Physical Activity of Older Women Living in Retirement Communities: Capturing the Whole Picture Through an Ecological Approach
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Interventions to increase physical activity among older populations may prevent or delay disability in activities of daily living and premature death. In our research, we focused on older women living independently in retirement communities, who commonly experience declining health. The purpose was to identify factors influencing physical activity in older women and to create a practical checklist to guide physical therapists in physical activity interventions within retirement communities. METHODS: The study was qualitative and guided by the Vancouver School of Doing Phenomenology. The data set comprised 12 in-depth interviews with 10 women, as 2 of the women were interviewed twice to deepen the understanding of their experience. They were aged 72 to 97 years (median = 84 years, interquartile range = 11 years) and lived in 7 different apartment buildings in the same urban area. The interviews were recorded, transcribed, and analyzed to identify factors influencing the physical activity behavior of the women. We then linked these factors to the Bronfenbrenner's ecological model and finally constructed a checklist for mapping the influencing factors. RESULTS: The physical activity experience of the older women reflected both facilitating and hindering factors from all layers of the ecological model. The largest part of the women's description was constructed around personal factors and the immediate physical and social environment. Yet, important influencing factors were expressed reflecting community, society, and the lifespan. Finally, the practical checklist created to guide physical activity interventions included 40 questions reflecting 24 influencing factors covering important layers of the ecological model. CONCLUSION: To deal with the epidemic of a sedentary lifestyle in older populations, physical therapists must join forces with health authorities and work with the complexities of physical activity promotion at appropriate levels. Our results and the checklist are a potential resource to aid in identifying physical activity influencing factors that are appropriate for physical therapy intervention, within retirement communities. Moreover, this checklist may be used to recognize factors that are more suitable for public health interventions at the community or national levels.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".