O041 Exploring the Landscape of Sleep Data Resources: A Literature Survey

INTRODUCTION: The complex physiologic interdependencies of sleep make sleep medicine a data-rich discipline. Emerging big data approaches hold promise for novel discoveries, however, scholars have suggested these advances may be hindered by dataset limitations. We conducted a review of the field to assess the drawbacks of available sleep data resources. METHODS: A systematic literature survey on PubMed. A keyword search identified studies based on sleep datasets which combined polysomnography records and contextual patient health information. For eligible studies, we extracted information on data provenance (existing repositories versus self-collected), data accessibility for onward research, dataset size, cohort characteristics, data/metadata inclusions, and use of systematic nomenclatures. RESULTS: Only approximately 40% of identified studies used public datasets, suggesting that these repositories only partially met current research needs. Alternative, self-collected cohorts were typically small, with just 20% having populations greater than 1000 subjects. Most were closed-source or lacking data access indications, which may impact the strength and reproducibility of study results. Large-size resources focussed predominantly on sleep apnoea, limiting the reach of research efforts targeting less prevalent conditions. Datasets featuring comprehensive medical histories and long-term follow-up information were rare. Where available, this information generally lacked systematisation of nomenclatures and metadata, impairing the ability of users to extract meaning from data entities and their interrelationships. DISCUSSION: Limitations in current sleep data practices were substantiated by our review. There is a clear need to develop larger, diverse, multimodal and clinically representative sleep data collections that can be more easily shared, and analysed using emergent data-driven discovery tools.