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Background <p>Sleep provides a&#xa0;stable physiological window that reduces behavioral and environmental confounders, making it a&#xa0;promising source of digital biomarkers. Signals such as electroencephalography (EEG), heart rate variability (HRV), respiration, oxygen saturation, and movement, when recorded longitudinally, may offer clinically meaningful insights beyond traditional sleep medicine, with applications across cardiovascular, neurological, and other indications.</p> Methods <p>A&#xa0;narrative review of the current literature on sleep-derived digital biomarkers was conducted, focusing on conceptual foundations, sensor technologies, validation requirements, and translational applications beyond sleep disorders. Evidence was synthesized across seven major clinical domains, alongside consideration of patient-reported outcomes, regulatory frameworks, and challenges to validation and implementation.</p> Results <p>Nocturnal signals consistently show prognostic and monitoring value across various medical conditions. In cardiovascular medicine, reduced nocturnal HRV and abnormal oxygen saturation trajectories during sleep have been linked to adverse outcomes, while in neurology, diminished slow-wave activity and lower spindle density correlate with neurodegenerative processes. Psychiatric research suggests that alterations in nocturnal HR and HRV reflect mood states and may predict treatment response. In metabolic and maternal–fetal fields, wearable-derived measures of sleep regularity and autonomic profiles are emerging as early predictors of glycemic variability and gestational complications. Although these findings demonstrate potential for broad applicability, the evidence remains heterogeneous, with most data derived from observational or cohort studies. Large-scale prospective trials as well as standardized frameworks for validation and regulatory clearance are currently lacking and represent priorities for the field.</p> Conclusion <p>Sleep-derived digital biomarkers represent an advancing field with high translational potential for various conditions beyond sleep medicine. With the emergence of multimodal at-home sensor technologies, longitudinal monitoring can be implemented, which can generate disease trajectories rather than clinical snapshots. Robust validation and regulatory alignment will be critical to move these biomarkers from exploratory research and into routine clinical use.</p>

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Digital biomarkers generated during sleep: translational potential for conditions beyond sleep medicine

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Marcel Braun,
  • Christoph Schoebel,
  • Sarah Dietz-Terjung

摘要

Background

Sleep provides a stable physiological window that reduces behavioral and environmental confounders, making it a promising source of digital biomarkers. Signals such as electroencephalography (EEG), heart rate variability (HRV), respiration, oxygen saturation, and movement, when recorded longitudinally, may offer clinically meaningful insights beyond traditional sleep medicine, with applications across cardiovascular, neurological, and other indications.

Methods

A narrative review of the current literature on sleep-derived digital biomarkers was conducted, focusing on conceptual foundations, sensor technologies, validation requirements, and translational applications beyond sleep disorders. Evidence was synthesized across seven major clinical domains, alongside consideration of patient-reported outcomes, regulatory frameworks, and challenges to validation and implementation.

Results

Nocturnal signals consistently show prognostic and monitoring value across various medical conditions. In cardiovascular medicine, reduced nocturnal HRV and abnormal oxygen saturation trajectories during sleep have been linked to adverse outcomes, while in neurology, diminished slow-wave activity and lower spindle density correlate with neurodegenerative processes. Psychiatric research suggests that alterations in nocturnal HR and HRV reflect mood states and may predict treatment response. In metabolic and maternal–fetal fields, wearable-derived measures of sleep regularity and autonomic profiles are emerging as early predictors of glycemic variability and gestational complications. Although these findings demonstrate potential for broad applicability, the evidence remains heterogeneous, with most data derived from observational or cohort studies. Large-scale prospective trials as well as standardized frameworks for validation and regulatory clearance are currently lacking and represent priorities for the field.

Conclusion

Sleep-derived digital biomarkers represent an advancing field with high translational potential for various conditions beyond sleep medicine. With the emergence of multimodal at-home sensor technologies, longitudinal monitoring can be implemented, which can generate disease trajectories rather than clinical snapshots. Robust validation and regulatory alignment will be critical to move these biomarkers from exploratory research and into routine clinical use.