Examining the Relationship Between Reactive Stepping Outcomes and Falls in People With Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Reactive stepping is critical for preventing falls and is impaired in people with multiple sclerosis (PwMS); however, which aspects of stepping relate to falls remains poorly understood. Identifying outcomes most related to falls is a first step toward improving rehabilitation for fall prevention. The purpose of this study was to assess whether reactive step latency or length during forward and backward losses of balance were related to a history of falls in PwMS. METHODS: Of the 111 PwMS who participated in this study, 76 reported no falls in the previous 6 months, whereas 36 reported 1 or more falls. Participants completed 3 forward and 3 backward treadmill-induced reactive steps from stance. Step length (centimeters) and step latency (milliseconds) were measured using motion capture and analyzed via MATLAB. RESULTS: Participants with a history of falls had significantly slower step latencies during backward stepping, but not forward stepping, than those without a history of falls. Step length did not differ between groups. Slower step latencies during backward stepping significantly increased the odds of having experienced a fall (β = .908, SE = 0.403, odds ratio = 2.479, 95% CI = 1.125 to 5.464). CONCLUSION: PwMS and a history of falling show delayed step onsets during backward reactive stepping. Specifically, for every 10-millisecond increase in step latency, PwMS were 2.5 times more likely to have a fall history. Although clinical trials are necessary to determine whether interventions targeting reactive stepping reduce falls in PwMS, the current work indicates that the latency of steps may be a relevant target for this work. IMPACT: Subsequent fall prevention clinical trials should consider targeting backward reactive step latency to further assess its relevance for rehabilitation in PwMS. LAY SUMMARY: If you have MS and a history of falls, you may be more likely to have delayed reactive step latencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".