Effects of a Dart Game Intervention in Community-Dwelling Older Adults with Suspected Mild Dementia: An Exploratory Study Using the Japanese Version of the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: With the aging of the population, awareness of various dementias, including Alzheimer's disease, is spreading. The causes of these dementias are being elucidated, while prevention is also becoming more important. Aerobic exercise, strength training, and flexibility exercise (physical exercise) alone are not sufficient to improve cognitive function, and dual-task activities that engage both physical and cognitive functions are effective for its prevention. Therefore, the present study investigated the effects of an intervention with dart games on cognitive function in older adults suspected of having mild cognitive impairment (MCI) who reside in the community. Factors contributing to these effects were also examined. METHODS: The intervention with dart games lasted for six months and was tested on healthy older individuals (aged ≥ 65 years) recruited from the community (Mihara City and Kumano Town in Hiroshima Prefecture and Iki City in Nagasaki Prefecture). The dart game intervention was the Count-Up and 01 Game, which took place every two weeks over a six-month period, with each session lasting 100 minutes. Data from 62 participants with or without MCI were analyzed. An intervention was conducted in which older adults suspected of having MCI who reside in the community played dart games for six months. Cognitive function was assessed before and after the intervention using the Montreal Cognitive Assessment-Japanese version (MoCA-J) to evaluate the effects of the intervention. Participants were classified based on their MoCA-J scores, and those scoring ≤25 were categorized as suspected MCI. For those classified as suspected MCI before the intervention, it was determined whether they were MCI (non-improved MCI group) or not MCI (improved MCI group) after the intervention. In both groups, MoCA-J subdomain scores (orientation (six points), language (three points), visuospatial/executive function (five points), delayed recall (five points), abstraction (two points), attention (six points), and naming (three points)) were compared. The predictive ability of MoCA-J scores for MCI improvement due to the dart game intervention was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: Among patients classified as suspected MCI before the intervention (n=27), 11 were in the non-improved MCI group and 16 in the improved MCI group after the intervention, with some subjects showing improvement in cognitive function following the intervention. In a comparison of the MoCA-J subdomain scores before and after intervention for those classified as suspected MCI before the intervention, improvements were revealed in "delayed recall (non-improved MCI group 0.0 vs. improved MCI group 2.5, median comparison)" and "attention (non-improved MCI group 0.0 vs. improved MCI group 1.0, median comparison)". The predictive ability of the dart game intervention to improve MCI for subjects classified as MCI before the intervention was determined using an ROC curve, with an AUC of 0.841. The optimal cut-off score based on Youden's index was 22 points. CONCLUSION: The dart game intervention demonstrated potential cognitive benefits, particularly in brain regions associated with "delayed recall" and "attention." Among older adults with MoCA-J scores of approximately 22, participating in a six-month dart game intervention may help improve MCI.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".