Self-Management Combined With Digital Health Interventions to Improve Dietary Behavior, Exercise Behavior, Stress Management Behavior, and Blood Pressure Among Thais With Uncontrolled Hypertension: Protocol for an Explanatory Sequential Study
Notice bibliographique
Résumé
Background: Uncontrolled hypertension causes substantial morbidity and mortality as well as rising health care costs. Adherence to self-management is critical for minimizing the risk of hypertensive complications. Traditional self-management is hindered by delayed management and insufficient support. Digital health interventions offer a feasible solution for closing these gaps and enhancing hypertension self-management. Little is known about patients' perspectives and experiences concerning how digital health interventions influence their self-management. Objective: This study aims to determine the effectiveness of a self-management intervention combined with digital health interventions on dietary behavior, exercise behavior, stress management behavior, and blood pressure among Thais with uncontrolled hypertension. The study explored and compared experiences and perceptions among participants with varying levels of blood pressure control. Methods: This study uses an explanatory sequential design performed in 2 phases comprising (1) a quasi-experimental design with 2 groups using repeated measures to determine the effects of self-management combined with digital health intervention and (2) an in-depth interview approach to explore the perceptions and experiences of 24 participants regarding the combination of self-management and digital health interventions after the intervention. In phase 1, participants were allocated by lottery to either the intervention group, which underwent an 8-week self-management intervention combined with digital health interventions, or the control group. The Dietary Approaches to Stop Hypertension Questionnaire, the Exercise Behavior Questionnaire, the Brief COPE inventory (Thai Version), and an automatic blood pressure measurement were used for data collection at baseline and the 4th and 8th weeks. In phase 2, semistructured interviews were used to conduct in-depth interviews. The analysis will take into account the effects of the interventions on dietary, exercise, and stress management behaviors, as well as blood pressure, using generalized estimating equations and linear mixed-effects modeling. We will perform the method by Colaizzi for the qualitative portion of the analysis. Results: Funded in December 2024, this study recruited 86 patients with uncontrolled hypertension at the Siriraj Primary Care Unit, Thailand. This study received ethical approval on May 31, 2025, and participant recruitment began in August 2025. In phase 1, this study began recruiting participants in August 2025, with data collection occurring from August through the first half of November 2025. Phase 2 was completed at the end of November 2025. The data analysis is expected to be completed by December 2025. The expected date for the results to be submitted for publication is March 2026. Conclusions: This study has the potential to address the gap between traditional self-management and digital health interventions for improving self-management behaviors and reducing blood pressure. The findings may offer practical guidance for nurses and other health care providers for managing uncontrolled hypertension in Thailand and contribute valuable insights for shaping future health care policies.
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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 | 0,021 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,005 |
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 ».