Exploring the Influence of a Novel App for Training and Evaluating Walking Aid Skills in Walking Aid Users: Protocol for a Pragmatic Single-Blind Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: More than 12% of the world's population and more than 1 million Canadians use walking aids to support mobility. Unsafe use of walking aids due to a lack of training may lead to injuries and an increased risk of falls. A novel interactive video-based feedback mobile app to train walking aid fitting and safe use, called ICanWALK (Improving Canadians' Walking Aid Skills, Learning, and Knowledge), was recently developed. OBJECTIVE: The primary objective of this study is to explore the efficacy of the walking aid skills training app on the balance confidence of walking aid users. The secondary objective is to explore the influence of the mobile app on mobility and knowledge of walking aid users. METHODS: A 2-site single-blind pragmatic randomized controlled trial is proposed. A total of 52 adults who use walking aids will be recruited through clinical and community organizations. Participants will complete measures of balance confidence (Activities-specific Balance Confidence scale; the primary outcome), mobility (6-Minute Walk Test and Timed Up and Go test), walking aid skills (Walking Aids Skills Test, WAST), and knowledge of and confidence in walking aid fit and use (self-reported questionnaire) at baseline (T1). Participants will then be randomly assigned to the experimental (ICanWALK app) or attention-matched control group (breathing activity app) and will complete two 20-minute sessions interacting with the assigned app. Participants will be reassessed 2 to 4 days after the intervention (T2) and again 4 weeks later (T3). Analysis of covariance will be performed for primary and secondary outcomes by using SPSS software. RESULTS: The study protocols were approved by the institutional review boards of both recruitment sites in 2023 and 2024. A feasibility study was conducted from 2023 to 2024 across the 2 sites. As of November 2025, participant recruitment is ongoing and expected to conclude in December 2026. To date, 26 individuals have been enrolled and have successfully completed all 3 assessment time points: T1, T2, and T3. CONCLUSIONS: Using a video-based feedback training approach, a novel app is hypothesized to improve balance confidence, mobility, and knowledge of and confidence in walking aid fitting and use. This structured educational program for fitting and training of walking aids may improve balance confidence. Better walking aid fitting and use may improve mobility, especially for older adults, thereby increasing independence and social participation. Establishing efficacy is an important first step before exploring how the ICanWALK app may be used by walking aid users and clinicians in clinical and community settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05347875; https://clinicaltrials.gov/study/NCT05347875. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71060.
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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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".