Factors influencing sedentary behaviour in older adults: An ecological approach
Bibliographic record
Abstract
Older adults represent one of the most sedentary populations worldwide, and as a result are at risk of negative health outcomes. Knowledge translation tools and public health promotion strategies are needed; however, little evidence is available to inform framing of such tools or development of intervention programs. Using Owen et al.'s (2011) ecological model, the current study examined the four sedentary behaviour domains; household, transport, leisure time and occupation with participants drawn from community seniors centres. The participants (n=26, 74±8.5 years) were involved in a range of sedentary and physical activities at the centres. Four focus groups were conducted to examine perceptions of sedentary behaviour, the range of activities participated in, factors influencing participation etc. Two dominant themes emerged, barriers and promoters of sedentary behaviour and these were further delineated into intrinsic and extrinsic factors. Results were synthesized to develop appropriate messaging and greater uptake of programming and guidelines thereby informing public health strategies. For example, in order to develop successful programs to combat sedentary behaviours the program should include both a social component and a mentally stimulating component, as these were identified for enjoyment and motivation. In conclusion the results clearly supported that sedentary time reduction strategies will need to consider each of the four domains in which older adults accumulate sedentary time. Acknowledgments: UOIT Internal Funding
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".