Effect of Haptic Training During Manual Wheelchair Propulsion on Shoulder Joint Reaction Moments
Bibliographic record
Abstract
Background: Manual wheelchair propulsion remains a very ineffective means of locomotion in terms of energy cost and mechanical efficiency, as more than half of the forces applied to the pushrim do not contribute to move the wheelchair forward. Manual wheelchair propulsion training using the haptic biofeedback has shown an increase in mechanical efficiency at the handrim level. However, no information is available about the impact of this training on the load at the shoulders. We hypothesized that increasing propulsion mechanical efficiency by 10% during propulsion would not yield clinically significant augmentation of the load sustained at the shoulders. Methods: Eighteen long-term manual wheelchair users with a spinal cord injury propelled a manual wheelchair over a wheelchair simulator offering the haptic biofeedback. Participants were asked to propel without the Haptic Biofeedback (HB) and, thereafter, they were subjected to five training blocks BL1-BL5 of 3 min in a random order with the haptic biofeedback targeting a 10% increase in force effectiveness. The training blocs such as BL1, BL2 BL3, BL4, and BL5 correspond, respectively, to a resistant moment of 5, 10, 15, 20, and 25%. Pushrim kinetics, shoulder joint moments, and forces during the propulsive cycle of wheelchair propulsion were assessed for each condition. Results: The tangential force component increases significantly by 74 and 87%, whereas value for the mechanical effective force increases by 9% between the pretraining and training blocks BL3. The haptic biofeedback resulted in a significant increase of the shoulder moments with 1-7 Nm. Conclusion: Increases in shoulder loads were found for the corresponding training blocks but even though the percentage of the increase seems high, the amplitude of the joint moment remains under the values of wheelchair propulsion found in the literature. The use of the HB simulator is considered here as a safe approach to increase mechanical effectiveness. However, the longitudinal impact of this enhancement remains unknown for the impact on the shoulder joint. Future studies will be focused on this impact in terms of shoulder risk injury during manual wheelchair propulsion.
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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) | 0.003 | 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".