Association of dual impairment in physical performance and cognition with cognitive change over two years in older adults
Bibliographic record
Abstract
Abstract Background Dementia has a great impact in the ageing world. Much of the growing will happen in low and middle income regions, especially in Asia and Africa. Identifying the target population before clinical symptoms is most important for early intervention of preventing dementia. Recently, the concept of motoric cognitive risk syndrome has been introduced. However, the analogue of this syndrome is poorly evaluated. Therefore, we aim to explore the association of dual impairment in physical performance and cognition with cognitive impairment. Method This cohort study is part of the ongoing Taiwan Initiative of Geriatric Epidemiological Research. A total of 315 participants aged 65 and older were identified between 2015 and 2017 at the University Taiwan University hospital with a two‐ year follow‐up (2017‐2019). Dual impairment were defined as the concurrence of slow gait speed (< 1 m/s) and poor cognitive domain or low grip strength (< 28kg for men and < 18kg for women) and poor cognitive domain. Cognitive impairment was defined as the lowest tertile of the change of Taiwanese version of Montreal Cognitive Assessment (MoCA‐T) over 2 years. Logistic regression models were used to evaluate the association between dual impairment and the change of cognitive function adjusted for age, sex, education, and apolipoprotein e4 status. Result The mean age of this population was 75.17 years old. Forty‐eight participants (15.24%) with dual impairment of low handgrip strength and low verbal fluency were identified. Multivariable analyses showed that dual impairment of low grip strength and low verbal fluency increased the risk of cognitive decline [adjusted odds ratios (aOR)=2.42, 95% confidence interval (CI)=1.20−4.88]. However, dual impairment of slow gait speed and low verbal fluency were not associated with cognitive decline (aOR=1.62, 95% CI=0.94–2.78). Conclusion Our findings indicate that older adults with dual impairment of low grip strength and low verbal fluency were at a higher risk of cognitive impairment, which emphasizes the importance of complexity in pre‐dementia syndrome. Further research is warranted to clarity the underlying mechanisms of cognitive impairment and opportunities for early intervention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".