Lifestyle changes during the COVID-19 pandemic in Brazil: results from three consecutive cross-sectional web surveys
Bibliographic record
Abstract
OBJECTIVE: The importance of a healthy lifestyle in preventing morbidity and mortality is well-established. The COVID-19 pandemic brought about significant lifestyle changes globally, but the extent of these changes in the Brazilian population remains unclear. The objective of this study was to evaluate changes in lifestyle among the Brazilian general population during the first year of the pandemic. DESIGN: Three consecutive anonymous web surveys were carried out: survey 1 (S1)-April 2020, S2-August 2020 and S3-January 2021. SETTING: Brazil. PARTICIPANTS: The study included 19 257 (S1), 1590 (S2) and 859 (S3) participants from the general population, who were ≥18 years, of both sexes, with access to the internet, self-reporting living in Brazil and who agreed to participate after reading the informed consent. PRIMARY OUTCOME: Lifestyle changes were assessed using the Short Multidimensional Instrument for Lifestyle Evaluation-Confinement (SMILE-C). The SMILE-C assesses lifestyle across multiple domains including diet, substance use, physical activity, stress management, restorative sleep, social support and environmental exposures. We used a combination of bootstrapping and linear fixed-effect modelling to estimate pairwise mean differences of SMILE-C scores overall and by domain between surveys. RESULTS: In all the surveys, participants were mostly women and with a high education level. Mean SMILE-C scores were 186.4 (S1), 187.4 (S2) and 190.5 (S3), indicating a better lifestyle in S3 as compared with S1. The pairwise mean differences of the overall SMILE-C scores were statistically significant (p<0.001). We also observed a better lifestyle over time in all domains except for diet and social support. CONCLUSIONS: Our findings indicate that individuals from a large middle-income country, such as Brazil, struggled to restore diet and social relationships after 1 year of the pandemic. These findings have implications for monitoring the long-term consequences of the pandemic, as well as future pandemics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".