Feasibility of Adapted Aerobic Cycle Ergometry Tasks to Encourage Paretic Limb Use After Stroke: A Case Series
Bibliographic record
Abstract
Cardiorespiratory fitness, along with sensorimotor recovery, is important for optimal function after stroke. Development of exercises that simultaneously address aerobic training and increase paretic limb involvement may improve outcomes and maximize productivity of therapy sessions. This case series assessed the feasibility of and characterized the cardiorespiratory and sensorimotor demands of adapted aerobic cycle ergometer activities hypothesized to increase paretic limb use. Mechanically loaded and electromyographic (EMG) feedback pedaling were compared to traditional pedaling in three poststroke case studies and a healthy control group. Submaximal oxygen uptake (Vo2), heart rate, perceived rate of exertion (RPE), and EMG of four leg muscles were assessed. Mechanically loaded ergometry increased RPE and altered muscle activity in healthy participants, while participants with stroke did not consistently increase paretic limb activation. EMG feedback pedaling increased target limb activity in healthy participants and decreased nonparetic activity in stroke participants. This paper highlights the challenges involved in adapting training tasks for individuals who are not able to walk at training intensities. Further work is necessary to refine adapted tasks for optimal effectiveness, and consideration of additional methods that permit differential interlimb loading may have additional value.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".