These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: An emerging technology for the identification and characterization of postural-dependent obstructive sleep apnea.
    Author: Tate A, Walsh J, Kurup V, Shenoy B, Mann D, Freakley C, Eastwood P, Terrill P.
    Journal: J Clin Sleep Med; 2020 Feb 15; 16(2):309-318. PubMed ID: 31992410.
    Abstract:
    STUDY OBJECTIVES: Body posture has a significant impact on the presence and severity of obstructive sleep apnea (OSA). The majority of polysomnography (PSG) systems have the capacity to categorize body (torso) posture as supine, left-lateral, right-lateral or prone, each within a 90-degree range. However, such broad categorization may limit the identification of subtle relationships between posture and OSA severity. The aim of this study was to quantify sleeping posture as a continuous variable; and to develop an intuitive tool for visualizing the relationship between body posture and OSA severity. METHODS: A customized triaxial accelerometer-based posture sensor which quantifies torso posture as a continuous variable was developed. 38 participants attending the sleep laboratory for suspected OSA were recruited. Each participant underwent a diagnostic PSG with an additional customized posture sensor securely attached to the sternum. Individual data were presented using a novel circular histogram-based visualization which displays sleeping position and position-specific OSA severity. RESULTS: Acceptable measurements were obtained in 21 participants. The mean ± standard deviation percentage of total sleep time spent within ± 15 degrees of the center of supine, left-lateral, right-lateral and prone was 59.7 ± 26.0%. A further 40.3 ± 26.0% of sleep time was spent in intermediate positions outside these traditional categorizations. The novel visualization revealed a wide variety of positional OSA phenotypes. CONCLUSIONS: Quantification of torso posture as a continuous variable and analysis of these data using a novel visualization enables the identification of subtle relationships between body posture and OSA severity that are not apparent using standard clinical sensors and summary statistics.
    [Abstract] [Full Text] [Related] [New Search]