Impact of COVID-19 Pandemic on University Students' Physical Activity Levels: An Early Systematic Review
Bibliographic record
Abstract
Purpose: This systematic review aimed to analyze the impact that the COVID-19 lockdown had on the amount of physical activity performed by university students. Materials and Methods : A systematic electronic search for studies providing information regarding physical activity levels pre and during COVID-19 pandemic in university students was performed up to 20th October 2020 in the databases Cochrane Library, PubMed, SPORTDiscus, and Web of Science. The risk of bias of external validity quality of included studies was assessed by means of those the Newcastle-Ottawa Scale (NOS). The quality of the evidence for main outcomes was graded using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results and Conclusions : A total of 10 studies were selected. Physical activity levels were assessed by means of questionnaires (10 studies) and accelerometer (1 study). Risk of bias was regarded as low and high in six and four investigations, respectively. The quality of evidence was downgraded to low. A significant reduction of physical activity levels were observed in 9 studies. Compared to pre-lockdown values, five studies showed a reduction of light/mild physical activity (walking) between 32.5 and 365.5%, while seven studies revealed a reduction of high/vigorous physical activity between 2.9 and 52.8%. Walking, moderate, vigorous, and total physical activity levels have been reduced during the COVID-19 pandemic confinements in university students of different countries. Despite of the reductions, those who met the current minimum PA recommendations before the lockdown generally met the recommendations also during the confinements.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".