Comparison of Cardiorespiratory Demand and Rate of Perceived Exertion During Propulsion in a Natural Environment With and Without the Use of a Mobility Assistance Dog in Manual Wheelchair Users
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare cardiorespiratory demand during manual wheelchair (MWC) propulsion among MWC users with a spinal cord injury (SCI) in a natural environment with and without the use of a trained mobility assistance dog (MAD). DESIGN: In this quasi-experimental repeated-measures analysis of difference, 13 experienced MWC users with an SCI propelled themselves with and without their trained MAD at a self-selected natural speed along a standardized 630-m course in a natural environment. Participants were equipped with a portable gas analyzer to measure their oxygen consumption, ventilation, tidal volume, respiratory quotient, respiratory rate, and heart rate before, during, and after completing the course. Participants also rated their perceived exertion on a modified Borg scale following each trial. RESULTS: All cardiorespiratory outcome measures decreased significantly with the use of a MAD (P ≤ 0.013; mean difference, -9% to -38%). Furthermore, most participants completed the course significantly faster (P ≤ 0.001; mean difference, -34%), while reporting considerably lower perceived exertion rates (P = 0.007; mean difference, -65%). CONCLUSIONS: A trained MAD decreases cardiorespiratory demand and rate of perceived exertion during MWC propulsion on a 630-m course among experienced MWC users with SCI. Trained MADs represent a valuable mobility assistive technology option for MWC users.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".