The present meta-analysis was conducted following the guidelines of the Meta-analysis Of Observational Studies in Epidemiology (MOOSE)7. We systematically reviewed the literature in PubMed and EMBASE through May 2015 to identify prospective cohort studies published in English on the association between sleep duration and the risk of overweight/obesity in children and adolescents. We used the key words “sleep” and “obesity”, “adiposity’, “body mass index”, “body weight”, “waist circumference”, or “skinfold thickness” and “follow-up studies”, “longitudinal studies”, or “prospective studies” and “children” or “adolescents”. In addition, we searched Google Scholar and manually reviewed the reference lists from the relevant articles.

Eligible studies were prospective cohort studies conducted in children and/or adolescents and reported results on the association between sleep duration and the risk of overweight/obesity and/or anthropometric measures. The primary outcomes included the risk of overweight/obesity and annual body mass index (BMI) gain. The secondary outcomes included BMI, BMI z-score [created from BMI (kg/m2) according to the 2000 Centers for Disease Control (CDC) growth reference], weight, waist circumference (WC), percent body fat (PBF), fat free mass index (FFMI), fat mass (FM), fat mass index (FMI) and sum of skin-folds (SSF). For multiple publications using data from the same cohort, the one with the longest follow-up period or the largest sample size was selected for this meta-analysis.

Data extraction

Two authors reviewed the literature independently and extracted the information for the meta-analysis following a formal protocol written in advance that clearly stated the objectives, the hypotheses to be tested, the subgroups of interest and the proposed methods and criteria for identifying and selecting relevant studies and extracting and analyzing information8. Data extraction covered 1) general information of the study: first author’s name, study name (if applicable), year of publication and country where the study was conducted; 2) characteristics of study population: age, total number of participants and percent of boys; 3) assessment and categorization of exposure; 4) ascertainment of outcome; 5) covariates adjusted in the analysis; and 6) measures of the association, e.g., odds ratio (ORs) and β coefficients and corresponding 95% confidence intervals (CIs). Discrepancies on literature review and data extraction were resolved by group discussion.

Statistical Analysis

According to the recommendation of the World Health Organization (WHO), overweight was defined as an age and gender specific BMI between the 85th and 95th percentile and obesity was defined as a BMI above the 95th percentile9. The average follow-up time was calculated as the sum of person-years divided by the total number of participants.

To estimate the overall association between sleep duration and risk of overweight/obesity, we used the inverse of variance as the weight to calculate the pooled ORs and 95% CIs comparing the shortest to the longest category of sleep duration. Standard errors (SEs) were derived from the fully adjusted ORs and 95% CIs in the primary studies, which were transformed to natural logarithms (ln). To estimate the association between sleep duration and the continuous outcomes (e.g. annual BMI gain), we pooled the β regression coefficients weighted by the inverse of their variances considering that both exposure (i.e. sleep duration) and outcomes (e. g. annual BMI gain) were measured similarly in the primary studies10. If the information on linear association was not available in the primary study, it would be derived from generalized least-squares for trend test if the number of data points was ≥311, or calculated directly under a linear assumption if the number of data points was <3. If the extreme sleep duration category was open-ended (e.g. ≥12 hours/day), its lower/upper limit was estimated by assuming the range equivalent to its adjacent close-ended category.

To assess heterogeneity among the original studies, we inspected forest plots and conducted a Cochran’s Q test with a P ≤ 0.10 considered as significant heterogeneity. We also computed the I2 statistic to measure the magnitude of heterogeneity. The low, moderate and high levels of heterogeneity were defined as <30%, 30–50% and >50%, respectively. Sources of heterogeneity were explored using meta-regression and subgroup analyses with pre-defined factors including age (<3, 3- < 5, ≥5 years), gender, follow-up time (above or below median) and study region (USA vs. non-USA).

Small-study effects or publication bias was assessed by funnel plot asymmetry followed by Egger’s regression asymmetry test (when the number of studies was ≥3) or Begg’s adjusted rank correlation test (when the number of studies was <3). The Duval and Tweedie nonparametric “trim and fill” method was used to adjust for publication bias, if needed12.

Results from a random-effects model were presented as our main findings because we found that there were moderate/high heterogeneities in most of the pooled analyses and publication bias existed in some analyses13. Sensitivity analyses were performed to evaluate the robustness of the findings. Specifically, we determined the effects of a single study on the pooled results by removing one study at a time in the meta-analysis. Also, we explored the possible changes if replacing a random-effects model with a fixed-effects model.

All analyses were performed using STATA statistical software (Version 13.0; STATA Corporation LP, College Station, Texas, USA). A two-sided P value ≤0.05 was considered significant if not specified.