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Background: Nonadherence to medication contributes substantially to worse health outcomes. Especially among older adults with chronic illness, multimorbidity leads to complex medication regimes and high nonadherence rates. In previous research, depressive symptomology has been identified as a major contributor to nonadherence, and some authors hypothesize a link via motivational deficits and low self-efficacy. However, the exact mechanisms linking depressive symptomology and nonadherence are not yet understood. This is in part because the often-employed sum scores cannot do justice to the complexity of depressive symptomology; instead, it is recommended to assess the influence of individual symptoms. Methods: Following this symptom-based approach, we performed correlation, network and regression analysis using depressive symptoms as depicted by the items of the revised Beck Depression Inventory II (BDI) to assess their influence with nonadherence in N = 731 older adults with chronic neurological diseases. Nonadherence was measured with the self-report Stendal Adherence to Medication Score (SAMS). Results: Even when controlling for sociodemographic and health-related covariates, the BDI remained the most influential contributor to nonadherence. Across different methods, Loss of Interest and Difficulty with Concentration were identified as particularly influential for nonadherence, linking nonadherence with other affective or somatic BDI items, respectively. Additionally, Fatigue, Problems with Decision Making, Suicidal Thoughts, and Worthlessness contribute to nonadherence. Conclusion: Using a symptom-driven approach, we aimed to understand which depressive symptoms contribute to higher levels of nonadherence. Our results refine previous hypotheses about motivation and control beliefs by suggesting that it is not merely a lack of beliefs in the efficacy of medication that connects depressive symptoms and nonadherence, but rather an overall lack of interest in improving one's health due to feelings of worthlessness and suicidal tendencies. This lack of interest is further substantiated by already sparse resources caused by changes in concentration and fatigue. In order to improve health outcomes and reduce nonadherence, these associations between depressive symptoms must be further understood and targeted in tailored interventions. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Self‐rated health (SRH), a measure of self‐reported general health, is a robust predictor of morbidity and mortality in various populations, including people with diabetes. Diabetes is negatively associated with SRH and quality of life (QoL). Little is known about how people with diabetes rate their health and which aspects influence the rating. Also, the predictive value of SRH on future QoL has not yet been evaluated.We analyzed data from 46 592 participants of the Survey of Health, Ageing and Retirement in Europe (SHARE). Using linear regression, we aimed to determine which sociodemographic, socioeconomic, medical, social, mental, and health behavior factors determine SRH in people with diabetes. In addition, we analyzed the predictive value of SRH on future QoL using the generalized estimating equations procedure.We determined that country, current job situation, hospitalization, pain, polypharmacy, memory, eyesight, activities of daily living, number of chronic diseases, and depression are all linked to SRH. Together these variables explained 38% of the SRH's variance, whereas depression, pain, and memory had the greatest influence on SRH of people with diabetes. We also found that SRH independently predicted future QoL, supported by a regression coefficient of β = −1.261 (Wald chi‐square test, χ2 = 22.097, df = 1, p < .05).As SRH is linked to future QoL, we conclude that incorporating SRH assessment into medical evaluations can help health care professionals gaining a more comprehensive understanding of an individual's health trajectory and supporting patients to enhance their QoL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nonmotor symptoms negatively affect health-related quality of life (HRQoL) in patients with Parkinson's disease (PD). However, it is unknown which nonmotor symptoms are most commonly associated with HRQoL. Considering the complex interacting network of various nonmotor symptoms and HRQoL, this study aimed to reveal the network structure, explained HRQoL variance, and identify the nonmotor symptoms that primarily affect HRQoL. We included 689 patients with PD from the Cohort of Patients with Parkinson's Disease in Spain (COPPADIS) study who were rated on the Nonmotor Symptoms Scale in Parkinson's disease (NMSS) and the Parkinson´s Disease Questionnaire 39 (PDQ-39) at baseline. Network analyses were performed for the 30 items of the NMSS and both the PDQ-39 summary index and eight subscales. The nodewise predictability, edge weights, strength centrality, and bridge strength were determined. In PD, nonmotor symptoms are closely associated with the mobility, emotional well-being, cognition, and bodily discomfort subscales of the PDQ-39. The most influential nonmotor symptoms were found to be fatigue, feeling sad, hyperhidrosis, impaired concentration, and daytime sleepiness. Further research is needed to confirm whether influencing these non-motor symptoms can improve HRQoL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life (QoL) is a key outcome in healthcare. However, whether cognitively impaired people with Parkinson's disease (PD) can reliably self-report QoL is unclear, and patients are often excluded from studies based on cognition test scores. The aim of this analysis was to assess the validity of the Parkinson's Disease Questionnaire-39 (PDQ-39) in PD patients with and without cognitive impairment. Methods: In this study, 221 individuals with PD completed the PDQ-39, Montreal Cognitive Assessment (MOCA), and Beck's Depression Inventory (BDI-II). The PDQ-39's internal consistency, convergent validity with BDI-II, and floor and ceiling effects were analyzed for patients with and without cognitive impairment. Results: Ninety-four patients showed cognitive impairment (MOCA <21), whereas 127 patients had mild/no impairment. Both MOCA groups differed significantly with regards to PD severity. The PDQ-39's internal consistency was adequate for most subdomains in both MOCA groups, but floor effects were present for the subdomains Stigmatization, Social Support and Communication, regardless of impairment. For some subdomains, the PDQ-39's convergent validity with the BDI receded in the low MOCA group but remained significant for most PDQ-39 domains, especially for the PDQ total score (r =.386, p <.001) and for the subdomain emotional well-being (r =.446, p <.001). Conclusion: The PDQ-39 can be used to measure QoL in cognitively impaired PD patients, thus test scores indicating cognitive impairment alone should not lead to exclusion of PD patients from clinical studies. Although the correlation between BDI-II and PDQ-39 shrinks for some subdomains in cognitively impairment patients, this finding may be explained by the difference in PD severity, as factors influencing QoL may shift with increasing age and PD symptoms. [ABSTRACT FROM AUTHOR]

Background: In patients with Parkinson's disease (PD), depression has a strong impact on quality of life (QoL). However, little is known about the influence of subthreshold depression (STD) on QoL in PD patients.Methods: A total of 230 hospitalized PD patients with normal and impaired cognitive status were included in this observational study. We collected the following data for analysis: Beck Depression Inventory level, Montreal Cognitive Assessment (MOCA) score, non-motor symptoms questionnaire score, PD questionnaire-39 (PDQ-39) score, Hoehn-Yahr stage, and Movement Disorder Society-sponsored revision of the unified PD rating scale III (MDS-UPDRS III) score. To study the impact of STD on the PDQ-39 summary index (SI) and its domains, we used multivariate analysis of variance and multivariate analysis of covariance.Results: In this cohort, 80 (34.8%) patients had STD [44 (32.3%) with high MOCA score (> 21) and 36 (38.3%) with low MOCA score (< 21)]. In PDQ-39 SI, there was a significant effect on depression level. In patients with higher MOCA score, STD was associated with worse PDQ-39 domains emotional well-being and cognition, whereas in patients with lower MOCA score, STD had no significant effect on PDQ-39 SI or its subdomains.Conclusion: In PD patients, QoL is significantly affected by STD, and thus, more attention in medical care should be focused on treating STD. However, the impact is only observable in PD patients with normal cognitive function. STD patients show more reduced QoL than non-depressed patients, indicating that STD should be treated as a transition zone between normal mood and depression. [ABSTRACT FROM AUTHOR]

The clinical presentation of Parkinson's disease (PD) is often dominated by depressive symptoms, which can significantly impact the patients' quality of life (QoL). However, it is not clear how these depressive symptoms are interconnected, or if some symptoms are more influential in affecting QoL. In the Cohort of Patients with Parkinson's Disease in Spain (COPPADIS) study, 686 patients with PD were analyzed using network analyses. The patients completed the Beck Depression Inventory II (BDI-II) and provided their overall QoL (EUROHIS-QOL) at the beginning of the study. The study used centrality measures such as Expected Influence and Bridge Expected Influence to identify depressive symptoms that had the greatest impact on overall QoL. The results of exploratory network analyses indicate that the BDI-II items related to loss of energy, past failure, and tiredness or fatigue have the greatest impact on overall QoL as measured by the EUROHIS-QOL 8-item index. The loss of energy and tiredness or fatigue BDI-II items are also strongly associated with a number of different EUROHIS-QOL items, according to Bridge Expected Influences. For individuals suffering from PD, network analysis can aid in identifying significant non-motor symptoms that impact their QoL, thus paving the way for potential improvements. [ABSTRACT FROM AUTHOR]

As social exclusion can be linked to worse health and overall reduced quality of life, we describe social exclusion in people with diabetes and assess whether diabetes can be considered as a risk factor for social exclusion. We analyzed two waves (2014, 2017, N = 6604) from a survey of community-dwelling people aged > 40 using linear regression, group comparison and generalized estimating equations to explore the association between diabetes, social exclusion, socioeconomic, physical and psychosocial variables. In the entire cohort, diabetes was cross-sectionally associated with social exclusion after adjusting for covariates (p = 0.001). In people with diabetes, social exclusion was further associated with self-esteem (p < 0.001), loneliness (p = < 0.001), income (p = 0.017), depression (p = 0.001), physical diseases (p = 0.04), and network size (p = 0.043). Longitudinal data revealed that higher levels of social exclusion were already present before the diagnosis of diabetes, and future social exclusion was predicted by self-esteem, loneliness, depression, and income, but not by diabetes (p =.221). We conclude that diabetes is not a driver of social exclusion. Instead, both seem to co-occur as a consequence of health-related and psychosocial variables. [ABSTRACT FROM AUTHOR]

Background: Medication is often changed after inpatient treatment, which affects the course of the disease, health behavior and adherence. Thus, it is important to understand patterns of medication changes after discharge from hospital. Methods: Inpatients at the Department of Neurology received a comprehensive assessment during their stay, including adherence, depression, cognition, health and sociodemographic variables. A month after being discharged, patients were contacted to enquire about post-discharge medication changes. Results: 910 older adults aged 70 ± 8.6 years participated, of which 204 (22.4%) reported medication changes. The majority of changes were initiated by physicians (n = 112, 56.3%) and only 25 (12.6%) patients reported adjusting medication themselves. Reasons for medication changes differed between patients and doctors (p < 0.001), with side effects or missing effects cited frequently. Sociodemographic and patient-related factors did not significantly predict medication changes. Conclusion: Patients reported less post-discharge medication changes than expected, and contrary to previous literature on nonadherence, only a fraction of those changes were performed by patients themselves. Socioeconomic and clinical parameters regarding personality, mood and cognition were poorly associated with post-discharge medication changes. Instead, individual health-related factors play a role, with patient factors only indirectly influencing physicians' decisions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine how limited medication knowledge as one aspect of health literacy contributes to poorer health-related quality of life (HRQoL) in people with Parkinson's disease (PD). Methods: Demographical data, PD-specific data (MDS-Unified Parkinson's Disease-Rating Scale, Nonmotor symptom scale), and data about depressive symptoms (Beck's depression inventory), cognition (Montreal cognitive assessment), HRQoL (Short-Form Health Questionnaire-36, SF-36), and medication knowledge (names, time of taking, indication, dosage) were assessed in 193 patients with PD. Multivariate analysis of variance (MANOVA), multivariate analysis of covariance, and mediation analyses were used to study the relationship between medication knowledge and HRQoL in combination with different mediators and covariates. Results: Overall, 43.5% patients showed deficits in at least one of the 4 knowledge items, which was associated with higher age, number of medications per day and depression level, and poorer cognitive function, motor function, and lower education level. Using one-way MANOVA, we identified that medication knowledge significantly impacts physical functioning, social functioning, role limitations due to physical problems, and role limitations due to emotional problems. Mediation models using age, education level, and gender as covariates showed that the relationship between knowledge and SF-36 domains was fully mediated by Beck's Depression Inventory but not by Montreal Cognitive Assessment. Conclusions: Patients who expressed unawareness of their medication did not necessarily have cognitive deficits; however, depressive symptoms may instead be present. This concomitant depressive symptomatology is crucial in explaining the contribution of nonadherence and decreased medication knowledge to poor quality of life. [ABSTRACT FROM AUTHOR]

Purpose: Nonadherence is a complex behaviour that contributes to poor health outcomes; therefore, it is necessary to understand its underlying structure. Network analysis is a novel approach to explore the relationship between multiple variables. Patients and Methods: Patients from four different studies (N = 1.746) using the self-reported Stendal Adherence to Medication Score (SAMS) were pooled. Network analysis using EBICglasso followed by confirmatory factor analysis were performed to understand how different types of nonadherence covered in the SAMS items are related to each other. Results: Network analysis revealed different categories of nonadherence: lack of knowledge about medication, forgetting to take medication, and intentional modification of medication. The intentional modification can further be sub-categorized into two groups, with one group modifying medication based on changes in health (improvement of health or adverse effects), whereas the second group adjusts medication based on overall medication beliefs and concerns. Adverse effects and taking too many medications were further identified as most influential variables in the network. Conclusion: The differentiation between modification due to health changes and modification due to overall medication beliefs is crucial for intervention studies. Network analysis is a promising tool for further exploratory studies of adherence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]