Where there's a will, there's a way: Identifying characteristics of physical activity that influence participation in older adults
Bibliographic record
Abstract
As individuals age, many change their physical activity patterns and often become sedentary (Mechling & Netz, 2009). More specifically, Visser et al. (1997) found that some older adults transition from activities of high intensity and competition to activities centered on social relationships and enjoyment. It seems likely that older adults are influenced by certain physical activity characteristics, which could explain why many cease to participate. The aim of the present study was to identify characteristics that were perceived to be important for physical activity participation by adults aged 65-75 years. The study used a mixed-methods and multi-phase approach. In phase one, a Physical Activity and Aging Research Committee was created (n=7; age 67-75) and qualitative data were collected using focus group discussions. The group identified 25 characteristics that influenced physical activity selection. They also identified and defined the dimensions of structured/unstructured and individual/group activities. Phase two consisted of 43 participants (age 65-75) who provided quantitative data by identifying which factors were important across each dimension while rating the importance of each characteristic on a visual analogue scale. Participants ranked which characteristics were most important to their involvement in physical activity. Preliminary analysis suggests that certain characteristics are important across all four quadrants. However, specific trends appear within the dimensions of structured and unstructured activities, and within the group and individual spheres. The discussion will outline the most important characteristics within and across the dimensions. Finally, application to and recommendations for physical activity design for older adults 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".