Rapidly adapting an effective health promoting intervention for older adults—choose to move—for virtual delivery during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 (COVID) pandemic shifted way of life for all Canadians. 'Stay-at-home' public health directives counter transmission of COVID but may cause, or exacerbate, older adults' physical and social health challenges. To counter unintentional consequences of these directives, we rapidly adapted an effective health promoting intervention for older adults-Choose to Move (CTM)-to be delivered virtually throughout British Columbia (BC). Our specific objectives were to 1. describe factors that influence whether implementation of CTM virtually was acceptable, and feasible to deliver, and 2. assess whether virtual delivery retained fidelity to CTM's core components. METHODS: We conducted a 3-month rapid adaptation feasibility study to evaluate the implementation of CTM, virtually. Our evaluation targeted two levels of implementation within a larger socioeconomic continuum: 1. the prevention delivery system, and 2. older adult participants. We implemented 33 programs via Zoom during BC's 1st wave acute and transition stages of COVID (April-October 2020). We conducted semi-structured 30-45 min telephone focus groups with 9 activity coaches (who delivered CTM), and semi-structured 30-45 min telephone interviews with 30 older adult participants, at 0- and 3-months. We used deductive framework analysis for all qualitative data to identify themes. RESULTS: Activity coaches and older adults identified three key factors that influenced acceptability (a safe and supportive space to socially connect, the technological gateway, and the role of the central support unit) and two key factors that influenced feasibility (a virtual challenge worth taking on and CTM flexibility) of delivering CTM virtually. Activity coaches also reported adapting CTM during implementation; adaptations comprised two broad categories (time allocation and physical activity levels). CONCLUSION: It was feasible and acceptable to deliver CTM virtually. Programs such as CTM have potential to mitigate the unintended consequences of public health orders during COVID associated with reduced physical activity, social isolation, and loneliness. Adaptation and implementation strategies must be informed by community delivery partners and older adults themselves. Pragmatic, virtual health promoting interventions that can be adapted as contexts rapidly shift may forevermore be an essential part of our changing world.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".