Trial reportSleep2024
Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.
Trial report in Sleep, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04254484 (SIESTA), which is not on this map. Cited by 8 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.
SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation
Who cites it
8 citing papers in PubMed.
- Comparison of Sleep Architecture in Individuals Aged 65 to 80 Years With and Without Mild Cognitive Impairment Using Multi-Channel Sleep Monitoring.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Zero burden multi night monitoring with AI enabled technology reduces obstructive sleep apnea misdiagnosis.NPJ digital medicine · 2026Article
- Detection of apnea and hypopnea events using a wireless Abdomen-Worn sensor with SpO₂ integration.Sleep & breathing = Schlaf & Atmung · 2026Article
- Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.Stroke · 2026Review
- Detecting sleep stages after stroke using wearable sensors: machine learning design and challenges.BMJ digital health & AI · 2026Article
- ACCESS-OSA: a randomized controlled trial for accurate, accessible, and cost-effective screening solutions for obstructive sleep apnoea: study protocol.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Revolutionizing Stroke Management with Wearable Technology: A Narrative Review of Clinical Applications, Opportunities, Challenges, and the Future Path Towards Equitable Implementation.Annals of Indian Academy of Neurology · 2025Article
- Assessing sleep metrics in stroke survivors: a comparison between objective and subjective measures.Sleep & breathing = Schlaf & Atmung · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
STUDY
objectivesTo evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF).
methodsA total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least 1 night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the apnea-hypopnea index (AHI ≥ 5, AHI ≥ 15).
resultsSeventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only 48 participants (63%) could be successfully assessed for obstructive sleep apnea by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger-temperature features to detect moderate-severe sleep apnea (AHI ≥ 15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions.
conclusionsThis research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes. CLINICAL TRIAL: SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation, https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1, NCT04254484.
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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.