Lifestyle changes and mental health during the COVID-19 pandemic: A repeated, cross-sectional web survey
Bibliographic record
Abstract
BACKGROUND: This study aimed to compare self-reported changes on lifestyle behaviors during two phases of the COVID-19 pandemic in Spain, and to evaluate clinical and sociodemographic factors associated with lifestyles. METHODS: Two cross-sectional web surveys were conducted during lockdown (April 15-May 15, 2020) and seven months later (November 16-December 16, 2020). Lifestyle behaviors were self-reported by a multidimensional scale (SMILE-C). Two separate samples of respondents were analyzed. A multivariate regression model was performed to evaluate the association of SMILE-C scores with demographic and clinical variables. RESULTS: The sample comprised, 3412 participants from the first survey (S1) and in the S1 and 3635 from the second (S2). SMILE-C score decreased across surveys (p < 0.001). The rates of positive screenings for depression and anxiety were similar between the surveys, whereas those for alcohol abuse decreased (p < 0.001). Most participants in S2 reported that their lifestyle had not changed compared to those before the pandemic. Variables independently associated with an unhealthier lifestyle were working as an essential worker, lower educational level, previous mental disease, worse self-rated health, totally/moderate changes on diet, sleep or social support, as well as positive screenings for alcohol abuse, anxiety and depression. LIMITATIONS: The cross-sectional design and recruitment by non-probabilistic methods limit inferring causality and the external validity of the results. CONCLUSIONS: Overall lifestyle worsened seven months after the lockdown in Spain. Several demographic and clinical factors were associated with lifestyle scores. The contribution of common mental disorders to unhealthier lifestyles should be considered in order to prevent the negative impact of the pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".