The Effect of Balance-Based Interventions on Cognitive Functions of the Healthy and MCI Elderly: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Aging is an inseparable part of life, accompanied by mild to severe cognitive disorders. This study aimed to investigate the influence of balance-based interventions on cognitive function in older adults, encompassing both healthy individuals and those with mild cognitive impairment (MCI). Methods: A systematic review was conducted by searching multiple databases up to April 2023, and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was followed for reporting. Sixteen studies, comprising 1148 participants aged 43 to 89 years, were analyzed. Balance exercises were administered 1 to 3 times per week, lasting 30 to 60 minutes per session. Methodological quality was assessed using the Downs and Black checklist. A meta-analysis was conducted for executive functions (Stroop Test) and complex attention (Trail-Making Test, TMT A&B), while other outcomes underwent qualitative analysis. Results: Qualitative analysis revealed positive effects on specific executive functions and complex attention aspects. However, the meta-analysis did not show significant differences in scores between balance training and control groups, which included healthy adults receiving nonbalance interventions or no intervention. Conclusion: Limited research and methodological constraints hinder conclusive findings on balance-based interventions for older adults' cognitive functions. Yet, these interventions show the potential to enhance executive function and complex attention, emphasizing the need for further research in disability and rehabilitation.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".