Skipping Breakfast Is Associated with Diet Quality and the Distribution of Food Intake Throughout the Day: NHANES 2005–20016
Bibliographic record
Abstract
Distribution of carbohydrate intakes (carb choices) throughout the day are an important aspect to diabetes management and reducing blood glucose spikes. Skipping breakfast represents a behavior of concern, providing an extension of the overnight fast and may result in elevated sugar levels later in the day. Therefore, the purpose of this study was to evaluate dietary intake differences, including carbohydrates, based on consuming breakfast or not, and by diabetes status. Adults over 30 years from NHANES 2005–2016 were classified into nondiabetes (HbA1c <5.7%, n = 14,701), prediabetes (HbA1c 5.7–6.4%, n = 5855) and diabetes (HbA1c (≥6.5%, n = 2881). Dietary intakes were assessed using a multiple pass 24-hour recall to estimate intakes from the foods and beverages reported as consumed on the day prior to the NHANES visit. Breakfast was self-defined by participants. Total population-based means (95% CI) of nutrient intakes, MyPlate equivalents, and Healthy Eating Index 2015 scores from the day of intake were calculated across levels of glycemic control and skipping breakfast status. Across all groups, adults who reported breakfast consumption had a significantly better overall diet quality, while total intakes of whole grains and fiber were significantly lower in those who skipped breakfast. Intakes of added sugars were not significantly different between those who skipped versus consumed breakfast. The absence of breakfast on the day of intake was related to differential intakes of several nutrients related to healthy eating and glycemic management, resulting in a poorer overall diet quality. Healthcare professionals could evaluate meal skipping patterns and its impact on overall nutrient intakes, and the distribution of food intake throughout the day, in people with diabetes, to help improve disease management. Abbott Nutrition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".