The Effectiveness of Computerized Cognitive Training Program for Older Adults With Mild Cognitive Impairment: Preliminary Study
Bibliographic record
Abstract
Objective: As the elderly population increases, the proportion of neurocognitive disorder is increasing. Accordingly, research on cognitive intervention therapy to prevent neurocognitive disorder is also becoming active. In particular, this study attempted to evaluate changes in cognitive function before and after training and verify their effectiveness by implementing a computerized cognitive training program for patients with mild cognitive impairment. Methods: A computerized cognitive training program was conducted for 15 patients with mild cognitive impairment. It was conducted twice a week, 30 minutes, and 16 times for 8 weeks, and neurocognitive function was evaluated before and after training. The neurocognitive function tests are as follows; Seoul Neuropsychological Screening Battery, Korean version of Montreal Cognitive Assessment (K-MoCA), Clinical Dementia Rating (CDR), Korean Instrumental Activity of Daily Living (K-IADL), Korean Neuropsychiatric Inventory (K-NPI), and Memory Age-associated Complaint Questionnaire (MAC-Q). Results: There was a statistically significant improvement in memory domain after the implementation of the computerized cognitive training program, but no significant changes in attention, language ability, visuospatial function, and frontal lobe executive function. Among the memory areas, the Seoul Verbal Learning Test: Delayed Recall, Rey Complex Figure Test: Delayed Recall, and Rey Complex Figure Test: Recognition are showed significant improvement. In addition, there was a significant improvement in CDR (sum of box) and K-MoCA scores. There was no statistically significant difference in K-IADL and K-NPI. Conclusion: Computerized cognitive training programs have been effective in improving memory in patients with mild cognitive impairment. In order to verify the effectiveness of dementia prevention, a long-term study of a larger number is needed. The results of this preliminary study will help develop and apply cognitive training contents in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".