Associations between Sleep Duration and Indicators of Cardiometabolic Disease in Canadian Children and Adolescents: Analyses of the 2007

Abstract

Background:

Indicators of cardiometabolic disease—including obesity, hyperinsulinemia, and dyslipidemia—are associated with an increased risk of cardiovascular disease and type 2 diabetes. Rates of obesity and type 2 diabetes in Canadian children and adolescents have increased rapidly in recent years; research exploring modifiable risk factors is critical. Experimental and epidemiological research demonstrates that partial sleep loss is linked with deteriorations in indicators of cardiometabolic health. The objectives of this study are (1) to examine associations between short sleep duration and indicators of cardiometabolic disease in Canadian children and adolescents and (2) to identify determinants of short sleep duration in this population.

Methods:

Logistic regression models were developed to examine associations between sleep duration and indicators of cardiometabolic disease and to identify predictors of short sleep duration.

Results:

Compared with longer sleepers, children and adolescents with short sleep duration had greater odds of being overweight or obese. Sex- and age-stratified analyses indicated that short sleep duration was linked with greater odds of overweight/obesity in boys and adolescents only. Short sleepers did not have greater odds of having hyperinsulinemia, low HDL cholesterol, or high triglycerides. Age was a strong predictor of inadequate sleep duration.

Conclusion:

Future studies should include longitudinal designs that address whether short sleep duration in boys and in adolescents contributes directly to the development of overweight and obesity.

Increased rates of obesity have been observed in Canadian children and adolescents in recent years. For example, between 1981 and 2009, the prevalence of overweight or obesity in Canadian youth aged 15–19 years increased from 14% to 31% in boys and from 14% to 25% in girls.¹ This is particularly concerning since the symptoms of metabolic syndrome including obesity, dyslipidemia, hypertension, and impaired carbohydrate metabolism are strongly associated with the development of cardiovascular disease and type 2 diabetes later in life.² Type 2 diabetes was considered nonexistent in Canadian children 20 years ago,³ but in the last few decades, the prevalence of diabetes has been rising globally. At 0.3%, the prevalence of type 2 diabetes in children and youth remains low in Canada, but it is rising, especially among certain groups such as Aboriginal populations.⁴

A large body of research has demonstrated that short sleep duration is linked to the following indicators of cardiometabolic disease: overweight/obesity (OWOB), hyperinsulinemia, and dyslipidemia.^5,6 However, the majority of these studies have focused on adults. Several cross-sectional or longitudinal studies have demonstrated negative associations between sleep duration and OWOB in children and children and adolescents in Australia,⁷ the United States,^8–15 England,^16,17 New Zealand,¹⁸ and Norway.¹⁹ The nature and strength of the relationships between sleep duration and OWOB vary among these studies. For example, age-corrected data from a study of 5–10-year-old children in the Canadian Province of Québec demonstrated an age-adjusted negative linear relationship between sleep duration and OWOB.²⁰ By comparison, one study completed in the United States reported that among adolescents, short sleep duration was associated with increased BMI in males only.¹¹ At the other end of this continuum, a recent study in Iran of 14–17-year olds did not observe any relationship between sleep duration and BMI in either boys or girls.²¹

Only a single study has examined associations between sleep duration and OWOB in Canadian children,²⁰ and that study was not nationally representative. No studies have explored associations between sleep duration and OWOB in Canadian adolescents. Given the variety of studies on this topic—and the variability in the findings reported—it is important to clarify using the data available what sleep duration-OWOB relationships may exist for the entirety of the Canadian pediatric population. This will support establishing relevant public health measures or recommendations, if required, at a national level in Canada, within the framework of the healthcare system and federal health policies that already exist here.

Study Objectives

There are two main objectives of this study. The primary objective of this study is to address a gap in the literature by investigating associations between short sleep duration and cardiometabolic risk factors in a nationally representative sample of Canadian children and adolescents. This is done using data from the Canadian Health Measures Survey (CHMS, Cycle 1), an ongoing, nationally representative, cross-sectional Canadian population health survey.²²

Any sleep duration-OWOB relationship observed could be influenced by covariates through confounding and/or interaction.²³ For this study, covariates available in the CHMS data set can be used as variables that are likely to have confounding and/or interaction effects based on evidence from the literature. Accordingly, the secondary objective of this study is to identify determinants of short sleep duration in this population, using the available CHMS covariates.

Methods

Participants

Data for this research were obtained from the 2007 to 2009 CHMS (Cycle 1).²² Residents of First Nations reserves, Crown lands, certain remote regions, institutions, and full-time members of the Canadian Forces were excluded from the survey.²² Giroux describes the CHMS sampling strategy in detail.²⁴ For this study, inclusion criteria included participants aged 6–17 years old; participants with diabetes were excluded. Two data files were used for analyses, including the full sample (n = 1690) and the fasting subsample that only included participants who had fasted for at least 12 hours (n = 735). Chi-square tests were performed to examine differences between participants with and without missing data for the full sample and fasting subsample. Characteristics of the study population are presented in Table 1.

Table 1.

Characteristics of the Study Population (Bootstrapped Data)

	Full sample (n = 1690), % (SE)	Fasting sample (n = 735), % (SE)
Age category
6–11	49.8 (0.8)	49.4 (1.3)
11–17	50.2 (0.8)	50.6 (1.3)
Sex
Male	53.0 (0.9)	53.2 (1.2)
Female	47.0 (0.9)	46.7 (1.2)
Sleep adequacy
Short sleeper	56.0 (1.4)	55.3 (2.5)
Not a short sleeper	44.0 (1.4)	44.7 (2.5)
OWOB status
OWOB	25.5 (2.2)	28.1 (2.7)
No OWOB	74.5 (2.2)	71.9 (2.7)
Insulin levels
Hyperinsulinemia	—	23.0 (1.8)
No hyperinsulinemia	—	77.0 (1.8)
Triglyceride levels
High triglycerides	—	23.8 (2.0)
No high triglycerides	—	76.2 (2.0)
HDL levels
Low HDL	—	24.7 (2.0)
No low HDL	—	75.3 (2.0)
Household education
Below postsecondary	20.2 (2.7)	20.3 (3.5)
Some postsecondary	79.8 (2.7)	79.7 (3.5)
Household income
Low/lower middle	23.9 (2.9)	24.8 (2.8)
Upper middle/high	76.1 (2.9)	75.2 (2.8)
Chronic disease
Yes	69.5 (2.9)	67.0 (3.4)
No	30.5 (2.9)	33.0 (3.4)

Chronic disease status was based on survey questions that probed for clinically diagnosed long-term mental and/or physical health conditions. Respondents with at least one chronic condition were coded as having a chronic disease.

OWOB, overweight/obesity.

Materials and Procedures

Measures

Sleep duration was gathered by self-report through the survey question, “How many hours do you usually spend sleeping in a 24-hour period, excluding time spent resting?” Parents/guardians answered the question for children aged 6–11 years. Cut-points for short sleep duration were <10 hours of sleep per night for children aged 6–11 years and <9 hours per night for children aged 12–17 years. This definition of short sleep duration was based on age-related sleep requirements.^25,26

CHMS measurements for indicators of cardiometabolic disease were performed by trained professionals and were used to address Objective 1. Weight was measured with a Mettler–Toledo digital scale, and standing height was measured one time with a stadiometer. Insulin, HDL cholesterol, and triglycerides were measured using fasting blood tests. For participants with insulin levels coded as below the lowest detectable limit of 14.43 pmol/L, these data were imputed using the L/√2 method.²⁷ OWOB was defined as BMI ≥85th percentile based on z-scores standardized for age and sex from pooled international data.²⁸ Hyperinsulinemia and high triglycerides were defined as values ≥the 75th percentile.²⁹ Low HDL cholesterol was defined as values <the 25th percentile.²⁹

Age, sex, chronic disease status, household education level, and household income were self-reported and were used to address Objective 2. Chronic disease status was based on survey questions that probed for clinically diagnosed long-term mental and/or physical health conditions. Respondents with at least one chronic condition were coded as having a chronic disease. Household education was defined as the highest level of education achieved by any member of the household. The cut-point for household education was defined as participants in households with at least one household member who attended postsecondary education. Household income was derived from the CHMS “income adequacy” variable that was based on total income and number of individuals in the household.³⁰ The cut-point for household income was defined as low/lower middle and upper middle/high income.

Statistical Analyses

Data were analyzed using the statistical software packages SPSS 20.0 and WesVar 5.1. Statistical significance was assessed at p < 0.05. Univariate analyses were conducted using chi-square tests in SPSS, and logistic regression models were conducted in WesVar with bootstrap weights. In logistic regression models, statistical significance was assessed for the overall model (likelihood ratio test) and for the predictive ability of the individual variables (odds ratio with confidence interval). Population weights were utilized to develop nationally representative estimates. To account for the complex CHMS sampling design, bootstrapping was used to calculate the coefficient of variation and confidence intervals.

Objective 1

Four logistic regression models were developed to examine associations between sleep duration and the four indicators of cardiometabolic disease. The first regression model included sleep duration as the predictor variable and OWOB as the outcome variable, with having a chronic condition, household education, and household income included as covariates. This model was tested using both the full sample and the fasting subsample. Using the fasting subsample, subsequent parallel regression models were tested with each of hyperinsulinemia, high triglycerides, and low HDL cholesterol as the outcome variable, respectively.

Objective 2

To identify determinants of sleep duration, logistic regression models were developed with age, sex, chronic conditions, education, and income entered as predictor variables and sleep duration included as the outcome variable. Models were also stratified by age and sex to explore whether the predictors of short sleep duration are different for girls compared with boys, and for children compared with adolescents. The full sample was utilized for these regression models because the sample size was larger and it contained all variables necessary for the analyses.

Ethical approval for this project was obtained from the Social Sciences and Humanities Research Council of Canada and from Statistics Canada. All participants provided informed consent. Day et al. provide extensive detail regarding the ethical measures in place for the CHMS.³¹

Results

Data Screening and Cleaning

The full CHMS sample included 1690 participants with complete data, and the fasting subsample had 735 participants with complete data. Characteristics of the study participants are documented in Table 1.

Missing data

Participants with missing data for any study variable were excluded from analyses. In the full CHMS sample, of the 1876 participants who met inclusion criteria, 186 had missing data and were excluded, leaving a final sample size of 1690. In the fasting subsample, 828 participants met inclusion criteria, and 93 with missing data were excluded. In the full sample, participants with missing data were more likely to be older, to be short sleepers, to have a chronic condition, and to have low household education levels. The same general pattern was observed in the fasting subsample, except that missing data status was not associated with education level and sleep duration, and, in the fasting subsample, participants with missing data were more likely to have hyperinsulinemia.

Associations between short sleep duration and OWOB

Based on CHMS data, the national prevalence of OWOB in the Canadian population aged 6–17 years was 25.5%. Chi-square analyses indicated that participants with short sleep duration or household education of at least the postsecondary level were more likely to be OWOB. In a bivariate logistic regression model with sleep duration as the predictor variable and OWOB as the outcome variable, short sleepers had an odds ratio of 1.59 (95% CI 1.19–2.88) for OWOB compared with children and adolescents who reported meeting recommended sleep duration guidelines. In a multivariable model adjusting for the effect of age, sex, chronic disease, education, and income, the odds of OWOB in short sleepers were slightly attenuated to 1.56 (95% CI 1.15–2.19). In gender stratified models, only boys with short sleep had increased odds of OWOB (OR 1.85, 95% CI 1.19–2.88) but not girls (OR 1.27, 95% CI 0.63–2.57). In age-stratified analyses, short sleepers aged 12–17 years had increased odds of OWOB (OR 1.77, 95% CI 1.05–2.99). No significant effect was observed in children aged 6–11 years (OR 1.37, 95% CI 0.84–2.22). Table 2 presents the coefficients for all variables included in the models.

Table 2.

Odds Ratios (95% CI) for All Variables Included in Multivariable Models Predicting OWOB (Bootstrapped Data, Full Sample)

		Sex-stratified models^b		Age-stratified models^c
Predictor variable	Unstratified model^a	Boys	Girls	6–11 years	12–17 years
Short sleeper	1.56 (1.15–2.19)	1.85 (1.19–2.88)	1.27 (0.63–2.57)	1.37 (0.84–2.22)	1.77 (1.05–2.99)
Adolescent age	1.09 (0.78–1.52)	1.11 (0.75–1.65)	1.07 (0.57–1.99)
Male sex	0.80 (0.64–1.00)	—	—	0.87 (0.63–1.19)	0.75 (0.46–1.21)
Chronic disease	0.88 (0.62–1.25)	0.92 (0.58–1.45)	0.86 (0.54–1.38)	1.21 (0.73–2.01)	0.70 (0.38–1.29)
Low education	0.68 (0.44–1.07)	0.68 (0.30–1.54)	0.67 (0.47–0.94)	0.74 (0.31–1.82)	0.64 (0.34–1.22)
Low income	0.97 (0.59–1.62)	1.41 (0.62–3.22)	0.66 (0.44–1.01)	0.78 (0.34–1.76)	1.20 (0.74–1.95)
Model^d	<0.001	<0.001	0.007	0.040	0.001

Bold = p < 0.05. Chronic disease status was based on survey questions that probed for clinically diagnosed long-term mental and/or physical health conditions. Respondents with at least one chronic condition were coded as having a chronic disease.

Odds ratios adjusted for age, sex, chronic disease, education, and income.

Odds ratios adjusted for age, chronic disease, education, and income.

Odds ratios adjusted for sex, chronic disease, education, and income.

Likelihood ratio test for significance of full models versus constant-only models.

Associations between short sleep duration and hyperinsulinemia

The fasting subsample (n = 735) was used to conduct all analyses involving hyperinsulinemia, low HDL cholesterol, and high triglycerides. Based on the CHMS data set, the national prevalence of hyperinsulinemia in the Canadian population aged 6–17 years was 23.0%. Chi-square analyses indicated that participants with short sleep duration, of age 6–11 years (vs. being an adolescent), with higher household education, or with a chronic disease were more likely to have hyperinsulinemia. In a bivariate logistic regression model with sleep duration as the predictor variable and hyperinsulinemia as the outcome variable, short sleepers had significantly greater odds of having hyperinsulinemia (OR 1.76, 95% CI 1.07–2.92). However, after adjusting for age, sex, chronic disease, education, and income, short sleep duration was not significantly linked to increased odds of hyperinsulinemia (OR 1.27, 95% CI 0.73–2.19). Similarly, short sleep duration was not a significant predictor of hyperinsulinemia in age- nor sex-stratified models.

Associations between short sleep duration and low HDL cholesterol

Based on the CHMS data set, the national prevalence of low HDL cholesterol in the Canadian population aged 6–17 years was 24.7%. Chi-square analyses indicated that, compared with participants without low HDL, those with low HDL were more likely to be of adolescent age and to have lower household education and income levels, and were less likely to have a chronic condition.

Short sleep duration was not a statistically significant predictor of low HDL in multivariable or stratified logistic regression models.

Associations between short sleep and high triglycerides

The national prevalence of high triglycerides in the Canadian population aged 6–17 years was 23.8%. Chi-square analyses indicated that, compared with younger children, participants of adolescent age were more likely to have high triglycerides. Short sleepers did not have increased odds of high triglycerides in multivariable models accounting for age, sex, chronic disease, education, and income (OR 0.99, 95% CI 0.64–1.53). Similarly, sex- and age-stratified models did not demonstrate any significant associations between sleep duration and high triglycerides.

In summary, short sleepers had higher odds of OWOB but not hyperinsulinemia, low HDL, or high triglycerides. The odds ratios for all indicators of cardiometabolic disease in short sleepers are summarized in Table 3, including OWOB, hyperinsulinemia, low HDL, and high triglycerides.

Table 3.

Comparison of Odds Ratios for Indicators of Cardiometabolic Disease in Short Sleepers (Bootstrapped, Fasting Subsample)

	OWOB		Hyperinsulinemia		Low HDL		High Triglycerides
Model	OR (95% CI)	Model^a	OR (95% CI)	Model^a	OR (95% CI)	Model^a	OR (95% CI)	Model^a
Bivariate	1.73 (1.15–2.60)	0.001	1.76 (1.07–2.92)	0.001	n/a^b	0.069	n/a^b	0.398
Multivariable^c	1.58 (1.00–2.48)	<0.001	1.27 (0.73–2.19)	<0.001	1.06 (0.58–1.92)	<0.001	0.99 (0.64–1.53)	0.002
Boys^d	1.93 (1.04–3.56)	<0.001	1.36 (0.73–2.54)	<0.001	1.60 (0.74–3.47)	<0.001	1.45 (0.85–2.47)	<0.001
Girls^d	n/a^b	0.110	1.21 (0.58–2.50)	<0.001	0.73 (0.37–1.42)	0.048	n/a^b	0.088
Ages 6–11^e	n/a^b	0.269	1.63 (0.87–3.06)	0.030	0.89 (0.43–1.85)	0.014	1.17 (0.56–2.43)	0.012
Ages 12–17^e	1.90 (0.91–3.97)	<0.001	1.19 (0.62–2.30)	0.013	1.22 (0.55–2.73)	<0.001	n/a^b	0.112

Bold = p < 0.05.

Likelihood ratio test for significance of full model versus constant-only model.

Odds ratios not displayed because overall model was not significant.

Odds ratios adjusted for age, sex, chronic disease, education, and income.

Odds ratios adjusted for age, chronic disease, education, and income.

Odds ratios adjusted for sex, chronic disease, education, and income.

Objective 2: Identify Determinants of Short Sleep Duration

Based on the full sample of the CHMS, the national prevalence of short sleep duration in the Canadian population aged 6–17 years was 44.0%. Chi-square analyses indicated that characteristics linked with short sleep duration included adolescent age (as opposed to childhood), female sex, and having a chronic condition.

To further explore risk and protective factors associated with being a short sleeper, logistic regression analyses were performed using the full sample (n = 1690). Using short sleep duration as the outcome variable and age, sex, chronic disease, education, and income as predictor variables, five logistic regression models were developed (Table 4). In the unstratified multivariable model, age was the strongest predictor of short sleep duration. Compared with children aged 6–11 years, teens aged 12–17 years had greater odds of not meeting sleep guidelines (OR 2.62, 95% CI 1.88–3.66). Sex, chronic disease, education, and income were not significant predictors of short sleep duration. In sex-stratified analyses, age was a predictor of short sleep duration both in girls (OR 3.25, 95% CI 2.16–4.90) and in boys (OR 2.15, 95% CI 1.30–3.55). The only other significant predictor of short sleep duration was low income, which was associated with lower odds of short sleep duration in children aged 6–11 years only (OR 0.60, 95% CI 0.40–0.91).

Table 4.

Predictors of Short Sleep Duration (Full Sample, Bootstrapped Data)

		Sex-stratified models^b		Age-stratified models^c
Predictor variable	Unstratified model^a	Boys	Girls	6–11 years	12–17 years
Adolescent age	2.62 (1.88–3.66)	2.15 (1.30–3.55)	3.25 (2.16–4.90)	—	—
Male sex	1.30 (0.90–1.88)	—	—	1.05 (0.72–1.53)	1.64 (0.97–2.77)
Chronic disease	0.83 (0.58–1.19)	0.78 (0.49–1.23)	0.88 (0.48–1.61)	0.94 (0.56–1.56)	0.74 (0.41–1.32)
Low education	1.34 (0.92–1.94)	1.70 (1.04–2.76)	1.03 (0.61–1.74)	1.23 (0.79–1.91)	1.40 (0.74–2.66)
Low income	0.81 (0.63–1.05)	0.99 (0.70–1.41)	0.67 (0.37–1.25)	0.60 (0.40–0.91)	1.15 (0.71–1.87)
Model^d	<0.001	<0.001	<0.001	0.018	0.004

Odds ratios adjusted for age, sex, chronic disease, education, and income.

Odds ratios adjusted for age, chronic disease, education, and income.

Odds ratios adjusted for sex, chronic disease, education, and income.

Likelihood ratio test for significance of full model versus constant-only model.

Discussion

Odds of OWOB in Short Sleepers

The first objective of this study was to explore associations between short sleep duration and indicators of cardiometabolic disease, including OWOB, hyperinsulinemia, low HDL, and high triglycerides in a nationally representative population of Canadian children and adolescents. Age- and sex-stratified analyses indicated that short sleep duration was associated with higher odds of OWOB for boys and adolescents only. After adjusting for the effects of age, chronic disease, education, and income on OWOB, boys with short sleep duration had 1.85 times the odds of OWOB compared with boys who met sleep guidelines; the relationship was not significant in girls. Similarly, after adjusting for sex, chronic disease, education, and income, short sleepers aged 12–17 years had increased odds of OWOB.

The finding that, in this Canadian data set, boys are more vulnerable to the obesogenic effect of sleep loss is consistent with data from other published literature.^7–20,32,33 Several theories could explain why boys may be more vulnerable to the obesogenic effects of short sleep duration. An evolutionary-based hypothesis suggests that girls may be more physiologically resilient to environmental stressors such as sleep loss.^7,34 Another possibility is that females reach their biological maturity earlier than males, and thus, their circadian rhythms may be attuned to sleep patterns that more closely match their social environment. For example, an Italian study found that adolescent females reached their peak in evening circadian rhythms (i.e., the preference to stay up late at night and sleep in later in the morning) at an earlier age compared with males.³⁵ Sex differences in sleep architecture may also account for this effect: slow wave sleep plays an important role in human metabolism³⁶ and, compared with males, females spend more time in slow wave sleep.³⁷ Therefore, females may require a higher threshold of sleep loss to experience a negative health impact.

Age-stratified analyses in our data set demonstrate that the odds of OWOB in short sleepers were significant in adolescents, but not in children aged 6–11 years. No studies that calculated similar estimates could be located. A potential explanation for this finding is that adolescents who report not getting enough sleep have been short sleepers since childhood, and consequently have been exposed to the obesogenic effects of insufficient sleep for a longer time period. This theory is supported by longitudinal studies on children and adolescents that demonstrate that short sleepers are more likely to gain weight over time.^8,15,38 Such obesogenic changes over time are concerning because the development of obesity, dyslipidemia, or impaired carbohydrate metabolism at a younger age increases the potential years of risk exposure, thereby lowering the age of potential disease onset, increasing years of morbidity over the life span, and reducing life expectancy. Furthermore, children and adolescents who exhibit milder stages of cardiometabolic risk factors tend to progress to more severe categories of risk over time. For example, overweight children are at high risk of advancing from overweight to obesity,³⁸ and youth with prehypertension tend to progress to adulthood hypertension.³⁹

Odds of Hyperinsulinemia, Low HDL, and High Triglycerides in Short Sleepers

The results of logistic regression analyses conducted on the fasting subsample did not support the hypothesis that short sleepers had greater odds of hyperinsulinemia, low HDL, or high triglycerides compared with longer sleepers. Although other studies that examined associations between short sleep duration and cardiometabolic indicators other than OWOB in children and adolescents are sparse, a few studies have demonstrated that short sleep is linked with adverse cardiometabolic health. In a population of obese American children, short sleepers had higher fasting insulin levels.⁴⁰ Spanish adolescents with short sleep had higher C-reactive protein even after adjusting for obesity.⁴¹ A German study found that short sleep duration was linked to lower adiponectin in boys, and higher leptin, insulin, and degree of insulin resistance in girls.⁴²

Several explanations could account for this finding of short sleepers having increased odds of OWOB but not increased odds for other indicators of cardiometabolic disease. First, children and adolescents may need a longer exposure time to short sleep duration to develop cardiometabolic risk factors other than OWOB. Longitudinal studies examining the long-term effect of short sleep duration on indicators of cardiometabolic health other than OWOB are needed to test this hypothesis. A second potential reason that short sleepers did not have greater odds of hyperinsulinemia is that alternative measures could have provided a superior assessment of the specific dysregulations in carbohydrate metabolism that occur secondary to sleep loss. Sleep loss affects both insulin secretion (e.g., beta cell function) and insulin resistance.³⁶ This study measured hyperinsulinemia, which is an appropriate measure to evaluate insulin resistance.⁴³ However, it is possible that a measure that evaluates both insulin resistance and beta cell function (e.g., the homeostatic model assessment of insulin resistance) would have been more appropriate given that sleep loss affects both of these aspects of carbohydrate metabolism.³⁶ Finally, compared with the regressions that examined the associations between sleep duration and OWOB, the regression models that examined associations between sleep duration and indicators of cardiometabolic disease used the smaller fasting sample. Because a smaller sample generally results in wider confidence intervals, the smaller sample may have resulted in incorrectly accepting the null hypothesis.⁴⁴

Factors Associated with Greater Odds of Short Sleep Duration Include Adolescent Age, Chronic Disease, Low Household Income, and Low Household Education

The second objective of this research was to identify risk and protective factors associated with short sleep duration. Among Canadian children and adolescents, the rising prevalence of obesity and type 2 diabetes has been accompanied by a trend of decreasing total sleep time.⁴⁵ Data from 20 countries and 690,747 children and adolescents reveal that sleep duration declined by a median of 0.75 min/year between 1905 and 2008—a total decline of more than 1 hour over the last century.⁴⁵ In Canadian children and adolescents specifically, there was a median decline of 1.10 (min·day)/year over the study period.⁴⁵

In this study, age was the strongest predictor of short sleep duration, with teens aged 12–17 years having greater odds of not getting enough sleep compared with children aged 6–11 years. These findings are consistent with other published research studies that report that age explains most of the variance in sleep duration, with older children more likely to experience inadequate sleep.^42,46 Adolescents are less likely to meet sleep requirements because they restrict their sleep to meet social demands, and this reduction in sleeping hours overcomes the slight decline in sleep requirement that occurs in adolescence.⁴⁶

The only other significant predictor variable for short sleep duration was low income, which was linked with lower odds of short sleep duration in 6–11-year olds only. This finding was unexpected because previous research indicated that sleep duration is inversely related to socioeconomic status in Canadian children and adolescents.⁴⁷ It is uncertain why the results of these studies contradict each other. Possibly children of higher income families are more likely to have opportunities to participate in organized activities that encroach on sleep time or, alternatively, children of higher income families could have more technology available to them. The use of electronic devices such as Smartphones and televisions has recently been linked to decreased sleep duration.⁴⁸

Limitations

In this CHMS sample, several variables were self-reported, which creates the potential for misclassification because, for example, it is uncertain how closely the respondents’ perception of an average night's sleep length corresponds to actual sleep length. Further to the issue of self-reported sleep duration, our measure of average night sleep duration is limited by the fact that it was queried by a single, sweeping question in the CHMS: “How many hours do you usually spend sleeping in a 24 hour period, excluding time spent resting?” Respondents answered this question using only increments of 30 minutes (i.e., on the hour and half-hour). Obviously, a quantitative experimental measure of sleep duration using, for example, actigraphy/accelerometry over a period of multiple nights¹⁷ would be more accurate. Even in observational designs similar to this study, more specific questions have been used to estimate sleep duration. For example, in the 2006 study of sleep versus OWOB relationships in Québec, two questions were used. As described by the authors, parents were asked, “When does your child usually go to bed during the week?” and “When does your child usually get up in the morning during the week?” with several options presented from which the respondents could choose to answer each question.²⁰

Other self-reported variables that are likely to have been affected by misclassification include education and income. In addition, respondents with missing data on any study variable were excluded from analysis, and participants with missing data were more likely to be short sleepers and to have hyperinsulinemia. Thus, exclusion of these participants may have resulted in selection bias that underestimated the association between sleep duration and indicators of cardiometabolic disease. Finally, in this cross-sectional study, it is not known whether short sleep duration preceded the onset of OWOB, so, we cannot be certain of the direction of the observed association between short sleep duration and OWOB.

Conclusion

In summary, this analysis of a nationally representative Canadian sample demonstrates that inadequate sleep duration is linked to greater odds of OWOB in boys and in adolescents. Short sleepers did not have increased odds of other indicators of cardiometabolic disease, including hyperinsulinemia, low HDL, or high triglycerides. Age predicted inadequate sleep duration in both sexes and all age groups. Further research is required to elucidate the relationship between sleep duration and obesity in Canadian children and adolescents. In particular, emphasis should be placed on longitudinal studies that help determine whether short sleep duration in boys and in adolescents contributes directly to the development of OWOB.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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Associations between Sleep Duration and Indicators of Cardiometabolic Disease in Canadian Children and Adolescents: Analyses of the 2007–2009 Canadian Health Measures Survey

Abstract

Abstract

Background:

Methods:

Results:

Conclusion:

Study Objectives

Methods

Participants

Materials and Procedures

Measures

Statistical Analyses

Objective 1

Objective 2

Results

Data Screening and Cleaning

Missing data

Associations between short sleep duration and OWOB

Associations between short sleep duration and hyperinsulinemia

Associations between short sleep duration and low HDL cholesterol

Associations between short sleep and high triglycerides

Objective 2: Identify Determinants of Short Sleep Duration

Discussion

Odds of OWOB in Short Sleepers

Odds of Hyperinsulinemia, Low HDL, and High Triglycerides in Short Sleepers

Factors Associated with Greater Odds of Short Sleep Duration Include Adolescent Age, Chronic Disease, Low Household Income, and Low Household Education

Limitations

Conclusion

Footnotes

Author Disclosure Statement

References