P161 Regularity of sleep-wake patterns in the UK Biobank (N = 86 624) and an open-source tool to calculate the Sleep Regularity Index

INTRODUCTION: Regular sleep-wake patterns aid in the maintenance of optimal physical and mental health, by helping to align environmental, behavioural, and physiological rhythms. The distribution of sleep regularity across the population has not been well documented. Furthermore, researchers currently lack tools to easily quantify sleep regularity. METHOD: We have described sleep regularity in 86 624 UK Biobank participants (age (M±SD) = 62.45±7.84; 56.2% female) using data from wrist-worn accelerometers. Regularity was measured using the Sleep Regularity Index (SRI), which quantifies day-to-day similarity in sleep-wake patterns, and which is linked to cardio-metabolic and mental health outcomes. We developed an R package to calculate SRI from accelerometer data, which works in conjunction with GGIR (a validated accelerometer processing tool) to identify sleep-wake state, including naps and broken sleep. RESULTS: The SRI distribution had M±SD = 78.02±11.53, and median = 80.49. The least regular quintile (SRI<70.2) had standard deviation of sleep onset = 2.23h, offset = 2.14h, and duration = 1.95h, compared with onset = 0.78h, offset = 0.85h, and duration = 0.95h in the most regular quintile (SRI>87.3). Approximately 14% of participants exhibited large day-to-day shifts in sleep timing (>3h) at least once per week. DISCUSSION: This is the largest description of sleep regularity to-date. The norms established here provide a reference for researchers and clinicians intending to quantify sleep regularity with the SRI. We have combined methods described here into an open-source R package to calculate SRI from accelerometer or sleep diary data, available for download via GitHub.