Statistical Techniques for Interpreting Body Size–Related Exercise Performance during Growth

2000 ◽  
Vol 12 (2) ◽  
pp. 112-127 ◽  
Author(s):  
Joanne R. Welsman ◽  
Neil Armstrong
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Body Mass ◽  
Exercise Performance ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
Regression Techniques ◽  
Ontogenetic Allometry ◽  
Log Linear ◽  
Individual Size

This paper reviews some of the statistical methods available for controlling for body size differences in the interpretation of developmental changes in exercise performance. For cross-sectional data analysis simple per body mass ratio scaling continues to be widely used, but is frequently ineffective as the computed ratio remains correlated with body mass. Linear regression techniques may distinguish group differences more appropriately but, as illustrated, only allometric (log-linear regression) scaling appropriately removes body size differences while accommodating the heteroscedasticity common in exercise performance data. The analysis and interpretation of longitudinal data within an allometric framework is complex. More established methods such as ontogenetic allometry allow insights into individual size-function relationships but are unable to describe adequately population effects or changes in the magnitude of the response. The recently developed multilevel regression modeling technique represents a flexible and sensitive solution to such problems allowing both individual and group responses to be modeled concurrently.

scholarly journals Hubungan IMT (Indeks Massa Tubuh) dengan Ketahanan (Endurance) Kardiorespirasi pada Mahasiswa Pendidikan Dokter Unand 2009-2012

2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Reny Jayusfani ◽  
Afriwardi Afriwardi ◽  
Eti Yerizel
Keyword(s):  
Body Mass Index ◽  
Medical Student ◽  
Linear Regression ◽  
Body Mass ◽  
The Body ◽  
Physical Work ◽  
Physical Work Capacity ◽  
Cross Sectional ◽  
Average Maximum ◽  
Cardiorespiratory Endurance

AbstrakSaat ini terjadi peningkatan kelebihan berat badan terutama pada generasi muda disebabkan oleh diet yang tidak tepat dan gaya hidup yang tidak aktif. Peningkatan berat badan ini akan berakibat pada penurunan daya tahan kardiorespirasi hingga berdampak pada kapasitas kerja fisik. Tujuan penelitian ini adalah untuk menentukan hubungan Indeks Massa Tubuh (IMT) dengan ketahanan kardiorespirasi pada mahasiswa FK Unand. Penelitian dilakukan pada mahasiswa FK Unand Padang dari Desember 2012 sampai Februari 2013. Studi observasional analitik ini menggunakan desain cross sectional study dengan jumlah subjek 30 orang. Ketahanan kardiorespirasi didapat dengan menghitung nilai VO2maks menggunakan tes ergometer sepeda metode Astrand 6 minute cycle test. Dilakukan pengukuran berat badan dan tinggi badan. Analisis statistik yang digunakan adalah uji regresi linear sederhana. Hasil penelitian menemukan bahwa rerata IMT 23,2 ± 5,1 dan rerata volume oksigen maksimal 39,5 ± 12,1. Uji regresi linear menunjukkan terdapat hubungan antara IMT dengan ketahanan kardiorespirasi dengan tingkat hubungan sedang (r=0,567, p<0,05) dengan pengaruh sebesar 32,1% (R2=0,321) dan persamaan regresi yang didapat adalah Y=70,827 – 1,349X. Kesimpulan hasil studi ini adalah semakin tinggi indeks massa tubuh semakin rendah ketahanan kardiorespirasi.Kata kunci: volume oksigen maksimal, indeks massa tubuh, ketahanan kardiorespirasi AbstractNowadays, there are many cases about increasing the weight of body, especially at younger generation. It is caused by anappropriate diet and inactive lifestyle. Increasing of weight will cause declining of cardiorespiratory endurance. So that, it will impact on physical work capacity. The objective of this study was to determine the relationship between cardiorespiratory with Body Mass Index (BMI) in medical student of Andalas University.The research was done on medical student of Andalas University Padang in December 2012 until February 2012. This research used observational study with cross sectional design study. The subject of this research were 30 people. Cardiorespiratory endurance was obtained by calculate the value of VO2max. This measurement used ergometer bicycle with the method was using Astrand 6 minute cycle test. This test measured the weight and height body. Statistical analysis was simple linear regression. The result found that the average of BMI is 23.2 ± 5.1 and an average maximum oxygen volume is 39.5 ± 12.1. Linear regression found that there is a moderate significant effect between BMI and cardiorespiratory endurance (r=0.567, p <0.05) with the effect about 32.1% (R2 = 0.321) and the regression equation was Y = 70.827 to 1.349 X.In conclusion, the subjects have average cardiorespiratory endurance level and normal body mass index. If the body of mass index is higher, the cardiorespiratory endurance

Age Differences in Change-of-Direction Performance and Its Subelements in Female Football Players

2015 ◽  
Vol 10 (4) ◽  
pp. 440-445 ◽  
Author(s):  
Norikazu Hirose ◽  
Chikako Nakahori
Keyword(s):  
Body Size ◽  
Body Mass ◽  
Age Differences ◽  
Muscle Power ◽  
Age Groups ◽  
Football Players ◽  
Cross Sectional ◽  
Sprint Speed ◽  
Change Of Direction ◽  
Age Related

Purpose:To describe cross-sectional age differences in change-of-direction performance (CODp) in female football players and investigate the relationship between CODp and linear-sprint speed, muscle power, and body size.Methods:A sample of 135 well-trained female football players was divided into 8 age groups. Anthropometry (height, body mass, and lean body mass) and athletic performance (10-m sprint speed, 10-m × 5-CODp, and 5-step bounding distance) were compared to determine interage differences using ANOVA. Then, the participants were divided into 3 age groups: 12- to 14-y-olds, 15- to 17-y-olds, and ≥ 18 y-olds. Simple- and multiple-regression analyses were conducted to determine the correlation between CODp and the other measurement variables in each age group.Results:Age-related differences were found for CODp (F = 10.41, P < .01), sprint speed (F = 3.27, P < .01), and bounding distance (F = 4.20, P < .01). Post hoc analysis revealed that the CODp of 17-y-old players was faster than that of 16-y-old players (P < .01), with no interage differences in sprint speed and bounding distance. Sprint speed and bounding distance were weakly correlated with CODp in 15- to ≥18-y-old players, but only sprint speed was correlated with CODp in 12- to 14-y-old players.Conclusions:CODp improves from 16 to 17 y of age in female players. Linear-sprint speed, muscle power, and body size were weakly correlated with the age differences in CODp.

scholarly journals Predictors of Hypertension among Nonpregnant Females Attending Health Promotion Clinic with Special Emphasis on Smokeless Tobacco: A Cross-Sectional Study

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Devika Bhatt ◽  
Shashi Sharma ◽  
Ruchika Gupta ◽  
Dhirendra N. Sinha ◽  
Ravi Mehrotra
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Health Promotion ◽  
Regression Analysis ◽  
Linear Regression ◽  
Body Mass ◽  
Smokeless Tobacco ◽  
Linear Regression Analysis ◽  
High Body Mass Index ◽  
Cross Sectional

Aim. To determine the predictors of hypertension among nonpregnant females attending a health promotion clinic. Design and Setting. A cross-sectional study was conducted during March to June 2016, at the National Institute of Cancer Prevention and Research, India. Methods. The study included 319 nonpregnant females of age 20–70 years. Demographics such as age, literacy, and income were noted. History regarding use, frequency, and quantity of smokeless tobacco was taken. Height, weight, and blood pressure were measured and body mass index was calculated. Statistical Analysis. Pearson’s product-moment correlation coefficient was calculated between each of the variables of age, smokeless tobacco consumption, and body mass index versus systolic and diastolic blood pressure, respectively. The linear as well as multiple linear regression analysis was employed to identify the risk factors for hypertension. Results. A univariate linear regression analysis showed that age, smokeless tobacco consumption, and body mass index were associated with systolic blood pressure (P value < 0.001 for each). For diastolic blood pressure, high body mass index was a predictor. Multiple linear regression analysis showed that both systolic and diastolic hypertension were associated with high body mass index and low level of education. Moreover, the systolic hypertension was associated with higher age and smokeless tobacco use. Conclusion. Health promotion requires control of body mass index and smokeless tobacco cessation for preventing hypertension and its complications.

scholarly journals Biological scaling analyses are more than statistical line fitting

2021 ◽  
Vol 224 (11) ◽  
Author(s):  
Douglas S. Glazier
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Traditional Method ◽  
Biological Traits ◽  
Log Transformation ◽  
Size Scaling ◽  
Non Linear ◽  
Scaling Analyses ◽  
Log Linear ◽  
Best Fit

ABSTRACT The magnitude of many biological traits relates strongly and regularly to body size. Consequently, a major goal of comparative biology is to understand and apply these ‘size-scaling’ relationships, traditionally quantified by using linear regression analyses based on log-transformed data. However, recently some investigators have questioned this traditional method, arguing that linear or non-linear regression based on untransformed arithmetic data may provide better statistical fits than log-linear analyses. Furthermore, they advocate the replacement of the traditional method by alternative specific methods on a case-by-case basis, based simply on best-fit criteria. Here, I argue that the use of logarithms in scaling analyses presents multiple valuable advantages, both statistical and conceptual. Most importantly, log-transformation allows biologically meaningful, properly scaled (scale-independent) comparisons of organisms of different size, whereas non-scaled (scale-dependent) analyses based on untransformed arithmetic data do not. Additionally, log-based analyses can readily reveal biologically and theoretically relevant discontinuities in scale invariance during developmental or evolutionary increases in body size that are not shown by linear or non-linear arithmetic analyses. In this way, log-transformation advances our understanding of biological scaling conceptually, not just statistically. I hope that my Commentary helps students, non-specialists and other interested readers to understand the general benefits of using log-transformed data in size-scaling analyses, and stimulates advocates of arithmetic analyses to show how they may improve our understanding of scaling conceptually, not just statistically.

Anthropometry, physique, body composition, and maturity

2013 ◽  
Author(s):  
Albrecht L. Claessens ◽  
Gaston Beunen ◽  
Robert M. Malina
Keyword(s):  
Body Composition ◽  
Body Size ◽  
Young People ◽  
Exercise Performance ◽  
Physiological Function ◽  
Research Question ◽  
Data Sets ◽  
Scaling Method ◽  
Cross Sectional ◽  
Scaling Methods

The appropriate normalization of exercise performance data for differences in body size underpins the clarification of growth and maturational influences on physiological function. Therefore, scaling is an issue of fundamental importance for all paediatric exercise scientists. The selection and application of a scaling method appropriate for the data and research question being addressed is at least as important as ensuring that the methodology used to collect the data is valid, reliable, and appropriate for use with young people. Several scaling methods are available and some methods can be applied in different ways. Unfortunately, taken as a whole, the extant literature presents a confusing picture as to which of these techniques is preferable, how they should be applied, and the meaning of the results obtained. The aim of this chapter is to clarify these issues through a description of the techniques available for analysing both cross-sectional and longitudinal data sets, highlighting their statistical and theoretical derivations.

Scaling of submaximal oxygen uptake with body mass and combined mass during uphill treadmill bicycling

1998 ◽  
Vol 85 (4) ◽  
pp. 1376-1383 ◽  
Author(s):  
Daniel P. Heil
Keyword(s):  
Linear Regression ◽  
Confidence Interval ◽  
Oxygen Uptake ◽  
Body Mass ◽  
Regression Models ◽  
Dynamic Friction ◽  
Scaling Relationships ◽  
Competitive Cyclists ◽  
Log Linear

This study examined the scaling relationships of net O2 uptake [V˙o 2(net)= V˙o 2 − restingV˙o 2] to body mass ( M B) and combined mass ( M C= M B + bicycle) during uphill treadmill bicycling. It was hypothesized thatV˙o 2(net)(l/min) would scale proportionally with M C [i.e.,V˙o 2(net)∝ [Formula: see text]] and less than proportionally with M B [i.e.,V˙o 2(net)∝ [Formula: see text]]. Twenty-five competitive cyclists [73.9 ± 8.8 and 85.0 ± 9.0 (SD) kg for M B and M C, respectively] rode their bicycles on a treadmill at 3.46 m/s and grades of 1.7, 3.5, 5.2, and 7.0% whileV˙o 2 was measured. Multiple log-linear regression procedures were applied to the pooledV˙o 2(net)data to determine the exponents for M C and M B after statistically controlling for differences in treadmill grade and dynamic friction. The regression models were highly significant ( R 2 = 0.95, P < 0.001). Exponents for M C (0.99, 95% confidence interval = 0.80–1.18) and M B (0.89, 95% confidence interval = 0.72–1.07) did not differ significantly from each other or 1.0. It was concluded that the 0.99 M C exponent was due to gravitational resistance, whereas the M B exponent was <1.0 because the bicycles were relatively lighter for heavier cyclists.

scholarly journals Physical Activity and Obesity Indicators: National Cross Sectional Study on Lebanese Adults

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Soha Hourani ◽  
Nagham Hamadeh ◽  
Mohamad Al-Iskandarani ◽  
Sarine El Daouk ◽  
Maha Hoteit
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Linear Regression ◽  
Body Mass ◽  
Activity Index ◽  
Cross Sectional Study ◽  
Inverse Association ◽  
Sectional Study ◽  
Cross Sectional ◽  
Physical Activity Index

<p>Association between higher levels of physical activity and lower rates of obesity has been shown. The aim is to assess the relation between the prevalence of physical activity and the Physical Activity Index (PAI) of 300 Lebanese healthy adults, with age, gender, occupation, body mass indices and waist circumferences (WC). The cutoff points of WC for both genders were determined using the values of Body Mass Index (BMI). A cross-sectional study using self-reported valid questionnaire was conducted randomly on 150 men and 150 women, between 18 and 74 years, from Beirut region. Association between variables was performed using chi2, T-Test and ANOVA. Linear regression determined the WC cutoffs based on BMI. 22% of the population was obese with WC mean level of 92.47±14.4cm (87.71±14.4 cm for women and 97.24±12.96 cm for men). The prevalence of physical activity was 34% in overall population (27% in women and 40% in men).There was no significant association between BMI values and PAI (p&lt; 0.085 for men and p&lt; 0.300 for women). However there was an inverse association between WC values and PAI in both genders (p&lt;0.043 in men and p&lt; 0.036 in women). Linear regression showed WC cut-off point in Lebanese women with BMI ≥25 kg/m2 and ≥30 kg/m2 of 86 cm and 100 cm respectively, whereas for men it was 92.12 cm and 105 cm respectively. The prevalence of physical activity in Beirut is low with differences among genders. The highest physical activity index is associated with the decreased values of waist circumference.</p>

scholarly journals Physical Activity and Obesity Indicators: National Cross Sectional Study on Lebanese Adults

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Soha Hourani ◽  
Nagham Hamadeh ◽  
Mohamad Al-Iskandarani ◽  
Sarine El Daouk ◽  
Maha Hoteit
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Linear Regression ◽  
Body Mass ◽  
Activity Index ◽  
Cross Sectional Study ◽  
Inverse Association ◽  
Sectional Study ◽  
Cross Sectional ◽  
Physical Activity Index

<p>Association between higher levels of physical activity and lower rates of obesity has been shown. The aim is to assess the relation between the prevalence of physical activity and the Physical Activity Index (PAI) of 300 Lebanese healthy adults, with age, gender, occupation, body mass indices and waist circumferences (WC). The cutoff points of WC for both genders were determined using the values of Body Mass Index (BMI). A cross-sectional study using self-reported valid questionnaire was conducted randomly on 150 men and 150 women, between 18 and 74 years, from Beirut region. Association between variables was performed using chi<sup>2</sup>, T-Test and ANOVA. Linear regression determined the WC cutoffs based on BMI. 22% of the population was obese with WC mean level of 92.47±14.4cm (87.71±14.4 cm for women and 97.24±12.96 cm for men). The prevalence of physical activity was 34% in overall population (27% in women and 40% in men).There was no significant association between BMI values and PAI (p&lt; 0.085 for men and p&lt; 0.300 for women). However there was an inverse association between WC values and PAI in both genders (p&lt;0.043 in men and p&lt; 0.036 in women). Linear regression showed WC cut-off point in Lebanese women with BMI ≥25 kg/m<sup>2</sup> and ≥30 kg/m<sup>2</sup> of 86 cm and 100 cm respectively, whereas for men it was 92.12 cm and 105 cm respectively. The prevalence of physical activity in Beirut is low with differences among genders. The highest physical activity index is associated with the decreased values of waist circumference.</p>

Body Size and the Growth of Maximal Aerobic Power in Children: A Longitudinal Analysis

1997 ◽  
Vol 9 (3) ◽  
pp. 262-274 ◽  
Author(s):  
Thomas Rowland ◽  
Paul Vanderburgh ◽  
Lee Cunningham
Keyword(s):  
Gender Differences ◽  
Body Size ◽  
Body Mass ◽  
Allometric Scaling ◽  
Aerobic Power ◽  
Maximal Aerobic Power ◽  
Scaling Exponent ◽  
Cross Sectional ◽  
Treadmill Testing ◽  
The Mean

Adjustment of VO2max for changes in body size is important in evaluating aerobic fitness in children. It is important, therefore, to understand the normal relationship between changes VO2max and body size during growth. Over the course of 5 years, 20 children (11 boys, 9 girls) underwent annual maximal treadmill testing to determine VO2max. The mean longitudinal allometric scaling exponent for VO2max relative to body mass (M) was 1.10 ± 0.30 in the boys and 0.78 ± 0.28 in the girls (p < .05). Respective cross-sectional values were 0.53 ± 0.08 and 0.65 ± 0.03. VO2max expressed relative to M1.0, M0.75, and M0.67 rose during the 5 years in the boys, but not the girls. Significant gender differences remained when VO2max was related to lean body mass. These findings suggest (a) factors other than body size affect the development of VO2max in children, and (b) gender differences exist in VO2max during childhood which are independent of body composition.

Interpreting exercise performance data in relation to body size

2013 ◽  
Author(s):  
Joanne R. Welsman ◽  
Neil Armstrong
Keyword(s):  
Body Size ◽  
Young People ◽  
Exercise Performance ◽  
Physiological Function ◽  
Research Question ◽  
Performance Data ◽  
Data Sets ◽  
Scaling Method ◽  
Cross Sectional ◽  
Scaling Methods

The appropriate normalization of exercise performance data for differences in body size underpins the clarification of growth and maturational influences on physiological function. Therefore, scaling is an issue of fundamental importance for all paediatric exercise scientists. The selection and application of a scaling method appropriate for the data and research question being addressed is at least as important as ensuring that the methodology used to collect the data is valid, reliable, and appropriate for use with young people. Several scaling methods are available and some methods can be applied in different ways. Unfortunately, taken as a whole, the extant literature presents a confusing picture as to which of these techniques is preferable, how they should be applied, and the meaning of the results obtained. The aim of this chapter is to clarify these issues through a description of the techniques available for analysing both cross-sectional and longitudinal data sets, highlighting their statistical and theoretical derivations.

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