Experiences and overall impact reported by people living with obesity: results from a multinational study
Bibliographic record
Abstract
Objectives: To explore the impact of obesity on clinical outcomes, health-related quality of life, emotional well-being, and work productivity in people/patients with obesity across six countries by body mass index and the presence of complications. Methods: Adelphi Real World Obesity Disease Specific Programme™ captured data related to physicians and their consulting people/patients with obesity on a weight management program or anti-obesity medication in Brazil, Canada, China, Japan, Kingdom of Saudi Arabia, and the United Arab Emirates from April to December 2022. Physicians reported data for up to eight qualifying people/patients with obesity. People/patients with obesity voluntarily completed outcome measures: 36-Item Short Form Health Survey, emotional well-being, and Work Productivity and Activity Impairment. Results: ). Overall, 82% had ⩾1 complication (median and interquartile range: 2.0 (1.0-3.0)). People/patients with obesity struggled with weight for a mean ± SD of 64.1 ± 86.6 months with a median (interquartile range) of 3.0 (2.0-5.0) weight-loss attempts. Mean ± standard deviation 36-Item Short Form Health Survey scores for people/patients with obesity above the obesity threshold showed impairment in general health (42.3 ± 11.5), social functioning (44.3 ± 9.2), role-emotional (46.0 ± 9.3), and mental health (45.7 ± 9.6). People/patients with obesity with complications showed similar impairment. People/patients with obesity reported bother/embarrassment about their weight, with 72% worried about future weight gain. Work productivity and activity impairment scores among people/patients with obesity above the obesity threshold showed impairment in activity (36%), overall work (34%), and presenteeism (31%). Findings were similar for people/patients with obesity with complications. Conclusion: Results suggest that obesity alongside the presence of complications negatively impacts health-related quality of life, emotional well-being, and work productivity, reinforcing the need for comprehensive interventions for this complex health issue.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".