Enhancing the Safe Mobility of Individuals with Multiple Sclerosis by Advancing Approaches to Ambulatory Monitoring
Bibliographic record
Abstract
People with multiple sclerosis (MS) experience a progressive loss of mobility and a high incidence of falls. Assistive mobility devices—such as rollators (i.e., four-wheeled walkers)—are commonly used to compensate for mobility impairment; however, their use has been identified as a fall risk factor in the MS population. This thesis examined the relationship between MS, assistive device use, and fatigue on gait performance. Three studies were conducted, which focused on understanding: 1) how MS influences spatiotemporal gait parameters; 2) how gait with a rollator differs across disability subgroups; and 3) how prolonged gait with a rollator differs as a function of time and disability. In study 1, several spatiotemporal parameters were found to have a significant linear relationship with disability (i.e., gait speed, step length, cadence, step time, step time variability, stride time, stance phase, and double-support time). As well, fallers had a reduced gait speed relative to non-fallers. These results provided reference values that could be used to assess the effectiveness of gait training programs and were consistent with the individuals with greater disability adopting a more cautious gait strategy. The second study revealed that new measures of trunk motion can differ by disability level in people with MS (e.g., roll velocity range, roll velocity variability, pitch velocity range, two-dimensional roll and pitch velocity variability). This provided insight into how the rollator could be incorporated into a deliberate strategy to achieve a more cautious gait pattern. The third and final study revealed that, during prolonged walking, trunk roll velocity variability differed in accordance with disability, whereas rollator yaw angle and yaw angle variability could distinguish between the movement strategies of distinct subgroups of people with MS in a manner that overall disability scores could not (e.g., pausing behaviour during the six-minute walk test). The clinical significance of these findings require further investigation. Some of these new rollator and trunk parameters may become plausible candidates for future therapeutic interventions and the implications for balance and stability are discussed.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".