Impact of mHealth on Postoperative Quality of Life, Self-Management, and Dysfunction in Patients With Oral and Maxillofacial Tumors: Nonrandomized Controlled Trial
Notice bibliographique
Résumé
Background: With a focus on postoperative dysfunctions that may occur after maxillofacial tumor resection and the difficulties faced during home rehabilitation, we developed a mobile health app based on nurse-patient cooperation. The app extends rehabilitation care from hospital to home with the help of artificial intelligence, Internet of Things, and other technologies, thus helping patients to better carry out their home functional rehabilitation and meet their health needs. Objective: The primary objective of this quasi-experimental study is to evaluate the impact of the Intelligent Home Rehabilitation Care Platform on the quality of life, self-management, and functional impairment in patients with oral and maxillofacial head and neck tumors. We aim to determine whether the intervention through this platform can lead to significant improvements in these areas compared with traditional postdischarge care methods. Methods: In this study, patients with oral and maxillofacial head and neck tumors who had undergone surgery were recruited from allied hospitals in the Yangtze River Delta region, divided into an experimental group (n=138) and a control group (n=123), and received either the Intelligent Home Rehabilitation Care Platform intervention or the conventional health care and guidance, respectively. The intervention lasted 3 months, and the patients' quality of life, self-management efficacy, and improvement in dysfunction were assessed at 1 week (baseline), 1 month (T1), and 3 months (T2) postoperatively. SPSS software (IBM Corp) was used to perform the chi-square test, rank sum test, t test, repeated-measures ANOVA, and generalized estimation equation for data analysis. Results: We analyzed the effects of the quality of life and self-management using the generalized estimating equation method. The generalized estimating equation results showed that after adjusting for age, sex, pathological histology, cancer stage, and primary site, the intervention group had a significantly higher improvement in quality of life than the control group at the T2 (regression coefficient, β=-68.020, 95 % CI -116.639 to -19.412; P=.006) stage. The degree of improvement in self-management efficacy was significantly higher in the T1 (regression coefficient, β =-7.030, 95 % CI -9.540 to -4.520; P<.001) and T2 (regression coefficient, β =-13.245, 95 % CI -16.923 to -9.566; P<.001) stages than in the control group. The results of repeated-measures ANOVA and rank sum test showed that the experimental group showed improvements in shoulder function, dysphagia, and trismus after mHealth intervention; however, the differences were not significant. Conclusions: The Intelligent Home Rehabilitation Care Platform interventions can effectively enhance patients' self-management efficacy, improving quality of life and facilitate recovery from dysfunctions. Therefore, mHealth may be used in oncology care to provide smarter home self-care for patients.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».