Effectiveness of gait aid prescription for improving spatiotemporal gait parameters and associated outcomes in community-dwelling older people: a systematic review
Bibliographic record
Abstract
PURPOSE: To integrate the evidence of gait aid prescription for improving spatiotemporal gait parameters, balance, safety, adherence to gait aid use, and reducing falls in community-dwelling older people. METHODS: Seven health databases were searched to June 2021. Experimental studies investigating gait aid prescription (provision and instruction for use) for older people, reporting gait parameters, balance, falls, and safety of or adherence to gait aid use was included. Mean differences with 95% confidence intervals of gait and balance outcomes in participants at the program's last follow-up were analyzed. The safety of and adherence to gait aid use were described. RESULTS: = 555 older people). No meta-analyses could be performed. Five studies used a single gait aid instruction session. Gait aid prescription had inconsistent effects on gait velocity, and no reported benefits in reducing gait variability in older people with mobility problems or fall risks, including Parkinson's or Alzheimer's disease. No study investigated gait aid prescription on falls and balance performance. Effects on safety and adherence to gait aid use were unclear. CONCLUSION: Research is needed to investigate the benefits of extensive gait aid training in older people with mobility problems, including those with dementia or high falls risk.IMPLICATIONS FOR REHABILITATIONThere is little evidence currently addressing the benefits of gait aid prescription on gait and associated outcomes in older people with mobility problems or fall risks.Gait aid prescription yielded inconsistent effects on increasing gait velocity and did not appear to reduce gait variability in older people with mobility problems or fall risks, nor in those with Parkinson's disease or Alzheimer's disease.Clinicians may consider using a more extensive gait aid training approach to optimize learning of safe gait patterns and gait aid use, which may produce better outcomes.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".