Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity (iWatch)
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|ClinicalTrials.gov Identifier: NCT01775826|
Recruitment Status : Completed
First Posted : January 25, 2013
Last Update Posted : August 20, 2019
|Condition or disease||Intervention/treatment||Phase|
|Physical Activity Sedentary Lifestyle||Other: Measurement||Not Applicable|
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||225 participants|
|Intervention Model:||Single Group Assignment|
|Masking:||None (Open Label)|
|Primary Purpose:||Basic Science|
|Official Title:||Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity|
|Study Start Date :||March 2013|
|Actual Primary Completion Date :||April 2016|
|Actual Study Completion Date :||April 2016|
Measured usual (day-to-day) behavior with body-worn sensors.
- physical activity behavior classification using study sensors (accelerometers, Sensecam and GPS) [ Time Frame: Baseline ]
Using an annotated data set of SenseCam images in three free-living population subgroups, we will compare sensitivity, specificity and percent agreement between behavioral classifiers derived from: (a) single axis vs. multi axis accelerometers; (b) aggregated movement counts vs. raw acceleration data; (c) hip vs. wrist mounted accelerometers.
Determine (a) the extent to which adding GPS data improves discrimination accuracy over accelerometer only behavior classification (i.e., best classifier resulting from Aim 1); and (b) the extent to which adding GIS data improves discrimination accuracy over accelerometer and GPS behavior classification alone (i.e., best classifier resulting from Aim 2a).
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01775826
|United States, California|
|La Jolla, California, United States, 92093|