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Record W4388836561 · doi:10.2196/47630

Improving Social Isolation and Loneliness Among Adolescents With Physical Disabilities Through Group-Based Virtual Reality Gaming: Feasibility Pre-Post Trial Study

2023· article· en· W4388836561 on OpenAlexvenueno aff
Byron Lai, Raven Young, Mary Craig, Kelli Chaviano, Erin Swanson‐Kimani, Cynthia Wozow, Drew Davis, James H. Rimmer

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessSocial isolationPsychologyPsychological interventionFriendshipSocializationIsolation (microbiology)PopulationMental healthIntervention (counseling)Clinical psychologyMedicineApplied psychologyDevelopmental psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescents with disabilities experience alarmingly higher rates of depression and isolation than peers without disabilities. There is a need to identify interventions that can improve mental health and isolation among this underserved population. Innovations in virtual reality (VR) gaming "standalone" headsets allow greater access to immersive high-quality digital experiences, due to their relatively low cost. OBJECTIVE: This study had three purposes, which were to (1) examine the preliminary effects of a low-cost, home-based VR multiplayer recreation and socialization on depression, socialization, and loneliness; (2) quantify the acceptability of the program as measured by participant adherence, total play time, and exercise time; and (3) identify and describe behavioral mechanisms that affected participant engagement. METHODS: This was a single-group, pre- to postdesign trial. The intervention was conducted at home. Participants were recruited from a children's hospital. The intervention lasted 4 weeks and included 2×1-hour sessions per week of supervised peer-to-peer gaming. Participants used the Meta Quest 2 headset to meet peers and 2 coaches in a private party held digitally. Aim 1 was evaluated with the Children's Depression Inventory 2 Short Form and the University of California, Los Angeles Loneliness Scale 20 items, which are measures of social isolation and loneliness, respectively. Aim 2 was evaluated through the following metrics: participant adherence, the types of games played, friendship building and playtime, and program satisfaction and enjoyment. RESULTS: In total, 12 people enrolled (mean age 16.6, SD 1.8 years; male: n=9 and female: n=3), and 8 people completed the program. Mean attendance for the 8 participants was 77% (49 sessions of 64 total possible sessions; mean 6, SD 2 sessions). A trend was observed for improved Children's Depression Inventory 2 Short Form scores (mean preintervention score 7.25, SD 4.2; mean postintervention score 5.38, SD 4.1; P=.06; effect size=0.45, 95% CI -0.15 to 3.9), but this was not statistically significant; no difference was observed for University of California, Los Angeles Loneliness Scale 20 items scores. Most participants (7/8, 88%) stated that they became friends with a peer in class; 50% (4/8) reported that they played with other people. Participants reported high levels of enjoyment and satisfaction with how the program was implemented. Qualitative analysis resulted in 4 qualitative themes that explained behavioral mechanisms that determined engagement in the program. CONCLUSIONS: The study findings demonstrated that a brief VR group program could be valuable for potentially improving mental health among adolescents with physical disabilities. Participants built friendships with peers and other players on the web, using low-cost consumer equipment that provided easy access and strong scale-up potential. Study findings identified factors that can be addressed to enhance the program within a larger clinical trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT05259462; https://clinicaltrials.gov/study/NCT05259462. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/42651.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.440
Teacher spread0.349 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2023
Admission routes1
Has abstractyes

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