Teen girls–but not boys–who prefer to go to bed later are more likely to gain weight, compared to same-age girls who go to bed earlier, suggests a study funded by the National Institutes of Health.
The findings by researchers at Kaiser Permanente in Oakland, California, and other institutions appear in JAMA Pediatrics.
A total of 804 adolescents (418 girls and 386 boys) ages 11 to 16 took part in the study.
The children responded to questionnaires on their sleep habits and wore an actigraph–a wrist device that tracks movement.
Researchers measured their waist size and calculated their proportion of body fat using a technique called dual-energy x-ray absorptiometry.
They also estimated the children’s social jet lag–the difference between their weeknight and weekend bedtimes.
Those who stayed up far later on weekends than weeknights were considered to have high social jet lag.
The authors noted that previous studies had found that adults who preferred to stay up late and had high social jet lag were more likely to gain weight than those who went to be earlier and did not have social jet lag.
The researchers undertook the current study to determine if the same associations would be seen in young people.
For girls, staying up later was associated with an average .58 cm increase in waist size and a .16 kg/m2 increase in body fat.
For girls, staying up later was associated with an average .58 cm increase in waist size and a .16 kg/m2 increase in body fat.
Each hour of social jet lag was associated with a 1.19 cm larger waist size and a 0.45 kg/m2 increase in body fat.
These associations were reduced–but still remained–after the researchers statistically adjusted for other factors known to influence weight, such as sleep duration, diet, physical activity, and television viewing. Although the researchers found slight associations between these measures and waist size and body fat in boys, they were not statistically significant.
researchers concluded that improving sleep schedules may be helpful in preventing obesity in childhood and adolescence, especially in girls.
Funding: Funding was provided by NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Diabetes and Digestive and Kidney Diseases, National Cancer Institute, and National Heart, Lung and Blood Institute.
An accumulating body of literature has recently taken to investigating the nexus between insufficient sleep and the current global epidemic of obesity among children [2,4]. Predominantly, this research has referred to insufficient sleep (sometimes referred to as sleep deprivation) in terms of duration, or amount, of time asleep deemed adequate for optimal outcomes .
This is in part due to guidelines that recommend approximately 9–11 h of sleep per night for children ages 5–12 [1,6]. Using this framing, representative data of children and adolescents in Canada and the USA report approximately 25% to one third are sleep deprived [7,8].
Another study of 9- to 11-year-olds across 12 different countries (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the United Kingdom and the United States) indicated that 58% of children slept less than the recommended guidelines . While trends in Australia are less clear, a sample of 3495 children aged 5–15 years old in South Australia reported 24% had insufficient sleep .
The concept of a sleep-obesity nexus is supported by observed global trends in declining sleep durations among children occurring concurrently with detected increases in the prevalence of childhood overweight and obesity [2,4].
Research on this sleep-obesity nexus, amongst both cross sectional and longitudinal samples, has consistently demonstrated that shorter sleeps among children increase the risk of overweight/obesity [10,11,12,13,14,15,16].
A meta-analysis of 30,002 children from around the globe reported an increased odds of obesity (pooled OR = 1.89 95% CI = 1.43–1.68) amongst children with insufficient sleep durations compared with their longer sleeping counterparts .
They argue that sleep is multidimensional, and any consideration of sleep sufficiency should include elements such as the “quality” (perceived satisfaction, or structure of sleep wave cycles), “efficiency” (the ability to initiate and maintain sleep in an efficient manner); and “timing” (the placement of sleep within the 24 h of the day) [11,17,18].
These dimensions have been shown to play noteworthy roles in the sleep-obesity nexus, with poor quality, poor efficiency and later timings leading to poorer weight status among children, independent of sleep duration [5,18,19].
Considerably higher odds for obesity have been reported amongst children aged 9–16 years old with sleep-wake categories of late to bed/early to rise (OR = 1.55, 95% CI = 1.12–2.14) or late to bed/late to rise (OR = 1.47, 95% CI = 1.14–1.90), when compared with children with a sleep-wake category of early to bed/late to rise . Jarrin et al. (2013) demonstrated that independent from adolescents’ sleep durations, participants’ risks of overweight and obesity were greater if they presented with poorer sleep quality, poor sleep maintenance/efficiency or delayed sleep timing .
Furthermore, Morrissey et al.  found that while scoring poorly on any one sleep dimension did not increase odds of obesity (OR = 1.17, 95% CI = 0.90–1.54), an increased odds of obesity was shown for having a combination of any two (OR = 1.68, 95% CI = 1.29–2.18), three (OR = 1.45, 95% CI = 1.03–2.01) or four or more (OR = 2.25, 95% CI = 1.41–3.58) sleep issues. These results suggest that the multiple sleep dimensions must be considered simultaneously in order to gain a stronger understanding of the sleep-obesity nexus.
A longitudinal study of Finnish children between 1989 and 2005 demonstrated that the prevalence of frequent sleep problems increased from 15% to 20% among girls . While the drivers of this high prevalence are not fully understood, today’s profuse availability of food products (leading to higher caloric consumption)  as well as an exponential growth in technology (leading to higher daily use of screen devices) [28,29] are both suggested as potential influential factors [29,30].
Sleep problems have also displayed a bidirectional negative association with physical activity (PA) levels among 13-year-old girls in Estonia .
In terms of the sleep-obesity nexus, evidence of children with lower sleep quality engaging in less daytime PA  may in turn increase their risk of overweight and obesity. F
urthermore, chronic sleep restriction for five consecutive nights has been shown to lead to increased consumption of high glycemic index foods among a sample of adolescents , contributing to energy imbalance and increased risk of obesity. So whilst sleep deprivation may not directly influence weight gain among children, it may considerably increase the risk of overweight and obesity as a result of associated behavioral determinants (e.g., physical activity, sedentary behavior and diet).
Behavioral factors such as physical activity, sedentary behavior and diet have previously been accepted as key modifiable determinants of childhood overweight and obesity, yet require further investigation to provide understanding of their potential influence on the sleep-obesity nexus [34,35]. This study aims to:
- investigate the influence of daily physical activity (PA), screen time (ST) and dietary behaviors on children’s sleep dimensions; and
- examine whether the association between children’s sleep and weight status is modified by daily physical activity, screen time and dietary behaviors.
Materials and Methods
The sample comprised data of Grade 4 (aged approx. 9–10 years) and Grade 6 (aged approx. 11–12 years) students participating in the Great South Coast Childhood Obesity Monitoring Study (GSC)  and the Goulburn Valley Health Behaviours Monitoring Study (GV) , collected between July 2016 and June 2017. Detailed information on the study design, sampling strategy, school and student recruitment process and measure have previously been published for the GSC study , which has been replicated in the GV study .
Briefly, all 146 primary schools (government, independent and catholic) within the nine local government areas of the Great South Coast region of Victoria (Colac-Otway, Corangamite, Southern Grampians, Glenelg, Moyne, Warrnambool) and Goulburn Valley region (Shepparton, Moira and Strathbogie) were invited to participate, of which 64% agreed (participation rates: GSC 56 of 83 schools; GV 38 of 63 schools).
A passive (opt-out) consent approach was used to invite all Grade 4 and 6 students within each participating school (GSC N = 2197; GV N = 1356). Children were excluded from participation if their parent or guardian completed an opt-out form, or if the student verbally declined on the day (GSC N = 228; GV N = 300). Children were also free to participate in as little or as much of the data collection process as they wished (e.g., just have their height measured and not their weight, just complete the self-report behavioral questionnaire, etc.).
In total, data from 2862 Grade 4 and Grade 6 children (GSC N = 1817; GV N = 1045; combined participant response rate 85%) from 94 primary schools across Victoria were collected. All data collection took place at the school during school time. Students were invited to have anthropometric measurements taken and to complete a self-report electronic questionnaire that examined demographic characteristics, health behaviors and well-being.
This study received ethical approvals from Deakin University’s Human Research Ethics Committee (DUHREC 2014-279), the Victorian Department of Education and Training (DET 2015_002622) and the Catholic Archdiocese of Sandhurst and Ballarat.
Children completed self-report items on date of birth and gender, as well as providing information on ancestry, primary language and socioeconomic position (via postcode). School Index of Community Socio-Educational Advantage (ICSEA) scores were also retrieved from the MySchool website .
Developed by Australian Curriculum, Assessment and Reporting Authority (ACARA) , ICSEA takes into account both student- and school-level factors to summarize educational advantages and disadvantages, with 1000 being the average benchmark score.
A 16-item questionnaire was developed from previous studies and questionnaires [40,41,42,43,44,45,46,47,48,49,50,51] to assess aspects of children’s sleep (duration, quality, efficiency and timing) and behavioral factors. A detailed description including reliability and validity properties (where possible) of these are available (forthcoming).
Children self-reported their usual bedtimes and wake times on school nights within 15-min increments, allowing for the calculation of sleep duration (the difference between the two time points); with short sleep defined as less than nine hours, as per national guidelines [6,52].
Median splits of bed and wake times were used to determine categorization early/late bedtimes and wake times, following previously used criteria [20,41,53]. Here, late to bed was determined as 8:30 p.m. or later, and early to rise as 7:00 a.m. or earlier, similar to previous literature [41,53,54,55].
From this, sleep-wake categories were categorized as either: early to bed/late to rise (≤8:15 p.m./≥7:15 a.m.); early to bed/early to rise (≤8:15 p.m./≤7:00 a.m.); late to bed/late to rise (≥8:30 p.m./≥7:15 a.m.); or late to bed/early to rise (≥8:30 p.m./≤7:00 a.m.). Sleep quality was based on the questionnaire item “overall, how well do you think you sleep” with children required to select a response on a six-option Likert scale ranging from “Very Good” to “Very Bad”.
These response options were dichotomized as “good” or “bad/very bad” sleep quality. Efficiency was assessed via questions on sleep initiation (“Over the last two weeks, have you found it hard to fall asleep (longer than 20 mins)”) and maintenance/waking episodes (“Some people wake up during the night, others never do. How often did you wake up”). Sleep initiation had a five-option Likert scale (ranging from “never” to “almost always”), and was dichotomized as poor from responses of “often” or “almost always”. Poor sleep maintenance/waking episode responses were determined as reporting having three or more waking episodes per night from a four-option Likert scale ranging from “never (I don’t wake up during the night)” to “often (3 or more times per night)”.
Trained staff, using a standardized protocol and equipment (portable stadiometer: HM200P stadiometer, Charder Electronic Co. Ltd, (Taichung City, Taiwan, China) and electronic weight scale: A&D Precision Scale UC-321, A&D Australasia Pty Ltd, (Adelaide, Australia), took measurements of height and weight. Measures were taken without shoes and wearing only light clothing.
Each measure was taken twice (to the nearest 0.1 cm for height, and the 0.1 kg for weight), with a third measure taken if the difference was greater than 0.1 kg for weight or 0.5 cm for height.
The average of two or three measurements was used to generate each child’s height and weight. Participants’ height, weight, gender and age were used to calculate BMI (Body Mass Index) z-score and weight status, using the WHO growth standards for children ages 5–19 .
Physical Activity, Screen Time and Dietary Behaviors
Based on items from the Core Indicators and Measures of Youth Health survey , students were asked to select how much time they had been physically active for each of the last seven days, with six possible duration options (“none”, “1 to 14 min”, “15 to 29 min”, “30 to 59 min”, “1 to 2 h” or “more than two hours”).
Students then reported screen usage (outside of school hours) for each of the last seven days, selecting from five duration options (“none”, “less than 1 hour”, “1 to 2 h”, “more than 2 h, but less than three” or “more than 5 h”).
Items adapted from the Children’s Sleep Hygiene and the Adolescent’s Sleep Hygiene questionnaires [58,59] assessed physical activity and screen-use behaviors around bedtime over the last two weeks. Children were asked whether they had “been very active (e.g., playing sports, playing outside, running, wrestling)” or “used electronic devices (e.g., computer/gaming console/ tablet/phone)” in the one hour prior to bedtime, with responses ranging from “never” to “almost always”. The last question was then repeated to assess usage of these devices while in bed/during bedtime.
Consumption of fruit and vegetables, sugar-sweetened beverages (SSBs), snack foods and takeaway (meals purchased from restaurants or fast-food vendors) were assessed from items extracted from the Simple Dietary Questionnaire [60,61].
Students selected from options starting from “none/don’t eat” to “7 or more per day” (options increasing by half servings) for both average fruit and vegetable consumption. Options ranging from “rarely or never” to “almost every day” and up to “three times per day” (eight options total) were used to report consumption of snack foods and SSBs. Takeaway food consumption options ranged from “rarely or never” to “2–4 times per week”, up to “every meal” (eight options total).
Of the 2862 consenting students, 2253 were included in the analysis (816 GV; 1437 GSC); 169 students were excluded due to missing BMI z-scores, six due to extreme BMI z-scores being ±3 standard deviations from the mean and 434 due to inaccurate/missing data or irregular bed/wake times.
Bedtimes and wake times were screened, with exclusion criteria representing inaccurate or irregular bed/wake times generated as described below. Cut-points for bedtime and wake times were created to exclude irregular and inaccurate/missing data. We excluded bedtimes reported before 6:00 p.m. or after 2:15 a.m. and wake times before 3:00 a.m. or after 8:45 a.m.
Results from each sleep variable were dichotomized as positive or negative (as outlined above), with negative defined as: sleeping less than nine hours per night, going to bed later than 8:30 p.m.; waking earlier then 7:00 a.m.; sleep quality perceived as bad/very bad; an initiation problem of 20 min or more to fall asleep; and three or more waking episodes per night.
From these, a sleep score was created to indicate the number of sleep dimensions scored as negative, out of a maximum of six. The sleep score was categorized ranging from 0 “no sleep problem” to 3 “three or more sleep problems”.
Physical activity (PA), screen time (ST) and dietary behaviors were dichotomized to reflect whether the guidelines or recommendations were met, or to indicate a negative versus positive behavior. Based on the recommended one hour of moderate-to-vigorous PA per day and less than two hours of ST per day for leisure , children were categorized as meeting the guidelines if they reported being active for one or more hours, or less than two hours, of ST on five out of the seven days.
Fruit guidelines were considered met if students reported consuming two servings of fruit per day. Vegetable guidelines were considered met if they reported consuming the recommended 5.5 servings per day for boys aged 12 years or older, or five servings per day for girls and younger boys . Screen time, physical activity and sugar-sweetened beverage consumption before bed, along with screen usage in bed, were positively scored if reported to be never or almost never. Snack and average sugar-sweetened beverages consumption were positively scored if reported as being consumed once a day or less, and takeaway consumption as once a week or less.
A detailed outline of each variable and the coding is available (Supplementary Table S1).
The current study suggests that behavioral factors, including usage of screen devices and the consumption of sugar-sweetened beverages, are influential factors on the sleep-obesity nexus. While dietary factors and screen-usage behaviors were not associated with children’s weight status, more frequent screen usage and the consumption of sugar-sweetened beverages were reported amongst those with more sleep problems.
Furthermore, higher numbers of sleep problems increased overweight or obesity odds, regardless of controlling for all other behavioral factors.
The outlined association between the number of sleep problems with the usage of screen devices and consumption of sugar-sweetened beverages could suggest that these factors might influence children’s weight statuses via the sleep-obesity nexus. Further developing this understanding through monitoring cohort studies could provide insight into strategies to improve children’s sleep and reduce overweight and obesity rates.
Robert Bock – NIH
The image is in the public domain.
Original Research: Closed access
“Chronotype, social jet lag, and cardiometabolic risk factors in early adolescence”. Cespedes Feliciano EM, et al.
JAMA Pediatrics doi:10.1001/jamapediatrics.2019.3089.