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Weight loss and changes in body weight due to active training programs
Research Areas
  • Physical education and sport
Introduction The world is facing major problems associated with the rapid increase in levels of overweight and obesity, such as chronic diseases and higher rates of disability (1, 2). Some studies (3-5) have analyzed the problem showing that diet is a prerequisite for weight loss. The aim of the present study was to analyze what was the influence of some factors in the body weight loss and generate an equation to predict it. Methods 87 overweight people (BMI: 25-29,9 kg?m-2), aged 18-50 years, participated in the study (36 men, 51 women). They were randomly assigned to four intervention groups: resistance training (RT, n=22), endurance training (ET, n=25), combined training (CT, n=23) and control group (C, n=19). All of them in combination with diet restriction during 6 months of intervention. The Pearson Product Moment Correlations were used to assess the relationships between weight loss and different descriptive variables. Step by step regression analysis was used to estimate the weight loss. Determination coefficient (R2) and standard error of estimation (SEE) was used to show the accuracy of estimation. Probability level for statistical significance was set at p=0.05. Results We obtained the following correlation coefficients for weight loss and body weight, sex, age, BMI and efficacy BMI 25 Index: r=0,222 (p=0,038), r=0,256 (p=0,016), r=0,184 (p=0,087), r=0,222 (p=0,038) and r=0,569 (p<0,001), respectively. With the regression analysis it was obtained the following predictive weight loss models: Weight loss (kg) =-8,507+0,524*(BMI)+1,721*(Sex, men=1, women=0) (R2=0.093; SEE=3.8 kg) Weight loss (kg) =-115,4+2,481*(BMI)+0,541*(Efficacy BMI 25 Index) (R2=0,768; SEE=1,92 kg) Discussion Hagan (6) showed that the weight loss is different according to sex. Other studies (4, 7, 8) have shown differences between the type of intervention realized. Del Corral (4) found that adherence to diet is a strong predictor of weight loss during a wide range of dietary restriction, so this variable should be included in the new prediction models. We conclude that the weight loss is related to the variables BMI and sex. References 1. Macfarlane DJ. British Journal of Sports Medicine. 2010;44(16):1197-201. 2. Worthy SL, et al. Health Education Journal. 2010;69(4):372-80. 3. Ballor DL, et al. The American Journal of Clinical Nutrition. 1988;47:19-25. 4. Del Corral P, et al. J Clin Endocrinol Metab. 2009 May;94(5):1602-7. 5. Kraemer WJ, et al. Journal of Applied Physiology. 1997;83:270-9. 6. Hagan RD, et al. Medicine & Science in Sports & Exercise. 1986;18(1):87-94. 7. Redman LM, et al. PLoS One. 2009;4(2):e4377. 8. Volpe SL, et al. J Am Coll Nutr. 2008 Apr;27(2):195-208.
16th Annual Congress of the European College os Sport Science
Start Date
End Date
From page
To page
Book of abstracts
  • Autor: Pedro Jose Benito Peinado (UPM)
  • Autor: Ana Belen Peinado Lozano (UPM)
  • Autor: Fco. Javier Calderon Montero (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Ejercicio Físico, Salud y sus Fundamentos Fisiológicos (EFISAF)
  • Departamento: Salud y Rendimiento Humano
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)