Bidirectional associations between sleep quality or quantity, and dietary intakes or eating behaviors in children 6–12 years old: a systematic review with evidence mapping
Bibliographic record
Abstract
CONTEXT: Although dietary advice has long been a cornerstone of a healthy lifestyle, how sleep quality and quantity may interact with dietary intake or eating behaviors remains unclear. OBJECTIVE: To consider a bidirectional relationship between sleep and diet in children aged 6-12 years via a systematic review following PRISMA guidelines. DATA SOURCES: Relevant trials and observational studies were identified by searching the PubMed, Medline, Embase, and CENTRAL databases up to June 1, 2019, without language or date restrictions and supplemented with hand searching. Recognized procedures and reporting standards were applied. DATA EXTRACTION: Data on participant characteristics, study parameters, diet measures, sleep measures, and findings of study quality assessment criteria were collected. DATA ANALYSIS: Forty-five articles involving 308 332 participants on a diverse range of topics were included. Meta-analyses were planned but were impossible to perform due to high study heterogeneity. Most studies (82%) were cross-sectional, which prevented examining directionality of the observed associations. Risk of bias was assessed for trial, cohort studies, and cross-sectional studies, using the Cochrane Risk of Bias Tool or Newcastle Ottawa Scale. RESULTS: Of 16 studies in which the effect of sleep on dietary intake was investigated, 81% (n = 13) reported a significant association. All studies (n = 8) of sugar-sweetened or caffeinated beverages reported a negative association with sleep, and in 6 of 7 studies in which eating behaviors were investigated, associations with sleep were reported. The use of objective measures of sleep and diet were scarce, with most trials and studies relying on subjective measures of sleep (68%) or diet (93%). CONCLUSION: Because most studies investigating the relationship between sleep and diet in this age group are cross-sectional, temporality could not be determined. Additional randomized controlled trials and long-term cohort studies in middle childhood, particularly those using objective rather than questionnaire measures of sleep, are required to better understand interactions between diet and sleep. SYSTEMATIC REVIEW REGISTRATION: Prospectively registered with PROSPERO International Prospective Register of Systematic Reviews (CRD42018091647).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".