Proprioception and muscle performance unchanged by in-home step training in multiple sclerosis: secondary outcomes analysis
Bibliographic record
Abstract
Background The Interactive Step Training to Reduce Falls in People with Multiple Sclerosis (iFIMS) trial was a multi-centre, parallel-designed, randomised controlled trial testing an in-home, computerised exergame playing system ( smart±step ) in people with multiple sclerosis (Expanded Disability Status Scale 2–6). Objective This study was nested within the iFIMS trial to assess whether the smart±step system could improve secondary outcomes of ankle proprioception and plantarflexor muscle performance. Methods Tests of ankle proprioception and plantarflexor muscle performance were performed before and after 6 months of intervention with the smart±step system (intervention group; n = 33), or 6 months of usual care (control group; n = 33). Ankle proprioception outcomes included movement detection threshold and reaction time. Plantarflexor muscle performance outcomes included maximal voluntary torque, twitch torque from electrical stimulation, voluntary activation (level of neural drive), decrease in these parameters after a 2-min sustained isometric contraction, and time-to-recovery of these parameters. Results There were no differences between the intervention and control groups for all proprioception and muscle performance outcomes (95% CI of mean differences crossed 0), and no difference in time-to-recovery after the sustained contraction (95% CI of hazard ratios crossed 1). Conclusions The smart±step system did not improve proprioception or muscle performance over a 6-month intervention, compared to usual care, in people with multiple sclerosis. However, at-home interventions are cost effective and convenient, and the smart±step system could help maintain physical activity in an engaging way in this group.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".