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.

Background: Detailed knowledge about nonadherence to medication could improve medical care in elderly patients. We aimed to explore patterns and reasons for nonadherence in people with Parkinson's disease (PD) aged 60 years and older. Methods: Detailed clinical data and adherence (German Stendal Adherence with Medication Score) were assessed in 230 patients with PD (without dementia). Descriptive statistics were used to study reasons for nonadherence in detail, and general linear models were used to study associations between clusters of nonadherence and clinical parameters. Results: Overall, 14.2% (n = 32) of the patients were fully adherent, 66.8% (n = 151) were moderately nonadherent, and 19.0% (n = 43) showed clinically meaningful nonadherence. In the multivariable analysis, nonadherence was associated with a lower education level, higher motor impairment in activities of daily living, higher number of medications per day, and motor complications of PD. Three clusters of nonadherence were observed: 59 (30.4%) patients reported intentional nonadherence by medication modification; in 72 (37.1%) patients, nonadherence was associated with forgetting to take medication; and 63 (32.5%) patients had poor knowledge about the prescribed medication. A lower education level was mainly associated with modification of medication and poorer knowledge about prescribed medication, but not with forgetting to take medication. Patients with motor complications, which frequently occur in those with advanced disease stages, tend to be intentionally nonadherent by modifying their prescribed medication. Increased motor problems and a higher total number of drugs per day were associated with less knowledge about the names, reasons, and dosages of their prescribed medication. Conclusions: Elderly patients with PD report many reasons for intentional and non-intentional nonadherence. Understanding the impact of clinical parameters on different patterns of nonadherence may facilitate tailoring of interventions and counseling to improve outcomes.

Background: Nonadherence to medication is a common and serious issue in the treatment of patients with Parkinson's disease (PD). Among others, distinct nonmotor symptoms (NMS) were found to be associated with nonadherence in PD. Here, we aimed to confirm the association between NMS and adherence. Methods: In this observational study, the following data were collected: sociodemographic data, the German versions of the Movement Disorder Society-sponsored revision of the unified Parkinson's disease rating scale for motor function (MDS-UPDRS III), Hoehn and Yahr (H&Y) stage, levodopa equivalent daily dose (LEDD), Becks depression inventory II (BDI-II), nonmotor symptoms questionnaire (NMSQ), and the Stendal adherence to medication score (SAMS). Results: The final sample included 137 people with PD [54 (39.4%) females] with a mean age of 71.3 ± 8.2 years. According to SAMS, 10.9% of the patients were fully adherent, 73% were moderately nonadherent, and 16.1% showed clinically significant nonadherence. Nonadherence was associated with LEDD, BDI-II, education level, MDS-UPDRS III, and the NMSQ. The number of NMS was higher in nonadherent patients than in adherent patients. In the multiple stepwise regression analysis, the items 5 (constipation), 17 (anxiety), and 21 (falls) predicted nonadherence to medication. These NMSQ items also remained significant predictors for SAMS after correction for LEDD, MDS-UPDRS III, BDI-II, age, education level, gender, and disease duration. Conclusion: Our study, in principle, confirms the association between NMS burden and nonadherence in PD. However, in contrast to other clinical factors, the relevance of NMSQ in terms of nonadherence is low. More studies with larger sample sizes are necessary to explore the impact of distinct NMS on adherence.

Although delirium is often investigated, little is known about the outcomes of patients having acute neuropsychological changes at a single time point without fulfilling the criteria of full delirium. Our aim was to determine point prevalence, predictors, and long-term outcomes of delirium and acute neuropsychological changes in patients aged 60 years and older across different departments of a university hospital with general inpatient care. DESIGN: Prospective observational study. SETTING: University hospital excluding psychiatric wards. PARTICIPANTS: At baseline, 669 patients were assessed, and follow-ups occurred at months 6, 12, 18, and 36. MEASUREMENTS: Measurements were obtained using the Confusion Assessment Method (CAM), comprehensive geriatric assessment, health-related quality of life, functional state (month 6), and mortality rates (months 6, 12, 18, and 36). Subjects were classified into (1) patients with delirium according to the CAM, (2) patients with only two positive CAM items (2-CAM state), and (3) patients without delirium. RESULTS: Delirium was present in 10.8% and the 2-CAM state in an additional 12.7% of patients. Highest prevalence of delirium was observed in medical and surgical intensive care units and neurosurgical wards. Cognitive restrictions, restricted mobility, electrolyte imbalance, the number of medications per day, any fixations, and the presence of a urinary catheter predicted the presence of delirium and 2-CAM-state. The mean Karnofsky Performance Score and EuroQol-5D were comparable between delirium and the 2-CAM state after 6 months. The 6-, 12-, 18-, and 36-month mortality rates of patients with delirium and the 2-CAM state were comparable. The nurses' evaluation of distinct patients showed high specificity (89%) but low sensitivity (53%) for the detection of delirium in wide-awake patients. CONCLUSION: Patients with an acute change or fluctuation in mental status or inattention with one additional CAM symptom (ie, disorganized thinking or an altered level of consciousness) have a similar risk for a lower quality of life and death as patients with delirium.

Objective To develop multidimensional approaches for pain management, this study aimed to understand how PD patients cope with pain. Design Cross-sectional, cohort study. Setting Monocentric, inpatient, university hospital. Participants 52 patients with Parkinson’s disease (without dementia) analysed. Primary and secondary outcome measures Motor function, nonmotor symptoms, health-related quality of life (QoL), and the Coping Strategies Questionnaire were assessed. Elastic net regularization and multivariate analysis of variance (MANOVA) were used to study the association among coping, clinical parameters, and QoL. Results Most patients cope with pain through active cognitive (coping self-statements) and active behavioral strategies (increasing pain behaviors and increasing activity level). Active coping was associated with lower pain rating. Regarding QoL domains, active coping was associated with better physical functioning and better energy, whereas passive coping was associated with poorer emotional well-being. However, as demonstrated by MANOVA, the impact of coping factors (active and passive) on the Short Form 36 domains was negligible after correction for age, motor function, and depression. Conclusion Passive coping strategies are the most likely coping response of those with depressive symptoms, whereas active coping strategies are the most likely coping response to influence physical function. Although coping is associated with pain rating, the extent that pain coping responses can impact on QoL seems to be low.