Using accelerometer to estimate energy expenditures with four equations in four training sessions
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This study compares estimated energy expenditure (EE) from four equations using accelerometer counts in Zumba, interval 4x4 spinning, interval 4x4 running and pyramid running. The study also characterizes differences in EE and accelerometer counts during activity and recovery periods for these activities. Twenty six men and women (21.8±2.4 years) completed four training sessions. Vector magnitude counts along three axes were measured using ActiGraph GT3X accelerometers. EE was estimated using four equations. Results show that EE varied by 34.2%, 19.7%, 18.0% and 20.0% depending on which equation was used in Zumba, 4x4 running, 4x4 spinning and pyramid running, respectively (p<0.001). Compared with 4x4 running, Zumba had 22.0% lower EE and 4x4 spinning had 47.8% lower EE in kcal/min (p<0.0001). There was no significant difference in EE between 4x4 running and pyramid running. The mean VM/min (vector magnitude counts per min) for Zumba was 22.1% and 20.4% lower than for 4x4 running and pyramid running, respectively (p<0.0001). An 85.3% higher VM/min was found in 4x4 running compared to 4x4 spinning (p<0.0001). The various equations caused substantial differences in the estimation of EE, particularly in Zumba, which is explained. Interval running provided the highest EE and counts/min. When 4x4 spinning was carried out both in sitting and standing positions, the underestimation in EE from accelerometer was about 50% compared with 4x4 running.
Abstract
This study compares estimated energy expenditure (EE) from four equations using accelerometer counts in Zumba, interval 4x4 spinning, interval 4x4 running and pyramid running. The study also characterizes differences in EE and accelerometer counts during activity and recovery periods for these activities. Twenty six men and women (21.8±2.4 years) completed four training sessions. Vector magnitude counts along three axes were measured using ActiGraph GT3X accelerometers. EE was estimated using four equations. Results show that EE varied by 34.2%, 19.7%, 18.0% and 20.0% depending on which equation was used in Zumba, 4x4 running, 4x4 spinning and pyramid running, respectively (p<0.001). Compared with 4x4 running, Zumba had 22.0% lower EE and 4x4 spinning had 47.8% lower EE in kcal/min (p<0.0001). There was no significant difference in EE between 4x4 running and pyramid running. The mean VM/min (vector magnitude counts per min) for Zumba was 22.1% and 20.4% lower than for 4x4 running and pyramid running, respectively (p<0.0001). An 85.3% higher VM/min was found in 4x4 running compared to 4x4 spinning (p<0.0001). The various equations caused substantial differences in the estimation of EE, particularly in Zumba, which is explained. Interval running provided the highest EE and counts/min. When 4x4 spinning was carried out both in sitting and standing positions, the underestimation in EE from accelerometer was about 50% compared with 4x4 running.
참고문헌 (21)
- "What is the difference among the Energy Expenditure Algorithms? Actigraph support home page" (2011)[URL]
- Campbell, K. L., "Field evaluation of energy expenditure in women using Tritrac accelerometers", Medicine and Science in Sports and Exercise, 34, pp.1667~1674 (2002)
- Crouter, S. E., "Estimating energy expenditure using accelerometers", European Journal of Applied Physiolog, 98, pp.601~612 (2006)
- Crouter, S. E., "A novel method for using accelerometer data to predict energy expenditure", Journal of Applied Physiology, 100, pp.1324~1331 (2006)
- Crouter, S. E., "Refined Two-Regression Model for the ActiGraph Accelerometer", Medicine and Science in Sports and Exercise, 42, pp.1029~1037 (2010)
- Freedson, P. S., "Calibration of the Computer Science and Applications, Inc. accelerometer", Med Sci Sports Exerc, 30, pp.777~781 (1998)
- Hausken, K., "Evaluating Performance Training and Step Aerobics in Intervals", International Journal of Performance Analysis in Sport, 10, pp.279~294 (2010)
- Helgerud, J., "Aerobic high-intensity intervals improve VO2max more than moderate training", Medicine and Science in Sports and Exercise, 39, pp.665~671 (2007)
- Howe, C. A., "Accelerometer Prediction of Energy Expenditure : Vector Magnitude Versus Vertical Axis", Medicine and Science in Sports and Exercise, 41, pp.2199~2206 (2009)
- Howley, E. T., "Fitness Professional's Handbook" (2012), Human Kinetics
- Härtel, S., "Estimation of energy expenditure using accelerometers and activitybased energy models—validation of a new device", European Review of Aging and Physical Activity, 8, pp.109~114 (2011)
- Jakicic, J. M., "The accuracy of the TriTrac-R3D accelerometer to estimate energy expenditure", Medicine and Science in Sports and Exercise, 31, pp.747~754 (1999)
- "Zumba brings the dance party into the health club" (2012)[URL]
- Luettgen, M., "Zumba (R): Is the “fitness-party“ a good workout?", Journal of Sports Science & Medicine, 11, pp.357~358 (2012)
- Lyden, K., "A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations", European journal of applied physiology, 111, pp.187~201 (2011)
- Rothney, M. P., "Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers", Obesity, 16, pp.1946~1952 (2008)
- Sanders, M. E., "Zumba(R)Fitness is Gold for All Ages", ACSM's Health & Fitness Journal, 16, pp.25~28 (2012)
- Sasaki, J. E., "Validation and comparison of ActiGraph activity monitors", J Sci Med Sport, 14, pp.411~416 (2011)
- Vanhelst, J., "Comparison of uniaxial and triaxial accelerometry in the assessment of physical activity among adolescents under free-living conditions: the HELENA study", BMC Medical Research Methodology, 12 (2012)
- Williams, R., "Kcal Estimates from Activity Counts using the Potential Energy Method" (1998), CSA, Inc.
- Yokoyama, Y., "Comparison of accelerometry and oxymetry for measuring daily physical activity", Circulation Journal, 66, pp.751~754 (2002)