The impact of the COVID-19 pandemic on dietary behaviors among school-age children, adolescents, and young adults: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: The COVID-19 pandemic has had a global impact on food security and nutrition, both in the short and long term. The influence on school-age children, adolescents, and young adults may be particularly significant and long-lasting. OBJECTIVE: This systematic review and meta-analysis aimed to quantify the impact of the COVID-19 pandemic on dietary habits among school-age children, adolescents, and young adults worldwide. DATA SOURCES: PubMed, Web of Science, and Embase were searched from inception to October 5, 2023. DATA EXTRACTION: We included observational studies published in English that reported dietary quality scores and dietary intake quantities during and before the COVID-19 pandemic among school-age children, adolescents, and young adults. We included a total of 22 cohort studies and 20 cross-sectional studies of high or moderate quality. DATA ANALYSIS: We conducted a meta-analysis, expressing dietary quality scores and dietary intake quantities as standardized mean differences (SMD) with 95% confidence intervals (CIs). For studies with low heterogeneity, we used a fixed-effects model; otherwise, we applied a random-effects model. The Newcastle-Ottawa Scale was employed by 2 reviewers independently to evaluate methodological quality. The analysis indicated that, overall, juice intake increased (SMD = 0.12, 95% CI: 0.04 to 0.20), while alcohol consumption reduced during the COVID-19 pandemic (SMD = -0.28, 95% CI: -0.47 to -0.08). However, the age-stratified results varied. Among school-age children, intake of fruit, dairy products, sugar, and juice increased. Adolescents showed an increase in meal frequency and vegetable intake. Young adults showed reduced carbohydrate and alcohol intakes, while protein and dairy product intakes increased, based on limited included studies. CONCLUSION: Dietary changes in school-age children from before to during the pandemic were mixed, while dietary behavior changes in adolescents and young adults tended to be more positive. Considering the lasting effects of negative dietary behaviors, attention should be given to addressing the increased sugar and juice intakes. It is also crucial that caregivers and researchers monitor whether positive dietary behaviors will rebound after returning to normal study and life. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42023420923.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".