Twenty-four hour activity-count behavior patterns associated with depressive symptoms: A big data-machine learning approach

Abstract

AbstractDepression 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. 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 found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group. 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.