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Record W4386229145 · doi:10.2196/48208

Effectiveness of Social Virtual Reality Training in Enhancing Social Interaction Skills in Children With Attention-Deficit/Hyperactivity Disorder: Protocol for a Three-Arm Pilot Randomized Controlled Trial

2023· article· en· W4386229145 on OpenAlexvenueno aff
Ka Po Wong, Jing Qin

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
FundersHong Kong Polytechnic University
KeywordsSocial skillsSocial competencePsychologyRandomized controlled trialIntervention (counseling)Attention deficit hyperactivity disorderCompetence (human resources)Clinical psychologyDevelopmental psychologyMedicinePsychiatrySocial changeSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders among children. Children with ADHD have challenges in understanding social cues and behavioral problems when entering a social setting. Virtual reality (VR) has been applied to improve cognitive behaviors in children with ADHD. Previous studies have not adopted VR to improve social interaction competence and appropriateness in children with ADHD. VR offers a more effective alternative to therapeutic strategies for children with ADHD. OBJECTIVE: This study aims to examine the feasibility and effectiveness of social VR training in enhancing social interaction skills compared to traditional social skills training in children with ADHD. We hypothesize that participants in the social VR training group are likely to perform better on social interaction skills than those in the traditional social skills training group. METHODS: In this nonblinded, 3-arm randomized controlled trial (RCT), 90 participants with ADHD recruited from the community will be randomized 1:1:1 to the social VR intervention group, traditional social skills training group, or waitlist control group. The child psychiatrist will conduct assessments for each participant at baseline and after the intervention. The Social Skills Rating Scale-Parent will be used to assess the social interaction skills of the participants before and after the intervention. Participants in the social VR intervention group and traditional social skills training group will receive twelve 20-minute training sessions for 3 weeks. The participants in the waitlist control group will receive no training. The primary outcome measure is training acceptability and compliance. The secondary outcome measures are the child psychiatrist's assessment and the Social Skills Rating Scale-Parent before and after the intervention. Another outcome measure is the Behavior Rating Inventory of Executive Function and Attention. Differences in the scale scores will be examined using a t test and an F test. RESULTS: This study is set to commence in the fourth quarter of 2023. It is anticipated that participants in the social VR intervention group will exhibit superior social interaction skills than those in the traditional social skills training group. CONCLUSIONS: To our knowledge, this RCT is the first study examining the feasibility and effectiveness of a social VR-based intervention for enhancing the social interaction skills of children with ADHD in Hong Kong. The VR-based social skills training is expected to provide a safer and more effective environment for children with ADHD to learn than the traditional approach. This study can lead to a full-scale RCT. TRIAL REGISTRATION: ClinicalTrials.gov NCT05778526; https://clinicaltrials.gov/study/NCT05778526. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48208.

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)0.025
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.025
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0150.007
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0520.007

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 head0.129
GPT teacher head0.513
Teacher spread0.384 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations15
Published2023
Admission routes1
Has abstractyes

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