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.

翻译：加速度计数据在体力活动研究、运动科学及公共卫生研究中普遍存在，其目标在于理解并比较不同群体和/或受试人群之间的体力活动差异，识别体力活动行为的模式与趋势，从而为改善公共健康的干预措施提供依据。本研究提出采用混合效应平滑样条方差分析（SSANOVA）作为分析加速度计数据的新工具。通过将数据表示为函数或曲线，平滑样条能够精确建模全天的潜在体力活动模式，尤其在加速度计数据为连续且高频采样时表现优异。SSANOVA框架能够将估计函数分解为群体间共有的部分（即平均活动）和群体间差异的部分。通过以这种方式分解体力活动测量函数，我们可以估计群体差异并识别差异区域。本研究展示了利用SSANOVA模型分析来自社区居住且处于不同跌倒风险类别老年人的加速度计体力活动数据的优势。基于贝叶斯置信区间，SSANOVA结果可用于可靠量化跌倒风险群体间的体力活动差异，并识别全天中不同的时间区域。