43
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Relationship between CrossFit ® Performance and Laboratory-Based Measurements of Fitness

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          To date, research has examined the physiological determinants of performance in standardized CrossFit ® (CF) workouts but not without the influence of CF familiarity. Therefore, the purpose of this present study was to examine the predictive value of aerobic fitness, body composition, and total body strength on performance of two standardized CF workouts in CF-naïve participants. Twenty-two recreationally trained individuals (males = 13, females = 9) underwent assessments of peak oxygen consumption (VO 2 peak), ventilatory thresholds, body composition, and one repetition maximum tests for the back squat, deadlift, and overhead press in which the sum equaled the CF Total. Participants also performed two CF workouts: a scaled version of the CF Open workout 19.1 and a modified version of the CF Benchmark workout Fran to determine scores based on total repetitions completed and time-to-completion, respectively. Simple Pearson’s r correlations were used to determine the relationships between CF performance variables (19.1 and modified Fran) and the independent variables. A forward stepwise multiple linear regression analysis was performed and significant variables that survived the regression analysis were used to create a predictive model of CF performance. Absolute VO 2 peak was a significant predictor of 19.1 performance, explaining 39% of its variance (adjusted R 2 = 0.39, p = 0.002). For modified Fran, CF Total was a significant predictor and explained 33% of the variance in performance (adjusted R 2 = 0.33, p = 0.005). These results suggest, without any influence of CF familiarity or experience, that performance in these two CF workouts could be predicted by distinct laboratory-based measurements of fitness.

          Related collections

          Most cited references25

          • Record: found
          • Abstract: found
          • Article: not found

          Contribution of phosphocreatine and aerobic metabolism to energy supply during repeated sprint exercise.

          This study examined the contribution of phosphocreatine (PCr) and aerobic metabolism during repeated bouts of sprint exercise. Eight male subjects performed two cycle ergometer sprints separated by 4 min of recovery during two separate main trials. Sprint 1 lasted 30 s during both main trials, whereas sprint 2 lasted either 10 or 30 s. Muscle biopsies were obtained at rest, immediately after the first 30-s sprint, after 3.8 min of recovery, and after the second 10- and 30-s sprints. At the end of sprint 1, PCr was 16.9 +/- 1.4% of the resting value, and muscle pH dropped to 6.69 +/- 0.02. After 3.8 min of recovery, muscle pH remained unchanged (6.80 +/- 0.03), but PCr was resynthesized to 78.7 +/- 3.3% of the resting value. PCr during sprint 2 was almost completely utilized in the first 10 s and remained unchanged thereafter. High correlations were found between the percentage of PCr resynthesis and the percentage recovery of power output and pedaling speed during the initial 10 s of sprint 2 (r = 0.84, P < 0.05 and r = 0.91, P < 0.01). The anaerobic ATP turnover, as calculated from changes in ATP, PCr, and lactate, was 235 +/- 9 mmol/kg dry muscle during the first sprint but was decreased to 139 +/- 7 mmol/kg dry muscle during the second 30-s sprint, mainly as a result of a approximately 45% decrease in glycolysis. Despite this approximately 41% reduction in anaerobic energy, the total work done during the second 30-s sprint was reduced by only approximately 18%. This mismatch between anaerobic energy release and power output during sprint 2 was partly compensated for by an increased contribution of aerobic metabolism, as calculated from the increase in oxygen uptake during sprint 2 (2.68 +/- 0.10 vs. 3.17 +/- 0.13 l/min; sprint 1 vs. sprint 2; P < 0.01). These data suggest that aerobic metabolism provides a significant part (approximately 49%) of the energy during the second sprint, whereas PCr availability is important for high power output during the initial 10 s.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            End Criteria for Reaching Maximal Oxygen Uptake Must Be Strict and Adjusted to Sex and Age: A Cross-Sectional Study

            Objective To describe different end criteria for reaching maximal oxygen uptake (VO2max) during a continuous graded exercise test on the treadmill, and to explore the manner by which different end criteria have an impact on the magnitude of the VO2max result. Methods A sample of 861 individuals (390 women) aged 20–85 years performed an exercise test on a treadmill until exhaustion. Gas exchange, heart rate, blood lactate concentration and Borg Scale6–20 rating were measured, and the impact of different end criteria on VO2max was studied;VO2 leveling off, maximal heart rate (HRmax), different levels of respiratory exchange ratio (RER), and postexercise blood lactate concentration. Results Eight hundred and four healthy participants (93%) fulfilled the exercise test until voluntary exhaustion. There were no sex-related differences in HRmax, RER, or Borg Scale rating, whereas blood lactate concentration was 18% lower in women (P<0.001). Forty-two percent of the participants achieved a plateau in VO2; these individuals had 5% higher ventilation (P = 0.033), 4% higher RER (P<0.001), and 5% higher blood lactate concentration (P = 0.047) compared with participants who did not reach a VO2 plateau. When using RER ≥1.15 or blood lactate concentration ≥8.0 mmol•L–1, VO2max was 4% (P = 0.012) and 10% greater (P<0.001), respectively. A blood lactate concentration ≥8.0 mmol•L–1 excluded 63% of the participants in the 50–85-year-old cohort. Conclusions A range of typical end criteria are presented in a random sample of subjects aged 20–85 years. The choice of end criteria will have an impact on the number of the participants as well as the VO2max outcome. Suggestions for new recommendations are given.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              High-Intensity Functional Training (HIFT): Definition and Research Implications for Improved Fitness

              High-intensity functional training (HIFT) is an exercise modality that emphasizes functional, multi-joint movements that can be modified to any fitness level and elicit greater muscle recruitment than more traditional exercise. As a relatively new training modality, HIFT is often compared to high-intensity interval training (HIIT), yet the two are distinct. HIIT exercise is characterized by relatively short bursts of repeated vigorous activity, interspersed by periods of rest or low-intensity exercise for recovery, while HIFT utilizes constantly varied functional exercises and various activity durations that may or may not incorporate rest. Over the last decade, studies evaluating the effectiveness of HIIT programs have documented improvements in metabolic and cardiorespiratory adaptations; however, less is known about the effects of HIFT. The purpose of this manuscript is to provide a working definition of HIFT and review the available literature regarding its use to improve metabolic and cardiorespiratory adaptations in strength and conditioning programs among various populations. Additionally, we aim to create a definition that is used in future publications to evaluate more effectively the future impact of this type of training on health and fitness outcomes.
                Bookmark

                Author and article information

                Journal
                Sports (Basel)
                Sports (Basel)
                sports
                Sports
                MDPI
                2075-4663
                11 August 2020
                August 2020
                : 8
                : 8
                : 112
                Affiliations
                [1 ]Human Performance Research Laboratory, Department of Kinesiology and Health Promotion, California State University Pomona, Pomona, CA 92805, USA; ekatzeitz@ 123456gmail.com (E.K.Z.); lfcook@ 123456cpp.edu (L.F.C.); slemez@ 123456cpp.edu (S.L.); whitleyva@ 123456gmail.com (W.D.L.); immanuelterbio@ 123456gmail.com (I.Y.T.); justinrtran@ 123456gmail.com (J.R.T.)
                [2 ]Department of Kinesiology, Azusa Pacific University, Azusa, CA 91702, USA; jdexheimer@ 123456apu.edu
                Author notes
                [* ]Correspondence: ejo@ 123456cpp.edu ; Tel.: +1-909-869-5499
                Author information
                https://orcid.org/0000-0002-6770-7778
                Article
                sports-08-00112
                10.3390/sports8080112
                7466681
                32796573
                69956ecc-d8f6-4189-b7a4-91dceb9122c7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 June 2020
                : 07 August 2020
                Categories
                Article

                high-intensity functional training,aerobic fitness,body composition

                Comments

                Comment on this article