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Record W2586492349

Off the couch and onto the playing court: Does sport involvement in older adulthood influence sedentary behaviour?

2016· article· en· W2586492349 on OpenAlexaffabout
Amy Gayman, Jessica Fraser Thomas, Jamie Spinney, Rachael C. Stone, Joseph Baker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsYork University
Fundersnot available
KeywordsGerontologyAthletesPsychologySedentary behaviorPhysical activityLeisure timeMedicinePhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Although sport involvement may motivate older people to engage in health-promoting behaviour including exercise and strength training, participation in physical activity (PA) does not necessarily equate to a reduction in sedentary behaviour (SB). Research has indicated that older adults who meet recommended levels of PA may spend a great portion of their day engaged in sedentary pursuits (Gennuso et al., 2013). Given that SB in older adulthood is associated with a range of maladaptive health outcomes independent of involvement in moderate to vigorous PA, more detailed information on this relationship is warranted. For instance, it is unclear whether the type of PA involvement later in life influences levels of SB. The present study examined the relationship between sport participation and SB in comparison to inactivity and other PA involvement. Data from 1,723 respondents (age 65 and older) who completed the Sport Module of the 2010 Canadian General Social Survey – Time Use was used to investigate the influence of PA participation on the daily duration of time spent in low effort activities (i.e., MET < 1.5). Results indicated that physically active respondents are less sedentary than those who are inactive. More specifically, athletes reported less SB time than those involved in general leisure activity. The types of SB contributing to overall sedentary time and implications of these findings in relation to previous work in the area and public health strategies aimed at reducing SB in ageing populations will be discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.054
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.288
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes2
Has abstractyes

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