A Qualitative Exploration of Factors Influencing Physical Activity Behaviour for Individuals with Parkinson's Disease Using the Social Ecological Model
Bibliographic record
Abstract
Physical activity (PA) can benefit individuals with Parkinson’s disease (PD), however, many individuals tend to be sedentary. This qualitative study explored factors influencing PA behaviour for individuals with PD using the social ecological model. Twelve individuals with PD took part in semi-structured interviews. Data were thematically analysed. Results suggest that individuals with PD experience several constraining and facilitating factors to PA behaviour. Intrapersonal constraints revolved around uncertainties that PA is beneficial for individuals with PD, a lack of interest in available PA programs, and disease-specific issues whereas intrapersonal facilitators included prior experiences of enjoyment in PA, seeing improvements, and wanting to maintain independence. Interpersonal constraints related to lacking social support and perceived stigma whereas interpersonal facilitators were, passionate PA program staff, and being active with similar others. Environmental constraints pertained to PA programs failing to adapt program activities, lack of time, and transportation accommodations, whereas environmental facilitators were exposure to non-traditional PA programs, access to resources on PA, and accessibility to community PA programs. Findings highlight the need to address factors influencing PA behaviour for individuals with PD.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".