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    Improving sleep quality involves understanding its link to sleep patterns and lifestyle. This study developed a model to identify key factors influencing subjective sleep quality, aiding personalized sleep strategies.

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    Area of Science:

    • Sleep Science
    • Behavioral Science
    • Biostatistics

    Background:

    • Effective sleep quality management requires understanding links between subjective assessments and objective sleep metrics.
    • Individual demographic characteristics influence sleep patterns and lifestyle choices impacting sleep quality.

    Purpose of the Study:

    • To construct a regression model for subjective sleep quality (SRS) to identify key influencing factors.
    • To develop a framework for personalized sleep strategies based on individual sleep characteristics.
    • To enhance self-awareness of sleep quality for promoting healthier sleep practices.

    Main Methods:

    • Utilized data from a previous study correlating subjective sleep ratings with quantitative sleep features and habitual lifestyle factors.
    • Employed backward stepwise Linear Mixed Effect (LME) modeling to analyze relationships across different SRS categories.
    • Characterized correlation profiles involving sleep ratings, quantitative sleep metrics, chronotype, social jetlag, and habitual sleep-wake patterns (HSWP).

    Main Results:

    • The LME model demonstrated acceptable accuracy in representing subjective sleep quality ratings (SRS).
    • Identified specific determinant factors contributing to each category of subjective sleep quality.
    • Established fundamental correlation profiles between daily subjective sleep assessments and objective sleep/lifestyle data.

    Conclusions:

    • The developed regression model provides a framework for understanding and predicting subjective sleep quality.
    • Identified factors can inform the design of practical, individualized strategies for achieving comfortable sleep.
    • Increased self-awareness of sleep quality through model-based predictors can facilitate healthier sleep practices.