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Record W2899704744 · doi:10.2196/10965

Implementations of Virtual Reality for Anxiety-Related Disorders: Systematic Review

2018· review· en· W2899704744 on OpenAlexvenueno aff
Theodore Oing, Julie Prescott

Bibliographic record

VenueJMIR Serious Games · 2018
Typereview
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPhobiasVirtual Reality Exposure TherapyVirtual realityExposure therapyPsychologySpecific phobiaImplementationClinical psychologyPsychotherapistPanic disorderComputer sciencePsychiatryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Although traditional forms of therapy for anxiety-related disorders (eg, cognitive behavioral therapy, CBT) have been effective, there have been long-standing issues with these therapies that largely center around the costs and risks associated with the components comprising the therapeutic process. To treat certain types of specific phobias, sessions may need to be held in public, therefore risking patient confidentiality and the occurrence of uncontrollable circumstances (eg, weather and bystander behavior) or additional expenses such as travel to reach a destination. To address these issues, past studies have implemented virtual reality (VR) technologies for virtual reality exposure therapy (VRET) to provide an immersive, interactive experience that can be conducted privately and inexpensively. The versatility of VR allows various environments and scenarios to be generated while giving therapists control over variables that would otherwise be impossible in a natural setting. Although the outcomes from these studies have been generally positive despite the limitations of legacy VR systems, it is necessary to review these studies to identify how modern VR systems can and should improve to treat disorders in which anxiety is a key symptom, including specific phobias, posttraumatic stress disorder and acute stress disorder, generalized anxiety disorder, and paranoid ideations. OBJECTIVE: The aim of this review was to establish the efficacy of VR-based treatment for anxiety-related disorders as well as to outline how modern VR systems need to address the shortcomings of legacy VR systems. METHODS: A systematic search was conducted for any VR-related, peer-reviewed articles focused on the treatment or assessment of anxiety-based disorders published before August 31, 2017, within the ProQuest Central, PsycINFO, and PsycARTICLES databases. References from these articles were also evaluated. RESULTS: A total of 49 studies met the inclusion criteria from an initial pool of 2419 studies. These studies were a mix of case studies focused solely on VRET, experimental studies comparing the efficacy of VRET with various forms of CBT (eg, in vivo exposure, imaginal exposure, and exposure group therapy), and studies evaluating the usefulness of VR technology as a diagnostic tool for paranoid ideations. The majority of studies reported positive findings in favor of VRET despite the VR technology's limitations. CONCLUSIONS: Although past studies have demonstrated promising and emerging efficacy for the use of VR as a treatment and diagnostic tool for anxiety-related disorders, it is clear that VR technology as a whole needs to improve to provide a completely immersive and interactive experience that is capable of blurring the lines between the real and virtual world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.391
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations160
Published2018
Admission routes1
Has abstractyes

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