ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Predicting Poor Sleep Quality in Fibromyalgia with Wrist Sensors.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 3 citations in OpenAlex.
- Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia - overlap, differences, and emerging insights.Journal of translational medicine · 2026Review
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Fibromyalgia is a musculoskeletal disorder characterized by chronic, widespread muscle pain. This condition is associated with disturbed sleep, which has a direct impact on patient quality of life. Patient-reported outcomes are frequently used to assess sleep quality, but show modest correlations with objective measures of sleep, such as polysomnography. Working towards our goal of an automated ambulatory system of assessing sleep quality, we use features from blood volume pulse (BVP) and electrodermal activity (EDA) collected with a wearable device during sleep. We compare these measurements between individuals with fibromyalgia who experienced poor sleep and individuals in a control group who experienced refreshing sleep. By applying Learning Using Concave and Convex Kernels (LUCCK) and Support Vector Machines (SVM), we achieve mean Area Under the Receiver Operating Characteristic Curve (AUC) of 0.6573 and 0.6526, respectively, by using BVP data for classifying individuals to the two groups.
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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.