TECH preserves global cognition of older adults with MCI compared with a control group: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Cognitive training using touchscreen tablet casual game applications (apps) has potential to be an effective treatment method for people with mild cognitive impairment (MCI). AIMS: This study aimed to establish the effectiveness of 'Tablet Enhancement of Cognition and Health' (TECH), a novel cognitive intervention for improving/preserving cognition in older adults with MCI. METHODS: A single-blind randomized controlled trial with assessments pre-, post-, and at 6-month follow-up was conducted. TECH entailed 5 weeks of daily self-training utilizing tablet apps, facilitated by weekly group sessions. Global cognition was assessed by the Montreal Cognitive Assessment (MoCA), and specific cognitive components were assessed using WebNeuro computerized battery. Short Form Health Survey (SF-12) assessed health-related quality of life (HRQoL). Intention-to-treat analysis was conducted and the %change was calculated between pre-post and between pre-follow-up. Cohen's d effect size was also calculated. RESULTS: Sixty-one participants aged 65-89 years were randomly allocated to TECH (N = 31, 14 women) or to standard care (N = 30, 14 women). Pre-post and pre-follow-up MoCA %change scores were significantly higher in TECH than control (U = 329.5, p < .05; U = 294.5, p < .05) with intermediate effect size values (Cohen's d = .52, Cohen's d = .66). Forty percent of TECH participants versus 6.5% of control participants achieved a minimal clinical important difference in MoCA. Pre-post between-group differences for specific cognitive components were not found and HRQoL did not change. DISCUSSION AND CONCLUSIONS: TECH encouraged daily self-training and showed to preserve global cognition of older adults with MCI. The implementation of TECH is recommended for older adults with MCI, who are at risk for further cognitive decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".