Can mild cognitive impairment with depression be improved merely by exercises of recall memories accompanying everyday conversation? A longitudinal study 2016-2019
Bibliographic record
Abstract
Purpose The purpose of this study is to find out a simple cognitive intervention method to use MCI and suffering people with depression. As the elderly society increases around the world, the number of elderly people with diseases and dementia is increasing rapidly. Mild cognitive impairment (MCI), a pre-stage to dementia, is a critical treatment time to slow disease progression. However, there is currently no appropriate medication. Furthermore, MCI patients with depression are more difficult to treat. Design/methodology/approach To overcome these problems, the authors confirmed improvements and delayed effects in MCI patients in this study for three years through cognitive intervention, demonstrating its effectiveness. Cognitive interventions were conducted for memory retrieval and steadily stimulated the brain by performing tasks to solve problems during daily conversations. Findings As a result, the intervention group retained mini-mental state examination and Montreal cognitive assessment scores on the domains of cognitive function and also instrumental activities of daily living in the domain of motion compared to the non-intervention group. Moreover, significant improvements in geriatric depression scales-15 and quality-of-life scales enabled the patients to maintain stable living compared to before the intervention. In addition, the intervention group showed a change in patterns that allowed them to voluntarily devote time to going out at the end of the study. Research limitations/implications This study was originally planned to compare the rates of transmission from MCI to dementia by tracking over five years (2016–2021). However, due to the impact of COVID-19, which began to spread around the world in 2020, further face-to-face visits and cognitive intervention became impossible. Thus, only half of the data in the existing plans were collected. Although it is difficult to present accurate results for the rate of transmission from MCI to dementia, the tendency was confirmed, indicating sufficient implications as an intervention. Originality/value This study was originally planned to compare the rates of transmission from MCI to dementia by tracking three years (2016–2019). The authors had studied for long-term effect.
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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".