Abstract
Mobile health (mHealth) applications are increasingly integral to healthcare delivery, yet traditional randomized trials face practical challenges in evaluating these dynamic tools. Real-world data (RWD), collected during routine app use, offers a complementary pathway to real-world evidence (RWE) that may reflect how mHealth applications are used in everyday settings. We conducted a scoping review to map how naturally emerging RWD are currently used in peer-reviewed studies to evaluate patient-facing mHealth applications. We systematically searched PubMed, Scopus, and Web of Science (January 2007-November 2024) and extracted data on application type, RWD characteristics and study design aspects. Study-level, design-centred evidence levels for RWD-based effectiveness evaluations were assigned using a combined Oxford Centre for Evidence-Based Medicine and FDA RWE framework. Seventy-two studies evaluating 61 unique mHealth applications were included. Most studies focused on mental health or metabolic conditions and relied predominantly on data actively reported by users, often via in-app surveys, with comparatively limited use of device-generated data or integration with system-generated data such as clinical or claims data. Single-group pre-post approaches were most frequently observed, while only a minority employed comparative observational, quasi-experimental, or randomized designs. These findings illustrate current patterns in the use of RWD for mHealth evaluation and highlight both opportunities and constraints in longitudinal and comparative assessments of mHealth applications in real-world contexts.