Use of online food retail platforms throughout the COVID‐19 pandemic and associated diet‐related chronic disease risk factors: A systematic review of emerging evidence
Bibliographic record
Abstract
Food accessibility was considerably impacted by restrictions during the coronavirus disease 2019 (COVID-19) pandemic, leading to growth in the online food retail sector, which offered contact-free delivery. This systematic review aimed to assess the change in use of online food retail platforms during COVID-19. The secondary aim was to identify diet-related chronic disease risk factors including dietary intake, eating behaviors, and/or weight status associated with the use of online food retail platforms during the pandemic. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022320498) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Nine electronic databases were searched between January 2020 and October 2023. Studies that reported the frequency or change in use of online groceries, meal delivery applications, and/or meal-kit delivery services before and during the pandemic were included. A total of 53 studies were identified, including 46 cross-sectional studies, 4 qualitative studies, 2 longitudinal cohort studies, and 1 mixed-methods study. Overall, 96% (43/45) of outcomes showed an increase in the use of online groceries during COVID-19, while 55% (22/40) of outcomes showed a decrease in meal delivery applications. Eight of nine outcomes associated the use of online food retail with weight gain and emotional eating. Further research is needed to investigate the links between online food retail and obesity.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".