Sleep and circadian informatics data harmonization: a workshop report from the Sleep Research Society and Sleep Research Network

Abstract

The increasing availability and complexity of sleep and circadian data are equally exciting and challenging. The field is in constant technological development, generating better high-resolution physiological and molecular data than ever before. Yet, the promise of large-scale studies leveraging millions of patients is limited by suboptimal approaches for data sharing and interoperability. As a result, integration of valuable clinical and basic resources is problematic, preventing knowledge discovery and rapid translation of findings into clinical care. To understand the current data landscape in the sleep and circadian domains, the Sleep Research Society (SRS) and the Sleep Research Network (now a task force of the SRS) organized a workshop on informatics and data harmonization, presented at the World Sleep Congress 2019, in Vancouver, Canada. Experts in translational informatics gathered with sleep research experts to discuss opportunities and challenges in defining strategies for data harmonization. The goal of this workshop was to fuel discussion and foster innovative approaches for data integration and development of informatics infrastructure supporting multi-site collaboration. Key recommendations included collecting and storing findable, accessible, interoperable, and reusable data; identifying existing international cohorts and resources supporting research in sleep and circadian biology; and defining the most relevant sleep data elements and associated metadata that could be supported by early integration initiatives. This report introduces foundational concepts with the goal of facilitating engagement between the sleep/circadian and informatics communities and is a call to action for the implementation and adoption of data harmonization strategies in this domain.

Data integration and analysis for circadian medicine

“…On the data level, there is heterogeneity in how PROs are measured through various types of questionnaire instruments and are evaluated by the research community as well as heterogeneity in the data representations of questionnaire software and their licensing models. 3 Recent standardization efforts, such as the inclusion of questionnaire structures into the HL7 FHIR model 45 and the representation of items from the Patient Reported Outcomes Measurement Information System (PROMIS) 48 as Logical Observation Identifiers Names and Codes (LOINC) 49 provide an important step forward. Still, the joint use and common analysis of patient-reported data usually require researchers to align regarding the instruments used upfront.…”

“…A particular challenge in regard to the integration of sensor data is the need to calibrate scales of measurements from different devices, analogous to what needs to be done to make laboratory values comparable across institutions. The challenge is aggravated by the rapid development of sensor technology, algorithms and methods, which need to be considered when integrating data from different devices, studies, and populations 3 . Also, the standardization of the collected data can be an issue 47 .…”

Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers

“…The development of common frameworks for storing and processing BioMeTs data will be a key in facilitating "big data" analyses of light dosimetry, and other types of data collected via digital sensors. [103][104][105] How to handle non-wear periods and quality control?. The assessment of rest-activity cycles with actigraphy is often paired with the use of a sleep diary, [106][107][108][109][110][111] sometimes indicating non-wear periods which can then be masked in later analysis steps.…”

“…The development of common frameworks for storing and processing BioMeTs data will be a key in facilitating “big data” analyses of light dosimetry, and other types of data collected via digital sensors. 103 – 105 …”

Is exposure to chemical pollutants associated with sleep outcomes? A systematic review

“…Actigraphy, which is based on detecting rest and activity patterns, can also be employed to derive measures of sleep-wake behavior and may be less onerous, more cost-effective, and allow for longer measurement than PSG. While the myriad specifications of these devices should be thoroughly considered in study design [379], in-home PSG and actigraphy both have the potential to advance our understanding of environmental contributors to sleep health, particularly among low-income populations that may face barriers to undergoing overnight in-lab sleep studies.…”