Comparison of Functional Mobility Performances of People with Alzheimer’s Disease, Mild Cognitive Impairment, and Cognitively Healthy Individuals
Bibliographic record
Abstract
Abstract Background One of the important factors that make it difficult to carry out activities of daily living independently is the decrease in functional mobility. Mobility‐related disability is a major public health issue that reduces the quality of life and increases the need for care, the burden, and the cost of care. However, in the early stages of dementia, the deterioration in motor skills is often overlooked. This study aimed to compare the mobility performance of healthy individuals and people with Mild Cognitive Impairment (pwMCI) and Alzheimer’s disease (pwAD). Method Cognitive level was measured with Montreal Cognitive Assessment (MoCA) and mobility performance was measured using Timed Up and Go Test (TUG) and 5 Times Sit to Stand Test (5XSTST). Statistical analysis was performed using One Way ANOVA in SPSS v.26 and the level of significance was accepted as p<0.05. Results 36 pwAD (Age: 74.90±9.25, 53.12% Female), 14 pwMCI (Age: 63.82±12.55 years, 52.6% Female), and 10 cognitively healthy individuals (Age: 60.25±10.23 years, 82% Female) were included the study. The mean of MoCA scores were 12.62±6.36, 22.25±3.04 and 28.5±0.5; TUG results were 15.34±11.83 sec, 7.47±1.41 sec and 6.86±1.98 sec; 5XSTST results were 16,31±7.75 sec, 11.06±1.98 sec and 9.34±2.37 sec. A significant difference was found between the three groups in terms of TUG and 5XSTST results (p = 0.027, p = 0.015, respectively). Conclusion In pwAD and pwMCI, the decrease in cognition appears to be accompanied by impairment in functional mobility. Motor skills should be evaluated not only in AD diagnosis but also during the prodromal stage to provide early intervention for loss of motor function. Improving motor function should be added to rehabilitation goals before impaired functional mobility begins to affect activities of daily living.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".