Short sleep duration and large variability in sleep duration are independently associated with dietary risk factors for obesity in Danish school children



Lack of sleep and increased consumption of energy-dense foods and sugar-sweetened beverages (SSBs) have all been suggested as factors contributing to the increased prevalence of overweight and obesity.


To evaluate whether objectively measured sleep duration (average and day-to-day variability) as well as parent-reported sleep problems are independently associated with proposed dietary risk factors for overweight and obesity in 8–11-year-old children.


In this cross-sectional study, data on sleep duration and day-to-day variability in sleep duration were measured in 676 Danish, apparently healthy children by an objective measure (actigraphy) for 8 nights, and the Children’s Sleep Habits Questionnaire (CSHQ) was filled out by the parents. Diet was recorded using a web-based food record for 7 consecutive days. Fasting blood samples were obtained for measurements of plasma leptin and ghrelin levels.


Sleep duration (h per night) was negatively associated with energy density (ED) of the diet (β=−0.32 kJ g−1), added sugar (β=−1.50 E%) and SSBs (β=−1.07 E%) (all P0.003). Furthermore, variability in sleep duration (10-min per night) was positively associated with SSBs (β=0.20 E%, P=0.03), independent of sleep duration, and CSHQ score was positively associated with ED (β=0.16 kJ g−1, P=0.04). All of these associations were independent of potential confounders (age, sex, pubertal status, height, weight, screen time, moderate-to-vigorous physical activity and parental education and ethnicity).


Our study suggests that short sleep duration, high sleep duration variability and experiencing sleep problems are all associated with a poor, obesity-promoting diet in children.


The high prevalence of overweight and obesity among children is of serious concern, as childhood overweight and obesity track into adulthood and increase the risk of its related comorbidities including type 2 diabetes and cardiovascular disease.1, 2 Overweight and obesity result from a long-term energy excess; however, the underlying mechanisms and effective strategies for prevention and treatment are still lacking. During the past century, there have been consistent declines in the sleep duration of children and adolescents.3 Although the evidence for the optimal sleep duration for children has recently been discussed,4 a growing body of evidence suggests that lack of sufficient sleep could be a contributing factor to the increased prevalence of overweight and obesity among children and adolescents.5, 6, 7 The increased availability and use of electronic entertainment and communication devices, especially during late evening, has been reported to delay bedtimes and to be associated with shortened sleep duration in children.8

Observational and experimental studies have suggested that short sleep duration and sleep restriction adversely affect selected endocrine system functions, and lead to alterations in the levels of the appetite-stimulating hormone ghrelin and the anorexogenic hormone leptin.9, 10, 11 Enhanced motivation to eat, together with the increased time available for food consumption linked to short sleep durations, might increase the risk of gaining weight.12, 13 Based on experimental studies, it is still unclear whether sleep restriction leads to increased energy intake (EI).14, 15, 16 However, associations between short sleep duration or sleepiness with increased consumption of and preferences for energy-dense foods and snacks have previously been reported in children and adolescents as well as in adults.17, 18, 19, 20 Furthermore, experimental studies have recently suggested that sleep restriction is associated with overeating in the morning driven by both homeostatic and hedonic factors21 and that it enhances the brain’s response to food stimuli,22, 23 which might cause a shift in the intake of foods favoring an increased EI.22 These foods could be energy-dense foods and sugar-sweetened beverages (SSBs), as the World Health Organization rates the evidence for these to be linked to weight gain as convincing and probable, respectively.24

Variations in sleep durations and the sleep–wake rhythm are common phenomena in school children.25, 26 One could hypothesize that an irregular sleep pattern is associated with a less regulated household, which might also be associated with poorer dietary habits. Moreover, it has recently been suggested that self-reported late bedtime is associated with a poor diet high in energy-dense, nutrient-poor foods in children and adolescents,27 and that late sleep preference is associated with consumption of fast foods in adolescents.28 However, whether large variations in objectively measured sleep durations are associated with specific dietary patterns has, to our knowledge, not yet been elucidated. Therefore, the aim of the present study was to investigate whether objectively measured sleep duration, variability in sleep duration and parent-reported sleep problems are independently associated with selected dietary risk factors for overweight and obesity in 8–11-year-old Danish school children. We hypothesized that short sleep duration, high sleep duration variability and experiencing sleep problems would be associated with dietary risk factors for overweight and obesity.

Materials and methods

Study population

The initial sample comprised 834 of the 1021 invited third and fourth grade students (8–11 years old) from nine Danish municipal schools enrolled in the OPUS (Optimal well-being, development and health for Danish children through a healthy New Nordic Diet) school meal study. The main aim of the OPUS project is to investigate health effects of a New Nordic Diet served at school.29 Exclusion criteria were diseases or conditions that could obstruct measurements or place the children at risk when eating the diet (that is, severe malabsorption and strong food intolerances), or concomitant participation in other research involving radiation or blood sampling. In this paper we used baseline data from the OPUS project. Because of missing data (<3 weekdays and 1 weekend day of sleep measurement, and no completed Children’s Sleep Habits Questionnaire (CSHQ; n=83) or extremely low and high reported EI, defined as EI divided by basal metabolic rate of <1.05 (n=63) and >2.29 (n=12),30 our cross-sectional analytical sample comprised baseline data from 676 children obtained between August and November 2011. Questionnaire data were collected first, and within the next 2 weeks physical activity (PA), sleep and dietary intake were measured. Within the following week, anthropometric measurements and blood samples were collected.

Questionnaire data

A baseline questionnaire ascertained age, sex, grade, highest education of parents, number of parents born in Denmark and screen time. Screen time was computed based on parent-reported time spent watching television, playing video games or using the computer for leisure on weekdays and weekend days. Playing Nintendo Wii or similar active video game devices was not included in screen time. Weekly average of screen time was calculated in the proportion 5 to 2 between weekdays and weekend days. Pubertal status was self-reported (parent and child) on the basis of breast development among girls and pubic hair among boys.31 Furthermore, the parents were asked to fill out the validated 33-item CSHQ, in which a higher score indicates more disturbed sleep,32 and the questionnaire has been shown to be a useful screening tool to identify sleep problems in school-aged children.32

PA and sleep assessment

The children were asked to wear an ActiGraph tri-axis accelerometer monitor (GT3X+ or GT3X, Pensacola, FL, USA) tightly on the right hip using an elastic belt for 7 consecutive days and 8 consecutive nights, and to remove it only during water activities (that is, showering or swimming). At the end of the observation period, data were reintegrated to 60-s epochs and analyzed using ActiLife6 (The ActiGraph 2012, ActiLife version 6; Pensacola, FL, USA).

Data obtained between 0600 h and midnight were used to derive PA and sedentary time. Periods of at least 15 min of consecutive zero counts using vector magnitudes were assumed to be nonwear time and removed before analysis. To remove nocturnal activity during late evening and early morning, consecutive wear time of <60 min was disregarded before analysis. PA was only estimated if monitor wear time was at least 10 h per day for a minimum of 3 weekdays and 1 weekend day. Moderate-to-vigorous physical activity was defined as 2296 c.p.m., which is a recently suggested pediatric cutoff.33 Weekly average of all PA variables was calculated in the proportion 5 to 2 between weekdays (Monday–Friday) and weekend days (Saturday and Sunday).

The parents and children were instructed to keep logs for bedtimes (lights off and trying to sleep) and waking time the same week as the monitor was worn. To estimate sleep duration, the self-reported bedtimes and waking times were used as the possible window of sleep and scored in ActiLife6 (The ActiGraph 2012, ActiLife version 6) using the algorithm by Sadeh et al.34 The self-reported sleep logs were missing for 58 children (9%), and in these cases sleep was scored visually from the individual actograms as the difference between time when activity stops and time of resumed activity. In a random subsample of 105 children, we found the mean differences in sleep duration between these two approaches to be very small (3.8 min, P<0.001). Weekly average sleep duration was calculated in the proportion 5 to 2 between weekdays (Sunday–Thursday) and weekend days (Friday and Saturday). Sleep duration was also divided into quartiles, with 1st quartile representing ‘short sleep’ duration (7.47–8.56 h per night; n=169), 2nd and 3rd quartile representing ‘medium sleep’ duration (8.57–9.31 h per night; n=339) and 4th quartile representing ‘long sleep’ duration (9.32–10.50 h per night; n=168). Finally, a sleep variability score (min per night) was constructed by adding the absolute difference between the mean and each day of measurement divided by the number of days measured. A higher score then indicates a large weekly variability in sleep durations. The accelerometer was worn for a median (interquartile range (IQR)) duration of 7 (7–7) days and 8 (7–8) nights.

Dietary assessment

The daily intake of food and beverages were recorded during 7 consecutive days, along with the actigraph, using a Web-based Dietary Assessment Software for Children (WebDASC) that has been validated against fruit and vegetables.35 Further description of WebDASC is available elsewhere.36 Variables used in the present study were energy density (ED) of the diet that was calculated as energy (kJ) divided by weight (g) of solid food and liquids consumed as food (for example, soups and yoghurts), intake of sugar from SSBs (E%) (including fizzy lemonade, ice tea, lemonade; exclusive cocoa milk, soy and oat beverages) and dietary energy from added sugar (E%).

Anthropometry and blood sampling

Fasting, barefooted and wearing light clothes, the children were weighed to the nearest 0.1 kg (Tanita BWB-800S, Tokyo, Japan) and height was measured three times to the nearest 0.1 cm (average used) (CMS Weighing Equipment Ltd, London, UK). Basal metabolic rate was calculated using height, weight, age and sex.37 Body mass index Z-score was calculated based on the World Health Organization Growth Reference from 2007,38 and the prevalence of underweight, normal-weight, overweight and obese children was calculated based on age- and sex-specific cutoffs defined to pass through body mass index at 18.5, 25 and 30 kg m−2, respectively, at age 18 years.39, 40 A total of 35 ml venous blood was drawn from the forearm in fasting state (time interval: 7.25–10.45 h). Blood was stored at −80 °C for later analysis of plasma leptin (Bio-Tek EL808, R&D Systems, Abingdon, UK) and plasma ghrelin (Bio-Tek EL808, Millipore, Hellerup, Denmark). The inter- and intraassay coefficients of variation were 9.0% (n=61) and 3.7% (n=10) for ghrelin and 9.2% (n=71) and 3.7% (n=10) for leptin, respectively. Finally, body fat percentage was determined by dual-energy X-ray absorptiometry scanning (Lunar Prodigy Pro, GE Healthcare, EnCore software, Madison, WI, USA).


The study was approved by The Committees on Biomedical Research Ethics for the Capital region of Denmark ( H-1-2010-123), and written informed parental consent of both custody holders and child assent were obtained for all participants. The study was registered in the database (no. NCT 01577277).


Descriptive characteristics of the study sample are presented as means and s.d., median and IQR or as proportions divided into short, medium and long sleep duration, and tested using a one-way analysis of variance with post hoc Bonferroni test, the Kruskal–Wallis test or Pearson’s χ2 test. Multiple regression analysis was carried out to test sleep duration, sleep duration variability and sleep problems as potential correlates of certain dietary variables and leptin and ghrelin levels adjusted for a number of possible confounding factors (age, sex, pubertal status (1 or 2), height, weight, screen time, moderate-to-vigorous physical activity, parent education and parent ethnicity). The results are presented as unstandardized β-coefficients (β) and 95% confidence intervals. Leptin was positively skewed and therefore log-transformed for analysis, following which coefficients were back-transformed. Thus, regression coefficients of leptin are presented as (10β−1) × 100% (95% confidence interval). Linearity between residuals and the dependent variables in the model was visually checked along with normal distribution of the residuals and homogeneity of variance of the residuals. Partial correlation coefficients (r) were carried out between sleep duration, sleep duration variability and CSHQ score adjusted for age, sex and pubertal status. Figures are presented as means and 95% confidence intervals of the dietary variables divided into short, medium and long sleep duration, and tested using a one-way analysis of variance with post hoc Bonferroni test. The level of significance was set at P<0.05 and analyses were performed using STATA/IC (version 11.2, Houston, TX, USA).


Descriptive characteristics of the participants are presented in Table 1. Overall, short sleepers were older and had a higher body mass index Z-score compared with medium and long sleepers (P<0.05). Parental education, ethnicity and screen time also differed among sleep groups (P0.006). Median (IQR) CSHQ score was 42 (39-46) and sleep duration variability was 25 (19-34) min per night. Sleep duration was negatively associated with all three dietary variables (P0.003; Table 2), whereas sleep duration variability was positively associated with intake of SSBs (P=0.03; Table 3), and the CSHQ score with ED (P=0.04; Table 4). All of these associations were independent of age, sex, pubertal status, height, weight, screen time, moderate-to-vigorous PA, highest education of the parents and the number of parents born in Denmark. Sleep duration variability was also independent of sleep duration.

Table 1 Descriptive characteristics according to short, medium and long sleep duration in 8–11-year-old school children (n=676)
Table 2 Associations between sleep duration (h per night) and food variables, leptin and ghrelin in 8–11-year-old Danish children (n=662)
Table 3 Associations between sleep duration variability (min per night) and food variables, leptin and ghrelin in 8–11-year-old Danish children (n=662)
Table 4 Associations between the Children’s Sleep Habit Questionnaire score and food variables, leptin and ghrelin in 8–11-year-old Danish children (n=658)

Median (IQR) levels of leptin and ghrelin were 3625 (2067–7132) and 972 (773–1255) pg ml−1, respectively. Leptin levels were positively associated (P=0.02) with both sleep duration (26.7 (3.3–55.3)%) and CSHQ (1.8 (0.3–3.3)%) when only adjusted for total body fat. Ghrelin levels were positively associated with sleep duration when adjusting for age, sex and pubertal status (P=0.02); however, these associations disappeared after further adjustments (see Table 2).

Sleep duration and sleep duration variability (r=−0.15) as well as sleep duration and CSHQ (r=−0.14) were negatively correlated (P<0.001), whereas sleep duration variability and CSHQ were not associated (r=0.06; P=0.13).

Mean (s.d.) EI of boys and girls were 8264 (1398) and 7203 (1180) kJ, respectively. Added sugar contributed with a mean (s.d.) of 11.2 (4.2) and SSBs with a median (IQR) of 2.7 (1.3–4.5) E% of the total diet. ED of the diet was 7.6 (1.1) kJ g−1.

ED, added sugar and SSBs were higher (P0.03) among short sleepers compared with long sleepers when divided into quartiles (see Figures 1a–c). There were no associations between ghrelin or leptin levels and any of the three dietary variables of interest (P>0.35).

Figure 1

(a) Associations between energy density (kJ g−1) of the diet and sleep duration. (b) Associations between added sugar (E%) and sleep duration. (c) Associations between intake of sugar-sweetened beverages (E%) and sleep duration. Data are presented as means (95% confidence interval (CI)) and tested using a one-way analysis of variance (ANOVA) with post hoc Bonferroni test. *Indicates significant difference (P<0.05). Sleep duration was divided into short (1st quartile of sleep duration (7.47–8.56 h per night; n=169)), medium (2nd and 3rd quartile of sleep duration (8.57–9.31 h per night; n=339)) and long (4th quartile of sleep duration (9.32–10.50 h per night; n=168)).


We observed that not only short sleep duration, but also a large variability in sleep duration regardless of mean sleep duration of the week was associated with higher intakes of SSBs (E%) in this cohort of Danish school children. Moreover, short sleep duration and experiencing some degree of sleep problems (CSHQ score) were both linked with a higher ED of the diet, and short sleep duration was associated with higher intakes of added sugar (E%). All of these associations persisted after adjustment for potential confounders including screen time, physical activity, parental education level and ethnicity of the parents, suggesting that insufficient sleep appears to be an independent risk factor for making poor dietary choices in children.

The observation that sleep duration was negatively associated with consumption of energy-dense foods is in line with previous findings in Finnish children within the same age group.18 However, the findings in the study by Westerlund et al.18 were obtained using a single questionnaire covering sleep habits and a short food frequency questionnaire. In adults, Spiegel et al.9 showed increased appetite for energy-dense foods with high carbohydrate content following experimentally imposed partial sleep curtailment, and Nedeltcheva et al.20 reported increased intake of calories from snacks high in carbohydrate, especially between 1900 and 0700 h, in sleep curtailed individuals.

It is tempting to suggest that EI and the intake of energy-dense foods might increase along with time spent awake, as time is often spent on sedentary activities with palatable high energy-dense foods being easily accessible.41, 42, 43 Moreover, children getting insufficient sleep may be less active because of tiredness and fatigue, and therefore spend more time on sedentary activities such as watching television, which is also likely to be associated with a high EI from snacks and SSBs.44, 45 However, our findings were independent of screen time, suggesting an independent effect of short sleep duration on the outcome variables.

The underlying mechanisms for a higher consumption of energy-dense foods among children with short sleep durations or with large variability in sleep duration are unclear, and may include behavioral as well as reward-related aspects of food consumption. Sleep duration variability and CSHQ score were not associated in the present study. However, variations in children’s sleep–wake rhythm seem to be pronounced from the age of 9 years,25, 26 and abnormal sleep–wake pattern and sleep at the incorrect circadian phase could likely lead to lower sleep quality and possibly disrupt glucose homeostasis and the circadian rhythm of appetite-regulating hormones, and cause changes in food intake and/or food preferences.9, 10 Although it remains speculative, we cannot exclude the possibility that short sleep duration and large variability in sleep durations in children alters reward-related brain functions because of circadian misalignment46, 47 and brain responses to specific food stimuli, as observed after acute sleep restriction in adults,22, 23 that consequently may lead to a shift in food preferences towards more energy-dense and sugar-containing foods. Moreover, having sleep problems as indicated by a high CSHQ score could possibly also influence brain function associated with reward-driven eating behavior, as observed in adolescents with lower sleep quality,47 and reduce reward sensitivity as well as receipt of rewards. The potential enhancement of hedonic stimulus underlying food consumption associated with insufficient sleep may be of serious concern if adaptation to palatable foods high in fat and sugars occurs after prolonged early exposure to these foods as suggested by animal studies.48 Conversely, it cannot be entirely excluded that the relationship goes in the opposite direction with food intake adversely affecting sleep, as suggested in a recent study by Crispim et al.49 in which late-night food consumption was negatively associated with sleep quality variables in adults. However, this was not confirmed in a recent study by Herzog et al.50 in which daytime energy intake was not associated with subsequent objectively measured sleep quality in adults. Another recent study suggested that weekend catch-up sleep in children is associated with a decreased risk of being overweight,26 which was more pronounced in children with shorter sleep duration during weekdays. However, we found that variability in sleep durations, when expressed independently of sleep duration, was positively associated with intake of SSBs, suggesting an adverse effect of changes in sleep timing per se.

Furthermore, a recent study found self-reported late bedtimes and late wake up times to be associated with poorer diet quality, independent of sleep duration in 9–16-year-old children and adolescents.27 In support of these findings, we found objectively assessed sleep onset and wake up time, independent of sleep duration, to be (borderline) associated with intake of added sugar (P=0.02 and P=0.08) and SSBs (P=0.07 and P=0.055), respectively (data not shown). This suggests that not only sleep duration, but also sleep timing could influence diet quality.

Children with insufficient sleep may experience daytime sleepiness and sensations of fatigue. Tiredness during the day has previously been shown to be associated with consumption of energy-dense foods in children.18 Therefore, it could be speculated that daytime sleepiness following short sleep and/or variability in sleep duration may be associated with consumption of added sugar and SSBs to achieve ‘energy’ to deal with daily tasks. Our observations that sleep duration and variability in sleep duration were associated with dietary risk factors for overweight and obesity independent of potential confounders such as watching television, suggest tiredness, following inadequate sleep, as a trigger for consumption of energy-dense foods high in sugar content. Prevalent consumption of SSBs containing caffeine among Danish school children has recently been a matter of debate, and we cannot exclude the possibility that these beverages may have contributed to the reported quantity of SSBs consumed by children in the present study. The stimulating effect of caffeine may also cause difficulties falling asleep, wakefulness and disrupted sleep.51 Sugar from SSBs contributed considerably to the total caloric intake, which is in accordance with observations of SSBs being the major source of added sugar in the diet of many children.1, 52 The higher intake of SSBs with shorter sleep and larger variability in sleep duration may in addition to their energy content lead to a subsequent higher EI, because of a lower satiety response and compensation after consumption of liquid energy compared with solid foods.53

The observation that ghrelin was positively associated with sleep duration independent of age, sex and pubertal status is in contrast to the findings by Spiegel et al.,9 reporting elevated ghrelin levels following acute sleep restriction. However, long-term regulation of ghrelin levels appears to be quite complex, and may be associated with several factors such as changes in energy balance, body weight and adiposity, and to be affected by fasting and the previous diet.54, 55, 56 As ghrelin was not associated with any of the dietary variables, the observed association between ghrelin and sleep duration cannot be attributed to differences in the current diet composition or the other way around, at least not explained by the variables of interest. Leptin levels were associated with sleep duration and CSHQ score after adjustment for fat mass, as suggested by observational and experimental studies observing positive associations between sleep duration and leptin levels in adults.9, 10, 11 However, when adjustments for the rest of the confounders of interest were made, the associations disappeared. Moreover, our findings do not suggest an effect of plasma leptin levels on the dietary variables investigated within this specific population. It is, however, worth keeping in mind that hormonal levels were determined based on a single blood sample drawn in the morning. It is likely that the children woke up at different times, and that factors related to circadian phase may therefore have increased the variability in hormone levels.

Previous studies investigating associations between sleep and food intake in children are based on either food frequency questionnaire or nonconsecutive dietary records conducted for a limited number of days, whereas children in the present study recorded their food intake in the WebDASC software for 1 week. The WebDASC software was well accepted by children and their parents, which may have increased the likelihood of correct recording, and thereby the validity of the dietary data. The algorithm used to calculate sleep duration was developed in adults by Sadeh et al.34 with wrist as the attachment site. The algorithm has been validated with equally good results in both adults and adolescents,34 and we have recently tested the agreement between wrist and waist-worn ActiGraph monitors using this algorithm, and concluded that waist-worn devices can provide a valid proxy measure of sleep duration in epidemiologic studies.57

In this cross-sectional analysis, we evaluated exposure and outcome at the same point in time, and we are thereby not able to establish a temporal sequence and infer causality. Finally, the observation that some of the associations between sleep and dietary variables persisted even after adjusting for potential confounders suggests that similar associations could be expected in other cultures, environments and age groups.

In summary, the present study suggests that short sleep duration and a high variability in sleep duration, as well as experiencing sleep problems, are associated with a poor, obesity-promoting diet in children. These findings suggest that either sleep influences diet or both a poor diet and poor sleep may be part of a cluster that characterizes certain subsets of the population. The causal relationship needs randomized controlled trials to be elucidated.


  1. 1

    Kjøller M, Juel K, Kamper-Jørgensen F . Folkesundhedsrapporten. Statens Institut for Folkesundhed, Syddansk Universitet 2007.

  2. 2

    Lakshman R, Elks CE, Ong KK . Childhood obesity. Circulation 2012; 126: 1770–1779.

    Article  Google Scholar 

  3. 3

    Matricciani L, Olds T, Petkov J . In search of lost sleep: secular trends in the sleep time of school-aged children and adolescents. Sleep Med Rev 2012; 16: 203–211.

    Article  Google Scholar 

  4. 4

    Matricciani LA, Olds TS, Blunden S, Rigney G, Williams MT . Never enough sleep: a brief history of sleep recommendations for children. Pediatrics 2012; 129: 548–556.

    Article  Google Scholar 

  5. 5

    Chaput JP, Lambert M, Gray-Donald K, McGrath JJ, Tremblay MS, O'Loughlin J et al. Short sleep duration is independently associated with overweight and obesity in Quebec children. Can J Public Health 2011; 102: 369–374.

    PubMed  PubMed Central  Google Scholar 

  6. 6

    Carter PJ, Taylor BJ, Williams SM, Taylor RW . Longitudinal analysis of sleep in relation to BMI and body fat in children: the FLAME study. BMJ 2011; 342: d2712.

    Article  Google Scholar 

  7. 7

    Cappuccio FP, Taggart FM, Kandala NB, Currie A, Peile E, Stranges S et al. Meta-analysis of short sleep duration and obesity in children and adults. Sleep 2008; 31: 619–626.

    Article  Google Scholar 

  8. 8

    Chahal H, Fung C, Kuhle S, Veugelers PJ . Availability and night-time use of electronic entertainment and communication devices are associated with short sleep duration and obesity among Canadian children. Pediatr Obes 2012; 7: 1–10.

    Article  Google Scholar 

  9. 9

    Spiegel K, Tasali E, Penev P, Van Cauter E . Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med 2004; 141: 846–850.

    Article  Google Scholar 

  10. 10

    Spiegel K, Leproult R, L'hermite-Baleriaux M, Copinschi G, Penev PD, Van Cauter E . Leptin levels are dependent on sleep duration: relationships with sympathovagal balance, carbohydrate regulation, cortisol, and thyrotropin. J Clin Endocrinol Metab 2004; 89: 5762–5771.

    CAS  Article  Google Scholar 

  11. 11

    Chaput JP, Despres JP, Bouchard C, Tremblay A . Short sleep duration is associated with reduced leptin levels and increased adiposity: results from the Quebec family study. Obesity (Silver Spring) 2007; 15: 253–261.

    CAS  Article  Google Scholar 

  12. 12

    Chaput JP, Tremblay A . Insufficient sleep as a contributor to weight gain: an update. Curr Obes Res 2012; 1: 245–256.

    Article  Google Scholar 

  13. 13

    Penev PD . Update on energy homeostasis and insufficient sleep. J Clin Endocrinol Metab 2012; 97: 1792–1801.

    CAS  Article  Google Scholar 

  14. 14

    Klingenberg L, Chaput JP, Holmback U, Jennum P, Astrup A, Sjodin A . Sleep restriction is not associated with a positive energy balance in adolescent boys. Am J Clin Nutr 2012; 96: 240–248.

    CAS  Article  Google Scholar 

  15. 15

    St-Onge MP, Roberts AL, Chen J, Kelleman M, O'Keeffe M, Roychoudhury A et al. Short sleep duration increases energy intakes but does not change energy expenditure in normal-weight individuals. Am J Clin Nutr 2011; 29: 410–416.

    Article  Google Scholar 

  16. 16

    Schmid SM, Hallschmid M, Jauch-Chara K, Wilms B, Benedict C, Lehnert H et al. Short-term sleep loss decreases physical activity under free-living conditions but does not increase food intake under time-deprived laboratory conditions in healthy men. Am J Clin Nutr 2009; 90: 1476–1482.

    CAS  Article  Google Scholar 

  17. 17

    Garaulet M, Ortega FB, Ruiz JR, Rey-Lopez JP, Beghin L, Manios Y et al. Short sleep duration is associated with increased obesity markers in European adolescents: effect of physical activity and dietary habits. The HELENA study. Int J Obes (Lond) 2011; 35: 1308–1317.

    CAS  Article  Google Scholar 

  18. 18

    Westerlund L, Ray C, Roos E . Associations between sleeping habits and food consumption patterns among 10-11-year-old children in Finland. Br J Nutr 2009; 102: 1531–1537.

    CAS  Article  Google Scholar 

  19. 19

    Weiss A, Xu F, Storfer-Isser A, Thomas A, Ievers-Landis CE, Redline S . The association of sleep duration with adolescents’ fat and carbohydrate consumption. Sleep 2010; 33: 1201–1209.

    Article  Google Scholar 

  20. 20

    Nedeltcheva AV, Kilkus JM, Imperial J, Kasza K, Schoeller DA, Penev PD . Sleep curtailment is accompanied by increased intake of calories from snacks. Am J Clin Nutr 2009; 89: 126–133.

    CAS  Article  Google Scholar 

  21. 21

    Hogenkamp PS, Nilsson E, Nilsson VC, Chapman CD, Vogel H, Lundberg LS et al. Acute sleep deprivation increases portion size and affects food choice in young men. Psychoneuroendocrinology 2013. e-pub ahead of print 18 February 2013 doi:10.1016/j.psyneuen.2013.01.012.

  22. 22

    Benedict C, Brooks SJ, O'Daly OG, Almen MS, Morell A, Aberg K et al. Acute sleep deprivation enhances the brain’s response to hedonic food stimuli: an fMRI study. J Clin Endocrinol Metab 2012; 97: E443–E447.

    CAS  Article  Google Scholar 

  23. 23

    St-Onge MP, McReynolds A, Trivedi ZB, Roberts AL, Sy M, Hirsch J . Sleep restriction leads to increased activation of brain regions sensitive to food stimuli. Am J Clin Nutr 2012; 95: 818–824.

    CAS  Article  Google Scholar 

  24. 24

    WHO. Diet, nutrition and the prevention of chronic diseases Report of a Joint WHO/FAO Consultation. WHO Technical Report Series 916 2003.

  25. 25

    Thorleifsdottir B, Bjornsson JK, Benediktsdottir B, Gislason T, Kristbjarnarson H . Sleep and sleep habits from childhood to young adulthood over a 10-year period. J Psychosom Res 2002; 53: 529–537.

    CAS  Article  Google Scholar 

  26. 26

    Kim CW, Choi MK, Im HJ, Kim OH, Lee HJ, Song J et al. Weekend catch-up sleep is associated with decreased risk of being overweight among fifth-grade students with short sleep duration. J Sleep Res 2012; 12: 546–551.

    Article  Google Scholar 

  27. 27

    Golley RK, Maher CA, Matricciani L, Olds TS . Sleep duration or bedtime? Exploring the association between sleep timing behaviour, diet and BMI in children and adolescents. Int J Obes (Lond) 2013; 37: 546–551.

    CAS  Article  Google Scholar 

  28. 28

    Fleig D, Randler C . Association between chronotype and diet in adolescents based on food logs. Eat Behav 2009; 10: 115–118.

    Article  Google Scholar 

  29. 29

    Damsgaard CT, Dalskov SM, Petersen RA, Sorensen LB, Molgaard C, Biltoft-Jensen A et al. Design of the OPUS School Meal Study: a randomised controlled trial assessing the impact of serving school meals based on the New Nordic Diet. Scand J Public Health 2012; 29: 1–11.

    Google Scholar 

  30. 30

    Black AE . The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity. Eur J Clin Nutr 2000; 54: 395–404.

    CAS  Article  Google Scholar 

  31. 31

    Morris NM, Udry KR . Validation of a self-administered instrument to assess stage of adolescent development. J Youth Adolesc 1980; 9: 271–280.

    CAS  Article  Google Scholar 

  32. 32

    Owens JA, Spirito A, McGuinn M . The Children’s Sleep Habits Questionnaire (CSHQ): psychometric properties of a survey instrument for school-aged children. Sleep 2000; 23: 1043–1051.

    CAS  Article  Google Scholar 

  33. 33

    Trost SG, Loprinzi PD, Moore R, Pfeiffer KA . Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc 2011; 43: 1360–1368.

    Article  Google Scholar 

  34. 34

    Sadeh A, Sharkey KM, Carskadon MA . Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep 1994; 17: 201–207.

    CAS  Article  Google Scholar 

  35. 35

    Biltoft-Jensen A, Bysted A, Trolle E, Christensen T, Knuthsen P, Damsgaard CT et al. Evaluation of Web-based Dietary Assessment Software for Children: comparing reported fruit, juice and vegetable intakes with plasma carotenoid concentration and school lunch observations. Br J Nutr 2012; 27: 1–10.

    Google Scholar 

  36. 36

    Biltoft-Jensen A, Trolle E, Christensen T, Islam N, Andersen LF, Egenfeldt-Nielsen S et al. WebDASC: a web-based dietary assessment software for 8-11-year-old Danish children. J Hum Nutr Diet 2012; 18: 1–11.

    Google Scholar 

  37. 37

    Henry CJ . Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 2005; 8: 1133–1152.

    CAS  Article  Google Scholar 

  38. 38

    de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J . Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85: 660.

    Article  Google Scholar 

  39. 39

    Cole TJ, Flegal KM, Nicholls D, Jackson AA . Body mass index cut offs to define thinness in children and adolescents: international survey. BMJ 2007; 335: 194.

    Article  Google Scholar 

  40. 40

    Cole TJ, Bellizzi MC, Flegal KM, Dietz WH . Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000; 320: 1240–1243.

    CAS  Article  Google Scholar 

  41. 41

    Chaput JP, Klingenberg L, Sjodin A . Do all sedentary activities lead to weight gain: sleep does not. Curr Opin Clin Nutr Metab Care 2010; 13: 601–607.

    Article  Google Scholar 

  42. 42

    Chaput JP, Visby T, Nyby S, Klingenberg L, Gregersen NT, Tremblay A et al. Video game playing increases food intake in adolescents: a randomized crossover study. Am J Clin Nutr 2011; 93: 1196–1203.

    CAS  Article  Google Scholar 

  43. 43

    Robinson TN . Reducing children’s television viewing to prevent obesity: a randomized controlled trial. JAMA 1999; 282: 1561–1567.

    CAS  Article  Google Scholar 

  44. 44

    Park S, Blanck HM, Sherry B, Brener N, O'Toole T . Factors associated with sugar-sweetened beverage intake among United States high school students. J Nutr 2012; 142: 306–312.

    CAS  Article  Google Scholar 

  45. 45

    Van den Bulck J, Van Mierlo J . Energy intake associated with television viewing in adolescents, a cross sectional study. Appetite 2004; 43: 181–184.

    Article  Google Scholar 

  46. 46

    Hasler BP, Dahl RE, Holm SM, Jakubcak JL, Ryan ND, Silk JS et al. Weekend-weekday advances in sleep timing are associated with altered reward-related brain function in healthy adolescents. Biol Psychol 2012; 91: 334–341.

    Article  Google Scholar 

  47. 47

    Holm SM, Forbes EE, Ryan ND, Phillips ML, Tarr JA, Dahl RE . Reward-related brain function and sleep in pre/early pubertal and mid/late pubertal adolescents. J Adolesc Health 2009; 45: 326–334.

    Article  Google Scholar 

  48. 48

    Lutter M, Nestler EJ . Homeostatic and hedonic signals interact in the regulation of food intake. J Nutr 2009; 139: 629–632.

    CAS  Article  Google Scholar 

  49. 49

    Crispim CA, Zimberg IZ, dos Reis BG, Diniz RM, Tufik S, de Mello MT . Relationship between food intake and sleep pattern in healthy individuals. J Clin Sleep Med 2011; 7: 659–664.

    PubMed  PubMed Central  Google Scholar 

  50. 50

    Herzog N, Friedrich A, Fujita N, Gais S, Jauch-Chara K, Oltmanns KM et al. Effects of daytime food intake on memory consolidation during sleep or sleep deprivation. PLoS One 2012; 7: e40298.

    CAS  Article  Google Scholar 

  51. 51

    Pollak CP, Bright D . Caffeine consumption and weekly sleep patterns in US seventh-, eighth-, and ninth-graders. Pediatrics 2003; 111: 42–46.

    Article  Google Scholar 

  52. 52

    Reedy J, Krebs-Smith SM . Dietary sources of energy, solid fats, and added sugars among children and adolescents in the United States. J Am Diet Assoc 2010; 110: 1477–1484.

    CAS  Article  Google Scholar 

  53. 53

    de Ruyter JC, Olthof MR, Seidell JC, Katan MB . A trial of sugar-free or sugar-sweetened beverages and body weight in children. N Engl J Med 2012; 367: 1397–1406.

    CAS  Article  Google Scholar 

  54. 54

    Hansen TK, Dall R, Hosoda H, Kojima M, Kangawa K, Christiansen JS et al. Weight loss increases circulating levels of ghrelin in human obesity. Clin Endocrinol (Oxf) 2002; 56: 203–206.

    CAS  Article  Google Scholar 

  55. 55

    Cummings DE, Foster-Schubert KE, Overduin J . Ghrelin and energy balance: focus on current controversies. Curr Drug Targets 2005; 6: 153–169.

    CAS  Article  Google Scholar 

  56. 56

    Williams DL, Cummings DE . Regulation of ghrelin in physiologic and pathophysiologic states. J Nutr 2005; 135: 1320–1325.

    CAS  Article  Google Scholar 

  57. 57

    Hjorth MF, Chaput JP, Damsgaard CT, Dalskov S, Michaelsen KF, Tetens I et al. Measure of sleep and physical activity by a single accelerometer: can a waist-worn Actigraph adequately measure sleep in children? Sleep Biol Rhythms 2012; 10: 328–335.

    Article  Google Scholar 

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The study is part of the OPUS project ‘Optimal well-being, development and health for Danish children through a healthy New Nordic Diet’ supported by a grant from the Nordea Foundation. We are grateful to the participants and would also like to acknowledge the school staffs as well as other researchers and staff in the OPUS project.

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Correspondence to J S Kjeldsen.

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The authors declare no conflict of interest.

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Designed research: AA, KFM, IT, J-PC and AS; coordinated data collection: MFH and RA; analyzed and interpreted data: JSK and MFH; discussed the analysis and interpretation of the data: AS and J-PC; wrote paper: JSK and MFH; had primary responsibility of the final content: AS. All authors reviewed the manuscript critically and approved the final manuscript.

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Kjeldsen, J., Hjorth, M., Andersen, R. et al. Short sleep duration and large variability in sleep duration are independently associated with dietary risk factors for obesity in Danish school children. Int J Obes 38, 32–39 (2014).

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  • sleep
  • diet
  • sugar intake
  • sugar-sweetened beverages
  • children
  • overweight

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