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Ehsan Ghahramanloo, Adrian W. Midgley and David J. Bentley

Background:

There is little information regarding the effects of concurrent training (endurance and resistance training performed in the same overall regimen) on blood lipid profile in sedentary male subjects. This study compared the effects of 3 different 8-wk training programs [endurance training (ET), strength training (ST) and concurrent training (CT)] on blood lipid profile and body composition in untrained young men.

Methods:

A total of 27 subjects were randomly allocated to an ET, ST or CT group which performed either progressive treadmill (ET), free weight (ST) or both the endurance and strength training requirements for 8 weeks.

Results:

High-density lipoprotein and low-density lipoprotein profiles significantly improved in the ET and CT groups (P < .01) but not in the ST group. Triglyceride and total cholesterol profiles significantly improved in all 3 training groups. Total fat mass significantly decreased in the ET and CT groups (P < .001) but not in the ST group, whereas fat free mass significantly increased in the ST and CT groups (P < .01) but not in the ET group.

Conclusions:

These results indicate that CT can be used to simultaneously improve both the serum lipid profile and body composition of previously untrained, apparently health young men.

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James P. Veale, Alan J. Pearce, David Buttifant and John S. Carlson

Purpose:

Body structure and physical development must be addressed when preparing junior athletes for their first season in a senior competition. The aim of this preliminary study was to measure the extent of the assumption that final year junior Australian Football (AF) athletes are at a physical mismatch to their senior counterparts.

Methods:

Twenty-one male participants (17.71 ± 0.27 y) were recruited from one state based elite junior AF competition and forty-one male participants (22.80 ± 4.24 y) were recruited from one club competing in the senior elite Australian Football League (AFL), who were subsequently divided into two groups; professional rookies aged 18-20 y (19.44 ± 0.70 y; n = 18) and professional seniors aged 21+ y (25.43 ± 3.98 y; n = 23). Dual energy X-ray absorptiometry (DEXA) scans of all participants were completed.

Results:

Despite being an average 6.0% and 6.1% lighter in total weight and lean mass respectively, no significant difference was found between the elite junior athletes and their professional AFL rookie counterparts. However, significant differences were demonstrated in comparison with the professional AFL senior athletes (P < .01). Both professional AFL groups demonstrated greater than 0.3 kg total bone mineral content (BMC) than the elite junior athletes (P < .01) and significantly greater segmental BMC and bone mineral density (BMD) results (P < .05).

Conclusion:

While the results identify the differences in body composition of the elite junior athletes, development in a linear fashion is noted, providing useful information for the creation of age appropriate expectations and training programs.

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Alis Bonsignore, David Field, Rebecca Speare, Lianne Dolan, Paul Oh and Daniel Santa Mina

treatment, which include deleterious changes to body composition (eg, bone mineral density, muscular atrophy, increased body fat percentage), physical capacity, fatigue, metabolism (eg, increased blood triglycerides, cholesterol, and impaired glycemic control), psychological well-being, and health

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Simone A. Tomaz, Alessandra Prioreschi, Estelle D. Watson, Joanne A. McVeigh, Dale E. Rae, Rachel A. Jones and Catherine E. Draper

favorable measures of body composition, along with a range of positive psychosocial and health outcomes. 6 Research from high-income countries shows that complying with the PA and SB components of the guidelines is associated with better health and developmental outcomes. 6 – 10 Evidence supporting the

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Alessandra Madia Mantovani, Manoel Carlos Spiguel de Lima, Luis Alberto Gobbo, Enio Ricardo Vaz Ronque, Marcelo Romanzini, Bruna Camilo Turi-Lynch, Jamile Sanches Codogno and Rômulo Araújo Fernandes

not engaged (Table  2 ). Lean soft tissue was higher in women engaged in sports participation in early life compared with women not engaged ( P -value = .001), but not in men ( P -value = .07). Table 2 Comparison of Body Composition Variables Between Early Sports Participation in Childhood or

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Nathaniel S. Nye, Drew S. Kafer, Cara Olsen, David H. Carnahan and Paul F. Crawford

, fitness level, biomechanics, genetic factors, training progression strategies (or lack thereof), age, gender, tobacco use, footwear, previous history of injury, lumbopelvic core strength/stability, intrinsic foot muscle strength/stability, body composition], it is not surprising that the AUCs are somewhat

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D. Enette Larson-Meyer, Kathleen Woolf and Louise Burke

obtaining, verifying and interpreting data needed to identify nutrition-related problems, their causes and their significance” ( Academy of Nutrition and Dietetics, 2015 ). A complete assessment should ideally include dietary evaluation, anthropometry and body composition analysis, biochemical testing

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Rodrigo Antunes Lima, Lisbeth Runge Larsen, Anna Bugge and Lars Bo Andersen

in the association between physical fitness and academic performance. However, several recent studies have proposed plausible mechanisms for the association between academic performance and body composition, such as evidence showing that excess adiposity might impair cognitive function and thereby

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Mary O. Hearst, John R. Sirard, Leslie Lytle, Donald R. Dengel and David Berrigan

Background:

The association of physical activity (PA), measured 3 ways, and biomarkers were compared in a sample of adolescents.

Methods:

PA data were collected on 2 cohorts of adolescents (N = 700) in the Twin Cities, Minnesota, 2007–2008. PA was measured using 2 survey questions [Modified Activity Questionnaire (MAQ)], the 3-Day Physical Activity Recall (3DPAR), and accelerometers. Biomarkers included systolic (SBP) and diastolic blood pressure (DBP), lipids, percent body fat (%BF), and body mass index (BMI) percentile. Bivariate relationships among PA measures and biomarkers were examined followed by generalized estimating equations for multivariate analysis.

Results:

The 3 measures were significantly correlated with each other (r = .22–.36, P < .001). Controlling for study, puberty, age, and gender, all 3 PA measures were associated with %BF (MAQ = −1.93, P < .001; 3DPAR = −1.64, P < .001; accelerometer = −1.06, P = .001). The MAQ and accelerometers were negatively associated with BMI percentile. None of the 3 PA measures were significantly associated with SBP or lipids. The percentage of adolescents meeting the national PA recommendations varied by instrument.

Conclusions:

All 3 instruments demonstrated consistent findings when estimating associations with %BF, but were different for prevalence estimates. Researchers must carefully consider the intended use of PA data when choosing a measurement instrument.