A Theory-Based Approach to Adapting a Task-Oriented Community Exercise Programme for Virtual Delivery for People With Balance and Mobility Limitations
Bibliographic record
Abstract
Abstract Purpose: Despite the health benefits for people with mobility limitations of participating in community-based exercise programmes (CBEPs), accessing and implementing in-person CBEPs can be challenging. We describe a theory-based approach to adapting a group task-oriented, in-person CBEP targeting balance and mobility to a virtual format. Method: We used the Knowledge-to-Action and Medical Research Council frameworks to guide adaptation of an established CBEP for virtual delivery. We undertook consultations with knowledge users (i.e., CBEP providers, health care professionals, managers, policy-makers) and researchers on adapting programme components to optimize the feasibility of virtual delivery and retain benefits of the in-person CBEP. Concurrently, we conducted three feasibility studies involving post-programme participant surveys to evaluate and refine programme components. Results: Programme components, including a pre-programme safety video and use of trained facilitators to stream exercise videos via videoconferencing and facilitate social times, met safety, feasibility, and acceptability benchmarks. In studies 1, 2, and 3, the majority of participants described benefit to physical function (59%, 64%, and 79%, respectively) and emotional well-being (64%, 62%, and 75%, respectively). Conclusions: Theoretical frameworks were useful to guide adaptation of an established in-person CBEP to a virtual format. This theoretical approach may inform virtual programme development for other populations and targeted 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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".