Maynardbritt2160

e Daly, Miriam Davidson, Bonnie Spring. Originally published in JMIR Formative Research (http//formative.jmir.org), 13.05.2020.BACKGROUND Virtual reality exposure therapy is an efficacious treatment of anxiety disorders, and recent research suggests that such treatments can be automated, relying on gamification elements instead of a real-life therapist directing treatment. Such automated, gamified treatments could be disseminated without restrictions, helping to close the treatment gap for anxiety disorders. Despite initial findings suggesting high efficacy, very is little is known about how users experience this type of intervention. OBJECTIVE The aim of this study was to examine user experiences of automated, gamified virtual reality exposure therapy using in-depth qualitative methods. METHODS Seven participants were recruited from a parallel clinical trial comparing automated, gamified virtual reality exposure therapy for spider phobia against an in vivo exposure equivalent. Participants received the same virtual reality treatment as in the trial and completed a semistructured interview afterward. The transcribed material was analyzed using thematic analysis. RESULTS Many of the uncovered themes pertained directly or indirectly to a sense of presence in the virtual environment, both positive and negative. The automated format was perceived as natural and the gamification elements appear to have been successful in framing the experience not as psychotherapy devoid of a therapist but rather as a serious game with a psychotherapeutic goal. CONCLUSIONS Automated, gamified virtual reality exposure therapy appears to be an appealing treatment modality and to work by the intended mechanisms. Findings from the current study may guide the next generation of interventions and inform dissemination efforts and future qualitative research into user experiences. ©Philip Lindner, Alexander Rozental, Alice Jurell, Lena Reuterskiöld, Gerhard Andersson, William Hamilton, Alexander Miloff, Per Carlbring. Originally published in JMIR Serious Games (http//games.jmir.org), 29.04.2020.BACKGROUND Cancer has become the second leading cause of death globally. Most cancer cases are due to genetic mutations, which affect metabolism and result in facial changes. OBJECTIVE In this study, we aimed to identify the facial features of patients with cancer using the deep learning technique. METHODS Images of faces of patients with cancer were collected to build the cancer face image data set. A face image data set of people without cancer was built by randomly selecting images from the publicly available MegaAge data set according to the sex and age distribution of the cancer face image data set. Each face image was preprocessed to obtain an upright centered face chip, following which the background was filtered out to exclude the effects of nonrelative factors. A residual neural network was constructed to classify cancer and noncancer cases. Transfer learning, minibatches, few epochs, L2 regulation, and random dropout training strategies were used to prevent overfitting. Moreover, guided gradient-weithat facial skin and adiposity were closely related to the presence of cancer. ©Bin Liang, Na Yang, Guosheng He, Peng Huang, Yong Yang. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 29.04.2020.BACKGROUND Well-being has multiple domains, and these domains are unique to the population being examined. Therefore, to precisely assess the well-being of a population, a scale specifically designed for that population is needed. OBJECTIVE The goal of this study was to design and validate a comprehensive well-being scale for people in a university environment, including students, faculty, and staff. METHODS A crowdsourcing approach was used to determine relevant domains for the comprehensive well-being scale in this population and identify specific questions to include in each domain. A web-based questionnaire (Q1) was used to collect opinions from a group of university students, faculty, and staff about the domains and subdomains of the scale. A draft of a new well-being scale (Q2) was created in response to the information collected via Q1, and a second group of study participants was invited to evaluate the relevance and clarity of each statement. A newly created well-being scale (Q3) was then used by a tniversity environment. ©Leming Zhou, Bambang Parmanto. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 29.04.2020.BACKGROUND Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. OBJECTIVE This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. METHODS First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exporon included partner trust, partner dependence, relationship commitment, and partner power. Prograf CONCLUSIONS The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.BACKGROUND Prior research has demonstrated the efficacy of internet-based cognitive behavioral therapy (ICBT) for social anxiety disorder (SAD). However, it is unclear how shame influences this treatment effect. OBJECTIVE This study aimed to investigate the role shame played in the ICBT treatment process for participants with SAD. METHODS A total of 104 Chinese participants (73 females; age mean 24.92 years, SD 4.59) were divided into self-help ICBT, guided ICBT, or wait list control groups. Participants were assessed before and immediately after the intervention using Social Interaction Anxiety Scale (SIAS), Social Phobia Scale (SPS), and Experience of Shame Scale (ESS). RESULTS Participants' social anxiety symptoms (self-help differences between pre and post-treatment SIAS=-12.71; Cohen d=1.01; 95% CI 9.08 to 16.32; P less then .001 and differences between pre and post-treatment SPS=11.13; Cohen d=0.89; 95% CI 6.98 to 15.28; P less then .001 and guided SIAS=19.45; Cohen d=1.20; 95% CI 14.67 to 24.24; P less then .