Trends in added sugars intake and sources among U.S. adults using the National Health and Nutrition Examination Survey (NHANES) 2001–2018
Bibliographic record
Abstract
Research on trends over time in added sugars intake is important to help gain insights into how population intakes change with evolving dietary guidelines and policies on reducing added sugars. The purpose of this study was to provide an analysis of dietary trends in added sugars intakes and sources among U.S. adults from 2001 to 2018, with a focus on variations according to the sociodemographic factors, age, sex, race and ethnicity and income, and the health-related factors, physical activity and body weight. Data from nine consecutive 2 year cycles of the National Health and Nutrition Examination Survey (NHANES) were combined and regression analyses were conducted to test for trends in added sugars intake and sources from 2001 to 2018. Trends were examined in the whole sample (19+ years) and in subsamples stratified by age (19–50, 51+ years), sex, race and ethnicity (Asian, Black, Hispanic, White), household income (poverty income ratio low, medium, high), physical activity level (sedentary, moderate, vigorous) and body weight status (normal, overweight, obese). From 2001 to 2018, added sugars intake (% kcal) decreased significantly ( P < 0.01), from 16.2 to 12.7% among younger adults (19–50 years), mainly due to declines in added sugars from sweetened beverages, which remained the top source. There were no changes in intake among older adults, and by 2018, the 23% difference in intake between younger and older adults that existed in 2001 almost disappeared. Declines in added sugars intake were similar among Black and White individuals, and all income, physical activity and body weight groups. Population-wide reductions in added sugars intake among younger adults over an 18 year time span coincide with the increasing public health focus on reducing added sugars intake. With the updated Nutrition Facts label now displaying added sugars content, it remains to be seen how added sugars intake trends carry forward in the future.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".