The Feasibility and Potential Impact of Brain Training Games on Cognitive and Emotional Functioning in Middle-Aged Adults
Bibliographic record
Abstract
OBJECTIVES: To investigate whether a commercially available brain training program is feasible to use with a middle-aged population and has a potential impact on cognition and emotional well-being (proof of concept). METHOD: Fourteen participants (ages 46-55) completed two 6-week training conditions using a crossover (counterbalanced) design: (1) experimental brain training condition and (2) active control "find answers to trivia questions online" condition. A comprehensive neurocognitive battery and a self-report measure of depression and anxiety were administered at baseline (first time point, before training) and after completing each training condition (second time point at 6 weeks, and third time point at 12 weeks). Cognitive composite scores were calculated for participants at each time point. RESULTS: Study completion and protocol adherence demonstrated good feasibility of this brain training protocol in healthy middle-aged adults. Exploratory analyses suggested that brain training was associated with neurocognitive improvements related to executive attention, as well as improvements in mood. CONCLUSION: Overall, our findings suggest that brain training programs are feasible in middle-aged cohorts. We propose that brain training games may be linked to improvements in executive attention and affect by promoting cognitive self-efficacy in middle-aged adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".