Trends in Added Sugars Intake and Sources Among US Children, Adolescents, and Teens Using NHANES 2001–2018
Bibliographic record
Abstract
BACKGROUND: Over the past 2 decades, there has been an increased emphasis on added sugars intake in the Dietary Guidelines for Americans (DGA), which has been accompanied by policies and interventions aimed at reducing intake, particularly among children, adolescents, and teens. OBJECTIVES: The present study provides a comprehensive time-trends analysis of added sugars intakes and contributing sources in the diets of US children, adolescents, and teens (2-18 years) from 2001-2018, focusing on variations according to sociodemographic factors (age, sex, race and ethnicity, income), food assistance, and health-related factors (physical activity level, body weight status). METHODS: Data from 9 consecutive 2-year cycles of the NHANES were combined and regression analyses were conducted to test for trends in added sugars intake and sources from 2001-2018 for the overall age group (2-18 years) and for 2 age subgroups (2-8 and 9-18 years). Trends were also examined on subsamples stratified by sex, race and ethnicity (Hispanic, non-Hispanic Asian, non-Hispanic Black, non-Hispanic White), income (household poverty income ratio), food assistance, physical activity level, and body weight status. RESULTS: From 2001-2018, added sugars intakes decreased significantly (P < 0.01), from 15.6% to 12.6% kcal among children (2-8 years) and from 18.4% to 14.3% kcal among adolescents and teens (9-18 years), mainly due to significant declines in added sugars from sweetened beverages, which remained the top source. Declines in added sugars intakes were observed for all strata, albeit to varying degrees. CONCLUSIONS: Declines in added sugars intakes were observed among children, adolescents, and teens from 2001-2018, regardless of sociodemographic factors, food assistance, physical activity level, or body weight status, but variations in the magnitudes of decline suggest persistent disparities related to race and ethnicity and to income. Despite these declines, intakes remain above the DGA recommendation; thus, continued monitoring is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".