Staying Physically Active During the Quarantine and Self-Isolation Period for Controlling and Mitigating the COVID-19 Pandemic: A Systematic Overview of the Literature
Bibliographic record
Abstract
The COVID-19 pandemic has created an unprecedented worldwide public health concern. Characterized by rapid and high frequency human-to-human transmission, the World Health Organization has recommended implementation of public health measures, including isolation of all suspected infectious individuals for a 14-day quarantine period, while governments have introduced "social distancing" and "lock-downs" of varying severity to curtail COVID-19 spread. Recent COVID-19 research further suggests there are major sleep problems and psychological disorders (e.g., stress, anxiety, depression) associated with the reduction of movement and activities, as well as the reduced social interaction. There have been no studies examining the effect of physical activity at home during such periods of isolation. However, based on previous research, potential tactics to overcome these negative effects include home-based exercise, exergaming, dancing to music, and participation in yoga. Adults should accumulate at least 150 min of moderate-intensity and at least 75 min of vigorous-intensity of activity divided in to 5-7 sessions per week. This training volume could be reduced by 30% for children and adolescents if replaced by recess or active play in and around the home. Additionally, exercises should be adapted to the fitness level of the participant and a progressive model of intensity and training volume should be utilized, preferably monitored by telephone applications and wearable sensors.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".