Smart Devices-Based Longitudinal Assessment of Patterns of Insomnia (SLEEPI): Protocol for Large-Scale, Digital, Longitudinal, and Decentralized Clinical Trials

Smart Devices-Based Longitudinal Assessment of Patterns of Insomnia (SLEEPI): Protocol for Large-Scale, Digital, Longitudinal, and Decentralized Clinical Trials. BACKGROUND: Personalized medicine requires an understanding of health conditions in real-world settings, but traditional clinical tools are often poorly suited for longitudinal tracking due to logistical hurdles, high participant burden, and data quality issues. While consumer wearables offer a scalable alternative, decentralized clinical trials introduce new challenges in patient recruitment, sustained engagement, and data management. Insomnia serves as a suitable use case for this protocol due to its global prevalence, frequent underdiagnosis, and the well-documented discrepancy between subjective patient reports and objective physiological metrics. OBJECTIVE: We present a research protocol for large-scale decentralized clinical trials and report its recruitment performance, applied to a longitudinal study on insomnia, called the SLEEPI (Smart Devices-Based Longitudinal Assessment of Patterns of Insomnia) study, using an established base of commercial devices with limited additional manual intervention. This study aims to develop a machine learning model classifying subjective insomnia presence at the participant level against DSM-5-TR (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision) criteria. METHODS: Two data streams are collected: subjective, clinically anchored patient reports and objective physiological metrics (eg, heart and respiratory rates) captured via unobtrusive wearable and nearable sensors. This study is executed through a digital protocol designed to conduct a large-scale, longitudinal clinical trial. This protocol replaces the manual interventions of site-based studies with digital tools that manage the full participation process. We detail this protocol from initial recruitment through engagement, adherence, study monitoring, and data management to stu…