Advancing Physical Activity Research Methods Using Real-Time and Adaptive Technology: A Scoping Review of "No-Code" Mobile Health App Research Tools

Mobile health applications (mHealth apps) and wearable devices have transformed physical activity research through the introduction of advanced research methods. These methods enable more accurate, context-sensitive and real-time evaluations, such as ecological momentary assessments (EMAs) and experience sampling methods. Further, these technological advancements facilitate the delivery of personalized physical activity interventions, employing techniques such as microrandomization trials and just-in-time adaptive interventions (JITAIs). However, developing these apps often requires extensive programming skills and resources. To address these challenges, "no-code" mHealth app design tools have recently been developed. This study aims to (a) identify and review current evidence of "no-code" mHealth physical activity research tools and (b) compare the features of these tools that can be utilized to implement innovative methodological approaches (i.e., EMA, JITAI, microrandomization trials). We conducted a scoping review and an internet search to identify current "no-code" mHealth app development tools, followed by a systematic comparison of their features. We found that current "no-code" mHealth app design tools that can be used for conducting EMA, JITAI, and microrandomization methods include Avicenna, Expiwell, LifeData, mEMA, Metricwire, movisensXS, mPath, MyCap, Pathverse, PiLR Health, and Teamscope. While these 11 tools offer diverse features, no single tool encompasses them all, necessitating selection based on study needs. Additional considerations include data privacy and security when selecting no-code app building tools. Overall, this study aims to inform researchers of the commercially available "no-code" mHealth research app development tools and their features.