Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma

Background: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. Objective: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. Methods: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. Results: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. Discussion: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied

Validation of the International IgA Nephropathy Prediction Tool in the Greek Registry of IgA Nephropathy

Background: Immunoglobulin A nephropathy (IgAN) is among the commonest glomerulonephritides in Greece and an important cause of end-stage kidney disease (ESKD) with an insidious chronic course. Thus, the recently published International IgAN prediction tool could potentially provide valuable risk stratification and guide the appropriate treatment module. This study aimed to externally validate this prediction tool using a patient cohort from the IgAN registry of the Greek Society of Nephrology.& nbsp;Methods: We validated the predictive performance of the two full models (with or without race) derived from the International IgAN Prediction Tool study in the Greek Society of Nephrology registry of patients with IgAN using external validation of survival prediction models (Royston and Altman). The discrimination and calibration of the models were tested using the C-statistics and stratified analysis, coefficient of determination (RD2) for model fit, and the regression coefficient of the linear predictor (beta(PI)), respectively.& nbsp;Results: The study included 264 patients with a median age of 39 (30-51) years where 65.2% are men. All patients were of Caucasian origin. The 5-year risk of the primary outcome (50% reduction in estimated glomerular filtration rate or ESKD) was 8%. The RD2 for the full models with and without race when applied to our cohort was 39 and 35%, respectively, and both were higher than the reported RD2 for the models applied to the original validation cohorts (26.3, 25.3, and 35.3%, respectively). Harrel’s C statistic for the full model with race was 0.71, and for the model without race was 0.70. Renal survival curves in the subgroups (< 16th, similar to 16 to < 50th, similar to 50 to < 84th, and > 84th percentiles of linear predictor) showed adequate separation. However, the calibration proved not to be acceptable for both the models, and the risk probability was overestimated by the model.& nbsp;Conclusions: The two full models with or without race were shown to accurately distinguish the highest and higher risk patients from patients with low and intermediate risk for disease progression in the Greek registry of IgAN

Development of the monolithic "MALTA" CMOS sensor for the ATLAS ITK outer pixel layer

Depleted Monolithic Active Pixel Sensors (DMAPS) are an option for the outermost layer of the upgraded ATLAS ITk Pixel Detector at the CERN LHC. Two large size DMAPS named TJ MALTA and TJ Monopix were produced in the TowerJazz 180 nm CMOS imaging process in a small collection electrode design. The MALTA chip combines a low power front end with a novel matrix readout design to achieve a low power consumption of <80 mW/cm2. Threshold values of 250 e- with a dispersion of 30 e- and an ENC of < 10e- can be achieved before irradiation which is consistent with the results from TJ Monopix. Test beam measurements indicate an average efficiency of 96% before irradiation, with the inefficiency mainly due to problems with the slow control of the chip. After neutron irradiation to 1e15 neq/cm2 the efficiency in pixel centres is retained, but it is reduced in pixel corners. A proposal to improve charge collection in the corners is backed up by TCAD simulations and promises an improved performance with small modifications