Factors Influencing Sedentary Behaviour in Older Adults: An Ecological Approach
Bibliographic record
Abstract
Sedentary behaviour is negatively associated with several health outcomes and is particularly problematic among older adults. 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. The aim of the present study was to use data on the perceptions of sedentary time and the programs or supports older adults identify as important for reducing their sedentary time, to inform knowledge translation strategies targeting this population. Focus groups were conducted with four groups of older adults (n = 26) at local seniors' centres (Ontario, Canada). Participants were 74 ± 8.5 years old and were engaging in both sedentary and physical activities in a social environment. Using the Ecological Model for sedentary time in adults, we categorized data into leisure time, household, transport and occupation domains. Intrinsic and extrinsic factors that worked to either discourage or promote sedentary behaviour were identified. Drawing on both groupings of data, results were synthesized to inform public health strategies on appropriate messaging and better uptake of programming and guidelines. For example, successful programs developed on the topic will need to include a social component and a mentally stimulating component, as these were identified as critical for enjoyment and motivation. It was clear from this analysis that sedentary time reduction strategies will need to consider the different domains in which older adults accumulate sedentary time.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".