Metacognitive accuracy differences in Parkinson's disease and REM sleep behavioral disorder relative to healthy controls.

Background: Metacognition is the ability to monitor and self-assess cognitive performance. It can be impaired in neurodegenerative diseases, with implications for daily function, and the ability of patients to reliably report their symptoms to health professionals. However, metacognition has not been systematically assessed in early-mid stage Parkinson's disease (PD) and REM sleep behavioral disorder (RBD), a prodrome of PD. Objectives: This study aimed to evaluate metacognitive accuracy and self-confidence in PD and RBD patients across various cognitive tasks. Methods: We conducted detailed computerized cognitive assessments with 19 cognitive tasks within an established PD and RBD cohort. Participants self-rated their performance post-task. Metacognitive accuracy was calculated by comparing these ratings against objective performance and further analyzed against clinical and mental health factors. Results: PD and RBD patients' metacognitive accuracy aligned with control subjects. However, they exhibited lower confidence across cognitive domains, reflecting their reduced cognitive performance. A notable inverse correlation was observed between their confidence and MDS-UPDRS I and II scales and HADS anxiety and depression scores. Conclusion: Our findings indicate that patients with early to mid-stage PD and RBD are generally aware of their cognitive status, differing from other neurological disorders. The inverse relationship between patient confidence and symptoms of depression, anxiety, and daily life challenges underscores the impact of emotional and functional difficulties on their self-perception of cognitive abilities. This insight could be significant for understanding how these conditions affect mental health, aiding clinicians in developing more effective patient care strategies.

Online cognitive monitoring technology for people with Parkinson's disease and REM sleep behavioural disorder.

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.