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Record W1511546690 · doi:10.1002/oby.20701

Change in sleep duration and visceral fat accumulation over 6 years in adults

2014· article· en· W1511546690 on OpenAlexafffund
Jean‐Philippe Chaput, Claude Bouchard, Angelo Tremblay

Bibliographic record

VenueObesity · 2014
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité LavalChildren's Hospital of Eastern Ontario
FundersMedical Research CouncilMedical Research Council CanadaCanadian Institutes of Health ResearchUniversité Laval
KeywordsMedicineDuration (music)Sleep (system call)Longitudinal studyWeight changeWeight gainInternal medicineObesityDemographyWeight lossBody weight

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the relationship between change in sleep duration and long-term visceral adiposity change in adults. METHODS: A longitudinal analysis was conducted on 293 participants, aged 18-65 years, followed for a mean of 6.0 ± 0.9 years. At baseline and year 6, sleep duration was self-reported and visceral adipose tissue (VAT) assessed using computed tomography. Multivariable modeling was used to examine the association between change in sleep duration and VAT change over the 6-year time period, with adjustments made for age, sex, change in BMI, personal characteristics, energy intake, and physical activity. RESULTS: Participants gained an average of 19.2 ± 37.3 cm(2) in VAT over the follow-up period. Baseline short (≤6 h/day) and long (≥9 h/day) sleepers gained significantly more VAT than those reporting sleeping 7-8 hours a night (23.4 and 20.2 cm(2) vs. 14.1 cm(2) , respectively, P < 0.05). Using continuous data, we observed that the change in sleep duration was not associated with VAT change. However, a change in sleep duration from ≤6 h/day to 7-8 h/day was associated with 6 cm(2) fewer VAT gain after multivariable adjustment (P < 0.05). CONCLUSIONS: A spontaneous change in sleep duration (from a short to an adequate duration) is independently and inversely associated with long-term VAT accumulation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.032
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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

Citations63
Published2014
Admission routes2
Has abstractyes

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