Off the couch and onto the playing court: Does sport involvement in older adulthood influence sedentary behaviour?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".