Effect of Functional Electrical Stimulation Cycling Exercise on Lower Limb Strength Asymmetry in Persons With Multiple Sclerosis
Bibliographic record
Abstract
Abstract Background: Lower limb strength asymmetry (ie, significant difference between contralateral limbs) has been associated with mobility impairment in persons with multiple sclerosis (MS). However, whether an adaptive exercise modality can be used to modify lower limb strength and potentially improve mobility is unclear. The effect of functional electrical stimulation (FES) cycling on lower limb strength asymmetry in persons with MS with mobility impairment was assessed, and the association between change in lower limb strength asymmetries and changes in functional and self-reported mobility outcomes was explored. Methods: Eight adults with MS (Expanded Disability Status Scale scores, 5.5–6.5) were included. Outcomes included knee extensor and knee flexor strength asymmetry, Timed 25-Foot Walk (T25FW) test, 2-Minute Walk Test (2MWT), Timed Up and Go (TUG) test, and 12-item Multiple Sclerosis Walking Scale (MSWS-12). Participants received 24 weeks (3 times per week) of FES cycling or passive leg cycling. Results: The FES condition demonstrated a small decrease in knee extensor (d = −0.33) and knee flexor (d = −0.23) strength asymmetry compared with passive leg cycling. With both groups combined, weak-to-strong associations were observed between change in knee extensor asymmetry and change in T25FW test time (rs = −0.43), 2MWT time (rs = −0.24), TUG test time (rs = 0.55), and MSWS-12 score (rs = 0.43). Moderate correlations were observed between change in knee flexor asymmetry and change in T25FW test time (rs = −0.31), TUG test time (rs = 0.33), and MSWS-12 score (rs = 0.35). Conclusions: FES cycling may be an efficacious exercise modality for reducing lower limb strength asymmetry and improving mobility in persons with MS.
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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".