Effect of Using Mobile Games on Patients with Acute Stroke during Cognitive Rehabilitation at the Intensive Care Unit
Bibliographic record
Abstract
Background: Using mobile games during the rehabilitation phase of patients with acute stroke will have a progressive influence on cognitive and memory impairment, which will reduce costs and enrich their prognosis. The aim of the study to evaluate the effect of using mobile games on patients with acute stroke during cognitive rehabilitation at the intensive care unit. Design: A quasi-experimental research design (pretest and posttest research designs). Setting: The study was conducted at the neurological intensive care unit at Assuit University Hospital, Egypt. Methods: Fifty patients with acute stroke were chosen randomly; they ranged in age from 18 to 60 years old, were able to write and read, and had recently been diagnosed with an ischemic or hemorrhagic stroke. Tools: Two tools were used, the general patient’s assessment questionnaire and the Montreal Cognitive Assessment (MOCA), to assess the acute stroke patients’ cognitive abilities before and after the application of mobile games. Results: More than half of patients with acute stroke had mild cognitive impairment (58%) before application of mobile games, and the majority of them had normal cognitive ability after application of mobile games (100%). There were statistical significance differences in cognitive assessment between patients before and after the mobile game application (P value = 0.001**). Conclusion: Patients with acute stroke had better cognitive and memory functions after playing mobile games. Recommendations: Incorporating brain games into the rehabilitation protocol to improve cognitive function in patients with acute stroke at the ICU will produce significant results and shorten the length of stay.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".