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Record W4296717764 · doi:10.2196/40705

Virtual Reality–Augmented Physiotherapy for Chronic Pain in Youth: Protocol for a Randomized Controlled Trial Enhanced With a Single-Case Experimental Design

2022· article· en· W4296717764 on OpenAlexaffvenue
Laura E. Simons, Courtney W. Hess, Ellison Choate, Amanda R Van Orden, Alexandra G Tremblay-McGaw, Maria Menendez, Derek Boothroyd, Gomathy Parvathinathan, Anya Griffin, Thomas J. Caruso, Jennifer Stinson, Amy Weisman, Timothy Liu, Kurt Koeppen

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsHospital for Sick Children
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsRandomized controlled trialPhysical therapyVirtual realityMedicineIntervention (counseling)RehabilitationChronic painProtocol (science)Physical medicine and rehabilitationMindfulnessAlternative medicineClinical psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic musculoskeletal (MSK) pain is a prominent health concern, resulting in pain-related disability, loss of functioning, and high health care costs. Physiotherapy rehabilitation is a gold-standard treatment for improving functioning in youth with chronic MSK pain. However, increasing physical activity can feel unattainable for many adolescents because of pain-related fear and movement avoidance. Virtual reality (VR) offers an immersive experience that can interrupt the fear-avoidance cycle and improve engagement in physiotherapy. Despite promising initial findings, data are limited and often lack the rigor required to establish VR as an evidence-based treatment for MSK pain. OBJECTIVE: This trial evaluates physiorehabilitation with VR in adolescents with MSK pain. This protocol outlines the rationale, design, and implementation of a randomized controlled trial enhanced with a single-case experimental design. METHODS: This study is a 2-group randomized controlled trial assessing the use of physiorehabilitation with VR in adolescents with MSK pain. The authors will collaborate with physical therapists to integrate VR into their standard clinical care. For participants enrolled in standard physiotherapy, there will be no VR integrated into their physical therapy program. Primary outcomes include physical function and engagement in VR. Secondary outcomes include pain-related fear and treatment adherence. Moreover, we will obtain clinician perspectives regarding the feasibility of integrating the intervention into the flow of clinical practice. RESULTS: The pilot study implementing physiorehabilitation with VR demonstrated that high engagement and use of physiorehabilitation with VR were associated with improvements in pain, fear, avoidance, and function. Coupled with qualitative feedback from patients, families, and clinicians, the pilot study results provide support for this trial to evaluate physiorehabilitation with VR for youth with chronic MSK pain. Analysis of results from the main clinical trial will begin as recruitment progresses, and results are expected in early 2024. CONCLUSIONS: Significant breakthroughs for treating MSK pain require mechanistically informed innovative approaches. Physiorehabilitation with VR provides exposure to progressive challenges, real-time feedback, and reinforcement for movement and can include activities that are difficult to achieve in the real world. It has the added benefit of sustaining patient motivation and adherence while enabling clinicians to use objective benchmarks to influence progression. These findings will inform the decision of whether to proceed with a hybrid effectiveness-dissemination trial of physiorehabilitation with VR, serving as the basis for potential large-scale implementation of physiorehabilitation with VR. TRIAL REGISTRATION: ClinicalTrials.gov NCT04636177; https://clinicaltrials.gov/ct2/show/NCT04636177. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40705.

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)0.025
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.249
GPT teacher head0.531
Teacher spread0.282 · 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.

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

Citations20
Published2022
Admission routes2
Has abstractyes

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