Pickleball for Inactive Mid-Life and Older Adults in Rural Utah: A Feasibility Study
Bibliographic record
Abstract
Many diseases, disabilities, and mental health conditions associated with aging can be delayed or prevented through regular exercise. Several barriers to exercise, many of which are exacerbated in rural communities, prevent mid-life and older adults from accessing its benefits. However, recently, a racquet sport named pickleball has become popular among older adults, and it appears to overcome some of these barriers. We conducted a feasibility study to evaluate the impact of a six-week pickleball intervention on measures of muscle function, cognitive function, perceived pain, and cardio-metabolic risk, as well as several psychosocial factors contributing to adherence in sedentary rural participants. Participants improved their vertical jump, cognitive performance, and reported a decrease in self-reported pain, suggesting improved physical and cognitive health across the sample. Participants also reported high levels of satisfaction and demonstrated good adherence over the duration of the study. Perhaps of greatest value was the overwhelmingly positive response from participants to the intervention and follow-up interviews reporting a desire to continue pickleball play beyond the study period. Overall, pickleball appears to be a promising intervention to, (1) elicit functional- and cognitive-related improvements, and (2) motivate mid-life and older adults to adhere to exercise sufficiently long to benefit their health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".