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Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness.

Authors
  • Muza, Stephen R1
  • 1 Strategic Science Management Office, US Army Research Institute of Environmental Medicine , Natick, Massachusetts.
Type
Published Article
Journal
Journal of Applied Physiology
Publisher
American Physiological Society
Publication Date
Mar 01, 2018
Volume
124
Issue
3
Pages
557–563
Identifiers
DOI: 10.1152/japplphysiol.00367.2017
PMID: 29074708
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.

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