ArticleSleep & breathing = Schlaf & Atmung2021
Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography.
Article in Sleep & breathing = Schlaf & Atmung, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07345312 (The Use of the BodySleep Algorithm), which is not on this map. Cited by 7 papers.
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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.
The Use of the BodySleep Algorithm (ResMed) for the Diagnosis of Obstructive Sleep Apnea Syndrome in Children
Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Modified scoring criteria to improve the accuracy of the home sleep apnea test.Sleep & breathing = Schlaf & Atmung · 2026Article
- Detecting arousals and sleep from respiratory inductance plethysmography.Sleep & breathing = Schlaf & Atmung · 2025Article
- Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers.JMIR formative research · 2024Article
- Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm "Nox Body Sleep™".Nature and science of sleep · 2024Article
- Capacitively-Coupled ECG and Respiration for Sleep-Wake Prediction and Risk Detection in Sleep Apnea Patients.Sensors (Basel, Switzerland) · 2021Article
- Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients.Frontiers in digital health · 2021Article
- Sleep Monitoring of Children With Nocturnal Enuresis: A Narrative Review.Frontiers in pediatrics · 2021Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep
methodsPatients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).
resultsWe found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem.
conclusionThe algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.
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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.