%0 Journal Article
%@ 2291-5222
%I JMIR Publications Inc.
%V 3
%N 2
%P e36
%T Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies
%A Hekler,Eric B
%A Buman,Matthew P
%A Grieco,Lauren
%A Rosenberger,Mary
%A Winter,Sandra J
%A Haskell,William
%A King,Abby C
%+ Arizona State University, School of Nutrition and Health Promotion, 500 N. 3rd St., Phoenix, AZ, 85003, United States, 1 6028272271, ehekler@asu.edu
%K telemedicine
%K cell phones
%K accelerometry
%K motor activity
%K validation studies
%D 2015
%7 15.04.2015
%9 Original Paper
%J JMIR mHealth uHealth
%G English
%X 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. 
%M 25881662
%R 10.2196/mhealth.3505
%U http://mhealth.jmir.org/2015/2/e36/
%U https://doi.org/10.2196/mhealth.3505
%U http://www.ncbi.nlm.nih.gov/pubmed/25881662