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Record W2057440674 · doi:10.1016/j.slsci.2014.09.009

Sedentary behavior and sleep efficiency in active community-dwelling older adults

2014· article· en· W2057440674 on OpenAlexafffund
Kenneth Madden, Maureen C. Ashe, Chris Lockhart, Jocelyn M. Chase

Bibliographic record

VenueSleep Science · 2014
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsSleep (system call)SittingSedentary behaviorObservational studyMedicineAssociation (psychology)DemographyPsychological interventionGerontologyPhysical activityPhysical therapyPsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Previous studies have demonstrated that aerobic exercise interventions have a positive impact on sleep efficiency in older adults. However, little work has been done on the impact of sedentary behavior (sitting, watching television, etc.) on sleep efficiency. METHODS: 54 Community-dwelling men and women >65 years of age living in Whistler, British Columbia (mean 71.5 years) were enrolled in this cross-sectional observational study. Measures of sleep efficiency as well as average waking sedentary (ST), light (LT), and moderate (MT) activity were recorded with Sensewear accelerometers worn continuously for 7 days. RESULTS: From the univariate regression analysis, there was no association between sleep efficiency and the predictors LT and MT. There was a small negative association between ST and sleep efficiency that remained significant in our multivariate regression model containing alcohol consumption, age and gender as covariates. (standardized β correlation coefficient -0.322, p=0.019). Although significant, this effect was small (an increase in sedentary time of 3 hours per day was associated with an approximately 5% reduction in sleep efficiency). CONCLUSIONS: This study found a small significant association between the time spent sedentary and sleep efficiency, despite high levels of activity in this older adult group.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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

Citations41
Published2014
Admission routes2
Has abstractyes

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