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Cited by 137Physical Activity and HealthContext-Aware Activity Recognition SystemsMobile Health and mHealth Applications

The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.

Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies

Open access
Hekler, Eric B, Buman, Matthew P, Grieco, Lauren, Rosenberger, Mary, Winter, Sandra J, Haskell, William, King, Abby C·2015·JMIR mhealth and uhealth
DOI 10.2196/mhealth.3505PMC PMC4414958
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Abstract

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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Crossref · 10.2196/mhealth.3505

Cites

36 of 36 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Phone to ActiGraph comparison across activities (laboratory study).
Figure 1 Phone to ActiGraph comparison across activities (laboratory study).CC BY · PubMed Central
Figure 2 Comparison of the stability of different regression model estimates (laboratory study). The error bars represent the root mean standard error across all models run utilizing a leave one out procedure.
Figure 2 Comparison of the stability of different regression model estimates (laboratory study). The error bars represent the root mean standard error across all models run utilizing a leave one out procedure.CC BY · PubMed Central
Figure 3 Bland-Altman plot comparing estimated minutes of MVPA per day between the phone and ActiGraph, full model (free-living study).
Figure 3 Bland-Altman plot comparing estimated minutes of MVPA per day between the phone and ActiGraph, full model (free-living study).CC BY · PubMed Central
Figure 4 Bland-Altman plot comparing estimated minutes of light activity per day between the phone and ActiGraph, full model (free-living study).
Figure 4 Bland-Altman plot comparing estimated minutes of light activity per day between the phone and ActiGraph, full model (free-living study).CC BY · PubMed Central
Figure 5 Bland-Altman plot comparing estimated minutes of sedentary behavior per day between the phone and ActiGraph, full model (free-living study).
Figure 5 Bland-Altman plot comparing estimated minutes of sedentary behavior per day between the phone and ActiGraph, full model (free-living study).CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 4

  1. 2008, Physical Activity Guidelines Advisory Committee Report
  2. Lau, Sl, 2010, Future Network and Mobile Summit’, 10
  3. Vankipuram, M, 2012, AMIA Annu Symp Proc, 2012, 931
  4. Tudor-Locke, C, 2012, Prev Chronic Dis, 9, E113

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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
ORCID
Venue
JMIR mhealth and uhealth
Publisher
JMIR Publications Inc.
Published
2015-04-15
Volume
3
Issue
2
Pages
e36
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/mhealth.3505
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
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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