We describe a pilot study that incorporated an innovative hybrid simulation designed to increase the perception of realism in a high-fidelity simulation. Prelicensure students (N = 12) cared for a manikin in a simulation lab scenario wearing Google Glass, a wearable head device that projected video into the students' field of vision. Students reported that the simulation gave them confidence that they were developing skills and knowledge to perform necessary tasks in a clinical setting and that they met the learning objectives of the simulation. The video combined visual images and cues seen in a real patient and created a sense of realism the manikin alone could not provide.

Graduating nursing and medical students missed several hazards of hospitalization, especially those related to the intensive care unit. Orientation for residents and new nurses should include education on hospitalization hazards. Ideally, this orientation should be interprofessional to allow appreciation for each other's roles and responsibilities.

BackgroundStudies show that students who use fidelity-based simulation technology perform better and have higher retention rates than peers who learn in traditional paper-based training. Augmented reality is increasingly being used as a teaching and learning tool in a continual effort to make simulations more realistic for students.ObjectiveThe aim of this project was to assess the feasibility and acceptability of using augmented reality via Google Glass during clinical simulation scenarios for training health science students.MethodsStudents performed a clinical simulation while watching a video through Google Glass of a patient actor simulating respiratory distress. Following participation in the scenarios students completed two surveys and were questioned if they would recommend continued use of this technology in clinical simulation experiences. ResultsWe were able to have students watch a video in their field of vision of a patient who mimicked the simulated manikin. Students were overall positive about the implications for being able to view a patient during the simulations, and most students recommended using the technology in the future. Overall, students reported perceived realism with augmented reality using Google Glass. However, there were technical and usability challenges with the device.ConclusionsAs newer portable and consumer-focused technologies become available, augmented reality is increasingly being used as a teaching and learning tool to make clinical simulations more realistic for health science students. We found Google Glass feasible and acceptable as a tool for augmented reality in clinical simulations.

Objective The purpose of this study was to examine the use of multiple mobile health technologies to generate and transmit data from diverse patients with type 2 diabetes mellitus (T2DM) in between clinic visits. We examined the data to identify patterns that describe characteristics of patients for clinical insights. Methods We enrolled 60 adults with T2DM from a US healthcare system to participate in a 6-month longitudinal feasibility trial. Patient weight, physical activity, and blood glucose were self-monitored via devices provided at baseline. Patients also responded to biweekly medication adherence text message surveys. Data were aggregated in near real-time. Measures of feasibility assessing total engagement in device submissions and survey completion over the 6 months of observation were calculated. Results It was feasible for participants from different socioeconomic, educational, and racial backgrounds to use and track relevant diabetes-related data from multiple mobile health devices for at least 6 months. Both the transmission and engagement of the data revealed notable patterns and varied by patient characteristics. Discussion Using multiple mobile health tools allowed us to derive clinical insights from diverse patients with diabetes. The ubiquitous adoption of smartphones across racial, educational, and socioeconomic populations and the integration of data from mobile health devices into electronic health records present an opportunity to develop new models of care delivery for patients with T2DM that may promote equity as well.

Background Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors. Objective The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics. Methods This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test. Results The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A1c level were more likely to be in the low and waning engagement group. Conclusions We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition. International Registered Report Identifier (IRRID) RR2-10.2196/13517