Second Wave of COVID-19 Global Pandemic and Athletes’ Confinement: Recommendations to Better Manage and Optimize the Modified Lifestyle
Bibliographic record
Abstract
Coronavirus 2019 (COVID-19) is an infectious viral disease that has spread globally, resulting in the ongoing pandemic. Currently, there is no vaccine or specific treatment for COVID-19. Preventive measures to reduce the chances of contagion consist mainly of confinement, avoiding crowded places, social distancing, masks, and applying strict personal hygiene as recommended by the World Health Organization (WHO). After the first wave of infection in many countries, the potential effects of relaxing containment and physical distancing control measures suggest that as a result of these measures, a second wave of COVID-19 appears probable in these countries. In sport, the period of self-isolation, and quarantine, for COVID-19 affects the physical preparation of athletes as well as their mental health and quality of life to an even greater extent (i.e., nutrition, sleep, healthy lifestyle), and thus, relevant and practical recommendations are needed to help alleviate these physical and mental health concerns. Our review aims to summarize the physiological and psychological effects of detraining associated with athletes' confinement during the proposed second wave of COVID-19. This article also proposes answers to questions that concern the advantages and disadvantages of different types of social media platforms, the importance of nutrition, and the effects of sleep disturbance on the health and modified lifestyle of athletes during this worldwide pandemic. Thus, this review provides some general guidelines to better manage their modified lifestyle and optimally maintain their physical and mental fitness with respect to measures taken during this restrictive proposed second wave of the COVID-19 confinement period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".