A Review of the Impact of the COVID-19 Pandemic on Lifestyle Behaviors
Bibliographic record
Abstract
Introduction/Objective: Although the COVID-19 pandemic primarily affected human health and medicine, it has also had positive and negative impacts on various aspects of life. Hence, this review aimed to explore the impact of the COVID-19 epidemic on lifestyle behaviors. Methods: The current review was conducted in 2023. PubMed, Scopus, and Google Scholar databases were searched using standard keywords. Related articles were included in the study after qualitative review and inclusion and exclusion criteria were applied, and data were extracted. Results: In the current review, 32 articles were included. Most reviewed studies referred to decreased physical activity (78.12%), unhealthy dietary habits (65.62%), and sleep disturbance (56.25), as the most affected lifestyle behaviors following the COVID-19 situation. Among the age groups, the 18- 89-year-old group reported more smoking and alcohol consumption than the other groups, and the 1- 21-year-old group reported the least smoking and alcohol consumption. Also, the age groups of 1 to 21 years and 18 to 69 years reported psychological problems, such as depression, anxiety, and stress, compared to other age groups. There has been a relationship between overweight and reduced physical activity, increased consumption of fast food and sweets, and also between decreased income and increased mental problems, increased consumption of cigarettes and alcoholic beverages, and decreased consumption of fruits and vegetables. Conclusion: The outbreak of the COVID-19 pandemic has had a significant impact on people's lifestyles, which can negatively affect overall health and well-being. The combination of reduced physical activity, unhealthy eating habits, and poor sleep has become a common consequence of the pandemic. By recognizing the potential negative impacts of reduced physical activity, unhealthy eating habits, and poor sleep, individuals can take steps to mitigate these effects.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".