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.
Parkinson Disease , Humans , Quality of Life , Bayes Theorem , Surveys and Questionnaires , Emotions
Introduction: Parkinson'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. Methods: We 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. Results: Our 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. Conclusion: Our 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.
OBJECTIVES: Measuring the degree of adherence to medication is essential in healthcare However, the cut-offs provided for adherence scales are often arbitrary and disease-specific, and need to be validated against a clinical outcome. Here, we used health-related quality of life (QoL) to determine cut-offs for a self-report adherence questionnaire in patients with neurological diagnoses. DESIGN: Cross-sectional study. PARTICIPANTS: 910 patients (age 70±8.6 years) with neurological disorders were recruited from the wards of neurology at a local university hospital. All patients received a comprehensive geriatric assessment, including assessments of adherence (Stendal Adherence to Medication Score, SAMS) and QoL (Short Form Survey SF-36). OUTCOME MEASURES: The main aim of the study was to define a cut-off for non-adherence at which QoL is significantly impaired. Thus, we used Spearman's rank correlation, multivariate and univariate analyses of variance to test the impact of different adherence levels on QoL. Receiver operating characteristics and area under curve measures were then used to determine cut-off scores for adherence based on significant differences in QoL. RESULTS: Correlations between SAMS and SF-36 domains were weak (ranging between r=-0.205 for emotional well-being and r=-0.094 for pain) and the effect of non-adherence on QoL disappeared in the multivariate analysis of variance (p=0.522) after adjusting for demographical and clinical factors. SAMS cut-offs in terms of SF-36 domains varied greatly, so that an overall SAMS cut-off for this cohort could not be defined. CONCLUSIONS: QoL as measured by the SF-36 is not suitable as a single outcome parameter to study the impact of non-adherence on QoL in a mixed neurological cohort. Since both QoL and adherence are heterogeneous, multifaceted constructs, it is unlikely to find an overarching cut-off applicable for all patients. Thus, it may be necessary to use disease or cohort-specific external outcome parameters to measure the indirect effect of interventions to enhance adherence. TRIAL REGISTRATION NUMBER: DRKS00016774.
Nervous System Diseases , Neurology , Humans , Aged , Middle Aged , Quality of Life , Cross-Sectional Studies , Surveys and Questionnaires , Germany
Introduction: Previous longitudinal studies identified various factors predicting changes in Quality of Life (QoL) in people with diabetes mellitus (PwDM). However, in these studies, the stability of QoL has not been assessed with respect to individual differences. Methods: We studied the predictive influence of variables on the development of QoL in PwDM across three waves (2013-2017) from the cross-national panel dataset Survey of Health, Ageing, and Retirement in Europe (SHARE). To determine clinically meaningful changes in QoL, we identified minimal clinically important difference (MCID). Linear regressions and Linear Mixed Models (LMM) were conducted to determine factors associated with changes in QoL. Results: On average, QoL remained stable across three waves in 2989 PwDM, with a marginal difference only present between the first and last wave. However, when looking at individual trajectories, 19 different longitudinal patterns of QoL were identified across the three time-points, with 38.8% of participants showing stable QoL. Linear regression linked lower QoL to female gender, less education, loneliness, reduced memory function, physical inactivity, reduced health, depression, and mobility limitations. LMM showed that the random effect of ID had the strongest impact on QoL across the three waves, suggesting highly individual QoL patterns. Conclusion: This study enhances the understanding of the stability of QoL measures, which are often used as primary endpoints in clinical research. We demonstrated that using traditional averaging methods, QoL appears stable on group level. However, our analysis indicated that QoL should be measured on an individual level.
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.
Medication Adherence , Nervous System Diseases , Adult , Aged , Humans , Nervous System Diseases/drug therapy , Nervous System Diseases/psychology , Personality , Quality of Life
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.
Nonadherence to medication is associated with increased morbidity, mortality, and healthcare costs, especially in older adults with higher chances of multimorbidity. However, comprehensive data on factors influencing adherence in this patient group are rare. Thus, data for 910 patients were acquired, including demographic data, nonadherence (Stendal Adherence to Medication), depression (Beck Depression Inventory), cognition (Montreal Cognitive Assessment), personality (Big Five Inventory), satisfaction with healthcare (Health Care Climate Questionnaire), quality of life (36-item Short Form Survey), mobility, diagnoses, and medication. Elastic net regularization was used to analyze the predictors of adherence. Principal component and general estimation equations were calculated to analyze the underlying patterns of adherence. Only 21.1% of patients were fully adherent. Nonadherence was associated with male gender, higher number of medications, diagnosis, depression, poor patient-physician relationship, personality, impaired cognition, and impaired mobility. Nonadherence was classified into three sub-factors: forgetting (46.2%), missing knowledge about medication (29%), and intentional modification of medication (24.8%). While depression exerted the strongest influence on modification, a high number of medications was associated with missing knowledge. The different patterns of nonadherence (i.e., modification, missing knowledge, and forgetting) are influenced differently by clinical factors, indicating that specific approaches are needed for interventions targeting adherence.
