The Effect of Brain Training Using Video Games to Improve Cognitive Functions in Lebanese Individuals with Chronic Moderate to Severe Traumatic Brain Injury: A Pilot Study
Bibliographic record
Abstract
Objective: This pilot study investigated the potential of a video game-based cognitive rehabilitation program, utilizing Nintendo Wii Big Brain Academy, to enhance cognitive functions in Lebanese adults with chronic traumatic brain injury (TBI). Methods: Six adult males with moderate to severe chronic TBI were randomly assigned to either an intervention group (n=3), which received 12 individualized sessions of Wii-based cognitive training over six weeks, or a control group (n=3), which continued with standard rehabilitation without gaming. Cognitive performance was assessed before and after the intervention using the Montreal Cognitive Assessment (MoCA), the Stroop Word-Color Test, and in-game task performance metrics targeting domains such as memory, attention, and analytical reasoning. Main results: Post-intervention results indicated marked cognitive improvements in the gaming group. The intervention group showed a mean increase of 4.67 points on the MoCA (Cohen's d=4.06) and a mean improvement of 25.33 points on the Stroop test (Cohen's d=1.71), reflecting substantial gains in executive function and cognitive flexibility. Additionally, participants demonstrated performance enhancements in multiple game-based cognitive domains and reported subjective improvements in memory, concentration, and problem-solving skills. No significant changes were observed in the control group. Implications: These preliminary findings suggest that Wiibased cognitive training may serve as an engaging and effective adjunct to traditional rehabilitation for individuals with chronic TBI. Given the small sample size, further research involving a larger and more diverse population is needed to confirm these results and evaluate long-term outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".