Combining ambulatory and laboratory assessment of rollator use for balance and mobility in neurologic rehabilitation in-patients
Bibliographic record
Abstract
PURPOSE: Despite the common use of rollators (four-wheeled walkers), understanding their effects on gait and balance is limited to laboratory testing rather than everyday use. This study evaluated the utility of an ambulatory assessment approach to examine balance and mobility in everyday conditions compared to a laboratory assessment. METHODS: Standing and walking with a rollator was assessed in three neurological rehabilitation in-patients under two conditions: (1) in laboratory (i.e. forceplate, GaitRite), and (2) while performing a natural walking course within and outside of the institution. An instrumented rollator (iWalker) was used to measure variables related to the balance control (e.g. upper limb kinetics), destabilizing events (e.g. stumbling), and environmental context. RESULTS: Two of three patients demonstrated greater reliance on the rollator for standing balance (2.3-5.9 times higher vertical loading, 72-206% increase in COP excursion) and 29-42% faster gait during the walking course compared to the laboratory. Importantly, destabilizing events (collisions, stumbling) were recorded during the walking course. Such events were not observed in the laboratory. CONCLUSION: This study illustrated a greater reliance on the rollator during challenges in everyday use compared to laboratory assessment and provided evidence of specific circumstances associated with destabilizing events that may precipitate falls in non-laboratory settings. Implications for Rehabilitation The value of combining laboratory and ambulatory assessment approaches to provide a more comprehensive profile of the risks and benefits of rollator use to prevent falling was studied. Patients demonstrated greater reliance on rollator assistive devices for standing balance and exhibited higher gait speeds during ambulatory assessment, compared to standard laboratory protocols. Repeated instances of events that may precipitate falls (e.g. collisions, stumbling, and unloading behaviors) were observed only during the ambulatory assessment. Individual challenges to balance can be used to identify specific training targets, assess suitability for assistive devices, and recommend rehabilitation goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".