The Effects of Computerized Cognitive Training With and Without Physical Exercise on Cognitive Function in Older Adults: An 8-Week Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Aging is characterized by cognitive changes in specific domains, such as declines in memory and executive functions. Given the world's aging population, it is important to identify and evaluate strategies that promote healthy cognitive aging. Besides exercise, computerized cognitive training (CCT) is a promising approach to promote cognitive function. Moreover, a single bout of exercise immediately prior to CCT may provide additional cognitive benefits. METHODS: An 8-week proof-of-concept randomized controlled trial to investigate the effect of a commercial CCT intervention, alone and when preceded by exercise, on cognitive function. Participants (124; aged 65-85 years) performed 8 weeks of: (i) Group-based CCT (Fit Brains) 3×/week for 1 hour plus 3×/week home-based training; (ii) Group-based CCT preceded by exercise (Ex-CCT) 3×/week for 1 hour plus 3×/week home-based training (exercise+CCT); or (iii) Group-based balanced and toned (BAT) classes 3×/week for 1 hour (control). Memory was assessed by the Rey Auditory Verbal Learning Test. Executive functions were assessed using the: (i) Stroop Test, (ii) Trail Making Tests (TMT), (iii) Flanker Test, and (iv) Dimensional Change Card Sort Test (DCCS). RESULTS: At trial completion, there were no significant between-group differences in memory (p > .05). However, compared with BAT, CCT, and Ex-CCT significantly improved Stroop performance (-10.72, 95% confidence interval [CI]: -16.53, -4.91; -7.95, 95% CI: -13.77, -2.13, respectively). Moreover, Ex-CCT significantly improved the performance on TMT (-13.65, 95% CI: -26.09, -1.22), the Flanker Test (6.72, 95% CI: 2.55, 10.88), and the DCCS Test (6.75, 95% CI: 0.99, 12.50). CONCLUSION: An 8-week CCT program may promote executive functions in older adults and combining it with a bout of exercise may provide broader benefits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".