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Record W2339405710 · doi:10.5993/ajhb.40.3.8

Correlates of General and Domain-Specific Sitting Time among Older Adults

2016· article· en· W2339405710 on OpenAlexaff
Jeff K. Vallance, Dean T. Eurich, Brigid M. Lynch, Paul A. Gardiner, Lorian Taylor, Barbara J. Jefferis, Steven T. Johnson

Bibliographic record

VenueAmerican Journal of Health Behavior · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of AlbertaAlberta Health ServicesAthabasca University
Fundersnot available
KeywordsSittingBody mass indexMedicineDemographyPopulationScreen timePhysical activityYoung adultGerontologyPhysical therapyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined the correlates of sitting time in a population-based sample of older adults. METHODS: Adults >55 years of age (N = 1296; N = 515 employed; N = 781 unemployed) self-reported measures of demographic and health-related variables, and a measure of sitting time (ie, SIT-Q). RESULTS: Employed total sitting time (min/day) was positively associated with home Internet access (B = 71.2, 95% CI, 8.9 to 133.4, p = .025), body mass index (BMI) (kg/m(2); B = 7.0, 95% CI, 2.1 - 11.9, p = .005), and negatively associated with physical health (B = -2.3; 95% CI, -4.9 to 0.3, p = .013). Unemployed total sitting time was negatively associated with age (B per year = -3.0, 95% CI, -4.9 to -1.1, p = .002), and being male (B = -54.0, 95% CI, -86 .7 to -21.3, p = .001). Unemployed total sitting time was positively associated with Internet access (B = 54.1, 95% CI, 17.7 to 90.4, p = .004) and BMI (B = 4.1, 95% CI, .94 to 7.3, p = .011). CONCLUSIONS: Older adults reported low levels of sitting time. Different correlates emerged for the employed and unemployed samples across sitting domains.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.356
Teacher spread0.335 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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