Treating Lower Phantom Limb Pain in the Postoperative Acute Care Setting Using Virtual Reality: Protocol for a 4-Phase Development and Feasibility Trial
Notice bibliographique
Résumé
BACKGROUND: Phantom limb pain (PLP) affects most people living with lower limb amputations (LLAs). Nonpharmacological interventions, such as graded motor imagery (GMI), have demonstrated promise as PLP treatments. However, GMI access is limited by low patient buy-in and long public outpatient wait times. Considering PLP has been shown to be more prevalent and severe immediately following LLA, there is an urgent need to bypass barriers to allow for prompt access to PLP interventions. In response to this need, the multidisciplinary research team in this study developed a virtual reality (VR) program that administers GMI treatment. This novel intervention may be completed independently and promptly within the postoperative acute care setting. Before conducting a randomized controlled trial, the VR-GMI program must be developed and refined through a rigorous and multistage feasibility assessment. OBJECTIVE: This protocol aims to outline the development and feasibility of the VR-GMI prototype for treating people with LLAs in the postoperative acute care setting (ie, inpatient and home settings) through an iterative, patient-centered, and descriptive approach. METHODS: Four phases of prototype development and assessment were conducted. In phase 1 (completed), the VR-GMI prototype was developed in collaboration with engineers at the National Research Council and in consultation with patient partners. In phase 2 (completed), people with lived experience with amputations were recruited from local physiotherapy and prosthetic clinics to trial the VR-GMI program and provide feedback through semistructured interviews and self-report measures. Phase 3 (completed) consisted of a descriptive case series of individuals who trialed the VR-GMI prototype immediately following their LLAs in the hospital. Results from phase 3 informed the development of a primary quantitative feasibility study. Phase 4 (underway) aims to evaluate the acceptability and pilot outcomes of the VR-GMI program in hospital and home settings as well as improve study procedures for a future randomized controlled trial (phase 4A). Iterative developments were made to the VR-GMI program between each phase to improve prototype fidelity. These iterative developments will also be reviewed in a series of focus groups to finalize the VR-GMI prototype (phase 4B). RESULTS: Recruitment for phases 1 and 2 was completed in September 2023. Phase 3 was completed in July 2024, and phase 4A is currently underway with 15 participants recruited as of March 2025. CONCLUSIONS: The intervention developed is the first VR PLP treatment implementing GMI and prioritizing an in-depth, patient-centered approach before assessing its efficacy. Doing so will improve the likelihood of successful clinical implementation. Moreover, very few PLP interventions have been assessed in the acute postoperative period when they may prevent PLP before its onset. TRIAL REGISTRATION: ClinicalTrials.gov NCT06638918; https://clinicaltrials.gov/study/NCT06638918. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68008.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,039 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».