共0篇 平均IF=NaN (-) 更多分析

    加载中

    logo
    Effectiveness of Self-guided App-Based Virtual Reality Cognitive Behavior Therapy for Acrophobia: A Randomized Clinical Trial. Donker Tara,Cornelisz Ilja,van Klaveren Chris,van Straten Annemieke,Carlbring Per,Cuijpers Pim,van Gelder Jean-Louis JAMA psychiatry Importance:Globally, access to evidence-based psychological treatment is limited. Innovative self-help methods using smartphone applications and low-cost virtual reality have the potential to significantly improve the accessibility and scalability of psychological treatments. Objective:To examine the effectiveness of ZeroPhobia, a fully self-guided app-based virtual reality cognitive behavior therapy (VR CBT) using low-cost (cardboard) virtual reality goggles compared with a wait-list control group and to determine its user friendliness. Design, Setting, and Participants:In a single-blind randomized clinical trial, participants were enrolled between March 24 and September 28, 2017, and randomly assigned (1:1) by an independent researcher to either VR CBT app or a wait-list control group. A total of 193 individuals aged 18 to 65 years from the Dutch general population with acrophobia symptoms and access to an Android smartphone participated. The 6 animated modules of the VR-CBT app and gamified virtual reality environments were delivered over a 3-week period in participants' natural environment. Assessments were completed at baseline, immediately after treatment, and at 3-month follow-up. Analysis began April 6, 2018, and was intention to treat. Intervention:Self-guided app-based VR CBT. Main Outcomes and Measures:The primary outcome measure was the Acrophobia Questionnaire. The hypothesis was formulated prior to data collection. Results:In total, 193 participants (129 women [66.84%]; mean [SD] age, 41.33 [13.64] years) were randomly assigned to intervention (n = 96) or a wait-list control group (n = 97). An intent-to-treat analysis showed a significant reduction of acrophobia symptoms at posttest at 3 months for the VR-CBT app compared with the controls (b = -26.73 [95% CI, -32.12 to -21.34]; P < .001; d = 1.14 [95% CI, 0.84 to 1.44]). The number needed to treat was 1.7. Sensitivity and robustness analysis confirmed these findings. Pretreatment attrition was 22 of 96 (23%) because of smartphone incompatibility. Of the 74 participants who started using the VR-CBT app, 57 (77%) completed the intervention fully. Conclusions and Relevance:A low-cost fully self-guided app-based virtual reality cognitive behavioral therapy with rudimentary virtual reality goggles can produce large acrophobia symptom reductions. To our knowledge, this study is the first to show that virtual reality acrophobia treatment can be done at home without the intervention of a therapist. Trial Registration:Trialregister.nl identifier: NTR6442. 10.1001/jamapsychiatry.2019.0219
    Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. Place Skyler,Blanch-Hartigan Danielle,Rubin Channah,Gorrostieta Cristina,Mead Caroline,Kane John,Marx Brian P,Feast Joshua,Deckersbach Thilo,Pentland Alex Sandy,Nierenberg Andrew,Azarbayejani Ali Journal of medical Internet research BACKGROUND:There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. OBJECTIVE:The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. METHODS:A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. RESULTS:Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). CONCLUSIONS:Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed. 10.2196/jmir.6678