Participant perceptions were generally positive-only one app was rated as less helpful and satisfactory than the control-and the studies that measured engagement and usability found relatively high study completion rates (mean 83%; n=18, N=39) and ease-of-use ratings (3 significantly better than control, 9/15 rated >70%). However, there was little evidence of changed behavior or health outcomes. CONCLUSIONS There was no strong evidence in support of the effectiveness of mobile apps in improving health behaviors or outcomes because few studies found significant differences between the app and control groups. Further research is needed to identify the BCTs that are most effective at promoting behavior change. Improved reporting is necessary to accurately evaluate the mobile health app effectiveness and risk of bias. ©Madison Milne-Ives, Ching Lam, Caroline De Cock, Michelle Helena Van Velthoven, Edward Meinert. Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 18.03.2020.BACKGROUND Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning-based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. OBJECTIVE This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. METHODS A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set 80% and validation set 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on t.jmir.org), 18.03.2020.BACKGROUND Telemedicine is defined by three characteristics (1) using information and communication technologies, (2) covering a geographical distance, and (3) involving professionals who deliver care directly to a patient or a group of patients. It is said to improve chronic care management and self-management in patients with chronic diseases. Epacadostat However, currently available guidelines for the care of patients with diabetes, hypertension, or dyslipidemia do not include evidence-based guidance on which components of telemedicine are most effective for which patient populations. OBJECTIVE The primary aim of this study was to identify, synthesize, and critically appraise evidence on the effectiveness of telemedicine solutions and their components on clinical outcomes in patients with diabetes, hypertension, or dyslipidemia. METHODS We conducted an umbrella review of high-level evidence, including systematic reviews and meta-analyses of randomized controlled trials. On the basis of predefined eligibility criteria,iews or meta-analyses reporting lipid outcomes in patients with diabetes were found. GRADE assessment revealed that the overall quality of the evidence was low to very low. CONCLUSIONS The results of this umbrella review indicate that telemedicine has the potential to improve clinical outcomes in patients with diabetes. Although subgroup-specific effectiveness rates favoring certain intervention and population characteristics were found, the low GRADE ratings indicate that evidence can be considered as limited. Future updates of clinical care and practice guidelines should carefully assess the methodological quality of studies and the overall certainty of subgroup-specific outcomes before recommending telemedicine interventions for certain patient populations. ©Patrick Timpel, Sarah Oswald, Peter E H Schwarz, Lorenz Harst. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 18.03.2020.Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control. © 2020, Jana et al.Mechanical forces are fundamental regulators of cell behaviors. However, molecular regulation of mechanotransduction remain poorly understood. Here, we identified the mechanosensitive channels Piezo1 and Piezo2 as key force sensors required for bone development and osteoblast differentiation. Loss of Piezo1, or more severely Piezo1/2, in mesenchymal or osteoblast progenitor cells, led to multiple spontaneous bone fractures in newborn mice due to inhibition of osteoblast differentiation and increased bone resorption. In addition, loss of Piezo1/2 rendered resistant to further bone loss caused by unloading in both bone development and homeostasis. Mechanistically, Piezo1/2 relayed fluid shear stress and extracellular matrix stiffness signals to activate Ca2+ influx to stimulate Calcineurin, which promotes concerted activation of NFATc1, YAP1 and ß-catenin transcription factors by inducing their dephosphorylation as well as NFAT/YAP1/ß-catenin complex formation. Yap1 and ß-catenin activities were reduced in the Piezo1 and Piezo1/2 mutant bones and such defects were partially rescued by enhanced ß-catenin activities. © 2020, Zhou et al.