STUDY OBJECTIVES This study aimed to quantify the temporal associations between nightly sleep quantity and timing with daytime eating behavior and activity levels in free-living (i.e., non-experimental) settings. METHODS Generally… Click to show full abstract
STUDY OBJECTIVES This study aimed to quantify the temporal associations between nightly sleep quantity and timing with daytime eating behavior and activity levels in free-living (i.e., non-experimental) settings. METHODS Generally healthy young adults (N=63; 28.9±7.1 years) completed concurrent sleep (wrist actigraphy), eating (photo-assisted diet records), and activity (waist actigraphy) assessments over 14 days. Multilevel models quantified the associations between nightly sleep (total sleep time, timing of sleep and wake onset) with next-day eating behavior (diet quality, caloric intake, timing of eating onset/offset, eating window duration) and activity levels (total physical activity, sedentary time). Associations in the reverse direction (i.e., eating and activity predicting sleep) were explored. Models adjusted for demographic and behavioral confounders and accounted for multiple testing. RESULTS At within- and between-subject levels, nights with greater-than-average total sleep time predicted a shorter eating window the next day (all p≤0.002). Later-than-average sleep and wake timing predicted within- and between-subject delays in next-day eating onset and offset, and between-subject reductions in diet quality and caloric intake (all p≤0.008). At within- and between-subject levels, total sleep time was bidirectionally, inversely associated with sedentary time (all p<0.001), while later-than-average sleep and wake timing predicted lower next-day physical activity (all p≤0.008). CONCLUSIONS These data underscore the complex interrelatedness between sleep, eating behavior, and activity levels in free-living settings. Findings also suggest that sleep exerts a greater influence on next-day behavior, rather than vice versa. While testing in more diverse samples is needed, these data have potential to enhance health behavior interventions and maximize health outcomes.
               
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