Management of Hypertension Using a Plant-Based Diet Among Farmers: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Farmers in Indonesia have a high risk for hypertension owing to their lifestyle and working environment. Diet management is a solution to reduce hypertension, and Indonesia has natural resources in the agricultural sector that could help manage hypertension. Optimizing vegetable and fruit intake in a plant-based diet (PBD) could help maintain blood pressure among farmers in Indonesia. OBJECTIVE: This study aims to explore the health problem of hypertension and the characteristics of local food sources to formulate a PBD menu for treating hypertension, as well as assess the prevalence of hypertension, level of acceptability of a PBD, and associated sociodemographic factors. Further, we want to examine the effectiveness of a community-based nursing program for managing hypertension using a PBD. METHODS: We will use the exploratory sequential mixed methods approach. There will be a qualitative study (phase I) in 2022 and a quantitative study (phase II) in 2023. We will analyze data using a thematic framework in phase I. In phase II, the study will involve (1) questionnaire development and validation; (2) examination of the prevalence of hypertension, the level of acceptability of a PBD, and the associated factors; and (3) a randomized controlled trial. We will recruit farmers with hypertension who meet the study criteria. Moreover, in phase II, we will invite expert nurses and nutritionists to assess the face and content validity of the questionnaire. We will use multiple logistic regression models to estimate the associated sociodemographic factors and the level of acceptability of a PBD. Furthermore, a linear generalized estimating equation will be used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different time points for systolic and diastolic blood pressure. RESULTS: A model PBD for hypertension management is expected to be developed. In 2022, we will collect information on hypertension and the characteristics of local food sources for managing hypertension, and will formulate a PBD menu to treat hypertension among farmers. In 2023, we will develop a questionnaire to assess the acceptability of a PBD to manage hypertension among farmers, the prevalence of hypertension, and the sociodemographic factors associated with hypertension among farmers. We will implement a community-based nursing program for managing hypertension using a PBD among farmers. CONCLUSIONS: The PBD model will not be readily available for other agricultural areas since validation of local food variation is required to design the menu. We expect contributions from the local government to implement the intervention as one of the policies in the management of hypertension for farmers in the agricultural plantation areas of Jember. This program may also be implemented in other agricultural countries with similar problems, so that hypertension can be optimally treated among farmers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41146.
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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 | 0.056 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".