Generally applicable qualifications of sleep and principles for achieving better sleep are difficult to design, because sleep quality can depend on individual demographic characteristics and lifestyles. In this study, the static and dynamic features of sleep–wake patterns were analyzed in association with quantitative sleep-related parameters and self-rated sleep quality to serve as a practical selection of sleep–wake patterns fitted to individual conditions. Data obtained over a 2-week period by actigraphy from university students and information technology workers were measured to obtain a daily subjective rating of sleep quality using the Oguri–Shirakawa–Azumi (OSA) sleep inventory. Qualitative sleep quality in terms of OSA score and quantitative sleep-related and chronobiological features were analyzed with regard to their dependency on the demographic characteristics, habitual sleep–wake patterns (HSWP), and distinction of weekdays/weekends. Multi-factor ANOVA was used to further investigate their dependencies regarding multiple ways of interactions between the demographic characteristics, HSWP, and distinction of weekdays/weekends. Subjective sleep quality and quantitative sleep-related parameters depended on the demographic characteristics, and so did their associations. The classification of day-to-day variations in HSWP showed four clusters that were effective factors for understanding their dependencies. Multi-factor analysis revealed demographic characteristics, HSWP, distinction of weekdays/weekends, and their multi-way interactions up to 3rd order as significant effectors of qualitative and quantitative quality of sleep. This study clarified how quantitative sleep-related parameters, subjective sleep quality, and their associations depended on demographic characteristics. Furthermore, their dependency was understood as a combination of multi-way interactions between the demographic characteristics, HSWP, and the distinction of weekdays/weekends. Our findings could provide a basis for the design of individually matched sleep–wake patterns.
- Clustering of sleep–wake patterns
- Multi-way interactions