Search

ObjectiveThis study assessed how Health-Related Quality of Life (HRQoL) and nonadherence to medication are linked, to determine whether HRQoL is a suitable endpoint for clinical trials evaluating nonadherence.BackgroundHRQoL is often used as an endpoint in clinical trials to determine the effectiveness of nonadherence interventions. However, the relationship between HRQoL and nonadherence is not clear, as some interventions find an effect of nonadherence on HRQoL while others do not. Since both HRQoL and nonadherence are latent constructs, it is of interest to understand the factors that link them.MethodsMedication nonadherence was assessed in 731 older adults with neurological disorders using the Stendal Adherence to Medication Score (SAMS). Regression and network analyses were performed to examine the association between the SAMS and HRQoL (SF-36). Cognitive function, depressive symptoms, mobility, and healthcare satisfaction were included as covariates.ResultsThere was a weak association between the SAMS and HRQoL only for the mental component scale. The relationship between the SAMS and HRQoL appears indirect, as its effect is nullified upon the inclusion of covariates, especially depressive symptoms. Network analyses showed that the effect of nonadherence on HRQoL is mainly delivered by depressive symptoms, while cognition and satisfaction with healthcare contribute to a lesser extent.ConclusionNonadherence and HRQoL are both latent variables influenced by similar factors. The effect of nonadherence on HRQoL seems to be indirect and mainly delivered by depressive symptoms, possibly via motivational pathways. These associations need to be considered when selecting clinical endpoints and planning interventions.

Abstract 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 Background Nonadherence to medication is a major issue in patients with chronic disorders such as Parkinson’s disease (PD). Many interventions for increasing adherence have been tested, and these have shown weak-to-moderate efficiency. Although the best methods to improve adherence remain unclear, it is reasonable to use tailored interventions instead of the “one-size-fits-all” approach. Methods A randomized, controlled, triple-blinded trial in elderly patients with PD is conducted to test the efficacy of AdhCare, a tailored intervention to enhance adherence compared with that achieved with routine care (64 participants per arm). Motor function, quality of life, and adherence measures will be assessed at baseline and at 3 and 6 months of follow-up. The type of intervention depends on the main personal reason for nonadherence (e.g., forgetting to take the medication or poor knowledge about the medication). Discussion The results of this study will provide valuable information for health professionals and policymakers on the effectiveness of tailored interventions in elderly patients with PD. Trial registration German Clinical Trials Register DRKS00023655. Registered on 24 February 2021. Last update on 22 March 2023.

IntroductionPrevious 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.MethodsWe 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.ResultsOn 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.ConclusionThis 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.

Measurement(s) adherence Technology Type(s) questionnaires Factor Type(s) personal factors Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment inpatient and outpatient setting Sample Characteristic - Location Germany

IntroductionIn previous cross-sectional and longitudinal studies, depressive symptoms have been associated with lower hand grip strength (HGS), which is a convenient measure of overall muscular strength and serves as a marker of poor health. Most studies have considered low sample sizes or highly selective patient cohorts.MethodsWe studied the association between depressive symptoms (EURO-D) and HGS in three waves from the cross-national panel dataset Survey of Health, Aging, and Retirement in Europe (SHARE). Linear regressions and Generalized Estimating Equations (GEE) were conducted to determine factors associated with depressive symptoms and investigate whether HGS predicts future depressive symptoms.ResultsCross-sectional HGS explained 7.0% (Wave 4), 5.7% (Wave 5), and 6.4% (Wave 6) of the EURO-D variance. In the GEE, we analyzed people without depression in Wave 4 (N = 39,572). HGS predicted future EURO-D (B = −0.21, OR = 0.979, 95%CI (0.979, 0.980), p

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.

BackgroundDizziness is a common complaint in older adults. To know which factors are instrumental in enabling patients with chronic dizziness to tolerate their symptoms to a certain degree in everyday life can help to develop tailored therapies.MethodsData from 358 patients with chronic dizziness and vertigo who had attended a multimodal daycare treatment program were recorded. Data included sociodemographic parameters, dizziness-related characteristics, the Vertigo Symptom Scale (VSS), and the Hospital Anxiety and Depression Scale (HADS). Descriptive statistics, elastic net regression, and mediation analysis were used.ResultsA higher tolerance of dizziness was associated with higher age, higher intensity of dizziness, lower burden of dizziness, higher HADS depression, structural reason for dizziness (type), permanent dizziness, absence of attacks, and longer disease duration. In contrast, younger persons with attack-like dizziness reported to tolerate less dizziness. Age had a significant direct effect on tolerance (72% of the total effect) and a significant indirect effect via intensity on tolerance (28% of the total effect) in the mediation analysis.ConclusionIt can only be speculated that negative stereotypes about age-related complaints may play a role in this. Why older people tolerate more dizziness and to what extent this may contribute to lower healthcare utilization need to be investigated in further studies.

This article presents data about coping with pain and health-related quality of life from 52 patients with Parkinson’s disease (PD) (without PD dementia). Coping was assessed using Coping Strategy Questionnaire (CSQ), including active/passive and cognitive/behavioral coping strategies and the felt efficacy of the coping strategies used. In addition, common PD specific assessments were recorded. For pain rating the corresponding items from the Short-Form-36 were used. The dataset allows determining factors related pain and coping in PD. The dataset can be utilized by clinicians, academics and pharmacists for further research and reference purposes. The data presented herein is associated with the research article “Pain coping strategies and their association with quality of life in people with Parkinson's Disease: a Cross-Sectional study” [1] and available on Dryad, Dataset 10.5061/dryad.2280gb5s7.

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.

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.

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 article presents demographic, socio-economic and detailed adherence to medication data from 429 patients with neurological disorders. Adherence to medication was assessed using the German Stendal Adherence to Medication Score (SAMS). The SAMS questionnaire includes 18 questions forming a cumulative scale (0 – 72) in which 0 indicates complete adherence and 72 complete non-adherence. The SAMS covers different aspects of adherence/non-adherence, such as intentional modification of medication, missing knowledge about reasons/dosage/timing of medication and forgetting to take medication. The dataset allows determining different reasons and clusters of adherence to medication. The dataset can be used as by clinicians, pharmacists and academia for further research and as reference. The dataset can also be used in a large range of other topics where demographic and socio-economic parameters are relevant. The data presented herein is associated with the research article “Clusters of non-adherence to medication in neurological patients” [1] and available on Mendeley https://data.mendeley.com/datasets/ny2krr3vgg/1.

Nonadherence is a growing issue in the treatment of Parkinson’s disease (PD). Many factors are known to influence nonadherence, but little is known about the influence of quality of life (QoL). Detailed clinical data were obtained from 164 patients with PD using the Parkinson’s Disease Questionnaire-39 (PDQ-39) and the German Stendal Adherence with Medication Score (SAMS). Descriptive statistics were used to identify reasons for nonadherence, and multivariable linear models were used to study associations between QoL and clinical parameters as well as nonadherence. Multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA) were used to study the effect of the SAMS on PDQ domains and other medical covariates. The results showed that 10.4% (n = 17) of patients were fully adherent, 66.4% (n = 109) were moderately nonadherent, and 23.2% (n = 38) were nonadherent. Nonadherence was associated with male gender, lower Montreal Cognitive Assessment (MoCA) score, higher non-motor symptoms questionnaire (NMS-Quest) score, greater number of medications per day (an indicator of comorbidity), and higher Beck Depression Inventory (BDI) score. QoL was correlated with male gender, lower MoCA score, higher NMS-Quest score, more comorbidities, and higher BDI score, but was not correlated with nonadherence.