Download PDF
Int J Sports Med 2014; 35(10): 847-850
DOI: 10.1055/s-0034-1371837
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Accuracy of Energy Expenditure Estimation by Activity Monitors Differs with Ethnicity

A.-S. Brazeau
1   Plateforme PROMD, Institut de recherches cliniques de Montreal, Montreal, Canada
,
C. Suppere
1   Plateforme PROMD, Institut de recherches cliniques de Montreal, Montreal, Canada
,
I. Strychar
2   Nutrition, Universite de Montreal, Montreal, Canada
,
V. Belisle
1   Plateforme PROMD, Institut de recherches cliniques de Montreal, Montreal, Canada
,
S.-P. Demers
1   Plateforme PROMD, Institut de recherches cliniques de Montreal, Montreal, Canada
,
R. Rabasa-Lhoret
1   Plateforme PROMD, Institut de recherches cliniques de Montreal, Montreal, Canada
› Author Affiliations
Further Information

Publication History



accepted after revision 26 January 2014

Publication Date:
09 May 2014 (online)

Also available at
    Permissions and Reprints

Abstract

The aim of this project is to explore the accuracy of 2 activity monitors (SenseWear Armband & Actical) to estimate energy expenditure during rest and light to moderate intensity exercises in 2 ethnic groups. 18 Caucasian and 20 Black adults (age: 26.8±5.2 years; body mass index: 23.9±3.0 kg/m2) wore the 2 devices simultaneously during 3 standardised activities: 30-min rest, 45-min of treadmill at 40% of their V˙O2peak and 45-min of stationary cycling at 50% of their V˙O2peak. Energy estimated with the 2 devices was compared to indirect calorimetry measurements. Both devices overestimated energy expenditure during rest (SenseWear: 36% in Black vs. 16% in Caucasian; Actical: 26% vs. 11%, p<0.01 between groups) and treadmill (SenseWear: 50% vs. 25%; Actical: 67% vs. 32%, p<0.01 between groups). Both devices significantly underestimated energy expenditure during stationary cycling (SenseWear: 24% vs. 26%; Actical: 58% vs. 70%, p=NS between groups). Equations used to estimate energy expenditure from accelerometer data is less precise among Black adults than Caucasian adults. Ethnic-specific formulas are probably required.

Key words

physical activity energy expenditure - resting energy expenditure - activity monitoring - accelerometry
 

  • References

  • 1 Crouter SE, Bassestt Jr DR. A new 2-regression model for the Actical accelerometer. Br J Sports Med 2008; 42: 217-224
    CrossrefPubMedSearch in Google Scholar
  • 2 Crouter SE, Churilla JR, Bassett Jr DR. Estimating energy expenditure using accelerometers. Eur J Appl Physiol 2006; 98: 601-612
    CrossrefPubMedSearch in Google Scholar
  • 3 Douglas CC, Lawrence JC, Bush NC, Oster RA, Gower BA, Darnell BE. Ability of the Harris Benedict formula to predict energy requirements differs with weight history and ethnicity. Nutr Res 2007; 27: 194-199
    CrossrefPubMedSearch in Google Scholar
  • 4 Foster GD, Wadden TA, Vogt RA. Resting energy expenditure in obese African American and Caucasian women. Obes Res 1997; 5: 1-8
    CrossrefPubMedSearch in Google Scholar
  • 5 Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc 2005; 105: 775-789
    CrossrefPubMedSearch in Google Scholar
  • 6 Harris DJ, Atkinson G. Ethical standards in sport and exercise science research: 2014 update. Int J Sports Med 2013; 34: 1025-1028
    Thieme ConnectPubMedSearch in Google Scholar
  • 7 Johannsen DL, Calabro MA, Stewart J, Franke W, Rood JC, Welk GJ. Accuracy of armband monitors for measuring daily energy expenditure in healthy adults. Med Sci Sports Exerc 2010; 42: 2134-2140
    CrossrefPubMedSearch in Google Scholar
  • 8 Malavolti M, Pietrobelli A, Dugoni M, Poli M, Romagnoli E, De Cristofaro P, Battistini NC. A new device for measuring resting energy expenditure (REE) in healthy subjects. Nutr Metab Cardiovasc Dis 2007; 17: 338-343
    CrossrefPubMedSearch in Google Scholar
  • 9 Martin K, Wallace P, Rust PF, Garvey WT. Estimation of resting energy expenditure considering effects of race and diabetes status. Diabetes Care 2004; 27: 1405-1411
    CrossrefPubMedSearch in Google Scholar
  • 10 McDuffie JR, Adler-Wailes DC, Elberg J, Steinberg EN, Fallon EM, Tershakovec AM, Arslanian SA, Delany JP, Bray GA, Yanovski JA. Prediction equations for resting energy expenditure in overweight and normal-weight black and white children. Am J Clin Nutr 2004; 80: 365-373
    PubMedSearch in Google Scholar
  • 11 Mifflin St MD, Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990; 51: 241-247
    CrossrefPubMedSearch in Google Scholar
  • 12 Rothney MP, Schaefer EV, Neumann MM, Choi L, Chen KY. Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers. Obesity (Silver Spring) 2008; 16: 1946-1952
    CrossrefPubMedSearch in Google Scholar
  • 13 Spierer DK, Hagins M, Rundle A, Pappas E. A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging. Eur J Appl Physiol 2011; 111: 659-667
    CrossrefPubMedSearch in Google Scholar
  • 14 St-Onge M, Mignault D, Allison DB, Rabasa-Lhoret R. Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr 2007; 85: 742-749
    PubMedSearch in Google Scholar
  • 15 Wouters-Adriaens MPE, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring) 2008; 16: 2212-2216
    CrossrefPubMedSearch in Google Scholar
  • 16 Zeno SA, Kim-Dorner SJ, Deuster PA, Davis JL, Remaley AT, Poth M. Cardiovascular fitness and risk factors of healthy African Americans and Caucasians. J Natl Med Assoc 2010; 102: 28-35
    PubMedSearch in Google Scholar