exercise energy expenditure
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Author(s):  
Jose M. Moris ◽  
Samantha A Olendorff ◽  
Chelsie M. Zajac ◽  
Maria Fernandez del Valle ◽  
Benjamin L. Webb ◽  
...  

The primary purpose of this study was to determine prevalence of the Male Athlete Triad (MAT) conditions: low energy availability (EA), low bone mineral density (BMD), and low testosterone in male collegiate athletes from different sports. Participants included 44 collegiate male athletes (age, 20.4 ± 0.2 yr; BMI, 25.3 ± 1.3 kg/m2) from seven sports (cross country, soccer, basketball, wrestling, track, golf, and baseball). Resting metabolic rate, three-day food intake, seven-day exercise energy expenditure, body composition, and reproductive and metabolic hormones were assessed. Of the total participants, 15% had low EA, 0% had low BMD, 28% had low total testosterone (TT), and 80% had low calculated free testosterone (cFT). There were no significant correlations between EA, BMD, TT, and cFT. Insulin and sex hormone binding globulin (SHBG) were below and on the upper end of the reference range for healthy male adults, respectively. Insulin was negatively correlated with total (r = -0.330, p = 0.043) and lumbar spine BMD z-scores (r = -0.413, p = 0.010). Low TT and low cFT were the most prevalent MAT conditions among all athletes. Further research should investigate the relationship between insulin and SHBG and the role of these hormones in the MAT. Novelty Bullets • Assessment of energy availability alone is not sufficient to identify physiological disturbances in collegiate male athletes. • Low total and/or free testosterone may be present in some collegiate male athletes, regardless of BMD status. • Low insulin and high SHBG concentration may portray the presence of conditions of the MAT in male collegiate athletes.


2021 ◽  
Vol 11 (18) ◽  
pp. 8618
Author(s):  
Iva Jurov ◽  
Nicola Keay ◽  
Samo Rauter

The aim of this study was to severely reduce energy availability (EA) in controlled conditions in trained male endurance athletes to observe any effects on health, performance, and psychological and energy markers. EA was reduced by 50% over 14 days in athletes by maintaining identical energy intake and increasing exercise energy expenditure. Blood was drawn, performance was measured by three specific tests (endurance, explosive power and agility) and two psychological questionnaires were used. Reduced EA (17.3 ± 5.0 kcal/kg FFM/day) resulted in lower body fat% (t(12) = 3.36, p = 0.006), lower power output and relative power output (t(12) = 2.69, p = 0.021 t(12) = 2.34, p = 0.036), explosive power was reduced (t(12) = 6.41, p = 0.000), lactate metabolism was altered (p = 0.001). EA was negatively correlated with haemoglobin and testosterone (r = −0.557, p = 0.30 and r = −0.532, p = 0.037), anaerobic threshold (r = −0.597, p = 0.02) and respiratory compensation point (r = −0.575, p = 0.025). There were significant differences in Well-being (t(12) = 4.11, p = 0.002) and the Three Factor Eating Questionnaire (t(12) = −2.71, p = 0.020). This is the first study to demonstrate that endurance performance and explosive power can be affected before detrimental health effects occur in male athletes. Drastic reductions of EA could lead to poor eating behaviours. The two psychological questionnaires seem to be more sensitive to EA changes than blood markers.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jinchao Gao ◽  
Fahd S Alotaibi ◽  
Ragab Ibrahim. Ismail

Abstract Carbohydrate metabolism can provide energy for human exercise. However, different exercise intensities will consume different amounts of energy. For this reason, the paper uses a fractional linear regression equation to study the characteristics of carbohydrate metabolism and energy consumption of other groups of human beings with the same oxygen consumption during exercise. In addition, the thesis measures energy consumption during exercise and body recovery by analyzing gas metabolism methods. As a result, we found that the sugar, fat metabolism, and energy consumption of heavier volunteers under the same exercise intensity were lower than those of regular weight volunteers. Thus, the fractional linear regression method can help us analyze the relationship between glucose metabolism and exercise energy.


Author(s):  
Karine Schaal ◽  
Marta D VanLoan ◽  
Christophe Hausswirth ◽  
Gretchen A Casazza

Low energy availability (EA) suppresses many physiological processes, including ovarian function in female athletes. Low EA could also predispose athletes to develop a state of overreaching. This study compared the changes in ad libitum energy intake (EI), exercise energy expenditure (ExEE), and EA among runners completing a training overload (TO) phase. We tested the hypothesis that runners becoming overreached would show decreased EA, suppressed ovarian function and plasma leptin, compared to well-adapted (WA) runners. After 1 menstrual cycle (baseline), 16 eumenorrheic runners performed 4 weeks of TO followed by a 2-week recovery (131±3% and 63±6% of baseline running volume respectively). Seven-day ExEE, EI, running performance (RUNPERF) and plasma [leptin] were assessed for each phase. Salivary [estradiol] was measured daily. Urinary [luteinizing hormone] tests confirmed ovulation. Nine runners adapted positively to TO (WA,ΔRUNPERF: +4±2%); seven were non-functionally overreached (NFOR, ΔRUNPERF –9±2%) as RUNPERF remained suppressed after the recovery period. WA increased EI during TO, maintaining their baseline EA despite a large increase in ExEE (ΔEA=+1.9±1.3 kcal.kgFFM-1.d-1, P=0.17). By contrast, NFOR showed no change in EI, leading to decreased EA (ΔEA=-5.6±2.1 kcal.kgFFM-1.d-1, P=0.04). [Leptin]b, mid-cycle and luteal [estradiol]s decreased in NFOR only. Contrasting with WA, NFOR failed to maintain baseline EA during TO, resulting in poor performance outcomes and suppressed ovarian function.NCT02224976. NOVELTY BULLETS: -Runners adapting positively to training overload (TO) increased ad libitum energy intake, maintaining baseline EA and ovarian function through TO. -By contrast, NFOR runners failed to increase energy intake, showing suppressed EA and ovarian function during TO.


2021 ◽  
Vol 56 (3) ◽  
pp. 311-320
Author(s):  
Toni Marie Torres-McGehee ◽  
Dawn M. Emerson ◽  
Erin M. Moore ◽  
Stacy E. Walker ◽  
Kelly Pritchett ◽  
...  

Context Research exists on energy balances (EBs) and eating disorder (ED) risks in physically active populations and occupations by settings, but the EB and ED risk in athletic trainers (ATs) have not been investigated. Objective To assess ATs' energy needs, including the macronutrient profile, and examine ED risk and pathogenic behavioral differences between sexes (men, women) and job statuses (part time or full time) and among settings (college or university, high school, nontraditional). Design Cross-sectional study. Setting Free living in job settings. Patients or Other Participants Athletic trainers (n = 46; male part-time graduate assistant ATs = 12, male full-time ATs = 11, female part-time graduate assistant ATs = 11, female full-time ATs = 12) in the southeastern United States. Main Outcome Measure(s) Anthropometric measures (sex, age, height, weight, body composition), demographic characteristics (job status [full- or part-time AT], job setting [college/university, high school, nontraditional], years of AT experience, exercise background, alcohol use), resting metabolic rate, energy intake (EI), total daily energy expenditure (TDEE), EB, exercise energy expenditure, macronutrients (carbohydrates, protein, fats), the Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist. Results The majority of participants (84.8%, n = 39) had an ED risk, with 26.1% (n = 12) engaging in at least 1 pathogenic behavior, 50% (n = 23) in 2 pathogenic behaviors, and 10.8% (n = 5) in >2 pathogenic behaviors. Also, 82.6% of ATs (n = 38) presented in negative EB (EI < TDEE). Differences were found in resting metabolic rate for sex and job status (F1,45 = 16.48, P = .001), EI (F1,45 = 12.01, P = .001), TDEE (F1,45 = 40.36, P < .001), and exercise energy expenditure (F1,38 = 5.353, P = .026). No differences were present in EB for sex and job status (F1,45 = 1.751, P = .193); χ2 analysis revealed no significant relationship between ATs' sex and EB (\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\({\rm{\chi }}_{1,46}^2\)= 0.0, P = 1.00) and job status and EB (\({\rm{\chi }}_{1,46}^2\) = 2.42, P = .120). No significant relationship existed between Daily Reference Intakes recommendations for all macronutrients and sex or job status. Conclusions These athletic trainers experienced negative EB, similar to other professionals in high-demand occupations. Regardless of sex or job status, ATs had a high ED risk and participated in unhealthy pathogenic behaviors. The physical and mental concerns associated with these findings indicate a need for interventions targeted at ATs' health behaviors.


Nutrients ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3262 ◽  
Author(s):  
Thomas Egger ◽  
Joelle Leonie Flueck

Background: Low energy availability (LEA) is a major problem as athletes often restrict their energy intake. It has been shown that LEA occurs often in female and endurance athletes and in athletes from weight-sensitive or aesthetic sports. The purpose of this study was to investigate energy availability (EA) in elite wheelchair athletes. Methods: Fourteen elite wheelchair athletes (8 males; 6 females) participated. Data were collected using a weighed seven-day food and training diary to estimate energy intake and exercise energy expenditure. Resting energy expenditure and body composition were measured, whereas energy balance (EB) was calculated. Results: Measured over 7 days, EA was significantly different (36.1 ± 6.7 kcal kg−1 FFM day−1) in male compared to female (25.1 ± 7.1 kcal kg−1 FFM day−1) athletes (p < 0.001). From all analyzed days, LEA occurred in 73% of the days in female athletes and in 30% of the days in male athletes. EB was positive in male athletes (+169.1 ± 304.5 kcal) and negative (−288.9 ± 304.8 kcal) in female athletes. Conclusions: A higher prevalence of LEA was found in female compared to male athletes. A higher energy intake would be recommended to meet energy needs and to maximize training adaptation.


2020 ◽  
Vol 52 (7S) ◽  
pp. 518-519
Author(s):  
Byan Smith ◽  
Olivia Hanzel ◽  
Joshua Patterson ◽  
Kayley Stock

2020 ◽  
Vol 52 (7S) ◽  
pp. 43-43
Author(s):  
Elvis Alvarez Carnero ◽  
Christopher Bock ◽  
Steven R. Smith ◽  
Bret H. Goodpaster

2020 ◽  
Vol 52 (7S) ◽  
pp. 344-344
Author(s):  
Gregory Hand ◽  
Robin Shook ◽  
Daniel O'Connor ◽  
Clemens Drenowatz ◽  
Steven Blair

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