The Impact of mHealth-Based Continuous Care on Disease Knowledge, Treatment Compliance, and Serum Uric Acid Levels in Chinese Patients With Gout: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: In patients with gout, suboptimal management refers to a lack of disease knowledge, low treatment compliance, and inadequate control of serum uric acid (SUA) levels. Several studies have shown that continuous care is recommended for disease management in patients with gout. However, in China, the continuous care model commonly used for patients with gout requires significant labor and time costs, and its efficiency and coverage remain low. Mobile health (mHealth) may be able to address these issues. Objective: This study aimed to explore the impact of mHealth-based continuous care on improving gout knowledge and treatment compliance and reducing SUA levels. Methods: This study was a single-center, single-blind, and parallel-group randomized controlled trial. Participants were recruited at the West China Hospital of Sichuan University in Chengdu, China, between February 2021 and July 2021 and were randomly assigned to the intervention and control groups. The intervention group received continuous care via an mHealth app, which includes modules for health records, 24 weeks of gout-related health education materials, and interactive support. The control group received routine continuous care, including face-to-face health education, paper-based health education materials consistent with the content for the intervention group, and telephone consultations initiated by the patient. Follow-up was conducted at 6 months. Participants' gout knowledge levels and treatment compliance were measured at baseline and the 12th and 24th weeks, and participants' SUA levels were measured at baseline and the 24th week. The intention-to-treat principle and a generalized estimating equation model were used to test the effect of the intervention. Results: Overall, 258 potential participants underwent eligibility assessments, and 120 were recruited and randomized into the intervention (n=60, 50%) and control (n=60, 50%) groups. Of the 120 participants, 93 (77.5%) completed the 24-week study. The 2 groups had no significant differences in sociodemographic or clinical characteristics, and the baseline measurements were comparable (all P>.05). Compared with the control group, the intervention group exhibited a significant improvement in gout knowledge levels over time (β=0.617, 95% CI 0.104-1.129; P=.02 and β=1.300, 95% CI 0.669-1.931; P<.001 at the 12th and 24th weeks, respectively). There was no significant difference in treatment adherence between the 2 groups at the 12th week (β=1.667, 95% CI -3.283 to 6.617; P=.51), while a statistical difference was observed at the 24th week (β=6.287, 95% CI 1.357-11.216; P=.01). At the 24th week, SUA levels in both the intervention and control groups were below baseline, but there was no significant difference in SUA changes between the 2 groups (P=.43). Conclusions: Continuous care based on the mHealth app improved knowledge levels and treatment compliance among patients with gout. We suggest incorporating this intervention modality into standard continuous care for patients with gout.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| 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 | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 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 ».