ed to wearables and vice versa. This is especially interesting as we are seeing the traditionally "dumb" body prosthesis becoming smarter and thus a natural merging of technology and body. Future longitudinal studies could focus on the dilemma people might experience of whether to believe the information of the device over their own thoughts and feelings. These studies might take into account constructs, such as technology reliance, autonomy, and levels of self-awareness.
  Intensive lifestyle modifications have proved effective in preventing type 2 diabetes mellitus (T2DM), yet the efficiency and effectiveness of these modifications need to be improved. Emerging social media interventions are considered useful in promoting these lifestyles; nevertheless, few studies have investigated the effectiveness of combining them with behavior theory.
 
  This study aims to examine the effectiveness of a 6-month mobile-based intervention (DHealthBar, a WeChat applet) combined with behavioral theory compared with a printed intervention in improving dietary behaviors, physical activity, and intention to change these behaviors among populations at high risk for T2DM.
 
  Participants aged 23 to 67 years were recruited offline in Beijing, China, and were randomized into the intervention group or the control group, which received educational content via DHealthBar or a printed handbook, respectively. Educational materials were culturally tailored recommendations on improving dietary behaviors, p differences in the changes of outcomes over the 6-month intervention period.
 
  Chinese Clinical Trial Registry ChiCTR2000032323; https//tinyurl.com/y4h8q4uf.
 Chinese Clinical Trial Registry ChiCTR2000032323; https//tinyurl.com/y4h8q4uf.
  Cardiac rehabilitation participants are encouraged to meet physical activity guidelines to reduce the risk of repeat cardiac events. However, previous studies have found that physical activity levels are low and sedentary behavior is high, both during and after cardiac rehabilitation. There is potential for smartphone apps to be effective in reducing sedentary behavior, although among the few studies that have investigated smartphone apps in cardiac rehabilitation, none targeted sedentary behavior.
 
  This study aims to evaluate the feasibility of a behavioral smartphone app (Vire) and a web-based behavior change program (ToDo-CR) to decrease sedentary behavior in cardiac rehabilitation participants.
 
  Using a single-center, pre-post design, participants were recruited by nursing staff on admission to cardiac rehabilitation. All eligible participants installed the Vire app, were given a Fitbit Flex, and received the 6-week ToDo-CR program while attending cardiac rehabilitation. The ToDo-CR program uses persoac rehabilitation. A larger randomized controlled trial is warranted to determine the effectiveness of the app.
 The use of a behavioral smartphone app to decrease sitting time appears to be feasible in cardiac rehabilitation. A larger randomized controlled trial is warranted to determine the effectiveness of the app.
  Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks.
 
  This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed.
 
  We introduce a novel probabilistic approach to running federated network queries.  https://www.selleckchem.com/products/heparan-sulfate.html  It combines an algorithmd queries of clinical data repositories that both protect patient privacy and scale to large networks.
 Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.
  People living with a tic disorder (TD)-such as Tourette syndrome (TS)-experience many negative psychological and social challenges arising from chronic tics, such as stigmatization from peers and poorer quality of life, and these can impact upon their families too. It can be difficult for this population to access face-to-face support for tics, and so online support communities offer one avenue for support from peers facing similar experiences. However, little is known about how online support communities may be used by people with TS and other TDs, and by others (eg, parents, caregivers) supporting a person with TS/TD.
 
  This study aimed to explore users' experiences of participation in online support communities for TS and TDs.
 
  In total, 90 respondents (aged 13-62 years; 62% [56/90] female) from 13 countries completed an online survey exploring their experiences of using online support communities for TS and TDs. Respondents were people living with TS/TD themselves (n=68) or supportive others of someonemprovements in psychosocial well-being. Users reported some drawbacks in engaging with online support communities, such as conflict between different types of users and triggering content, which negatively affected experiences of community participation.
  Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.
 
  This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models.
 
  To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology.