Weight gain, weight management and medical care for individuals living with overweight and obesity during the COVID‐19 pandemic (EPOCH Study)
Bibliographic record
Abstract
Objective: Medical care and weight related experiences have been challenged by the coronavirus disease 2019 (COVID-19) pandemic for those living with obesity. The magnitude of this impact requires further attention in order to optimize patient care and outcomes. The aim of this study was to assess the impact of the COVID-19 pandemic and lockdown on access to, and experience of, medical care, weight gain and management strategies, as well as predictors of weight gain. Methods: = 980). Results: Less than half of the total respondents thought that their providers were available for their medical care and most preferred in-person appointments over telemedicine. Only one quarter were satisfied with their obesity care. Sixty percent of the respondents reported weight gain (on average 5.65 kilograms [kg] gained), with 39.0% gaining more than 5% of their body weight (10.2% gained more than 10%). Over half of the respondents experienced decreased motivation for healthy eating or exercise. One third experienced more frequent and greater food consumption. Although worsening sleep occurred in approximately 20%, there was no significant increase in smoking, alcohol, or cannabis use. Predictors of weight gain were younger patients, higher weight categories, those who struggled with obtaining medical care during the pandemic, as well as those who struggled with eating. Conclusion: These results suggest that the COVID-19 pandemic negatively impacted patient care for those living with overweight and obesity and was associated with weight gain and interfered with weight management strategies. Greater attention to personalized weight management and interventions that focus on the predictors of weight gain should be undertaken.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".