysis of mobile-or web-based lifestyle interventions demonstrated that personalization is a strong determinant of intervention effectiveness [14, 34].”
Validity of muscular fitness self-assessments in the ecofit smartphone application: A correlation study
Jansson, A. K., Lubans, D. R., Duncan, M. J., Smith, J. J., Bauman, A. E., Attia, J., & Plotnikoff, R. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278374 · Cited by 5
Remote maintenance cardiac rehabilitation (MAINTAIN): A protocol for a randomised feasibility study
Denton, F., Waddell, A., Kite, C., Hesketh, K. L., Atkinson, L., Cocks, M. S., Jones, H., Randeva, H. S., Davenport, N., Powell, R., et al. (2023) · Digital Health · doi:10.1177/20552076231152176 · Cited by 7
“When combined with personalised text messages from health professionals, wearable activity monitors are often more effective at improving exercise and PA behaviour.(24,25) Thus, remotely monitored and prescribed CR programs supported by wearable activity monitors may be a feasible alternative or adjunct to centrebased…”
Digital Health Promotion For Fitness Enthusiasts In Africa
Arueyinzho, O., & Sanyaolu, K. (2022) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh55609.2022.00017 · Cited by 5
Motor Activity Sensorics for mHealth Support of Human Resilience in Daily Life
Meigal, A. Y., Gerasimova-Meigal, L. I., Rego, G. E., & Korzun, D. G. (2022) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct56874.2022.9953829 · Cited by 5
“In addition, mHealth components (smartphone apps, physical activity trackers) have a significant effect in increasing physical activity, for example by 1850 steps [18], or 1126 step daily [64], which roughly equals 0,7-1 km.”
Background
Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis
Kim, J., Choi, J.-Y., Kim, H., Lee, T., Ha, J., Lee, S., Park, J., Jeon, G., & Cho, S. (2023) · JMIR mhealth and uhealth · doi:10.2196/50663 · Cited by 5
“Most previous studies either provided the participants with a specific device model or restricted participation to individuals using a particular model [21-23].”
Background
Effective management of sedentary behavior among Indian university students: An empirical exploration into health-related behavior
Khatri, S., & Sharma, R. (2024) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_1489_23 · Cited by 5
The effectiveness of digital tools in physical activity interventions for individuals with severe mental illness: a scoping review
Forde, S. A., Coppinger, T. C., Rea, S., & Hanrahan, S. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2508938 · Cited by 4
Digital Technologies in the Activities of Physical Culture and Sports Specialists: Features of Professional Training
Rybalko, P., Шукатка, О. В., Лазоренко, С. А., Кисельов, В. О., Скачедуб, Н. М., & Paryshkura, Y. K. (2023) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro57284.2023.10159719 · Cited by 4
“It includes other concepts such as “eHealth”, “mHealth”, and “Wireless Health” [9; 10; 11].”
Adherence Promotion With Tailored Motivational Messages: Proof of Concept and Message Preferences in Older Adults
Zhang, S., Dieciuc, M., Dilanchian, A. T., Lustria, M. L. A., Carr, D. C., Charness, N. H., He, Z., & Boot, W. R. (2024) · Gerontology and Geriatric Medicine · doi:10.1177/23337214231224571 · Cited by 6
“Numerous studies have demonstrated that tailored short messages and reminders can improve intervention adherence and promote health outcomes in the general population (see, e.g., Laranjo et al., 2021; Mauch et al., 2022; Sahin et al., 2019; Tong et al., 2021 for reviews).”
Background
Hourly step recommendations to achieve daily goals for working and older adults
Ang, G., Tan, C. S., Lim, N., Tan, J., Müller‐Riemenschneider, F., Cook, A. R., & Chen, C. (2024) · Communications Medicine · doi:10.1038/s43856-024-00537-4 · Cited by 4
WAT’s up? Exploring the Impact of Wearable Activity Trackers on Physical Activity and Wellbeing: A Systematic Research Review
Scudds, A., & Lasikiewicz, N. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00442-6 · Cited by 6
“Prior studies have observed this when exploring the effect of WATs on physical activity (Laranjo et al., 2021), but in this context, its influence may also extend to psychological wellbeing.”
The curvilinear effects of relative positions in smartphone app leaderboards on physical activity
Yang, Y., & Koenigstorfer, J. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108532 · Cited by 4
A 12-Month Digital Peer-Supported App Intervention to Promote Physical Activity Among Community-Dwelling Older Adults: Follow-Up Study of a Nonrandomized Controlled Trial
Tabira, K., Oguma, Y., Yoshihara, S., Shibuya, M., Nakamura, M., Doihara, N., Hirata, A., Manabe, T., & Yamashita, T. (2025) · JMIR Aging · doi:10.2196/66610 · Cited by 4
“Recently, mobile apps have been successfully used to increase physical activity levels [15,16]. eHealth, or electronic health, encompasses health care services supported by information and communication technology, including computers, mobile phones, and satellite communications, for health…”
‘I’m doing it for myself’: Using a smartphone-based exercise service during the COVID-19 lockdown in the Faculty of Health Sciences, University of the Witwatersrand, South Africa
Constantinou, D., Torres, G., Neophytou, N., Fourie, P., Buntting, X., & Gradidge, P. J.-L. (2021) · South African Journal of Sports Medicine · doi:10.17159/2078-516x/2021/v33i1a9053 · Cited by 4
“A recent metaanalysis investigated the effect of activity trackers and mobile apps with the features mentioned above in increasing physical activity in healthy adults (18–65 years old).[5] The meta-analysis found that mobile app and tracker users were more active than control groups by an average of 1 850 additional s…”
Background
Perceptions of Physical Activity and the Use of Activity Monitors to Increase Activity Levels in Patients Undergoing Total Knee Replacement
Arant, K. R., Zimmerman, Z. E., Bensen, G. P., Losina, E., & Katz, J. N. (2021) · ACR Open Rheumatology · doi:10.1002/acr2.11324 · Cited by 4
“increasing PA levels (13), indicating that accelerometers such as the Fitbit may be useful to both collect PA data and motivate participants.”
Background
The effect of fixed physical usage patterns on the engagement of physical activity apps: a real-world data analysis
Ma, L., Zhao, X., & Zhou, Z. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2159875 · Cited by 4
“The common BCTs embedded in the PA apps include feedback and monitoring, goals and planning, reward and threat, coaching, informational and emotional support, and social support (Du et al. 2016; Ehlers and Huberty 2014; Laranjo et al. 2021; Luhanga et al. 2018; Mollee et al. 2017).”
BackgroundMethodInfluential
Acceptable, useful, and ineffective? Recent retirees’ experiences of a 12-month activity tracker-based physical activity intervention
Tuominen, M., Koski, P., Axelin, A., Stenholm, S., & Leskinen, T. (2023) · Digital Health · doi:10.1177/20552076221147419 · Cited by 4
“58 21 – 23 These features should be further developed and tested by future activity tracker-driven interventions by introducing personalized, dynamic, and adaptive daily physical activity goals together with the provision of feedback and support delivered via text-messaging.”
Background
Mobile Health Fitness Interventions
Razaghizad, A., McKee, T. J., Malhamé, I., Friedrich, M. G., Giannetti, N., Coristine, A. J., Johnson, A., Ashley, E. A., Hershman, S. G., Struck, B., et al. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100613 · Cited by 6
What do adults living with obesity want from a chatbot for physical activity? – a qualitative study
Larbi, D., Wynn, R., Trondsen, M. V., Årsand, E., Zanaboni, P., Denecke, K., & Gabarrón, E. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00070-3 · Cited by 6
“Interventions aiming to change behaviour, like a physical activity chat-bot, should include personalisation features because they are a critical component of users’ adherence [23] and are highly effective in engaging users [45].”
Global Perspectives on Physical Activity
Zengaro, S. A., & Zengaro, F. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3952-7.ch020 · Cited by 4
Exercise and Fitness Quantification in Clinical Practice: Why and How; and Where Are We Going?
Riopel-Meunier, J., Piché, M., & Poirier, P. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.11.032 · Cited by 4
Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis
Wyatt, B., Forstmann, N., Badier, N., Hamy, A., Larochelambert, Q. D., Antero, J., Danino, A., Vercamer, V., de Villèle, P., Vittrant, B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/68199 · Cited by 4
User Experience With a Personalized mHealth Service for Physical Activity Promotion in University Students: Mixed Methods Study
Wittmar, S., Frankenstein, T., Timm, V., Frei, P., Kurpiers, N., Wölwer, S., & Schäfer, A. G. M. (2025) · JMIR Formative Research · doi:10.2196/64384 · Cited by 4
Validation of the Activ8 Activity Monitor for Monitoring Postures, Motions, Transfers, and Steps of Hospitalized Patients
Becker, M. L., Hurkmans, H. L. P., Verhaar, J. A. N., & Bussmann, J. B. J. (2023) · Sensors · doi:10.3390/s24010180 · Cited by 4
Using Commercially Available Measurement Devices for the Intake-Balance Method to Estimate Energy Intake: Work in Progress
Gebel, K., & Ding, D. (2021) · Journal of Nutrition · doi:10.1093/jn/nxab393 · Cited by 4
Self-citation
Evaluation of Digital Public Health Interventions
Lange, O., Boskamp, P., Brannath, W., Santis, K. K. D., Muellmann, S., Rogowski, W. H., & Rothgang, H. (2025) · Springer series on epidemiology and public health · doi:10.1007/978-3-031-90154-6_8 · Cited by 3
Understanding rider:horse bodyweight ratio trends, weight management practices and rider weight perceptions within leisure and amateur riders in the UK
Challinor, C., Randle, H., & Williams, J. M. (2021) · Comparative Exercise Physiology · doi:10.3920/cep200082 · Cited by 3
Interventions utilizing smartwatches in healthcare: A comprehensive literature review
Alabeidi, F., Aljawad, ‏. M., Alswaied, ‏. A., Alanazi, ‏. N., Aljabri, ‏. S., Jaafari, ‏. A., Alanazi, ‏. J., Alhomod, ‏. A., Alhamed, ‏. A., AlHizan, K., et al. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15262 · Cited by 3
“Previous reviews have primarily addressed wearable technology as a whole [16], [17], as well as their application in specific health conditions like cardiovascular disease [18], diabetes [19], and depression [20], or general physical activity trackers [21], [22].”
Background
ВИКОРИСТАННЯ МОБІЛЬНИХ ДОДАТКІВДЛЯ ПІДВИЩЕННЯ МОТИВАЦІЇ ДО ЗАНЯТЬ ФІЗКУЛЬТУРНО-ОЗДОРОВЧОЮ ДІЯЛЬНІСТЮ ОСІБ ЗРІЛОГО ВІКУ
Skaliy, A., & Mulyk, K. (2024) · Education Innovation Practice · doi:10.31110/2616-650x-vol12i10-006 · Cited by 3
Ongoing incentives' 36-month effect on physical activity in a Japanese insurance-based health-promotion program
Kanai, M., Onozato, N., Fujisawa, Y., Hamatani, T., Tateyama, K., Miki, T., Toda, T., Sugawara, T., & Hagiwara, Y. (2025) · Preventive Medicine · doi:10.1016/j.ypmed.2025.108327 · Cited by 3
Population-Wide mHealth Interventions Promoting Healthy Movement Behaviors: Systematic Review of the Real-World Evidence
Edney, S. M., Mair, J. L., Topothai, T., Chua, X. H., Na, W., & Müller‐Riemenschneider, F. (2025) · Journal of Physical Activity and Health · doi:10.1123/jpah.2025-0151 · Cited by 3
The Effect of Commercial Activity Tracker Based Physical Activity Intervention on Body Composition and Cardiometabolic Health Among Recent Retirees
Leskinen, T., Suorsa, K., HEINONEN, I. H. A., Löyttyniemi, E., Pentti, J., Vahtera, J., & Stenholm, S. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.757080 · Cited by 3
“Modern fitness and activity trackers have increased their popularity among consumers but also as an intervention method because wearable technology can be harnessed with evidence-based behavioral change techniques, such as selfmonitoring, goal setting and feedback, without face-to-face contact (Chia et al., 2019; Lara…”
Background
Ervaringen en ondersteuningsbehoeften van leefstijlcoaches in het gebruik van digitale coachingsmiddelen bij cliënten met overgewicht
Bak, M., Bossen, D., Braam, K. I., Holla, J., Visser, B., & Dallinga, J. M. (2023) · TSG - Tijdschrift voor gezondheidswetenschappen · doi:10.1007/s12508-023-00379-w · Cited by 3
Virtual training with real-life benefits: A survey investigating online fitness communities during lockdown level 5 in South Africa
Pienaar, H., Boer, P., van Rensburg, A. J., Ramagole, D., & van Rensburg, D. C. J. (2023) · South African Journal for Research in Sport Physical Education and Recreation · doi:10.36386/sajrsper.v45i2.234 · Cited by 3
Effect of Physical Exercise Programs Based on Mobile Health and Ecological Momentary Assessment on the Physical and Mental Health, Cognitive Functions, and Social Environment of Adults in Developing Countries: A Systematic Review
Aniotz, A. F., Reyes‐Molina, D., Cigarroa, I., García-Merino, S., Alonso, M. R., Pérez, M., & Zapata‐Lamana, R. (2024) · Medicina · doi:10.3390/medicina60040578 · Cited by 3
“In addition, the following few studies were identified, and of these, most had small sample sizes and very short durations of interventions [23,27,29,31,32].”
BackgroundInfluential
Step count accuracy and precision of the Xiaomi Mi Smart Band 5 in healthy young individuals
Bellini, A., Nicolò, A., Bustos, A. S. O., & Sacchetti, M. (2021) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot51437.2021.9488479 · Cited by 3
Promoting Work Ability With a Wearable Activity Tracker in Working Age Individuals With Hip and/or Knee Osteoarthritis: A Randomized Controlled TrialPreprint
Östlind, E., Eek, F., Stigmar, K. G. E., Sant’Anna, A., & Hansson, E. E. (2021) · Research Square · doi:10.21203/rs.3.rs-634847/v1 · Cited by 3
How Participatory Health Informatics Catalyzes One Digital Health
Denecke, K., Rivera-Romero, O., Merolli, M., Miron‐Shatz, T., Gabarrón, E., & Petersen, C. C. (2023) · Yearbook of Medical Informatics · doi:10.1055/s-0043-1768727 · Cited by 2
“…techniques “feedback and monitoring” and “goals and planning” in smartphones and activity trackers or gamification interventions have proven to be effective in increasing physical activity [29]; or the use of game elements is effective to engage children to establish healthy eating behavior [30].”
Background
Working Women Walking Program: A Sequential Multiple Assignment Randomized Trial
Buchholz, S. W., Daniel, M. N., Kitsiou, S., Schoeny, M. E., Halloway, S., Johnson, T. J., Vispute, S., & Wilbur, J. (2025) · Journal of Physical Activity and Health · doi:10.1123/jpah.2024-0790 · Cited by 4
Evaluation of the effects of a technical sock on sleep dynamics
Bertuccioli, A., Belli, A., Zonzini, G. B., Cazzaniga, M., Cardinali, M., Pierro, F. D., Gregoretti, A., Zerbinati, N., Matera, M. R., Cavecchia, I., et al. (2025) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.25.16871-0 · Cited by 2
Can fitness apps work long term? A 24-month quasiexperiment of 516 818 Canadian fitness app users
Nguyen, L., Faulkner, G., Taylor, C. M., & Mitchell, M. (2025) · British Journal of Sports Medicine · doi:10.1136/bjsports-2025-109901 · Cited by 3
Artificial intelligence for breast cancer prevention: the vision ahead
Sardanelli, F., & Scaperrotta, G. P. (2025) · European Radiology · doi:10.1007/s00330-025-12036-8 · Cited by 3
“Interesting mobile-phone app-based experiences are available for physical activity [52], nutrition behavior [53], and smoking cessation [54], also in combination with pharmacotherapy [55].”
Stronger Together: a community-based intervention using Behaviour Change Wheel to promote healthier lifestyles among women with low socioeconomic status: a feasibility study
Alageel, S., Alsukait, R. F., Alharbi, A., Bilal, L., Aldakhil, H., & Altwaijri, Y. A. (2025) · International Journal for Equity in Health · doi:10.1186/s12939-025-02654-5 · Cited by 2
“They also took part in a weekly competition for the highest steps per day, which could positively influence walking behaviour [51].”
‘The tracking was in control of me’: exploring affordances of self-tracking tools for adolescents’ psychological wellbeing
Freeman, J. L. (2025) · International Journal of Adolescence and Youth · doi:10.1080/02673843.2025.2590907 · Cited by 4
“Much of both the academic literature and marketing around self-tracking tools has centered on physical health and self-improvement (Crawford et al., 2015; Jin et al., 2020; Laranjo et al., 2021).”
Feasibility and usability of a ChatGPT-based app to support physical activity: A pilot study
Larbi, D., Zanaboni, P., Årsand, E., Randine, P., Trondsen, M. V., Denecke, K., Wynn, R., & Gabarrón, E. (2026) · Digital Health · doi:10.1177/20552076261417860 · Cited by 3
“42,47 Similarly, our previous qualitative study of user preferences for a PA chatbot found that participants preferred chatbots with integrated goal setting and feedback functionalities to increase motivation for PA. 14 These ﬁ ndings suggest that incorporating goal setting, self-monitoring, and…”

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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