# Sensewear Armband for prediction of energy expenditure in controlled and free-living conditions

Sylvie Rousset, Christophe Montaurier, S. Normand, V. Sauvinet, Philippe Lacomme, Yves Boirie, Béatrice Morio

► To cite this version:

Sylvie Rousset, Christophe Montaurier, S. Normand, V. Sauvinet, Philippe Lacomme, et al. Sensewear Armband for prediction of energy expenditure in controlled and free-living conditions. 2. International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM 2011), May 2011, Glasgow, United Kingdom. 2011, 2th. International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM 2011). [hal-01601783](https://hal.science/hal-01601783)

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https://hal.science/hal-01601783

Submitted on 3 Jun 2020

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# ACTIHEART VS. SENSEWEAR ARMBAND FOR PREDICTION OF ENERGY EXPENDITURE IN CONTROLLED AND FREE-LIVING CONDITIONS

**Rousset S.**<fnref n="1" />, Montaurier C.<fnref n="1" />, Normand S.<fnref n="2" />, Sauvinet V.<fnref n="2" />, Lacomme P.<fnref n="3" />, Boirie Y.<fnref n="1" />, Morio B.<fnref n="1" />

**Introduction** Evaluating variations in free-living energy expenditure during the day and on a day-to-day basis is of major interest in clinical trials as well as for individual use. Several monitors are available today for research on energy expenditure (EE) prediction.

**Aim** The aim of our study was to compare the accuracy of EE estimation of two of these devices (Actiheart and Armband), compared to measurements using calorimetric chambers for the assessment of changes of energy expenditure during the day, and the doubly-labelled water (DLW) technique for the evaluation of free-living total energy expenditure.

## Methods

All volunteers were normal weight and wore both monitors (Actiheart & Armband)

<table><thead><tr><th></th><th>Calorimetric room(0:00-17:00)</th><th>Free living conditions(10 days)</th></tr></thead><tbody><tr><td>Reference</td><td>Indirect calorimetry</td><td>Doubly-labelled water</td></tr><tr><td>Size</td><td>23 men, 26 women</td><td>27 men, 28 women</td></tr><tr><td>Age</td><td>45 ± 5y</td><td>juniors: 28 ± 5y, seniors:46 ± 5y</td></tr><tr><td>Activity</td><td>sleep, rest, walking 3, 4, 5, 6 km.h<sup>-1</sup>, step ...</td><td>free</td></tr></tbody></table>

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Data mining and EE calculation over activity periods by F... developed in-house

**Error of EE estimation (%)** : |EE predicted by monitor - EE reference|

EE reference

**Concordance prediction/reference:** Bland & Altman plots

**Paired t-test** to compare the levels of errors from the 2 monitors

## Results Comparison of EE predictions compared to indirect calorimetry

Mean errors of Actiheart and Armband during the stay in calorimetric room was similar in women. In men Actiheart error was higher than that of Armband. Analysis by activity showed that Actiheart more effectively predicted EE during standing recovery, walking 4kmh⁻<fnref n="1" />, exercising with a stepper and recovery after stepper activity. In contrast, the prediction of EE during post-absorptive rest, sleeping, standing-sitting and recovery after walking 6kmh⁻<fnref n="1" /> was improved with the SenseWear Armband.

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## Comparison of EE predictions compared to doubly-labelled water

In free-living conditions, the mean error was significantly higher for Actiheart (11.6±7.8%) compared to the Armband (8.30±5.5%) (t=-3.1, p=0.003) mainly for men. The errors averaged 7.5±4.7% and 11.5±7.9% in juniors, and 9.1±6.0% and 11.9±7.4% in seniors for the SenseWear Armband and Actiheart, respectively. The Armband underestimated EE in the high value range (> 3100 kcal/d) and vice versa for Actiheart.

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**Conclusion** The measurements of energy expenditure using Actiheart may be altered by either the delicate procedure for skin preparation or the positioning of the device or of the algorithm. Thus, the error of energy expenditure predicted by Actiheart during sleeping was higher. Since this period is long, the sleeping energy expenditure error accounts for a significant part of the total error. An adaptation in the algorithm for low intensity activity and low heart rate might improve prediction accuracy.

<footnote n="1">¹ INRA, Unité de Nutrition Humaine UMR1019, CRNH d'Auvergne, Clermont Ferrand, France</footnote>

<footnote n="2">² CRNH Rhône-Alpes, Lyon, France</footnote>

<footnote n="3">³ Laboratoire d'Informatique (LIMOS, UMR CNRS 6158), Campus des Cézeaux, 63177 Aubière Cedex, France</footnote>