Michael H Haischer, Joseph P Carzoli, Daniel M Cooke, Joshua C Pelland, Jacob F Remmert, Michael C Zourdos
{"title":"从重复速度和速度损失预测后蹲的总重复次数。","authors":"Michael H Haischer, Joseph P Carzoli, Daniel M Cooke, Joshua C Pelland, Jacob F Remmert, Michael C Zourdos","doi":"10.5114/jhk/162021","DOIUrl":null,"url":null,"abstract":"<p><p>The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed in the back squat. Fifty-six resistance-trained individuals participated in the study (male = 41, age = 23 ± 3 yrs, 1RM = 162.0 ± 40.0 kg; female = 15, age = 21 ± 2 yrs, 1RM = 81.5 ± 12.5 kg). After 1RM testing, participants performed single repetition sets with 70% of 1RM and a set to failure with 70% of 1RM. ACV was recorded on all repetitions. Regression model comparisons were performed, and Akaike Information Criteria (AIC) and Standard Error of the Estimate (SEE) were calculated to determine the best model. Neither single repetition ACV at 70% of 1RM (R<sup>2</sup> = 0.004, p = 0.637) nor velocity loss (R<sup>2</sup> = 0.011, p = 0.445) were predictive of total repetitions performed in the set to failure. The simple quadratic model using the first repetition of the set to failure (<math><mrow><mi>Y</mi><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>X</mi><mrow><mi>A</mi><mi>C</mi><mi>V</mi><mi>F</mi><mi>i</mi><mi>r</mi><mi>s</mi><mi>t</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>Z</mi><mo>+</mo><mi>ε</mi></mrow></math>) was identified as the best and most parsimonious model (R<sup>2</sup> = 0.259, F = 9.247, p < 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. This average error of ~2 repetitions warrants only cautious utilization of this method to predict total repetitions an individual can perform in a set, with additional autoregulatory or individualization strategies being necessary to finalize the training prescription.</p>","PeriodicalId":16055,"journal":{"name":"Journal of Human Kinetics","volume":"87 ","pages":"167-178"},"PeriodicalIF":1.9000,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203840/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss.\",\"authors\":\"Michael H Haischer, Joseph P Carzoli, Daniel M Cooke, Joshua C Pelland, Jacob F Remmert, Michael C Zourdos\",\"doi\":\"10.5114/jhk/162021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed in the back squat. Fifty-six resistance-trained individuals participated in the study (male = 41, age = 23 ± 3 yrs, 1RM = 162.0 ± 40.0 kg; female = 15, age = 21 ± 2 yrs, 1RM = 81.5 ± 12.5 kg). After 1RM testing, participants performed single repetition sets with 70% of 1RM and a set to failure with 70% of 1RM. ACV was recorded on all repetitions. Regression model comparisons were performed, and Akaike Information Criteria (AIC) and Standard Error of the Estimate (SEE) were calculated to determine the best model. Neither single repetition ACV at 70% of 1RM (R<sup>2</sup> = 0.004, p = 0.637) nor velocity loss (R<sup>2</sup> = 0.011, p = 0.445) were predictive of total repetitions performed in the set to failure. The simple quadratic model using the first repetition of the set to failure (<math><mrow><mi>Y</mi><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>X</mi><mrow><mi>A</mi><mi>C</mi><mi>V</mi><mi>F</mi><mi>i</mi><mi>r</mi><mi>s</mi><mi>t</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>Z</mi><mo>+</mo><mi>ε</mi></mrow></math>) was identified as the best and most parsimonious model (R<sup>2</sup> = 0.259, F = 9.247, p < 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. This average error of ~2 repetitions warrants only cautious utilization of this method to predict total repetitions an individual can perform in a set, with additional autoregulatory or individualization strategies being necessary to finalize the training prescription.</p>\",\"PeriodicalId\":16055,\"journal\":{\"name\":\"Journal of Human Kinetics\",\"volume\":\"87 \",\"pages\":\"167-178\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203840/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Human Kinetics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5114/jhk/162021\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/4/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"SPORT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Human Kinetics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5114/jhk/162021","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/4/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss.
The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed in the back squat. Fifty-six resistance-trained individuals participated in the study (male = 41, age = 23 ± 3 yrs, 1RM = 162.0 ± 40.0 kg; female = 15, age = 21 ± 2 yrs, 1RM = 81.5 ± 12.5 kg). After 1RM testing, participants performed single repetition sets with 70% of 1RM and a set to failure with 70% of 1RM. ACV was recorded on all repetitions. Regression model comparisons were performed, and Akaike Information Criteria (AIC) and Standard Error of the Estimate (SEE) were calculated to determine the best model. Neither single repetition ACV at 70% of 1RM (R2 = 0.004, p = 0.637) nor velocity loss (R2 = 0.011, p = 0.445) were predictive of total repetitions performed in the set to failure. The simple quadratic model using the first repetition of the set to failure () was identified as the best and most parsimonious model (R2 = 0.259, F = 9.247, p < 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. This average error of ~2 repetitions warrants only cautious utilization of this method to predict total repetitions an individual can perform in a set, with additional autoregulatory or individualization strategies being necessary to finalize the training prescription.
期刊介绍:
The Journal of Human Kinetics is an open access interdisciplinary periodical offering the latest research in the science of human movement studies. This comprehensive professional journal features articles and research notes encompassing such topic areas as: Kinesiology, Exercise Physiology and Nutrition, Sports Training and Behavioural Sciences in Sport, but especially considering elite and competitive aspects of sport.
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