Background Therapeutic virtual reality (VR) has emerged as an efficacious treatment modality for a wide range of health conditions. However, despite encouraging outcomes from early stage research, a consensus for the best way to develop and evaluate VR treatments within a scientific framework is needed. Objective We aimed to develop a methodological framework with input from an international working group in order to guide the design, implementation, analysis, interpretation, and communication of trials that develop and test VR treatments. Methods A group of 21 international experts was recruited based on their contributions to the VR literature. The resulting Virtual Reality Clinical Outcomes Research Experts held iterative meetings to seek consensus on best practices for the development and testing of VR treatments. Results The interactions were transcribed, and key themes were identified to develop a scientific framework in order to support best practices in methodology of clinical VR trials. Using the Food and Drug Administration Phase I-III pharmacotherapy model as guidance, a framework emerged to support three phases of VR clinical study designs—VR1, VR2, and VR3. VR1 studies focus on content development by working with patients and providers through the principles of human-centered design. VR2 trials conduct early testing with a focus on feasibility, acceptability, tolerability, and initial clinical efficacy. VR3 trials are randomized, controlled studies that evaluate efficacy against a control condition. Best practice recommendations for each trial were provided. Conclusions Patients, providers, payers, and regulators should consider this best practice framework when assessing the validity of VR treatments.

Variances and covariances were estimated for birth weight (BWT), weaning weight (WW), yearling gain (YG), yearling weight (YW), yearling height (YH), relative growth from birth to weaning (RGW) and weaning to yearling (RGY), and mature weight (MW). Field records on 572,446 Polled Hereford cattle were analyzed. Because multiple dam weights were collected on a cow the MW used in the analysis was calculated using a nonlinear regression correction factor computed by fitting a quadratic with a plateau to the data. If the cow had an observation(s) beyond the inflection point (IP), the closest weight to that point was used. If the cow only had observation(s) before the IP the closest weight to that point was nonlinearly adjusted to the plateau. The IP for this data set was 1,506 d and the plateau was 554.7 kg. Heritabilities and genetic and environmental correlations were calculated from the appropriate (co)variances and phenotypic correlations were computed. Heritability estimates for BWT, WW, YG, YW, YH, RGW, RGY, and MW were .49, .24, .23, .30, .59, .24, .15, and .52, respectively. Genetic correlations between MW with BWT, WW, YG, YW, YH, RGW, and RGY were .64, .80, .76, .89, .73, -.29, and .35, respectively, environmental correlations were .15, .43, .05, .40, 1.03, .32, and -.10, respectively, and phenotypic correlations were .33, .32, .28, .46, .70, .00, and .07, respectively.

Conversational artificial intelligence (AI) is changing the way mental health care is delivered. By gathering diagnostic information, facilitating treatment, and reviewing clinician behavior, conversational AI is poised to impact traditional approaches to delivering psychotherapy. While this transition is not disconnected from existing professional services, specific formulations of clinician-AI collaboration and migration paths between forms remain vague. In this viewpoint, we introduce four approaches to AI-human integration in mental health service delivery. To inform future research and policy, these four approaches are addressed through four dimensions of impact: access to care, quality, clinician-patient relationship, and patient self-disclosure and sharing. Although many research questions are yet to be investigated, we view safety, trust, and oversight as crucial first steps. If conversational AI isn’t safe it should not be used, and if it isn’t trusted, it won’t be. In order to assess safety, trust, interfaces, procedures, and system level workflows, oversight and collaboration is needed between AI systems, patients, clinicians, and administrators.

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Background Major depressive disorder (MDD) is a global problem with an increasing incidence and prevalence. There has additionally been an increase in depression due to the COVID-19 pandemic. Behavioral activation is considered an evidence-based treatment for MDD. However, there are many barriers that could hinder one’s ability to engage in behavioral activation, with COVID-19 “shelter-in-place” and social distancing orders being current and large impediments. Virtual reality (VR) has been successfully used to help treat a variety of mental health conditions, but it has not yet been used as a method of administering behavioral activation to a clinical population. Using VR to engage in behavioral activation could eliminate barriers that pandemic precautions place and help decrease symptoms of depression that are especially exacerbated in these times. Objective The following case report examines the feasibility, acceptability, and tolerability of VR behavioral activation for an adult with MDD during a global pandemic. This participant was part of a larger pilot study, and the case serves as a description of the VR intervention. Methods The participant engaged in a weekly 50-minute psychotherapy Zoom session for 4 weeks, in which a modified behavioral activation protocol was administered using a VR headset to simulate activities. Data on mood ratings, homework compliance, and headset use were obtained from the headset. Acceptability, tolerability, and depression symptoms were obtained using self-report rating scales. Results The intervention was feasible, acceptable, and tolerable, as reported by this participant. The participant’s depressive symptoms decreased by five-points on the Patient Health Questionnaire-9 over a month, with a beginning score of 10 (moderate depression) and a final score of 5 (mild depression). Conclusions The implications of these findings for future research are discussed. Trial Registration ClinicalTrials.gov NCT04268316; http://clinicaltrials.gov/ct2/show/NCT04268316