Work

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

Year: 2026

Type: article

Abstract: 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 study completion or withdrawal. The data collected are intended to train and evaluate a machine learning model for subjective insomnia classification. The recruitment objectives were determined by using simulations to determine the minimal sample size for that purpose and by applying a prospective attrition model to calculate the initial contact volume needed to reach that targeted sample. Results This protocol enables the continuous monitoring of over 10,000 individuals for 26 weeks. Enrollment metrics exceeded projections: initial survey response rates reached 16% (vs 10% projected), the consent rate was 96% (vs 77% projected), and the final inclusion rate was 90% (vs 81% projected). Recruitment opened in February 2025, and the initial wave closed 6 weeks later with 10,823 individuals included. Data collection is projected to end in June 2027. Preliminary analyses have been conducted, and the first results are expected in the summer of 2027. Conclusions This protocol uses preexisting user bases of connected devices to conduct the trial with limited additional manual intervention. Beyond insomnia, the protocol is intended as a model for generating large-scale, real-world clinical datasets, presenting new opportunities for research in other complex areas such as immunology, mental health, and cardiovascular diseases.

Source: JMIR Research Protocols

DOI: 10.2196/92341

Authors: Marie-Ange Stefanos, Martin de Gourcuff, Guillaume de Laboulaye, David Campo, Pierre Alexis Geoffroy, Anna Sigridur Islind