Sequential multiple assignment randomised trial to develop an adaptive mobile health intervention to increase physical activity in people poststroke in the community setting in Ireland: TAPAS trial protocol
Bibliographic record
Abstract
INTRODUCTION: Stroke is the second-leading cause of death and disability globally. Participation in physical activity (PA) is a cornerstone of secondary prevention in stroke care. Given the heterogeneous nature of stroke, PA interventions that are adaptive to individual performance are recommended. Mobile health (mHealth) has been identified as a potential approach to supporting PA poststroke. To this end, we aim to use a Sequential Multiple Assignment Randomised Trial (SMART) design to develop an adaptive, user-informed mHealth intervention to improve PA poststroke. METHODS AND ANALYSIS: The components included in the 12-week intervention are based on empirical evidence and behavioural change theory and will include treatments to increase participation in Structured Exercise and Lifestyle or a combination of both. 117 participants will be randomly assigned to one of the two treatment components. At 6 weeks postinitial randomisation, participants will be classified as responders or non-responders based on participants' change in step count. Non-responders to the initial treatment will be randomly assigned to a different treatment allocation. The primary outcome will be PA (steps/day), feasibility and secondary clinical and cost outcomes will also be included. A SMART design will be used to evaluate the optimum adaptive PA intervention among community-dwelling, ambulatory people poststroke. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Health Service Executive Mid-Western Ethics Committee (REC Ref: 026/2022). The findings will be submitted for publication and presented at relevant national and international academic conferences TRIALS REGISTRATION NUMBER: NCT05606770.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".