P161 Regularity of sleep-wake patterns in the UK Biobank (N = 86 624) and an open-source tool to calculate the Sleep Regularity Index

Abstract Introduction Regular sleep-wake patterns aid in the maintenance of optimal physical and mental health, by helping to align environmental, behavioural, and physiological rhythms. The distribution of sleep regularity across the population has not been well documented. Furthermore, researchers currently lack tools to easily quantify sleep regularity. Method We have described sleep regularity in 86 624 UK Biobank participants (age (M±SD) = 62.45±7.84; 56.2% female) using data from wrist-worn accelerometers. Regularity was measured using the Sleep Regularity Index (SRI), which quantifies day-to-day similarity in sleep-wake patterns, and which is linked to cardio-metabolic and mental health outcomes. We developed an R package to calculate SRI from accelerometer data, which works in conjunction with GGIR (a validated accelerometer processing tool) to identify sleep-wake state, including naps and broken sleep. Results The SRI distribution had M±SD = 78.02±11.53, and median = 80.49. The least regular quintile (SRI<70.2) had standard deviation of sleep onset = 2.23h, offset = 2.14h, and duration = 1.95h, compared with onset = 0.78h, offset = 0.85h, and duration = 0.95h in the most regular quintile (SRI>87.3). Approximately 14% of participants exhibited large day-to-day shifts in sleep timing (>3h) at least once per week. Discussion This is the largest description of sleep regularity to-date. The norms established here provide a reference for researchers and clinicians intending to quantify sleep regularity with the SRI. We have combined methods described here into an open-source R package to calculate SRI from accelerometer or sleep diary data, available for download via GitHub.

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Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab088 ◽

2021 ◽

Author(s):

Shahram Nikbakhtian ◽

Angus B Reed ◽

Bernard Dillon Obika ◽

Davide Morelli ◽

Adam C Cunningham ◽

...

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Risk ◽

Sleep Onset ◽

Sensitivity Analyses ◽

Poor Sleep ◽

Accelerometer Data ◽

Uk Biobank ◽

Cvd Risk ◽

Onset Timing ◽

Sleep Parameters

Abstract Aims Growing evidence suggests that poor sleep health is associated with cardiovascular risk. However, research in this area often relies upon recollection dependent questionnaires or diaries. Accelerometers provide an alternative tool for measuring sleep parameters objectively. This study examines the association between wrist-worn accelerometer-derived sleep onset timing and cardiovascular disease (CVD). Methods and results We derived sleep onset and waking up time from accelerometer data collected from 103 712 UK Biobank participants over a period of 7 days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. A total of 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00 p.m.–10:59 p.m. was associated with the lowest CVD incidence. An additional model, controlling for sleep duration, sleep irregularity, and established CVD risk factors, did not attenuate this association, producing hazard ratios of 1.24 (95% confidence interval, 1.10–1.39; P < 0.005), 1.12 (1.01–1.25; P= 0.04), and 1.25 (1.02–1.52; P= 0.03) for sleep onset <10:00 p.m., 11:00 p.m.–11:59 p.m., and ≥12:00 a.m., respectively, compared to 10:00 p.m.–10:59 p.m. Importantly, sensitivity analyses revealed this association with increased CVD risk was stronger in females, with only sleep onset <10:00 p.m. significant for males. Conclusions Our findings suggest the possibility of a relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

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Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study

10.1101/2021.06.23.21259390 ◽

2021 ◽

Author(s):

Shahram Nikbakhtian ◽

Angus B Reed ◽

Bernard Dillon Obika ◽

Davide Morelli ◽

Adam C Cunningham ◽

...

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Risk ◽

Disease Incidence ◽

Sleep Onset ◽

Onset Time ◽

Sensitivity Analyses ◽

Accelerometer Data ◽

Uk Biobank ◽

Onset Timing ◽

Sleep Parameters

Aims Growing evidence suggests that sleep quality is associated with cardiovascular risk. However, research in this area often relies upon recollection dependant questionnaires or diaries. Accelerometers provide an alternative tool for deriving sleep parameters measuring sleep patterns objectively. This study examines the associations between accelerometer derived sleep onset timing and cardiovascular disease (CVD). Methods and Results We derived sleep onset and waking up time from accelerometer data collected from 103,712 UK Biobank participants over a period of seven days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00pm-10:59pm was associated with the lowest CVD incidence. A fully adjusted model, additionally controlling for sleep duration, sleep irregularity, and established CVD risk factors, was unable to eliminate this association, producing hazard ratios of 1.24 (95% CI, 1.10-1.39; p<0.005), 1.12 (1.01-1.25; p=0.04), and 1.25 (1.02-1.52; p=0.03) for sleep onset <10:00pm, 11:00pm-11:59pm, and & ≥12:00am, respectively, compared to 10:00pm-10:59pm. Importantly, sensitivity analyses revealed this association was stronger in females, with only sleep onset <10:00pm significant for males. Conclusions Our findings suggest an independent relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

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706 Sleep and Mental Health in College Students Before and During the Initial COVID-19 Shutdown

SLEEP ◽

10.1093/sleep/zsab072.704 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A275-A276

Author(s):

Michele Okun ◽

Allison Walden ◽

Leilani Feliciano

Keyword(s):

Mental Health ◽

College Students ◽

Sleep Quality ◽

Online Survey ◽

Sleep Problems ◽

Negative Impact ◽

Sleep Onset ◽

Sleep Onset Latency ◽

Physical And Mental Health ◽

The U.S

Abstract Introduction The COVID-19 pandemic has had an unparalleled impact on college students. Following the initial and abrupt shutdown of campuses in March 2020, several investigators assessed the immediate effects on University students. Early reports found that college students reported a higher prevalence of anxiety and depression, sedentary behavior, and sleep problems. Most were conducted outside the U.S. Data from U.S. college students are critical to identify which areas are should receive resources and interventions as the U.S. continues to experience exponential COVID cases along with continued remote learning, social restrictions and/or lockdowns. Methods Students enrolled in the Spring 2020 semester (18 years of age +) were invited to participate in an online survey (April – May 2020). A final sample of 491 completed the entire survey (length ~45 minutes) which asked about sleep quality, psychological stress, depression, and exercise.Paired t-tests were conducted to compare pre-COVID and during COVID data. Results There were significant differences in sleep onset latency (26.44 ± 23.53 min vs 32.06 ± 26.88 min; t = -3.81, P < .001), sleep duration (7.30 ± 1.45 hours vs 7.63 ± 2.07 hours; t = -2.23, p = 0.027) and overall sleep quality (6.29 ± 3.29 vs 7.44 ± 3.86; t = -7.26, p < .001), as well as depression scores (IDS no sleep questions) (5.61 ± 4.18 vs 17.59 ± 5.45; t = -54.9, P < .001). There was no difference in perceived stress (28.03 ±5.27 vs 28.39 ±5.53, t = -1.49, p = .138). Exercise (vigorous, moderate and walking) all decreased with regards to days and time spent, (all P’s < .001), whereas minutes sitting significantly increased (426.50 ± 239.88 vs 542.26 ± 249.63, p < .001). Conclusion These data empirically support the claim that the pandemic is having a significant negative impact on physical and mental health among college students. In the best of times, college students have irregular sleep patterns and significant depression, but these behaviors are worsened under government restrictions. These findings underscore the need to prioritize prevention and intervention of modifiable behaviors, especially if the pandemic extends into 2021. Support (if any):

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Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia

Molecular Psychiatry ◽

10.1038/mp.2015.225 ◽

2016 ◽

Vol 21 (11) ◽

pp. 1624-1632 ◽

Cited By ~ 201

Author(s):

S P Hagenaars ◽

◽

S E Harris ◽

G Davies ◽

W D Hill ◽

...

Keyword(s):

Mental Health ◽

Cognitive Functions ◽

Uk Biobank ◽

Physical And Mental Health ◽

Shared Genetic

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Pleiotropy between neuroticism and physical and mental health: findings from 108 038 men and women in UK Biobank

Translational Psychiatry ◽

10.1038/tp.2016.56 ◽

2016 ◽

Vol 6 (4) ◽

pp. e791-e791 ◽

Cited By ~ 54

Author(s):

C R Gale ◽

◽

S P Hagenaars ◽

G Davies ◽

W D Hill ◽

...

Keyword(s):

Mental Health ◽

Uk Biobank ◽

Physical And Mental Health ◽

Men And Women

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261 Accelerometer-Derived Sleep and Circadian Domains and Sociodemographic Correlates in the UK Biobank

SLEEP ◽

10.1093/sleep/zsab072.260 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A105-A105

Author(s):

Philip Gehrman ◽

Susan Malone ◽

Freda Patterson ◽

Jonathan Mitchell ◽

Diego Mazzotti

Keyword(s):

Open Source ◽

Linear Models ◽

Activity Patterns ◽

Population Based ◽

Accelerometer Data ◽

Uk Biobank ◽

Sleep Health ◽

Variable Clustering ◽

Clinically Significant ◽

The Uk

Abstract Introduction Sleep health encompasses sleep regularity, duration, timing, efficiency and satisfaction. Accelerometry is an established method to estimate sleep in ecologically valid contexts, capturing key characteristic of rest-activity patterns, and facilitating population sleep health research. While hundreds of traits can be generated from open-source algorithms applied to raw acceleration data, the lack of clarity around their meaningful use beyond conventional measures limit the ability of these data to systematically inform evidence-based practices promoting sleep health. Here, we propose a method to identify key sleep and circadian domains, using data reduction methods for hundreds of accelerometer-derived traits to inform population-based sleep heath research. We also aimed to validate our findings by assessing whether the identified domains captured known sociodemographic associations. Methods We analyzed sociodemographic and raw triaxial accelerometer data recorded for 7 days from 79,876 adults (mean age 56.3±2.1 years, 56.3% women) participating in the UK Biobank. Standardized data processing using the open-source package GGIR (v1.7-1) resulted in the generation of 107 sleep and circadian traits. Variable clustering was used to identify key sleep and circadian domains, pertinent to sleep health, representing interpretable biological constructs minimizing correlation with other domains. Associations between identified domains and sociodemographic factors were evaluated using general linear models, and clinically significant differences were determined by standardized mean differences (SMD) ≥0.3. Results We identified 25 sleep and circadian domains explaining ≥80% of the variability of all 107 included traits. Domains capturing measures of variability tended to cluster together. The most clinically significant associations with sociodemographic characteristics were: women (vs. men) had higher sleep efficiency and lower accumulation of diurnal sleep periods; older (vs. younger) individuals had earlier most active starting time, lower acceleration amplitude and lower number of nocturnal sleep periods; and shift (vs. non-shift) workers had higher variability in sleep timing on weekends. Conclusion We demonstrate that variable clustering on accelerometer-derived data can identify meaningful sleep and circadian domains. In addition, identified domains captured known sociodemographic associations commonly observed in the sleep and circadian literature, suggesting that they could be relevant to inform public health practices that promote sleep health. Support (if any) NHLBI 5R01HL143790-02(PG); NIMHHD R01MD012734(FP); NIDA R01DA051321(FP); NIH/NHLBI K01HL123612(JM)

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GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2018-0063 ◽

2019 ◽

Vol 2 (3) ◽

pp. 188-196 ◽

Cited By ~ 39

Author(s):

Jairo H. Migueles ◽

Alex V. Rowlands ◽

Florian Huber ◽

Séverine Sabia ◽

Vincent T. van Hees

Keyword(s):

Physical Activity ◽

Open Source ◽

R Package ◽

Research Community ◽

Accelerometer Data ◽

Scientific Publications ◽

Research Areas ◽

Technological Advances ◽

Sleep Parameters ◽

One Stop

Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.

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