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

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.

Abstract

In a cross-sectional study, Michael Wainberg and colleagues investigate the association between accelerometer-derived sleep measures and lifetime psychiatric diagnoses.

Author summary

Why was this study done?

• Sleep problems are both symptoms of and risk factors for many mental health conditions.

• This study aimed to determine how objectively measured sleep differs among individuals with lifetime psychiatric diagnoses.

What did the researchers do and find?

• This cohort study of 89,205 individuals from the UK Biobank analyzed 10 accelerometer-derived sleep measures.

• The study found a rich suite of associations with lifetime diagnoses of psychopathology and psychiatric polygenic risk scores, though effect sizes were generally small.

What do these findings mean?

• Sleep pattern differences are the norm among patients with lifetime psychiatric illness.

• Accelerometry provides a scalable way to objectively measure such differences in psychiatric research and practice.

• Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry.