Rationale, Design, and Intervention Development of a Mobile Health–Led Primary Care Program for Management of Type 2 Diabetes in Rural Thailand: Protocol for a SMARThealth Diabetes Study
Notice bibliographique
Résumé
Background Noncommunicable diseases (NCDs), particularly diabetes and chronic kidney diseases, pose a significant health burden in Thailand, especially among socioeconomically disadvantaged populations. The existing primary health care system faces challenges in providing optimal care for NCDs due to inadequate primary care workforce. The SMARThealth program offers a technology-based solution to enhance NCD management through task-sharing among nonphysician health care workers. Objective This study aims to adapt and implement the SMARThealth Diabetes program in rural Thailand to improve diabetes management. The main objectives are to (1) adapt, validate, and integrate the SMARThealth Diabetes program for improving the management of type 2 diabetes mellitus at the primary health care level; and (2) to determine the feasibility and acceptability of the SMARThealth Diabetes program in rural communities of Thailand. Methods A pragmatic, type 2 hybrid effectiveness or implementation, parallel-group cluster randomized controlled trial of 12 months duration and involving 51 subdistrict health offices in rural communities of Kamphaeng Phet province, Thailand, will be conducted. The intervention arm will receive the SMARThealth Diabetes program, including workforce restructuring, clinical decision support system, and continuous performance monitoring, while the control arm will continue with usual practice. Data will be collected using the SMARThealth platform and will be stored on a server in Thailand. The primary outcome measure will be the change in mean hemoglobin A1c (HbA1c) measured at randomization and 12 months from randomization between the intervention and control clusters. Secondary outcomes will include the difference in change in albuminuria status, estimated glomerular filtration rate, systolic blood pressure, and low-density lipoprotein cholesterol level. The analysis for change in HbA1c between baseline and end of study will be performed using linear mixed models. Any imbalances between the 2 arms will be addressed by sensitivity analyses. Additionally, a mixed methods process evaluation will be conducted to assess the implementation process, that will include in-depth interviews and focus group discussions, in addition to the quantitative data collected during the implementation process. The qualitative data will be thematically analyzed to explore factors that promote or inhibit the implementation and maintenance of the program. Results The data collection commenced in November 2022, and the results will be ready for publication by the first quarter of 2025. Effectiveness of the intervention package will be assessed by change in mean HbA1c measures, and detailed feasibility, barriers, and enablers for the implementation of the intervention will be documented through a detailed process evaluation. Conclusions The study protocol outlines a novel approach to enhancing diabetes management in rural Thailand through digital technology–based interventions that will facilitate task-sharing among health care workers. This can help inform future strategies for improving NCD care in low-resource settings globally. Trial Registration Thai Clinical Trials Registry TCTR20200322006; https://www.thaiclinicaltrials.org/show/TCTR20200322006 International Registered Report Identifier (IRRID) DERR1-10.2196/59266
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 | 0,042 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,008 |
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 ».