In the contemporary era, when life habits are largely determined by social needs and individual preferences, sleep is nevertheless affected by seasonal environmental changes. Japan has large seasonal and geographical alterations of photoperiod and climate. Japan does not adopt the daylight saving time (DST) system, making it a suitable country for the study of seasonal variations in natural human sleep. The aim of this study was to analyze the seasonal changes in the sleep properties (timing and quality) and identify their relationship with environmental changes. Here, we report an analysis of objective sleep data of 691 161 nights collected from 1856 Japanese participants (age 20-79 years, male 91%, female 9%) for 3 years using contactless biomotion sensors. Sleep onset time did not show clear seasonal variation, but sleep offset time showed a seasonal change with a single latest peak in winter. Seasonal variation was larger during weekends than during weekdays. Sleep offset time well correlated with sunrise time but was different in spring and autumn even when the sunrise time was same, suggesting the role of temperature difference. Sleep quality, estimated by wake time after sleep onset and sleep efficiency, showed seasonal changes with the lowest trough around mid-summer. In conclusion, despite profound social influences, the timing and quality of sleep showed seasonal fluctuation indicating that they were influenced by climate factors even in the developed country.

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.

Due to the busy lifestyles of people in developed countries, insufficient sleep has emerged as a central health issue in the general population. Objective home sleep monitoring is preferable for identifying sleep problems and improving sleep quality. Several instruments, most notably wrist actigraphy, have been used for this purpose. However, various impediments, including economic and practical concerns, have continued to hamper the widespread use of self‐sleep monitoring. In this study, we used the contactless biomotion sensor based on SleepMinderTM with a frequency band modified from 5.8 GHz to 10.525 Hz to comply with Japanese Radio Law. The purpose of the study was to validate the accuracy of the sleep–wake algorithm for adult outpatients used by this new sensing system. We examined the data using simultaneous polysomnography and compared the results with previous contactless biomotion sensor and actigraphy studies. Data were collected at two institutions, the Ota Memorial Sleep Center and Kuwamizu Hospital. In total, 211 adult subjects participated in the study, and 148 (99 subjects with apnea‐hypopnea index score >15) were used for the analysis. The overall accuracy of the new system was 84.1%, and its sensitivity and specificity were 91.8% and 37.6%, respectively. The automated algorithm slightly overestimated total sleep time (bias: +13 min) and sleep efficiency (bias: +3%) compared with polysomnography. As for sleep onset latency and wake time after sleep onset, these were slightly underestimated. The results are comparable to previous contactless biomotion sensor and actigraphy studies, and indicate that this modified contactless biomotion sensor is sufficiently accurate for monitoring sleep–wake patterns in this population.