When immersive virtual reality users employ digital self-representations, i.e., avatars, they may be subject to the Proteus effect. This effect describes changes in attitudes and behaviors in accordance with identity cues derived from the employed avatar’s appearance, which can persist after leaving virtual reality. Individual reactions to the experience can affect the strength of observed Proteus effects. Especially the experienced illusions of body ownership of avatars and of being in the virtual environment (spatial presence) have been discussed in this context. This study investigated a Proteus effect of avatar age on post-embodiment walking speed, with special focus on how body ownership and spatial presence moderated this effect. Participants who had previously embodied older avatars took significantly longer to walk a set distance than either young avatar or control group participants. This was only apparent during the first half of the walking phase, which may indicate fast decay rates of the effect after embodiment ended. The reported body ownership could not be shown to impact the strength of the Proteus effect. Participants reporting more pronounced spatial presence were subject to stronger Proteus effects, with only the two-thirds of the sample with higher spatial presence showing evidence of the effect.

A multidisciplinary journal on gaming and gamification for health education/promotion, teaching and social change.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background: The use of health apps to support the treatment of chronic pain is gaining importance. Most available pain management apps are still lacking in content quality and quantity as their developers neither involve health experts to ensure target group suitability nor use gamification to engage and motivate the user. To close this gap, we aimed to develop a gamified pain management app, Pain-Mentor. Objective: To determine whether medical professionals would approve of Pain-Mentor’s concept and content, this study aimed to evaluate the quality of the app’s first prototype with experts from the field of chronic pain management and to discover necessary improvements. Methods: A total of 11 health professionals with a background in chronic pain treatment and 2 mobile health experts participated in this study. Each expert first received a detailed presentation of the app. Afterward, they tested Pain-Mentor and then rated its quality using the mobile application rating scale (MARS) in a semistructured interview. Results: The experts found the app to be of excellent general (mean 4.54, SD 0.55) and subjective quality (mean 4.57, SD 0.43). The app-specific section was rated as good (mean 4.38, SD 0.75). Overall, the experts approved of the app’s content, namely, pain and stress management techniques, behavior change techniques, and gamification. They believed that the use of gamification in Pain-Mentor positively influences the patients’ motivation and engagement and thus has the potential to promote the learning of pain management techniques. Moreover, applying the MARS in a semistructured interview provided in-depth insight into the ratings and concrete suggestions for improvement. Conclusions: The experts rated Pain-Mentor to be of excellent quality. It can be concluded that experts perceived the use of gamification in this pain management app in a positive manner. This showed that combining pain management with gamification did not negatively affect the app’s integrity. This study was therefore a promising first step in the development of Pain-Mentor.

Background: Deep and slow abdominal breathing is an important skill for the management of stress and pain. However, despite multiple proofs on the effectiveness of biofeedback, most breathing apps remain limited to pacing specific breathing patterns, without sensor feedback on the actual breathing behavior. Objective: To fill this gap, an app named Breathing-Mentor was developed. This app combines effective visualization of the instruction with biofeedback on deep abdominal breathing, based on the mobile phone’s accelerometers. The aim of this pilot study was to investigate users’ feedback and breathing behavior during initial contact with the app. Methods: To reveal the possible effects of biofeedback, two versions of the mobile app were developed. Both contained the same visual instruction, but only the full version included additional biofeedback. In total, 40 untrained participants were randomly assigned to one of the two versions of the app. They had to follow the app’s instructions as closely as possible for 5 min. Results: The group with additional biofeedback showed an increased signal-to-noise ratio for instructed breathing frequency (0.1 Hz) compared with those using visual instruction without biofeedback (F1,37=4.18; P<.048). During this initial contact with the full version, self-reported relaxation effectivity was, however, lower than the group using visual instruction without biofeedback (t37=−2.36; P=.02), probably owing to increased cognitive workload to follow the instruction. Conclusions: This study supports the feasibility and usefulness of incorporating biofeedback in the Breathing-Mentor app to train abdominal breathing. Immediate effects on relaxation levels should, however, not be expected for untrained users.

The ability to capture human motion based on wearable sensors has a wide range of applications, e.g., in healthcare, sports, well-being, and workflow analysis. This article focuses on the development of an online-capable system for accurately capturing joint kinematics based on inertial measurement units (IMUs) and its clinical application, with a focus on locomotion analysis for rehabilitation. The article approaches the topic from the technology and application perspectives and fuses both points of view. It presents, in a self-contained way, previous results from three studies as well as new results concerning the technological development of the system. It also correlates these with new results from qualitative expert interviews with medical practitioners and movement scientists. The interviews were conducted for the purpose of identifying relevant application scenarios and requirements for the technology used. As a result, the potentials of the system for the different identified application scenarios are discussed and necessary next steps are deduced from this analysis.