Virtual motivational interviewing for physical activity among older adults: A non‐randomised, mixed‐methods feasibility study
Bibliographic record
Abstract
The objective of this study was to evaluate the feasibility of Virtual Motivational Interviewing (VIMINT) for improving physical activity among community-dwelling older adults. A feasibility study using a mixed-method single-group pre- and post-design. Each participant received five sessions of motivational interviewing (MI) through the Zoom platform. Feasibility and acceptability were assessed through recruitment, attrition and retention rates; adherence; satisfaction; counsellors' competency; and interviews with participants and counsellors. Other outcomes including physical activity were assessed at baseline, post- and 2-month follow-up. Eight participants were recruited; the mean age was 68.9 ± 3.9 years. The retention rate was 88%, 92.5% of the sessions were attended, and the participants' satisfaction score was 24.14 ± 7.3/32. The counsellors were rated as "good" and "fair" in relational and technical components, respectively. The categories derived from qualitative analysis were session composition, acceptability of outcome measures, positive impact of the VIMINT study and suggestions to improve future studies. The findings showed that VIMINT intervention should be feasible and acceptable for older adults. Evidence from this study provides relevant information that will guide the planning of future studies investigating the effectiveness of virtual MI on physical activity among community-dwelling older adults.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".