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Int J Sports Med 2014; 35(12): 1037-1043
DOI: 10.1055/s-0034-1368722
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Neural Network for Estimating Energy Expenditure in Paraplegics from Heart Rate

X. García-Massó
1   Department of Physical Education and Sports, University of Valencia, Valencia, Spain
,
P. Serra-Añó
2   Department of Physiotherapy, University of Valencia, Valencia, Spain
,
L. García-Raffi
3   Universidad Politécnica de Valencia, Instituto Universitario de Matemática Pura y Aplicada, Valencia, Spain
,
E. Sánchez-Pérez
3   Universidad Politécnica de Valencia, Instituto Universitario de Matemática Pura y Aplicada, Valencia, Spain
,
M. Giner-Pascual
4   Departmento de Medicina Física y Rehabilitación (Unidad de Lesionados Medulares), Hospital Universitario ‘La Fe’, Valencia, Spain
,
L.-M. González
1   Department of Physical Education and Sports, University of Valencia, Valencia, Spain
› Author Affiliations
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Publication History



accepted after revision 13 January 2014

Publication Date:
02 June 2014 (online)

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Abstract

The aim of the present study is to obtain models for estimating energy expenditure based on the heart rates of people with spinal cord injury without requiring individual calibration. A cohort of 20 persons with spinal cord injury performed a routine of 10 activities while their breath-by-breath oxygen consumption and heart rates were monitored. The minute-by-minute oxygen consumption collected from minute 4 to minute 7 was used as the dependent variable. A total of 7 features extracted from the heart rate signals were used as independent variables. 2 mathematical models were used to estimate the oxygen consumption using the heart rate: a multiple linear model and artificial neural networks. We determined that the artificial neural network model provided a better estimation (r=0.88, MSE=4.4 ml · kg−1 · min−1) than the multiple linear model (r=0.78; MSE=7.63 ml · kg−1 · min−1).The goodness of fit with the artificial neural network was similar to previous reported linear models involving individual calibration. In conclusion, we have validated the use of the heart rate to estimate oxygen consumption in paraplegic persons without individual calibration and, under this constraint, we have shown that the artificial neural network is the mathematical tool that provides the better estimation.

Key words

fitting - oxygen consumption - spinal cord injury - physical activity
 

  • References

  • 1 Ainslie P, Reilly T, Westerterp K. Estimating human energy expenditure: a review of techniques with particular reference to doubly labelled water. Sports Med 2003; 33: 683-698
    CrossrefPubMedSearch in Google Scholar
  • 2 Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC, Latimer AE, McColl MA, Potter PJ, Wolfe DL. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640-647
    CrossrefPubMedSearch in Google Scholar
  • 3 Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE. Energy cost of physical activities in persons with spinal cord injury. Med Sci Sports Exerc 2010; 42: 691-700
    PubMedSearch in Google Scholar
  • 4 Dugas LR, van der Merwe L, Odendaal H, Noakes TD, Lambert EV. A novel energy expenditure prediction equation for intermittent physical activity. Med Sci Sports Exerc 2005; 37: 2154-2161
    CrossrefPubMedSearch in Google Scholar
  • 5 García-Massó X, Serra-Añó P, García-Raffi L, Sánchez-Pérez E, López-Pascual J, González L. Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury. Spinal Cord 2013; 51: 898-903
    CrossrefPubMedSearch in Google Scholar
  • 6 Ginis KAM, Arbour-Nicitopoulos KP, Latimer AE, Buchholz AC, Bray SR, Craven BC, Hayes KC, Hicks AL, McColl MA, Potter PJ, Smith K, Wolfe DL. Leisure time physical activity in a population-based sample of people with spinal cord injury part II: activity types, intensities, and durations. Arch Phys Med Rehabil 2010; 91: 729-733
    CrossrefPubMedSearch in Google Scholar
  • 7 Ginis KAM, Latimer AE, Arbour-Nicitopoulos KP, Buchholz AC, Bray SR, Craven BC, Hayes KC, Hicks AL, McColl MA, Potter PJ, Smith K, Wolfe DL. Leisure time physical activity in a population-based sample of people with spinal cord injury part I: demographic and injury-related correlates. Arch Phys Med Rehabil 2010; 91: 722-728
    CrossrefPubMedSearch in Google Scholar
  • 8 Harriss DJ, Atkinson G. 2014 Update – Ethical standards in sport and exercise science research. Int J Sports Med 2013; 34: 1025-1028
    Thieme ConnectPubMedSearch in Google Scholar
  • 9 Hayes AM, Myers JN, Ho M, Lee MY, Perkash I, Kiratli BJ. Heart rate as a predictor of energy expenditure in people with spinal cord injury. J Rehabil Res Dev 2005; 42: 617-624
    CrossrefPubMedSearch in Google Scholar
  • 10 Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA. Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755-1759
    CrossrefPubMedSearch in Google Scholar
  • 11 Hiremath SV, Ding D. Evaluation of activity monitors in manual wheelchair users with paraplegia. J Spinal Cord Med 2011; 34: 110-117
    CrossrefPubMedSearch in Google Scholar
  • 12 Kurpad AV, Raj R, Maruthy KN, Vaz M. A simple method of measuring total daily energy expenditure and physical activity level from the heart rate in adult men. Eur J Clin Nutr 2006; 60: 32-40
    CrossrefPubMedSearch in Google Scholar
  • 13 Lee M, Zhu W, Hedrick B, Fernhall B. Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336-343
    CrossrefPubMedSearch in Google Scholar
  • 14 Lee M, Zhu W, Hedrick B, Fernhall B. Estimating MET values using the ratio of HR for persons with paraplegia. Med Sci Sports Exerc 2010; 42: 985-990
    CrossrefPubMedSearch in Google Scholar
  • 15 Livingstone MB. Heart-rate monitoring: the answer for assessing energy expenditure and physical activity in population studies?. Br J Nutr 1997; 78: 869-871
    CrossrefPubMedSearch in Google Scholar
  • 16 Manns PJ, Chad KE. Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566-1571
    CrossrefPubMedSearch in Google Scholar
  • 17 Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med 2001; 31: 439-454
    CrossrefPubMedSearch in Google Scholar
  • 18 Slater D, Meade MA. Participation in recreation and sports for persons with spinal cord injury: review and recommendations. NeuroRehabilitation 2004; 19: 121-129
    PubMedSearch in Google Scholar
  • 19 Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300-1307
    CrossrefPubMedSearch in Google Scholar
  • 20 Tudor-Locke C, Williams JE, Reis JP, Pluto D. Utility of pedometers for assessing physical activity: convergent validity. Sports Med 2002; 32: 795-808
    CrossrefPubMedSearch in Google Scholar
  • 21 Valanou EM, Bamia C, Trichopoulou A. Methodology of physical-activity and energy-expenditure assessment: a review. J Public Health 2006; 14: 58-65
    CrossrefPubMedSearch in Google Scholar
  • 22 Washburn R, Copay A. Assessing Physical Activity During Wheelchair Pushing: validity of a portable accelerometer. Adapt Phys Act Q 1999; 16: 290-299
    PubMedSearch in Google Scholar
  • 23 Wicks JR, Oldridge NB, Nielsen LK, Vickers CE. HR index – a simple method for the prediction of oxygen uptake. Med Sci Sports Exerc 2011; 43: 2005-2012
    PubMedSearch in Google Scholar