Clusters of non-adherence to medication in neurological patients

Impact of depressive symptoms on medication adherence in older adults with chronic neurological diseases

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.

Tailored interventions to improve adherence to medication in elderly patients with Parkinson's disease: a study protocol for a randomized controlled trial (AdhCare)

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.

Using a generic quality of life measure to determine adherence thresholds: a cross-sectional study on older adults with neurological disorders in Germany

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.

Data on medication adherence in adults with neurological disorders: The NeuroGerAd study

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.

Piloting of the virtual telecare technology 'Addison Care' to promote self-management in persons with chronic diseases in a community setting: protocol for a mixed-methods user experience, user engagement and usability pilot study

INTRODUCTION

Chronic diseases in older adults are one of the major epidemiological challenges of current times and leading cause of disability, poor quality of life, high healthcare costs and death. Self-management of chronic diseases is essential to improve health behaviours and health outcomes. Technology-assisted interventions have shown to improve self-management of chronic diseases. Virtual avatars can be a key factor for the acceptance of these technologies. Addison Care is a home-based telecare solution equipped with a virtual avatar named Addison, connecting older persons with their caregivers via an easy-to-use technology. A central advantage is that Addison Care provides access to self-management support for an up-to-now highly under-represented population-older persons with chronic disease(s), which enables them to profit from e-health in everyday life.

METHODS AND ANALYSIS

A pragmatic, non-randomised, one-arm pilot study applying an embedded mixed-methods approach will be conducted to examine user experience, usability and user engagement of the virtual avatar Addison. Participants will be at least 65 years and will be recruited between September 2022 and November 2022 from hospitals during the discharge process to home care. Standardised instruments, such as the User Experience Questionnaire, System Usability Scale, Instrumental Activities of Daily Living scale, Short-Form-8-Questionnaire, UCLA Loneliness Scale, Geriatric Depression Scale, Stendal Adherence with Medication Score and Self-Efficacy for Managing Chronic Diseases Scale, as well as survey-based assessments, semistructured interviews and think-aloud protocols, will be used. The study seeks to enrol 20 patients that meet the criteria.

ETHICS AND DISSEMINATION

The study protocol has been approved by the ethic committee of the German Society for Nursing Science (21-037). The results are intended to be published in peer-reviewed journals and disseminated through conference papers.

TRIAL REGISTRATION NUMBER

DRKS00025992.

Adherence to Medication in Neurogeriatric Patients: Insights from the NeuroGerAd Study

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.

Detecting Reasons for Nonadherence to Medication in Adults with Epilepsy: A Review of Self-Report Measures and Key Predictors

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.

The impact of poor medication knowledge on health-related quality of life in people with Parkinson's disease: a mediation analysis

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.

Effect of Pillbox Organizers with Alarms on Adherence to Pharmacotherapy in Parkinson Disease Patients Taking Three and More Daily Doses of Dopaminergic Medications

Improvement of adherence to pharmacotherapy in patients with Parkinson’s disease (PD) is a challenge in routine clinical practice. Our study was aimed at the effect of pillbox organizers with alarms improving adherence to pharmacotherapy and its impact on clinical outcomes. Forty nonadherent patients with PD being treated with ≥ 3 daily doses of levodopa and/or dopamine agonists were pseudorandomized and consecutively ranked to groups A (early-start intervention) and B (delayed-start intervention). We used the following validated diagnostic instruments: MMAS-8 (adherence), PDQ-8 (quality of life, QoL), GDS (depression), NMSS (non-motor symptoms), MDS-UPDRS III (motor involvement), MDS-UPDRS IV, and WOQ-9 (motor and non-motor fluctuations and dyskinesias). We proved a significantly improved rate of adherence with the use of pillbox organizers with alarms. Moreover, after only four weeks of using the pillbox organizer, we detected an improvement in QoL scores, motor involvement, motor-, and non-motor fluctuations. Our study showed that pillbox organizers with alarms are efficient in improving adherence to pharmacotherapy in PD. It also could contribute to better motor states, less severe fluctuations, and improved QoL.

Patterns and Predictors of Medication Change after Discharge from Hospital: An Observational Study in Older Adults with Neurological Disorders

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.

Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort

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.

Factors Influencing Self-Reported Medication Use in the Survey of Health Aging and Retirement in Europe (SHARE) Dataset

The validity of self-reported medication use in epidemiological studies is an important issue in healthcare research. Here we investigated factors influencing self-reported medication use for multiple diagnoses in the seventh wave of the Survey of Health Aging and Retirement in Europe (SHARE) dataset in n = 77,261 participants (ages: mean = 68.47, standard deviation = 10.03 years). The influence of mental, physical, and sociodemographic parameters on medication self-report was analyzed with logistic regressions and mediation models. Depression, memory function, and polypharmacy influenced the self-report of medication use in distinct disorders to varying degrees. In addition, sociodemographic factors, knowledge about diagnosis, the presence of several chronic illnesses, and restrictions of daily instrumental activities explained the largest proportion of variance. In the mediation model, polypharmacy had an indirect effect via depression and memory on self-reported medication use. Factors influencing medication self-report vary between different diagnoses, highlighting the complexity of medication knowledge. Therefore, it is essential to assess the individual parameters and their effect on medication behavior. Relying solely on medication self-reports is insufficient, as there is no way to gage their reliability. Thus, self-reported medication intake should be used with caution to indicate the actual medication knowledge and use.

Impact of mobile health and telehealth technology on medication adherence of stroke patients: a systematic review and meta-analysis of randomized controlled trials

Background Stroke patients have low medication adherence after discharge, which leads to a high recurrence rate and poor disease control. Various strategies have been explored to enhance medication adherence in this patient population. Aim To evaluate the effects of mobile health (mHealth) and telehealth technology on medication adherence in stroke patients. Method All English studies that met the inclusion criteria published before September 2021 were obtained from PubMed, EMBASE, Web of Science, and Cochrane Library. Two researchers independently screened articles, extracted data, and evaluated the quality of the included studies. All articles were about randomized controlled trials. Medication adherence was used as the outcome index of this review. Random or fixed-effect models were used in statistical methods. I² statistics were used to evaluate heterogeneity. Results A total of ten studies met the inclusion criteria, covering 2151 stroke patients. Compared with the Usual Care group, the medication adherence scores of the mHealth technology group were better (standard mean deviation 0.67, 95% confidence interval, CI [0.49, 0.85], P < 0.001). The medication adherence ratio of the mHealth technology group was higher (odds ratio, OR, 2.81, 95% CI [1.35, 5.85], P = 0.006). Subgroup analysis showed that application and messaging interventions were more effective than the telephone call intervention (OR 4.05, 95% CI [2.10, 7.80], P < 0.001). The shorter the interval of the intervention, the better the medication adherence of patients (OR 4.24, 95% CI [2.30, 7.81], P < 0.001). Conclusion Compared with Usual Care, mHealth can effectively improve the medication adherence of stroke patients.

Expectations and behaviour of older adults with neurological disorders regarding general practitioner consultations: an observational study

Background

Patients’ relationship with their GPs is linked to adherence, patient behaviour and satisfaction with healthcare. Several factors pertaining to this relationship have already been identified, however expectations and preferences vary depending on age and diagnosis. Chronically ill elderly patients constitute a group of patients with specific needs that are not yet understood.

Methods

For this observational study, 100 (44 female, mean age 72.72 + − 8.28 years) patients were interviewed. Multiple linear or binary logistic regression as well as analysis of variance was used to understand the link between factors pertaining to GP relationship and patient behaviour, and principal component analysis was performed to understand the underlying structure of patients’ needs.

Results

Patients attribute high importance to their GP’s opinion of them. On average, what the GP thinks about the patients is almost as important as what their partners think. Patients primarily want to be perceived as engaged, friendly and respected individuals, and it is important for patients to be liked by their GP. This importance is linked to active preparation; 65% of the patients prepared actively for GP consultations. Expectations regarding GP consultations can be split into two components: a medical aspect with a subfactor concerning emotional support, and a social component. Prominent factors influencing the relationship are the possibility to talk about emotions and mental well-being, trust, and GP competency. Satisfaction and trust were mainly linked to medical competency. Being able to show emotions or talk about mental well-being enhances perceived GP competence, satisfaction, and active patient preparation. However, a focus on the social component such as frequent talking about private topics reduces both perceived GP competency as well as active patient preparation.

Conclusion

Older patients take GP consultations seriously, and their expectations regarding GP consultations focus on medical competence and care as well as empathetic listening and understanding. Older persons seek a deeper connection to their GPs and are willing to be active and cooperative. As the patient–GP relationship influences health outcomes, treatment of older patients should be adjusted to enable this active participation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02469-3.

Association Between the Health Locus of Control and Medication Adherence: An Observational, Cross-Sectional Study in Primary Care

Background: Medication non-adherence is an important healthcare issue and a common problem. Many predictors of non-adherence have been found in different settings and cohorts.

Objective: Evaluate the impact of the health locus of control (HLC) on unintentional/intentional non-adherence in primary care.

Methods: In this observational, cross-sectional study, 188 patients (mean age 63.3 ± 14.9 years) were recruited from three primary care practices in Jena, Germany, over 4 months. The study assessed demographic data, self-reported adherence (German Stendal adherence to medication score, SAMS), HLC, and depression.

Results: According to the SAMS total score, 44 (27.5%) were fully adherent, 93 (58.1%) were moderately non-adherent, and 23 (14.4%) were clinically significantly non-adherent. The most common reasons for non-adherence were forgetting to take the medication or lacking knowledge about the prescribed medication. Multiple linear regression revealed that adherence was good in people with external HLC and poor in internal HLC. In particular, intentional non-adherence was positively associated with internal HLC and negatively with fatalistic external HLC. Depression had a negative influence on both intentional and unintentional non-adherence.

Conclusion: HLC is an independent predictor of medication non-adherence and is a promising target for interventions that enhance adherence.

Difficulties in reporting purpose and dosage of prescribed medications are associated with poor cognition and depression

Knowledge on prescribed medication is important for medication adherence. We determined the presence of cognitive impairment in neurological patients who report not to know reasons and dosages of their medication. Data from 350 patients were collected: sociodemographic data, German Stendal Adherence to Medication Score (SAMS), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory-II (BDI-II). Eighty-eight (29.0%) patients did not know the reasons for taking their prescribed medication and 83 (27.4%) did not know the doses. Sixty-three (20.8%) knew neither reasons nor dosage. The latter were characterized by higher nonadherence, higher number of prescribed medication per day, lower MoCA, higher BDI, and had more often a lower education level compared with patients who knew the reasons. The MANOVA revealed a significant multivariate effect for not knowing the reasons and not knowing the dosages of medication on MoCA and BDI. Significant univariate effects for not knowing reasons were found for depressive mood, but not for cognitive performance. Significant univariate effects for not knowing dosages were found for cognitive performance, but not for depressive mood. Inaccurate medication reporting is not solely associated with cognitive problems, but also with depression, which has to be taken into account in daily practice and research.

Self-Reported Nonadherence to Medication Is Not Associated with Health-Related Quality of Life in Parkinson's Disease

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.

Medication Adherence and Coping with Disease in Patients from a Neurological Clinic: An Observational Study

PURPOSE

Medication non-adherence is a huge concern for the medical community. For chronic, especially neurological diseases, taking medication is a central pillar of treatment. To improve adherence to these oftentimes complex medication regimens, the construct needs to be understood in more depth. The aim of this study was to investigate associations between adherence with sociodemographics, clinical variables, and coping in neurological patients.

PATIENTS AND METHODS

The sample consisted of 545 patients from a German neurological clinic. Adherence was assessed with the Stendal Adherence to Medication Score (SAMS). Patients were grouped as completely adherent (SAMS = 0), non-adherent (upper 25% of the sample), and moderately adherent. Associations with coping were assessed using the Essen Coping Questionnaire.

RESULTS

Medication adherence was low compared to other non-neurological patient samples. Differences between adherence groups were found regarding gender and facets of coping, namely "trivialisation, wishful thinking and defence" and "finding of inner stability".

CONCLUSION

Interventions to improve medication adherence should focus on facets of coping with disease, increasing acceptance of disease, willpower, and confidence in treatment.

Association Between Nonmotor Symptoms and Nonadherence to Medication in Parkinson's Disease

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.

Self-Reported Nonadherence Predicts Changes of Medication after Discharge from Hospital in People with Parkinson's Disease

Background

Medication is often changed after hospital discharge in people with Parkinson's disease (PD).

Objective

This observational study aimed to describe changes in PD medication after discharge and explore their association with self-reported adherence and clinical parameters.

Methods

During hospitalisation sociodemographic characteristics, the Movement Disorder Society-sponsored revision of the Unified PD Rating Scale for motor function (MDS-UPDRS III), Hoehn and Yahr (H&Y) stage, levodopa equivalent daily dose (LEDD), Beck Depression Inventory II (BDI-II) score, Montreal Cognitive Assessment (MoCA) score, nonmotor symptoms questionnaire (NMSQ), and Stendal Adherence to Medication Score (SAMS) were collected in 125 people with PD. A semistructured interview was conducted 1 month after discharge to determine the extent and reasons for medication changes.

Results

Thirty-eight patients (30.4%) changed their PD medication after discharge. Most changes (20.8%) were performed by physicians while 9.6% of patients changed their medication by themselves due to side effects, missing effect of the medication, missing knowledge about the indication, running out of medication, or nonspecific reason. This led to decreased doses while changes by physicians resulted in both increase and decrease of doses as well as new drug prescription. Patients without changes, patients with changes performed by them, and patients with changes performed by physicians did not differ in age, disease duration, MDS-UPDRS III, LEDD, NMSQ, MoCA, BDI-II, gender, marital status, or education. However, patients who themselves made the changes were more likely to be nonadherent according to baseline SAMS. Patients who made changes after discharge had higher SAMS modification and forgetting subscores than patients without changes or with changes made by physicians.

Conclusion

Both intended and unintended nonadherence occur in patients who change medication after discharge. The use of an adherence questionnaire during inpatient treatment may help detect patients with higher risk of changing medication after discharge.

Knowledge, Attitudes, Practices, and Burden During the COVID-19 Pandemic in People with Parkinson's Disease in Germany

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.

Treatment Adherence and Secondary Prevention of Ischemic Stroke Among Discharged Patients Using Mobile Phone- and WeChat-Based Improvement Services: Cohort Study

BACKGROUND

Real-world studies have indicated that adherence is important for guaranteeing medication effectiveness. Few studies have tested the feasibility and efficacy of WeChat-based improvement services, via mobile phone, in secondary prevention-specific follow-up among discharged stroke patients.

OBJECTIVE

We evaluated a quadruple-domain, WeChat-based service for ischemic stroke secondary prevention designed to improve treatment adherence of discharged patients. This service focuses on sending reminders for drug use, blood pressure recording, and glucose recording; it also records medication use. We compared the endpoint event rate between WeChat self-monitoring and traditional monitoring.

METHODS

A cohort study was used to determine the feasibility of a physician-assisted, WeChat-based improvement service and follow-up self-monitoring platform for the secondary prevention of ischemic stroke. The platform was developed by the Peking University Third Hospital based on the information-motivation-behavioral skills model. The overall adherence rate was calculated as the proportion of medication doses verified via uploading. The ischemic endpoint event rate and medication noncompliance rate were compared between traditional prevention monitoring and WeChat self-monitoring. Factors influencing adherence were summarized.

RESULTS

The 1-year follow-up event rate of the WeChat self-monitoring group was 11.9% (12/101), which was less than that of the traditional group (21/157, 13.4%). Compared with the traditional group, the risk ratio of the WeChat group was 0.983 (95% CI 0.895-1.080); this difference was not noted to be significant. The 1-year medication noncompliance ratio tended to be lower in the WeChat monitoring group (3/101, 3.0%) than in the traditional group (11/157, 7.0%; χ²=1.9, df=1, P=.16). Of the platform registry participants, 89.7% (210/234: 167 hospital-based and 43 community-based participants) adhered to inputting information into WeChat for 8-96 weeks. The average adherence time was 16.54 (SD 0.80, range 2-24) months. The average decrease in adherence was 4 participants (1.1%) per month. Being a member of a community-based population was an influencing factor for good adherence at the 2-year follow-up (OR 2.373, 95% CI 1.019-5.527, P=.045), whereas transient ischemic attack was an influencing factor for poor adherence at the 2-year follow-up (OR 0.122, 95% CI 0.016-0.940, P=.04).

CONCLUSIONS

Use of WeChat self-monitoring showed a trend of increasing medication compliance and decreasing ischemic endpoint event rate compared with traditional monitoring. However, there were ceiling effects in the outcomes, and a relatively small sample size was used. Male participants displayed better adherence to WeChat self-monitoring. The community-based population displayed good adherence when using WeChat self-monitoring.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02618265; https://clinicaltrials.gov/ct2/show/NCT02618265.

What Predicts Different Kinds of Nonadherent Behavior in Elderly People With Parkinson's Disease?

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.

Adherence to medication in neurogeriatric patients: an observational cross-sectional study

Background

Poor adherence is a major issue and is associated with increased morbidity, mortality, and immense costs for the healthcare system. Due to demographic changes, the burden of neurological diseases is increasing with a crucial exacerbation of the problem of nonadherence. However, comprehensive data on geriatric patients with neurological disorders do not exist to date. In this cross-sectional observational study we aim to identify disease-specific adherence-modulating factors in neurogeriatric patients.

Methods

Patients 60 years or older with neurological disorders will receive an assessment of adherence (Stendal Adherence with Medication Score) and a comprehensive geriatric assessment during their stay in the Department of Neurology or Geriatrics at the Jena University Hospital (baseline data). In addition disease specific data will be derived from medical records. After one and twelve months a telephone interview will be conducted to evaluate if and why changes of medication occurred (follow up data).

Discussion

This study aims to explore disease-specific patterns of nonadherence in elderly patients with neurological disorders and characteristics of information transfer between a specialized center, practicing neurologists, general practitioners, and the patients and their caregivers. This comprehensive data may help to develop and apply complex and disease-specific interventions to enhance adherence.

Trial registration

German Clinical Trials Register DRKS00016774. Registered 19.02.2019.

Data on adherence to medication in neurological patients using the German Stendal Adherence to Medication Score (SAMS)

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.