Environmental, Behavioral and Individual Determinants of Sleep Quality in Healthy Population: A Regression Tree Approach.

Global sleep quality is declining, while it plays a crucial role in maintaining overall health. Various environmental, behavioral and individual characteristics, such as light exposure, physical activity, or room ambiance, influence sleep quality. This study aims to identify key determinants of sleep health and develop evidence-based recommendations for improving sleep quality across diverse populations.195 healthy participants aged 18-80 were recruited, comprising 117 females (61.9%) and 72 males (38.1%). Participants received monitoring devices for seven days, including a sleep recording bracelet, light sensor, actigraphy device, heart rate sensor, and ambient sensor. Data on environmental and behavioral factors (light exposure, physical activity, heart rate, room ambient) and individual characteristics (age, chronotype, anxiety level, gender) were collected. These data were analyzed using regression tree modeling to identify optimal determinants for improving sleep quality.Sleep efficiency was most influenced by gender, with women exhibiting better efficiency. Among men, anxiety and heart rate variability negatively impacted sleep efficiency, while for women it was the age. The proportion of deep slow-wave sleep (%S3) was affected by aging and later sleep midpoint, and the highest number of awakenings was associated with a later sleep midpoint.This study revealed that gender was the strongest predictor of sleep efficiency. These results highlight the importance of personal characteristics in sleep health, suggesting a need for tailored interventions that consider individual factors over external synchronizers like light exposure.