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

Hekler EB, Buman MP, Grieco L, Rosenberger M, Winter SJ, Haskell W, King AC - JMIR Mhealth Uhealth (2015)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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.

No MeSH data available.


Related in: MedlinePlus

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.
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figure2: 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.

Mentions: Table 2 reports results of the mixed model regression equations for each cut-point estimate. The betas and intercepts were the values used to calculate the phone-based cut-points listed when the ActiGraph cut-points (ie, <100 and >1952) were imputed into the equation. Figure 2 provides estimates of the mean differences between the predicted values from the phones and the actual ActiGraph counts across phones and across different modeling datasets (eg, the full dataset, to the dataset that only included hip data and excluded biking/standing). Overall, there was some instability in the overall estimate depending on the observations included/excluded from the models using the leave-one-out technique based on the large root mean standard errors but that these differences were not greatly impacted by the specific phone used or the filtering strategies. Overall, the models were improved if data were aggregated across all phones as opposed to using phone-specific estimates. For the remainder of the paper, we only refer to the aggregated estimates of cut-points across phones as these appeared most stable and we continue to report both the “full model” and “no bike and standing” models because there appeared to be improved model fit using this filter but no improved model fit when excluding measurements from the phones while worn in the pocket.


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

Hekler EB, Buman MP, Grieco L, Rosenberger M, Winter SJ, Haskell W, King AC - JMIR Mhealth Uhealth (2015)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4414958&req=5

figure2: 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.
Mentions: Table 2 reports results of the mixed model regression equations for each cut-point estimate. The betas and intercepts were the values used to calculate the phone-based cut-points listed when the ActiGraph cut-points (ie, <100 and >1952) were imputed into the equation. Figure 2 provides estimates of the mean differences between the predicted values from the phones and the actual ActiGraph counts across phones and across different modeling datasets (eg, the full dataset, to the dataset that only included hip data and excluded biking/standing). Overall, there was some instability in the overall estimate depending on the observations included/excluded from the models using the leave-one-out technique based on the large root mean standard errors but that these differences were not greatly impacted by the specific phone used or the filtering strategies. Overall, the models were improved if data were aggregated across all phones as opposed to using phone-specific estimates. For the remainder of the paper, we only refer to the aggregated estimates of cut-points across phones as these appeared most stable and we continue to report both the “full model” and “no bike and standing” models because there appeared to be improved model fit using this filter but no improved model fit when excluding measurements from the phones while worn in the pocket.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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.

No MeSH data available.


Related in: MedlinePlus