mHealth and Digital Technologies for Community-Based Hypertension Screening, Monitoring, Treatment, and Long-Term Management: A Scoping Review and Evidence Gap Map

<title>Abstract</title> <p>Background Hypertension is the leading modifiable risk factor for cardiovascular disease globally, contributing to over 10 million preventable deaths annually. Community-based approaches leveraging mobile health (mHealth) and digital technologies offer a transformative opportunity to close persistent gaps in hypertension detection, monitoring, treatment adherence, and long-term management, particularly in low- and middle-income countries (LMICs). This scoping review and evidence gap map aimed to comprehensively characterise the breadth, depth, and geographic distribution of evidence on mHealth and digital technologies for community-based hypertension care, and to identify critical evidence gaps to guide future research and policy. Methods Guided by the Arksey and O'Malley methodological framework and the PRISMA-ScR guidelines, we conducted programmatic searches across six databases and manual searches across ten additional sources for the period spanning 2010 to 2026. A stratified purposive sampling strategy yielded 120 studies across ten thematic clusters. An evidence gap map (EGM) was constructed across nine geographic regions and ten thematic dimensions. Results Of 5,267 identified records, 120 studies met all inclusion criteria (69 RCTs, 21 systematic reviews/meta-analyses). Sub-Saharan Africa accounted for 40 studies (33.3%). The strongest evidence existed for SMS/app-based adherence interventions (T3–T4) and community health worker-led digital programmes (T5). Major evidence gaps were identified for AI/clinical decision support in LMICs (T6), cost-effectiveness analyses in Sub-Saharan Africa (T9), and MENA-region studies across all themes. Conclusions mHealth and digital technologies demonstrate substantial potential to improve community-based hypertension care globally. However, critical evidence gaps—particularly regarding AI-driven tools, economic evaluations, and health equity in LMICs—must be addressed to enable evidence-based scale-up and sustainable integration into routine health systems.</p>