Introduction: Ecological Momentary Assessment (EMA) and Ecological Momentary Intervention (EMI), including Just-In-Time Adaptive Intervention (JITAIs), are increasingly implemented through (native) mobile health (mHealth) applications.Existing reviews emphasize methodological and clinical aspects, while the technical architectures and reusable framework characteristics of native EMA/EMI systems remain fragmented across disciplines.Given the heterogeneity and rapid evolution of these systems, a literature review was conducted to systematically map this emerging field. Objective: This review systematically maps configurable and reusable native mHealth platforms that integrate EMA and EMI/JITAI, focusing on architectural patterns, configurability mechanisms, and the operationalization of adaptive interventions, as well as gaps in technical reporting and evaluation maturity. Methods: Following PRISMA guidelines and a preregistered protocol (OSF), we searched ACM Digital Library, APA PsycINFO, PubMed, IEEE Xplore, Web of Science, and Google Scholar for studies published from 2009 to July 11, 2024.Peer-reviewed publications describing implemented native mobile applications with integrated EMA and EMI functionality were included.Three reviewers screened 3,724 records at title–abstract level, and two reviewers assessed 399 full texts.Data were extracted using a structured form and synthesized descriptively. Results: Forty-one publications describing 34 distinct systems were included.Most platforms were presented as configurable frameworks enabling multi-study deployment and runtime configuration of assessment and intervention logic.EMA functionality was typically implemented through hybrid sampling strategies combining signal-based prompting (40/41), event-contingent sampling (34/41), and passive sensing (24/41).Automated intervention delivery was widely supported (39/41), whereas explicit AI-driven decision logic was rare (4/41), indicating that adaptivity was predominantly implemented through rule-based or configurable mechanisms rather than data-driven models.Evaluation maturity was limited, with feasibility and pilot studies predominating and 16 publications lacking empirical outcomes.Visual reporting was common but varied in completeness, with 27/41 publications including user interface screenshots, 21/41 system architecture figures, 10/41 code architecture visualizations, and all 41/41 presenting primarily tabular data visualizations.Technical reporting was frequently incomplete—particularly for backend architecture, databases, cloud infrastructure, and interoperability—limiting comparability and reproducibility across systemsOverall, the field shows substantial architectural heterogeneity without clear convergence toward standardized design patterns. Conclusions: Configurable native EMA/EMI frameworks and reusable systems represent a diverse and expanding design space characterized by broad configurability and automated intervention capabilities, but still a minority of systems and with uneven evaluation maturity and insufficient architectural transparency.Standardized reporting of system architectures, clearer definitions of adaptivity and AI, and stronger alignment between platform capabilities and empirical validation are needed to improve reproducibility, comparability, and practical reuse.