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Cited by 136Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsPhysical Activity and Health

The DOI matches its Crossref record; 206 of the 207 references it cites were found in a registry or an index.

Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review

Open access
McCallum, Claire, Rooksby, John, Gray, Cindy M·2018·JMIR mhealth and uhealth
DOI 10.2196/mhealth.9054PMC PMC5889496

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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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Figure 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram.CC BY · PubMed Central

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Research works printed but not linked: 6

  1. Blair, SN, 2009, Br J Sports Med, 43, 1
  2. 10.1007/978-3-642-27-2_39 · DOI
  3. Taylor, D, 2016, Stud Health Technol Inform, 220, 414
  4. Godin, G, 1985, Can J Appl Sport Sci, 10, 141
  5. ConnellyKOn developing a technology acceptance model for pervasive computingWorkshop of Ubiquitous System Evaluation (USE)20079th International Conference on Ubiquitous Computing (UBICOMP) 2007September 16-19Innsbruck, Austria
  6. Quesenbery, W, 2003, Content and complexity: Information design in technical communication, 81

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Metadata

Authors
McCallum, C., Rooksby, J., & Gray, C. M.
Affiliations
University of Glasgow (GB)
Corresponding author
McCallum, Claire
ORCID
Venue
JMIR mhealth and uhealth
Publisher
JMIR Publications Inc.
Published
2018-03-23
Volume
6
Issue
3
Pages
e58
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/mhealth.9054
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
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

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