Body Mass Index as a Predictor of Percent Fat in College Athletes and Nonathletes

Ode, Joshua J.; Pivarnik, James M.; Reeves, Mathew J.; Knous, Jeremy L.

doi:10.1249/01.mss.0000247008.19127.3e

Cited by 263 publications

(201 citation statements)

References 27 publications

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“…Participants with a BMI 27.5 kg/m 2 were identified as overweight. Although the traditional BMI overweight cutoff value for a general population is 25, the higher value is in agreement with previous research in trained individuals with higher muscle mass 21 and is based on the Army screening table. 22 Smoking: Participants who answered yes to ''Do you smoke cigarettes regularly?''…”

Section: Health Risk Factorssupporting

confidence: 89%

“…Finally, the BMI cutoff value of 27.5 kg/m 2 is higher than the typical cutoff value for being overweight (25.0 kg/ m 2 ). 21 This higher rate was based on the enrollment of a relatively active population, and this cutoff value is aligned with current Army standards for determining weight status. 21,22 Based on the exploratory nature of this study and the multiple comparisons in this study, there is a potential for type I error.…”

Section: Discussionmentioning

confidence: 99%

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Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting

Teyhen

Rhon

Butler

et al. 2016

Journal of Athletic Training

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Context: Although inactivity, being overweight, smoking, and a history of injury are identified as risk factors for poor health and injury, few authors have examined their association on physical performance. Young adults may be more likely to adopt healthier lifestyles if they understand the effect of health behaviors on performance.Objective: To determine the association of being overweight, smoking, inactivity, and a history of injury with physical performance.Design: Cross-sectional study.Setting: Military population. Patients or Other Participants: Active-duty service members (N ¼ 1466; 1380 men, 86 women; age ¼ 24.7 6 5.0 years; body mass index ¼ 26.7 6 3.4 kg/m 2 ). Main Outcome Measure(s): Participants performed 8 measures (the triple-crossover hop for distance, the 6-m timed-hop test, the Functional Movement Screen, the Lower Quarter YBalance Test, the Upper Quarter Y-Balance Test, and the 3-event Army Physical Fitness Test) for evaluation of endurance, strength, muscular endurance, power, agility, balance, and motor control. Participants were categorized based on the number of health risk factors present. Using an analysis of covariance, we assessed the relationship between risk factors and physical performance with age and sex as covariates.Results: Compared with those who had no risk factors (27.9% of men, 34.9% of women), physical performance was worse in those who had 1, 2, or 3 to 4 risk factors present by 4.3%, 6.7%, and 10.3%, respectively. Decrements in performance for those with 3 to 4 risk factors ranged from 3.3% to 14.4%.Conclusions: An unhealthy lifestyle habit or a history of injury was negatively associated with physical performance. Physical performance decrements were associated with the number of risk factors present. Understanding how risk factors contribute to decreased physical performance may enable clinicians to improve compliance with injury-prevention programs in occupational settings in which a young and relatively healthy workforce may be more concerned about performance than health.Key Words: functional movement, health, injury prediction, risk factors Key PointsPhysical performance was worse in those with health risk factors; performance was lowest in those with 3 to 4 health risk factors. Risk factors can be responsible for a 10-to 47-point decrease in scores on the Army Physical Fitness Test. Future researchers should determine if strategies focused on improving performance may be more effective in decreasing health risk factors in a relatively young and healthy population. S ubstantial evidence exists that risk factors negatively affect health. Specifically, physical inactivity, being overweight, smoking, and a history of injury have been identified as having negative associations with health.1-4 Less clear is the association between these health risk factors and physical performance. Defining that association is of particular interest to organizations that value both health and physical performance. The US Army, in addition to funding health care for its members, must e...

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Section: Health Risk Factorssupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting

Teyhen

Rhon

Butler

et al. 2016

Journal of Athletic Training

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“…The inability of BMI to distinguish fat from lean mass can lead to the inappropriate diagnosis of obesity. This shortcoming has been shown in many studies including that of Ode et al 38 in which the specificity of BMI to diagnose excess adiposity in varsity male athletes was only 27%, whereas the sensitivity was excellent at 100%. This study's results showed a pooled specificity of 90% in diagnosing excess adiposity, thereby indicating that 10% of the studied individuals were misdiagnosed.…”

Section: Discussionmentioning

confidence: 96%

Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis

Okorodudu

Jumean

Montori³

et al. 2010

Int J Obes

888

681

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Objective: We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity. Design: Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity. Results: The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43-0.57) and a pooled specificity of 0.90 (CI: 0.86-0.94). Positive LR was 5.88 (CI: 4.24-8.15), I 2 ¼ 97.8%; the negative LR was 0.43 (CI: 0.37-0.50), I 2 ¼ 98.5%; and the DOR was 17.91 (CI: 12.56-25.53), I 2 ¼ 91.7%. Analysis of studies that used BMI cutoffs X30 had a pooled sensitivity of 0.42 (CI: 0.31-0.43) and a pooled specificity of 0.97 (CI: 0.96-0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates. Conclusion: Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.

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“…It should be also noted that body mass index may not be reliable in identifying overweight/obesity in athletic and military populations. 76,77 Determining baseline characteristics based on ICD-9 codes was another limitation, given the variable sensitivity and specificity of each disease group. However, we are not aware of any reason for differential ascertainment bias between statin users and nonusers (i.e., underestimation or overestimation is likely to affect both treatments equally).…”

Section: Discussionmentioning

confidence: 99%

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

et al. 2015

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BACKGROUND: Statin use is associated with increased incidence of diabetes and possibly with increased body weight and reduced exercise capacity. Data on the longterm effects of these associations in healthy adults, however, are very limited. In addition, the relationship between these effects and diabetic complications has not been adequately studied. OBJECTIVE: To examine the association between statin use and new-onset diabetes, diabetic complications, and overweight/obesity in a cohort of healthy adults. RESEARCH DESIGN: This was a retrospective cohort study. PARTICIPANTS: Subjects were Tricare beneficiaries who were evaluated between October 1, 2003 and March 1, 2012. Patients were divided into statin users and nonusers. INTERVENTION: We excluded patients who, at baseline, had a preexisting disease indicative of cardiovascular diseases, any positive element of the Charlson comorbidity index (including diabetes mellitus), or life-limiting chronic diseases. Using 42 baseline characteristics, we generated a propensity score to match statin users and nonusers. MAIN MEASURES: Outcomes assessed included newonset diabetes, diabetic complications, and overweight/ obesity. KEY RESULTS: A total of 25,970 patients (3982 statin users and 21,988 nonusers) were identified as healthy adults at baseline. Of these, 3351 statins users and 3351 nonusers were propensity score-matched. Statin users had higher odds of new-onset diabetes (odds ratio [OR] 1.87; 95 % confidence interval [95 % CI] 1.67-2.01), diabetes with complications (OR 2.50; 95 % CI 1.88-3.32), and overweight/obesity (OR 1.14; 95 % CI 1.04-1.25). Secondary and sensitivity analyses demonstrated similar findings. CONCLUSIONS: Diabetes, diabetic complications, and overweight/obesity were more commonly diagnosed among statin-users than similar nonusers in a healthy cohort of adults. This study demonstrates that short-term clinical trials might not fully describe the risk/benefit of long-term statin use for primary prevention.

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Body Mass Index as a Predictor of Percent Fat in College Athletes and Nonathletes

Abstract: BMI should be used cautiously when classifying fatness in college athletes and nonathletes. Our results support the need for different BMI classifications of overweight in these populations.

Cited by 263 publications

References 27 publications

Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting

Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting

Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

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