Studies of physical activity and COVID-19 during the pandemic: an updated scoping review
Bibliographic record
Abstract
BACKGROUND: This review is an update of the previous study aiming to identify the available evidence related to physical activity (PA) in the context of the coronavirus disease (COVID-19) pandemic. METHODS: We searched 6 databases (PubMed, Embase, SPORTDiscus, Scopus, Web of Science, and CINAHL) in April 2024. Medical subject headings and keywords related to PA and COVID-19 were combined to conduct the online search, which covered the period from July 2020 to April 2024. RESULTS: Overall, 49,579 articles were retrieved. After duplicate removal and title, abstract, and full-text screening, 1,976 articles were included in this update. Most of the studies were observational with a cross-sectional design (68.0%). Most of COVID-19 and PA studies came from high-income countries. Most studies explored the changes in PA levels due to the COVID-19 pandemic and its effects on mental health outcomes. CONCLUSION: Research on PA and COVID-19 prioritized online approach and cross-sectional designs. Most of the evidence identified a decrease in PA levels due to social distancing measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".