Literature DB >> 16503424

Measurement of exercise ventilation by a portable respiratory inductive plethysmograph.

Jonathan D Witt1, Jason R K O Fisher, Jordan A Guenette, Krystie A Cheong, Brock J Wilson, A William Sheel.   

Abstract

The purpose of this study was to evaluate the accuracy of a respiratory inductive plethysmograph (RIP) designed for ambulatory data collection during exercise by comparison to a pneumotachograph. Healthy young males (n=10) wore an elastic body garment embedded with inductance sensors encircling the rib cage and abdomen. Breathing frequency (f(R)), tidal volume (V(T)) and minute ventilation (V (I)) were monitored during 5min of rest, slow walking (3.7kmh(-1)), fast walking (6.1kmh(-1)) and slow running (8.9kmh(-1)) followed by an incremental treadmill test to exhaustion (14.4+/-2.7kmh(-1)). Mean f(R), V(T) and V (I) values were not statistically different between the two methods (P>0.05). Within each of the subjects at rest and different exercise intensities, the average coefficient of determination was high for f(R), V(T) and V (I) (R(2)=0.9233, 0.8743 and 0.9652, respectively) and the mean bias values were low (-0.102+/-2.91, 0.033+/-0.207 and -0.715+/-8.362, respectively). These data suggest that the ambulatory RIP provides reasonable estimates of ventilation during rest and exercise.

Mesh:

Year:  2006        PMID: 16503424     DOI: 10.1016/j.resp.2006.01.010

Source DB:  PubMed          Journal:  Respir Physiol Neurobiol        ISSN: 1569-9048            Impact factor:   1.931


  22 in total

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2.  Bioharness(™) Multivariable Monitoring Device: Part. II: Reliability.

Authors:  James A Johnstone; Paul A Ford; Gerwyn Hughes; Tim Watson; Andrew T Garrett
Journal:  J Sports Sci Med       Date:  2012-09-01       Impact factor: 2.988

3.  Field based reliability and validity of the bioharness™ multivariable monitoring device.

Authors:  James A Johnstone; Paul A Ford; Gerwyn Hughes; Tim Watson; Andrew C S Mitchell; Andrew T Garrett
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5.  Comparison of artificial neural network (ANN) and partial least squares (PLS) regression models for predicting respiratory ventilation: an exploratory study.

Authors:  Ming-I Brandon Lin; William A Groves; Andris Freivalds; Eun Gyung Lee; Martin Harper
Journal:  Eur J Appl Physiol       Date:  2011-08-23       Impact factor: 3.078

6.  Assessment of an alternative calibration technique to record breathing pattern and its variability with respiratory inductive plethysmography.

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Journal:  J Clin Monit Comput       Date:  2016-06-11       Impact factor: 2.502

7.  Simple to complex modeling of breathing volume using a motion sensor.

Authors:  Dinesh John; John Staudenmayer; Patty Freedson
Journal:  Sci Total Environ       Date:  2013-03-27       Impact factor: 7.963

8.  Design and evaluation of a ubiquitous chest-worn cardiopulmonary monitoring system for healthcare application: a pilot study.

Authors:  Jiewen Zheng; Congying Ha; Zhengbo Zhang
Journal:  Med Biol Eng Comput       Date:  2016-05-13       Impact factor: 2.602

9.  Tissue artifact removal from respiratory signals based on empirical mode decomposition.

Authors:  Shaopeng Liu; Robert X Gao; Dinesh John; John Staudenmayer; Patty Freedson
Journal:  Ann Biomed Eng       Date:  2013-01-17       Impact factor: 3.934

10.  Measurement accuracy of heart rate and respiratory rate during graded exercise and sustained exercise in the heat using the Zephyr BioHarness.

Authors:  J-H Kim; R Roberge; J B Powell; A B Shafer; W Jon Williams
Journal:  Int J Sports Med       Date:  2012-11-22       Impact factor: 3.118

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