The relationship of physical activitiy levels with the body mass index and the body fat percentage according to different age groups and genders among the Turkish children

2013 ◽  
Vol 5 (6) ◽  
pp. 17-22
Author(s):  
Ozlem Orhan
2021 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Suci Eka Putri ◽  
Adelina Irmayani Lubis

Body mass index (BMI) is to monitor nutritional status adults, especially those related to deficiency and overweight. Body fat percentage can describe the risk of degenerative diseases.This study was conducted to measure the relationship between BMI and body fat percentage. Methods An analytical study was conducted to 41 male and 51 female participant from Universitas Teuku Umar. The body weight was measured using scales, whereas the body height was measured using microtoise. The body fat percentage was measured using Karada Scan. The BMI was calculated by dividing the body weight in kilogram divided by body height in meter square. Data was collected from 16-18th February 2021 and analyzed by Pearson’s correlation test. The results showed BMI underweight, normal, and overweight were 10,9, 57,6, and 31,5. High body fat percentage in men were 75,6% and in women were 35,5%. There is a relationship between the nutritional status of the women group and the body fat percentage with p-value is obtained = 0.021. Furthermore, for men, there is no relationship between nutritional status in the men group and the body fat percentage. There is a relationship between nutritional status and body fat percentage in women. Among this population, BMI can still be used to determine body fat percentage


2008 ◽  
Vol 40 (Supplement) ◽  
pp. S272-S273
Author(s):  
Joshua C. Neuman ◽  
Sigurbjörn Á. Arngrímsson ◽  
Daina M. Mallard ◽  
Gunnhildur Hinriksdóttir ◽  
Annie L. Parrett ◽  
...  

2013 ◽  
Vol 72 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Maria E. Irigoyen-Camacho ◽  
Leonor Sanchez-Perez ◽  
Nelly Molina-Frechero ◽  
Consuelo Velazquez-Alva ◽  
Marco Zepeda-Zepeda ◽  
...  

2021 ◽  
Author(s):  
Bojan Masanovic ◽  
Veselin Veljovic ◽  
Zeljko Spalevic ◽  
Marija Bubanja

2021 ◽  
Vol 5 (3) ◽  
pp. 242
Author(s):  
Nurul Hikma ◽  
Zakiyatul Faizah ◽  
Rize Budi Amalia

AbstractBackground: The menstrual cycle can be said to be normal if the interval is between 21-35 days. The prevalence rates associated with menstrual cycle disorders were found in the range of 15.8-89.5. Nutritional status is one of the factors that cause menstrual cycle disorders, where nutritional status can be determined using body mass index and measuring the percentage of fat in the body. The purpose of this study was to determine the relationship between nutritional status and the menstrual cycle using the literature review method. The formulation of the problem in this research is determined by PICO, namely: "is there a relationship between nutritional status and menstrual cycle disorders?". A total of 749 literary works obtained from the Google Scholar, Science Direct, PubMed and ProQuest databases were used as a literature review source, where in screening the literature the inclusion and exclusion criteria were seen, which had previously been determined so that finally six literatures were reviewed. Results: The cause of menstrual cycle disorders has been found in adolescents with an average value of 18.4-37.8 and the percentage of body fat between 12.80-34.80. Conclusion: Based on all literature that has been reviewed, it can be concluded that menstrual cycle disorders have a significant relationship with body mass index and body fat percentage.


Author(s):  
alexandru godescu

The classic Body Mass Index, (BMI), developed in the 19th century by the Belgian mathematician Adolphe Quetelet [1] is an important indicator of the risk of death, of obesity, of negative health consequences, body fat percentage and of the shape of the body. While he BMI is assumed to indicate obesity in sedentary people and in people who do not practice sports, it is undisputed and a consensus among researchers [2][3][4][5][9][25] that Body Mass Index (BMI) is not a good indicator for obesity in people who developed their body through heavy physical work or sport but also in other segments of population such as those who appear to have a normal weight but in fact have a high body fat percentage and obese methabolism. The BMI also does not include all the variables essential for a health predictor. The BMI is not always a good predictor of metabolic disease, people who appear of healthy weight according to BMI have in some cases an obese metabolic syndrome. The BMI was developed as a law of natural sciences and “social physics” [1], as it was called then, before the middle of the 19th century, and it had been used from the 70s for medical purposes, to detect obesity and the risk of mortality [6][7]. The BMI has a huge importance for modern society, affected by an obesity epidemic [8]. BMI has applications in medicine, sport medicine, sport, fitness, bodybuilding, insurance, nutrition, pharmacology. The main limitation of the BMI is that it does not account for body composition including non fat body mass such as muscles, joints, body frame and makes no difference between fat and non fat components of the body weight. The body composition and the proportion of fat and muscles make a difference in health outcomes [12][13][14][25][26][27][35][36][37] [38][39][40][41][42][43][44]…[100]. Body composition makes a difference also in the level of sport performance for athletes of every level. In nearly two centuries since the Body Mass Index was developed, no formula had been successfully developed to account for body composition and make the difference between muscle and fat in a consistent way. This can be considered a longstanding open problem of major importance for society. The objective of this analysis is to develop new formulae taking into account the health implication of body composition measured through indirect, simple indicators and making the difference between muscles and fat, healthy and non healthy metabolism. The formulae developed in this article are the only formula to successfully generalize BMI and make this difference. I develop a direct generalization of BMI, in the mathematical and physiological sense to account for fat and fat free mass and muscles, small and large body frames. It is the first such generalization because the classic BMI can be determined as a particular case of my formulae in the strict mathematical and practical physiologic sense. No other formula generalized the BMI to make the difference between fat and a large frame and muscles has ever been published in nearly two centuries since the BMI formula had been developed. The formulae I developed explain and generalize the conclusions of a large number of highly cited empirical experiments cited in the reference section. [35][36][37][38][38][39] [40][42][43][44]..[100] Most of the experimental proof I bring in support of my formulae and bodyweight quantification theory comes from many highly cited experimental research publications in medicine, sports medicine, sport science and physiology. My formulae explain also performance in decades of competitive sports and athletics


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