Citation

Nawrin SS, Inada H, Momma H, Nagatomi R. BMC Public Health 2024; 24(1): e1254.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12889-024-18759-5

PMID

38714982

Abstract

BACKGROUND: Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity.

METHODS: To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-h temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011-2012 data collected from the noninstitutionalized civilian resident population of the United States. The number of participants that had the whole set of physical activity data collected by the accelerometer is 6613. Among 6613 participants, 4242 participants had complete demography and Patient Health Questionnaire-9 (PHQ-9) questionnaire, a tool to quantify depressive symptoms. The association between activity-count behaviors and depressive symptoms was analyzed using multivariable logistic regression to adjust for confounding factors in sequential models.

RESULTS: We identified four physical activity-count behaviors based on five physical activity-counting patterns classified by unsupervised machine learning. Regarding PHQ-9 scores, we found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group.

CONCLUSIONS: Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability of assessing depressive symptoms effectively.

Language: en

Keywords

*Depression/epidemiology; *Exercise/psychology; *Machine Learning; Accelerometry; Activity pattern; Adult; Aged; Big Data; Cross-Sectional Studies; Depressive symptoms; Female; Humans; Kernel K-means; Male; Middle Aged; Nutrition Surveys; Objectively measured physical activity; Time Factors; Time-series clustering; United States/epidemiology; Unsupervised machine learning