Should, and how can, exercise be done during a coronavirus outbreak? An interview with Dr. Jeffrey A. Woods
Bibliographic record
Abstract
The Coronavirus disease (COVID-19) crisis is now present in China.It started in December, 2019 and has, so far, led 213 individuals died and at least 9066 infected in China by local time 17:26, January 30, 2020.It has also spread to a number of Asian countries, as well as to Canada, France, Germany, and the United States.As a result, the Chinese government has put several major cities in Hubei Province on lockdown and has thrown plans for the Lunar New Year holiday into chaos for millions of people.On January 30, 2020, the World Health Organization also declared the COVID-19 outbreak a global health emergency because it could spread to countries that are not prepared.Furthermore, to prevent the spread of the new and deadly virus, all cities in China now have shut down most public places and facilities, including parks, leaving many people with no place to exercise.As a result, people may wonder if one should exercise at all during the outbreak and if so, how?These questions made Journal of Sport and Health Science remember some well-known studies done by my colleague, Dr. Jeffrey A. Woods and his team at the University of Illinois at Urbana-Champaign (UIUC), in which they found a protective effect of exercise on mortality due to influenza in mice.Dr. Woods is a Mottier Family Professor at UIUC.His research focuses on the effects of exercise on the immune system, the gut microbiome, and aging.He was among the first scholars to demonstrate that regular exercise can have an anti-inflammatory effect on the body and showed that exercise can improve the immune response to the flu vaccine in older adults.I interviewed Dr. Woods for the "Should and How" questions.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".