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Record W4404298115 · doi:10.2196/58734

Virtual Reality–Applied Home-Visit Rehabilitation for Patients With Chronic Pain: Protocol for Single-Arm Pre-Post Comparison Study

2024· article· en· W4404298115 on OpenAlexvenueno aff
Hiroki Funao, Ryo Momosaki, Mayumi Tsujikawa, Eiji Kawamoto, Ryo Esumi, Motomu Shimaoka

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintRehabilitationProtocol (science)Virtual realityPhysical therapyMedicinePhysical medicine and rehabilitationChronic painPsychologyComputer scienceHuman–computer interactionWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Pain inhibits rehabilitation. In rehabilitation at medical institutions, the usefulness of virtual reality (VR) has been reported in many cases to alleviate pain. In recent years, the demand for home rehabilitation has increased. Unlike in medical situations, the patients targeted for in-home rehabilitation often have chronic pain due to physical and psychosocial factors, and the environment is not specialized for rehabilitation. However, VR might be effective for in-home rehabilitation settings. OBJECTIVE: This study aims to evaluate the feasibility of applying VR to home-visit rehabilitation for homebound patients with chronic pain. METHODS: This study will test the feasibility of VR applied to home-visit rehabilitation for patients with chronic pain. A single-arm pre-post comparison will be conducted to evaluate its feasibility. Screening will be conducted on patients who have given consent to participate in the study, and those who have pain that persists or recurs for more than 3 months and receive home-visit rehabilitation will be enrolled in the study. Baseline measurements will be conducted on study participants before the start of the VR intervention. VR-applied home-visit rehabilitation will be conducted once a week for a total of 10 VR interventions. The primary endpoint is the change in pain from the baseline to the tenth intervention. Pain is a subjective symptom of the study participants and will be subjectively assessed by the Numerical Rating Scale of 11 levels from 0 to 10. Pain as the primary endpoint will be measured at 3-time points per rehabilitation session: before, during, and after the rehabilitation so that changes between time points can be evaluated. Secondary endpoints are heart rate variability, range of motion of the area in the musculoskeletal system where the pain occurs, motivation for rehabilitation, catastrophic thoughts of pain, mood state, quality of life, and interviews. Assessments will be conducted at the baseline, first, fifth, and tenth interventions. After completing the clinical study (10 VR interventions), patients will continue their regular home-visit rehabilitation as usual. RESULTS: Recruitment of participants began on February 22, 2022, and data collection is ongoing as of November 2024. The research results will be published in international peer-reviewed journals and through presentations at national and international conferences. CONCLUSIONS: This study will contribute to the development of novel rehabilitation-based solutions for homebound patients who have had difficulty obtaining adequate relief from chronic pain. Future studies will consider conducting randomized controlled trials as clinical trials to validate the efficacy of VR during home-visit rehabilitation for patients with chronic pain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58734.

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)0.013
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.009
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0410.007

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 head0.146
GPT teacher head0.523
Teacher spread0.377 · 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 designNon-randomized 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

Citations2
Published2024
Admission routes1
Has abstractyes

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