Comparison of cohesive powder flowability measured by Schulze Shear Cell, Raining Bed Method, Sevilla Powder Tester and new Ball Indentation Method

Poor powder flow leads to many problems during manufacturing and can lead to inaccurate dosing and off-specification products. Powder flowability is commonly assessed under relatively high applied loads using shear cells by characterising the unconfined yield strength at a range of applied loads. For applied stresses below 1 kPa, it becomes increasingly difficult to obtain reliable values of the unconfined yield strength. The bulk cohesion and tensile strength of the powder are then obtained by extrapolating the yield locus to zero and negative loads, respectively. However, the reliability of this approximation for a given material is not known. To overcome this limitation, techniques such as the Raining Bed Method, Sevilla Powder Tester and the newly-developed Ball Indentation Method may be used. In this paper, we report our measurement results of the tensile strength of glass beads, α-lactose monohydrate and various sizes of fluid catalytic cracking powders determined by the Sevilla Powder Tester and Raining Bed Method and compare them with those inferred from the Schulze Shear Cell. The results of the latter are also compared with those of the Ball Indentation Method. The outcome suggests that in the case of shear cell tests, the extrapolation of the yield locus to lower or negative loads is unsafe. The ball indentation method enables the characterisation of highly cohesive powders at very low compressive loads; however extrapolation to negative loads is still not reliable. In contrast, the Sevilla Powder Tester and Raining Bed Methods are able to characterise the tensile strength directly, but high bulk cohesion poses difficulties as the internal bed failure needs to be analysed in order to reliably estimate the tensile strength. These methods provide a better understanding of powder flow behaviour at low stresses, thus enabling a greater control of manufacturing processes

Effects of self-monitoring of glucose in non-insulin treated patients with type 2 diabetes: design of the IN CONTROL-trial

<p>Abstract</p> <p>Background</p> <p>Diabetes specific emotional problems interfere with the demanding daily management of living with type 2 diabetes mellitus (T2DM). Possibly, offering direct feedback on diabetes management may diminish the presence of diabetes specific emotional problems and might enhance the patients' belief they are able to manage their illness. It is hypothesized that self-monitoring of glucose in combination with an algorithm how and when to act will motivate T2DM patients to become more active participants in their own care leading to a decrease in diabetes related distress and an increased self-efficacy.</p> <p>Methods and design</p> <p>Six hundred patients with T2DM (45 ≤ 75 years) who receive care in a structured diabetes care system, HbA1c ≥ 7.0%, and not using insulin will be recruited and randomized into 3 groups; Self-monitoring of Blood Glucose (SMBG), Self-monitoring of Urine Glucose (SMUG) and usual care (n = 200 per group). Participants are eligible if they have a known disease duration of over 1 year and have used SMBG or SMUG less than 3 times in the previous year. All 3 groups will receive standardized diabetes care. The intervention groups will receive additional instructions on how to perform self-monitoring of glucose and how to interpret the results. Main outcome measures are changes in diabetes specific emotional distress and self-efficacy. Secondary outcome measures include difference in HbA1c, patient satisfaction, occurrence of hypoglycaemia, physical activity, costs of direct and indirect healthcare and changes in illness beliefs.</p> <p>Discussion</p> <p>The IN CONTROL-trial is designed to explore whether feedback from self-monitoring of glucose in T2DM patients who do not require insulin can affect diabetes specific emotional distress and increase self-efficacy. Based on the self-regulation model it is hypothesized that glucose self-monitoring feedback changes illness perceptions, guiding the patient to reduce emotional responses to experienced threats, and influences the patients ability to perform and maintain self-management skills.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN84568563</p

The value of episodic, intensive blood glucose monitoring in non-insulin treated persons with type 2 diabetes: Design of the Structured Testing Program (STeP) Study, a cluster-randomised, clinical trial [NCT00674986]

<p>Abstract</p> <p>Background</p> <p>The value and utility of self-monitoring of blood glucose (SMBG) in non-insulin treated T2DM has yet to be clearly determined. Findings from studies in this population have been inconsistent, due mainly to design differences and limitations, including the prescribed frequency and timing of SMBG, role of the patient and physician in responding to SMBG results, inclusion criteria that may contribute to untoward floor effects, subject compliance, and cross-arm contamination. We have designed an SMBG intervention study that attempts to address these issues.</p> <p>Methods/design</p> <p>The Structured Testing Program (STeP) study is a 12-month, cluster-randomised, multi-centre clinical trial to evaluate whether poorly controlled (HbA1c ≥ 7.5%), non-insulin treated T2DM patients will benefit from a comprehensive, integrated physician/patient intervention using structured SMBG in US primary care practices. Thirty-four practices will be recruited and randomly assigned to an active control group (ACG) that receives enhanced usual care or to an enhanced usual care group plus structured SMBG (STG). A total of 504 patients will be enrolled; eligible patients at each site will be randomly selected using a defined protocol. Anticipated attrition of 20% will yield a sample size of at least 204 per arm, which will provide a 90% power to detect a difference of at least 0.5% in change from baseline in HbA1c values, assuming a common standard deviation of 1.5%. Differences in timing and degree of treatment intensification, cost effectiveness, and changes in patient self-management behaviours, mood, and quality of life (QOL) over time will also be assessed. Analysis of change in HbA1c and other dependent variables over time will be performed using both intent-to-treat and per protocol analyses. Trial results will be available in 2010.</p> <p>Discussion</p> <p>The intervention and trial design builds upon previous research by emphasizing appropriate and collaborative use of SMBG by both patients and physicians. Utilization of per protocol and intent-to-treat analyses facilitates a comprehensive assessment of the intervention. Use of practice site cluster-randomisation reduces the potential for intervention contamination, and inclusion criteria (HbA1c ≥ 7.5%) reduces the possibility of floor effects. Inclusion of multiple dependent variables allows us to assess the broader impact of the intervention, including changes in patient and physician attitudes and behaviours.</p> <p>Trial Registration</p> <p>Current Controlled Trials NCT00674986.</p

What are the basic self-monitoring components for cardiovascular risk management?

<p>Abstract</p> <p>Background</p> <p>Self-monitoring is increasingly recommended as a method of managing cardiovascular disease. However, the design, implementation and reproducibility of the self-monitoring interventions appear to vary considerably. We examined the interventions included in systematic reviews of self-monitoring for four clinical problems that increase cardiovascular disease risk.</p> <p>Methods</p> <p>We searched Medline and Cochrane databases for systematic reviews of self-monitoring for: heart failure, oral anticoagulation therapy, hypertension and type 2 diabetes. We extracted data using a pre-specified template for the identifiable components of the interventions for each disease. Data was also extracted on the theoretical basis of the education provided, the rationale given for the self-monitoring regime adopted and the compliance with the self-monitoring regime by the patients.</p> <p>Results</p> <p>From 52 randomized controlled trials (10,388 patients) we identified four main components in self-monitoring interventions: education, self-measurement, adjustment/adherence and contact with health professionals. Considerable variation in these components occurred across trials and conditions, and often components were poorly described. Few trials gave evidence-based rationales for the components included and self-measurement regimes adopted.</p> <p>Conclusions</p> <p>The components of self-monitoring interventions are not well defined despite current guidelines for self-monitoring in cardiovascular disease management. Few trials gave evidence-based rationales for the components included and self-measurement regimes adopted. We propose a checklist of factors to be considered in the design of self-monitoring interventions which may aid in the provision of an evidence-based rationale for each component as well as increase the reproducibility of effective interventions for clinicians and researchers.</p

Dynamic measurement and simulation of bulk solids during silo discharge

This paper deals with the experimental investigation and numerical simulation of silo discharge processes, including dynamic interactions between silo filling and elastic silo walls. The experiments have taken place in a large model silo with a height of 3m and a rectangular base of 800 to 400mm. Optical measurement techniques have been applied to investigate the flow profile, while load cells on the silo walls have registered the stress' evolution, e.g. a stress peak (switch) move from the outlet to the transition of hopper and shaft. The measured data have been compared with simulation results of the Institute of Applied Mechanics at the Technical University of Braunschweig. It has been possible because the numerical simulation examples have been chosen to be similar to the experimental test silo. The discharge process in the simulation is described by a system of nonlinear differential equations. Via the Finite Element Method (FEM) based on an Eulerian reference frame deformation rate, velocity field, porosity and stress distribution can be calculated without the need for re-meshing the FE grid