Effect of an Online Mobility Self-Management Program on Walking Speed in Older Adults With Preclinical Mobility Limitation: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Walking difficulties are a common and costly problem. However, disability associated with the decline in walking ability is not an inevitable consequence. With an aging population, it is increasingly important to establish strategies to help older adults preserve the capacity to live independently and function well in late life. Preclinical mobility limitation (PCML), which is characterized by subtle changes or limitations in mobility that precede disability, manifests as changes in how daily tasks such as walking are performed. Persons with PCML are at increased risk for the onset of disability and chronic disease. For that reason, PCML is a critical stage in the natural history of functional change when there is the opportunity for primary prevention interventions. OBJECTIVE: To evaluate the effect of a 12-week online mobility self-management program (Stepping Up) on mobility outcomes, including walking speed, aerobic exercise capacity, dual-task cost, physical functioning (balance and strength), balance confidence, extent of community mobility, self-management of mobility, and quality of life in older adults with PCML. METHODS: This randomized controlled trial (RCT) will recruit 249 older adults (aged 55-75 years) screened for PCML. Participants will be assigned to the Stepping Up program, a telephone-based coaching walking program or an online chair-based yoga program. Programs will be delivered over 12 weeks and participants will undergo virtual assessments with a blinded physiotherapist at baseline, 12, 24, and 36 weeks. An economic evaluation will be conducted alongside this RCT. RESULTS: A total of 253 participants were enrolled in the trial. Data collection commenced in August 2021 and will be completed in October 2025. Data analysis will begin in November 2025 and results will be published in the Summer of 2026. CONCLUSIONS: To our knowledge, PCML has not been addressed by primary prevention interventions that incorporate both task-oriented motor learning exercise and mobility self-management sessions. Results will establish if the Stepping-Up program has the potential to serve as a model for sustainable, accessible, and cost-effective programming for individuals with early mobility limitations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04368949; https://clinicaltrials.gov/study/NCT04368949. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72585.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".