JMIR Publications

ABSTRACT

Background:

Personalized medicine requires understanding 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 (DCTs) 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. By providing a large-scale dataset, this protocol addresses these limitations to better reflect the real-world conditions of patient monitoring than using traditional assessment methods.

Objective:

We present and validate a novel research protocol for large-scale DCTs, applied to a longitudinal study on insomnia called the SLEEPI study, by leveraging an established commercial ecosystem with limited additional manual intervention.

Methods:

Two data streams are collected: subjective, clinically-anchored patient reports, and objective physiological metrics (e.g., heart and respiratory rates) captured via unobtrusive wearable and nearable sensors. This study is executed through a highly automated, end-to-end digital protocol designed to conduct a large-scale, longitudinal clinical trial. This protocol deliberately reduces the resource-intensive, manual interventions of traditional studies with a suite of digital tools and strategies that manage the entire participant journey. We detail this highly automated framework, from initial recruitment to engagement, adherence, study monitoring, data management, and withdrawal. The data collected are intended to train a machine learning model. 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 enabled the continuous monitoring of over 10,000 individuals for 26 weeks, creating the largest and most diverse longitudinal insomnia dataset to date. Enrolment metrics significantly 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), therefore validating the recruitment efficiency and scalability of this protocol.

Conclusions:

This framework reduces operational demands by leveraging pre-existing user bases of connected devices. Beyond insomnia, the protocol provides a blueprint 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. Clinical Trial: ClinicalTrials.gov NCT07430878; https://clinicaltrials.gov/study/NCT07430878