Summary: People diagnosed with mental health disorders are more likely to report poor sleep quality, including sleep disturbances and problems falling back to sleep, than the general population.
Source: CAMH
People who have been diagnosed with a mental illness are more likely to have poor sleep quality compared to the general population, according to the largest study of its kind ever conducted.
The CAMH-led study, “Accelerometer-derived sleep measures and lifetime psychiatric diagnoses,” has just been published in the journal PLOS Medicine.
“The differences in sleep patterns indicated worse sleep quality for participants with a previous diagnosis of mental illness, including waking up more often and for longer periods of time,” said senior author Dr. Shreejoy Tripathy, an Independent Scientist at CAMH’s Krembil Centre for Neuroinformatics. He also emphasized that gauging the quality of sleep was just as important as measuring the total amount with regard to its impact on mental health.
“The relationship between sleep and mental health is bi-directional,” said lead author Dr. Michael Wainberg, a postdoctoral researcher at the Krembil Centre for Neuroinformatics. “Poor sleep contributes to poor mental health and poor mental health contributes to poor sleep. Sleep pattern differences were a feature of all mental illnesses we studied regardless of diagnosis.”
The study was based on data collected from 89,205 participants in the United Kingdom who agreed to wear an accelerometer on their wrist that tracked body movement 24 hours a day for seven days. They also consented to having their data stored in a digital biobank for research purposes.
The authors used computational algorithms—including machine learning—to summarize this vast amount of data into ten metrics, including bedtime, wake time, naps and the longest duration of uninterrupted sleep. They then compared these metrics between participants who had received a previous diagnosis of mental illness in their lifetime and those who had not.
“We know that up to 80 per cent of people with mental health disorders can have problems with falling asleep, staying asleep or waking up earlier than they intended,” said CAMH psychiatrist and sleep disorder specialist Dr. Michael Mak. “We know that sleep disturbances cause a great burden to society, including an economic one. And we know that treatments that improve sleep quality, whether it is therapy or some types of medication, can improve mental health outcomes.”
This is the first large-scale transdiagnostic study of objectively measured sleep and mental health, and the study’s unique methodology allowed for sleep monitoring to be conducted in each individual’s natural home sleep environment rather than in a laboratory setting.
“Until now nobody has looked at objectively measured sleep in the context of mental illness at quite this scale before,” said Dr. Tripathy. “Part of why we wanted to do this study is that with the emergence of smartphones and wearables, we have access to data streams that we never had before.”
The Krembil Centre for Neuroinformatics is currently developing a patient data biobank similar to the one in the UK that was used for this study. The core goal of the CAMH BrainHealth Databank is to use patient data, including the use of wearables outside of a hospital setting, to deliver improved, personalized mental health care in the present, while also accelerating future clinical research, discovery and innovation.
About this sleep and mental health research news
Author: Hayley Chazan
Source: CAMH
Contact: Hayley Chazan – CAMH
Image: The image is in the public domain
Original Research: Open access.
“Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank” by Michael Wainberg, Samuel E. Jones, Lindsay Melhuish Beaupre, Sean L. Hill, Daniel Felsky, Manuel A. Rivas, Andrew S. P. Lim, Hanna M. Ollila, Shreejoy J. Tripathy. PLOS Medicine
Abstract
Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank
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.
