Reimagining Medication Adherence: A Novel Holistic Model for Hypertension Therapy

Purpose: Patients' adherence to the prescribed therapy is influenced by several personal and social factors. However, existing studies have mostly focused on individual aspects. We took a holistic approach to develop a higher-level impact factor model. Patients and Methods: In this independent, non-interventional, cross-sectional and anonymous study design the pharmacist recruited patients who entered the pharmacy and handed in a prescription for a blood pressure medication. The patients received a paper questionnaire with a stamped return envelope to volunteer participation. A total of 476 patients in Germany who reported having at least high normal blood pressure according to the Global Hypertension Practice Guidelines were surveyed. In this study, each patient received an average of 2.49 antihypertensive prescriptions and 7.9% of all patients received a fixed-dose combination. Partial least squares structural equation modeling was performed for model analytics since it enables robust analysis of complex relationships. Results: was significant. The patient's overall therapy satisfaction determined medication adherence. The medication scheme's complexity also influenced the adherence levels. Therapy satisfaction was significantly shaped by the complexity of the medication scheme, behavioral control, and emotional attitude. The results demonstrated the superior performance of fixed-dose combinations against combinations of mono-agents according to the adherence level. Additionally, patient-physician and patient-pharmacist relationships influenced behavioral control of medication therapy execution. According to the A14-scale to measure the level of adherence, 49.6% of patients were classified as adherent and the remainder as non-adherent. Conclusion: The results enable healthcare stakeholders to target attractive variables for intervention to achieve maximum effectiveness. Moreover, the proven predictive power of the model framework enables clinicians to make predictions about the adherence levels of their hypertensive patients.