Weight gain, weight management and medical care for individuals living with overweight and obesity during the COVID‐19 pandemic (EPOCH Study)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it