Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

doi:10.1093/sleep/zsaf035

. 2025 Jun 13;48(6):zsaf035.

doi: 10.1093/sleep/zsaf035.

Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

Lawrence Baizer¹, Regina Bures², Girish Nadkarni³, Carolyn Reyes-Guzman⁴, Sweta Ladwa², Brian Cade⁵, Michael Brandon Westover⁶, Jeffrey Durmer⁷, Massimiliano de Zambotti⁸, Manisha Desai⁹, Ankit Parekh¹⁰, Bing Si¹¹, Julio Fernandez-Mendoza¹², Kelton Minor¹³, Diego R Mazzotti¹⁴, Soomi Lee¹⁵, Dina Katabi¹⁶, Orsolya Kiss¹⁷, Adam P Spira¹⁸, Jonna Morris¹⁹, Azizi Seixas²⁰, Marianthi-Anna Kioumourtzoglou¹³, John F P Bridges²¹, Marishka Brown¹, Lauren Hale²², Shaun Purcell⁵

Affiliations

¹ National Center on Sleep Disorders Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
² National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
³ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴ National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁵ Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁷ Sleep & Circadian Science, Absolute Rest, Denver, CO, USA.
⁸ Science, Ouraring Inc. San Francisco, CA, USA.
⁹ Quantitative Sciences Unit, Stanford University Medical School, Stanford, CA, USA.
¹⁰ Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai New York, NY, USA.
¹¹ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
¹² Penn State College of Medicine Sleep Research and Treatment Center, Pennsylvania State University College of Medicine, Hershey, PA, USA.
¹³ Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
¹⁴ Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, USA.
¹⁵ Department of Human Development and Family Studies, Center for Healthy Aging, Pennsylvania State University, University Park, PA, USA.
¹⁶ MIT Center for Wireless Networks and Mobile Computing, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁷ Center for Health Sciences, SRI International, Menlo Park, CA, USA.
¹⁸ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹⁹ School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
²⁰ Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, FL, USA.
²¹ College of Medicine, Ohio State University, Columbus, OH, USA.
²² Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.

PMID: 39945146
PMCID: PMC12163129 (available on 2026-02-13)
DOI: 10.1093/sleep/zsaf035

Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

Lawrence Baizer et al. Sleep. 2025.

. 2025 Jun 13;48(6):zsaf035.

doi: 10.1093/sleep/zsaf035.

Authors

Affiliations

¹ National Center on Sleep Disorders Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
² National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
³ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴ National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁵ Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
⁷ Sleep & Circadian Science, Absolute Rest, Denver, CO, USA.
⁸ Science, Ouraring Inc. San Francisco, CA, USA.
⁹ Quantitative Sciences Unit, Stanford University Medical School, Stanford, CA, USA.
¹⁰ Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai New York, NY, USA.
¹¹ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
¹² Penn State College of Medicine Sleep Research and Treatment Center, Pennsylvania State University College of Medicine, Hershey, PA, USA.
¹³ Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
¹⁴ Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, USA.
¹⁵ Department of Human Development and Family Studies, Center for Healthy Aging, Pennsylvania State University, University Park, PA, USA.
¹⁶ MIT Center for Wireless Networks and Mobile Computing, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁷ Center for Health Sciences, SRI International, Menlo Park, CA, USA.
¹⁸ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹⁹ School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
²⁰ Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, FL, USA.
²¹ College of Medicine, Ohio State University, Columbus, OH, USA.
²² Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.

PMID: 39945146
PMCID: PMC12163129 (available on 2026-02-13)
DOI: 10.1093/sleep/zsaf035

Abstract

The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-term health consequences of sleep disorders and disparities of these health problems in specific populations.

Keywords: artificial intelligence; data science; obstructive sleep apnea; remote monitoring; sleep.

Published by Oxford University Press on behalf of Sleep Research Society (SRS) 2025.

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References

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[1] Baughman DJ. Technology in medicine: optimizing electronic health records. FP Essent. 2024;537:7–13. - PubMed

[2] Baughman DJ. Technology in medicine: optimizing electronic health records. FP Essent. 2024;537:7–13. - PubMed

[3] Ramesh J, Keeran N, Sagahyroon A, Aloul F.. Towards validating the effectiveness of obstructive sleep apnea classification from electronic health records using machine learning. Healthcare (Basel). 2021;9(11):1450. doi: https://doi.org/ 10.3390/healthcare9111450 - DOI - PMC - PubMed

[4] Ramesh J, Keeran N, Sagahyroon A, Aloul F.. Towards validating the effectiveness of obstructive sleep apnea classification from electronic health records using machine learning. Healthcare (Basel). 2021;9(11):1450. doi: https://doi.org/ 10.3390/healthcare9111450 - DOI - PMC - PubMed

[5] Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI.. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521–533. doi: https://doi.org/ 10.5664/jcsm.10930 - DOI - PMC - PubMed

[6] Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI.. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521–533. doi: https://doi.org/ 10.5664/jcsm.10930 - DOI - PMC - PubMed

[7] Zheng NS, Annis J, Master H, et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the all of us research program. Nat Med. 2024;30:2648. - PMC - PubMed

[8] Zheng NS, Annis J, Master H, et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the all of us research program. Nat Med. 2024;30:2648. - PMC - PubMed

[9] Trujillo R, Zhang E, Templeton JM, Poellabauer C.. Predicting long-term sleep deprivation using wearable sensors and health surveys. Comput Biol Med. 2024;179:108749. doi: https://doi.org/ 10.1016/j.compbiomed.2024.108749 - DOI - PubMed

[10] Trujillo R, Zhang E, Templeton JM, Poellabauer C.. Predicting long-term sleep deprivation using wearable sensors and health surveys. Comput Biol Med. 2024;179:108749. doi: https://doi.org/ 10.1016/j.compbiomed.2024.108749 - DOI - PubMed

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Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

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Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

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Abstract

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