Foot placement patterns of female rollator users with multiple sclerosis in the community
Bibliographic record
Abstract
Purpose: The objective of this study was to investigate the relationship between medio-lateral foot placement characteristics and environmental context when individuals with Multiple Sclerosis (MS) walk with a rollator (four-wheeled walker). Method: Ten women diagnosed with MS, who used rollators regularly, participated in this study. First, a rollator-based technique of calculating step width (SW) was validated in this patient population by comparing the output of an instrumented rollator (iWalker) to a laboratory-based Vicon Motion Capture System. Secondly, the iWalker-based technique was used to calculate the participants’ SW and SW variability as they used the iWalker in four common outdoor community environments. Results: There was a strong level of agreement between the iWalker and Vicon SW calculations, with an intra-class correlation coefficient of 0.993 and a root-mean-square difference of 0.71 cm. Furthermore, SW variability (P = 0.002), but not SW (P = 0.288), was influenced by the walking environment (i.e. between the in-lab vs. up-ramp and up-ramp vs. down-ramp conditions). Conclusions: Foot placement variability appears to be influenced by the walking environment of MS patients. Therefore, an enhanced understanding of walker-user interactions in relation to the environment must be established in order to improve mobility.Implications for RehabilitationThe walking environment may have an effect on the foot placement patterns of females with Multiple Sclerosis who use rollators to compensate for their gait difficulties.Developing an enhanced understanding of the environmental factors that affect rollator-user interactions may inform how to more effectively improve the mobility of individuals who rely on these devices in the community.
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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 | 0.001 | 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".