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Predicting muscular strength using demographics,skeletal dimensions,and body composition measures 被引量:1
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作者 Sean T.Stanelle stephen f.crouse +3 位作者 Tyler R.Heimdal Steven E.Riechman Alexandra L.Remy Bradley S.Lambert 《Sports Medicine and Health Science》 2021年第1期34-39,共6页
The purpose of this study was to develop an equation to predict strength for seven common resistance training exercises using anthropometric and demographic measures.One-hundred forty-seven healthy adults(74 males,73 ... The purpose of this study was to develop an equation to predict strength for seven common resistance training exercises using anthropometric and demographic measures.One-hundred forty-seven healthy adults(74 males,73 females,3512 yr,17410 cm,8819 kg)volunteered to participate.Body composition values(regional/total)and body dimensions were assessed using dual-energy x-ray absorptiometry(DEXA).Subjects underwent the following maximal strength assessments:Leg Press,Chest Press,Leg Curl,Lat Pulldown,Leg Extension,Tri-ceps Pushdown,and Biceps Curl.Multiple linear regression with stepwise removal was used to determine the best model to predict maximal strength for each exercise.Independent predictor variables identified(p<0.05)were height(cm);weight(kg);BMI;age;sex(0=F,1=M);regional lean masses(LM,kg);fat mass(FM,kg);fat free mass(FFM,kg);percent fat(%BF);arm,leg,and trunk lengths(AL,LL,TL;cm);and shoulder width(SW,cm).Analyses were performed with and without regional measures to accommodate scenarios where DEXA is un-available.All models presented were significant(p<0.05,R^(2)=0.68-0.83),with regional models producing the greatest accuracy.Results indicate that maximal strength for individual resistance exercises can be reasonably estimated in adults. 展开更多
关键词 Strength training Exercise testing Regression equation DEXA
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Wearable positive end-expiratory pressure valve improves exercise performance
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作者 stephen f.crouse Jason R.Lytle +4 位作者 Sean Boutros William Benton Michael Moreno Patrick C.McCulloch Brad S.Lambert 《Sports Medicine and Health Science》 2020年第3期159-165,共7页
We tested a PEEP(4.2 cmH_(2)O)mouthpiece(PMP)on maximal cycling performance in healthy adults.Experiment-1,PMP vs.non-PMP mouthpiece(CON)[n=9(5♂),Age=30±2 yr];Experiment-2,PMP vs.no mouthpiece(NMP)[n=10(7♂),Age... We tested a PEEP(4.2 cmH_(2)O)mouthpiece(PMP)on maximal cycling performance in healthy adults.Experiment-1,PMP vs.non-PMP mouthpiece(CON)[n=9(5♂),Age=30±2 yr];Experiment-2,PMP vs.no mouthpiece(NMP)[n=10(7♂),Age=27±1 yr].At timepoint 1 in both experiments(mouthpiece condition randomized)subjects performed graded cycling testing(GXT)(Corival®cycle ergometer)to determine VO_(2peak)(ml*kg*min^(-1)),O2pulse(mlO2*bt^(-1)),GXT endurance time(GXT-T(s)),and VO_(2)(ml*kg*min^(-1))-at-ventilatory-threshold(VO_(2)@VT).At timepoint 272 h later,subjects completed a ventilatory-threshold-endurance-ride[VTER(s)]timed to exhaustion at VO_(2)@VT power(W).One week later at timepoints 3 and 4(time-of-day controlled),subjects repeated testing protocols under the alternate mouthpiece condition.Selected results(paired T-test,p<0.05):Experiment 1 PMP vs.CON,respectively:VO_(2peak)=45.2±2.4 vs.42.4±2.3 p<0.05;VO_(2)@VT=33.7±2.0 vs.32.3±1.6;GXTTTE=521.7±73.4 vs.495.3±72.8(p<0.05);VTER=846.2±166.0 vs.743.1±124.7;O2pulse=24.5±1.4 vs.23.1±1.3(p<0.05).Experiment 2 PMP vs.NMP,respectively:VO_(2peak)=43.3±1.6 vs.41.7±1.6(p<0.05);VO_(2)@VT=31.1±1.2 vs.29.1±1.3(p<0.05);GXT-TTE=511.7±49.6 vs.486.4±49.6(p<0.05);VTER 872.4±134.0 vs.792.9±122.4;O2pulse=24.1±0.9 vs.23.4±0.9(p<0.05).Results demonstrate that the PMP conferred a significant performance benefit to cyclists completing high intensity cycling exercise. 展开更多
关键词 Oxygen uptake Cycling exercise Pulmonary system Maximal working capacity Oxygen saturation RESPIRATION
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