Observational studySleep & breathing = Schlaf & Atmung2025
Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.
Observational study in Sleep & breathing = Schlaf & Atmung, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03803098 (Evaluation of the Metrological Reliability of Connected Objects Available to the Public in the Measurement of Medical Physiological Parameters), which is not on this map. Cited by 3 papers.
What it found
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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.
Evaluation of the Metrological Reliability of Connected Objects Available to the Public in the Measurement of Medical Physiological Parameters
Who cites it
3 citing papers in PubMed.
- Clinical correlates of sleep profiles in dementia with Lewy bodies: a latent class approach.Alzheimer's research & therapy · 2026Article
- Article
- Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeobstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal
methodsmonocentric proof-of-concept study with a first manual scoring step, then automatic detection of respiratory events from recorded signals using a sequential deep-learning model (version 0.1 of Apneal
results46 patients (women 34%, BMI 28.7 kg/m²) were included. Sensitivity of manual scoring was 0.91 (95% CI [0.8-1]) for IAH > 15 and 0.85 [0.67-1] for AHI > 30, and positive predictive values (PPV) 0.89 [0.76-0.97] and 0.94 [0.8-1]. We obtained an AUC-ROC of 0.85 (95% CI [0.69-0.96]) and AUC-PR of 0.94 (95% CI [0.84-0.99]) for the identification of AHI > 15, and AUC-ROC of 0.95 [0.860.99] and AUC-PR of 0.93 [0.81-0.99] for AHI > 30. The ICC between the AHI estimated manually, and from the PSG is 0.89 (p = 6.7 × 10
conclusionManual scoring of smartphone-based signals is possible and accurate compared to PSG-based scorings. Automatic scoring method based on a deep learning model provides promising results.
trial registrationNCT03803098.
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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.