Athlete Biological Passport: Need and Challenges

Comprehensive analysis of prohibited substances and methods in sports: Unveiling trends, pharmacokinetics, and WADA evolution

This review systematically compiles sports-related drugs, substances, and methodologies based on the most frequently detected findings from prohibited lists published annually by the World Anti-Doping Agency (WADA) between 2003 and 2021. Aligned with structure of the 2023 prohibited list, it covers all proscribed items and details the pharmacokinetics and pharmacodynamics of five representatives from each section. Notably, it explores significant metabolites and metabolic pathways associated with these substances. Adverse analytical findings are summarized in tables for clarity, and the prevalence is visually represented through charts. The review includes a concise historical overview of doping and WADA's role, examining modifications in the prohibited list for an understanding of evolving anti-doping measures.

Perceptions of Cheating and Doping in E-Cycling

E-cycling is a growing area of cycling appealing to competitive cyclists and fitness enthusiasts. Zwift is the most popular e-cycling platform, with approx. 1 million subscribers and is a virtual environment that hosts regular races, including the UCI e-cycling world championships. The popularity of Zwift has given rise to cases of cheating and hacking the system to gain an advantage in e-racing. As a result, some high-profile professional riders have faced bans. We set out to understand the thoughts and concerns e-cyclists have about cheating, hacking, and doping in e-cycling. A total of 337 females and 1130 males were recruited over a 7-week period via social networking sites to complete an online survey. Forty-four per cent had experienced cheating during e-racing, which made them feel angry, annoyed, disappointed, and cheated. However, 15% of those who experienced cheating said they did not care, possibly because many see e-racing as a game or training tool rather than a competitive event. Eighty-seven per cent of participants were in favour of enforcing a ban on cheaters in e-cycling, while 34% wanted cheaters to be banned during in-person cycling events too. Results indicate that many e-cyclists have experienced cheating and would like clearer rules and bans for cheaters during e-races.

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Athletes usually arrange their training plans and determine their training intensity according to the coach's experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes' training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete's exercise intensity prediction model constructed by extracting the features of the athlete's ECG and PCG signals realizes the real-time monitoring of the athlete's exercise intensity and has high accuracy and generalization ability.

Annual banned-substance review: Analytical approaches in human sports drug testing 2019/2020

Analytical chemistry-based research in sports drug testing has been a dynamic endeavor for several decades, with technology-driven innovations continuously contributing to significant improvements in various regards including analytical sensitivity, comprehensiveness of target analytes, differentiation of natural/endogenous substances from structurally identical but synthetically derived compounds, assessment of alternative matrices for doping control purposes, and so forth. The resulting breadth of tools being investigated and developed by anti-doping researchers has allowed to substantially improve anti-doping programs and data interpretation in general. Additionally, these outcomes have been an extremely valuable pledge for routine doping controls during the unprecedented global health crisis that severely affected established sports drug testing strategies. In this edition of the annual banned-substance review, literature on recent developments in anti-doping published between October 2019 and September 2020 is summarized and discussed, particularly focusing on human doping controls and potential applications of new testing strategies to substances and methods of doping specified the World Anti-Doping Agency's 2020 Prohibited List.