The COVID-19 lockdown does not necessarily worsen diabetes control, in spite of lower physical activity — a systematic review
Bibliographic record
Abstract
INTRODUCTION: This review aimed to synthesize evidence on the impact of the COVID-19 lockdown on the glycaemic control, physical activity, and diet of diabetic patients. MATERIAL AND METHODS: Two electronic databases (PubMed and Scopus) were searched from January 2020 to February 2021. A total of 161 unique records were retrieved. Out of these, 25 articles met the eligibility criteria and were included in the final review. The quality of the studies was assessed by using the modified Newcastle-Ottawa Quality Assessment Scale for observational studies. RESULTS: Out of the 25 studies included in the review, 18 (72%) were cross sectional, 5 (20%) were retrospective analyses, and 2 (8%) were cohort studies. Thirteen studies included type I diabetics, 8 studies included type2 diabetics, and 4 studies included both. In the quality assessment, 17 (68%) of the studies met the criteria of satisfactory quality. Overall glycaemic parameters were improved during the lockdown. Dietary patterns were affected during the lockdown, but the direction of change- either negative or positive- could not be inferred. However, physical activity patterns were found to be deteriorated during the lockdown. CONCLUSION: The review found that lockdowns for curbing COVID-19 had no negative impact on glucose control, while there was a decline in the physical activity among diabetics. Furthermore, available studies are subject to various biases, which calls for robust studies in future with representative samples. There is also a need to promote physical activity and a healthy diet among diabetic patients, with follow-up through telemedicine during such confinement periods.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".