How did lockdown and social distancing policies change the eating habits of diabetic patients during the COVID-19 pandemic? A systematic review
Bibliographic record
Abstract
Background: After the declaration of the COVID-19 pandemic, governments established national lockdowns and social distancing as an effective plan to control this disease. As a result of the lockdown policies, diabetic patients` access to food products, medication, and routine follow-ups is disrupted, making it difficult for them to control their disease. Methods: International databases, including PubMed/Medline, Web of Science, and Scopus, were searched until April 2022. All observational studies included assessing the impact of lockdown and social distancing on eating habits (as primary outcome), and glycemic and anthropometric indices (as secondary outcomes) of diabetic patients during the COVID-19 pandemic. The Newcastle-Ottawa Quality Scale was used to assess the quality rating of the studies. Results: Overall, 22 studies were included in this systematic review, the results of which varied in different communities. In most studies, consumption of grains, fruits, and vegetables was reported to increase. On the other hand, consumption of snacks and sweets was reported to increase in other surveys. During the COVID-19 lockdown, most diabetic patients preferred to cook meals at home, using less takeout, fast foods, and alcoholic drinks. Although the patients mostly improved their eating habits, the glycemic and anthropometric indices were contradictory in different studies. Studies showed that the eating habits of diabetic patients vary from country to country, even in some cases and studies done in the same country showed different results. For example, all the studies done in Japan showed an increase in the consumption of snacks and sweets, leading to weight gain in the patients. However, conflicting results in eating habits have been observed in studies conducted in India. Conclusion: The lockdown policies have led to a beneficial change in the eating habits of diabetic patients to consume more fruits and vegetables and reduce the consumption of animal protein products and alcoholic beverages. While some diabetic patients have increased consumption of snacks and sweets, leading to a disturbance in their glycemic and anthropometric indices control. Understanding the consequences of lockdown and social distancing of the diabetic patient during the COVID-19 pandemic can help public health authorities make better recommendations to improve glycemic control.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".