Wearable-Derived Sleep Patterns and Everyday Well-Being

Michael W. L. Chee

Wearable-Derived Sleep Patterns and Everyday Well-Being

Author(s)	Prof. Michael W. L. Chee
Country	United States
Abstract	Wearable devices have expanded the possibility of studying sleep under everyday conditions by repeatedly estimating sleep duration, sleep timing, sleep efficiency, nocturnal wakefulness, and related physiological signals outside conventional sleep laboratories. This capability is particularly relevant to well-being research because a single retrospective sleep questionnaire may obscure night-to-night variability and temporal relationships between sleep and next-day functioning. Contemporary sleep-health theory also emphasizes that healthy sleep is multidimensional and cannot be reduced to duration alone. Buysse's sleep-health framework identifies duration, timing, efficiency, regularity, alertness, and satisfaction as complementary dimensions, while objective and wearable research has increasingly shown the value of examining consistency and timing alongside conventional measures of sleep quantity. The present study develops a longitudinal framework connecting wearable-derived sleep patterns with everyday well-being. A synthetic dataset representing 360 hypothetical adults monitored across 21 consecutive days was generated, producing 7,560 participant-day observations. Nightly sleep duration, sleep efficiency, wake after sleep onset, and deviation from habitual sleep timing were modeled as predictors of next-day well-being. Cluster-robust standardized regression was used to account for repeated observations nested within individuals. Sleep duration was modeled nonlinearly around approximately 7.5 hours rather than assuming that progressively longer sleep always predicts superior outcomes. The simulated analysis showed that deviation from the 7.5-hour sleep-duration reference was the strongest predictor of lower next-day well-being (standardized β = −.429). Greater night-to-night sleep-timing deviation was also negatively associated with well-being (β = −.241), whereas higher sleep efficiency was positively associated with well-being (β = .150). Greater wake after sleep onset showed a smaller negative relationship (β = −.097). The model explained 28.2% of variance in the simulated daily well-being outcome. Mean well-being declined from 80.36 among nights with less than 30 minutes of timing deviation to 75.71 among nights exceeding 60 minutes of deviation. The findings illustrate the value of treating wearable sleep information as a pattern rather than a single nightly score. The manuscript further emphasizes that consumer wearables are not equivalent to polysomnography. Validation studies published through 2021 indicate promising performance for sleep–wake estimation in several devices, but device-specific error and inconsistent sleep-stage classification remain important limitations. Future empirical research should therefore prioritize transparent device validation, longitudinal modeling, multidimensional sleep metrics, and cautious interpretation of wearable-derived sleep stages.
Keywords	wearable technology; sleep patterns; everyday well-being; sleep regularity; sleep efficiency; sleep duration; actigraphy; consumer sleep trackers; digital health; longitudinal monitoring
Field	Engineering
Published In	Volume 4, Issue 4, July-August 2023
Published On	2023-08-02

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Wearable-Derived Sleep Patterns and Everyday Well-Being

Share this