Twenty-four-hour activity-count behavior patterns associated with depressive symptoms: Cross-sectional study by a big data-machine learning approach

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. We hypothesize that the temporal patterns of daily physical activity could better explain the association of physical activity with depressive symptoms. Methods To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-hour temporal patterns of physical activity. We conducted a cross-sectional study on NHANES 2011–2012 data where the data is 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. 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.