Pulmonary and Physical Virtual Reality Exercises for Patients With Blunt Chest Trauma: Randomized Clinical Trial
Notice bibliographique
Résumé
BACKGROUND: Adequate pain relief, early restoration of breathing, and rapid mobilization pose a clinical challenge in patients with blunt chest trauma. Virtual reality (VR) has the potential to achieve these 3 interrelated treatment objectives with enhanced self-efficacy and autonomy of patients and limited support by clinicians. OBJECTIVE: This study aimed to assess the effectivity of breathing and physical exercises using VR on the pulmonary recovery of patients with blunt chest trauma at the ward. METHODS: A pilot randomized controlled trial was performed. The control group received usual physiotherapy consisting of protocolized breathing exercises (8 times daily for 10 minutes) and physical exercises (2 times daily for 10 minutes). The VR group was instructed to perform these exercises using VR. The primary outcome was vital lung capacity at day 5 or earlier at discharge. Secondary outcomes were patient mobility (time standing, lying, and sitting), clinical outcomes (length of hospital stay, pulmonary complications, transfer to intensive care unit, and readmission within 30 days), pain, activities of daily living, patient-reported outcome measures (satisfaction and quality of recovery). Patient experiences and barriers and facilitators toward implementation were assessed through interviews. RESULTS: The study was prematurely ended due to enrollment failure combined with poor protocol adherence to exercises in both groups. A total of 27 patients were included, of which 19 patients completed 3 or more days. Vital lung capacity at 5 days (or last measurement) was equal between groups with 1830 (SD 591) mL and 1857 (SD 435) mL in the control and VR groups, respectively. No marked differences were observed in secondary outcomes. Patient interviews showed positive attitudes toward the use of VR, describing that visualization of the exercises helped patients to perform the exercises correctly and to continue the exercises for a longer duration. Also, patients experienced the immersiveness of VR as an analgesic. However, patients did not experience added value over usual care and reported that better integration in treatment and the hectic hospital environment could improve the use of the VR exercises. CONCLUSIONS: The suitability of patients to use virtual reality therapy (VRx) in a hospital (trauma) ward setting is lower than generally expected. Effective application of VRx requires professional guidance and needs thorough alignment with clinical practice. For future research, we recommend to chart adherence to study protocol before designing a VR clinical trial. Patient-reported experiences need to be prioritized in evaluating VR acceptance, usability, and effectiveness. In line, we recommend performing a systematic analysis (eg, using the technology acceptance model) on the acceptance before pilot or main effectiveness studies. Finally, the eligibility of patients and exclusion of patients due to the inability to use VRx should be routinely reported. TRIAL REGISTRATION: ClinicalTrials.gov NCT05194176; https://tinyurl.com/2bzh4tzx.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».