Abstract
Many people find that their sleep is restricted or disturbed by social obligations, including work. Sleep phase delays can affect an individual’s circadian rhythms on the following day and cause daytime sleepiness and/or poor performance. In this study, to examine weekly variations in sleep patterns, we analyzed sleep data for seven-day periods (from Sunday to Saturday) that had been collected from 2914 subjects (aged 20–79 years) over a total of 24,899 subject-weeks using contactless biomotion sensors. On the weekend, the subjects’ mean sleep midpoint, bedtime, and wake-up time were delayed by 40, 26 and 53 min, respectively, compared with those seen on weekdays. In addition, on weekdays, the mean difference between the maximum and median sleep midpoint ranged from 35 to 47 min among the subjects in their 20 s–70 s. The weekend delay and weekday variation in the subjects’ sleep patterns tended to decrease with age. This study detected sleep pattern disturbances on both weekdays and weekends. The serial changes in weekday bedtimes detected in this study suggest that sleep habits are influenced by changes in the temporal patterns of social activities/duties. We need further study the advantages of getting extra sleep and the disadvantages of sleep pattern disturbances in daily lifestyle.
Highlights
Sleep plays an important role in a healthy lifestyle together with nutrition and exercise
The contactless biomotion sensor used in this study enables the start and end of the measurement period to be determined in two ways
We examined six sleep parameters in this study: bedtime, wake-up time, time spent in bed (TIB), total sleep time (TST), sleep efficiency (SE), and initial sleep index
Summary
Sleep plays an important role in a healthy lifestyle together with nutrition and exercise. We recently developed and commercialized a contactless biomotion sensor, which objectively and accurately record sleep/wake state in daily life. The data of the users are collected through an Internet system, which enables us to gather large-scale data. With this innovative sensor technology, we are analyzing sleep data at epidemiologic level. Getting less sleep on weekdays and compensating for this on the weekend are associated with the nighttime use of electronic devices such as cellphones, computers, and televisions [4]. Many individuals restrict the amount of sleep they get on weekdays because of the demands of modern lifestyles [5]. People get insufficient sleep on weekdays and accumulate a sleep debt throughout the week, and they frequently sleep for longer on the weekend
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