Evaluating the Feasibility and Acceptability of a Mobile Health–Based Female Community Health Volunteer Program for Hypertension Control in Rural Nepal: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hypertension is a major modifiable risk factor for cardiovascular disease, the world's leading cause of death. The prevalence of hypertension is disproportionately higher in South Asian countries than in other regions of the world. Screening for hypertension in primary care settings remains a challenge in many South Asian countries, including Nepal. Nepal is located in the Himalayan Mountains region, posing significant geographical challenges for its rural citizens to access primary health care and service delivery. This barrier increases the costs and inconvenience for rural Nepalis to access hypertension screening and treatment. As a result, the prevalence of hypertension in Nepal tripled in the last 25 years to 22.4%-38.6%. Nepal's Ministry of Health and Population relies on female community health volunteers to link health centers and communities to provide basic health services. Over 50,000 of these volunteers in Nepal have received basic health care training and are assigned to take care of maternal and child health. Due to limited health care resources, adopting new methods to control hypertension is an urgent need in Nepal. Several recent studies in Nepal have recommended extending the role of female community health volunteers to include hypertension management through blood pressure monitoring and home-based education. OBJECTIVE: The goal of this study was to assess if a mobile health-based female community health volunteer approach of combining the traditional community health volunteer program with digital technologies would be feasible and acceptable in rural Nepal. METHODS: In this study, we recruited 17 female community health volunteers and extended their role from maternal and child health to hypertension management through screening blood pressures. RESULTS: All 17 female community health volunteers successfully measured 1113 rural Nepalis' blood pressures, identified 169 hypertensive patients, and collected health behaviors data of the 169 hypertensive patients. Among the 169 patients, 70% of them had a mobile phone, and 92% were interested in receiving health-related information via a mobile phone. Among those who were interested in receiving information via a mobile phone, 84% preferred voice calls, and 7% and 1% preferred texting and apps, respectively. CONCLUSIONS: Results from this study indicate that a digital health intervention that leverages feature-phones combined with female community health volunteers may be an acceptable and pragmatic way to implement an evidence-based program to reduce hypertension in rural Nepal.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".