Effectiveness of Neuromuscular Physical Therapy with Cognitive-Motor Dual Task Training in Reducing Fall Risk in Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
Background: A study investigating the effectiveness of neuromuscular physical therapy combined with cognitive-motor dual-task training could provide valuable insights into optimizing fall prevention strategies. Such research would help establish evidence-based protocols that maximize functional outcomes and improve the quality of life for older adults at risk of falls. Methods: A quasi-experimental study with pre- and post-intervention assessments was conducted to evaluate the effectiveness of combining neuromuscular physical therapy with cognitive-motor dual-task training in reducing the risk of falls among older adults with mild cognitive impairment (MCI). Results: The effects of interventions were determined by comparing the average values of outcome measures taken at baseline and after 12 weeks of intervention and the findings provide significant improvement where the values of Tinetti Performance-Oriented Mobility Assessment at baseline were 22.63±3.27 that was significantly (p<0.001) improved to 27.21±3.11 after twelve weeks of intervention. In addition to that value of cognitive functioning as observed at baseline was 21.36±2.12 was also significantly (p<0.001) improved to 25.66±3.21 after intervention. Conclusion: The 12-week intervention program combining neuromuscular physical therapy and cognitive-motor dual-task training significantly improved functional mobility, cognitive function, and dynamic balance in older adults with mild cognitive impairment. The results indicate that the intervention was effective in enhancing balance and reducing fall risk, as evidenced by the improvements in Tinetti Performance-Oriented Mobility Assessment (POMA), Montreal Cognitive Assessment (MoCA), and Timed Up and Go (TUG) test scores. DOI: https://doi.org/10.59564/amrj/03.01/016
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".