ABSTRACT
The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00058-x.
ABSTRACT
Aim is to determine if the training with heart rate variability biofeedback allows to improve performance in athletes of different disciplines. Methods such as database search on Web of Science, SpringerLink, EBSCO Academic Search Complete, SPORTDiscus, Pubmed/Medline, and PROQUEST Academic Research Library, as well as manual reference registration. The eligibility criteria were: (a) published scientific articles; (b) experimental studies, quasi-experimental, or case reports; (c) use of HRV BFB as main treatment; (d) sport performance as dependent variable; (e) studies published until October 2016; (f) studies published in English, Spanish, French or Portuguese. The guidelines of the PRISMA statement were followed. Out of the 451 records found, seven items were included. All studies had a small sample size (range from 1 to 30 participants). In 85.71% of the studies (n = 6) the athletes enhanced psychophysiological variables that allowed them to improve their sport performance thanks to training with heart rate variability biofeedback. Despite the limited amount of experimental studies in the field to date, the findings suggest that heart rate variability biofeedback is an effective, safe, and easy-to-learn and apply method for both athletes and coaches in order to improve sport performance.
