ABSTRACT
BackgroundThe COVID-19 pandemic has negatively impacted sporting activities across the world. However, practical training strategies for athletes to reduce the risk of infection during the pandemic has not been definitively studied. ObjectiveThe purpose of this report was to provide an overview of our challenges encountered during the reboot of high-performance sporting activities of the Japanese national handball team during the 3rd wave of the COVID-19 pandemic in Tokyo, Japan. MethodsTwenty-nine Japanese national womens handball players and 24 staff participated in the study. To initiate the reboot of their first training camp after COVID-19 stay-home social policy, we conducted: web-based health-monitoring, SARS-CoV-2 screening with polymerase chain reaction (PCR) test, real-time automated quantitative monitoring of social distancing on-court using video-based artificial intelligence (AI) algorithm, physical intensity evaluation with wearable heart rate (HR) and acceleration sensors, and self-reported online questionnaire. ResultsThe training camp was conducted successfully with no COVID-19 infections. The web-based health monitoring and the frequent PCR testing with short turnaround times contributed remarkably in early detection of athletes health problems and risk screening. During handball, the AI based on-court social-distancing monitoring revealed key time-dependent spatial metrics to define player-to-player proximity. This information facilitated positive team members on and off-game distancing behavior. Athletes regularly achieved around 80% of maximum HR during training, indicating anticipated improvements in achieving their physical intensities. Self-reported questionnaires related to the COVID management in the training camp revealed a sense of security among the athletes allowing them to focus singularly on their training. ConclusionThe current challenge provided us considerable know-how to create and manage a safe environment for high-performing athletes in the COVID-19 pandemic via the Japan Sports-Cyber Physical System (JS-CPS) of SRIP (Japan Sports Agency, Tokyo, Japan). This report is envisioned to provide informed decisions to coaches, trainers, policymakers from the sports federations in creating targeted, infection-free, sporting and training environments.
Subject(s)
COVID-19ABSTRACT
High-throughput, high-accuracy detection of emerging viruses allows for pandemic prevention and control. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is used to diagnose the presence of SARS-CoV-2. The principle of the test is to detect RNA in the virus using a pair of primers that specifically binds to the base sequence of the viral RNA. However, RT-PCR is a sophisticated technique requiring a time-consuming pretreatment procedure for extracting viral RNA from clinical specimens and to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity using artificial intelligent nanopores utilizing a simple procedure that does not require RNA extraction. Artificial intelligent nanopore platform consists of machine learning software on the servers, portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. Here we show that the artificial intelligent nanopores are successful in accurate identification of four types of coronaviruses, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2, which are usually extremely difficult to detect. The positive/negative diagnostics of the new coronavirus is achieved with a sensitivity of 95 % and specificity of 92 % with a 5-minute diagnosis. The platform enables high throughput diagnostics with low false negatives for the novel coronavirus.