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Background: Anxiety is one of the most common but often overlooked mood-related nonmotor symptoms in people with Parkinson's disease (PD). To improve the well-being of people with PD, it is important to understand the impact of anxiety in PD, especially its association with depressive and motor symptoms and its impact on health-related quality of life (HRQoL). Methods: 91 people with PD were assessed between June 2017 and June 2018. Anxiety was measured using the Geriatric Anxiety Scale (GAS) and its cognitive, somatic, and affective subscales. HRQoL was assessed using the Parkinson's Disease Questionnaire 39 (PDQ-39). Moreover, sociodemographic information, depressive symptoms, cognition, motor and nonmotor symptoms were assessed. Descriptive statistics, regression analyses, and path analyses were performed to understand predictors of anxiety and its influence on HRQoL. Results: Of the 91 people with PD, 35 (38.5%) experienced anxiety. Anxiety symptoms in these individuals primarily manifest as somatic sensations. Anxiety, motor, and depressive symptoms are interlinked but contribute individually to HRQoL. Beyond motor symptoms, cognitive and affective aspects of anxiety impact HRQoL. While anxiety and depression overlap, the somatic and cognitive aspects of anxiety play a significant role in determining HRQoL in addition to depressive symptoms. Conclusion: Our study used the GAS and its three subscales to shed light on the connections between anxiety, depression, and motor impairment in people with PD. Although anxiety is linked to depression and motor symptoms, it independently affects the HRQoL of people with PD. Thus, it is crucial to adopt a comprehensive diagnostic approach that detects and considers the impact of anxiety on HRQoL in PD. [ABSTRACT FROM AUTHOR]

The clinical presentation of Parkinson's disease (PD) is often dominated by depressive symptoms, which can significantly impact the patients' quality of life (QoL). However, it is not clear how these depressive symptoms are interconnected, or if some symptoms are more influential in affecting QoL. In the Cohort of Patients with Parkinson's Disease in Spain (COPPADIS) study, 686 patients with PD were analyzed using network analyses. The patients completed the Beck Depression Inventory II (BDI-II) and provided their overall QoL (EUROHIS-QOL) at the beginning of the study. The study used centrality measures such as Expected Influence and Bridge Expected Influence to identify depressive symptoms that had the greatest impact on overall QoL. The results of exploratory network analyses indicate that the BDI-II items related to loss of energy, past failure, and tiredness or fatigue have the greatest impact on overall QoL as measured by the EUROHIS-QOL 8-item index. The loss of energy and tiredness or fatigue BDI-II items are also strongly associated with a number of different EUROHIS-QOL items, according to Bridge Expected Influences. For individuals suffering from PD, network analysis can aid in identifying significant non-motor symptoms that impact their QoL, thus paving the way for potential improvements. [ABSTRACT FROM AUTHOR]

Quality of life (QoL) in people with Parkinson´s disease (PD) is commonly measured with the PD questionnaire-39 (PDQ-39), but its factor structure and construct validity have been questioned. To develop effective interventions to improve QoL, it is crucial to understand the connection between different PDQ-39 items and to assess the validity of PDQ-39 subscales. With a new approach based on network analysis using the extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator (EBICglasso) followed by factor analysis, we mostly replicated the original PDQ-39 subscales in two samples of PD patients (total N = 977). However, model fit was better when the "ignored" item was categorized into the social support instead of the communication subscale. In both study cohorts, "depressive mood", "feeling isolated", "feeling embarrassed", and "having trouble getting around in public/needing company when going out" were identified as highly connected variables. This network approach can help to illustrate the relationship between different symptoms and direct interventional approaches in a more effective manner. [ABSTRACT FROM AUTHOR]

Nonmotor symptoms negatively affect health-related quality of life (HRQoL) in patients with Parkinson's disease (PD). However, it is unknown which nonmotor symptoms are most commonly associated with HRQoL. Considering the complex interacting network of various nonmotor symptoms and HRQoL, this study aimed to reveal the network structure, explained HRQoL variance, and identify the nonmotor symptoms that primarily affect HRQoL. We included 689 patients with PD from the Cohort of Patients with Parkinson's Disease in Spain (COPPADIS) study who were rated on the Nonmotor Symptoms Scale in Parkinson's disease (NMSS) and the Parkinson´s Disease Questionnaire 39 (PDQ-39) at baseline. Network analyses were performed for the 30 items of the NMSS and both the PDQ-39 summary index and eight subscales. The nodewise predictability, edge weights, strength centrality, and bridge strength were determined. In PD, nonmotor symptoms are closely associated with the mobility, emotional well-being, cognition, and bodily discomfort subscales of the PDQ-39. The most influential nonmotor symptoms were found to be fatigue, feeling sad, hyperhidrosis, impaired concentration, and daytime sleepiness. Further research is needed to confirm whether influencing these non-motor symptoms can improve HRQoL. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life (QoL) is a key outcome in healthcare. However, whether cognitively impaired people with Parkinson's disease (PD) can reliably self-report QoL is unclear, and patients are often excluded from studies based on cognition test scores. The aim of this analysis was to assess the validity of the Parkinson's Disease Questionnaire-39 (PDQ-39) in PD patients with and without cognitive impairment. Methods: In this study, 221 individuals with PD completed the PDQ-39, Montreal Cognitive Assessment (MOCA), and Beck's Depression Inventory (BDI-II). The PDQ-39's internal consistency, convergent validity with BDI-II, and floor and ceiling effects were analyzed for patients with and without cognitive impairment. Results: Ninety-four patients showed cognitive impairment (MOCA <21), whereas 127 patients had mild/no impairment. Both MOCA groups differed significantly with regards to PD severity. The PDQ-39's internal consistency was adequate for most subdomains in both MOCA groups, but floor effects were present for the subdomains Stigmatization, Social Support and Communication, regardless of impairment. For some subdomains, the PDQ-39's convergent validity with the BDI receded in the low MOCA group but remained significant for most PDQ-39 domains, especially for the PDQ total score (r =.386, p <.001) and for the subdomain emotional well-being (r =.446, p <.001). Conclusion: The PDQ-39 can be used to measure QoL in cognitively impaired PD patients, thus test scores indicating cognitive impairment alone should not lead to exclusion of PD patients from clinical studies. Although the correlation between BDI-II and PDQ-39 shrinks for some subdomains in cognitively impairment patients, this finding may be explained by the difference in PD severity, as factors influencing QoL may shift with increasing age and PD symptoms. [ABSTRACT FROM AUTHOR]

Background: Medication is often changed after inpatient treatment, which affects the course of the disease, health behavior and adherence. Thus, it is important to understand patterns of medication changes after discharge from hospital. Methods: Inpatients at the Department of Neurology received a comprehensive assessment during their stay, including adherence, depression, cognition, health and sociodemographic variables. A month after being discharged, patients were contacted to enquire about post-discharge medication changes. Results: 910 older adults aged 70 ± 8.6 years participated, of which 204 (22.4%) reported medication changes. The majority of changes were initiated by physicians (n = 112, 56.3%) and only 25 (12.6%) patients reported adjusting medication themselves. Reasons for medication changes differed between patients and doctors (p < 0.001), with side effects or missing effects cited frequently. Sociodemographic and patient-related factors did not significantly predict medication changes. Conclusion: Patients reported less post-discharge medication changes than expected, and contrary to previous literature on nonadherence, only a fraction of those changes were performed by patients themselves. Socioeconomic and clinical parameters regarding personality, mood and cognition were poorly associated with post-discharge medication changes. Instead, individual health-related factors play a role, with patient factors only indirectly influencing physicians' decisions. [ABSTRACT FROM AUTHOR]

Background: Adherence to measures that have been adopted during the COVID-19 pandemic is crucial to control the spread of the coronavirus. Methods: Semi-structured telephone interviews were performed with 99 patients with Parkinson's disease (PD) and 21 controls to explore knowledge, attitudes, practices, and burden in order to elucidate nonadherence to preventive measures. Results: The majority of patients understood the preventive measures and felt sufficiently informed. Analysis of qualitative answers, however, showed that about 30% of patients had an insufficient level of knowledge, which was not associated with educational level, cognitive disorders, or depression. Changes in behaviour were reported by 73 patients (99% performed at least one specific preventive behavior, and 86.9% have reduced social contacts and stayed home). A closer analysis of qualitative answers showed that 27.3% of patients continued to meet relatives face-to-face almost daily. Anxiety and worries about the current situation were reported by 58.6% of patients; 31.3% complained about a decrease in their mobility since the beginning of the restrictions, mainly because of worsening of PD and because regular therapies (e.g., physiotherapy) were canceled. Conclusions: About 30% of PD patients are nonadherent to preventive measures. Use of simple dichotomous questions overestimates adherence to preventive measures in patients with PD. [ABSTRACT FROM AUTHOR]