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JMIR Mhealth Uhealth. 2015 Apr 15;3(2):e36. doi: 10.2196/mhealth.3505.

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

Author information

1
Arizona State University, School of Nutrition and Health Promotion, Phoenix, AZ, United States. ehekler@asu.edu.

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.

KEYWORDS:

accelerometry; cell phones; motor activity; telemedicine; validation studies

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