TY - JOUR
T1 - Longitudinal Associations Between Timing of Physical Activity Accumulation and Health
T2 - Application of Functional Data Methods
AU - Lin, Wenyi
AU - Zou, Jingjing
AU - Di, Chongzhi
AU - Sears, Dorothy D.
AU - Rock, Cheryl L.
AU - Natarajan, Loki
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Functional modeling
KW - Longitudinal data analysis
KW - Physical activity
KW - Principal component analysis
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U2 - 10.1007/s12561-022-09359-1
DO - 10.1007/s12561-022-09359-1
M3 - Article
AN - SCOPUS:85139165373
SN - 1867-1764
VL - 15
SP - 309
EP - 329
JO - Statistics in Biosciences
JF - Statistics in Biosciences
IS - 2
ER -