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

Abstract

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.

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

“…A principal component analysis (PCA) with varimax rotation was performed to assess the underlying structure of the adherence (SAMS) data and to confirm the three factors found in previous literature [12,13,25].…”

“…This study revealed several predictors of global nonadherence and different types of nonadherence, which can broadly be divided into patient factors, interpersonal factors, and medication factors [6]. As in our previous work, we used the SAMS to detect modification, missing knowledge, and forgetting to take medication [13,14,25]. These factors were influenced differently by clinical and demographic variables.…”

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

“…One if its advantages is that different facets of adherence are included, namely modification of medication, lack of knowledge and forgetting to take medication 18 22. The SAMS has previously been validated in neurological patients, patients with chronic pain and patients with kidney transplants, and has been used in a variety of studies since 18 23–27…”