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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".