Improving adherence to medication in stroke survivors (IAMSS): a randomised controlled trial: study protocol

Background: Adherence to therapies is a primary determinant of treatment success, yet the World Health Organisation estimate that only 50% of patients who suffer from chronic diseases adhere to treatment recommendations. In a previous project, we found that 30% of stroke patients reported sub-optimal medication adherence, and this was associated with younger age, greater cognitive impairment, lower perceptions of medication benefits and higher specific concerns about medication. We now wish to pilot a brief intervention aimed at (a) helping patients establish a better medication-taking routine, and (b) eliciting and modifying any erroneous beliefs regarding their medication and their stroke. Methods/Design: Thirty patients will be allocated to a brief intervention (2 sessions) and 30 to treatment as usual. The primary outcome will be adherence measured over 3 months using Medication Event Monitoring System (MEMS) pill containers which electronically record openings. Secondary outcomes will include self reported adherence and blood pressure. Discussion: This study shall also assess uptake/attrition, feasibility, ease of understanding and acceptability of this complex intervention. Trial Registration: Current Controlled Trials ISRCTN3827495

Improving medication adherence in diabetes type 2 patients through Real Time Medication Monitoring: a Randomised Controlled Trial to evaluate the effect of monitoring patients' medication use combined with short message service (SMS) reminders

Contains fulltext : 97026.pdf (publisher's version ) (Open Access)BACKGROUND: Innovative approaches are needed to support patients' adherence to drug therapy. The Real Time Medication Monitoring (RTMM) system offers real time monitoring of patients' medication use combined with short message service (SMS) reminders if patients forget to take their medication. This combination of monitoring and tailored reminders provides opportunities to improve adherence. This article describes the design of an intervention study aimed at evaluating the effect of RTMM on adherence to oral antidiabetics. METHODS/DESIGN: Randomised Controlled Trial (RCT) with two intervention arms and one control arm involving diabetes type 2 patients with suboptimal levels of adherence to oral antidiabetics (less than 80% based on pharmacy refill data). Patients in the first intervention arm use RTMM including SMS reminders and a personal webpage where they can monitor their medication use. Patients in the second intervention arm use RTMM without SMS reminders or webpage access. Patients in the control arm are not exposed to any intervention. Patients are randomly assigned to one of the three arms. The intervention lasts for six months. Pharmacy refill data of all patients are available from 11 months before, until 11 months after the start of the intervention. Primary outcome measure is adherence to oral antidiabetics calculated from: 1) data collected with RTMM, as a percentage of medication taken as prescribed, and as percentage of medication taken within the correct time interval, 2) refill data, taking the number of days for which oral antidiabetics are dispensed during the study period divided by the total number of days of the study period. Differences in adherence between the intervention groups and control group are studied using refill data. Differences in adherence between the two intervention groups are studied using RTMM data. DISCUSSION: The intervention described in this article consists of providing RTMM to patients with suboptimal adherence levels. This system combines real time monitoring of medication use with SMS reminders if medication is forgotten. If RTMM proves to be effective, it can be considered for use in various patient populations to support patients with their medication use and improve their adherence. TRIAL REGISTRATION: Netherlands Trial Register NTR1882

The human keratins: biology and pathology

The keratins are the typical intermediate filament proteins of epithelia, showing an outstanding degree of molecular diversity. Heteropolymeric filaments are formed by pairing of type I and type II molecules. In humans 54 functional keratin genes exist. They are expressed in highly specific patterns related to the epithelial type and stage of cellular differentiation. About half of all keratins—including numerous keratins characterized only recently—are restricted to the various compartments of hair follicles. As part of the epithelial cytoskeleton, keratins are important for the mechanical stability and integrity of epithelial cells and tissues. Moreover, some keratins also have regulatory functions and are involved in intracellular signaling pathways, e.g. protection from stress, wound healing, and apoptosis. Applying the new consensus nomenclature, this article summarizes, for all human keratins, their cell type and tissue distribution and their functional significance in relation to transgenic mouse models and human hereditary keratin diseases. Furthermore, since keratins also exhibit characteristic expression patterns in human tumors, several of them (notably K5, K7, K8/K18, K19, and K20) have great importance in immunohistochemical tumor diagnosis of carcinomas, in particular of unclear metastases and in precise classification and subtyping. Future research might open further fields of clinical application for this remarkable protein family

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to know something about how robust Bayesian methods are to the violation of those assumptions. In this paper, we focus on the problem of contaminated data (such as data with outliers or conflicts present), with specific application to the problem of estimating a credible interval for the population mean. We evaluate five Bayesian methods for constructing a credible interval, using toy examples to illustrate the qualitative behavior of different approaches in the presence of contaminants, and an extensive simulation study to quantify the robustness of each method. We find that the “default” normal model used in most Bayesian data analyses is not robust, and that approaches based on the Bayesian bootstrap are only robust in limited circumstances. A simple parametric model based on Tukey’s “contaminated normal model” and a model based on the t-distribution were markedly more robust. However, the contaminated normal model had the added benefit of estimating which data points were discounted as outliers and which were not