Preventing cognitive decline via digital literacy and virtual game management: a randomized controlled intervention study in community-dwelling elderly
Bibliographic record
Abstract
Objective: To evaluate the impact of cognitive stimulation via digital inclusion and game management on cognition in the elderly in primary health care. Method: This is a randomized controlled intervention study nested within a population-based cohort study. Based on the application of the Clinical Dementia Rating (CDR), individuals with scores of 0 and 0.5, and aged 60 years or older were included and randomly allocated to the Intervention Group (IG) and Control Group (CG). Initially, 160 participants met the selection criteria and underwent neuropsychological evaluation via Montreal Cognitive Assessment (MoCA), used before and after the intervention. The IG (n=62) participated in the computer-based intervention once a week for 1.5 hours for a total of 4 months. The CG (n=47) participated in Mindfulness workshops held with the same durability over the same time period. Results: The screening of cell phone and computer use in the initial sample (n=160) showed that 93% of the elderly have a cell phone, 82.5% of the devices is of the smatphone model with Internet access, 80% have a computer at home and, the majority (56.5%) fit with the basic level regarding use. Sixty-two elders in GI (50 women; 12 men), with a mean age of 74.8±6.3 years and 56.5% had high schooling, and 47 in CG (41 women; 6 men), with a mean age of 74±5.7 years and 46.8% with medium schooling, joined the study. 34% of the IG seniors had an income greater than or equal to 5 minimum wages, as well as 44.5% of the CG seniors. The IG who went through the Remembrance Workshops had on average 2.6 points more in MoCA after 4 months than the control group (p<0.001; 95%CI [1.90; 3.31]). The change in final MoCA decreased by 0.46 points (p<0.001; 95% CI [-0.57; -0.34]) for each additional unit in baseline MoCA. Individuals with average education had a 0.93 point (p=0.011; 95% CI [0.21; 1.64]) increase in MoCA change compared to individuals with low or high education. Conclusion: Digital inclusion combined with the practice of video games has the potential to improve cognition in the elderly. This prevention program can be structured to be applied in primary health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".