Promoting Optimal Physical Exercise for Life: An Exercise and Self-Management Program to Encourage Participation in Physical Activity after Discharge from Stroke Rehabilitation—A Feasibility Study
Bibliographic record
Abstract
People with stroke do not achieve adequate levels of physical exercise following discharge from rehabilitation. We developed a group exercise and self-management program (PROPEL), delivered during stroke rehabilitation, to promote uptake of physical activity after discharge. This study aimed to establish the feasibility of a larger study to evaluate the effect of this program on participation in self-directed physical activity. Participants with subacute stroke were recruited at discharge from one of three rehabilitation hospitals; one hospital offered the PROPEL program whereas the other two did not (comparison group; COMP). A high proportion (11/16) of eligible PROPEL program participants consented to the study. Fifteen COMP participants were also recruited. Compliance with wearing an accelerometer for 6 weeks continuously and completing physical activity questionnaires was high (>80%), whereas only 34% of daily heart rate data were available. Individuals who completed the PROPEL program seemed to have higher outcome expectations for exercise, fewer barriers to physical activity, and higher participation in physical activity than COMP participants (Hedge's g ≥ 0.5). The PROPEL program delivered during stroke rehabilitation shows promise for reducing barriers to exercise and increasing participation in physical activity after discharge. This study supports feasibility of a larger randomized trial to evaluate this program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".