ReviewCommunications medicine2025
Revealing sleep and pain reciprocity with wearables and machine learning.
Review in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Rotator cuff injury disrupts sleep architecture in mice and exhibits a light-dark phase-dependent pattern.BMC musculoskeletal disorders · 2026Article
- Rethinking Pain Assessment: Subjective Scales, Biomarkers, and Multimodal Integration.Journal of pain research · 2026Review
- Mixed pain: clinical practice recommendations.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.