Bidirectional relationship between depression and activities of daily living and longitudinal mediation of cognitive function in patients with Parkinson’s disease
Bibliographic record
Abstract
Objective To investigate the bidirectional relationship between depression and activities of daily living (ADL) in Parkinson’s disease (PD) patients and explore the mediating role of cognitive function over time. Methods Data from 892 PD patients from the Parkinson’s Progression Markers Initiative (PPMI) database were included in this study, and depression, cognitive function, and ADL were measured using the Geriatric Depression Scale (GDS-15), Montreal Cognitive Assessment Scale (MoCA), and Unified Parkinson’s Disease Rating Scale, Part II (UPDRS II) respectively. The cross-lagged panel model (CLPM) was employed to analyze the reciprocal relationship between depression and ADL. Then, we explored the mediating role of cognitive function in the bidirectional relationship between depression and ADL in patients with PD, and the mediation effect test was carried out using a bias-corrected nonparametric percentile bootstrap approach. Results Depression in patients with PD predicted their subsequent ADL (β = 0.079, p < 0.01), and ADL also predicted their subsequent depression (β = 0.069, p < 0.05), In addition, Bootstrap analysis showed that cognitive function played a significant mediating role in prediction of depression to ADL in patients with PD (β = 0.006, p = 0.074, 95%CI = 0.001 ~ 0.014), and cognitive function also played a significant mediating role in prediction of depression to ADL (β = 0.006, p = 0.067, 95%CI = 0.001 ~ 0.013). Conclusion There is a bidirectional relationship between depression and ADL in patients with PD. Furthermore, we found that cognitive function mediates the relationship that exists between depression and ADL in patients with PD. Interventions aimed at enhancing cognitive function could potentially lessen the vicious cycle of depression and ADL in PD, thus improving patient quality of life (QOL).
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".