Physical Activity Interventions for Patients With Poststroke Fatigue: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Poststroke fatigue (PSF) affects nearly 50% of stroke survivors, severely impacting functional recovery and quality of life. Physical activity (PA) interventions show promise in mitigating fatigue, yet evidence remains fragmented across study designs and intervention types. OBJECTIVE: This scoping review aims to systematically map the literature on PA interventions for PSF, identifying key concepts, evidence gaps, and implementation characteristics to guide future research. METHODS: Using a 5-step framework, we will search PubMed, Web of Science, Scopus, Embase, PsycINFO, CINAHL, and the Cochrane Library from the inception of the databases to 2025. Gray literature and trial registries will be included. Two reviewers will independently screen titles, abstracts, and full texts using predefined criteria. Data extraction will focus on intervention components, fatigue assessment tools, and implementation outcomes. RESULTS: Initial searches identified 8268 articles. The study selection process is expected to finish in February 2026, and the manuscript will be submitted in June 2026. CONCLUSIONS: The conduct of this scoping review is expected to synthesize a fragmented evidence base, providing a clear evidence map of PA interventions for PSF. The findings will identify key gaps and inform the design of future definitive studies and evidence-based clinical guidelines, ultimately contributing to improved care for stroke survivors experiencing fatigue. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/80703.
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 | 0.066 | 0.052 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.017 |
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".