Literature DB >> 24743390

A review of the evidence for the use of ventilation as a surrogate measure of energy expenditure.

Steven Gastinger1, Alan Donnelly2, Rémy Dumond3, Jacques Prioux4.   

Abstract

Precise measurement of sedentary behavior and physical activity is necessary to characterize the dose-response relationship between these variables and health outcomes. The most frequently used methods employ portable devices to measure mechanical or physiological parameters (eg, pedometers, heart rate monitors, accelerometers). There is considerable variability in the accuracy of total energy expenditure (TEE) estimates from these devices. This review examines the potential of measurement of ventilation (VE) to provide an estimate of free-living TEE. The existence of a linear relationship between VE and energy expenditure (EE) was demonstrated in the mid-20th century. However, few studies have investigated this parameter as an estimate of EE due to the cumbersome equipment required to measure VE. Portable systems that measure VE without the use of a mouthpiece have existed for about 20 years (respiratory inductive plethysmography). However, these devices are adapted for clinical monitoring and are too cumbersome to be used in conditions of daily life. Technological innovations of recent years (small electromagnetic coils glued on the chest/back) suggest that VE could be estimated from variations in rib cage and abdominal distances. This method of TEE estimation is based on the development of individual/group calibration curves to predict the relationship between ventilation and oxygen consumption. The new method provides a reasonably accurate estimate of TEE in different free-living conditions such as sitting, standing, and walking. Further work is required to integrate these electromagnetic coils into a jacket or T-shirt to create a wearable device suitable for long-term use in free-living conditions.
© 2014 American Society for Parenteral and Enteral Nutrition.

Entities:  

Keywords:  electromagnetic coils; physical activity; respiratory inductive plethysmography; sedentary behavior

Mesh:

Year:  2014        PMID: 24743390     DOI: 10.1177/0148607114530432

Source DB:  PubMed          Journal:  JPEN J Parenter Enteral Nutr        ISSN: 0148-6071            Impact factor:   4.016


  6 in total

1.  Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning.

Authors:  Rémy Dumond; Steven Gastinger; Hala Abdul Rahman; Alexis Le Faucheur; Patrice Quinton; Haitao Kang; Jacques Prioux
Journal:  Eur J Appl Physiol       Date:  2017-06-13       Impact factor: 3.078

Review 2.  Analysis of energy metabolism in humans: A review of methodologies.

Authors:  Yan Y Lam; Eric Ravussin
Journal:  Mol Metab       Date:  2016-09-20       Impact factor: 7.422

3.  Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals.

Authors:  Zhao Wang; Qiang Zhang; Ke Lan; Zhicheng Yang; Xiaolin Gao; Anshuo Wu; Yi Xin; Zhengbo Zhang
Journal:  Front Physiol       Date:  2022-08-25       Impact factor: 4.755

4.  New Respiratory Inductive Plethysmography (RIP) Method for Evaluating Ventilatory Adaptation during Mild Physical Activities.

Authors:  Yann Retory; Pauline Niedzialkowski; Carole de Picciotto; Marcel Bonay; Michel Petitjean
Journal:  PLoS One       Date:  2016-03-23       Impact factor: 3.240

5.  Energy expenditure estimation from respiration variables.

Authors:  Rahel Gilgen-Ammann; Marcel Koller; Céline Huber; Riikka Ahola; Topi Korhonen; Thomas Wyss
Journal:  Sci Rep       Date:  2017-11-22       Impact factor: 4.379

6.  Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation.

Authors:  Ke Lu; Liyun Yang; Fernando Seoane; Farhad Abtahi; Mikael Forsman; Kaj Lindecrantz
Journal:  Sensors (Basel)       Date:  2018-09-14       Impact factor: 3.576

  6 in total

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