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Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies
Open accessAbstract
BACKGROUND: There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach. OBJECTIVE: The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults. METHODS: A laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph. RESULTS: Study 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph's percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P<.001) between the raw ActiGraph counts/minute and the phone's raw counts/minute and a strong correlation on minutes of moderate-to-vigorous physical activity (MVPA; ie, ρ=.67, P<.001). Results from Bland-Altman plots suggested close mean absolute estimates of sedentary (mean difference=-26 min/day of sedentary behavior) and MVPA (mean difference=-1.3 min/day of MVPA) although there was large variation. CONCLUSIONS: Overall, results suggest that an Android smartphone can provide comparable estimates of physical activity to an ActiGraph in both a laboratory-based and free-living context for estimating sedentary and MVPA and that different Android smartphones may reliably confer similar estimates.
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“Smartphones are capable of accurately measuring physical activity in controlled and uncontrolled settings (30)”
From Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial (2020) · cites this paper as [30]
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Metadata
- Authors
- Hekler, E. B., Buman, M. P., Grieco, L., Rosenberger, M., Winter, S. J., Haskell, W., & King, A. C.
- Affiliations
- Arizona State University (US)
- Stanford University (US)
- Corresponding author
- Hekler, Eric B
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2015-04-15
- Volume
- 3
- Issue
- 2
- Pages
- e36
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4414958
- OpenAlex
- W2124775720
- Cited by
- 137
- References
- 37
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 6 · 2023: 17 · 2022: 8 · 2021: 12 · 2020: 28 · 2019: 13 · 2018: 15 · 2017: 15 · 2016: 8 · 2015: 5
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Heart, Lung, and Blood Institute (RC1HL099340)
- Topics
- 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
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- physical activity monitoring, smartphone accelerometer, Android smartphones, ActiGraph, accelerometer validity, reliability statistics, moderate-to-vigorous physical activity, sedentary behavior, activity intensity classification, free-living physical activity, middle-aged and older adults, Bland-Altman agreement, wearable devices, step count
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