Abstract:
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Research about the links between physical activity patterns and health outcomes has traditionally been based on fairly crude self-report instruments such as questionnaires. This field is being revolutionized by the availability of relatively cheap wearable accelerometer devices. These devices produce thousands of observations per person per day in some cases on a second by second basis, and depending on the device, can measure whether the person is asleep, lying down, sitting, standing, and, when a person is moving, produce data about the amount of energy expended by physical activity. Statistically, the problem can be cast as densely sampled high dimensional functional data with binary and continuous outcomes. We describe hierarchical multi-dimensional methods that exploit such functional data to show how lifestyle intervention can affect sedentary time, interruptions of sedentary behaviour and expenditure of energy from moderate to vigorous physical activity.
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