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

Validation of a New Patient-Reported Outcome Measure of the Functional Impact of Essential Tremor on Activities of Daily Living

Background

The Essential Tremor Rating Assessment Scale (TETRAS) is a popular scale for essential tremor (ET), but its activities of daily living (ADL) and performance (P) subscales are based on a structured interview and physical exam. No patient-reported outcome (PRO) scale for ET has been developed according to US regulatory guidelines.

Objective

Develop and validate a TETRAS PRO subscale.

Methods

Fourteen items, rated 0-4, were derived from TETRAS ADL and structured cognitive interviews of 18 ET patients. Convergent validity analyses of TETRAS PRO versus TETRAS ADL, TETRAS-P, and the Quality of Life in Essential Tremor Questionnaire (QUEST) were computed for 67 adults with ET or ET plus. Test-retest reliability was computed at intervals of 1 and 30 days. The influence of mood (Hospital Anxiety and Depression Scale, HADS) and coping behaviors (Essen Coping Questionnaire, ECQ) was examined with multiple linear regression.

Results

TETRAS PRO was strongly correlated (r > 0.7) with TETRAS ADL, TETRAS-P, and QUEST and exhibited good to excellent reliability (Cronbach alpha 95%CI = 0.853-0.926; 30-day test-retest intraclass correlation 95%CI = 0.814-0.921). The 30-day estimate of minimum detectable change (MDC) was 6.6 (95%CI 5.2-8.0). TETRAS-P (rsemipartial = 0.607), HADS depression (rsemipartial = 0.384), and the coping strategy of information seeking and exchange of experiences (rsemipartial = 0.176) contributed statistically to TETRAS PRO in a multiple linear regression (R2 = 0.67).

Conclusions

TETRAS PRO is a valid and reliable scale that is influenced strongly by tremor severity, moderately by mood (depression), and minimally by coping skills. The MDC for TETRAS PRO is probably sufficient to detect clinically important change.

Transforming Neurology Care Delivery Through a Population Health Data Strategy

Background

With more than 30% of global data originating from health care, deriving usable insights that improve health requires population health analytics. In neurology, data-driven approaches have grown in significance because of digital health records and advanced analytics. A vital aspect of this evolution is adopting a population health data strategy (PHDS).

Recent Findings

Crafting a tailored PHDS for neurology involves cataloging data points and measures spanning demographics, clinical history, genetics, and social determinants. Neurologic outcomes include mortality rates, functional and cognitive abilities, and imaging results. A robust strategy relies on interoperability, advanced analytics, and transparent AI algorithms.

Summary

Neurology is embracing data-driven health care. The PHDS synthesizes diverse patient data to provide personalized care. It includes a wide range of outcome measures to address neurologic complexities. Advanced analytics and collaboration among neurologists, data scientists, and business leaders uncover hidden patterns and promote outcome-driven medicine in the 21st century.

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.

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.

Association of Coping Strategies and Medication Adherence: A Systematic Review

Background: Medication adherence is difficult for most patients who take at least one medication. Poor drug adherence is a developing problem since it contributes to negative outcomes, prescription waste, increased healthcare expenses, and disease progression. Coping strategies are an important tool for managing a patient's condition because a patient's coping method influences how he or she perceives the situation and deals with the stress that comes with it, which can eventually affect adherence. Coping strategies are classified into five categories: problem-focused, emotion-focused, seeking understanding, support seeking, and problem avoidance. Objective: The goal of this study is to examine and illustrate the association of coping strategies on medication adherence. Method: A systematic review of PubMed/MEDLINE database was conducted in order to screen and select articles. A total of 15 studies were included where they were classified by endpoints. Endpoints that were considered are medication adherence, problem-solving/active coping strategy, emotion-focused coping strategy, seeking understanding coping strategy, support seeking coping strategy and problem avoidance coping strategy. The association of each coping strategy on medication adherence was then evaluated from each article assigned to every category of coping strategies to determine if it had a favorable, negative, or no impact on medication adherence. Results: Most studies which measured problem-solving/active coping strategy (78%) had a positive association on medication adherence, followed by studies which measured emotion-focused coping strategy (69%). Majority of the studies that evaluated for problem avoidance coping strategy (50%) showed a negative association on medication adherence and a small proportion of studies (30%) showed a positive association. Four(4) of the 5 coping strategies (problem-solving/active, emotion-focused, seeking understanding and support seeking) were found to have a greater number of studies showing positive association to medication adherence as opposed to problem avoidance. Conclusion: The findings may suggest that problem-solving and emotion-focused coping strategies can be useful to help people with chronic conditions improve their medication adherence. More study is needed to establish a link between coping strategies and medication adherence in patients, which will allow pharmacists and other healthcare professionals to deliver better interventions to patients and assess for medication nonadherence due to poor coping skills.

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.