Effects of physical, virtual reality-based, and brain exercise on physical, cognition, and preference in older persons: a randomized controlled trial
Bibliographic record
Abstract
Physical exercise (PE), virtual reality-based exercise (VRE), and brain exercise (BE) can influence physical and cognitive conditions in older persons. However, it is not known which of the three types of exercises provide the best effects on physical and cognitive status, and which exercise is preferred by older persons. This study compared the effects of PE, VRE, and BE on balance, muscle strength, cognition, and fall concern. In addition, exercise effort perception and contentment in older persons was evaluated. Eighty-four older persons ( n = 84) were randomly selected for PE, VRE, BE, and control groups. The exercise groups received 8-week training, whereas the control group did not. Balance was assessed by Berg Balance Scale (BBS) and Timed Up and Go test (TUG), muscle strength by 5 Times Sit to Stand (5TSTS) and left and right hand grip strength (HGS), cognition by Montreal Cognitive Assessment (MoCA) and Timed Up and Go test Cognition (TUG-cog), fall concern by Fall Efficacy Scale International (FES-I), exercise effort perception by Borg category ratio scale (Borg CR-10), and exercise contentment by a questionnaire. After exercise, PE significantly enhanced TUG and 5TSTS to a greater extent than VRE (TUG; p = 0.004, 5TSTS; p = 0.027) and BE (TUG; p = 0,012, 5TSTS; p < 0.001). VRE significantly improved MoCA ( p < 0.001) and FES-I ( p = 0.036) compared to PE, and 5TSTS ( p < 0.001) and FES-I ( p = 0.011) were improved relative to BE. MoCA was significantly enhanced by BE compared to PE ( p < 0.001) and both MoCA and TUG-cog were improved compared to VRE ( p = 0.04). PE and VRE significantly ( p < 0.001) increased Borg CR-10 in all exercise sessions, whereas BE showed a significant improvement ( p < 0.001) in the first 4 sessions. Participants had a significantly greater satisfaction with BE than controls ( p = 0.006), and enjoyed VRE and BE more than PE ( p < 0.001). Subjects in all exercise groups exhibited benefits compared to the control group ( p < 0.001). PE provided the best results in physical tests, VRE produced measurable improvements in physical and cognition scores, while BE enhanced cognition ability in older persons. Older persons preferred VRE and BE compared to PE. Both exercises are suggested to older persons to improve physical and cognitive conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".