Observational studyBMC public health2025
Efficacy of using a digital health intervention model using community health workers for primary health services in Bangladesh: a repeated cross-sectional observational study.
Observational study in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integration of Cardiovascular Disease Services within Primary Health Care Systems: Insights from South Asia.Current atherosclerosis reports · 2026Review
- "Beyond the Crisis: Forging a Climate-Resilient Health System for Bangladesh".Health science reports · 2026Article
- Article
- Smart Community Hypertension Management: A Narrative Review of Promises and Challenges.Journal of multidisciplinary healthcare · 2026Review
- The coupling coordination between digital village construction and rural healthcare service efficiency in China: dynamic evolution, spatial difference and driving factors.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to evaluate the effectiveness of a digital health intervention model by observing people's health conditions in a rural area of Bangladesh. Through a repeated cross-sectional design, health outcomes were assessed at six-month intervals over 18 months.
methodsThe data presented in this paper are obtained from the model's pilot implementation in one catchment area in Bangladesh. The model involved community health workers (CHWs) using a smart health kit and Artificial Intelligence(AI)-based mobile application to provide monthly doorstep health education, screenings, risk assessments, and digital referrals. Socio-demographic and health measurement data are presented as proportions with 95% confidence intervals (CIs). Multivariate logistic regression was used to analyze the association between diseases and their respective risk factors. We compared health vitals across three consecutive periods to determine the effectiveness of the model in improving health outcomes over time.
resultsThe model served 32,581 people from 7,090 households during this operation. We found that 21.76% of the served population were overweight, 8.18% had prehypertension, 16.45% had high blood glucose, and 11% children were malnourished. From the analysis of risk factors, we found that people aged
conclusionThis study highlights the feasibility and effectiveness of digital health interventions in enhancing primary healthcare in rural settings. Integration of AI-driven decision support with CHW-led health education and screenings can improve early detection, intervention and management of non-communicable diseases (NCDs). Scaling up this model through public-private partnerships offers a cost-effective approach to bridging healthcare gaps and promoting universal health coverage.
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