Influence of Mobile Application Based Brain Training Program on Cognitive Function and Quality of Life in Patients Post Stroke
Bibliographic record
Abstract
Background: Stroke often leads to cognitive impairment, which can significantly reduce one's independence as well as quality of life. Cognitive rehabilitation is a treatment strategy for restoring cognitive abilities following brain injuries. Cognitive functioning can be improved through the use of multimedia as well as informatics in computerized cognitive rehabilitation (CCR). Purpose: to investigate the influence of a mobile application-based brain training program on cognitive functions as well as quality of life in post stroke patients. Methodology: forty referred medically and radiologically diagnosed stroke patients from both genders experienced post stroke cognitive impairment (PSCI), aged from 45 to 60 years old, were randomized into two groups of the same number: a study group and a control group. The Study group received mobile application-based brain training program (Lumosity training application) as well as aerobic training on a bicycle ergometer, and the control group received only the aerobic training on a bicycle ergometer for 18 sessions every other day for 6 weeks, 3 sessions/week, each session for 60 minutes. All patients were evaluated with Computer-based cognitive device RehaCom, Addenbrooke’s Cognitive Examination Revised (ACE-R) test, Montreal Cognitive Assessment (MoCA) in addition to Stroke specific quality of life scale (SS-QoL) pre and post treatment. Results: a significant difference has been detected among the two groups as the (p-value = 0.001) indicating that the study group reported enhancement in the cognitive functions as well as the quality of life more than the control group and there was a correlation between RehaCom, MoCA, ACE-R and SS-QoL. Conclusion: This study showed that six weeks of mobile application-based brain training program (Lumosity training application) as well as aerobic training on a bicycle ergometer was a beneficial approach and is a successful treatment for patients suffering from (PSCI).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".