The Effects of a New Integrated and Multidisciplinary Cognitive Rehabilitation Program Based on Mindfulness and Reminiscence Therapy in Patients with Parkinson’s Disease and Mild Cognitive Impairment: A Pilot Study
Bibliographic record
Abstract
Background: Mindfulness trainings have shown promising results as treatment for behavioural symptoms in several pathologies. In addition, mindfulness protocols induced an improvement in memory and attention. Therefore, mindfulness could be an effective intervention for patients affected by Parkinson’s disease (PD) and mild cognitive impairment (MCI), who are characterized by both behavioural and cognitive dysfunctions. Methods: We assessed differences in Montreal Cognitive Assessment (MoCA) scores and in Beck Depression Inventory II (BDI-II) scores in patients affected by PD and MCI enrolled in two different rehabilitation programs (an experimental vs. an usual structured program for cognitive rehabilitation). Participants in the experimental group (MILC-tr) underwent innovative rehabilitation program involving mindfulness and reminiscence activities. Assessments were performed before (T0) and at the end of the rehabilitation program (T1). Results: Friedman test showed a significant improvement between timepoints in MoCA global score (x2 = 4.000, p = 0.046), MoCA memory sub-scale score (x2 = 4.571, p = 0.033), and BDI-II cognitive and affective factors (x2 = 4.000, p = 0.046) only for patients in MILC-tr group. Mann–Whitney test showed a significant difference between group comparing differences in Δ scores between T0 and T1 in the MoCA memory sub-scale score (U = 190.50, p = 0.035). Conclusions: Mindfulness-based rehabilitation programs could be effective in patients affected by PD and MCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".