A cross-sectional study examining multiple mobility device use and fall status among middle-aged and older adults with multiple sclerosis
Bibliographic record
Abstract
Purpose: To document the prevalence of multiple mobility device use among adults with multiple sclerosis (MS) (≥55 years) and examine the association between falls status (faller/non-faller) and the number of mobility devices used. Methods: Cross-sectional data generated through telephone interviews with 353 participants was used for this secondary analysis. Descriptive statistics were used to address the first study purpose. Multiple device use was measured by the number of devices used, which ranged from 0 (never use a cane, walker, manual wheelchair, or power wheelchair/scooter) to 4 (use all four mobility devices at least some of the time). Logistic regression analysis was used to address the second purpose, with fall status used as the dependent variable (non-fallers [<1 per year] versus fallers [≥1 per year]). Results: Just under 60% of participants reported the use of at least two mobility devices. For each additional mobility device used, the odds of being a faller increased by 1.47 times (95% CI = 1.14–1.90). Conclusion: Multiple mobility device use was common and the greater number of devices used, the greater the likelihood of being a faller. To prevent falls, this association requires further research to determine directionality. Implications for RehabilitationThe high prevalence of multiple mobility device use points to the importance of asking about all devices a person uses, not just the primary one.Regression findings point to the need for comprehensive fall risk assessment and raise questions about whether different approaches to fall prevention intervention may be required for different clients.Including discussions and education about how to select the “right” device for different activities may be useful in the context of improving mobility status and reducing fall risk. Adequate practice with each device and “refreshers” when a device is not used to regularly may also be valuable.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".