Dataset Information

Physiological Predictors of Competition Performance in CrossFit Athletes.

ABSTRACT: The aim of this study was to determine the physiological variables that predict competition performance during a CrossFit competition. Fifteen male amateur CrossFit athletes (age, 35 ± 9 years; CrossFit experience, 40 ± 27 months) performed a series of laboratory-based tests (incremental load test for deep full squat and bench press; squat, countermovement and drop jump tests; and incremental running and Wingate tests) that were studied as potential predictors of CrossFit performance. Thereafter, they performed the five Workouts of the Day (WODs) corresponding to the CrossFit Games Open 2019, and we assessed the relationship between the laboratory-based markers and CrossFit performance with regression analyses. Overall CrossFit performance (i.e., final ranking considering the sum of all WODs, as assessed by number of repetitions, time spent in exercises or weight lifted) was significantly related to jump ability, mean and peak power output during the Wingate test, relative maximum strength for the deep full squat and the bench press, and maximum oxygen uptake (VO2max) and speed during the incremental test (all p < 0.05, r = 0.58-0.75). However, the relationship between CrossFit Performance and most laboratory markers varied depending on the analyzed WOD. Multiple linear regression analysis indicated that measures of lower-body muscle power (particularly jump ability) and VO2max explained together most of the variance (R2 = 81%, p < 0.001) in overall CrossFit performance. CrossFit performance is therefore associated with different power-, strength-, and aerobic-related markers.

SUBMITTER: Martinez-Gomez R

PROVIDER: S-EPMC7277742 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Physiological Predictors of Competition Performance in CrossFit Athletes.

Martínez-Gómez Rafael R Valenzuela Pedro L PL Alejo Lidia B LB Gil-Cabrera Jaime J Montalvo-Pérez Almudena A Talavera Eduardo E Lucia Alejandro A Moral-González Susana S Barranco-Gil David D

International journal of environmental research and public health 20200524 10

The aim of this study was to determine the physiological variables that predict competition performance during a CrossFit competition. Fifteen male amateur CrossFit athletes (age, 35 ± 9 years; CrossFit experience, 40 ± 27 months) performed a series of laboratory-based tests (incremental load test for deep full squat and bench press; squat, countermovement and drop jump tests; and incremental running and Wingate tests) that were studied as potential predictors of CrossFit performance. Thereafter ...[more]

PMID: 32456306

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Project description:BackgroundCrossFit is a type of competitive exercise program that has gained widespread recognition. To date, there have been no studies that have formally examined injury rates among CrossFit participants or factors that may contribute to injury rates.PurposeTo establish an injury rate among CrossFit participants and to identify trends and associations between injury rates and demographic categories, gym characteristics, and athletic abilities among CrossFit participants.Study designDescriptive epidemiology study.MethodsA survey was conducted, based on validated epidemiologic injury surveillance methods, to identify patterns of injury among CrossFit participants. It was sent to CrossFit gyms in Rochester, New York; New York City, New York; and Philadelphia, Pennsylvania, and made available via a posting on the main CrossFit website. Participants were encouraged to distribute it further, and as such, there were responses from a wide geographical location. Inclusion criteria included participating in CrossFit training at a CrossFit gym in the United States. Data were collected from October 2012 to February 2013. Data analysis was performed using Fisher exact tests and chi-square tests.ResultsA total of 486 CrossFit participants completed the survey, and 386 met the inclusion criteria. The overall injury rate was determined to be 19.4% (75/386). Males (53/231) were injured more frequently than females (21/150; P = .03). Across all exercises, injury rates were significantly different (P < .001), with shoulder (21/84), low back (12/84), and knee (11/84) being the most commonly injured overall. The shoulder was most commonly injured in gymnastic movements, and the low back was most commonly injured in power lifting movements. Most participants did not report prior injury (72/89; P < .001) or discomfort in the area (58/88; P < .001). Last, the injury rate was significantly decreased with trainer involvement (P = .028).ConclusionThe injury rate in CrossFit was approximately 20%. Males were more likely to sustain an injury than females. The involvement of trainers in coaching participants on their form and guiding them through the workout correlates with a decreased injury rate. The shoulder and lower back were the most commonly injured in gymnastic and power lifting movements, respectively. Participants reported primarily acute and fairly mild injuries.

Project description:Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Project description:BackgroundThe maximal running speed (VMAX) determined on a graded treadmill test is well-recognized as a running performance predictor. However, few studies have assessed the variables that predict VMAX in recreationally active runners.MethodsWe used a mathematical procedure combining Fick's law and metabolic cost analysis to verify the relation between (1) VMAX versus anthropometric and physiological determinants of running performance and, (2) theoretical metabolic cost versus running biomechanical parameters. Linear multiple regression and bivariate correlation were applied. We aimed to verify the biomechanical, physiological, and anthropometrical determinants of VMAX in recreationally active runners. Fifteen recreationally active runners participated in this observational study. A Conconi and a stead-steady running test were applied using a heart rate monitor and a simple video camera to register the physiological and mechanical variables, respectively.ResultsStatistical analysis revealed that the speed at the second ventilatory threshold, theoretical metabolic cost, and fat-mass percentage confidently estimated the individual running performance as follows: VMAX = 58.632 + (-0.183 * fat percentage) + (-0.507 * heart rate percentage at second ventilatory threshold) + (7.959 * theoretical metabolic cost) (R2 = 0.62, p = 0.011, RMSE = 1.50 km.h-1). Likewise, the theoretical metabolic cost was significantly explained (R2 = 0.91, p = 0.004, RMSE = 0.013 a.u.) by the running spatiotemporal and elastic-related parameters (contact and aerial times, stride length and frequency, and vertical oscillation) as follows: theoretical metabolic cost = 10.421 + (4.282 * contact time) + (-3.795 * aerial time) + (-2.422 * stride length) + (-1.711 * stride frequency) + (0.107 * vertical oscillation).ConclusionCritical determinants of elastic mechanism, such as maximal vertical force and vertical and leg stiffness were unrelated to the metabolic economy. VMAX, a valuable marker of running performance, and its physiological and biomechanical determinants can be effectively evaluated using a heart rate monitor, treadmill, and a digital camera, which can be used in the design of training programs to recreationally active runners.

Dataset Information

Physiological Predictors of Competition Performance in CrossFit Athletes.

Publications

Physiological Predictors of Competition Performance in CrossFit Athletes.

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