THE EFFECTS OF PHYSICAL AND COGNITIVE TRAINING ON MOBILITY AND EXECUTIVE FUNCTIONS IN OLDER ADULTS
Bibliographic record
Abstract
Studies suggest that physical exercise and cognitive training interventions can help improve cognitive functions and mobility in older adults. However, the specific impact of different types of exercise programs and cognitive training on mobility and cognition in sedentary older adults is not fully understood. To investigate this, 68 healthy sedentary participants over the age of 60 (M=68.58, SD=4.68) have been randomized to one of the three 12 weeks training programs (Aerobic (AE)=23, Motor Functions (MF)=24, Cognition(COG)=21). Before and after the training program, the participants underwent physical fitness tests (VO2Max; timed-up-and-go – TUG; metabolic energy cost of walking – MECW), and cognitive evaluations (MMSE, and a modified computerized Stroop task). The AE consisted of high intensity training on a recumbent bicycle. The MF consisted of full-body exercises focusing on coordination, balance, stretching, flexibility without raising the heart rate. The COG training consisted of Ipad exercises focusing on executive functions. Repeated measures ANOVAs revealed a mobility improvement in TUG in all three groups (F(1,65)=9.25, P<.00), an improvement in VO2Max only in the AE group (F(2,65)=4.68, P<.01), an improvement in MECW only in the HM group (F(2,65)=3.35, P<.04), an improvement in Stroop inhibition reaction time only in the COG and MF groups (F(2,65)=25.83, P<.00), and an improvement in Stroop switching reaction time in all groups, with the highest benefit in the COG group (F(2,65)=38.75, P<.00). The study shows that three separate training programs targeting specific mechanisms can improve mobility and confirms the beneficial effects of exercise on cognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".