{"id":6647,"date":"2019-11-08T06:30:25","date_gmt":"2019-11-08T12:30:25","guid":{"rendered":"http:\/\/thesportjournal.org\/?p=6647"},"modified":"2020-06-02T13:45:30","modified_gmt":"2020-06-02T18:45:30","slug":"comparison-of-bmi-based-equations-and-plethysmography-for-estimating-body-fat-in-female-collegiate-gymnasts","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/comparison-of-bmi-based-equations-and-plethysmography-for-estimating-body-fat-in-female-collegiate-gymnasts\/","title":{"rendered":"Comparison of BMI-based equations and plethysmography for estimating body fat in female collegiate gymnasts"},"content":{"rendered":"\n

Authors:<\/strong> Jason C. Casey1<\/sup>, Robert L. Herron2<\/sup>, and Michael R. Esco3<\/sup><\/p>\n\n\n\n

1<\/sup>Department\nof Kinesiology, University of North Georgia, Oakwood, GA, USA
\n2<\/sup>Department of Sports Management, United States Sports Academy,\nDaphne, AL, USA
\n3<\/sup>Department of Kinesiology, The University of Alabama, Tuscaloosa,\nAL, USA<\/p>\n\n\n\n

Corresponding Author:
<\/strong>Robert L. Herron, MA, CSCS*D, ACSM-RCEP
1 Academy Drive
Daphne Al, 36526
rherron@ussa.edu
251-626-3303<\/p>\n\n\n\n

Jason C. Casey,\nPhD, CSCS*D, EP-C is an Assistant Professor of Kinesiology at the University of\nNorth Georgia in Oakwood, GA. His research interests focus on fatigue and\nrecovery associated with exercise, athlete monitoring, and sport-related\nmeasurement issues. <\/p>\n\n\n\n

Robert L. Herron, MA, CSCS*D, ACSM-RECP is\ncurrently faculty member and Sport Management doctoral student at the United\nStates Sports Academy.  Robert\u2019s areas of\nresearch interest include: measurement and evaluation in sport-related research\nand recovery from exercise stressors or sport injuries.  <\/p>\n\n\n\n

Michael R. Esco, PhD, CSCS*D, FACSM is an associate professor of exercise physiology in the Department of Kinesiology at the University of Alabama. His research interests are in the areas of heart rate variability, body composition, athletic monitoring, and cardiovascular physiology.<\/p>\n\n\n\n

Comparison of BMI-based equations and\nplethysmography for estimating body fat in female collegiate gymnasts <\/strong><\/h3>\n\n\n\n

ABSTRACT<\/strong><\/p>\n\n\n\n

The purpose of\nthis study was to assess the utility of using BMI-based equations (BEQ) to\nestimate body-fat percentage (BF%) in female-collegiate gymnasts.  As such, the agreement between BF% estimates\nwith BEQ and air-displacement plethysmography\n(AP) were compared in twenty-two gymnasts (n = 22).  Body mass, height, and BF% were assessed via\nAP and BEQ [Jackson et al. (JBMI<\/sub>), Deurenberg et al. (DBMI<\/sub>),\nand Womersley & Durnin (WBMI<\/sub>)]. Results: The assessments\nproduced the following estimated BF%: AP = 20.3 \u00b1 3.6%; JBMI<\/sub> = 26.9\n\u00b1 3.9%; DBMI<\/sub> = 26.4 \u00b1 2.2%; and WBMI<\/sub> = 27.9 \u00b1 2.5%. BF%\nestimated via AP was significantly lower (p < 0.001) than each BEQ. Weak\ncorrelations were found between AP and BEQ (JBMI<\/sub>, r = 0.12; DBMI<\/sub>,\nr = 0.07; WBMI<\/sub>, r = 0.12). The limits of agreement (constant error \u00b1\n1.96 SD) for each BEQ compared to AP were: JBMI<\/sub> = 6.6 \u00ad\u00ad\u00b1 9.5%;\nDBMI<\/sub> = 6.1 \u00ad\u00ad\u00b1 7.8%; and WBMI<\/sub> = 7.6 \u00b1 8.0%. These results\nsuggest a wide range of individual differences existed between BEQ and AP.\nFurthermore, BEQ significantly overestimated BF% relative to AP in this\ngymnastics population.  Coaches and sport\npractitioners are in need of a quick, practical, inexpensive, and accurate\nmethod of body composition assessment. Based on this study, BEQ does not meet\nthe needs of the practitioner when compared to AP. As a result, practitioners\nin the field need to consider other field methods of predicting BF% in\ncollegiate female gymnasts.<\/p>\n\n\n\n\n\n\n\n

Keywords<\/strong>: air-displacement, body mass index, percent fat, gymnastics<\/p>\n\n\n\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

Many factors,\nincluding body composition, influence performance and health of athletes.\nDesirable ranges of body fat percentage (BF%) and other anthropometric\nmeasurements vary between athletes, sports, and positions (9,\n20). However, it\nis generally considered that increased fat-free mass (FFM) is beneficial and\nexcess fat-mass (FM) is a detriment to athletic performance (11). <\/p>\n\n\n\n

Coaches\nand other sport practitioners need a body-composition assessment method to be\nvalid, accessible, and easy to use (1).\nBody mass index (BMI) is one of the most\ncommonly utilized body composition metrics and a popular method used to\ndetermine the body size clasifications in the general population (24). BMI assessment is non-invasive and requires only a\nmeasurement of height and weight. Thus, BMI is quite easy to assess, does not\nrequire a skilled technician, and is a time-efficient method of body\ncomposition analysis. A primary concern of BMI is that the metric fails\nto distinguish between fat mass and fat-free mass and BMI alone may misclassify\nobesity in some populations (14).\nDespite these limitation, research has shown that BMI and BF% are highly\ncorrelated in the general population (18).\nTo better utilize this metric, several\nregression equations have been developed to estimate BF% with BMI as a primary\nvariable (6, 13, 26). However, little research has been conducted evaluating these equations\nin an athletic population. <\/p>\n\n\n\n

Valid\nlaboratory methods of assessment have been used to estimate BF% and other\nbody-composition metrics in athletes \u2013 such as underwater weighing and\ndual-energy x-ray absorptiometry (10, 15).\nHowever, these methods are often expensive, difficult to access and require a\nhighly trained technician. Additionally, research has demonstrated value of\nvarious field techniques which are often popular in a practical setting due to\nthe accessibility and ease of use (8, 15).\nNevertheless, these techniques present different challenges. For instance,\nsmall changes in hydration status and fluid balance can influence the accuracy\nof BIA assessment. Thus, controlling for hydration should be a requirement with\nBIA but often ignored in practical settings (8, 17).\nSkinfold assessment has been shown to be accurate relative to laboratory\nmethods (15).\nHowever, skinfold assessment requires a skilled technician that is trained to\nprecisely locate anatomical markers and measure skinfold thickness at specific\nlocations on the body (3).\nFurthermore, there are and over 100 various body composition equations that may\ncontribute to validity and reliability issues (3).\nAn alternative laboratory-based body composition\nassessment method is air-displacement plethsymography (AP). AP is often\ndeemed a faster and more user-friendly laboratory measure (23)\nand is regularly used in athletic settings. Additionally, evidence supports the\nuse of  AP  as a valid estimate of BF% in female athletes\nwhen compared to dual energy x-ray\nabsorptiometry (DXA) (1). Furthermore, AP has recently been used in the\nestablishment of descriptive values for body composition across a large sample\nof female-collegiate athletes (9).<\/p>\n\n\n\n

In\nwomen, excessively low BF% is linked with the deleterious health conditions\ncomprising the female athlete triad including; disordered eating, low bone\nmineral density, and amenorrhea (7, 20).\nAdditionally, extreme weight loss and the related comorbidities are more common\nin sports where aesthetics is a prominent aspect of the culture. As noted in\nprevious research, some female gymnasts, attempting to aesthetically conform\nfor sport, are known to achieve these physical goals by engaging in extreme\nweight loss behaviors associated with disordered eating (12, 22).<\/p>\n\n\n\n

Furthermore,\nin sports where aesthetics are a prominent aspect of the associated culture,\nsuch as gymnastics (20),\nresearchers have documented that, in female athletes, appearance is a major\nfactor influencing body composition (19).\nMoreover, one\u2019s sense of body image may be distorted in female athletes\nparticipating in these sports (5, 25),\neven among athletes who present with a favorable body composition (5).\nAs a result, the importance of regular, accurate assessment of BF% are\nheightened in these female athletes and \u2013 more importantly \u2013 inaccurate\nestimates of BF% may detrimentally impact the physical and psychological\nwell-being of the athlete. <\/p>\n\n\n\n

Due to their speed and simplicity, BEQ may be\nattractive for coaches and sport practitioners who have limited access to more\nadvanced measures (such as AP), have minimal time for body composition\nassessment, or have limited training in techniques like skinfold assessment. However,\nthere is little research available to compare BF% estimations from BEQ versus more\nadvanced methods in athletes. The purpose of this study was to assess the\nutility of using BMI-based equations (BEQ) to estimate body-fat percentage\n(BF%) in female-collegiate gymnasts.<\/p>\n\n\n\n

METHODS<\/strong><\/p>\n\n\n\n

Participants<\/strong><\/p>\n\n\n\n

This\nstudy utilized data from twenty-two (n = 22) female collegiate gymnasts (age =\n19 \u00b1 1 yr, height = 158.2 \u00b1 1.9 cm, body mass = 57.4 \u00b1 5.6 kg, and BMI = 22.9 \u00b1\n1.8 kg.<\/sup>m-2<\/sup>). This study was approved by the Institutional\nReview Board at The University of Alabama. <\/p>\n\n\n\n

Procedures<\/strong><\/p>\n\n\n\n

To\ncompare three previously developed BEQ with AP, data was analysed from a\nde-identified database. Information obtained from this database for the\npurposes of this study consisted of height, body mass, body composition via AP,\nand age. BMI was then calculated and subsequently, BF% estimates were derived\nfrom the BEQ (6, 13, 26). BF% estimates from\neach BEQ were then compared to BF% estimates determined via AP.<\/p>\n\n\n\n

Participants were instructed to\nrefrain from exercise, eating, and drinking for at least 2 hours before\ntesting. Height was measured to the nearest 0.1 cm using a stadiometer\n(Detecto, Webb City, MO) and body mass was measured to the nearest 0.02 kg via\nthe BOD POD electronic scale, calibrated to manufacturer guidelines with participants\u2019\nbare foot. BMI was calculated as mass (kg) divided by height (m\u00ad\u00ad<\/sub>\u00ad\u00ad\u00ad<\/sub>\u00ad\u00ad2<\/sup>).\nAll values for BMI were rounded to the nearest 0.1 kg.<\/sup>m-2<\/sup>.\n<\/p>\n\n\n\n

Body composition was assessed\nvia AP using a calibrated BOD POD (BOD POD body-composition system, model\n2000A; Life Measurement Instruments, Concord, CA). Prior to each testing\nsession, all BOD POD calibration procedures were completed according to the\nmanufacturer guidelines by measuring an empty chamber and a calibrating\ncylinder of a standard volume (49.55 L). Researchers proceeded after successful\ncalibration. Participants wore a tight-fitting spandex sports bra, spandex\nshorts, removed all jewelry, and were provided a swim cap to wear over their\nhead to minimize the effect of hair on body volume assessment. A trained\ntechnician then performed the BOD POD assessment.<\/p>\n\n\n\n

Participants were asked to sit\nin the BOD POD in an erect position with hands folded in their laps for body\nvolume assessment. All BOD POD instructions were followed for the assessments.\nAs directed by the manufacturer, two tests were performed to ensure reliability\nof the assessment. If the two original tests were not within 150 mL of each\nother, two additional tests were performed to achieve reliable data. This\nmethod of assessment is recommended by the manufacturer. Previous literature\nhas demonstrated high test to test reliability of AP via these methods for body\nmass (r = 1.0), BF% (r = 0.997), and fat-free mass (FFM) (r = 1.0) (9).\n<\/p>\n\n\n\n

After completion of the test,\nthe software predicted the following in all participants: thoracic gas volume,\nfat-free mass (FFM), fat mass (FM), and BF%. BF% was also predicted via the\nfollowing three previously developed BEQ: Jackson et al. (JBMI<\/sub>) (13),\nDeurenberg et al. (DBMI<\/sub>) (6),\nand Womersley & Durnin (WBMI<\/sub>) (26).\nSee Table 1 for the equations.<\/p>\n\n\n\nTable 1.<\/strong> BMI-based BF% regression equations that were utilized within the study.\n\n \n \n \n \n \n
Abbreviation<\/strong><\/td>\n Equation<\/strong><\/td>\n <\/tr>\n
JBMI<\/td>\n (4.35 x BMI) \u2013 (0.05 x BMI2) \u2013 46.24<\/td>\n <\/tr>\n
DBMI<\/td>\n (1.20 x BMI) + (0.23 x age) \u2013 5.4<\/td>\n <\/tr>\n
WBMI<\/td>\n (1.37 x BMI) \u2013 3.47<\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

Data Analyses <\/strong><\/p>\n\n\n\n

Statistical\nanalyses were performed using SPSS version 20.0 (IBM, Somers, NY, USA). A\nrepeated-measures analysis of variance (ANOVA) was performed to determine mean\ndifferences between BF% assessed via AP and the three BEQ using an alpha level\nof 0.05. A Bonferonni post-hoc analysis was used as a follow-up procedure for\npair-wise comparisons. Pearson product-moment correlation coefficients, Cohen\u2019s\nd effect size (4),\nconstant error, and the 95% limits of agreement via the Bland-Altman method (2)\nwere also calculated. <\/p>\n\n\n\n

RESULTS<\/strong><\/p>\n\n\n\n

Table\n2 represents the comparative statistics between AP and the BEQ. As indicated by\nthe repeated measures ANOVA and follow-up post hoc analysis, each BEQ provided\na significantly higher estimation of BF% when compared to AP (p < 0.001). WBMI<\/sub>\nestimated the highest BF% and was statistically higher than JBMI<\/sub> (p\n= 0.03) and DBMI <\/sub>\u00ad(p < 0.001). Furthermore, the effect size for\nthe comparison of AP to each BEQ was classified as large (Cohen\u2019s d > 0.8).\nWeak correlations were found between AP and all BEQ. Additionally, AP had a\nweak correlation with BMI (r = 0.17).<\/p>\n\n\n\nTable 2.<\/strong> BMI-based equations compared to AP (n = 22) (mean \u00b1 SD).\n\n\n \n \n \n \n \n \n
Method<\/strong><\/td>\n Body Fat %<\/strong><\/td>\n Cohen\u2019s d<\/strong><\/td>\n r<\/strong><\/td>\n CE \u00b1 1.96 SD<\/strong><\/td>\n <\/tr>\n
AP<\/td>\n 20.3 \u00b1 3.6<\/td>\n –<\/td>\n –<\/td>\n –<\/td>\n <\/tr>\n
JBMI<\/td>\n 26.9 \u00b1 3.9*<\/td>\n 0.9<\/td>\n 0.12<\/td>\n 6.6 \u00b1 9.5%<\/td>\n <\/tr>\n
DBMI<\/td>\n 26.4 \u00b1 2.2*<\/td>\n 1.0<\/td>\n 0.07<\/td>\n 6.1 \u00b1 7.8%<\/td>\n <\/tr>\n
WBMI<\/td>\n 27.9 \u00b1 2.5*<\/td>\n 1.2<\/td>\n 0.12<\/td>\n 7.6 \u00b1 8.0%<\/td>\n <\/tr>\n
Cohen\u2019s d = effect size compared to with AP
\nr = Pearson product-moment correlation coefficient with AP.
\n*Significantly greater than AP, (p < 0.001). <\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

Bland-Altman\nplots evaluating the individual differences between AP and each BEQ are shown\nin Figures 1-3. The mean bias predicted for JBMI <\/sub>was 6.6% and the \u00b1\n1.96 SD ranged from -2.9% to 16.1% (Figure 1). As seen in Figure 2, the mean\nbias for DBMI<\/sub> was 6.1% ranging from (\u00b1 1.96 SD) -1.7% to 13.9%. The\nmean bias for WBMI <\/sub>was the largest at 7.6% and the \u00b1 1.96 SD ranged\nfrom -0.4% to 15.6% (Figure 3).<\/p>\n\n\n\n

\"Figure
Figure 1.<\/strong> Bland-Altman plot comparing the estimation of BF% between AP and JBMI<\/sub>. The solid middle line indicates mean difference between the predicted and the actual values of BF% (6.6%); the two outer dashed lines represents \u00b1 1.96 SD of the mean difference (-2.9% to 16.1%). The dashed middle line represents the trend between the difference of the methods and their mean. <\/figcaption><\/figure>\n\n\n\n
\"Figure
Figure 2<\/strong>. Bland-Altman plot comparing the estimation of BF% between AP and DBMI<\/sub>. The solid middle line indicates mean difference between the predicted and the actual values of BF% (6.1%); the two outer dashed lines represents \u00b1 1.96 SD of the mean difference (-1.75 to 13.9%). The dashed middle line represents the trend between the difference of the methods and their mean. <\/figcaption><\/figure>\n\n\n\n
\"Figure
Figure 3.<\/strong> Bland-Altman plot comparing the estimation of BF% between AP and WBMI<\/sub>. The solid middle line indicates mean difference between the predicted and the actual values of BF% (7.6%); the two outer dashed lines represents \u00b1 1.96 SD of the mean difference (-0.4% to 15.6%). The dashed middle line represents the trend between the difference of the methods and their mean. <\/figcaption><\/figure>\n\n\n\n

DISCUSSION<\/strong><\/p>\n\n\n\n

The\npurpose of this study was to determine the agreement between three BEQ and AP\nfor estimating BF% in female collegiate gymnasts. BEQ provide quick and simple\nmethods of estimating BF% that, if in agreement with more advanced assessment\nmethods, would allow for increased accessibility and ease of testing for\npractitioners in athletic settings. However, this study found that each BEQ\nprovided a significantly higher estimate of BF% than AP. Additionally, all BEQ\nshowed a wide range of individual error relative to AP (each BEQ demonstrated a\nconstant error of > 6.0%) according to the Bland-Altman method, as well as\nweak correlation coefficients. Furthermore, AP had a weak correlation with BMI\nalone (r = 0.17). Thus, it is recommended that practitioners avoid the use of\nBEQ in the female collegiate gymnast population.<\/p>\n\n\n\n

Gymnasts\ntypically maintain a lower BF% and greater FFM\n(considered an athletic body type) compared to the average population. This\nlikely serves as an explanation for the results found in this study, as BMI does not distinguish between FM and FFM often\nmisclassifying individuals with athletic body types (16). Thus as the results of this study indicate, the\nequations predominately dependent on BMI were not able to accurately predict\nBF% relative to AP in this sample of collegiate female gymnasts. It should be\nnoted that it may be advantageous for the gymnastics athlete at this level of\ncompetition to have a lower body height. Fields et al. (2017) demonstrated that\nbody height for female collegiate gymnasts was statistically lower than five\nother collegiate female sports evaluated (9). Due to this typical body type (lower BF%, increased FFM, and lower body\nheight) BMI-based categorical interpretation, serves little-to-no use in the\ncollegiate, female-gymnast population.<\/p>\n\n\n\n

There were limitations to this study that may have\nimpacted the results. Three specific limitations were: hydration status was not\nmeasured in the participants, phase of the menstrual cycle was not taken into\nconsideration, and residual volume was estimated via AP standard process as\nopposed to directly measuring. Each of these could have impacted body\ncomposition measurement. However, because all measurements were performed\nduring one session, the influence of total body water status would have influenced\nbody weight for all measures, making the impact on the results minimmal.    <\/p>\n\n\n\n

Further research to provide sport practioners with a\nquick and accurate method of assessing BF% should focus on formulas with\nadditional variables that do not rely as heavily on BMI. Taylor et al. (2012)\ndeveloped a formuala utilizing BMI, hand-grip strength, waist circumference,\nand sex as variables (21). The results of the study indicated the BF%\nestimation from the formula did not differ from dual-energy x-ray absoptiomitry\n(DXA). In additon, the formula had a high correlation and low standard error of\nthe estimate relative to DXA. However, this study included only participants\nfrom the general population. It is unknown if the inclusion of  these additional variables in the BF%\nestimation formula would result in a more accurate estimate of BF% in female athletes\n\u2013 more specifically \u2013 female gymnasts.<\/p>\n\n\n\n

CONCLUSIONS<\/strong><\/p>\n\n\n\n

Regular\nmonitoring of body composition is a valuable component of strength and\nconditioning programs. Coaches and sport practitioners are in need of a quick,\npractical, inexpensive, and valid method of body composition assessment. BEQ\nsignificantly overestimated BF% relative to AP in this gymnastics population.\nThis indicates that the BEQ did not appropriately estimate group-mean BF%.\nFurthermore, a wide range of individual differences existed when comparing the\nthree BEQ with AP estimates in female-collegiate gymnasts. <\/p>\n\n\n\n

APPLICATIONS IN SPORT<\/strong><\/p>\n\n\n\n

Due\nto the aesthetic focus of gymnastics, inaccurate body composition data can\nfoster unhealthy body-image perceptions and exacerbate psychological issues\nrelated to the development and progression of eating disorders  (5, 20).\nTherefore, coaches and other sport practitioners should refrain from using BEQ\nin female-collegiate gymnasts and should consider other practical means of\npredicting BF% in this population.<\/p>\n\n\n\n

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    Authors: Jason C. Casey1, Robert L. Herron2, and Michael R. […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[994,898],"tags":[1521,1523,323,1522],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-1Jd","jetpack-related-posts":[{"id":479,"url":"https:\/\/thesportjournal.org\/article\/physical-self-perception-profile-of-female-college-students-kinesiology-majors-vs-non-kinesiology-majors\/","url_meta":{"origin":6647,"position":0},"title":"Physical Self-Perception Profile of Female College Students: Kinesiology Majors vs. Non-Kinesiology Majors","date":"November 21, 2012","format":false,"excerpt":"Jay Thornton and Kim KatoABSTRACTThe purpose of this study was to compare college student\u2019s Physical Self-Perception Profile (PSPP) (18) scores in female kinesiology majors and non-kinesiology majors. Participants included 68 female kinesiology majors and 88 female non-majors in a mid-sized university. The mean age for the kinesiology majors was 20.8\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2012\/11\/Table1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":6762,"url":"https:\/\/thesportjournal.org\/article\/weight-discrimination-among-students-from-a-diverse-urban-university\/","url_meta":{"origin":6647,"position":1},"title":"Weight Discrimination among Students from a Diverse Urban University","date":"December 20, 2019","format":false,"excerpt":"Authors: Guillermo Escalante1, Rafael Alamilla1, Eric Vogelsang2, Christopher Gentry1, Jason Ng1 1Department of Kinesiology, California State University, San Bernardino, USA; 2Department of Sociology, California State University, San Bernardino, USA Corresponding Author:Guillermo Escalante, DSc, MBA, ATC, CSCS, CISSNCalifornia State University- San Bernardino, Department of Kinesiology5500 University ParkwaySan Bernardino, CA 92407gescalan@csusb.edu(909) 537-7236\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":6082,"url":"https:\/\/thesportjournal.org\/article\/an-investigation-of-youth-football-players-participation-motivations-and-health-related-behaviors\/","url_meta":{"origin":6647,"position":2},"title":"An Investigation of Youth Football Players\u2019 Participation Motivations and Health Related Behaviors","date":"October 18, 2018","format":false,"excerpt":"Authors: Zhenhao Zeng, Andria Cuello, Jonathan Skelly, Christopher Gigliello, Steven Riveras Corresponding Author: P.I. Zhen Hao Zeng, D.P.E. Professor of Sport Pedagogy Department of Kinesiology, Brooklyn College of The City University of New York, USA hzeng@brooklyn.cuny.edu 718-951-5014 Zhen Hao (Howard) Zeng is an associate professor of the Department of Kinesiology\u2026","rel":"","context":"In "Sports Studies and Sports Psychology"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2018\/10\/Table-1.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":5275,"url":"https:\/\/thesportjournal.org\/article\/comparison-of-laboratory-and-field-based-predictors-of-5-km-race-performance-in-division-i-cross-country-runners\/","url_meta":{"origin":6647,"position":3},"title":"Comparison of Laboratory and Field-Based Predictors of 5-km Race Performance in Division I Cross-Country Runners","date":"September 21, 2017","format":false,"excerpt":"Authors: Katie M. Sell, Ph.D., CSCS, TSAC-F, ACSM EP-C Department of Health Professions, Hofstra University, NY Jamie Ghigiarelli, Ph.D., CSCS, USAW, CISSN Department of Health Professions, Hofstra University, NY Corresponding Author: Katie M. Sell, Ph.D., CSCS, TSAC-F, ACSM EP-C Department of Health Professions, 101 Hofstra Dome, 220 Hofstra University, Hempstead,\u2026","rel":"","context":"In "Sports Coaching"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2017\/09\/table1-sell.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":4955,"url":"https:\/\/thesportjournal.org\/article\/body-image-in-division-i-male-athletes-why-is-baseball-high-and-outside\/","url_meta":{"origin":6647,"position":4},"title":"Body Image in Division I Male Athletes: Why is Baseball High and Outside?","date":"March 23, 2017","format":false,"excerpt":"Authors: Lorraine Killion & Dean Culpepper Corresponding Author: Lorraine Killion, Ed.D. Associate Professor Texas A&M University-Kingsville 700 University Blvd. Kingsville, TX 78363-8202 lorraine.killion@tamuk.edu 361.593.3095 Lorraine Killion is an Associate Professor in the Health & Kinesiology Department at Texas A&M University-Kingsville. She is also the EC-12 Physical Education Program Coordinator. Dean\u2026","rel":"","context":"In "Sports Health & Fitness"","img":{"alt_text":"Table 2","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2017\/03\/Table2-1.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":7848,"url":"https:\/\/thesportjournal.org\/article\/the-relationship-between-hip-extensor-strength-and-contralateral-and-ipsilateral-hip-flexor-muscle-length-in-healthy-men-and-women\/","url_meta":{"origin":6647,"position":5},"title":"The relationship between hip extensor strength and contralateral and ipsilateral hip flexor muscle length in healthy men and women","date":"April 9, 2021","format":false,"excerpt":"Authors: Ashley Calvillo1, Guillermo Escalante2, and Morey J. Kolber3 1Los Angeles Sunset Department of Physical Therapy, Kaiser Permanente, Los Angeles, CA, USA2Department of Kinesiology, California State University- San Bernardino, San Bernardino, CA, USA3Department of Physical Therapy, Nova Southeastern University, Fort Lauderdale, FL, USA Corresponding Author:Guillermo Escalante, DSc, MBA, ATC, CSCS*D,\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6647"}],"collection":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/comments?post=6647"}],"version-history":[{"count":7,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6647\/revisions"}],"predecessor-version":[{"id":7292,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/6647\/revisions\/7292"}],"wp:attachment":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/media?parent=6647"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/categories?post=6647"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/tags?post=6647"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}