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Later circadian timing of energy intake is associated with higher body fat percentage. Current methods for obtaining accurate circadian timing are labor- and cost-intensive, limiting practical application of this relationship. This study investigated whether the timing of energy intake relative to a mathematically modeled circadian time, derived from easily collected ambulatory data, would differ between participants with a lean or overweight/obesity body fat percentage.Participants (N = 87) wore a light- and activity-measuring device (actigraph) throughout a cross-sectional 30-day study. For 7 consecutive days within these 30 days, participants used a time-stamped-picture phone application to record energy intake. Body fat percentage was recorded. Circadian time was defined using melatonin onset from in-laboratory collected repeat saliva sampling or using light and activity or activity data alone entered into a mathematical model.Participants with overweight/obesity body fat percentages ate 50% of their daily calories significantly closer to model-predicted melatonin onset from light and activity data (0.61 hours closer) or activity data alone (0.86 hours closer; both log-rank p 0.05).Use of mathematically modeled circadian timing resulted in similar relationships between the timing of energy intake and body composition as that observed using in-laboratory collected metrics. These findings may facilitate use of circadian timing in time-based interventions.

The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hours. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: mean errors in estimating the time of DLMO are approximately 1.5–1.6 hours. We reframed the problem as a classification problem and estimated whether an individual’s current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation - identifying the time at which the switch in classification occurs - to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55–65% with a range of 20–80% for a given day; this accuracy was highly dependent upon the phase angle (i.e., time) between DLMO and sleep onset, with highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy-based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.