Eyeing the huge potential mHealth market in China, developers both inside and outside of China have created an increasing number of fitness mobile applications with Chinese-language user interfaces. The present study analyzes the content of those fitness mobile apps (N = 177), with a particular focus on their behavior change techniques and relevant theoretical mechanisms. It finds that three theoretical mechanisms, modeling/observational learning, self-regulation, and social comparison/social support, are prevalent among fitness mobile apps with Chinese-language user interfaces. Moreover, based on the configurations of the behavior change techniques, three distinct clusters are identified: "instructional apps" (N = 75), "self-regulation apps" (N = 58), and "triathlon apps" (N = 44). Among them, "triathlon apps" equipped with technical features reflecting all three theoretical mechanisms are found to be the most popular among users. This suggests the usefulness of health behavior change theories in promoting physical activity via mobile apps in that the inclusion of more theoretical content in the app design enhances the app's effectiveness. More theoretical and practical implications are also discussed.

Field-effect transistor (FET) biosensors based on two-dimensional (2D) materials have drawn significant attention due to their outstanding sensitivity. However, the Boltzmann distribution of electrons imposes a physical limit on the subthreshold swing (SS), and a 2D-material biosensor with sub-60 mV/dec SS has not been realized, which hinders further increase of the sensitivity of 2D-material FET biosensors. Here, we report tunnel FETs (TFETs) based on a SnSe2/WSe2 heterostructure and observe the tunneling effect of a 2D material in aqueous solution for the first time with an ultralow SS of 29 mV/dec. A bilayer dielectric (Al2O3/HfO2) and graphene contacts, which significantly reduce the leakage current in solution and contact resistance, respectively, are crucial to the realization of the tunneling effect in solution. Then, we propose a novel biosensing method by using tunneling current as the sensing signal. The TFETs show an extremely high pH sensitivity of 895/pH due to ultralow SS, surpassing the sensitivity of FET biosensors based on a single 2D material (WSe2) by 8-fold. Specific detection of glucose is realized, and the biosensors show a superb sensitivity (3158 A/A for 5 mM), wide sensing range (from 10–9 to 10–3 M), low detection limit (10–9 M), and rapid response rate (11 s). The sensors also exhibit the ability of monitoring glucose in complex biofluid (sweat). This work provides a platform for ultrasensitive biosensing. The discovery of the tunneling effect of 2D materials in aqueous solution may stimulate further fundamental research and potential applications.

In this demo, we build a practical mobile application, Moodee,to help detect and release users’ psychological stress byleveraging users’ social media data in online social networks,and provide an interactive user interface to present users’and friends’ psychological stress states in an visualized andintuitional way.Given users’ online social media data as input, Moodee intelligentlyand automatically detects users’ stress states. Moreover,Moodee would recommend users with different linksto help release their stress. The main technology of this demois a novel hybrid model - a factor graph model combinedwith Deep Neural Network, which can leverage social mediacontent and social interaction information for stress detection.We think that Moodee can be helpful to people’s mentalhealth, which is a vital problem in modern world.