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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.

This review presents individual reasons for self-reported nonadherence in people with epilepsy (PWE). A literature search was performed on the PubMed/Medline and Scopus databases for studies published up to March 2022. Thirty-six studies were included using the following inclusion criteria: original studies on adults with epilepsy, use of subjective self-report adherence measurement methods, and publication in English. Data were extracted using a standardized data extraction table, including the year of publication, authors, cohort size, study design, adherence measurement method, and self-reported reasons for nonadherence. Self-reported reasons for nonadherence were grouped following the WHO model with the five dimensions of nonadherence. In addition, study characteristics and sociodemographic information are reported. Of the 36 included studies, 81% were observational. The average nonadherence rate was nearly 50%. Across all studies, patient-associated, therapy-associated, and circumstance-related factors were the most frequently reported dimensions of nonadherence. These factors include forgetfulness, presence of side-effects, and history of seizures. Regarding healthcare system factors, financial problems were the most reported reason for nonadherence. Stigmatization and quality of life were the most frequently cited factors influencing nonadherence in the disease- and circumstance-related dimensions. The results suggest that interventions for improving adherence should incorporate all dimensions of nonadherence.

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.

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.