Correlations of Sleep and Sugar-Sweetened Beverages Intake with Body measurements in Children: a cross-sectional study

doi:10.21203/rs.3.rs-3919395/v1

Taiwan’s density of beverage shops was considered very high in the world. The intakes of sugar sweetened beverages (SSBs) were high in children and adolescents. This study investigated the relationship between sleep and sugar-sweetened beverages (SSBs) intake with body composition.

For this purpose, data was pooled from two waves of Nutrition and Health Survey in Taiwan (NAHSIT) survey conducted in 2012 and in 2013-2016. Children aged 6-12 years were included. Variables included behaviors (nocturnal sleep duration and SSBs consumption) and body measurements (including external indicators (BMI, waist circumference (WC), hip circumference, and upper-arm circumference) and internal indicators (bone mass, muscle mass, fat mass, and bone density)). Multiple regression was employed to examine the associations between behaviors and body measurements.

Results showed negative trends were observed between body measurements and sleep duration on weekdays (p-trend = 0.032 to <0.001) . In multiple regression, in the same SSBs consumption group, lower values of the external indicators (p = 0.013 to 0.002) and of the lean mass (p = 0.033 to 0.022) were associated with ≥ 9.5 h sleep in weekdays comparing to <8.5h. Inverse trends were also found in the external indicators (p-trend = 0.007 to 0.001) and lean mass (p-trend =0.014) which decreased with increasing sleep duration on weekdays..

In conclusion, among the children with the same frequency of SSBs consumption, external indicators and lean mass were lower in those with a longer nocturnal sleep duration on weekdays. Sleep and SSBs consumption are both crucial correlates for body measurements in children and should be considered together in public health policy formulation.

Health sciences/Biomarkers

Health sciences/Risk factors

The Guideline: Sugars Intake for Adults and Children published by the World Health Organization (WHO) recommends to reduce the daily intake of free sugars to less than 10% of the total energy intake and that particular attention be paid to the sugar content from sugare sweetened beverages (SSBs) ¹. Studies have reported rather strong associations between the consumption of any types of SSBs and sugar intake ² as well as sugar content from SSBs representing 20%-25% of the daily sugar intake among children or adolescents ^3,4. An increasing global trend was observed in the level of daily SSBs consumption globally between 1990 and 2015, with the lowest level being observed in Asia ⁵. However, although Taiwan is situated in Asia, it has particular behavior of the SSBs consumption. The densities of convenience stores and beverage shops ranked among the top three in the world. Up to the end of 2021, the numbers of local convenience stores and beverage shops (excluding liquor shops) were 20,779 and 27,414, respectively. Calculated on the basis of the population in Taiwan by the end of 2021 (23,375,314), there was one convenience store or beverage shops for every 492 people ⁶. Such density increased the accessibility to SSBs for children or adolescents. According to studies conducted in Taiwan, the average level of daily SSBs consumption among adolescents from 2010 to 2011 was up to 413 grams, the average frequency of intake was 10.8 times per week, and the proportion of those with a frequency of at least 1 time per week was 99.5% ^7,8.

Higher SSBs consumption leads to issues including obesity, diabetes, high uric acid level, and cardiovascular disease as well as lower diet quality scores ^7,8. It can also result in shorter sleep duration, and their correlation has been determined through cross-country studies and systematic reviews on children ^9,10. Similarly, studies conducted in Asia have reported more servings of daily SSBs consumption among children or adolescents with a sleep duration < 7 hours ¹¹ as well as less hours of nocturnal sleep on weekdays and sleep debt in children with high frequencies of SSBs consumption ¹². On the basis of the literature, the correlations of SSBs consumption and sleep with the health of children can be further clarified through analysis that combines both SSBs consumption and sleep.

The suitable sleep duration for children aged 6–13 years is 9–11 hours per day ¹³, but the sleep duration among children seem to have decreased following changes of the times ¹⁴. A significant decline, from 1995 to 2016, in the proportion of children with sleep durations on weekdays and weekends meeting the recommended sleep duration (9–11 hours) decreased from 87.05–80.87% for weekdays (p = 0.036) and from 83.17–69.26% on weekends (p < 0.001) ¹⁵. A study conducted in Asia over nine years reported a significant decline in the sleep duration among the population in a city ¹⁶. When the sleep duration among children decreases, attention should be paid to the effects of short sleep duration or sleep debt (the difference between weekday and weekend sleep durations) on health or other aspects, including obesity (involving higher values of BMI, WC, body fat, or weight) ^17–19, blood sugar level, blood pressure level,and psychological stress ^18,20.

Childhood obesity has an empirical relationship with sleep ¹⁸ and a strong association with SSBs consumption ^21,22. Common measures of obesity include BMI, WC, hip circumference, upper-arm circumference, and body fat percentage (%). Numerous studies have revealed correlations of sleep and SSBs consumption with these indicators ^17,23,24, whereas a few studies have identified their associations with bone mass, lean mass, and bone density ^25–28. The objective of current study is to investigate the correlations of both sleep duration and SSBs consumption with aforementioned obesity-related indicators and body composition measured by dual-energy X-ray absorptiometry (DXA or DEXA) instrument. We hypothesized that given the same amount of SSBs intakes, body composition would be better in children with better sleep duration.

Demographics

This study on school children aged 6–12 years involved a food frequency questionnaire as well as height, weight, number of hours of nocturnal sleep, and PA level. The data of those with frequencies of SSBs consumption > 50 times per week (n = 3) and those with a longer or shorter sleep duration (n = 4) were excluded to obtain the data on 2,628 school children, comprising 1,030 from the 2012 NAHSIT and 1,598 from the 2013–2016 NAHSIT (Fig. 1). Table 1 presents the distribution of their demographic characteristics, SSBs consumption, and sleep. The participants had a mean age of 9.4 years; 7.3% of them were non-SSBs consumption throughout the week. Their average nocturnal sleep duration was 8.8 hours on weekdays and 9.7 hours on weekends, and their average sleep debt was 0.9 hours. In the sensitivity analysis, after multiple imputation, the distribution of demographic variables of the BMI category differed by only 4% from before imputation. Finally, we still use the imputed BMI for current study.

Table 1

Characteristics of children aged 6–12 years.
	All (n = 2628)
	N	% (weight)
Age (years), mean (SE)	9.4 (0.1)
Age group (years)
6 to 9	1338	49.9
10 to 12	1290	50.1
Gender
Boys	1323	52.2
Girls	1305	47.8
BMI Categories *
Normal weight and Underweight	1926	71.5
Overweight and Obesity	701	28.5
Missing	1
PA (MET-minute/week)
Low	1277	50.0
High	1293	50.0
Missing	58
SSBs consumption
Low intake (Q1) ^†	641	24.3
Median intake	1246	49.8
High intake (Q3)	741	25.8
Sleep duration in weekdays (hours), mean (SE)	8.8 (0.0)
Sleep duration in weekdays
< 8.5 h (Q1)	718	26.8
≥ 8.5 h to < 9.5 h	1245	47.1
≥ 9.5 h (Q3)	665	26.1
Sleep duration in weekends (hours), mean (SE)	9.7 (0.0)
Sleep duration in weekends
< 9 h (Q1)	560	22.0
≥ 9 h to < 10.5h	1258	49.7
≥ 10.5 h (Q3)	810	28.3
Sleep debt (hours), mean (SE)	0.9 (0.1)
Sleep debt
= 0 h (Q1)	835	33.3
> 0 h to < 2h	1101	40.9
≥ 2h (Q3)	692	25.7
SE, standard error; BMI, body mass index; PA, physical activity; MET, metabolic equivalent of task; SSBs, sugar-sweetened beverages; Q1, first quartile; Q3, third quartile.
* multiple imputation.
† Including non-intake (n = 161, 7.3%)

Distribution of anthropometric assessments and body composition

The school children were grouped by the level of SSBs consumption, sleep duration on weekdays and weekends, and sleep debt. The mean values of the anthropometric assessments(external indicators) and body composition (internal indicators) are detailed in Table 2. Increasing trends were observed in the mean values of the four external indicators, which increased with increasing frequency of SSBs consumption (p-trend = 0.011 to 0.005) and decreasing nocturnal sleep on weekdays (p-trend < 0.001); decreasing trends were observed in the mean values of BMI, WC, and upper-arm circumference (p-trend = 0.005 to 0.004) in those with a longer nocturnal sleep duration on weekends; an increasing trend was observed in the average hip circumference which increased with increasing sleep debt (p-trend = 0.003). A trend was observed in the mean values of the four internal indicators, which decreased with increasing weekdays nocturnal sleep duration (p-trend 0.032 to < 0.001 ). An increasing trend was observed in the average lean mass which incrased with increasing frequency of SSBs consumption (p-trend = 0.016) and decreasing nocturnal sleep duration on weekends (p-trend = 0.029) .

Table 2

Distribution of the mean values of the indicators of body measurements among children grouped by frequency of SSBs consumption and sleep duration
	BMI (kg/m²)		WC (cm)		Hips (cm)		Upper arm size (cm)		Bone mass (kg)				Fat mass (kg)				Lean mass (kg)				Total BMD (g/cm²)
	Mean	SE	Mean	SE	Mean	SE	Mean	SE	Mean		SE		Mean		SE		Mean		SE		Mean	SE
All	18.19	0.15	63.34	0.57	77.04	0.62	21.40	0.16	1.45		0.02		12.12		0.31		28.70		0.33		0.91	0.00
SSBs consumption
Low intake (Q1) *	17.76	0.27	61.75	0.89	75.25	1.01	20.95	0.33	1.39		0.04		11.57		0.77		27.25		0.51		0.90	0.01
Median intake	18.17	0.17	63.54	0.68	77.27	0.64	21.39	0.21	1.46		0.03		11.97		0.54		28.75		0.51		0.91	0.01
High intake (Q3)	18.66	0.23	64.44	0.72	78.26	0.90	21.85	0.26	1.49		0.05		12.85		0.73		29.68		0.80		0.91	0.01
P-trend		0.005		0.011		0.021		0.030			0.103				0.158				0.016			0.287
Sleep duration
In weekdays
< 8.5 h (Q1)	18.97	0.26	66.44	0.76	80.99	0.74	22.47	0.23	1.53		0.03		12.87		0.56		30.18		0.52		0.92	0.01
≥ 8.5 h to < 9.5 h	18.24	0.18	63.02	0.66	76.59	0.74	21.34	0.23	1.43		0.03		12.12		0.50		28.23		0.42		0.91	0.01
≥ 9.5 h (Q3)	17.32	0.17	60.92	0.59	73.99	0.61	20.48	0.17	1.37		0.03		10.76		0.62		27.05		0.44		0.89	0.01
P-trend		< 0.001		< 0.001		< 0.001		< 0.001			0.002				0.032				< 0.001			0.003
In weekends
< 9 h (Q1)	18.85	0.23	65.21	0.87	78.40	0.99	22.08	0.29	1.51		0.04		12.90		0.78		29.67		0.57		0.92	0.01
≥ 9 h to < 10.5h	18.00	0.21	62.86	0.67	76.59	0.73	21.25	0.21	1.43		0.03		11.73		0.59		28.55		0.54		0.90	0.01
≥ 10.5 h (Q3)	18.02	0.18	62.68	0.61	76.73	0.60	21.13	0.21	1.46		0.02		12.18		0.50		28.11		0.32		0.91	0.00
P-trend		0.004		0.004		0.057		0.005		0.353				0.417				0.029				0.333
Sleep debt
= 0 h (Q1)	18.13	0.17	63.01	0.61	75.95	0.65	21.25	0.17	1.41	0.03		11.68		0.56		27.96		0.44		0.90		0.00
> 0 h to < 2h	18.10	0.25	63.18	0.88	76.99	1.03	21.35	0.31	1.48	0.04		12.25		0.73		29.32		0.59		0.91		0.01
≥ 2h (Q3)	18.43	0.17	64.05	0.58	78.60	0.55	21.69	0.21	1.47	0.03		12.41		0.61		28.61		0.48		0.91		0.01
P-trend		0.228		0.190		0.003		0.135		0.204				0.451				0.373				0.209
BMI, body mass index; WC, waist circumference; BMD, bone mineral density; SSBs, sugar-sweetened beverages; Q1, first quartile; Q3, third quartile.
* Including non-intake.

Association between anthropometric assessments and SSBs combined with sleep

Table 3 presents the correlations of the frequency of SSBs consumption variable combined with the sleep variable with the external indicators after correcting potential confounders. Among the three models, only Model 1 (both frequency of SSBs consumption and nocturnal sleep duration on weekdays were controlled) exhibited significant correlations with and trends in relation to the external indicators. Among children with the same frequency of SSBs consumption, the values of log-BMI (β − 0.023; 95% CI − 0.041, − 0.006), log-WC (β − 0.023; 95% CI − 0.039, − 0.007), hip circumference (β − 1.994; 95% CI − 3.191, − 0.797), and upper-arm circumference (β − 0.025; 95% CI − 0.043, − 0.006) were lower in those with a sleep duration > 9.5 hours compared with those with a sleep duration < 8.5 hours. Similarly, the values of log-WC (β − 0.016; 95% CI − 0.028, − 0.003) and hip circumference (β − 1.333; 95% CI − 2.296, − 0.371) of school children with a sleep duration of 8.5–9.5 hours were lower compared with those with a sleep duration < 8.5 hours. Additionally, a significant inversed trend (p-trend = 0.007 to 0.001) was observed in the correlation between nocturnal sleep duration on weekdays and the external indicators, indicating that the values of these indicators were lower in those with a longer sleep duration.

Table 3

Multiple regression analysis on the correlations between the indicators of anthropometric assessments with frequency of SSBs consumption and sleep duration by group
	Log-BMI (kg/m²)			Log-WC (cm)			Hips (cm)			Log-upper arm size (cm)
	Beta	(95% CI)	P value	Beta	(95% CI)	P value	Beta	(95% CI)	P value	Beta	(95% CI)	P value
Model 1
SSBs consumption
Low intake (Q1) *	ref			ref			ref			ref
Median intake	0.004	(-0.016, 0.025)	0.655	0.009	(-0.010, 0.029)	0.325	0.481	(-0.76, 1.722)	0.431	0.003	(-0.023, 0.028)	0.825
High intake (Q3)	0.008	(-0.009, 0.026)	0.329	0.004	(-0.018, 0.026)	0.701	0.375	(-1.321, 2.07)	0.652	0.003	(-0.022, 0.029)	0.781
P-trend			0.318			0.698			0.648			0.779
Sleep duration in weekdays
< 8.5 h (Q1)	ref			ref			ref			ref
≥ 8.5 h to < 9.5 h	-0.007	(-0.020, 0.006)	0.279	-0.016	(-0.028, -0.003)	0.017	-1.333	(-2.296, -0.371)	0.009	-0.011	(-0.027, 0.005)	0.173
≥ 9.5 h (Q3)	-0.023	(-0.041, -0.006)	0.013	-0.023	(-0.039, -0.007)	0.007	-1.994	(-3.191, -0.797)	0.002	-0.025	(-0.043, -0.006)	0.011
P-trend			0.007			0.003			0.001			0.006
Model 2
SSBs consumption
Low intake (Q1)	ref			ref			ref			ref
Median intake	0.005	(-0.016, 0.026)	0.617	0.010	(-0.010, 0.030)	0.301	0.531	(-0.733, 1.795)	0.394	0.003	(-0.022, 0.029)	0.789
High intake (Q3)	0.010	(-0.007, 0.027)	0.246	0.006	(-0.016, 0.028)	0.554	0.570	(-1.126, 2.266)	0.494	0.006	(-0.020, 0.031)	0.651
P-trend			0.234			0.548			0.487			0.647
Sleep duration in weekends
Low intake (Q1)	ref			ref			ref			ref
Median intake	-0.017	(-0.035, 0.002)	0.079	-0.014	(-0.031, 0.004)	0.116	-0.809	(-2.169, 0.551)	0.231	-0.017	(-0.039, 0.005)	0.117
High intake (Q3)	-0.015	(-0.032, 0.001)	0.068	-0.012	(-0.031, 0.007)	0.198	-0.409	(-1.781, 0.963)	0.544	-0.019	(-0.043, 0.004)	0.103
P-trend			0.055			0.185			0.538			0.090
Model 3
SSBs consumption
Low intake (Q1)	ref			ref			ref			ref
Median intake	0.004	(-0.016, 0.025)	0.659	0.009	(-0.010, 0.029)	0.332	0.479	(-0.760, 1.718)	0.432	0.003	(-0.023, 0.028)	0.829
High intake (Q3)	0.009	(-0.008, 0.027)	0.270	0.006	(-0.016, 0.028)	0.584	0.507	(-1.189, 2.203)	0.543	0.005	(-0.020, 0.031)	0.681
P-trend			0.258			0.579			0.537			0.677
Sleep debt
= 0 h (Q1)	ref			ref			ref			ref
> 0 h to < 2h	-0.000	(-0.015, 0.015)	0.981	-0.000	(-0.015, 0.014)	0.966	0.426	(-0.551, 1.404)	0.377	0.002	(-0.016, 0.020)	0.839
≥ 2h (Q3)	0.004	(-0.011, 0.018)	0.596	0.003	(-0.014, 0.019)	0.747	0.876	(-0.206, 1.957)	0.108	0.004	(-0.015, 0.024)	0.654
P-trend			0.591			0.744			0.940			0.649
BMI, body mass index; WC, waist circumference; SSBs, sugar-sweetened beverages; Q1, first quartile; Q3, third quartile.
* Including non-intake.

Association between body composition and SSBs combined with sleep

Table 4 presents the correlations of the frequency of SSBs consumption variable combined with the sleep variable with the internal indicators after correcting potential confounders. The results revealed that, among the children with the same frequency of SSBs consumption, the values of log-lean mass were significantly lower in those with a sleep duration ≥ 9.5 hours (β − 0.030; 95% CI − 0.054, − 0.005) and 8.5–9.5 (β − 0.025; 95% CI − 0.048, − 0.002) hours compared with those with a sleep duration < 8.5 hours, and a significant inverse trend (p-trend = 0.014) was observed between nocturnal sleep duration on weekdays and lean mass.

Table 4

Multiple regression analysis on the correlations between body composition indicators with frequency of SSBs consumption and sleep duration by group
	Log-bone mass (kg)			Log-fat mass (kg)			Log-lean mass (kg)			Total BMD (g/cm²)
	Beta	(95% CI)	P value	Beta	(95% CI)	P value	Beta	(95% CI)	P value	Beta	(95% CI)	P value
Model 1
SSBs consumption
Low intake (Q1) *	ref			ref			ref			ref
Median intake	0.015	(-0.013, 0.043)	0.278	-0.060	(-0.139, 0.019)	0.131	0.012	(-0.013, 0.037)	0.328	0.004	(-0.010, 0.018)	0.550
High intake (Q3)	-0.008	(-0.038, 0.023)	0.611	-0.059	(-0.134, 0.017)	0.122	0.008	(-0.026, 0.041)	0.641	-0.007	(-0.021, 0.007)	0.323
P-trend			0.606			0.108			0.637			0.312
Sleep duration in weekdays
< 8.5 h (Q1)	ref			ref			ref			ref
≥ 8.5 h to < 9.5 h	-0.017	(-0.042, 0.007)	0.164	0.062	(-0.021, 0.146)	0.135	-0.025	(-0.048, -0.002)	0.033	0.004	(-0.006, 0.013)	0.449
≥ 9.5 h (Q3)	-0.012	(-0.037, 0.013)	0.324	0.035	(-0.063, 0.133)	0.466	-0.030	(-0.054, -0.005)	0.022	0.003	(-0.008, 0.013)	0.610
P-trend			0.314			0.459			0.014			0.605
Model 2
SSBs consumption
Low intake (Q1)	ref			ref			ref			ref
Median intake	0.024	(-0.009, 0.057)	0.151	-0.057	(-0.136, 0.022)	0.151	0.010	(-0.014, 0.035)	0.388	0.006	(-0.009, 0.021)	0.413
High intake (Q3)	0.007	(-0.033, 0.047)	0.727	-0.065	(-0.142, 0.011)	0.089	0.011	(-0.024, 0.045)	0.527	-0.005	(-0.020, 0.011)	0.528
P-trend			0.724			0.076			0.520			0.521
Sleep duration in weekends
< 9 h (Q1)	ref			ref			ref			ref
≥ 9 h to < 10.5h	-0.014	(-0.047, 0.018)	0.372	-0.017	(-0.017, 0.083)	0.726	0.001	(-0.021, 0.024)	0.901	-0.002	(-0.015, 0.011)	0.803
≥ 10.5 h (Q3)	-0.008	(-0.041, 0.025)	0.615	0.002	(-0.076, 0.081)	0.953	-0.004	(-0.024, 0.016)	0.699	-0.003	(-0.014, 0.008)	0.584
P-trend			0.610			0.953			0.695			0.579
Model 3
SSBs consumption
Low intake (Q1)	ref			ref			ref			ref
Median intake	0.024	(-0.009, -0.057)	0.151	-0.060	(-0.139, 0.019)	0.129	0.011	(-0.013, 0.035)	0.348	0.006	(-0.009, 0.021)	0.413
High intake (Q3)	0.007	(-0.033, 0.047)	0.727	-0.065	(-0.143, 0.012)	0.094	0.011	(-0.024, 0.045)	0.524	-0.005	(-0.020, 0.011)	0.528
P-trend			0.724			0.081			0.518			0.521
Sleep debt
= 0 h (Q1)	ref			ref			ref			ref
> 0 h to < 2h	-0.014	(-0.047, 0.018)	0.372	-0.036	(-0.134, 0.062)	0.456	0.018	(-0.011, 0.047)	0.206	-0.002	(-0.015, 0.011)	0.803
≥ 2h (Q3)	-0.008	(-0.041, 0.025)	0.615	-0.039	(-0.112, 0.033)	0.272	0.010	(-0.012, 0.032)	0.353	-0.003	(-0.014, 0.008)	0.584
P-trend			0.610			0.260			0.343			0.579
BMD, bone mineral density; SSBs, sugar-sweetened beverages; Q1, first quartile; Q3, third quartile.
* Including non-intake

The results of this study revealed increasing trends in the mean values of the external indicators (including BMI, WC, hip circumference, and upper-arm circumference) and 1 internal indicator (lean mass) among the children with a higher frequency of SSBs consumption. For those with a longer nocturnal sleep duration on weekdays, decreasing trends were observed in the mean values of all the internal and external indicators (including bone mass, fat mass, lean mass, and bone density); nocturnal sleep duration on weekends and sleep debt exhibited correlations and related trends with some of the body measurements. After the potential confounders were controlled, and the SSBs consumption variable and the sleep variable were combined, among children with the same frequency of SSBs consumption, lower values of the external indicators and of lean mass were observed in those with a nocturnal sleep duration on weekdays ≥ 9.5 hours, and inverse trends were observed in these values which decreased with increasing sleep duration.

Among the children aged 6–12 years in this study, the average weekend sleep duration was 9.7 hours, and the average weekday sleep duration was 8.8 hours; the findings approximate to the results regarding nocturnal sleep duration obtained through objective measurements in cross-country systematic reviews ^36,37. One of the systematic reviews, which complied data on nocturnal sleep duration across 12 countries, reported an average of 8.8 hours (the weighted mean of nocturnal sleep durations on weekdays and weekends)³⁶. Another systematic review, which used actigraphy to compile data from 18 studies, reported an average nocturnal sleep duration on weekdays of 8.7 hours for children aged 6–11 years and of 7.8 hours for children aged 12–14, and an average nocturnal sleep duration on weekends of 8.8 hours for children aged 6–11 years and of 8.5 hours for children aged 12–14 years ³⁷. Although the average nocturnal sleep duration on weekends obtained through this study is longer, it is lower compared with the results of a questionnaire survey conducted in Asia, which reported average nocturnal sleep durations on weekends of boys and girls aged 6–11 years to be 10.1 hours and 10.3 hours, respectively ¹⁶. Differences in the number of hours of sleep might vary due to a number of factors, including age, geographical area, and adoption of subjective (questionnaire) or objective measurement (actigraphy). For example, sleep duration decreases with increasing age; the number of hours of sleep in Asia differs from those in other regions; sleep hours of questionnaires completed by parents are larger than those obtained through actual measurement ^38,39. In this study, the total nocturnal sleep duration plus the afternoon nap generally satisfies the sleep duration recommended by the American Academy of Sleep Medicine for children aged 6–12 years (9–12 hours) ⁴⁰.

In this study, the children reported never consuming SSBs only accounted for 7.3% per week, that is, the children consumed SSBs accounted for at least 90% per week. The result differs vastly from those of surveys conducted in other countries, one of which reported an increase prevalence from 47–50% of children aged 6–12 years who did not consume SSBs follow up over six years²³, the proportions of the boys and girls aged 10–12 years who did not consume Coca-Cola were 47% and 64.6%, respectively ⁴¹. In a study across 12 countries, children aged 9–11 years reported never consuming SSBs throughout the week accounted for 15.1%-75.9% (the percentage varies by SSBs type) ⁹. In a study on Asian children aged 6–18 years, the proportions of those who did not consume soft drinks and sweetened tea throughout the week accounted for 24.5%-27.7% and 9.9%-11%, respectively ⁴². These values approximate those in current study, but after deducting the adolescent population in the aforementioned study, the proportion of those who did not consume sweetened tea would be much higher than current study, which highlights the high percentage of SSBs consumption among children in Taiwan.

Numerous systematic reviews have reported correlations between SSBs consumption and obesity, including positive correlations with indicators such as weight, BMI, WC, and body fat ²¹. Similar associations were observed in the single variable analysis in this study, which, however, disappeared after controlling sleep duration. This was probably due to an effect by the moderation of hours of sleep. Similar results have been reported in a study ⁴³ through a predicted probability plot on overweight or obesity in children aged 10–12 years, showing a higher predictive probability of overweight or obesity in those with a higher level of daily SSBs consumption, particularly in those with a frequency of intake of milk containing added sweeteners (sugar, honey, or chocolate powder) ≥ 4 times per week, with such probability being more marked in those with a sleep duration < 10 hours ⁴³. Additionally, two studies conducted in Asia reported no magnitude of increase in the sleep variable, but after sleep and frequency of SSBs consumption were controlled, higher risk of overweight or obesity was observed in children with a shorter sleep duration and higher frequency of SSBs consumption ^44,45. Considering the potential confounder of the aforementioned associations, this study controlled the weight variable, and no correlations were observed in internal indicators, including bone mass, fat mass, lean mass, and bone density, with SSBs consumption. However, numerous children studies have reported an inverse relationship between SSBs consumption and bone health (including bone mass and bone density). Moreover, a few studies have reported a positive relationship between SSBs consumption with fracture ²⁷, including a recent study (2020) which reported higher risk of fracture for those with daily SSBs consumption ≥ 1 serving(s), and the odds ratios for boys and girls were 2.2 (95%CI = 1.0-4.3) and 4.6 (95%CI = 2.3–9.1), respectively ⁴⁶. Nevertheless, studies on lean mass in adolescents have yielded inconsistent results. A study on adolescents aged 14–18 years reported an inverse relationship between SSBs consumption and skeletal lean mass index ²⁸, whereas a 6-year follow-up study reported no significant associations with lean mass or bone mass but a higher fat mass value in the high SSBs consumption trajectory class ²⁴; in these studies, the sleep variable was not controlled, which prevented from identifying its moderation. Furthermore, associations between SSBs consumption of school children and other indicators of body composition has rarely been investigated, and additional research is required for verification.

Systematic reviews have reported significant negative correlations of sleep with the measurements of obesity, such as weight, BMI, WC, and body fat ¹⁷. Current study also verified correlations between children’s nocturnal sleep duration on weekdays and obesity-related indicators. After controlling the SSBs consumption variable, the values of BMI, WC, hip circumference, and upper-arm circumference decreased with increasing sleep duration. The associations are probably due to the variance in the PA level and basal metabolic rate (BMR). Studies have reported a lower PA level ( p < 0.05) as well as higher sugar and energy intake (%) ( p < 0.05) among children with a sleep duration < 7 hours ⁴⁷; shorter sedentary time ( P = 0.001) and longer duration of light PA ( P = 0.01) per day as well as the BMR value increasing with increasing sleep duration ( P = 0.009 ) in children meeting the recommended sleep duration ≥ 50% of the 7 days of the week ⁴⁸. However, not all studies showed associations between sleep and obesity indicators. A systematic review revealed inconsistent results, showing that, despite negative correlations between sleep duration and weight of children observed through interventional studies, no associations between sleep duration and BMI were observed through meta-analysis ⁴⁹. Similarly, although this study reported negative correlations between sleep and some of the measurements of obesity, no correlations were observed with the body fat variable, which was probably due to the moderation of SSBs consumption.

The results of this study revealed that, among children with the same SSBs consumption level, the values of lean mass were lower among those with a longer nocturnal sleep duration on weekdays. Among studies on lean mass, a follow-up study reported an inverse relationship between the sleep duration of children aged 3 years and their fat-free mass index (FFMI; the sum of lean mass and bone mass divided by height [m]²) at the age of four ²⁵. Additionally, a cross-sectional study has reported an inverse relationship between the sleep duration and FFMI among children aged 10–12 years, but this relationship turned positive after controlling sexual maturity ²⁶. For the aforementioned follow-up study, the respondents were younger; the follow-up lasted for one year only, and data on change in sleep and body composition was not provided during the year. Therefore, the effect of sleep on the body reported through the said study have yet to be verified and confirmed through further follow-up studies. Moreover, although the age of the respondents in the aforementioned cross-sectional study were similar to those in current study, that study was structurally based on prepubescent children. Therefore, controlling sexual maturity might be suitable for that study but not exactly for current study. Because half of the respondents in this study were children aged 6–9 years, and pubertal development had not yet begun in this stage, it was unnecessary to control the sexual maturity variable, whereas this variable may be considered in analyzing data on the other half, who were children aged 10–12 years. Additionally, the weight measured through DXA comprised bone mass, fat mass, and lean mass. Studies have reported negative correlations between sleep and weight, but no correlations of sleep with bone mass and fat mass were observed in this study, which probably indicates the crucial role of the correlation between sleep and lean mass. Furthermore, the lean mass measured through DXA involved skeletal muscle and visceral body fat. Further research is required to verify either change in skeletal lean mass or that in visceral body fat affects the increasing/decreasing trends between sleep duration and lean mass.

The strength of this study is its rarity in terms of investigating the correlations between children’s SSBs consumption and sleep with anthropometric assessments or body composition, which facilitates an understanding of the correlations of the aforementioned body measurements related to the health of children. Specifically, the findings of this study revealed negative correlations between sleep duration and lean mass of children. However, this study involved some limitations. First, it was a cross-sectional study, which prevents determination of the causal relationships involved, such as whether frequency of SSBs consumption and sleep duration affected body measurements or physical transformation in children affected SSBs consumption or sleeping habits. Second, the SSBs consumption variable only involved frequency of intake without the amount of intake. According to a systematic review that complied data from 51 countries between 2010 and 2019, the daily SSBs consumption of children ≤ 11 years of age was 303.6 ml ⁵⁰. By estimation based on the sugar content of Coca-Cola, the daily sugar intake among children approximates 32 grams. Consequently, the calories in the sugar provided through SSBs accounted for 6.7%-7.7% of the total daily energy intake (1666–1908 kcal) of Taiwanese children aged 7–12 years between 2013 and 2016. The percentages approximate 10%, the maximum proportion of free sugars in the daily intake recommended by the WHO, indicating the elevated frequency of daily SSBs consumption among children in Taiwan as well as the attention required to the sugar intake through SSBs. Third, this study did not include sexual maturity as a control variable, which weakened inferential statistics analysis. Because children aged 9–12 years undergo pubertal development, measurements of anthropometric measurement and body composition probably involve marked changes. However, as mentioned previously, this study participants were school children aged 6–12 years, and approximately 50% of them had yet to undergo the stage of sexual development. Meanwhile, this study reduced potential bias in relatively senior children by controlling the age variable. Therefore, the inference of this study is still of great referential value.

Among children with the same frequency of SSBs consumption, nocturnal sleep duration on weekdays was negatively correlated with and exhibited related trends in relation to BMI, WC, hip circumference, upper-arm circumference, and lean mass, which was particularly marked in children with a sleep duration > 9.5 hours compared with those with a sleep duration < 8.5 hours. After the sleep duration variable was controlled, the correlations between frequency of SSBs consumption and body measurements turned nonsignificant, showing the substantial moderation of sleep. Therefore, SSBs consumption and sleep should be considered for children obesity prevention and formulation of relevant public health policies so as to improve the outcomes intended.

Study population

This study was a cross-sectional study with children aged 6–12 years as participants. The data for analysis were from 2012 and 2013 and 2016 Nutrition and Health Survey in Taiwan (NAHSIT). National nutrition surveys have been implemented in Taiwan since 1980, and these were entitled NAHSIT in 1993. The research design and sampling principles of each of the NAHSIT were rather similar. The population of the 2012 NAHSIT comprised elementary school children ≥ 6 years of age; six strata were created on the basis of population density, geographical location, and ethnicity. The elementary schools in each of these strata were ordered by the total number of students; 4 schools were sampled according to the principles of probability proportional to its population sizes (PPS), resulting in a total of 24 schools. From these schools, 50 students were sampled, comprising 25 boys and 25 girls, with approximately the same number of students from the six grades, resulting in a total of 1,200 students. The response rate was 43.2%. For the 2013–2016 NAHSIT, the population comprised permanent residents countrywide ≥ 2 months of age; the 20 counties and cities in Taiwan were divided into 40 strata for sampling, from each of which were sampled 4 villages according to the PPS principles, resulting in a total of 160 villages where the survey was then conducted with a total of 2,800 respondents. Written consents were obtained from the guardians of children. The survey was completed after a four-year period, with a response rate of 74.2%. For these two surveys, respondents from whom consent was conducted interviews and physical examinations. Further details are provided in relevant studies ^29,30.

Measurement

For the 2012–2016 NAHSIT, the questions on sleep have been modified several times. In 2012, it was an open-ended question on school children’s “average sleep duration on weekdays and weekends;” between 2013 and 2016, it successively took the following forms: (1) 24-hour continuous recording with Three-day Physical Activity Log (3-d PAL) (school children’s activities were recorded every 15 minutes with corresponding codes); (2) a multiple-choice item on “nocturnal bedtime and wake-up time on weekdays and weekends” with options based on a 30-minute interval; and (3) an open-ended question on “nocturnal bedtime and wake-up time weekdays and weekends.” Finally, “hour” was adopted as the uniform unit for the sleep variable in current study; data were obtained on nocturnal sleep duration on weekdays (mean = 8.6–9.25 hours) and on weekends (mean = 9.6–10.3 hours) as well as on sleep debt (mean = 0.75–1.08 hours). Regarding the definition of sleep debt, it was generally defined in studies as the weekend-weekday difference in the hours of sleep ^31,32. This study referenced this definition and revised it into nocturnal sleep duration on weekends minus that on weekdays; a negative value was indicated as “no sleep debt” (i.e., 0 hours). Quartile 1 (Q1) and quartile 3 (Q3) were adopted as the cut-off points for the three variables which were divided into three groups: nocturnal sleep duration on weekdays (< 8.5 hours, ≥ 8.5 and < 9.5 hours, and ≥ 9.5 hours), three groups by nocturnal sleep duration on weekends (< 9 hours, ≥ 9 and < 10.5 hours, and ≥ 10.5 hours), and three groups by sleep debt (≤ 0 hours, > 0 and < 2 hours, and ≥ 2hours).

For this study, data on the intake of SSBs per week among school children were from the food frequency questionnaire (FFQ) in the NAHSIT survey questionnaire. The FFQ in the 2012 NAHSIT contained the following item: “Have you drunk coca-cola, milk tea, other SSBs last month? How often?” (the options were every month, every week, or every day). The FFQ in the 2013–2016 NAHSIT contained the following item: “Have you drunk lactic acid bacteria beverages, carbonated beverages, sports drinks or energy beverages, chocolate drinks, various types of milk tea, other tea drinks containing tea ingredients or not, or coffee drinks? How often? ”(the options were every month, every week, or every day). Along with a further item, i.e., “Were the beverages you drank sweetened? Frequency?” (with 4 sweetened level of frequency were always, mostly, half, or few). The data on frequency of SSBs consumption were adjusted by sweetened level and converted into the times of SSBs consumption per week. Quartiles were adopted as the cut-off points, and the frequency of SSBs consumption was divided into a low intake group (Q1: low < 0.57 times per week for 2012; < 2.2 times per week for 2013–2016) and a high intake group (Q3: ≥ 4 times per week for 2012; ≥ 8.35 times per week for 2013–2016).

Physical measurements in the NAHSIT comprised two parts including anthropometric assessments and body composition which were referred to as “external indicators” and “internal indicators” in current study, respectively. The anthropometric assessments covered the following items : height, weight, WC, hip circumference, and upper-arm circumference. Height and weight were measured using an automatic height and weight scale (Super-View HW-686, Kongho Instruments Co., Ltd., Taiwan). The BMI value (kg/m²) was obtained through dividing weight by height (meter). WC, hip circumference, and upper-arm circumference were measured using a soft tape. The BMI data from the 2013–2016 NAHSIT involved a missing value of 43.68%; a missing value of 27.55% remained after combining the data from the two surveys. Therefore, multiple imputation was performed using the SUDAAN software package, with survey year, age, sex, and level of physical activity (PA level) as the basis. Height and weight were submitted to 5 interpolations, the means of which was used to recalculate the BMI value. Finally, only 1 of these values could not be interpolated, that is, only 1 missing value existed. Sensitivity analyses were also performed before performing the imputation procedure. The same table of population characteristics was established to compare the differences before and after imputation. Body composition involved bone mass, fat mass, lean mass, and bone density, which were measured by the DXA instrument.

Data for other variables, including age, sex, and PA, were obtained from the NAHSIT questionnaire. The NAHSIT questionnaire on the PA variable regarding school children successively took the three following forms: (1) an open-ended question for 2012, i.e., “Which sports or activities have you engaged in for at least 5 minutes regularly every week over the past month, and how much time was spent on them?” ; (2) 3-d PAL ³³ ; and (3) the PA level obtained through International Physical Activity Questionnaire (IPAQ) for 2013–2016 ³⁴. For current study, data from the version in 2012 were organized using the calculation method for IPAQ ³⁵ and weekly PA level per week (metabolic equivalent of task (MET)-mins/week) was adopted for organizing data from the three aforementioned versions. Among the data from 3-d PAL, those on PAs including sleeping, sitting, and standing were deleted, and the remaining data were organized. However, the value obtained from each of these versions still varied. Therefore, the median of PA level in each version was adopted as the cut-off point for the PA variable to divide PA into a low PA group and a high PA group (such median was 372–632 MET-mins/week for the IPAQ version [with a different questionnaire design] and 2234.4 MET-mins/week for the 3-d PAL version). This study has been performed in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the National Health Research Institutes (EC1090206-E).

Statistical analyses

The data were merged and debugged and the variables were organized using the SAS 9.4 (SAS Institute Inc.) software package. Because the NAHSIT data was obtained through unequal probability sampling, estimation and analysis were conducted after weighting using the SUDAAN 11.0 software package; the weights that could reflect the charracteritics of the population were used in the weighting. The distribution of demographic variables was described using the weighted percentages; variances in the values of the body measurements were described using mean and standard error. A trend test was conducted for analyzing the trends in the body measurements in relation to frequency of SSBs consumption, sleep duration, and sleep debt of school children. The correlations between the SSBs consumption and sleep duration variables with the body measurements were analyzed using multiple regression model, with survey year, age, sex, and PA level as the control variables. Residual analysis involving histogram, box plot, Q-Q plot and residual plots were performed in the multiple regression model for testing the normality of residuals and outliers. Log transformation was performed on the variables without normal distribution, which did not include hip circumference and bone density. After the log transformation, the skewness value of the residuals in the multiple regression model ≌ 0, and the kurtosis value ≤ the absolute value (2). Two-sided P values < 0.5 were considered significant.

Acknowledgements

The assistance provided by the institutes and the considerable effort of all who contributed to the survey are greatly appreciated. The views expressed herein are solely those of the authors. This manuscript was edited by Wallace Academic Editing.

Authors' contributions:

YHS clean, analyzed and interpreted the data, and wrote the manuscript; HCW provided a critical review of the manuscript; WHP was responsible for the nutritional surveys and provided a critical review of the manuscript; HYC conceptualized the framework of this study, supervised the analysis, and revised the manuscript; and all authors read and approved the final manuscript.

Data Availability:

The datasets used and/or analysed during the current study can be applied through https://dep.mohw.gov.tw/DOS/cp-2503-44928-113.html

Ethics approval and consent to participate

This study was approved by the Institution Review Board of National Health research Institutes. Written informed consents were obtained from the guardians of the participants.

Competing interests:

The authors declare that they have no competing interests

Funding:

Data in 2012 were collected by the Nutrition and Health Survey in Taiwan on Elementary School Students (NAHSIT 2012), sponsored by the Food and Drug Administration, Department of Health, Executive Yuan (DOH101-FDA-31411). Data in 2013–2016 were collected by the research project "Nutrition and Health Survey in Taiwan (NAHSIT 2013-2016)", under the guidance of the Health Promotion Administration. The research project was carried out by the Division of Preventive Medicine and Health Services Research, the Institute of Population Health Sciences of the National Health Research Institutes, directed by Professor Wen-Harn Pan.

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No competing interests reported.

Correlations of Sleep and Sugar-Sweetened Beverages Intake with Body measurements in Children: a cross-sectional study

Status:

Version 1

Abstract

Figures

Introduction

Results

Demographics

Distribution of anthropometric assessments and body composition

Association between anthropometric assessments and SSBs combined with sleep

Association between body composition and SSBs combined with sleep

Discussion

Methods

Study population

Measurement

Statistical analyses

Declarations

References

Additional Declarations

Status:

Version 1