Effectiveness of WeChat-Based Plus Scene-Graphics Health Education for Rehabilitation After Open Elbow Arthrolysis: Historical Control Study
Notice bibliographique
Résumé
Background: Elbow stiffness often hinders daily tasks. Open elbow arthrolysis is effective but requires long-term postoperative rehabilitation. Traditional health education does not significantly improve patient cooperation or results. Handy and engaging tools such as WeChat and scene graphics may help. Objective: This study aims to assess the efficacy of WeChat-based health education combined with scene graphics following open elbow arthrolysis. Methods: This historical control study involved patients aged 18 years and older who underwent open elbow arthrolysis, had normal communication skills, and were proficient in using WeChat. Eligible patients were divided into 2 groups based on admission time: the control group (56 patients, enrolled from January to June 2021) and the WeChat group (56 patients, enrolled from July to December 2021). The control group had received traditional health education, whereas the WeChat group received health education using WeChat and scene graphics. Information in 4-part comics was shared through a WeChat public account. Patients accessed this account to receive daily lessons during hospitalization, followed by online instruction in a WeChat group after discharge until 12 weeks postoperatively. Outcome data were collected at 1, 6, and 12 weeks postoperatively. The primary outcome was elbow range of motion; secondary outcomes were elbow function, quality of life, and complication incidence. Results: The elbow flexion angle improved from 71.5° (SD 4.2°) to 124.2° (SD 11.7°) in the WeChat group and from 71.7° (SD 4.6°) to 114.4° (SD 13.6°) in the control group (difference 10.0°, 95% CI 4.9-15.1, P<.001). The mean elbow extension angle improved from 29.6° (SD 6.0°) to 6.4° (SD 2.5°) in the WeChat group and from 28.8° (SD 3.8°) to 10.1° (SD 3.4°) in the control group (difference -4.5°, 95% CI -6.5 to -2.5, P<.001). The mean forearm pronation angle improved from 31.9° (SD 4.0°) to 66.9° (SD 7.3°) in the WeChat group and from 33.0° (SD 4.2°) to 63.1° (SD 7.2°) in the control group (difference 4.9°, 95% CI 2.0-7.8, P=.001). The mean forearm supination angle improved from 30.2° (SD 3.7°) to 71.8° (SD 4.8°) in the WeChat group and from 30.4° (SD 4.1°) to 64.2° (SD 9.8°) in the control group (difference 7.7°, 95% CI 4.4-11.0, P<.001). The mean Mayo Elbow Performance Score increased from 58.0 (SD 3.7) to 80.4 (SD 5.7) in the WeChat group and from 58.9 (SD 2.8) to 75.8 (SD 6.9) in the control group (difference 5.5 points, 95% CI 2.8-8.2, P<.001). The mean 36-item Short Form Health Survey questionnaire score increased from 44.4 (SD 6.6) to 82.0 (SD 7.1) in the WeChat group and from 44.0 (SD 6.4) to 75.0 (SD 11.2) in the control group (difference 6.6 points, 95% CI 2.6-10.6, P=.002). Conclusions: WeChat-based health education combined with scene graphics was found to significantly improve elbow range of motion, elbow function, and quality of life.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».