  Accelerometer data is commonplace in physical activity research, exercise
science, and public health studies, where the goal is to understand and compare
physical activity differences between groups and/or subject populations, and to
identify patterns and trends in physical activity behavior to inform
interventions for improving public health. We propose using mixed-effects
smoothing spline analysis of variance (SSANOVA) as a new tool for analyzing
accelerometer data. By representing data as functions or curves, smoothing
spline allows for accurate modeling of the underlying physical activity
patterns throughout the day, especially when the accelerometer data is
continuous and sampled at high frequency. The SSANOVA framework makes it
possible to decompose the estimated function into the portion that is common
across groups (i.e., the average activity) and the portion that differs across
groups. By decomposing the function of physical activity measurements in such a
manner, we can estimate group differences and identify the regions of
difference. In this study, we demonstrate the advantages of utilizing SSANOVA
models to analyze accelerometer-based physical activity data collected from
community-dwelling older adults across various fall risk categories. Using
Bayesian confidence intervals, the SSANOVA results can be used to reliably
quantify physical activity differences between fall risk groups and identify
the time regions that differ throughout the day.
