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Record W4309765032 · doi:10.1093/sleepadvances/zpac042

Sleep efficiency and the metabolic risk score in very active older women and men

2022· article· en· W4309765032 on OpenAlexaffabout
Kenneth Madden, Boris Feldman, Jocelyn Chase

Bibliographic record

VenueSLEEP Advances · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineGerontologyDemographyInternal medicineSociology

Abstract

fetched live from OpenAlex

Abstract Study Objectives Sleep disruption is a risk factor for obesity, diabetes, and cardiovascular disease in older adults. How physical activity (PA) interacts with the negative cardiometabolic effects of poor sleep is not known. We objectively measured sleep efficiency (SE) in very active older adults and examined the association between SE and a continuous Metabolic Syndrome Risk Score (cMSy). Methods Very active older adults (age ≥65 years) from a Master’s Ski Team (Whistler, Canada) were recruited. Each participants wore an activity monitor (SenseWear Pro) continuously for 7 days to provide measures of both daily energy expenditure (metabolic equivalents, METs) and SE. All components of the metabolic syndrome were measured and a principal component analysis was used to compute a continuous metabolic risk score (cMSy, sum of eigenvalues ≥1.0). Results A total of 54 participants (mean age 71.4 years, SD 4.4 years, and 24 men and 30 women) were recruited and had very high PA levels (>2.5 h per day of exercise). Initially, there was no significant association between SE and cMSy (p = 0.222). When stratified by biological sex, only men showed a significant negative association between SE and cMSy (Standardized β = −0.364 ± 0.159, p = 0.032). Conclusions Only older men show a significant negative association between poor SE and increased cardiometabolic risk, despite high levels of PA.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.654

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)0.0010.000

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 head0.005
GPT teacher head0.249
Teacher spread0.244 · 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 designOther design
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

Citations1
Published2022
Admission routes2
Has abstractyes

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