Abstract
Accelerometers are person-worn sensors that provide objective measurements of movement based on minute-level activity counts, thus providing a rich framework for assessing physical activity patterns. New statistical approaches and computational tools are needed to exploit these densely sampled time-series data. We implement a functional principal component mixed model approach to ascertain temporal activity patterns in 578 overweight women (60% cancer survivors) and summarize individual patterns with unique personalized principal component scores. We then test if these patterns are associated with health by performing multiple regression of health outcomes (including biomarkers, namely, insulin, C-reactive protein, and quality of life) on activity patterns represented by these scores. Our model elucidates the most important patterns/modes of variation in physical activities. Results show that health outcomes including biomarkers and quality of life are strongly associated with the total volume, as well as temporal variation in activity. In addition, associations between physical activity and health outcomes are not modified by cancer status. Our findings suggest that employing a multilevel functional principal component analysis approach can elicit important temporal patterns in physical activity. It further allows us to study the relationship between health outcomes and activity patterns, and thus could be a valuable modeling approach in behavioral research.
Highlights
Physical inactivity and sedentary behavior are recognized risk factors for many chronic diseases [2,3,25,26], driving research on levels of physical activity needed to maintain a healthy lifestyle and prevent disease
Because 60% of the study sample were cancer survivors, we were able to test whether activity patterns differed by cancer status, and whether cancer status moderated the associations between physical activity and health outcomes
Accelerometer-based activity counts form irregular functions characterized by peaks of varying frequencies and locations
Summary
Physical inactivity and sedentary behavior are recognized risk factors for many chronic diseases [2,3,25,26], driving research on levels of physical activity needed to maintain a healthy lifestyle and prevent disease. While aggregate levels may provide a measure of average habitual physical activity, there is much to be gleaned from a more thorough analysis of temporal activity patterns throughout the day Analysis of these patterns would potentially allow a more refined understanding of the implications of the circadian rhythms of exercise for health, and possibly identify critical windows-of-opportunity for intervening to increase physical activity or reduce sedentary behavior. More recently functional analysis techniques such as wavelet-based or principal component functional models [14,15,24,27] have been proposed to study the full spectrum of accelerometer data These approaches model minute-level information, rather than reducing the data to daily or weekly summaries, exploiting these densely sampled measures. Because 60% of the study sample were cancer survivors, we were able to test whether activity patterns differed by cancer status, and whether cancer status moderated the associations between physical activity and health outcomes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
