Anticholinergic burden and cognitive aging: investigating the protective role of digital cognitive training
Bibliographic record
Abstract
Association between anticholinergic burden and cognitive decline in older adults remains unclear, particularly regarding the modulatory effect of digital cognitive training. This study investigated the relationship between anticholinergic burden, cognitive impairment, and cognitive remediation in healthy older adults (HOA) and individuals with mild cognitive impairment (MCI). A total of 165 participants aged 60 and older (37 men, 128 women; mean age=69.1 years) from two randomized controlled trials were assessed. Baseline data included sociodemographic, clinical status, medication, and cognitive measures. Anticholinergic burden was quantified using the Anticholinergic Cognitive Burden (ACB) scale. The mean Montreal Cognitive Assessment (MoCA) score was 24.1±3.5, and mean ACB score was 0.77±1.1. Participants with MCI were older and used more medications, with higher ACB scores, compared to HOA. Each additional medication increased the odds of MCI by 1.01 times (p<0.001). However, these associations lost significance after adjusting for age and education (OR=0.8, p=0.60; OR=0.9, p=0.69, respectively). Among MCI participants who completed 20 hours of digital cognitive training, higher ACB scores correlated with lower cognitive gains (Spearman’s r=-0.55, p<0.01). No significant correlations were found in the HOA group. Findings suggest that anticholinergic burden may hinder cognitive training benefits in MCI. Although not a significant predictor of MCI after adjustments, clinicians should consider minimizing anticholinergic load to optimize interventions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".