The Impacts of COVID-19 Restrictions on Physical Activity in Children and Youth: A Systematic Review of Qualitative Evidence
Bibliographic record
Abstract
BACKGROUND: The objectives of this systematic review were to synthesize qualitative evidence on the impacts of COVID-19 restrictions on physical activity (PA) for children and youth, and explore factors perceived to influence those impacts. METHODS: Five databases (MEDLINE, Embase, SPORTDiscus, ERIC, and CINAHL) were searched initially in June 2021 and updated in December 2021 to locate qualitative articles considering COVID-19 restrictions and PA for children and youth (≤18 y old), in any setting. Eligibility, quality assessments, and data extraction were completed by 2 independent reviewers. Data were synthesized using meta-aggregation with confidence of findings rated using ConQual. RESULTS: After screening 3505 records, 15 studies were included. Curriculum-based PA, organized sport, and active transportation were negatively impacted by COVID-19 restrictions. Negative changes were affected by COVID-19 exposure risks, inadequate instruction, poor access, screen time, and poor weather. Unstructured PA was inconsistently impacted; outdoor unstructured PA increased for some. Positive changes were facilitated by family co-participation, availability of outdoor space, and perceived mental health benefits. CONCLUSION: Qualitative data indicated restrictions had a predominantly negative impact on PA for children and youth, but inconsistent impacts on unstructured PA. The improved contextual understanding offered by our review will be foundational knowledge for health strategies moving forward.
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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 | 0.038 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".