Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods

Wenyi Lin, Jingjing Zou, Chongzhi Di, Dorothy D. Sears, Cheryl L. Rock and Loki Natarajan ()
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Wenyi Lin: University of California, San Diego
Jingjing Zou: University of California, San Diego
Chongzhi Di: Fred Hutchinson Cancer Research Center
Dorothy D. Sears: Arizona State University
Cheryl L. Rock: University of California, San Diego
Loki Natarajan: University of California, San Diego

Statistics in Biosciences, 2023, vol. 15, issue 2, No 1, 309-329

Abstract: Abstract Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.

Keywords: Accelerometer; Physical activity; Functional modeling; Principal component analysis; Longitudinal data analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09359-1

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