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Sleep and circadian health in the UK Biobank: report on the 2023 sleep questionnaire enhancement

Abstract:
Study Objectives: Our study introduced the 2023 UK Biobank sleep questionnaire and described variation in sleep health dimensions and the prevalence of disordered sleep. Methods: A questionnaire comprising validated measures and bespoke items was developed to capture key self-reported domains of sleep health and symptoms of sleep disorders. We quantified cohort prevalence of operationally defined sleep disorders and assessed the patterning of sleep health dimensions across key sociodemographic and clinically relevant variables. Results: A total of 183 704 individuals completed at least one module of the questionnaire after email invitation (representing 56 per cent of those with an active email address), and an additional 1352 individuals completed via the participant website. In total 185 056 individuals were included in the analysis. Respondents were predominantly from a White ethnic background (96.8%), had a mean age of 69.9 (SD, 7.5) years, 57.9 per cent were female, and 25.5 per cent were in employment. Compared to non-respondents, respondents were more likely to be female, tended to be better educated, healthier, and exhibit lower levels of socioeconomic deprivation, although baseline sleep variables were similar between respondents and non-respondents. Around 40 per cent of respondents reported sleep duration less than 7 h, and 49 per cent reported poor sleep quality (Pittsburgh Sleep Quality Index >5). Approximately one-quarter (25.2%) met the criteria for at least one operationally defined sleep disorder, with insomnia being the most common (14.4%) followed by obstructive sleep apnea (8.0%), restless legs syndrome (4.1%), and frequent nightmares (3.7%). Sleep disorders were associated with higher levels of anxiety, depression, fatigue, and cognitive complaints. Conclusions: Poor sleep quality and operationally defined sleep disorders are common in the UK Biobank cohort. Sleep questionnaire data can now be integrated with a range of biomedical information to advance understanding of sleep.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/sleep/zsag068

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-1158-5425
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Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Big Data Institute
Role:
Author
ORCID:
0000-0001-5944-1925
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Sub department:
Big Data Institute
Role:
Author
ORCID:
0009-0002-6924-3599
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Role:
Author
More by this author
Institution:
University of Oxford
Role:
Author


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Funder identifier:
https://ror.org/0187kwz08
Grant:
NIHR203667
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Funder identifier:
https://ror.org/0472gwq90
Grant:
UKRI687
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Funder identifier:
https://ror.org/03x94j517
Grant:
APP36241
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Funder identifier:
https://ror.org/029chgv08
Grant:
223100/Z/21/Z
More from this funder
Funder identifier:
10.13039/501100000266


Publisher:
Oxford University Press
Journal:
SLEEP More from this journal
Volume:
49
Issue:
6
Pages:
zsag068
Article number:
zsag068
Publication date:
2026-03-09
Acceptance date:
2026-02-27
DOI:
EISSN:
1550-9109
ISSN:
0161-8105


Language:
English
Keywords:
Source identifiers:
4231796
Deposit date:
2026-06-15
ARK identifier:
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