Abstract P025: Trying to Lose Weight: The Effect of Income and Age on Weight Loss Strategies in NHANES 1999-2010
Bibliographic record
Abstract
Background: Two-thirds of the general US population is overweight or obese; those living in poverty are disproportionately affected. Although 30-50% of Americans report currently trying to lose weight, some strategies can be counterproductive. Even though increased physical activity and decreased fat consumption were recently reported to be among the most successful weight-loss strategies for obese persons, little is known about how income may be associated with the type of strategies used to lose weight. Objective: determine the association between income and types of weight-loss strategies. Methods: Analyses included the 1999-2010 National Health and Nutrition Examination Survey participants that reported trying to lose weight in the last year and were stratified by age (youth: aged 8-19, n=3184; adults: 20+, n=5643) due to availability of covariates and different weight-loss questions. Annual household income was categorized as: <$20K, $20K-45K, $45-75K, and compared to the referent ($75K+). Multivariable models included sampling weights, and adjusted for sex, age, ethnicity, and whether the person was overweight or obese (body mass index > 25 for adults, or > 85th percentile for youth according to the CDC growth curves). Analyses among adults also adjusted for marital status and education. Multivariable logistic regression assessed the likelihood of using healthy weight-loss strategies (e. g., exercising) and unhealthy weight-loss strategies (e. g., skipping meals). Results: Compared to the referent, adults with lower household incomes were less likely to use healthy weight-loss strategies (Fig. 1) but unhealthy strategies were not associated; youth from lower income households were more likely to utilize unhealthy weight-loss strategies, and less likely to use healthy strategies. Conclusions: Weight-loss strategies differ based on income and age. Education efforts and environmental strategies should be done in tandem to increase use of healthy weight-loss strategies to curb the obesity epidemic.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".