---
title: "Accelerometer-derived sleep stages and incident disease risk in UK Biobank: phenome-wide cohort an"
id: "plos-medicine-0-accelerometer-derived-real-world-sleep-stages-and-risk-of-incident-diseases-a"
canonical_url: "https://medichelpline.com/clinical-feed/plos-medicine-0-accelerometer-derived-real-world-sleep-stages-and-risk-of-incident-diseases-a"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS Medicine"
source_url: "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213"
published_at: "2026-09-17T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Accelerometer-derived sleep stages and incident disease risk in UK Biobank: phenome-wide cohort an
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-medicine-0-accelerometer-derived-real-world-sleep-stages-and-risk-of-incident-diseases-a
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS Medicine
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213)
- **Published At:** 2026-09-17T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This cohort study used wrist-worn accelerometer data from **95,559** UK Biobank participants and the self-supervised **SleepNet** algorithm to derive real-world sleep stages (REM, N1, N2, N3), total sleep duration, sleep irregularity, and wake after sleep onset (WASO). - Participants were followed from the date of accelerometer wear to first disease event, death, or April 1, 2024, with a median follow-up of **8.9 years**. - A phenome-wide association study (PheWAS) examined associations between derived sleep metrics and **1,049** incident health outcomes using Cox proportional hazards models adjusted for demographics, lifestyle, and environmental exposures. - Variations in sleep patterns were associated with **156** incident diseases after multiple-testing correction. - Greater amounts of **REM sleep** and **deep sleep** (N3) were linked to lower risks for many conditions—REM with **83** diseases and deep sleep with **7** diseases. - Greater **sleep irregularity** and increased **WASO** were associated with higher risks for several outcomes (3 and 6 diseases, respectively). - Restricted cubic spline analyses identified significant non-linear associations between sleep duration and **86** disease phenotypes; for **69** phenotypes the minimum-risk sleep duration clustered in a **6–8 hours** window. - Extreme short sleep (<5 hours) showed the most widespread adverse associations, accounting for **37 of 41** significant category-specific adverse links compared with 6–8 hours. - The study underscores differential contributions of **sleep architecture** and duration to disease risk but is observational, limiting causal inference and remaining susceptible to residual confounding. - Data availability: individual-level UK Biobank data require access via UK Biobank; minimal dataset and analysis scripts are archived on Zenodo (DOI reported in source).
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosmedicine%2Farticle%3Fid%3D10.1371%2Fjournal.pmed.1005213) * * About * Browse * Publish * [](https://journals.plos.org/plosmedicine/ "PLOS Medicine") * Search [advanced search](https://journals.plos.org/plosmedicine/search) * 0 [Save](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#savedHeader) * 0 [Citation](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#citedHeader) * 0 [View](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#viewedHeader) * 0 [Share](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213#discussedHeader) Open Access Peer-reviewed Research Article # Accelerometer-derived real-world sleep stages and risk of incident diseases: A UK Biobank cohort study and phenome-wide association analysis * Jingsong Luo , Contributed equally to this work with: Jingsong Luo, Ruiyi Liu Roles Methodology, Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review & editing Affiliations School of Public Health, Peking University, Beijing, China, School of Public Health, Capital Medical University, Beijing, China, Beijing Key Laboratory of Environment and Aging, Capital Medical University, Beijing, China, Beijing Laboratory of Allergic Diseases, Beijing Municipal Education Commission, Beijing, China ⨯ * Ruiyi Liu , Contributed equally to this work with: Jingsong Luo, Ruiyi Liu Roles Data curation, Software, Writing – original draft, Writing – review & editing Affiliations School of Public Health, Capital Medical University, Beijing, China, Beijing Key Laboratory of Environment and Aging, Capital Medical University, Beijing, China, Beijing Laboratory of Allergic Diseases, Beijing Municipal Education Commission, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0004-7696-9130 ](https://orcid.org/0009-0004-7696-9130 "ORCID Registry") ⨯ * Jie Yin, Roles Writing – original draft, Writing – review & editing Affiliations School of Public Health, Capital Medical University, Beijing, China, Beijing Key Laboratory of Environment and Aging, Capital Medical University, Beijing, China, Beijing Laboratory of Allergic Diseases, Beijing Municipal Education Commission, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-8526-3402 ](https://orcid.org/0000-0002-8526-3402 "ORCID Registry") ⨯ * Wangnan Cao , Roles Conceptualization, Methodology, Supervision, Project administration, Writing – review & editing * E-mail: wangnancao@bjmu.edu.cn (WC); shengzhisun@ccmu.edu.cn (SS); ruichen@ccmu.edu.cn (RC) Affiliation School of Public Health, Peking University, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-6163-2760 ](https://orcid.org/0000-0002-6163-2760 "ORCID Registry") ⨯ * Shengzhi Sun , Roles Conceptualization, Methodology, Data curation, Formal analysis, Software, Resources, Supervision, Project administration, Funding acquisition, Writing – review & editing * E-mail: wangnancao@bjmu.edu.cn (WC); shengzhisun@ccmu.edu.cn (SS); ruichen@ccmu.edu.cn (RC) Affiliations School of Public Health, Capital Medical University, Beijing, China, Beijing Key Laboratory of Environment and Aging, Capital Medical University, Beijing, China, Beijing Laboratory of Allergic Diseases, Beijing Municipal Education Commission, Beijing, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-3708-1225 ](https://orcid.org/0000-0002-3708-1225 "ORCID Registry") ⨯ * Rui Chen Roles Conceptualization, Methodology, Supervision, Project administration, Writing – review & editing * E-mail: wangnancao@bjmu.edu.cn (WC); shengzhisun@ccmu.edu.cn (SS); ruichen@ccmu.edu.cn (RC) Affiliations School of Public Health, Capital Medical University, Beijing, China, Beijing Key Laboratory of Environment and Aging, Capital Medical University, Beijing, China, Beijing Laboratory of Allergic Diseases, Beijing Municipal Education Commission, Beijing, China ⨯ # Accelerometer-derived real-world sleep stages and risk of incident diseases: A UK Biobank cohort study and phenome-wide association analysis * Jingsong Luo, * Ruiyi Liu, * Jie Yin, * Wangnan Cao, * Shengzhi Sun, * Rui Chen ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 17, 2026 * * [Article](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213) * [Authors](https://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.pmed.1005213) * [Metrics](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1005213) * [Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005213) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pmed.1005213) * [Abstract](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#abstract0) * [Author summary](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#abstract1) * [1. Introduction](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#sec004) * [2. Methods](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#sec005) * [3. Results](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#sec011) * [4. Discussion](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#sec020) * [Supporting information](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#sec021) * [Acknowledgments](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#ack) * [References](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#references) * [Reader Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1005213) * [Figures](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213) ![Accessible Data Icon](https://journals.plos.org/resource/img/accessible_data.svg)Accessible Data [ See the data ![Link Icon](https://journals.plos.org/resource/img/data_link_icon.svg) ](https://doi.org/10.5281/zenodo.21515144) This article includes the Accessible Data icon, an experimental feature to encourage data sharing and reuse. [Find out how research articles qualify for this feature.](https://theplosblog.plos.org/2023/07/accessible-data/) ## Abstract ### Background Previous studies utilizing self-reported data or polysomnography have found that poor sleep quality is associated with increased risk of morbidity and mortality. However, the association between real-world sleep patterns, including sleep stages, duration, and fragmentation, measured objectively with physiological data and the risk of incident disease among middle-aged and older adults has not yet been systematically evaluated. ### Methods and findings In this cohort study, we analyzed wrist-worn accelerometer data from 95,559 Biobank participants and derived key metrics of real-world sleep patterns: rapid eye movement [REM], N1, N2, and N3; total sleep duration; sleep irregularity; and wakefulness after sleep onset using the SleepNet algorithm. Participants were followed up from the date of accelerometer wear until the first occurrence of disease, death, or April 1, 2024 with median follow-up length of 8.9 years. Phenome-wide association analysis and restricted cubic spline (RCS) analyses were performed using Cox proportional hazard regression, with adjustment for demographic characteristics, lifestyle factors, and environmental exposures, to map an atlas of associations between real-world sleep patterns and the incidence of 1,049 health outcomes. We found that variations in sleep patterns were associated with 156 incidences of diseases. Specifically, higher amount of REM sleep and deep sleep were associated with lower risks of 83 and 7 diseases, respectively, while greater sleep irregularity and increased WASO were linked to elevated risks of 3 and 6 diseases, respectively. RCS analyses revealed significant non-linear relationships between sleep duration and 86 disease phenotypes (_P_ for nonlinear . The minimal data set underlying the reported findings and all analysis scripts required to reproduce the analyses are publicly available through Zenodo (DOI: [10.5281/zenodo.21515144](https://doi.org/10.5281/zenodo.21515144)). This study did not generate any new unique materials or reagents. **Funding:** This work was supported by the National Science Fund for Distinguished Young Scholars (82025031 to RC; ), the Key Program of the National Natural Science Foundation of China (82230109 to RC; ), the Beijing Outstanding Young Scientist Program (JWZQ20240101024 to RC; ), and the Young Beijing Scholars Project, the Chinese Institutes for Medical Research, Beijing (CX23YZ01 to RC; ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. **Competing interests:** The authors have declared that no competing interests exist. **Abbreviations:** BMI, body mass index; FDR, false discovery rate; HRs, hazard ratios; IQR, interquartile ranges; LUR, Land Use Regression; PAR%, population attributable risk percent; PheWAS, phenome-wide association study; PSG, polysomnography; RECORD, Reporting of Studies Conducted using Observational Routinely-Collected Data; RCS, restricted cubic spline; REM, rapid eye movement; SD, standard deviation; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology; TDI, Townsend deprivation index; WASO, wake after sleep onset ## 1. Introduction Sleep is a fundamental human behavior that plays a critical role in overall health and wellbeing. While many previous studies have examined the association between sleep and risks of morbidity and mortality, most have primarily focused on sleep duration as measured by self-reported questionnaires [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref001),[2](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref002)]. However, self-reported sleep measures often show poor correlation with objective physiological assessments [[3](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref003)–[5](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref005)], making it difficult to accurately capture real-world sleep patterns. Moreover, there remains limited understanding of how other real-world sleep patterns, such as sleep stages (rapid eye movement [REM], N1, N2, and N3) and fragmentation, relate to health outcomes. The gold standard for objectively assessing sleep patterns is polysomnography (PSG), which provides accurate measurements of real-world sleep stages, including REM, N1, N2, and N3 [[6](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref006),[7](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref007)]. However, its high cost and technical complexity limit its feasibility for large-scale epidemiological studies. Although consumer-grade wrist-worn devices have popularized sleep monitoring, their sleep staging algorithms are typically proprietary and validated only in small samples, leaving their measurement validity uncertain [[8](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref008)–[11](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref011)]. Furthermore, these devices are predominantly used among younger and middle-aged adults, resulting in a lack of large-scale data in older populations [[12](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref012)]. In contrast, wrist-worn accelerometers have become widely used for objective measurement of sleep patterns in large middle-aged and elderly cohorts, such as nighttime sleep duration, onset, efficiency, and fragmentation [[13](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref013),[14](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref014)]. A landmark study developed a self-supervised machine learning algorithm, SleepNet, which demonstrated competitive performance in characterizing sleep architecture from wrist-worn accelerometer data [[7](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref007)]. Validated against 1,113 nights of laboratory-based polysomnography recordings, this approach provides a robust methodological framework for deriving sleep stages and sleep regularity metrics from accelerometer signals [[7](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005213#pmed.1005213.ref007)]. Building on these advances, recent studies have leveraged accelerometer-derived sleep features to investigate their associations with cardiometabolic and neurological outcomes [[15](
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