Factors associated with weight gain during COVID-19 pandemic: A global study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has resulted in changes in lifestyle habits and experiences of mental health outcomes, some of which were possibly related to weight gain, leading to an increase in the prevalence of obesity, which is associated with the development of several severe diseases. Concerns regarding weight gain and its impact on health outcomes are prevalent worldwide, with obesity being one of the highest causes of mortality in current society. METHODS: A self-reported questionnaire collected data from participants aged 18 years of age and above from 26 countries and regions worldwide. Post-hoc multiple logistic regression analyses have been done to evaluate the association between demographic and socioeconomic factors, and the perspectives that were identified to be associated with weight gain. RESULTS: Participants belonging to a younger age group; with a higher level of education; living in an urban area; living with family members; employed full-time; and had obesity were found to be more vulnerable to weight gain. After adjusting for socio-demographic factors, participants who were quarantined; exercised less prior to the pandemic; consumed unhealthy foods; and reported negative thoughts such as helplessness and the perceived risk of COVID-19, were more likely to experience weight gain; while negative thoughts such as having no means of control over the COVID-19 pandemic and the consequences of the COVID-19 pandemic will have great personal effect were associated with females, students, and people living in the rural area. CONCLUSIONS: Weight gain risk during the pandemic was significantly associated with certain socio-demographic and COVID-19 related factors. To improve public health outcomes, future research should conduct a longitudinal evaluation on the effects of COVID-19 experiences upon health choices. Streamlined mental support should also be provided to the vulnerable groups which were prone to negative thoughts that were associated with weight gain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".