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Record W4412652157 · doi:10.1101/2025.07.22.25332021

Associations of Device-Measured Sleep Duration, Regularity, and Efficiency with Cardiometabolic Health in Adults: Findings from the ProPASS Consortium

2025· preprint· en· W4412652157 on OpenAlexaff
Annemarie Koster, Raaj Kishore Biswas, Matthew Ahmadi, Joanna M. Blodgett, Nicholas A. Koemel, Andrew J. Atkin, Richard Pulsford, Borja del Pozo Cruz, Carlos Celis‐Morales, John J. Mitchell, Pasan Hettiarachchi, Peter Johansson, Magnus Svartengren, Hsiu‐Wen Chan, Kristin Suorsa, Esmée A. Bakker, Sari Stenholm, Thijs M.H. Eijsvogels, Hans H. C. M. Savelberg, Vegar Rangul, Alun D. Hughes, I‐Min Lee, Peter A. Cistulli, Andreas Holtermann, Mark Hamer, Emmanuel Stamatakis

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersForskningsrådet om Hälsa, Arbetsliv och VälfärdMedical Research CouncilVetenskapsrådetNational Health and Medical Research CouncilBritish Heart Foundation
KeywordsSleep (system call)SittingAssociation (psychology)MedicinePhysical therapyPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract Background Sleep is critical for cardiometabolic health, yet evidence on the independent and combined associations of device-measured sleep parameters remains limited. The aim of this study is to examine the independent and joint associations of sleep duration, regularity, and efficiency with cardiometabolic health outcomes in adults. Methods Cross-sectional data from six cohorts (n=14,085 participants; five countries, ≥4-day wear time) from the Prospective Physical Activity, Sitting and Sleep (ProPASS) consortium were used. Sleep duration (short: <7 h/day; adequate: 7-8 h/day; long: >8 h/day), sleep regularity index as a measure of day-to-day variability in sleep-wake patterns (regular: >87.3%; slightly irregular: 71.6-87.3%; irregular: <71.6%), and sleep efficiency as the ratio of total sleep time to total time in bed (high: >91.8%), medium: 85.3-91.8%, low: <85.3%; based on tertiles) were derived from thigh-worn accelerometer data. Cardiometabolic health markers included body mass index, waist circumference, HDL and LDL cholesterol, glycated haemoglobin, systolic and diastolic blood pressure, and a composite cardiometabolic risk z-score was computed. Generalized linear regression was used to examine individual and joint associations of sleep parameters with cardiometabolic health, adjusted for age, sex, cohort, smoking, alcohol consumption, medication use, prevalent cardiovascular disease and moderate-to-vigorous intensity physical activity. Results Short sleep duration (β: 0.05, 95%CI: 0.03-0.07), an irregular sleep pattern (β:0.14, 95%CI:0.11-0.18), and low sleep efficiency (β:0.10, 95%CI:0.07-0.13) were associated with a higher cardiometabolic risk z-score compared to adequate sleep duration, regular sleep patterns, and high sleep efficiency, respectively. A long sleep duration was not associated with cardiometabolic risk score (β:0.01, 95%CI:-0.03-0.03). The joint analysis of all three sleep parameters shows that individuals with an irregular and low sleep efficiency, regardless of sleep duration, had worse cardiometabolic health (in long sleepers: β:0.21, 95%CI: 0.15-0.28; in adequate sleepers: β:0.23, 95%CI: 0.16-0.30; in short sleepers: β:0.28, 95%CI: 0.22-0.35) compared to adequate, regular and highly efficient sleepers. Conclusion Our study suggests that sleep regularity and efficiency are important for cardiometabolic health beyond sleep duration. Future longitudinal studies and trials should evaluate multidimensional sleep indices that incorporate duration, regularity, and efficiency across diverse populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.291
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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