Browsing by Author "Malone, Susan K."
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Item Sleep Variability, Eating Timing Variability, and Carotid Intima‐Media Thickness in Early Adulthood(Journal of the American Heart Association Cardiovascular and Cerebrovascular Disease, 2023-10-03) Hoopes, Elissa K.; Witman, Melissa A.; D'Agata, Michele N.; Brewer, Benjamin; Edwards, David G.; Robson, Shannon M.; Malone, Susan K.; Keiser, Thomas; Patterson, FredaBackground Day‐to‐day variability in sleep patterns and eating timing may disrupt circadian rhythms and has been linked with various adverse cardiometabolic outcomes. However, the extent to which variability in sleep patterns and eating timing relate to atherosclerotic development in subclinical stages remains unclear. Methods and Results Generally healthy adults (N=62, 29.3±7.3 years, 66% female) completed 14 days of sleep and dietary assessments via wrist accelerometry and photo‐assisted diet records, respectively. Variability in sleep duration, sleep onset, eating onset (time of first caloric consumption), eating offset (time of last caloric consumption), and caloric midpoint (time at which 50% of total daily calories are consumed) were operationalized as the SD across 14 days for each variable. Separate regression models evaluated the cross‐sectional associations between sleep and eating variability metrics with end‐diastolic carotid intima‐media thickness (CIMT) measured via ultrasonography. Models adjusted for age, sex, systolic blood pressure, sleep duration, and total energy intake. Each 60‐minute increase in sleep duration SD and sleep onset SD were associated with a 0.049±0.016 mm (P=0.003) and 0.048±0.017 mm (P=0.007) greater CIMT, respectively. Variability in eating onset and offset were not associated with CIMT; however, each 60‐minute increase in caloric midpoint SD was associated with a 0.033±0.015 mm greater CIMT (P=0.029). Exploratory post hoc analyses suggested that sleep duration SD and sleep onset SD were stronger correlates of CIMT than caloric midpoint SD. Conclusions Variability in sleep patterns and eating timing are positively associated with clinically relevant increases in CIMT, a biomarker of subclinical atherosclerosis, in early adulthood.Item Temporal associations between nightly sleep with daytime eating and activity levels in free-living young adults(SLEEP, 2023-04-21) Hoopes, Elissa K.; Brewer, Benjamin; Robson, Shannon M.; Witman, Melissa A.; D’Agata, Michele N.; Malone, Susan K.; Edwards, David G.; Patterson, FredaStudy 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. Graphical Abstract: Available at https://doi.org/10.1093/sleep/zsad123