Effects of a Physical Activity Program that Incorporates Exercises Targeting Balance, Strength, and Proprioception on Cognitive Functions and Physical Performance in Old Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Aging often leads to cognitive function decline, sensory structure deterioration, and musculoskeletal system weakening. This impacts postural control during static and dynamic activities like walking, increasing the fall risk among the elderly. Older adults with mild cognitive impairment (MCI) face an elevated fall risk and cognitive decline, magnifying the public health concern. OBJECTIVE: This study aimed to explore solutions by investigating the effects of a multi-component physical activity program on cognitive and motor functions in MCI patients. METHODS: Twenty-three participants were enrolled in the study and assigned into two groups: an intervention group (n = 13; age = 85.7±5.5 years) and a control group (n = 9; age = 85±6.7 years). The study spanned two months, with participants engaging in three 60-minute weekly physical exercise sessions. The intervention focused on improving proprioception, muscle strength, and balance. RESULTS: Results demonstrated significant enhancements in physical performance, fall risk reduction, and balance (p < 0.05). Various tests, including the timed up and go test, Unipedal Stance test, Tinetti test, Short Physical Performance Battery, and 6-minute walking test, indicated these improvements. Cognitive function was evaluated with the Mini-Mental State Examination, revealing non-significant progress (p > 0.05). Predictive models for outcomes were developed using linear regression analysis during the follow-up stage. CONCLUSIONS: This study underscores the effectiveness of a multi-component physical activity program encompassing balance, proprioception, and muscle-strengthening exercises as a non-pharmaceutical approach in improving balance skills and playing a key role in mitigating the risk of falls among old adults with MCI.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".