An Iterative, Mixed Usability Approach Applied to the Telekit System from the Danish TeleCare North Trial

Objective. The aim of the present study is to evaluate the usability of the telehealth system, coined Telekit, by using an iterative, mixed usability approach. Materials and Methods. Ten double experts participated in two heuristic evaluations (HE1, HE2), and 11 COPD patients attended two think-aloud tests. The double experts identified usability violations and classified them into Jakob Nielsen’s heuristics. These violations were then translated into measurable values on a scale of 0 to 4 indicating degree of severity. In the think-aloud tests, COPD participants were invited to verbalise their thoughts. Results. The double experts identified 86 usability violations in HE1 and 101 usability violations in HE2. The majority of the violations were rated in the 0–2 range. The findings from the think-aloud tests resulted in 12 themes and associated examples regarding the usability of the Telekit system. The use of the iterative, mixed usability approach produced both quantitative and qualitative results. Conclusion. The iterative, mixed usability approach yields a strong result owing to the high number of problems identified in the tests because the double experts and the COPD participants focus on different aspects of Telekit’s usability. This trial is registered with Clinicaltrials.gov, NCT01984840, November 14, 2013

Reablement professionals' perspectives on client characteristics and factors associated with successful home-based reablement:a qualitative study

Abstract Background To understand what is needed to achieve a successful Danish home-based reablement service from the perspective of reablement professionals. Methods Semi-structured interviews and observations were conducted with nine professionals within a municipal visitation unit in the Northern Denmark Region. Thematic analysis was used to analyze the interviews. Results Four major themes emerged during this study: “Heterogeneity of clients and mixed attitudes towards the reablement intervention”, “Shared understanding and acknowledging the need for help as the first step in reablement”, “Commitment and motivation are essential for successful reablement”, and “Homecare helpers as most important team players”. The findings indicate that the clients had both mixed characteristics and attitudes about participating in the reablement intervention. Essential factors for successful reablement included a shared understanding of the reablement intervention, commitment, and motivation in terms of client involvement and staff group collaboration. Conclusions Shared understanding of the reablement intervention, commitment, and motivation was found to be essential factors and the driving forces in relation to successful reablement

Evaluation of a phosphate kinetics model in hemodialysis therapy-Assessment of the temporal robustness of model predictions

In-depth understanding of intra- and postdialytic phosphate kinetics is important to adjust treatment regimens in hemodialysis. We aimed to modify and validate a three-compartment phosphate kinetic model to individual patient data and assess the temporal robustness. Intradialytic phosphate samples were collected from the plasma and dialysate of 12 patients during two treatments (HD1 and HD2). 2-h postdialytic plasma samples were collected in four of the patients. First, the model was fitted to HD1 samples from each patient to estimate the mass transfer coefficients. Second, the best fitted model in each patient case was validated on HD2 samples. The best model fits were determined from the coefficient of determination (R2 ) values. When fitted to intradialytic samples only, the median (interquartile range) R2 values were 0.985 (0.959-0.997) and 0.992 (0.984-0.994) for HD1 and HD2, respectively. When fitted to both intra- and postdialytic samples, the results were 0.882 (0.838-0.929) and 0.963 (0.951-0.976) for HD1 and HD2, respectively. Eight patients demonstrated a higher R2 value for HD2 than for HD1. The model seems promising to predict individual plasma phosphate in hemodialysis patients. The results also show good temporal robustness of the model. Further modifications and validation on a larger sample are needed.</p

QT Measurement and Heart Rate Correction during Hypoglycemia: Is There a Bias?

Introduction. Several studies show that hypoglycemia causes QT interval prolongation. The aim of this study was to investigate the effect of QT measurement methodology, heart rate correction, and insulin types during hypoglycemia. Methods. Ten adult subjects with type 1 diabetes had hypoglycemia induced by intravenous injection of two insulin types in a cross-over design. QT measurements were done using the slope-intersect (SI) and manual annotation (MA) methods. Heart rate correction was done using Bazett's (QTcB) and Fridericia's (QTcF) formulas. Results. The SI method showed significant prolongation at hypoglycemia for QTcB (42(6) ms; P < .001) and QTcF (35(6) ms; P < .001). The MA method showed prolongation at hypoglycemia for QTcB (7(2) ms, P < .05) but not QTcF. No difference in ECG variables between the types of insulin was observed. Discussion. The method for measuring the QT interval has a significant impact on the prolongation of QT during hypoglycemia. Heart rate correction may also influence the QT during hypoglycemia while the type of insulin is insignificant. Prolongation of QTc in this study did not reach pathologic values suggesting that QTc prolongation cannot fully explain the dead-in-bed syndrome

Introducing a Problem Analysis Tool Implies Increasement in Understanding the Problem Analysis Among Students

Problem-based learning (PBL) is the through-going didactics at Aalborg University, but literature shows how integrating PBL into project work is challenging for students. Studies indicate that students especially struggle with the problem analysis section, i.e., what it consists of, how the structure of the analysis should be, etc. Moreover, literature shows that ignorance among students leads to conflicts among group members. The aim of the study was to evaluate the consequences of introducing a problem analysis tool to master students working with a PBL project. Data analysis showed an increase (with significant p values) in the following 5 topics: 1) the problem analysis term, 2) problem analysis structure, 3) scientific argumentation, 4) learn to analyze instead of explaining, and 5) using literature to argue for a scientific problem. Significant results showed that students believed that they had increased their understanding of the term problem analysis after being introduced to the problem analysis tool.

A phosphate kinetics model in hemodialysis therapy

&lt;p&gt;&lt;strong&gt;Kinetic model&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The model builds on a three-compartment model previously presented as model variation numbers 8 and 10 (fitted to four- and eight-hour HD, respectively) as described by Laursen et al. in the paper &lt;i&gt;Distribution Volume Assessment Compartment Modelling: Theoretic Phosphate Kinetics in Steady State Hemodialys Patients&lt;/i&gt; (2015). In this present version of the model, modifications have been made to the volumes of distribution in the three compartments (V1, V2, and V3), dialyzer phosphate clearance (kd) and two mass transfer coefficients (k1 and k2). The components V1, V2, V3, and kd were calculated and remained fixed for each patient, whereas k1 and k2&nbsp;were estimated. The calculation and estimation of the components are available in the subsections below.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;Determination of volumes of distribution&lt;/i&gt;&lt;/p&gt;&lt;p&gt;The volume of distribution in compartment 1 (V1) was assumed to be equal to the fluid in plasma. The volume of distribution in compartment 2 (V2) was assumed to be the remaining fluid in the extracellular fluid (ECF) from the expection that V2 = ECF - V1. The volume of distribution in compartment 3 (V3) was assumed to be the intracellular fluid and thus equal to total body water (TBW) minus ECF.&nbsp;&lt;/p&gt;&lt;p&gt;The formulas suggested by P.E. Watson in the paper &lt;i&gt;Total body water volumes for adult males and females estimated from simple anthropometric measurements&lt;/i&gt; (1980) were used to calculate TBW for each male and female patient. Ultrafiltration (UF) was ignored in the calculation of body weight. The individual predialytic body weight was entered into the equations.&lt;/p&gt;&lt;p&gt;The TBW was set to be equal to V1+V2+V3&nbsp;for each male or female patient based on knowledge about the distribution of physiological molecules in general.&lt;/p&gt;&lt;p&gt;Based on knowledge about fluid distribution in the body, ECF was set to 1/3 of TBW, and plasma was set to ¼ of ECF. This led to:&nbsp;V1 = TBW * 1/3 * ¼;&nbsp; &nbsp;&nbsp;V2= TBW*1/3 * ¾; &nbsp; &nbsp;V3 = TBW * 2/3.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;Determination of mass transfer coefficients and phosphate clearance&nbsp;&lt;/i&gt;&lt;/p&gt;&lt;p&gt;The dialyzer phosphate clearance (kd) was set to be equal to the mean dialyzer clearance value for each patient—i.e., the value was calculated based on the dialysate phosphate samples from each patient, mean dialysate flow rate, and plasma phosphate concentrations at the time points where dialysate was measured in HD1. The following equation illustrates the calculation.&lt;/p&gt;&lt;p&gt;&nbsp; &nbsp;kd&lt;/p&gt; &lt;p&gt;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&lt;/p&gt;&lt;p&gt;The np and nd components are the number of plasma samples and dialysate samples, respectively. The two mass transfer coefficients (k1 and k2)&lt;i&gt;&nbsp;&lt;/i&gt;were determined for each patient using the &lt;i&gt;Solver&lt;/i&gt; function in Excel. The &lt;i&gt;Solver&lt;/i&gt; function was used to obtain the optimum solutions for k1 and k2 and included minimization of the root mean square error (RMSE) using the measured plasma phosphate concentrations from HD1 and the corresponding modeled plasma phosphate concentrations.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Data analysis and validation&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The goodness of fit to the patient data was calculated for the model simulation showing the lowest RMSE value in each patient for HD1 and HD2, respectively. As described in the previous subsection, the lowest RMSE value was found using the &lt;i&gt;Solver&lt;/i&gt; function in Excel in each treatment case. To assess the goodness of fit to the patient data, the coefficient of determination (R2) was determined using the Excel RSQ function that returns the square of the Pearson product-moment correlation coefficient, R.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt