Where there's a will, there's a way: Identifying important factors of physical activity among older adults
Bibliographic record
Abstract
Older adults in Canada are largely sedentary and do not meet the recommended amount of daily physical activity. It has been suggested that older adults preferences for physical activities move from those characterized by high intensity and competition, to those of lower intensity and higher social components. However, limited research has examined which elements of physical activity influence participation or non-participation in the older adult population. Therefore, the purpose of this research was to determine which of these elements, or "factors", influence older adult's selection of physical activity. A two-phase design was used to collect data. Phase one consisted of a committee of seven older adults who generated a list of 25 factors affecting participation. Phase two consisted of 45 older adults who used the list of factors created by phase one to rank the most important factors across four categories: group structured, group unstructured, individual structured, and individual unstructured activities. The Cochran–Mantel–Haenszel chi–square test was used to investigate the differences between categories on the order of ranks. Results of this analysis show that there is a significant difference in rank orders of factors across the quadrants (χ2 = 75.9, p < 0.001). The second analysis used confidence intervals to investigated differences within categories to determine which factors were most important to older adults. Level A factors (most important) were identified as fun, satisfaction, commitment, and energize. Level B factors included safety, learning, awareness, and productive while level C factors were related to meaningful contribution, intensity, and motivation. In general, it was found that certain factors are more important than others when selecting physical activity during older adulthood. Results are discussed in light of these findings and it is concluded that including these factors into physical activity may lead to higher participation rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".