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Use of human body morphology as an indication of physical fitness: implications for police officers / Uso de la morfología del cuerpo humano como una indicación de la condición física: implicancias en oficiales de policía
Kukic, Filip; Dopsaj, Milivoj; Dawes, Jay; Orr, Robin; Cvorovic, Aleksandar.
  • Kukic, Filip; Police Sports Education Center. Abu Dhabi. AE
  • Dopsaj, Milivoj; University of Belgrade. Faculty of Sport and Physical Education. Department for Analysis and Diagnosis in Sport. RS
  • Dawes, Jay; University of Colorado-Colorado Springs. Health Sciences Department. US
  • Orr, Robin; Bond University. Tactical Research Unit. AU
  • Cvorovic, Aleksandar; Police Sports Education Center. Abu Dhabi. AE
Int. j. morphol ; 36(4): 1407-1412, Dec. 2018. tab, graf
Article in English | LILACS | ID: biblio-975716
ABSTRACT
Research with police officers (POs) suggests an association between body composition, physical performance and health. The aim of the study was to investigate the associations between body composition and measures of physical fitness, and their use to predict estimated physical fitness score (EPFS). The sample included 163 male POs (age = 31.61 ± 4.79 years, height = 172.97 ± 6.09 cm, body mass = 77.53 ± 11.66 kg). Eight body composition variables body mass index (BMI), body fat mass index (BFMI), percent of body fat (PBF), percent skeletal muscle mass (PSMM), index of hypokinezia (IH), skeletal muscle mass index (SMMI), protein mass index (PMI), and fat-free mass index (FFMI); and four physical fitness

measures:

a 3.2 km run, a 2-minute push-up, 2-minute sit-up and estimated physical fitness score (EPFS) were correlated, followed by the regression analysis for causal relationship between body composition and EPFS. Running 3.2 km test correlated to BMI, PBF, PSMM, BFMI, and SMMI (r = 0.274, 0.250, -0.234, 0.311, p<0.01, respectively); 2-minute push-up correlated to PBF, PSMM, BFMI, SMMI, PMI, IH, and FFMI (r = -0.413, 0.436, -0.375, 0.221, 0.231, -0.411, 0.261, p<0.01, respectively); 2-minute sit-up correlated to PBF, PSMM, BFMI, and IH (r = -0.237, 0.250, -0.236, -0.218, p<0.01, respectively); and EPFS correlated to BMI, FFMI, PBF, PSMM, BFMI, and IH (r = -0.200, 0.168, p<0.05, and r = -0.369, 0.378, 0.376, -0.317, p <0.01, respectively). Two models of predictions were extracted 1) PBF, BFMI, PMI and FFMI (R2 = 0.250, p<0.001); 2) PBF, BFMI and PMI (R2 = 0.244, p<0.001). Obtained prediction models may be a promising screening method of a POs' fitness, when conducting the physical tests is not possible or safe (obese and injured POs or bad weather conditions).
RESUMEN
En este trabajo realizado con oficiales de policía (OP) se sugiere una asociación entre la composición corporal y el rendimiento físico y la salud. El objetivo del estudio fue investigar las asociaciones entre la composición corporal y las medidas de aptitud física, y su uso para predecir el puntaje de aptitud física estimado (PAFE). La muestra incluyó 163 OP masculinos (edad = 31,61 ± 4,79 años, altura = 172,97 ± 6,09 cm, masa corporal = 77,53 ± 11,66 kg). Se analizaron ocho variables de composición corporal índice de masa corporal (IMC), índice de masa corporal grasa (IMCG), porcentaje de grasa corporal (PGC), porcentaje de masa muscular esquelética (PMME), índice de hipoquinezia (IH), índice de masa muscular esquelética (IMME), índice de masa proteica (IMP) e índice de masa libre de grasa (IMLG); y cuatro medidas de aptitud física se correlacionaron una carrera de 3,2 km, una elevación de 2 minutos, una postura de 2 minutos y un puntaje de aptitud física estimada (PAFE), seguido del análisis de regresión para la relación causal entre la composición corporal y el PAFE. La prueba de ejecución de 3,2 km se correlacionó con el IMC, PGC, PMME, IMCG y IMME (r = 0,274, 0,250, -0,234, 0,311, p <0,01, respectivamente); Push-up de 2 minutos correlacionado con PGC, PMME, IMCG, IMME, PMI, IH y IMLG (r = -0,413, 0,436, -0,375, 0,221, 0,231, 0,411, 0,261, p <0,01, respectivamente); Sit-up de 2 minutos correlacionado con PGC, PMME, IMCG e IH (r = -0,237, 0,250, 0,236, -0,218, p <0,01, respectivamente); y EPFS correlacionado con IMC, IMLG, PGC , PMME, IGMC e IH (r = -0,200, 0,168, p <0,05, y r = -0,369, 0,378, 0,376, -0,317, p <0,01, respectivamente). Se extrajeron dos modelos de predicción 1) PGC, IGMC, IMP y IMLG (R2 = 0,250, p <0,001); 2) PGC, IGMC y IMP (R2 = 0,244, p <0,00). Los modelos de predicción obtenidos pueden ser un método prometedor de detección de la condición física de los OP, cuando no es posible o seguro realizar las pruebas físicas (OP obesos y lesionados o condiciones climáticas adversas).
Subject(s)


Full text: Available Index: LILACS (Americas) Main subject: Anthropometry / Physical Fitness / Police Type of study: Prognostic study Limits: Adult / Humans / Male Language: English Journal: Int. j. morphol Journal subject: Anatomy Year: 2018 Type: Article / Project document Affiliation country: Australia / Brazil / United Arab Emirates / United States Institution/Affiliation country: Bond University/AU / Police Sports Education Center/AE / University of Belgrade/RS / University of Colorado-Colorado Springs/US

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Full text: Available Index: LILACS (Americas) Main subject: Anthropometry / Physical Fitness / Police Type of study: Prognostic study Limits: Adult / Humans / Male Language: English Journal: Int. j. morphol Journal subject: Anatomy Year: 2018 Type: Article / Project document Affiliation country: Australia / Brazil / United Arab Emirates / United States Institution/Affiliation country: Bond University/AU / Police Sports Education Center/AE / University of Belgrade/RS / University of Colorado-Colorado Springs/US