The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats

Introduction and objectives
Accelerometry is used as an objective measure of physical activity in humans and veterinary species. In cats, one important use of accelerometry is in the study of therapeutics designed to treat degenerative joint disease (DJD) associated pain, where it serves as the most widely applied objective outcome measure. These analyses have commonly used summary measures, calculating the mean activity per-minute over days and comparing between treatment periods. While this technique has been effective, information about the pattern of activity in cats is lost. In this study, functional data analysis was applied to activity data from client-owned cats with (n = 83) and without (n = 15) DJD. Functional data analysis retains information about the pattern of activity over the 24-hour day, providing insight into activity over time. We hypothesized that 1) cats without DJD would have higher activity counts and intensity of activity than cats with DJD; 2) that activity counts and intensity of activity in cats with DJD would be inversely correlated with total radiographic DJD burden and total orthopedic pain score; and 3) that activity counts and intensity would have a different pattern on weekends versus weekdays.
Results and conclusions
Results showed marked inter-cat variability in activity. Cats exhibited a bimodal pattern of activity with a sharp peak in the morning and broader peak in the evening. Results further showed that this pattern was different on weekends than weekdays, with the morning peak being shifted to the right (later). Cats with DJD showed different patterns of activity from cats without DJD, though activity and intensity were not always lower; instead both the peaks and troughs of activity were less extreme than those of the cats without DJD. Functional data analysis provides insight into the pattern of activity in cats, and an alternative method for analyzing accelerometry data that incorporates fluctuations in activity across the day.

引言与研究目标
加速度计法（accelerometry）被用作人类及兽医物种体力活动的客观评估手段。在猫类研究中，加速度计法的一项重要应用是针对退行性关节病（DJD）相关性疼痛的治疗药物研发，此时它是应用最广泛的客观结局评估指标。既往此类分析通常采用汇总统计方法，即计算每日每分钟的平均活动量，并在不同治疗周期间进行比较。尽管该方法已被证实有效，但会丢失猫类活动模式的相关信息。本研究将函数数据分析（functional data analysis）应用于83只患DJD及15只未患DJD的宠物猫的活动数据中。函数数据分析可保留24小时内活动模式的完整信息，从而深入解析随时间变化的活动特征。我们提出以下假设：①未患DJD的猫的活动总计数与活动强度均高于患DJD的猫；②患DJD的猫的活动总计数与活动强度与影像学DJD总负荷及总骨科疼痛评分呈负相关；③猫的活动总计数与活动强度在周末与工作日呈现不同的模式。

结果与结论
结果显示，猫之间的活动水平存在显著个体差异。猫的活动呈现双峰模式：早晨存在一个尖锐的活动峰值，晚间则存在一个更为宽泛的活动峰值。进一步分析显示，该活动模式在周末与工作日存在差异，早晨的活动峰值会向右偏移（即出现时间更晚）。患DJD的猫与未患DJD的猫的活动模式存在差异，尽管前者的活动量与活动强度并非始终更低；相较而言，患DJD的猫的活动峰值与谷值的极端程度均低于未患DJD的猫。函数数据分析可深入解析猫类的活动模式，同时为加速度计数据的分析提供了一种新方法，该方法可纳入全天活动的波动特征。