Revealing sleep and pain reciprocity with wearables and machine learning.

Abstract

Sleep disturbance and chronic pain share a bidirectional relationship with poor sleep exacerbating pain and pain disrupting sleep. Despite the substantial burden of sleep disturbance and pain, current treatments fail to address their interplay effectively, largely due to the lack of longitudinal data capturing their complex dynamics. Traditional sleep measurement methods that could be used to quantitate daily changes in sleep, such as polysomnography, are costly and unsuitable for large-scale studies in chronic pain populations. New wearable polysomnography devices combined with machine learning algorithms offer a scalable solution, enabling comprehensive, longitudinal analyses of sleep-pain dynamics. In this Perspective, we highlight how these technologies can overcome current limitations in sleep assessment to uncover mechanisms linking sleep and pain. These tools could transform our understanding of the sleep and pain relationship and guide the development of personalized, data-driven treatments.

View details for DOI 10.1038/s43856-025-00886-8

View details for PubMedID 40335627

View details for PubMedCentralID 7527024