Design and Implementation of mHealth-Based Early Warning Systems for Heart Disease: A Scoping Review - Ntwari, Richard; Wesonga, Bob; Vallence Ngabo Maniragaba; Engwau, Tonny; Kabarungi, Moreen (AI Society & STMIK Indonesia, 2025)
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, with sub-Saharan Africa including Uganda experiencing a growing burden due to limited access to early detection and specialist care. In response, mobile health (mHealth) technologies have emerged as promising tools to support early cardiac risk detection and intervention, especially in low-resource settings. Following Arksey and O’Malley’s scoping review framework with enhancements a systematic search was conducted across five databases from January 2000 to April 2025. Studies were screened and selected based on predefined inclusion criteria, and data were charted across design elements, outcomes, and implementation contexts. Thematic analysis was applied to synthesize findings.Twenty-seven studies met the inclusion criteria. mHealth-based EWS frequently incorporate wearable sensors, mobile apps, and AI-driven analytics for real-time monitoring and risk prediction. While user-centered design enhances acceptability, clinical efficacy evidence is mixed and scalability remains under-explored. AI/ML integration shows promise in improving prediction and personalization, but challenges persist around interoperability and health system integration.mHealth-based early warning systems hold significant potential to address cardiovascular care gaps in resource-limited settings. To maximize impact, future interventions should prioritize clinical validation, adaptive AI integration, and sustainable scale-up models tailored to local infrastructure and user needs. These insights are critical for guiding policymakers, developers, and researchers toward more effective digital health strategies for CVD prevention.