Sedentary behavior (SB) is a health risk, independent of moderate-to-vigorous physical activity (MVPA). Epidemiologic data reveal consistent associations between SB and numerous cancers, cardiovascular disease, as well as multiple markers of metabolic dysfunction. Reducing SB is recommended as a viable new cancer prevention strategy. Hip mounted accelerometry is a field-based criterion measure of PA but is inadequate for measuring SB. Misclassification most often occurs when movements are performed with limited trunk displacement, under load or on an incline, on a vibrating surface (e.g., in a car), or when they are too small to be distinguished from non-wear time. This is unacceptable because we may then mischaracterize the prevalence of both SB and PA. Measurement error obscures true relationships between behavior and health, and the effects of interventions may go undetected. [Our study will improve upon laboratory studies that do little to test SB in natural environments such as driving or watching TV. Lab-based machine learning algorithms for SB are aided by the artificial start and end points. Algorithms based upon research in free living settings will be more generalizable and applicable to intervention research]. In this study we will refine and validate new machine-learned classification algorithms for a continuum of behaviors from SB to MVPA using accelerometer, Global Positioning System (GPS), and Geographic Information System (GIS) data. This study will focus on behaviors that are most frequently misclassified: moderate intensity activities that are coded as light, light activities that are coded as sedentary, and sedentary activities that are coded as light or non wear time. [We will improve upon current self-report and accelerometer estimates by 30-50%.] We will focus on four primary behavioral classes: lying, sitting, standing, and ambulatory locomotion. A total of 210 participants (ages 6-10 yrs, n = 70;16-55 yrs, n=70;65-85 yrs, n= 70) will be recruited over a 2-yr period and will wear 3 ActiGraph accelerometers (two hip, one wrist);a GPS device, and a SenseCam (an automatic image capture device) for two weekdays and 1 weekend day. [A subsample will repeat the procedures for a further 3 days]. For a 6 hr period on each of these days, participants will also be directly observed by trained coders who will record free living behaviors using a novel portable behavioral assessment system developed for the iPad. Direct observation data will provide 'ground-truths'of behavior for an annotated data file recorded at one-second intervals. For free-living behaviors not directly observed, SenseCam images will be used. Machine learning Kernel methods will be employed. Sensitivity, specificity, accuracy and other ROC graph methods will be used to compare classifiers derived from: (a) single axis vs. multi axis accelerometer data;(b) movement 'counts'vs. raw acceleration data;and (c) hip vs. wrist mounted accelerometers. Analyses will determine the improvement in sensitivity and specificity when GPS and GIS data are added. We will evaluate the need for population specific classifiers and quantify measurement error in cut-points that are currently employed.
The majority of the US population spends most of the day sitting and we have new scientific evidence that this can contribute to poor health regardless of how much physical activity a person does. However, we do not measure sitting time very accurately and when we ask people to tell us how much they do, their answers are unreliable. Our study will use small sensors to objectively measure when people sit or do physical activity, and we will use sophisticated computational techniques to summarize these movement patterns.
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|Ellis, Katherine; Godbole, Suneeta; Marshall, Simon et al. (2014) Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms. Front Public Health 2:36|