COMPUTERIZED COGNITIVE TRAINING, WITH OR WITHOUT EXERCISE, TO PROMOTE COGNITIVE FUNCTION: A RANDOMIZED TRIAL
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Given the world’s aging population, it is important to identify strategies that promote healthy cognitive aging. Computerized cognitive training (CCT) may be a promising method to combat cognitive decline in older adults. Moreover, physical exercise immediately prior to CCT might provide additional cognitive benefits. We conducted a randomized controlled trial to examine the effect of a CCT intervention, alone or preceded by physical exercise, on memory and executive functions in older adults. 124 community-dwelling older adults aged 65-85 years were randomly assigned to either 8-weeks of: 1) 3x/week group-based CCT plus 3x/week CCT sessions at home; 2) 3x/week group-based CCT combined with a 15-minute brisk walk (Ex-CCT) plus 3x/week Ex-CCT sessions at home; or 3)3x/week group-based sham exercise and education sessions (CON). At baseline and 8-weeks standard neuropsychological tests of verbal memory and learning and executive functions were administered, including the Rey Auditory Verbal Learning Test (RAVLT), Stroop test, Flanker test, Trail Making Tests (TMT B-A), and Dimensional Change Card Sort (DCCS) Test. At trial completion, there were no differences in RAVLT performance. Compared with CON, FBT and Ex-FBT participants significantly improved performance on the Stroop test (p = .001 and p = .023, respectively). Additionally, those randomized to Ex-CCT improved performance on the Flanker test (p = .002), TMT B-A (p = .047), and the DCCS Test (p = .023) compared with BAT. These findings suggest that an 8-week CCT program could benefit executive functions, and that implementing exercise immediately prior to CCT could provide broader benefits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it