Objective assessment of sleep regularity in 60 000 UK Biobank participants using an open-source package

Human health and behavior are regulated by a complex and extensive network of circadian clocks. These clocks are entrained by rhythmic signals in the environment, such as daily light exposure. In individuals who have irregular sleep schedules, these signals that furnish time-of-day information to the circadian system are less robust, which may cause circadian disruption and poor health outcomes [1]. Sleep regularity can be quantified using the Sleep Regularity Index (SRI) [2], a metric that compares sleep patterns between consecutive days (sleeping at similar times each day results in a high SRI). The SRI captures day-to-day variability in bedtime, waketime, sleep duration, naps, and awakenings during sleep [3]. Lower SRI has been associated with substantially increased risk for obesity, diabetes, cardiovascular disease, hypertension, and depressed mood [4–6]. Although sleep regularity is now recognized as a critical dimension of sleep health, there are barriers to measuring and reporting sleep regularity consistently. First, there are no open-source options for calculating sleep regularity, meaning it cannot be computed with the same ease as other common sleep metrics. Second, there is a lack of clear benchmarks for what represents a high or low level of sleep regularity at a population level, both for the SRI and other sleep regularity metrics [3, 4, 6]. We developed an open-source package for computing SRI from accelerometer data, and we applied it to the single largest accelerometer sample available to researchers, within the UK Biobank.