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

This research presents a novel application of functional data analysis to study activity patterns in cats with degenerative joint disease (DJD). Traditional accelerometry studies in veterinary medicine have relied on summary measures that calculate mean activity per minute over days, but this approach loses valuable information about activity patterns.

The study reveals that cats exhibit a distinctive bimodal activity pattern characterized by a sharp morning peak and a broader evening peak. Interestingly, this pattern differs between weekdays and weekends, with the morning peak shifting later on weekends. The research demonstrates significant inter-cat variability in activity levels and patterns.

This work advances our understanding of feline behavior and provides a more sophisticated analytical framework for evaluating therapeutic interventions for DJD-associated pain in cats. The functional data analysis approach preserves the temporal structure of activity data, offering insights that traditional summary statistics might miss.

The findings have important implications for veterinary medicine, particularly in the development and assessment of treatments for degenerative joint disease in cats, where accelerometry serves as a key objective outcome measure. From the statistical point of view, it was a novel collaboration that I worked on as a grad student, learning how to use functional data analysis to answer questions posed by researchers at the Veterinary School at NCSU, and under the supervision of Dr. Staicu.

Recommended citation: Gruen, M. E., Alfaro-Córdoba, M., Thomson, A. E., Worth, A. C., Staicu, A. M., & Lascelles, B. D. X. (2017). "The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats." PLOS ONE. 12(1): e0169576.
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