Dataset on the effectiveness of cognitive training using the abacus-based mental calculation training application
Bibliographic record
Abstract
This study investigated the effect of a home-based computerized cognitive training program that utilizes a digital application for training abacus-based mental calculations, “SoroTouch,” on the cognitive functions of healthy middle-aged and older people using a randomized controlled trial. The participants were 20 adults (aged 42-79 years) who were involved in community-based activities for dementia prevention held by a certain organization. The participants were assigned randomly to the intervention (SoroTouch) group or control group. The SoroTouch group received home-based cognitive training with SoroTouch, being asked to use the software every day for 6 months, while the control group did not receive any intervention. To investigate the effect of SoroTouch, CogEvo, a cognitive functions test battery utilizing a tablet device, was administered to all participants once per month during the 6-month intervention period. In addition, before and after the intervention, all participants were asked to take the CogEvo and the Japanese version of the Montreal Cognitive Assessment (MoCAJ). The analyses showed that the SoroTouch group did not improve total scores of the CogEvo and MoCAJ, but large group differences were observed in the two tasks of the CogEvo as follows: ‘Follow the order’ (modified Trail Making Test) at 2 months after the beginning of the intervention (group differences; 39.4, 95% confidence interval; 7.6-71.2) and ‘Route 99’ at 6 months (group differences; 39.6, 95% confidence interval; 4.9-74.4). These results provide evidence that a home-based computerized cognitive training program SoroTouch has the potential to improve working memory and executive functions in healthy middle-aged and older adults.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".