O062 Sleep behaviour modification in shift workers: Applying models of arousal dynamics tailored to individual shift schedules and personal commitments

Abstract

Abstract Introduction Circadian-based biomathematical models of sleep-wake and alertness regulation can improve sleep health in shift workers by providing physiologically optimal times for sleep. It is unclear whether they can be successfully implemented in real-world contexts. This study, for the first time examined associations between model-based sleep-wake recommendations and modification of sleep behaviours in shift workers. Methods Nurses (n=28; 37.2±9.6yrs) with matching-rotating shift schedules received personalised sleep recommendations from either the Phillips-Robinson Model or Model of Arousal Dynamics. Nurses used sleep diaries to record sleep-wake behaviours: (i) pre-exposure to recommendations (baseline, 1-week), (ii) upon exposure (1-week) and completed PROMIS-sleep disturbance, sleep-related impairments pre and post. Results During baseline, participants usual bedtimes and waketimes were at least 180mins away from recommended times from both models. Upon exposure to recommendations, sleep behaviours were aligned within: (i) 62±11mins for bedtime, 71±9mins for waketime (Model of Arousal Dynamics), (ii) 104±16mins for bedtime, 89±12mins for waketime (Phillips-Robinson model) with large effect sizes (p<.00001 for both models). Participants sleep windows had better compliance with recommended sleep windows for the CRC Alertness model (79% overlap) compared to the Phillips-Robinson model (69% overlap). Sleep disturbance and sleep-related impairments improved significantly upon study conclusion (small-effect sizes both groups). Participants reported that automated recommendation delivery, and use of diurnal preference and real-time sleep behaviours to further personalise recommendations will facilitate greater uptake. Conclusion The study provides positive proof-of-concept for the implementation of biomathematical models of sleep-wake dynamics in shift work contexts. Accounting for individual differences may improve applicability of recommendations.