Background: Using traditional simulators (eg, cadavers, animals, or actors) to upskill health workers is becoming less common because of ethical issues, commitment to patient safety, and cost and resource restrictions. Virtual reality (VR) and augmented reality (AR) may help to overcome these barriers. However, their effectiveness is often contested and poorly understood and warrants further investigation. Objective: The aim of this review is to develop, test, and refine an evidence-informed program theory on how, for whom, and to what extent training using AR or VR works for upskilling health care workers and to understand what facilitates or constrains their implementation and maintenance. Methods: We conducted a realist synthesis using the following 3-step process: theory elicitation, theory testing, and theory refinement. We first searched 7 databases and 11 practitioner journals for literature on AR or VR used to train health care staff. In total, 80 papers were identified, and information regarding context-mechanism-outcome (CMO) was extracted. We conducted a narrative synthesis to form an initial program theory comprising of CMO configurations. To refine and test this theory, we identified empirical studies through a second search of the same databases used in the first search. We used the Mixed Methods Appraisal Tool to assess the quality of the studies and to determine our confidence in each CMO configuration. Results: Of the 41 CMO configurations identified, we had moderate to high confidence in 9 (22%) based on 46 empirical studies reporting on VR, AR, or mixed simulation training programs. These stated that realistic (high-fidelity) simulations trigger perceptions of realism, easier visualization of patient anatomy, and an interactive experience, which result in increased learner satisfaction and more effective learning. Immersive VR or AR engages learners in deep immersion and improves learning and skill performance. When transferable skills and knowledge are taught using VR or AR, skills are enhanced and practiced in a safe environment, leading to knowledge and skill transfer to clinical practice. Finally, for novices, VR or AR enables repeated practice, resulting in technical proficiency, skill acquisition, and improved performance. The most common barriers to implementation were up-front costs, negative attitudes and experiences (ie, cybersickness), developmental and logistical considerations, and the complexity of creating a curriculum. Facilitating factors included decreasing costs through commercialization, increasing the cost-effectiveness of training, a cultural shift toward acceptance, access to training, and leadership and collaboration. Conclusions: Technical and nontechnical skills training programs using AR or VR for health care staff may trigger perceptions of realism and deep immersion and enable easier visualization, interactivity, enhanced skills, and repeated practice in a safe environment. This may improve skills and increase learning, knowledge, and learner satisfaction. The future testing of these mechanisms using hypothesis-driven approaches is required. Research is also required to explore implementation considerations.

This is the abstract only. Read the full text free (open access) on the JMIR Serious Games website. JMIR is the leading ehealth publisher: fast peer-review - open access - high impact.

Background: Sustained engagement is essential for the success of telerehabilitation programs. However, patients’ lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science–based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR). Objective: This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert. Methods: In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users’ range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software’s response to their movements. During calibration, the time series of the users’ movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements. Results: The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%). Conclusions: Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation. Trial Registration:

This is the abstract only. Read the full text free (open access) on the JMIR Serious Games website. JMIR is the leading ehealth publisher: fast peer-review - open access - high impact.

This is the abstract only. Read the full text free (open access) on the JMIR Serious Games website. JMIR is the leading ehealth publisher: fast peer-review - open access - high impact.

Background: Parkinson disease is a neurodegenerative disease causing a progressive loss of autonomy. This requires long-term rehabilitation care. Currently, new technologies are being developed for use in daily life, and there is a progressive implementation of telerehabilitation. Objective: The aim of this study (the TELEP@RK study) is to evaluate the uses of a digital self-rehabilitation device in patients with Parkinson disease and their independent physiotherapists on the scale of a health territory. Methods: A total of 10 independent physiotherapists and 31 patients with Parkinson disease were followed for 1 year to evaluate the use of a telerehabilitation tool (digital tablet and inertial sensor) via questionnaires of the Unified Theory of Acceptance and Use of Technology (UTAUT). The questionnaires were submitted to participants at 0, 2, and 12 months from the start of follow-up. The averages of the scores of the different determinants and constructs of the UTAUT questionnaires were compared at the different follow-up times. Results: Among professionals, the averages of the various determinants were generally high at the beginning of the study with an average (out of 5) performance expectancy of 4.19, effort expectancy of 3.88, social influence of 3.95, facilitating conditions of 4, and intention to use of 3.97. These averages decreased over time. Conclusions: Acceptability, acceptance, and appropriation of the tool were very high among the physiotherapists as well as the patients, despite the tool’s lack of evolution during the study. In the current health care context, these results allow us to envision a new organization of the care pathway for patients with chronic diseases, with the increased use of new technologies associated with telecare.

This is the abstract only. Read the full text free (open access) on the JMIR Serious Games website. JMIR is the leading ehealth publisher: fast peer-review - open access - high impact.