Physical activity and quality of life/subjective well-being in people with disabilities: A look backwards and a way forwards
Bibliographic record
Abstract
The case for devoting resources to physical activity (PA) interventions and initiatives often hinges on demonstrating the impact of PA on people’s quality of life (QOL)/subjective well-being (SWB). In PA-intervention studies involving children, youth and adults with disabilities, the effects tend to be inconsistent and relatively small. In this paper, I argue that the true effects of PA on QOL/SWB in people with disabilities have been masked by mis-conceptualization and mis-measurement of QOL/SWB, and a lack of theoretical specification of how PA may influence QOL/SWB. I begin with an overview of the QOL and SWB concepts followed by a review of quantitative and qualitative research on the effects of PA on QOL/SWB among people living with disabilities. Research from sport and exercise psychology that aims to explain how PA improves QOL/SWB is synthesized along with QOL theorizing from the parent discipline of psychology. In the final section, I integrate these perspectives into a Quality Participation Model of Physical Activity and Quality of Life/Subjective Well-Being with recommendations for researchers and interventionists. PA can make a substantive difference in the lives of people with disabilities; however, researchers and interventionists must be more careful when designing and assessing PA interventions to improve QOL/SWB. • Effect sizes for PA-intervention studies tend to be small and inconsistent. • Studies confound measures of QOL/SWB with HRQOL, masking true effects on QOL/SWB. • Qualitative studies show clear, consistent effects and how PA may improve QOL/SWB. • A Quality Participation Model of PA and QOL/SWB is presented to guide future work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".