Adjusting athletes' body mass index to better reflect adiposity in epidemiological research

Nevill, Alan M.; Winter, Edward; Ingham, Stephen A.; Watts, Adam; Metsios, George S.; Stewart, Arthur D.

doi:10.1080/02640414.2010.487071

Cited by 38 publications

(35 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This inability of BMI to assess fatness or adiposity has been reported in the literature [90] . Despite this limitation, the use of MI instead of BMI may permit diagnosis of underweight and assessment of "optimum body weight" for high performance in sports on a finer scale.…”

Section: Body Mass Index (Bmi) and Mass Index (Mi)mentioning

confidence: 96%

Current Status of Body Composition Assessment in Sport

et al. 2012

Self Cite

View full text Add to dashboard Cite

Dette er siste tekst-versjon av artikkelen, og den kan inneholde små forskjeller fra forlagets pdf-versjon. Forlagets pdf-versjon finner du på link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 This is the final text version of the article, and it may contain minor differences from the journal's pdf version. The original publication is available at link.springer.com: http://dx.doi.org/10.2165/11597140-000000000-00000 . Figure 3. Semi-automatic image evaluation: The edge detection algorithm for subcutaneous adipose tissue (SAT) thickness determination enables selecting areas of interest, distances (d US ) measurement series, color-coding of distance values, and statistical evaluations [48] . In this example of a SAT-layer above the triceps muscle, with the transducer held parallel to the humerus, 119 d US values ranging from 2.3 mm to 4.3 mm were automatically detected by the algorithm; the median was 3.4 mm (c = 1470 m/s). Layers and interfaces: A: gel, B: gel-epidermis, C: dermis, D: dermis-SAT, E: SAT, F: SAT-fascia of muscle, G: muscle. [77] . Abbreviations: mid dist = middle distance track runners; long dist = long distance runners; Scot long dist = Scottish long distance runners; SASI mid dist = South Australian Sports Institute middle distance track runners; SASI sprint = sprint runners; SASI jump = jumpers. Figure 6. Selected skinfold ratios in extremely lean male and female endurance athletes, and mean values from 106 male and 33 female athletes [80] . 4 ABSTRACTQuantifying human body composition has played an important role in monitoring all athlete performance and training regimens, but especially so in gravitational, weight class and aesthetic sports wherein the tissue composition of the body profoundly affects performance or adjudication. Over the past century, a myriad of techniques and equations have been proposed, but all have some inherent problems, whether in measurement methodology or in the assumptions they make. To date, there is no universally applicable criterion or "gold standard" methodology for body composition assessment.Having considered issues of accuracy, repeatability and utility, the multi-component model might be employed as a performance or selection criterion, provided the selected model accounts for variability in the density of FFM in its computation. However, when profiling change in interventions, single methods whose raw data are surrogates for body composition (with the notable exception of the BMI) remain useful.

show abstract

Section: Body Mass Index (Bmi) and Mass Index (Mi)mentioning

confidence: 96%

Current Status of Body Composition Assessment in Sport

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…The estimated magnitude of this bias in middle-school students is consistent with, but smaller than, the elite athletic bias of more than 20% documented by direct measurement (Nevill et al, 2010). This indirect evidence should be supplemented by studies in which direct measurement of adiposity are compared with BMI and BMI percentile for individuals at various levels of physical activity performance.…”

Section: Discussionmentioning

confidence: 83%

“…This has led coaches and trainers to question the validity of BMI as a measure of health risk among athletes (Riewald, 2008;Wein & Palmer, 2008). Substantial athletic bias in elite athletes has been documented by comparing direct adiposity measures and BMI (Nevill et al, 2010); however, this is the first investigation to examine whether a similar bias occurs in adolescents.…”

Section: Discussionmentioning

confidence: 99%

“…This has led coaches and trainers to question the validity of BMI as a measure of health risk among athletes (Riewald, 2008;Wein & Palmer, 2008). Nevill et al (2010) used skinfold thickness and BMI data from elite (Olympic) athletes in seven sports with age-matched controls to examine the adjustments required for elite athletes that would allow BMI for athletes to reflect the adiposity in nonathletic populations. They documented adjustments in the range of 21% to 39% that differed by sport with middle-distance runners requiring a greater adjustment in BMI than other sports studied (including lightweight-and heavyweight-rowers, long-distance runners and triathletes) (Nevill et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Fitness Bias in Body Mass Index of Middle School Students

Erfle

2015

JET

View full text Add to dashboard Cite

Since muscle is more dense than fat, athletes tend to have greater mass and BMI than similarly sized non-athletes. Comparing direct adiposity measures and BMI confirms that BMI is a biased proxy for adiposity for elite athletes. A similar bias should exist for non-elite athletes as well as fit individuals.This paper provides a methodology for indirectly estimating the size of the fitness bias in BMI using median physical activity performances. Approximately 30% of females and 33% of males are fit using this definition.Using data from 9 062 students, regressions suggest 3.1, 95% CI [0.9, 5.3], of a female's BMI percentile of 85, and 3.6% of her weight, CI [1.6%, 5.6%], is due to being fit, but 5.6, CI [3.3, 7.9], of a male's BMI percentile of 85, and 5.9% of his weight, CI [3.9%, 7.9%], is due to being fit. These increases in weight are smaller than, but consistent with, the bias of more than 20% for elite athletes.Strong performance on individual physical activities decreases BMI percentile and BMI, but doing well on multiple physical activities has the reverse effect. This provides evidence of a fitness bias. BMI report cards should include the caveat that BMI may overstate the adiposity status of fit children.

show abstract

“…In the studies on students from selected universities in Warsaw, it was found that in the group with high levels of physical activity, there were only people with normal relative weight. In analyses of body composition in young training people, increased BMI does not indicate an increased fraction of fat in body composition [18], but it is a good predictor of fat in an average population [19]. Probably for this reason, there was no greater relationship between the weight-height proportions and the level of activity of the surveyed students of physical education, whereas this correlation was confirmed in relation to the fraction of fat in body composition (especially in women).…”

Section: Discussionmentioning

confidence: 99%

Determining the level of physical activity estimated by the Canada Fitness Survey questionnaire using criteria of the International Physical Activity Questionnaire

Kostencka

Pastuszak

Buśko

2016

Biomedical Human Kinetics

View full text Add to dashboard Cite

SummaryStudy aim: The aim of the study was to determine the weekly energy expenditure measuring MET/min/week based on data collected through the Canada Fitness Survey (CFS), according to the classification used in the International Physical Activity Questionnaire (IPAQ), and to verify the adopted method to assess the level of physical activity in students of physical education. Material and methods:The study involved 116 female students (21.1 ± 1.6) and 276 male students (21.2 ± 1.7), studying Physical Education at Kazimierz Wielki University. Physical activity (PA) of respondents assessed using the Canada Fitness Survey was converted to energy expenditure in MET/min/week using the criteria established in the IPAQ. Body composition was assessed according to bioelectrical impedance. Results: A significantly smaller fat fraction was observed in the group of students with high physical activity (PA) (p < 0.01). In women, there was a significant relation between FAT% and all analysed characteristics of physical activity: total physical activity (TPA) -0. Conclusions: Based on data collected using the CFS on the type and frequency of PA during a week, we can determine the level of activity in a measurable way, using the IPAQ classification. There is a significant relationship between thus determined physical activity levels and body composition in both women and men, which proves the accuracy of the adopted method of converting weekly energy expenditure to MET/min/week.

show abstract

Adjusting athletes' body mass index to better reflect adiposity in epidemiological research

Cited by 38 publications

References 14 publications

Current Status of Body Composition Assessment in Sport

Current Status of Body Composition Assessment in Sport

Estimating Fitness Bias in Body Mass Index of Middle School Students

Determining the level of physical activity estimated by the Canada Fitness Survey questionnaire using criteria of the International Physical Activity Questionnaire

Contact Info

Product

Resources

About