Positive effects of functional electrical stimulation-assisted cycling on perception of effort, cerebral blood flow and cognition in post-stroke patients
Bibliographic record
Abstract
Abstract Background FES-assisted cycling may reduce perceived effort by lowering the required motor command compared to voluntary cycling. While benefits on effort perception have been shown during walking in stroke and multiple sclerosis patients, its effectiveness during cycling in stroke rehabilitation remains unproven. Thus, this work aimed to test the effect of functional electrical stimulation-assisted cycling on stroke patients’ perception of effort (primary aim) in a randomized controlled study design. In an exploratory way, this work also aimed to examine the effect of FES-assisted cycling on cerebral blood flow and cognitive performance (exploratory aims) in a subsample. Methods Fifteen post-stroke patients completed functional electrical stimulation-assisted and traditional cycling sessions separated by 72h. Perceived effort, cardio, and cerebrovascular parameters were monitored during exercise. Cognitive performance was assessed before and after each session. Qualitative data were reported after both sessions. Results Patients reported a lower perceived effort during functional electrical stimulation- assisted cycling than traditional cycling. Both sessions increased heart rate, end-tidal CO 2 , cardiac output, and cerebral artery blood flow velocity, with higher blood lactate levels after functional electrical stimulation-assisted cycling. Functional electrical stimulation-assisted cycling positively impacted cognitive performance. Conclusion Traditional and functional electrical stimulation-assisted cycling induced similar increased cardio and cerebrovascular responses. However, patients perceived functional electrical stimulation-assisted cycling as less effortful than traditional cycling. As effort is a barrier to regular exercise engagement and adherence, these results are promising for implementing functional electrical stimulation-assisted cycling in stroke patients’ rehabilitation.
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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 | 0.001 |
| 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) | 0.002 | 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".