Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses : A cross-sectional study of 89,205 participants from the UK Biobank

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Wainberg , M , Jones , S E , Beaupre , L M , Hill , S L , Felsky , D , Rivas , M A , Lim , A S P , Ollila , H M & Tripathy , S J 2021 , ' Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses : A cross-sectional study of 89,205 participants from the UK Biobank ' , PLoS Medicine , vol. 18 , no. 10 , e1003782 . https://doi.org/10.1371/journal.pmed.1003782

Title: Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses : A cross-sectional study of 89,205 participants from the UK Biobank
Author: Wainberg, Michael; Jones, Samuel E.; Beaupre, Lindsay Melhuish; Hill, Sean L.; Felsky, Daniel; Rivas, Manuel A.; Lim, Andrew S.P.; Ollila, Hanna M.; Tripathy, Shreejoy J.
Contributor organization: Institute for Molecular Medicine Finland
University of Helsinki
Date: 2021-10
Language: eng
Number of pages: 18
Belongs to series: PLoS Medicine
ISSN: 1549-1277
DOI: https://doi.org/10.1371/journal.pmed.1003782
URI: http://hdl.handle.net/10138/341117
Abstract: Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.
Description: Funding Information: The authors acknowledge Milos Milic for data curation assistance. MW and SJT acknowledge support from the Kavli Foundation, Krembil Foundation, CAMH Discovery Fund, the McLaughlin Foundation, NSERC (RGPIN-2020-05834 and DGECR-2020-00048) and CIHR (NGN-171423). DF is supported by the Michael and Sonja Koerner Foundation New Scientist Program, Krembil Foundation, CAMH Discovery Fund, and the McLaughlin Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes?. The authors acknowledge Milos Milic for data curation assistance. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes.? Publisher Copyright: Copyright: © 2021 Wainberg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subject: 3124 Neurology and psychiatry
3121 General medicine, internal medicine and other clinical medicine
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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