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Sleep. May 1, 2008; 31(5): 619–626.
PMCID: PMC2398753

Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults



Recent epidemiological studies suggest that short sleep duration may be associated with the development of obesity from childhood to adulthood.


To assess whether the evidence supports the presence of a relationship between short sleep duration and obesity at different ages, and to obtain an estimate of the risk.


We performed a systematic search of publications using MEDLINE (1996-2007 wk 40), EMBASE (from 1988), AMED (from 1985), CINHAL (from 1982) and PsycINFO (from 1985) and manual searches without language restrictions. When necessary, authors were contacted. Criteria for inclusion were: report of duration of sleep as exposure, BMI as continuous outcome and prevalence of obesity as categorical outcome, number of participants, age, and gender. Results were pooled using a random effect model. Sensitivity analysis was performed, heterogeneity and publication bias were also checked. Results are expressed as pooled odds ratios (OR [95% confidence intervals, CIs]) and as pooled regression coefficients (β; 95% CIs).


Of 696 studies identified, 45 met the inclusion criteria (19 in children and 26 in adults) and 30 (12 and 18, respectively) were pooled in the meta-analysis for a total of 36 population samples. They included 634,511 participants (30,002 children and 604,509 adults) from around the world. Age ranged from 2 to 102 years and included boys, girls, men and women. In children the pooled OR for short duration of sleep and obesity was 1.89 (1.46 to 2.43; P < 0.0001). In adults the pooled OR was 1.55 (1.43 to 1.68; P < 0.0001). There was no evidence of publication bias. In adults, the pooled β for short sleep duration was −0.35 (−0.57 to −0.12) unit change in BMI per hour of sleep change.


Cross-sectional studies from around the world show a consistent increased risk of obesity amongst short sleepers in children and adults. Causal inference is difficult due to lack of control for important confounders and inconsistent evidence of temporal sequence in prospective studies.


Cappuccio FP; Taggart FM; Kandala NB; Currie A; Peile E; Stranges S; Miller MA. Meta-analysis of short sleep duration and obesity in children and adults. SLEEP 2008;31(5):619-626.

Keywords: Sleep duration, obesity, meta-analysis

IN THE LAST FEW DECADES THERE HAS BEEN A SIGNIFICANT INCREASE IN THE PREVALENCE OF OBESITY WORLDWIDE AND THE WORLD HEALTH organization has declared it a global epidemic.1 Obesity in childhood is a cause of psychosocial problems including low self esteem,2 and frequently continues into adulthood3 where it is a cause of major morbidity and mortality including cardiovascular disease and type 2 diabetes. At the same time there has been a reduction in sleep time. National surveys in USA have shown a decline in self-reported sleep duration over the past 50 years by 1.5 to 2 hours.4 This sleep curtailment has been attributed to lifestyle changes.

Several studies have reported associations between duration of sleep (short as well as long) and ill-health, including relationships with self-reported well-being,5 morbidity and mortality,612 and with chronic conditions including type 2 diabetes, respiratory disorders, hypertension, and obesity.1318 The associations between short duration of sleep and obesity, in particular, have stimulated a debate given the potential implications for children19,20 as well as adults.21,22 However, given the variety of studies and the large differences in the target populations, it is difficult to draw immediate conclusions on the consistency of the association, the direction of causality and the likely mechanisms involved. The aims of this article are to (i) systematically review published population-based studies, (ii) to carry out a meta-analysis to assess whether the evidence supports the presence of a relationship between short sleep duration and obesity at different ages, and (iii) to obtain a quantitative estimate of the risk in order to assess the consistency and potential public health relevance


Literature Search

We performed a systematic search for publications using Medline (1996-2007 week 40), EMBASE (from 1988), AMED (from 1985), CINAHL (from 1982) Psychinfo (from 1985). Search strategies used subject headings and key words and did not use language restrictions. We (FMT, N-BK, AC, SS, MAM, and FPC) examined reference lists of the relevant reviews and all identified studies and reviewed the cited literature. Two reviewers (FMT and N-BK) independently extracted the data. Differences about inclusion of studies and interpretation of data were resolved by arbitration (FPC), and consensus was reached after discussion with all authors. Of a total of 696 studies identified from the search (Figure 1), 12 studies in children met the inclusion criteria and provided suitable data on 13 population samples to be included in the pooled analysisw1–w12 (Table 1). Seven studiesw13–w19 were excluded, as they did not provide sufficient data for inclusion (Table 2). In adults 26 studies met the inclusion criteria, and 18 provided suitable data on 23 population samples to be included in the pooled analysisw20–w37 (Table 3). Eight studiesw38–w45 were excluded as they did not provide sufficient data for inclusion (Table 4). Note: All references beginning with a W are available in the website version of this paper on the SLEEP website at www.journalsleep.org

Figure 1
Flowchart indicating the results of the systematic review with inclusions and exclusions.
Table 1
Description of the Study Populations of Children Included in the Meta-Analyses (n=30,002)
Table 2
Description of the Studies in Children Excluded from the Meta-Analyses
Table 3
Description of the Study Populations of Adults Included in the Meta-Analyses (n=604,509)
Table 4
Description of the Studies in Adults Excluded from the Meta-Analyses

Inclusion Criteria

The main objective was to assess the relationship between sleep duration and either obesity or body mass index (BMI). No restriction was placed on populations included. When data were not readily available from the published reports, we wrote to authors to ask for raw data.


Exposure: Sleep Duration

Both the nature and quantity of sleep in children is different from that of adults. There is a gradual change with age and by age 10, sleep is similar to that of adults but the total time is longer (10 h).23 We analyzed the results of studies in children separately from those in adults. For children the definition of “short sleep” was <10 h or <10 h per night unless stated otherwise in Table 1.

In most of the studies in adults, short sleep was defined as either <5 h or <5 h per night for either average total sleep time (TST) in 24 h, nighttime sleep, weekday sleep, or based on a weighted average TST on weekdays and weekends, unless stated otherwise in Table 3. For odds ratios, short sleepers were compared to both middle and long sleepers, although in some studies they were compared to the reference category of either 7 h or 8 h sleep per night.

Outcome: Obesity

Unless stated otherwise (Table 1), obesity in children was defined either as BMI >95th percentile according to local national growth charts or by international growth charts where the thresholds for obesity is defined as the percentile which passes through BMI >30 kg/m2 at age 18 years.w46 Unless stated otherwise (Table 3), obesity in adults was defined as BMI >30 kg/m2.


Mean age of the populations, proportion of boys and girls, men and women, and sample size were collected and used in stratified analyses of heterogeneity, publication bias and sensitivity.

Statistical Analysis

To estimate the quantitative relation between short sleep duration and obesity, we obtained an estimate from each study of the unadjusted odds ratio (OR) with 95% confidence intervals (CIs) and the unadjusted regression coefficient β (95% CIs) for BMI as a continuous outcome. Some studies did not report the unadjusted OR and β for the relationship between short sleep duration and obesity. We requested from various authors the unadjusted OR (95% CIs) for <5 h sleep versus >5 h and obesity defined as BMI >30 kg/m2, its standard error (SE), and the exact sample size (N). We also requested the unadjusted β (95% CIs) for BMI (as a continuous outcome) on sleep duration, its standard error (SE), and the exact sample size (N). If the SE of either the OR or β were not supplied, it was algebraically computed from the 95% CIs. We used a random effect model and calculated pooled effects (95% CIs) for both OR and β. A problem which occurs in observational studies is selection bias and confounding. Selection bias is a feature of the study design and the possibility of this can be assessed by examining the methods of the study. Confounding can be due to known or unknown factors involved in the etiology and are related to both exposure and outcome variables. We examined possible sources of heterogeneity between the studies using a meta-regression technique. We performed the Breslow-Day test for homogeneity of ORs, Cochran-Mantel-Haenszel test for the null hypothesis of no effect (OR=1), and the Mantel-Haenszel common OR estimate. We assessed publication bias by using a funnel plot and Begg'sw47 test to find out whether there was a bias towards publication of studies with positive results among the smaller studies. In order to avoid bias in selection of papers, we tried to obtain all population studies which had data on the relationship between sleep duration and obesity which had been published worldwide and conducted the searches in an unbiased way using the main medical databases and reference lists from recent reviews. We also examined the influence of individual studies, in which the meta-analysis estimates are derived omitting one study at a time to see the extent to which inferences depend on a particular study or group of studies.



Thirteen population samples from 12 studies were included in the pooled analysis. They included 30,002 participants from France, Tunisia, Japan, Germany, USA (n = 2), Brazil, Portugal, United Kingdom, Canada, Taiwan, and China. Age ranged from 2 to 20 years and included boys and girls. Sample sizes ranged between 150 and 8,941. Data was extracted from the published articles when available and authors were contacted to request unavailable data or analyses. Details of the studies are summarized in Table 1.

For the meta-analysis sleep exposure was dichotomized for all studies. Figure 2 shows the Forest plot of 11 observational studies of short sleep and obesity involving 29,502 children studied around the world. Seven of 11 studies reported a significant association between short duration of sleep and obesity. The pooled OR was 1.89 (1.46 to 2.43). Publication bias was not detected by the Begg's test (P = 0.12) (Figure 3a). The heterogeneity test was significant (Q = 46.6, df = 10, P < 0.001). The sensitivity analysis indicated that the omission of any of the studies led to changes in estimates between 1.61 (1.33 to 1.96) and 2.07 (1.54 to 2.79) (Appendix 1).

Figure 2
Forest plot of the associations between short duration of sleep and obesity in studies carried out in children. OR and 95 CI indicate odds ratio and 95% confidence intervals.
Figure 3
Funnel plot for meta-analysis of studies in children (A: top) and in adults (B: bottom).


Twenty-two population samples from 17 studies met the inclusion criteria and provided suitable data for pooled analyses. They included 604,509 participants from Spain, Japan (n = 2), USA (n = 5), France, Switzerland, Sweden, Brazil, Finland (n = 3), Norway, Canada, Hong Kong, and United Kingdom. Age ranged from 15 to 102 years and included men and women. Sample sizes ranged between 437 and 497,037. Data were extracted from the published articles when available and authors were contacted to request unavailable data or analyses. Details of the studies included in the meta-analysis are summarized in Table 3. For the meta-analysis sleep exposure was used in two ways: as dichotomized variable and as continuous variable regressed over BMI used as continuous variable. Figure 4 shows the forest plot of 22 population samples from 17 observational studies of short sleep and obesity involving 603,519 adults studied around the world. Seventeen population samples showed a significant association between short duration of sleep and obesity. The pooled OR was 1.55 (1.43 to 1.68). There was no evidence of publication bias (Begg's test P = 0.09) (Figure 3b). The heterogeneity test was significant (Q = 64.0, df = 21, P < 0.001). The sensitivity analysis indicated that the omission of any of the studies led to changes in estimates between 1.50 (1.39 to 1.61) and 1.59 (1.44 to 1.76) (Appendix 2).

Figure 4
Forest plot of the associations between short duration of sleep and obesity in studies carried out in adults. OR and 95 CI indicate odds ratio and 95% confidence intervals.

Figure 5 shows the Forest plot of 7 studies in adults including 16,509 participants and reporting regression coefficients (β) between hours of sleep per night and BMI. Unlike studies in children, all studies in adults showed a consistent and significant negative association between hours of sleep and BMI. The pooled β was −0.35 (−0.57 to −0.12) unit of change in BMI per hour of sleep (P = 0.002; heterogeneity P < 0.001). The sensitivity analysis indicated that the omission of any of the studies led to changes in estimates between −0.30 (−0.51 to −0.09) and −0.41 (−0.53 to −0.28) (Appendix 3).

Figure 5
Forest plot of the associations between duration of sleep and body mass index in studies carried out in adults. β and 95 CI indicate regression coefficient and 95% confidence intervals.


This study provides for the first time a systematic review of the literature and quantitative estimates of the cross-sectional associations between duration of sleep and obesity (or measures of obesity) in population-based studies of children and adults around the world. It shows a consistent pattern of increased odds of being short sleeper if you are obese, both in childhood and in adulthood. A pooled regression analysis in adults also suggests that a reduction in one hour of sleep per day would be associated with a 0.35 kg/m2 increase in BMI. For a person approximately 178 cm tall it would be equivalent to approximately 1.4 kg in weight.

These results are of interest for several reasons. First, the association is consistent in different populations. Although the meta-analysis detected significant heterogeneity between studies, further sensitivity analyses and the exclusion of publication bias are in favor of a similar effect across the populations. Second, they indicate an effect size consistent across ages. The 60% to 80% increase in the odds of being short sleeper amongst obese was seen in both children and adults, even after some attenuation following sensitivity analyses. Third, the categorical results were corroborated by the meta-analysis of regression coefficients, at least in adults.

There are some limitations. First, the quality of the data cannot go beyond the quality of the individual studies included. Second a meta-analysis of observational studies is open to important fallacies in that it cannot directly control for confounding and therefore may be open to biased estimates. Third, the results can only be representative of the studies that have been included and are unable to provide a representative inference of all studies published. Nevertheless, these results are important in guiding the assessment of current evidence and the definition of future research strategies.

The pooled studies are cross-sectional and cannot, therefore, determine temporal sequence, hence, causality. They also cannot examine changes in sleeping habits with time. Moreover, all studies used sleep questionnaires to determine self-reported sleep duration within their populations. Self-reported duration of sleep has been validated against actigraphy.24

The variety of methods for analyzing weight and obesity reflects our current poor understanding of what the most effective measuring scheme is. Many different methods were used to determine obesity, particularly in children, making the various studies more difficult to reconcile.

The studies varied in the degree of control for confounders such as age, gender, ethnic background, socioeconomic status, degree of energy intake, energy expenditure, frequency of snacking, and other health-related behaviors and nutritional habits. A confounding factor in the relationship between sleep duration and obesity in adults is psychiatric comorbidity, particularly depression.2527 Chronic illness, physical disability, use of hypnotics, etc., would also be important confounders to consider. In the pooled analysis we used exclusively unadjusted estimates from the individual studies. The consistency of the pooled associations between children and adults suggests that it is unlikely that a differential bias due to common unaccounted confounders might have occurred in the two age categories.

The results of prospective studies do not provide consistency in support of the view that short sleep duration predicts the future development of obesity,22,2831 with the exception of a very large study (n >60,000), carried out in women only, which shows a small effect (hazard ratio of 1.15 and 1.06 for those sleeping <5 h and <6 h per night, respectively).32 The relationship may be confounded by comorbidity, such as chronic mental illness, causing a decrease in the levels of physical activity and a reduction in sleep, or by physical illnesses associated with pain—hence disrupted sleep—and severe limitation in energy expenditure through limited physical activity. More recent studies have adjusted for these potential confounders and found no prospective association.22

Our study does not allow us to study mechanism. However, it has been suggested that short sleep may lead to obesity through the activation of hormonal responses33 leading to an increase in appetite and caloric intake. Short sleep is associated to reciprocal changes in leptin and ghrelin.33 This in turn would increase appetite and contribute to the development of obesity. The evidence in humans comes from short-lived severe sleep deprivation experiments35,36 that cannot be extrapolated to long term effects in the population.

Activation of inflammatory pathways by short sleep may also be implicated in the development of obesity.37 Finally, it is not inconceivable that short sleep is just a marker of unfavorable health status and of lifestyle characteristics.38,39

The potential public health implications of a causal relationship between short duration of sleep and obesity have already been widely disseminated in the media. The findings of our analysis suggest that whilst sustained sleep curtailment and ensuing excessive daytime sleepiness are undoubtedly cause for concern, the link to obesity is of interest but still to be proven as a causal link. Many questions still need an answer to determine causality. Prospective studies in which weight, height, waist measurements. and adiposity are measured at baseline and again at subsequent data collection times together with more accurate objective measurement of sleep duration (including naps) and confounding factors or mediators such as depression are needed.

Further prospective studies with improved assessment of long-term exposure (repeated self-reported sleep duration or repeated actigraphy), more specific outcomes (including measures of adiposity) and better control for confounders are needed before causality can be determined.


We thank Drs. Agras, Bjorkelund, Chaput, Chen, Cournot, Gangwisch, Giugliano, Hasler, Kripke, Locard, Sekine and Vioque for supplying additional data or analyses not available in the published article. This work is part of the Programme ‘Sleep, Health & Society’ of the University of Warwick.


Disclosure Statement

This was not an industry supported study. Dr. Peile has received research support from Cephalon. The other authors have indicated no financial conflicts of interest.

Authors' Contribution

FPC conceived the study aims and design, contributed to the data extraction, planned the analysis, interpreted the results and drafted the final version of the paper. FMT carried out the systematic review and contributed to data extraction and analysis. N-BK contributed to data extraction and carried out the statistical analysis. AC, SS and MAM contributed to the systematic review, analysis and interpretation. EP contributed to interpretation of results. All authors contributed to the revision of the manuscript.


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