Management of patients with uncontrolled arterial hypertension--the role of electronic compliance monitoring, 24-h ambulatory blood pressure monitoring and Candesartan/HCTZ.
ABSTRACT Incomplete drug regimen compliance (DRC) and white-coat hypertension are two of several possible causes of uncontrolled hypertension. Therefore the aim of the present study was to compare DRC in hypertensives treated with combination therapy whose blood pressures (BP) were controlled vers. uncontrolled after 4 weeks of self-monitored BP measurement. To observe the consequences in uncontrolled patients of switching one drug of the combination therapy to candesartan/HCTZ (16 mg/12.5 mg) with and without a compliance intervention program.
Self-and ambulatory-monitoring of BP were done with upper arm oscillometric devices. Patients' dosing histories were compiled electronically (MEMS, AARDEX). Patients with office blood pressure (OBP) >140/90 mmHg despite combination therapy were begun on MEMS monitoring and self BP measurement for 4 weeks of run-in. Of 62 such patients, 18 (29%) patients were normotensive according to self BP measurement and ambulatory BP measurement at 4 weeks (Group A); in the remaining 44 still uncontrolled patients, candesartan/HCTZ was substituted for one of the combination therapy drugs, with half these patients receiving passive compliance monitoring (B) and half a DRC intervention program (C). All groups were then followed for 8 weeks.
DRC before week 4 was significantly higher in A than in the uncontrolled patients (B&C). DRC was stable during run-in A, but declined in B and C. DRC after week 4 was not different in the three groups and stayed constant over time. DRC during weekends was lower than during weekdays in all groups. In group A no significant change in blood pressure was observed with all three methods of BP measurements. In groups B and C significant reductions of systolic and diastolic BP were observed for ABPM and SBPM. After the change to candesartan/HCTZ in B&C ambulatory 24-h-BP (ABPM) was normalized in 39% of patients.
Normalization of BP was associated with superior drug regimen compliance in previously uncontrolled patients treated with a combination drug regimen. Switching still-uncontrolled patients to candesartan/HCTZ significantly improved BP control and stabilized a declining DRC.
Full-textDOI: · Available from: Thomas Mengden, May 24, 2015
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ABSTRACT: Medication nonadherence, which has been estimated to affect 28% to 31% of US patients with hypertension, hyperlipidemia, and diabetes, may be improved by electronic medication packaging (EMP) devices (adherence-monitoring devices incorporated into the packaging of a prescription medication).JAMA The Journal of the American Medical Association 09/2014; 312(12):1237-1247. DOI:10.1001/jama.2014.10059 · 30.39 Impact Factor
05/2009, Degree: PhD
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ABSTRACT: Background Mobile health (mHealth) services cannot easily adapt to users’ unique needs. Purpose We used simulations of text messaging (SMS) for improving medication adherence to demonstrate benefits of interventions using reinforcement learning (RL). Methods We used Monte Carlo simulations to estimate the relative impact of an intervention using RL to adapt SMS adherence support messages in order to more effectively address each non-adherent patient’s adherence barriers, e.g., forgetfulness versus side effect concerns. SMS messages were assumed to improve adherence only when they matched the barriers for that patient. Baseline adherence and the impact of matching messages were estimated from literature review. RL-SMS was compared in common scenarios to simple reminders, random messages, and standard tailoring. Results RL could produce a 5-14 % absolute improvement in adherence compared to current approaches. When adherence barriers are not accurately reported, RL can recognize which barriers are relevant for which patients. When barriers change, RL can adjust message targeting. RL can detect when messages are sent too frequently causing burnout. Conclusions RL systems could make mHealth services more effective.Annals of Behavioral Medicine 08/2014; 49(1). DOI:10.1007/s12160-014-9634-7 · 4.20 Impact Factor