Virtual Reality for Supporting Depression and Anxiety: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Nilufar Baghaei ◽  
Vibhav Chitale ◽  
Andrej Hlasnik ◽  
Lehan Stemmet ◽  
Hai-Ning Liang ◽  
...  
Keyword(s):  
Mental Health ◽  
Virtual Reality ◽  
Virtual Environment ◽  
Scoping Review ◽  
Google Scholar ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Virtual Reality Exposure Therapy ◽  
Mental Health Conditions ◽  
Virtual Reality Exposure

BACKGROUND Mental health conditions pose a major challenge to healthcare providers and society at large. The World Health Organization (WHO) predicts that by 2030, mental health conditions will be the leading disease burden globally. The current need for mental health care is overwhelming. In New Zealand, one in six adults have been diagnosed with common mental disorders such as depression, and anxiety disorders according to a national survey. Cognitive behavioral therapy (CBT) has been shown to effectively help patients overcome a wide variety of mental health conditions. Virtual Reality Exposure Therapy (VRET) might be one of the most exciting technology that is emerging in the clinical setting for the treatment of anxiety and depression. OBJECTIVE This study aimed to investigate what VR technologies are currently being used to help suppress depression and anxiety. Primarily we identified whether the CBT was included as part of the virtual reality exposure therapy treatment (VRET), and if so, how? Equally important, the focus was set not only on VR hardware and used software tools but also on what the participants did in the virtual environment and how the virtual environment looked like METHODS We performed a scoping review. To identify significant studies, we decided to use already aggregated sources in Google Scholar Database. Overall, the goal of our search strategy was to limit the number of initial results related to virtual reality in mental health to only a relevant minimum. RESULTS Using our defined key words, Google Scholar identified more than 17300 articles. After applying all inclusion and exclusion criteria, we identified a total of 369 articles for further processing. After manual evaluation, 34 articles were shortlisted, of which 9 reported the usage of CBT with VR. All these articles were published between 2017 and 2021. CONCLUSIONS Majority of the studies demonstrated the use of VR to be effective for suppressing anxiety or depression in a range of settings and recommended its potential as tool for usage in a clinical environment. As standalone headsets are much easier to work with and more suitable for home usage, the shift from tethered VR headsets to standalone headsets in the mental health environment was not observed. A total of 9 studies explicitly mentioned the usage of CBT. Out of these, CBT was conducted within a virtual reality environment in 5 studies while in the remaining 4 studies CBT was used as an addition to VRET. All 9 studies reported the use of CBT either in vivo or inside a virtual environment to be effective in suppressing anxiety or depression.

Food insecurity and mental health outcomes among homeless adults: a scoping review

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Elizabeth I Loftus ◽  
James Lachaud ◽  
Stephen W Hwang ◽  
Cilia Mejia-Lancheros
Keyword(s):  
Mental Health ◽  
Food Security ◽  
Food Insecurity ◽  
Scoping Review ◽  
Health Conditions ◽  
Homeless Adults ◽  
Mental Health Conditions ◽  
Household Food ◽  
Potential Association ◽  
Health Related

Abstract Objective: This review summarises and synthesises the existing literature on the relationship between food insecurity (FS) and mental health conditions among adult individuals experiencing homelessness. Design: Scoping review. Papers published between 1 January 2008 and 2 November 2018, searched in PubMed, Web of Science, Scopus, PsycINFO, Cochrane Library and CINAHL, using homelessness, food security and mental health keywords. Setting: Global evidence. Participants: Homeless adults aged 18 years or more. Results: Nine articles (eight cross-sectional and one longitudinal) were included in the present review. FS was measured using the Household Food Insecurity Access Scale, the United States Department of Agriculture Household Food Security Survey Module, as well as single-item or constructed measures. Depression and depressive symptoms were the most common mental health conditions studied. Other mental health conditions assessed included alcohol and substance use, emotional disorders, mental health problems symptoms severity and psychiatric hospitalisations. Composite measures such as axis I and II categories and a cluster of severe mental conditions and mental health-related functioning status were also analysed. FS and mental health-related problems were considered as both exposure and outcome variables. The existing evidence suggests a potential association between FS and several mental health conditions, particularly depression, mental health symptoms severity and poor mental health status scores. Conclusions: This review suggests the potential association between some mental health conditions and FS among homeless adults. However, there is a need for more longitudinal- and interventional-based studies, in order to understand the nature and directionality of the links between FS and mental health in this population group.

26 Supporting the Transition to Postsecondary for Students with Mental Health Conditions: A Scoping Review

2021 ◽  
Vol 26 (Supplement_1) ◽  
pp. e19-e21
Author(s):  
Dan Devoe ◽  
Thomas Lange ◽  
Pauline MacPherson ◽  
Dillon Traber ◽  
Rosemary Perry ◽  
...  
Keyword(s):  
Mental Health ◽  
Postsecondary Education ◽  
Scoping Review ◽  
Life Stage ◽  
Subject Area ◽  
Health Conditions ◽  
Future Research ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Mental Health Conditions

Abstract Primary Subject area Mental Health Background The transition from high school to postsecondary is a critical milestone for independence and empowerment. This life stage frequently coincides with the emergence of most mental health conditions (MHCs). Without adequate support to assist with the transition to postsecondary education, the mental health of arriving students with existing MHCs is likely to decline or remain unmet. Declining mental health is strongly associated with students withdrawing from both secondary and postsecondary education. However, a scoping review of interventions aiming to support youth with MHCs transition to postsecondary has not been conducted. Objectives The objectives of this scoping review were to identify: (1) researched interventions that support youth with MHCs during the transition to postsecondary; (2) best practices used to support this transition; (3) methods of evaluating these interventions and any limitations; and (4) gaps where future research is warranted. Design/Methods A database search of MEDLINE, PsycINFO, Embase, SocINDEX, ERIC, CINHAL, and Education Research Complete was undertaken. Two reviewers independently screened studies and extracted the data. Thematic analysis and risk-of-bias assessment were conducted on included studies. Results Nine studies were included in this review, describing eight unique interventions (Figure 1). Sixty-two percent of interventions were nonspecific in the MHCs that they were targeting in postsecondary students. These interventions were designed to support students upon arrival to postsecondary. Peer mentorship, student engagement, and interagency collaboration were found to be beneficial approaches to supporting youth transitioning into postsecondary (Table 1). The overall quality and level of evidence in these studies was low. Three knowledge gaps were found: evidence was not generalizable to the diversity of MHCs, intervention studies were mostly cross-sectional in nature and lacked follow-up data, and sustaining intervention funding remained a challenge for postsecondary institutions. Conclusion The volume of research identified was limited but indicated overall that offering support during the transition to postsecondary was beneficial for students with MHCs. Further evidence is needed that is generalizable across the mental health spectrum, and that assesses intervention outcomes in relation to intervention costs.

Effectiveness of virtual reality exposure therapy for active duty soldiers in a military mental health clinic

2011 ◽  
Vol 24 (1) ◽  
pp. 93-96 ◽  
Author(s):  
Greg M. Reger ◽  
Kevin M. Holloway ◽  
Colette Candy ◽  
Barbara O. Rothbaum ◽  
JoAnn Difede ◽  
...  
Keyword(s):  
Mental Health ◽  
Virtual Reality ◽  
Exposure Therapy ◽  
Active Duty ◽  
Health Clinic ◽  
Mental Health Clinic ◽  
Virtual Reality Exposure Therapy ◽  
Military Mental Health ◽  
Virtual Reality Exposure

scholarly journals Association of Depression and Anxiety with Social Network Types: Results from a Community Cohort Study

2021 ◽  
Vol 18 (11) ◽  
pp. 6120
Author(s):  
Saju Madavanakadu Devassy ◽  
Lorane Scaria ◽  
Natania Cheguvera ◽  
Kiran Thampi
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Assessment Tools ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Lower Odds ◽  
Mental Health Conditions ◽  
Catchment Areas ◽  
Network Type

Social networks protect individuals from mental health conditions of depression and anxiety. The association between each social network type and its mental health implications in the Indian population remains unclear. The study aims to determine the association of depression and anxiety with different social network types in the participants of a community cohort. We conducted a cross-sectional household survey among people aged ≥30 years in geographically defined catchment areas of Kerala, India. We used cross-culturally validated assessment tools to measure depression, anxiety and social networks. An educated male belonging to higher income quartiles, without any disability, within a family dependent network has lower odds of depression and anxiety. Furthermore, 28, 26.8, 25.7, 9.8 and 9.7% of participants belonged to private restricted, locally integrated, wider community-focused, family-dependent and locally self-contained networks, respectively. Close ties with family, neighbours, and community had significantly lower odds of anxiety and depression than private restricted networks. The clustering of people to each social network type and its associated mental health conditions can inform social network-based public health interventions to optimize positive health outcomes in the community cohort.

scholarly journals Machine learning in mental health: a scoping review of methods and applications

2019 ◽  
Vol 49 (09) ◽  
pp. 1426-1448 ◽  
Author(s):  
Adrian B. R. Shatte ◽  
Delyse M. Hutchinson ◽  
Samantha J. Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Big Data Applications ◽  
Study Results ◽  
Detection And Diagnosis

AbstractBackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

scholarly journals Heterogeneous Network Approach to Predict Individuals’ Mental Health

2021 ◽  
Vol 15 (2) ◽  
pp. 1-26
Author(s):  
Shikang Liu ◽  
Fatemeh Vahedian ◽  
David Hachen ◽  
Omar Lizardo ◽  
Christian Poellabauer ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Health Conditions ◽  
Depression And Anxiety ◽  
Health States ◽  
Mental Health Conditions ◽  
Health Related ◽  
Types Of Information ◽  
The University ◽  
Critical Public Health

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.

Evidence-Based Virtual Reality Use for Mental Health Conditions

2022 ◽  
pp. 171-192
Author(s):  
Jagrika Bajaj ◽  
Aparna Sahu
Keyword(s):  
Mental Health ◽  
Virtual Reality ◽  
Mental Healthcare ◽  
Health Conditions ◽  
Post Traumatic Stress ◽  
Mental Health Conditions ◽  
Comprehensive Picture ◽  
Post Traumatic ◽  
The Impact ◽  
Immersive Technologies

The advancements in immersive technologies have impacted various sectors, with mental healthcare being one of them. The subsequent interaction between immersive technologies, particularly virtual reality and mental health, has created interesting effects that call for a closer look. This chapter intends to provide a comprehensive picture of mental health conditions, namely anxiety and related disorders, post-traumatic stress disorder, and major depressive disorder, as tackled by VR-based therapy. The focus is on its effectiveness and how the results compare to the traditional modes of treatment in terms of efficacy. The impact of user experience towards this approach of intervention and the importance of ethical consideration when VR intersects with the field of mental health are addressed.

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