Nonadherence to medication is a common issue that goes along with increased morbidity and mortality and immense health care costs. To improve medication adherence and outcome in ill people, their reasons of not taking their prescribed medication must be known. Here a dataset is presented based on the longitudinal observational NeuroGerAd study in adults with neurological disorders (N = 910). The dataset contains demographic background variables as well as measures of adherence, medication changes after hospital discharge, comprehensive geriatric assessments, personality, patient-physician relationship, and health-related quality of life. As such, the dataset offers unique opportunities to enable a plethora of analyses on personal, social, and institutional factors influencing medication adherence.
IntroductionParkinson's disease (PD) is a multisystem neurodegenerative disorder characterized by motor and non-motor symptoms. In particular, non-motor symptoms have become increasingly relevant to disease progression. This study aimed to reveal which non-motor symptoms have the highest impact on the complex interacting system of various non-motor symptoms and to determine the progression of these interactions over time.MethodsWe performed exploratory network analyses of 499 patients with PD from the Cohort of Patients with Parkinson's Disease in Spain study, who had Non-Motor Symptoms Scale in Parkinson's Disease ratings obtained at baseline and a 2-year follow-up. Patients were aged between 30 and 75 years and had no dementia. The strength centrality measures were determined using the extended Bayesian information criterion and the least absolute shrinkage and selection operator. A network comparison test was conducted for the longitudinal analyses.ResultsOur study revealed that the depressive symptoms anhedonia and feeling sad had the strongest impact on the overall pattern of non-motor symptoms in PD. Although several non-motor symptoms increase in intensity over time, their complex interacting networks remain stable.ConclusionOur results suggest that anhedonia and feeling sad are influential non-motor symptoms in the network and, thus, are promising targets for interventions as they are closely linked to other non-motor symptoms.
Self-care and self-management are essential for well-being, especially in advancing age or chronic illness. To assess these complex behaviors, validated questionnaires are needed. The Appraisal of Self-Care Agency Scale-Revised (ASAS-R) is a self-report questionnaire to evaluate the actions people take to manage their health. This manuscript reports the psychometric properties of the German ASAS-R translation. After standardized translation, convergent validity was assessed with the Patient Activation Measure (PAM) controlling for sociodemographic and health factors. Internal consistency, descriptive statistics, and principal component analysis (PCA) are reported. We analyzed data of 215 community-dwelling German adults aged 51.6 ± 14.7 years with at least one chronic illness. Similar to the original ASAS-R, PCA revealed three factors, although item allocation differed. The ASAS-R showed good internal consistency overall and for each factor, although ceiling effects were present for some items. Convergent validity was good, and the ASAS-R was as a predictor for the PAM irrespective of other variables. As self-care is highly complex, we conclude that factor structure should be assessed for each dataset. Overall, the German ASAS-R is a valid instrument to measure self-care and self-management of chronic diseases that may enhance research on this fundamental health behavior in German-speaking countries.
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