Patterns of Weight Gain in Middle-Aged and Older US Adults, 1992–2010
Bibliographic record
Abstract
BACKGROUND: Cross-sectional analyses of national data have found that persons with high baseline body mass index (BMI) gain weight faster than persons at the median and that those whose weight was below the median gain very little weight. However, it is not clear whether these population-level changes reflect patterns at the individual level. METHODS: We examined longitudinal changes in BMI in initially underweight, normal-weight, overweight, and obese US men and women using individual-level repeat data from the Health and Retirement Study (n = 15,895; age range, 40-69 years at baseline). Linear mixed-effect regression was used to model 6-year change in self-reported BMI during 4 study periods (1992/1994-1998/2000, 1996/1998-2002/2004, 2000/2002-2006/2008, and 2004-2010). RESULTS: In the first 6-year period, the mean increase in BMI was greatest among persons who were initially normal weight (0.3 kg/m [95% confidence interval = 0.2 to 0.4]) and overweight (0.2 kg/m [0.1 to 0.3]). Weight gain accelerated in these groups with each subsequent period. Weight gain was less for initially class-I obese participants, and a net decrease in BMI was observed for class-III obese participants. CONCLUSION: These analyses suggest that the change in mean BMI among middle-aged and older US adults between 1992 and 2010 resulted mainly from accelerated weight gain among persons who were initially normal weight and overweight.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".