Cognitive and Psychosocial Outcomes of Self-Guided Executive Function Training and Low-Intensity Aerobic Exercise in Healthy Older Adults
Bibliographic record
Abstract
OBJECTIVES: Prior work has demonstrated that executive function training or physical exercise can improve older adults' cognition. The current study takes an exploratory approach to compare the feasibility and efficacy of online executive function training and low-intensity aerobic exercise for improving cognitive and psychosocial functioning in healthy older adults. METHOD: Following a standard pretest-training-posttest protocol, 40 older adults (aged 65 and above) were randomly assigned to an executive function or a physical training group. A battery of cognitive and psychosocial outcome measures were administered before and after training. During the 10 weeks of self-guided training at home (25-30 min/day, 4 days/week), the executive function training group practiced a set of adaptive online executive function tasks designed by Lumos Labs, whereas the physical training group completed an adaptive Digital Video Disc (DVD)-based low-intensity aerobic exercise program. RESULTS: Training transfer effects were limited. Relative to low-intensity aerobic exercise, executive function training yielded cognitive improvement on the 64-card Wisconsin Card Sorting Task (WCST-64), a general executive function measure. Depression and stress levels dropped following both training programs, but this could be driven by decreased stress or excitement in performing the tasks over time. DISCUSSION: The results revealed limited cognitive benefits of the online executive function training program, specifically to a near transfer test of general executive control. Importantly, the current study supports the feasibility of home-based self-guided executive function and low-intensity physical training with healthy older 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".