Determination of a CrossFit Benchmark Performance Profile.

Sports (Basel)

Institute for Sports Science, Faculty of Human Sciences, University of the Federal Armed Forces Munich, 85579 Neubiberg, Germany.

Published: June 2021

In the trend sport CrossFit, international competition is held at the CrossFit Games, known worldwide as the definitive fitness test. Since American athletes are the best in the world regarding CrossFit, there might be influencing factors on international competition performance. Here, we characterize the benchmark performance profile of American and German CrossFit athletes (n = 162). To collect the common benchmark performance by questionnaire, 66 male and 96 female CrossFit athletes (32.6 ± 8.2 years) participated in our survey in both nations. By comparing the individual performance variables, only a significant difference in total power lift performance by males was identified between the nations ( = 0.034). No other significant differences were found in the Olympic lift, running, or the "Girl" Workout of the Day (Fran, Grace, Helen) performance. Very large to extremely large (r = 0.79-0.99, < 0.01) positive correlations were found between the power lift and Olympic lift variables. Further linear regression analysis predicted the influence of back squat performance on performance in the Olympic lifts, snatch (R = 0.76) and clean and jerk (R = 0.84). Our results suggested a dominant role of back squat performance in the assessment of physical fitness of CrossFit athletes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228530PMC
http://dx.doi.org/10.3390/sports9060080DOI Listing

Publication Analysis

Top Keywords

benchmark performance
12
crossfit athletes
12
performance
10
performance profile
8
international competition
8
power lift
8
olympic lift
8
squat performance
8
crossfit
6
determination crossfit
4

Similar Publications

A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.

Brief Bioinform

November 2024

Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.

The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.

View Article and Find Full Text PDF

Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification.

Heliyon

January 2025

Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, Iraq.

Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.

View Article and Find Full Text PDF

Achieving the smallest crystallite/particle size of zinc oxide nanoparticles (ZnO NPs) reported to date, measuring 5.2/12.41 nm with () leaf extract, this study introduces a facile green synthesis.

View Article and Find Full Text PDF

SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions.

Front Artif Intell

January 2025

Department of Physics and Astronomy, The University of Alabama, Tuscaloosa, AL, United States.

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline).

View Article and Find Full Text PDF

Background: The Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS) grade the severity of injuries and are useful for trauma audit and benchmarking. However, AIS coding is complex and requires specifically trained staff. A simple yet reliable scoring system is needed.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!