Methodological Challenges in Studies Examining the Effects of Breakfast on Cognitive Performance and Appetite in Children and Adolescents
Bibliographic record
Abstract
Breakfast is purported to confer a number of benefits on diet quality, health, appetite regulation, and cognitive performance. However, new evidence has challenged the long-held belief that breakfast is the most important meal of the day. This review aims to provide a comprehensive discussion of the key methodological challenges and considerations in studies assessing the effect of breakfast on cognitive performance and appetite control, along with recommendations for future research. This review focuses on the myriad challenges involved in studying children and adolescents specifically. Key methodological challenges and considerations include study design and location, sampling and sample section, choice of objective cognitive tests, choice of objective and subjective appetite measures, merits of providing a fixed breakfast compared with ad libitum, assessment and definition of habitual breakfast consumption, transparency of treatment condition, difficulty of isolating the direct effects of breakfast consumption, untangling acute and chronic effects, and influence of confounding variables. These methodological challenges have hampered a clear substantiation of the potential positive effects of breakfast on cognition and appetite control and contributed to the debate questioning the notion that breakfast is the most important meal of the day.
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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 | 0.146 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".