LAUSR

Big data collected using consumer sleep technology can provide objectively measured insights on sleep behavior in the real-life environment. It has the advantage over self-report data of being less prone to bias. Here we used a non-contact bio-motion sensor to remotely capture objective sleep data. We analyzed 168432 nights of sleep data to test if differences between weekday versus weekend sleep behavior, known from self-report, would still hold using objective data in a large population. Sleep data was acquired using the SleepScore Max remote sleep sensor and included 168432 nights (2730 users, mean age: 46.6 +/- 11.8 years, 33% female, all resident in the USA). Analysis was restricted to those of working age; adults between 20-65. Any sleep which ended from Monday to Friday was considered weekday sleep, and any ending on Saturday or Sunday as weekend sleep. Data records were inspected and cleaned before analyzing. Descriptive statistics and independent t-tests were used to analyze the data. Total Sleep Time, Time In Bed and Sleep Onset Latencies were longer during weekend (TST: + 20.6 mins, TIB: +22.9 mins, SOL: +1.1 min, all p <0.001), resulting in a slightly poorer Sleep Efficiency (-.016%, p<0.01) for weekend nights. Time to bed and final awakening were both delayed in weekends as compared to weekdays (Time to bed +30.0 mins, and final awakening +53.4 mins, both p<0.001). This big data analysis confirms the earlier observed difference in sleep and sleep behavior between weekdays and weekends. This should be considered for optimizing (automated) sleep interventions, that may not normally take the weekend effect into consideration.