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Record W2969865377 · doi:10.1136/bmjopen-2018-026942

Sleep duration and multimorbidity in Luxembourg: results from the European Health Examination Survey in Luxembourg, 2013–2015

2019· article· en· W2969865377 on OpenAlexaff
María Ruiz‐Castell, Tatjana T. Makovski, Valéry Bocquet, Saverio Stranges

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSocioeconomic statusDemographyCross-sectional studyDuration (music)Sleep (system call)National Health and Nutrition Examination SurveyPopulationGerontologyEpidemiologyPediatricsEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We estimated the prevalence of short sleep duration and multimorbidity in Luxembourg, and assessed whether sleep duration was associated with multimorbidity after adjusting for sociodemographic and behavioural characteristics. DESIGN: Cross-sectional study. PARTICIPANTS: Data from 1508 Luxembourg residents (48% men and 52% women) aged 25 to 64 years came from the European Health Examination Survey 2013-2015. OUTCOME MEASURES: Short sleep duration and multimorbidity. RESULTS: Participants reported sleeping 6.95 hours/night during work days, nearly 1 hour less than during non-work days (7.86 hours/night). Nearly half of participants reported having been diagnosed with ≥2 chronic conditions/diseases. Short sleep duration was associated with the number of chronic conditions (OR 4.65, 95% CI 1.48 to 14.51; OR 7.30, 95% CI 2.35 to 22.58; OR 6.79, 95% CI 2.15 to 21.41 for 1, 2 and ≥3 chronic conditions/diseases, respectively), independently of socioeconomic and behavioural characteristics. CONCLUSIONS: Health promotion programmes should aim at improving and promoting healthy lifestyles among the general population to improve sleep habits as well as decrease multimorbidity in middle-aged adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.162
GPT teacher head0.423
Teacher spread0.260 · 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 teacher head, 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

Citations74
Published2019
Admission routes1
Has abstractyes

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