The relationship between daytime napping and obesity: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Daytime napping, a habit widely adopted globally, has an unclear association with obesity. In this study, we executed a meta-analysis to explore the relationship between daytime napping and obesity. We conducted a comprehensive search of the PubMed, Embase, Cochrane Library, Scopus, PsycINFO, and Web of Science databases for pertinent articles published up to April 2023. Random-effects models were utilized to calculate odds ratios (ORs) with 95% confidence intervals (CIs), and we assessed the heterogeneity of the included studies using the I2 statistic. To explore potential sources of heterogeneity, subgroup analyses were performed. The methodological quality of the studies was evaluated using the Newcastle–Ottawa Scale (NOS), and funnel plots were employed to detect any publication bias. Sensitivity analyses were conducted by sequentially omitting each study. We conducted a meta-analysis of twelve studies that included one each from the UK and Spain, five from the USA, and five from China, totalling 170,134 participants, to probe the association between napping and obesity. The pooled analysis suggested a higher risk of obesity in individuals who nap (OR: 1.22 [1.10–1.35], p < 0.001, I2 = 87%) compared to non-nappers. The meta-analysis results revealed variations in the summary ORs for studies conducted in China, Spain, the USA, and the UK. The ORs for China, Spain, the USA, and the UK were 1.05 (95% CI 0.90–1.23), 9.36 (95% CI 4.74–18.45), 1.27 (95% CI 1.10–1.47), and 1.39 (95% CI 1.32–1.47), respectively. A subgroup analysis based on age within the American population disclosed that napping in both adults and children heightened obesity incidence. A subgroup analysis based on nap duration found a significant rise in obesity occurrence when nap duration exceeded one hour, but no clear relationship emerged when nap duration was less than 1 h. In a subgroup analysis based on the definition of obesity, napping did not demonstrate a significant relationship with obesity when diagnostic criteria set obesity at a BMI of 25 or above. However, when the criteria were set at a BMI of 28 or 30 or more, napping significantly increased obesity risk. Our meta-analysis indicates a positive association between daytime napping and the risk of obesity. However, given the limited number of included studies, potential confounding factors might not have been fully addressed. Future well-designed prospective studies are required to further investigate this relationship. Large-scale studies are necessary to confirm our findings and elucidate the underlying mechanisms that drive these associations and causation.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".