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The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare’s service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution. 

We show, via molecular simulations, that not only does cholesterol induce a lipid order, but the lipid order also enhances cholesterol localization within the lipid leaflets. Therefore, there is a strong interdependence between these two phenomena. In the ordered phase, cholesterol molecules are predominantly present in the bilayer leaflets and orient themselves parallel to the bilayer normal. In the disordered phase, cholesterol molecules are mainly present near the center of the bilayer at the midplane region and are oriented orthogonal to the bilayer normal. At the melting temperature of the lipid bilayers, cholesterol concentration in the leaflets and the bilayer midplane is equal. This result suggests that the localization of cholesterol in the lipid bilayers is mainly dictated by the degree of ordering of the lipid bilayer. We validate our findings on 18 different lipid bilayer systems, obtained from three different phospholipid bilayers with varying concentrations of cholesterol. To cover a large temperature range in simulations, we employ the Dry Martini force field. We demonstrate that the Dry and the Wet Martini (with polarizable water) force fields produce comparable results. 

The main two mechanisms of morphing wall flow control are direct injection of momentum in the streamwise direction and indirect momentum transfer via triggering instabilities. Traveling waves have been shown to perform better than standing waves, probably because they can use both mechanisms. However, the relative importance of the two mechanisms is not known. To differentiate between the mechanisms, a range of parameters (frequency, amplitude, and starting location) at stall (15 deg angle of attack) and poststall (20 deg angle of attack) is tested using wall-resolved large-eddy simulations with a sharp-interface curvilinear immersed boundary method at a low Reynolds number of [Formula: see text] over a NACA0018 airfoil. The results of the simulations demonstrate that the flow is reattached within a range of nondimensional frequencies, actuation amplitudes, and starting locations of oscillation at the stall and poststall angles of attack. Significant lift enhancement and drag reduction are also observed within these ranges. The nondimensional frequency range at which the flow is reattached is found to be similar to the dominant nondimensional frequencies of leading-edge vortex shedding of the unactuated airfoil. These indicate that the indirect transfer of momentum is the dominant mechanism because direct injection of momentum increases with the increase of amplitude and frequency; that is, separation should reduce as they increase. Nevertheless, direct injection of momentum improves the performance relative to pure excitations of standing waves when instabilities are triggered. 

BackgroundMaternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. ObjectiveThe aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection. MethodsWe conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. We leveraged participants’ smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. ResultsThe gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness. ConclusionsOur results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1-score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being through early detection of loneliness.