Effects of Home Based Serious Game Training (Brain Talk™) in the Elderly With Mild Cognitive Impairment: Randomized, a Single-Blind, Controlled Trial
Bibliographic record
Abstract
Mild cognitive impairment (MCI) increases with aging society. Serious games may be effective in improving cognitive function in patients with MCI; however, research on their effects remains insufficient. This study aimed to confirm the efficacy and safety of cognitive rehabilitation training using a serious game (Brain Talk™) for the elderly with MCI. Twenty-four elderly individuals with MCI were randomized into study and control groups. The study group received 12 training sessions (30 min/session, 3 times/week), whereas the control group did not receive training. Blinded evaluations were conducted before and after the training and four weeks after the training. The primary outcome measures were the Korean Mini-Mental State Examination (K-MMSE) and K-MoCA (Korean Montreal Cognitive Assessment). Secondary outcome measures were the Semantic Verbal Fluency Task (SVFT), Trail-Making Test-B, and 2-back test. In the study group, the K-MMSE, K-MoCA, and SVFT scores after finishing the training and 4 weeks after training showed a significant increase; however, there was no significant change in the control group. No significant differences were observed between the two groups. Cognitive function significantly improved in the study group after training. Home-based serious games are considered helpful in improving cognitive function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".