Accessible exercise for wheelchair users: comparing the usability of two adapted exercise machines
Bibliographic record
Abstract
INTRODUCTION: Exercise is an important occupation for wheelchair users. Limited access to adapted aerobic exercise equipment in the community and lack of knowledge on how to exercise are barriers to exercise participation among wheelchair users. To address these barriers, the adapted rower (aROW) and adapted skier (aSKI) exercise machines and educational materials were created. PURPOSE: 1) To compare wheelchair users' perspectives of the effectiveness and usability of the aROW and aSKI. 2) To explore perceptions of educational materials to support use of the machines. MATERIALS AND METHODS: A sequential, mixed-methods study design was used. Six wheelchair users trialled the machines, and completed an interview and two usability questionnaires. Qualitative data were analysed using thematic and conventional content analysis. Usability scores of both machines were compared using the Wilcoxon Signed Ranks Test. RESULTS: Data show high usability of the aROW and aSKI. More set up challenges were reported for the aROW than the aSKI. Participants perceived both machines provided effective cardiovascular workouts, and each met their exercise goals differently. Participants preferred the instructional videos over instructional sheets and provided suggestions for improving both. The Wilcoxon Signed Ranks Test showed no statistically significant difference in usability between the aROW and aSKI. CONCLUSION: Implementing the aROW and aSKI in the community may address some equity issues that wheelchair users face by providing more aerobic exercise options. Results will inform educational material revisions to support use of the machines.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".