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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. [ABSTRACT FROM AUTHOR]

[Display omitted] • The use of ATUS in energy-related occupant-building interactions is reviewed. • The purpose of using ATUS in occupant-building interaction is categorized into three models. • Markov Chain is the most used approach in developing occupant behavior models. • This study suggested four new research directions in the focused topic. Occupant-building interaction plays a critical role in determining energy consumption and enhancing energy efficiency in the built environment. The advancement in data-driven approaches has provided new opportunities to investigate occupant-building interaction in building energy modeling and simulation. Selecting an enriched and reliable data source is a key factor in accurately modeling energy-related occupant-building interactions. The American Time Use Survey (ATUS), the nationally representative estimation of how Americans spend their time in the built environment, has been one of the primary data sources for such studies in the United States. This study provides a comprehensive review of 51 articles in which ATUS has been used to better study energy-related occupant-building interactions. We found out that the purpose of using ATUS in occupant-building interaction studies can be categorized into modeling (1) occupancy schedules, (2) occupant energy-related activity patterns, and (3) building energy performance. The results of this study summarize the current limitations and future research directions in this topic. The findings of this study not only contribute to the knowledge domain, but also assist practitioners by revealing the current practice and research needs in this field. In addition, it solves the gap of previous studies regarding lack of a comprehensive review on the use of ATUS in occupant-building interaction studies. [ABSTRACT FROM AUTHOR]

Nonconsumptive effects of predators potentially have negative fitness consequences on prey species through changes in prey behavior. Coyotes (Canis latrans) recently expanded into the eastern United States, and raccoons (Procyon lotor) are a common mesocarnivore that potentially serve as competitors and food for coyotes. We used camera traps at baited sites to quantify vigilance behavior of feeding raccoons and used binomial logistic regression to analyze the effects of social and environmental factors. Additionally, we created raccoon and coyote activity patterns from the camera trap data by fitting density functions based on circular statistics and calculating the coefficient of overlap (Δ). Overall, raccoons were vigilant 46% of the time while foraging at baited sites. Raccoons were more vigilant during full moon and diurnal hours but less vigilant as group size increased and when other species were present. Raccoons and coyotes demonstrated nocturnal activity patterns, with coyotes more likely to be active during daylight hours. Overall, raccoons did not appear to exhibit high levels of vigilance. Activity pattern results provided further evidence that raccoons do not appear to fear coyotes, as both species were active at the same time and showed a high degree of overlap (Δ = 0.75) with little evidence of temporal segregation in activity. Thus, our study indicates that nonconsumptive effects of coyotes on raccoons are unlikely, which calls into question the ability of coyotes to initiate strong trophic cascades through some mesocarnivores. [ABSTRACT FROM AUTHOR]

We investigated winter activity patterns of American Martens, Manes americana, Snowshoe Hares, Lepus americanus, and Red Squirrels, Tamiasciurus hudsonicus, in westcentral Montana between November 1994 and March 1995 using dual-sensor remote cameras. One hundred percent of Snowshoe Hare (n = 25) observations occurred at night while Martens (n= 85) exhibited random activity during diel and nocturnal hours and Red Squirrels (n = 22) were diurnal. Marten activity coincided with the nocturnal and diurnal microtines and diurnal Red Squirrels though they could take advantage of the strictly nocturnal Snowshoe Hares. [ABSTRACT FROM AUTHOR]