THE IMPACT OF COMBINED PHYSICAL AND COGNITIVE TRAINING ON MOBILITY OUTCOMES—DOES FORMAT MATTER?
Bibliographic record
Abstract
Researchers have demonstrated that with age, declining sensorimotor abilities are compensated for by the recruitment of higher level cognitive processes. This view was recently supported by showing that cognitive dual-task training improved mobility and posture among older adults. Moreover, preliminary evidence suggests that combined physical and cognitive training is more effective than single domain training in improving mobility among older adults. However, to date there has been no investigation contrasting sequential and simultaneous training conditions. To explore this hypothesis, 41 older adults were assigned to either a sequential or simultaneous training group consisting of 12 weeks of computerized divided attention training and aerobic training. All participants completed pre- and post-training assessments consisting of mobility (STS: Sit-to-Stand) and cognitive (n-back working memory) tasks performed singly and concurrently. Additionally, the sound intensity of the cognitive stimuli was manipulated in order to increase auditory challenge. Pre-post comparisons on the STS mobility task revealed that both groups improved under dual-task conditions at both levels of auditory challenge. Additionally, participants in the sequential training group demonstrated improved dual-task performance on the cognitive task. These results suggest that while both training protocols were successful in improving mobility under challenging dual-task conditions, sequential training was more effective in improving dual-task cognitive performance. Therefore, focusing on one training intervention at a time appears to be more beneficial than dual-task training where participants are required to divide their attention between two tasks.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".