Impact of the COVID-19 pandemic on the changes in dietary habits, lifestyle and physical activity in the Slovak population
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused by the coronavirus was accompanied by the emergence of various adverse conditions, as well as the deterioration of health from the point of view of chronic diseases, as well as lifestyle changes related to the preference of certain foods and changes in body weight due to the restriction of free movement. Objective: The objective of our survey was to assess the impact of the pandemic and lockdown on selected key lifestyle elements affecting the overall health status of the Slovak population and subsequently to evaluate subjectively assessed changes in the respondents' eating habits, daily routine, physical activity and body weight. Material and Methods: The research group consisted of 528 participants who took part in an online distributed questionnaire survey. Results: Respondents subjectively evaluated the change in lifestyle rather negatively. Up to 48.37% of men and 38.93% of women reported a change for the worse. Almost 59% of participants reported no change in their health, while almost a quarter reported a slight deterioration in their health. A change in eating habits for the worse was reported by 22.88% of men and 28.26% of women (p<0.05). Increased appetite during the lockdown was reported by 24.18% of men and 35.47% of women (p<0.05), more frequent overeating during the pandemic occurred in 30.07% of men and 38.13% of women. When evaluating the consumption of individual food commodities, the increased consumption of fresh fruit, fresh vegetables (p<0.01), homemade bread (p<0.05), homemade pastries (p<0.05) and dairy products (p<0.05) is very favourable. We also found a significant increase in the consumption of sweets (p<0.01) and coffee (p<0.001). When evaluating the sleep pattern, we noted an increase in sleep during the pandemic, as well as more time spent sitting. Over half of the respondents reported a change in body weight, in most cases it was an increase in both sexes. Conclusions: The results show that the pandemic and the restrictions during it caused changes not only in diet, but also in physical activity, daily routine and overall lifestyle. However, this is a very specific issue that needs to be assessed in a comprehensive and strictly personalized manner. The positive or negative impact of the pandemic on the health of the population will be the subject of research in the near future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".