Exploring the Feasibility and Efficacy of a Telehealth Stroke Self-Management Programme: A Pilot Study
Bibliographic record
Abstract
PURPOSE: Moving On after STroke (MOST) is an established self-management programme for persons with stroke and their care partners. Through 18 sessions over 9 weeks, each including discussion and exercise, participants learn about goal-setting, problem-solving, exercise, and community-reintegration skills. This study was undertaken to evaluate the feasibility and efficacy of telehealth delivery of MOST. METHOD: Efficacy was evaluated using an experimental non-randomized trial comparing a telehealth MOST intervention group (T-MOST) (n = 10) with a waiting list control group (WLC) (n = 8). Outcome measures included the Berg Balance Scale (BBS), the Reintegration to Normal Living Index, the Stroke-Adapted Sickness Impact Profile, Goal Attainment Scaling, and the Geriatric Depression Scale. The feasibility evaluation included attendance rates, focus groups, and facilitator logs. In MOST Telehealth, one co-facilitator was local and the other was connected by videoconference. RESULTS: Attendance rates for persons with stroke (83.9%, SD = 2.6) and care partners (76.7%, SD = 2.9) and participant and facilitator experiences indicated feasibility of this mode of programme delivery. There was a significant difference in BBS scores between the T-MOST group and the WLC group (mean difference -4.27, 95%CI: -6.66 to -1.87). Participants reported additional benefits, including increased motivation and awareness of partners' needs. Videoconferencing was reported to decrease their sense of isolation. CONCLUSION: It appears feasible to deliver the MOST programme with two facilitators, one connected by videoconference and one in person. In addition, preliminary evidence suggests that the programme is associated with improved well-being in persons with stroke and their care partners. Practitioners delivering self-management programmes may consider wider dissemination using videoconferencing.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".