Effects of combined physical and cognitive training on fitness and neuropsychological outcomes in healthy older adults
Bibliographic record
Abstract
PURPOSE: Physical exercise and cognitive training have been shown to enhance cognition among older adults. However, few studies have looked at the potential synergetic effects of combining physical and cognitive training in a single study. Prior trials on combined training have led to interesting yet equivocal results. The aim of this study was to examine the effects of combined physical and cognitive interventions on physical fitness and neuropsychological performance in healthy older adults. METHODS: Seventy-six participants were randomly assigned to one of four training combinations using a 2×2 factorial design. The physical intervention was a mixed aerobic and resistance training program, and the cognitive intervention was a dual-task (DT) training program. Stretching and toning exercises and computer lessons were used as active control conditions. Physical and cognitive measures were collected pre- and postintervention. RESULTS: All groups showed equivalent improvements in measures of functional mobility. The aerobic-strength condition led to larger effect size in lower body strength, independently of cognitive training. All groups showed improved speed of processing and inhibition abilities, but only participants who took part in the DT training, independently of physical training, showed increased task-switching abilities. The level of functional mobility after intervention was significantly associated with task-switching abilities. CONCLUSION: Combined training did not yield synergetic effects. However, DT training did lead to transfer effects on executive performance in neuropsychological tests. Both aerobic-resistance training and stretching-toning exercises can improve functional mobility in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".