Frequent sweet consumption associated with poor dietary habits and increased screen time in children and adolescents: Results from the National Action for Children’s Health program

Abstract

BACKGROUND:

Frequent sweet consumption constitutes a significant health issue among children which leads to a predisposition towards overweight and cardiovascular disease risk factors.

OBJECTIVE:

To examine the prevalence of sweet consumption and to identify associated lifestyle factors.

METHODS:

Cross-sectional, observational study. Population data derived from a health survey carried out in 2015 on a representative sample of 177091 children aged 8 to 17 years. Sweet consumption and dietary habits were evaluated using questionnaires (KIDMED index). Frequency of sweets consumption constitutes a question of KIDMED and it was classified as rare or frequent. Physical activity status, screen time and sleeping habits were assessed through self-completed questionnaires.

RESULTS:

More boys than girls (19.6% vs. 17.5%, p < 0.001) consumed sweet frequently (more than twice daily). Sweet consumption was strongly associated with unhealthy dietary habits such as skipping breakfast and fast food consumption. Adjusting for several covariates, insufficient dietary habits, insufficient sleep and increased screen time were increased on participant’s odds of being frequent sweet consumers by 80% (95% CI 0.17–0.23), 18% (95% CI1.05–1.29) and 218% (95% CI 1.96–2.41) in boys and by 80% (95% CI 0.17–0.24), 31% (95% CI1.17–1.47) and 241% (95% CI 2.15–2.72) in girls.

CONCLUSIONS:

Frequent sweet consumption was associated with an unhealthy lifestyle profile.

Keywords

Sweet consumption dietary habits children adolescents lifestyle factors

1 Introduction

Childhood and adolescence are important time periods for the onset of healthy or unhealthy dietary habits which frequently track into adulthood [1]. Poor dietary habits could predetermine to the upgrowth of insulin dependent diabetes, obesity, cardiovascular diseases and reduced immunity even in children [2]. The excessive consumption of dietary sugar among children and adolescents has emerged an alarmingly public health concern over recent years [3, 4]. Children prefer sweet foods as sweet sensory preferences are high among them [5]. Sweet taste characterized by a liking for elevated levels of sweetness in foods and beverages in childhood [5]. Sugars in sweet and candy not only contribute with energy that can lead to an increased amount of body fat but also could cause the development of insulin dependent diabetes, overweight, caries and other health effects [6]. Also, sugar consumption has been connected to elevated intake of unhealthy dietary patterns such as fats and salt, which contribute to increased energy densities and glycemic loads [7]. Specifically, a study among Spanish adolescents has speculated increased frequency of consumption of food rich in calories, sugar and fat, and poor in vitamins, minerals, and fibers [8]. As a consequence, the recommendation by the World Health Organization (WHO) relating to sugars intake for adults and children, emphasizes the need to decrease consumption of sugars at all stages of life, in order to prevent the current and future occurrence of diseases among children [9]. Furthermore, a recent position study of the European Society for Paediatric Gastroenterology, Hepatology and Nutrition Committee on nutrition has proposed that the intake of free sugars should be decreased and minimized with a desirable aim of <5% energy intake in children and adolescents aged≥2 to 18 years [10]. There are large differences among countries in the frequency of sweet intake [11, 12]. A study that has examined the frequent daily intake of sweets among 11– year-old children in 20 European countries, Israel and Canada during the 1993/1994 school year, referred percentages of consumption ranged from 4% (Norway, Finland and Sweden) to 41% (Scotland) [11]. Another study among Brazilian schoolchildren has reported that 19.2% of them consumed sweets regularly [12].

Socio-demographic and lifestyle factors have been associated with the consumption of sweets, as well as with the risk of developing health problems among children [13]. Previous studies focusing on the association between obesity status and sweet consumption in children and adolescents reported inconsistent results [14 –16]. Screen time has been associated with increased consumption of foods with high energy density factors that can promote sweet consumption [12 , 18]. Association of sleeping hours [19, 20], physical activity [21] and physical fitness [22] with sweet consumption has been explored among schoolchildren; however, most studies have examined the association of each factor separately and in specific age-groups. On the other hand, sweet consumption is more consistently associated with aspects such as unhealthy dietary habits [14 , 24]. Moreover, there are no representative national studies that analyzed the prevalence of sweets consumption among children and adolescents as well as the factors associated with its consumption.

As a consequence, the most important contribution of the current study to the existing scientific literature is to present descriptive information about the association of sweet consumption and its negative outcomes and correlates among schoolchildren. This information could support the development of health actions focused on improving dietary habits. For the purpose of this study, “sweet consumption” is considered any consumption of sweets (e.g. croissant, cakes, biscuits, e.t.c.), or/and chocolates or/and candies.

Thus, the objectives of this study were (a) to incorporate the prevalence of sweet consumption and (b) to explore the association between frequent sweet consumption and lifestyle factors taking into consideration several potential confounders, in a wide, national representative sample of Greek children and adolescents aged 8– to 17– year old. The hypothesis was that frequent sweet consumption would be associated with increased total and/or abdominal obesity, poor dietary habits levels, increased screen time and insufficient sleep duration.

2 Materials and methods

2.1 Participants

The study conducted based on population-representative data from a nation-wide school-based health survey in Greece, under the auspices of the Ministry of Education. Anthropometric, nutrition, physical activity (PA), screen time, and physical fitness (PF) data along with information on age and sex were collected from March 2015 to May 2015. In total, 232401 (51% boys and 49% girls) children aged 8 to 17 years old from elementary (8 to 12– yrs-old) and middle (13 to 17– yrs-old) public and private schools agreed to participate in the study (participation rate was almost 40% of the total population). Parents were informed in writing for the purposes of this school health survey and gave their written consent.

2.2 Assessment of demographic and anthropometric data

Demographic information of students (e.g., school, class, gender and date of birth) was obtained from the school principals. Children’s body weight, height and waist circumference were measured in the morning, using a standardized procedure. Body Mass Index status (e.g. normalweight, overweight, obese) was classified using the International Obesity Task Force age- and gender-specific BMI cut-off criteria [25]. Central obesity was defined as waist circumference-to-height ratio (WHtR)≥0.5 [26]. Physical Education (PE) professionals performed all anthropometric measurements.

2.3 Assessment of physical fitness levels

The Euro-fit PF test battery was used to evaluate children’s PF levels [27]. The battery consists of 5 tests, i.e. (a) a multi-stage 20|m shuttle run test (20 m SRT), to estimate aerobic performance; (b) a maximum 10×5|m shuttle run test (10×5|m SRT) to evaluate speed and agility; (c) a sit-ups test in 30|s (SUs), to measure the endurance of the abdominal and hip-flexor muscles; (d) a standing long jump (SLJ), to evaluate lower body explosive power; and (e) a sit and reach (SR) test to measure flexibility. All fitness tests were administered during the PE class by PE professionals, who were instructed through a detailed manual of operations and followed a standardized procedure of measurements in order to minimize the inter-rate variability among schools. For the purposes of this paper we have used data only from the 20 m SRT.

2.4 Assessment of dietary habits

Participating children’s dietary, PA and sedentary habits were recorded via the use of an electronic questionnaire that was completed at school with the assistance of their teachers and/or Information Technology professors. Students’ dietary habits were assessed using the KIDMED (Mediterranean Diet Quality Index for children and adolescents) [28]. This index contains 16 yes or no questions, including dietary habits that are in accordance with the principles of the MD dietary pattern and the general dietary guidelines for youth, and habits that undermine them. Questions denoting a negative connotation with respect to a high-quality diet are assigned a value of – 1, while those with a positive aspect are assigned a value of +1. Thus, the total KIDMED score ranges from 0 to 12 and is classified into 3 levels:≥8, suggesting an optimal adherence to the MD (sufficient dietary habits); 4–7, suggesting an average adherence to the MD and an improvement needed to adjust dietary intake to guidelines (relatively sufficient dietary habits); and≤3, suggesting a low adherence to the MD and generally a low diet quality (insufficient dietary habits).

2.5 Main outcome (sweet consumption)

Frequency of sweets consumption constitutes a question in KIDMED index. Participants were characterized as “frequent sweet consumers” based on whether they consumed sweets (e.g. croissant, cakes, biscuits, e.t.c.), or/and chocolates or/and candies several (more than twice) times every day. Participants who did not consume it several times a day were characterized as “rare sweet consumers.” Specifically, to measure child’s sweet consumption, we asked the question, “Takes sweets, chocolates and/or candies several times every day?”. Response categories to this question were “No or Yes”. Responses to the above question were incorporated for analysis.

2.6 Assessment of self-reported physical activity and sedentary time

Patterns of PA were also self-reported. The questionnaire applied has been previously validated [29], and included simple closed-type questions regarding children’s frequency, time and intensity of participation in (i) school-related PA (including activity during physical education classes); (ii) organized sports activities and (iii) PA during leisure time. The frequency of all reported activities were multiplied by the minutes of moderate to vigorous physical activities (MVPA) and then divided by 7 to obtain the mean daily time children engaged in MVPA. Children who participated in MVPA at least for 60 min per day were considered as meeting the recommendations for PA. Daily time spent in sedentary activities (e.g. television viewing, playing with computer or/and console games, use of Internet for non-study reasons,) was also calculated for each student (via multiplying the weekly frequency of participation with the duration per bout of participation in sedentary activities, and then dividing by 7). In current study, screen-viewing was used as a proxy for sedentary behaviors. Using the threshold of two hours per day, as per current guidelines, students were classified as sedentary or not, i.e., exceeding (>2 h/d) or not (≤2 h/d) [30]. Sleep time was assessed through self reported recordings. Children who were sleeping at least 9 h daily and adolescents who were sleeping at least 8 h daily were classified as meeting the recommendations of sufficient sleep. Children and adolescents sleeping daily fewer than the number of recommended hours were classified as having insufficient sleep [31].

2.7 Ethical approval

Ethical approval for the health survey was granted by the Ethical Review Board of the Ministry of Education and the Ethical Review Committee of Harokopio University.

2.8 Statistical analyses

Descriptive statistics were expressed as mean±standard deviation or frequency (percentages). The chi-square test evaluated associations between the categorical variables and the Student’s t test were applied to evaluate differences in mean values of normally distributed variables. In order to assess the potential effect of several dietary habits on the ‘frequent’ vs. ‘rare’ sweet consumption, binary logistic regression analysis was implemented and OR with the corresponding 95% CI were calculated, adjusted for confounders. Furthermore, aiming to assess the potential effect of several demographic and lifestyle factors on the frequency of sweet consumption, hierarchical binary logistic regression analysis was implemented and OR with the corresponding 95% CI were calculated to obtain adjusted association of covariates while controlling for confounding. The Hosmer and Lemeshow’s goodness-of-fit test was calculated in order to evaluate the model’s goodness-of-fit and residual analysis was implicated using the dbeta, the leverage, and Cook’s distance D statistics in order to identify outliers and influential observations. Finally, discriminant analysis was used to explore the strength of each component in relation to the outcome. All statistical analyses were performed using the SPSS version 23.0 software for Windows (SPSS Inc., Chicago, Il, USA). Statistical significance level from two-sided hypotheses was set at p-value < 0.05.

3 Results

A total of 177,091 children aged 8– to 17– year-old participated in the study. Basic descriptive statistics of the total sample by gender of participants in the health survey are presented in Table 1. A greater proportion of boys as compared to girls reported that they consume sweets more than 2 times per day (19.6% vs. 17.5%, p < 0.001). Significant differences between boys and girls were incorporated in anthropometric variables (e.g. BMI, waist circumference), dietary habits (e.g. KIDMED Index), physical activity, screen time and physical fitness measurements (all p-values < 0.001).

Table 1
Baseline characteristics (means, SD) of participants in the study aged 8– to 17– year-old

Total (n = 177109) Boys (n = 90821) Girls (n = 86288) P-value^*

Age, (years) 9.88 (2.8) 9.91 (2.8) 9.84 (2.8) <0.001

Children 8–11 years, n (%) 100 134 51 161 (50.9) 48 973 (49.1) <0.001

Adolescents 12–17 years, n (%) 76 975 39 660 (52.5) 37 315 (47.5) <0.001

Height (cm) 149 (13.5) 150 (14.5) 148 (12.3) <0.001

Weight (kg) 44.5 (14.2) 45.5 (15.2) 43.5 (12.9) <0.001

BMI (kg/m²) 19.7 (3.8) 19.8 (3.8) 19.5 (3.7) <0.001

Waist circumference (cm) 70.4 (10.7) 71.6 (11.1) 69.2 (10.2) <0.001

Waist to height ratio 0.30 (0.46) 0.32 (0.47) 0.28 (0.45) <0.001

KIDMED score (0, 12)^† 6.7 (2.4) 6.7 (2.4) 6.8 (2.4) <0.001

Sweet consumption > 2 per d, n (%) 31,778 (18.6) 17,184 (19.6) 14,595 (17.5) <0.001

Physical activity (h/wk) 9.4 (5.5) 10.4 (5.9) 8.4 (5.2) <0.001

Screen time (h/wk) 8.6 (8.5) 9.3 (8.8) 7.8 (7.8) <0.001

Sleeping time weekdays, (h/d) 8.6 (1.6) 8.6 (1.6) 8.7 (1.6) <0.001

20m shuttle run (stages) 3.5 (2.1) 4.0 (2.3) 2.8 (1.5) <0.001

	Total (n = 177109)	Boys (n = 90821)	Girls (n = 86288)	P-value^*
Age, (years)	9.88 (2.8)	9.91 (2.8)	9.84 (2.8)	<0.001
Children 8–11 years, n (%)	100 134	51 161 (50.9)	48 973 (49.1)	<0.001
Adolescents 12–17 years, n (%)	76 975	39 660 (52.5)	37 315 (47.5)	<0.001
Height (cm)	149 (13.5)	150 (14.5)	148 (12.3)	<0.001
Weight (kg)	44.5 (14.2)	45.5 (15.2)	43.5 (12.9)	<0.001
BMI (kg/m²)	19.7 (3.8)	19.8 (3.8)	19.5 (3.7)	<0.001
Waist circumference (cm)	70.4 (10.7)	71.6 (11.1)	69.2 (10.2)	<0.001
Waist to height ratio	0.30 (0.46)	0.32 (0.47)	0.28 (0.45)	<0.001
KIDMED score (0, 12)^†	6.7 (2.4)	6.7 (2.4)	6.8 (2.4)	<0.001
Sweet consumption > 2 per d, n (%)	31,778 (18.6)	17,184 (19.6)	14,595 (17.5)	<0.001
Physical activity (h/wk)	9.4 (5.5)	10.4 (5.9)	8.4 (5.2)	<0.001
Screen time (h/wk)	8.6 (8.5)	9.3 (8.8)	7.8 (7.8)	<0.001
Sleeping time weekdays, (h/d)	8.6 (1.6)	8.6 (1.6)	8.7 (1.6)	<0.001
20m shuttle run (stages)	3.5 (2.1)	4.0 (2.3)	2.8 (1.5)	<0.001

^†KIDMED score (≤3: insufficient dietary habits, 4–7: relatively sufficient dietary habits,≥8: sufficient dietary habits. ^*P-values for differences between boys and girls.

Table 2 presents a description of the study participants based on frequency of daily sweet consumption (frequent or rare). Participants from both genders classified as frequent consumers of sweet incorporated worse anthropometric measurements (e.g. BMI, WHtR), poorer dietary habits, increased screen time, sleeping less and having lower physical fitness levels in comparison to rare consumers from the same gender (all p-values < 0.05).

Table 2

Anthropometric and behavioral characteristics (mean±SD) according to daily frequent or rare sweet consumption, in Greek boys and girls (8 to 17– y-old) of the study

Children	Frequent sweet consumption		Rare sweet consumption
	Boys	Girls	Boys	Girls
Age (years)	11.7 (2.5)	12.0 (2.6)	11.2 (2.2)^*	11.1 (2.1)^*
BMI (kg/m²)	19.8 (4.0)	19.6 (3.7)	19.6 (3.8)^*	19.4 (3.7)^*
Waist circumference (cm)	71.5 (10.8)	69.5 (10.2)	71.4 (11.0)	69.1 (10.2)
WHtR	0.33 (0.48)	0.29 (0.45)	0.28 (0.45)^*	0.25 (0.43)^*
KIDMED score	4.9 (2.2)	4.9 (2.3)	7.1 (2.4)^*	7.2 (2.2)^*
Physical activity (h/wk)	10.4 (5.7)	8.5 (5.5)	10.5 (6.1)	8.4 (5.1)
Screen time (h/wk)	13.2 (10.4)	11.9 (9.8)	8.4 (8.2)^*	7.0 (7.2)^*
Sleeping time (h/day)	8.3 (1.6)	8.3 (1.7)	8.6 (1.7)^*	8.7 (1.6)^*
20m SRT (laps)	36.1 (22.1)	25.2 (14.3)	36.4 (20.6)	25.5 (13.8)

BMI: Body Mass Index; WHtR: Waist to height ratio; ^*P-values < 0.01 for differences between rare (< = 1/day) and frequent (>2/day) consumers of sweet, from the same gender.

Binary logistic regression analysis (unadjusted) shown that skipping breakfast, eating pasta or rice almost every day and consuming fast food frequently enlarged the odds of being a frequent sweet consumer, while eating a second fruit every day, eating pulses more than once weekly, using olive oil at home and eating two yogurts and/or cheese (40 g) daily was associated with decreased odds of being a frequent sweet consumer, in both genders (Table 3, Model 1). After adjusting for several covariates (e.g. age, BMI, waist circumference and physical activity levels), the food habits previously reported, remained significantly associated with frequent consumption of sweets, in both genders (Table 3, Model 2). Further adjustment for screen time and sleeping hours did not significantly change the results (Table 3, Model 3).

Table 3

Results (OR, 95% CI) from logistic regression models that used to evaluate the association of children’s (8 to 17– y-old) dietary habits with sweet consumption (rare vs. frequent)

Predictors	Model 1 OR (95% CI)	Model 2 R (95% CI)	Model 3 (OR 95% CI)
Boys
Skips breakfast (no vs. yes)	1.40 (1.28–1.53)	1.43 (1.30–1.56)	1.37 (1.25–1.50)
Has a second fruit every day (no vs. yes)	0.65 (0.60–0.70)	0.66 (0.61–0.72)	0.69 (0.64–0.76)
Has fresh or cooked vegetables more than once a day (no vs. yes)	0.96 (0.87–1.06)	0.97 (0.88–1.07)	1.01 (0.91–1.11)
Consumes fish regularly (at least 2–3/week) (no vs. yes)	0.93 (0.86–1.01)	0.93 (0.86–1.01)	0.99 (0.91–1.08)
Eats pulses > 1/week (no vs. yes)	0.73 (0.67–0.79)	0.71 (0.65–0.77)	0.75 (0.69–0.82)
Eats pasta or rice almost every day (no vs. yes)	1.42 (1.31–1.54)	1.39 (1.28–1.51)	1.37 (1.26–1.49)
Consumes nuts regularly (at least 2–3/week) (no vs. yes)	1.08 (0.99–1.16)	1.08 (0.99–1.17)	1.08 (0.99–1.17)
Uses olive oil at home (no vs. yes)	0.84 (0.66–0.99)	0.84 (0.67–0.99)	0.81 (0.66–0.99)
Takes two yoghurts and/or some cheese (40 g) daily (no vs. yes)	0.82 (0.75–0.91)	0.82 (0.75–0.91)	0.82 (0.74–0.91)
Fast food consumption (< = 1/week vs.>1/week)	4.00 (3.69–4.35)	3.84 (3.53–4.17)	3.44 (3.16–3.75)
Girls
Skips breakfast (no vs. yes)	1.39 (1.26–1.53)	1.42 (1.29–1.56)	1.37 (1.24–1.51)
Has a second fruit every day (no vs. yes)	0.62 (0.57–0.68)	0.67 (0.61–0.73)	0.70 (0.63–0.76)
Has fresh or cooked vegetables more than once a day (no vs. yes)	0.83 (0.75–0.92)	0.86 (0.77–0.95)	0.85 (0.77–0.95)
Consumes fish regularly (at least 2–3/week) (no vs. yes)	0.76 (0.69–0.83)	0.80 (0.73–0.87)	0.85 (0.78–0.94)
Eats pulses > 1/week (no vs. yes)	0.80 (0.73–0.88)	0.77 (0.70–0.84)	0.80 (0.72–0.88)
Eats pasta or rice almost every day (no vs. yes)	1.65 (1.51–1.80)	1.643 (1.50–1.79)	1.62 (1.48–1.77)
Consumes nuts regularly (at least 2–3/week) (no vs. yes)	1.06 (0.97–1.16)	1.07 (0.97–1.16)	1.07 (0.97–1.16)
Uses olive oil at home (no vs. yes)	0.86 (0.54–0.99)	0.85 (0.53–0.99)	0.84 (0.64–0.99)
Takes two yoghurts and/or some cheese (40 g/daily) (no vs. yes)	0.82 (0.74–0.91)	0.83 (0.74–0.92)	0.84 (0.75–0.94)
Fast food consumption (< = 1/week vs.>1/week)	4.27 (3.88–4.70)	4.03 (3.65–4.44)	3.57 (3.23–3.95)

Model 1: Unadjusted; Model 2: Adjusted for age, BMI, waist circumference and physical activity levels; Model 3: Model 2 + screen time and sleeping hours.

Taking into consideration that frequent sweet consumers had a worse lifestyle profile as compared to their infrequent peers, stepwise logistic regression analyses (4 Models) was applied to explore the potential associations of several factors on sweet consumption (frequent vs. rare), in both genders. The first analysis (Model 1) revealed that for every one year raise in the age the odds of being a frequent sweet consumer increased by 9% and 17% in boys and girls, respectively; while being overweight/obese or centrally obese increased the odds of being a frequent sweet consumer (Table 4). When the KIDMED index was added in the analysis (Table 4, Model 2), results related to the effect of age and obesity status did not change, while insufficient dietary habits were increased the odds of frequent sweet consumption by almost 83%, in both genders. After further adjustment for screen time and insufficient (<8–9 h/d) sleeping status (Model 3), the results revealed an unfavourable influence of both on frequency of sweet consumption. Ultimately, when inadequate (<60 min daily MVPA) PA levels and aerobic fitness measurements were included in the analysis (Model 4), the influence of previous factors did not change significantly.

Table 4

Results (OR, 95% CI) from logistic regression models that used to evaluate the association of children’s (8 to 17 y-old) characteristics with sweet consumption (rare vs. frequent)

Predictors	Model 1OR (95% CI)	Model 2 OR (95% CI)	Model 3 OR (95% CI)	Model 4 (OR 95% CI)
Boys
Age (per 1 year)	1.09 (1.07–1.11) (1.097–1.112)	1.07 (1.05–1.09) (1.097–1.112)	1.05 (1.03–1.07) (1.097–1.112)	1.06 (1.03–1.09) (1.097–1.112)
BMI group (normal-weight vs. overweight/obese)	1.21 (1.11–1.31)	1.20 (1.10–1.30)	1.18(1.08–1.28)	1.09 (0.98–1.18)
Abdominal obesity (no vs. yes)	1.11 (1.03–1.20)	1.11 (1.01–1.19)	1.09 (0.99–1.17)	1.04 (0.94–1.11)
KIDMED index (insufficient vs. relatively/sufficient dietary habits)		0.18 (0.16–0.20)	0.20 (0.18–0.22)	0.20 (0.17–0.23)
Sleeping hours (sufficient vs. insufficient)			1.21 (1.11–1.32)	1.17 (1.05–1.29)
Screen time (acceptable vs. increased)			2.18 (2.00–2.37)	2.18 (1.96–2.41)
Physical activity (adequate vs. inadequate)				0.99 (0.89–1.09)
20m shuttle run (per 1 lap)				0.99 (0.98–0.99)
Girls
Age (per 1 year)	1.17 (1.15–1.19)	1.15 (1.12–1.17)	1.11 (1.09–1.13)	1.12 (1.09–1.15)
BMI group (normal-weight vs. overweight/obese)	1.33 (1.16–1.51)	1.31 (1.13–1.48)	1.20 (1.04–1.38)	1.10 (0.93–1.27)
Central obesity (no vs. yes)	1.15 (1.02–1.29)	1.13 (1.01–1.27)	1.08 (0.95–1.22)	0.98 (0.84–1.15)
KIDMED index (relatively/sufficient vs. insufficient		0.17 (0.15–0.19)	0.20 (0.17–0.22)	0.20 (0.17–0.24)
Sleeping hours (sufficient vs. insufficient)			1.30 (1.18–1.42)	1.31 (1.17–1.47)
Screen time (acceptable vs. increased)			2.34 (2.13–2.58)	2.41 (2.15–2.72)
Physical activity (adequate vs. inadequate)				1.10 (0.99–1.23)
20m shuttle run (per 1 lap)				0.99 (0.98–0.99)

Model 1: Age and BMI group and Abdominal obesity; Model 2: Model 1 + KIDMED index; Model 3: Model 2 + Screen time and Sleeping hours; Model 4: Model 3 + Physical activity levels and aerobic fitness measurements.

Discriminant analysis was applied to assess whether the predictors could better distinguish frequent from rare sweet consumers. Standardized function coefficients suggest that dietary habits (0.79); screen time (0.45), and age (– 0.26), contributes more to distinguishing those who consume sweet frequently from those with rare consumption, in both genders. The classification results show that the model correctly predicts 80% of frequent sweet consumers and 55% of infrequent ones.

4 Discussion

As far as we know, this study is among few that have explored several anthropometric and lifestyle correlates of sweet consumption in a population representative cohort. We have involved data from 177091 schoolchildren (aged 8– to 17– year-old) to make available reliable, current, standardized, and comparable findings. The most important results of this study are: (a) almost one to five of schoolchildren consumed sweet more than one time, daily (b) participants from both genders who were frequent consumers of sweet had a worse lifestyle profile, and (c) frequent sweet consumption was strongly associated with poor dietary habits and increased screen time, in both genders.

We found that nearly 18.5% of the surveyed population consumes sweet more than once daily. There are scarce data relating to the frequency of sweet intake among children [11 , 32]. In accordance with our study, findings from Brazil indicated that 19.2% of schoolchildren consumed sweets regularly [12]. Also, studies among children aged 7–12 from Poland and Saudi Arabia shown that almost 18.2% and 26.1% of the participants, respectively, consumed sweets daily [14, 32]. Another project that has examined the daily sweet consumption among children from several countries presented percentages of frequent consumption ranged from 4% (Norway, Finland and Sweden) to 41% (Scotland) [11].

A greater percentage of boys as compared to girls (19.6% vs. 17.5%, p < 0.001) reported frequent sweet intake. In line with this result, most studies have concluded that boys consume most commonly sweets than girls [11 , 32]. In opposite, data among Lithuanian children aged 11– to 15– y-old indicated that more girls (39.4%) than boys (34.3%) had a frequent weekly sweet consumption (p < 0.001) [33].

Regarding age effect, our data are in agreement with those from Brazilian adolescents that reported higher odds of frequent sweet consumption being associated with older ages [12]. These results probably attributed to the fact that older children in Greece had more autonomy and chances to sweet consumption than younger ones. Also, children were more possible to be under their parents influence as compared to adolescents.

Although obesity in Greek schoolchildren is of great concern, frequent sweet consumption did not significantly contribute to total or central obesity in our study, after adjustment for several covariates. Several studies that has examined the association between obesity status and sweet consumption among children and adolescents presented contradictory findings [14 –16].

Our results suggest that dietary habits, screen time and sleeping time were significantly associated with sweet consumption. Particularly, participants who classified as frequent sweet consumers had 80% decreased odds of having sufficient dietary habits. Furthermore, unhealthy dietary habits such as skipping breakfast and frequent fast food intake enlarged odds of being a frequent sweet consumer. A review study among children of several countries has concluded that unhealthy dietary patterns including sweet, fast foods, ice cream, fried food, potato chips, cakes e.t.c. are strongly associated to each other [34]. Furthermore, in accordance with our results, a study among US children shown that children with unhealthy dietary patterns had increased intake of sugar-sweetened beverages and fried potatoes and decreased consumption of fruits, vegetables and low-fat mixed dishes than their peers with healthy dietary patterns [7]. Surprisingly, our results revealed that consuming of pasta or rice increased the probabilities of frequent sweet consumption. A study among Spanish schoolchildren proved that boys preferred to eat more commonly fast food and pasta or rice [35]. We hypothesized that the augment in unhealthy dietary habits in Greece, possibly leads to higher intake of meals that are easier to prepare such as pasta or rice. Conversely, healthy dietary habits such as eating a second fruit every day, using olive oil at home and consuming two yoghurts daily were associated with lowers odds of being a frequent sweet consumer. Our results are in line with studies that have incorporated a consistent association of sweet intake with dietary habits [14 , 24]. The previous referred dietary habits (e.g. skipping breakfast, fast food intake, pasta or rice consumption, eating fruits, e.t.c.) have assessed through KIDMED questionnaire.

The participants of the present study with increased screen time (>2 h/d) had higher odds of being frequent sweet consumers by almost 2.3 times, in both genders. In line with our findings, a study among Danish schoolchildren shown that increased television viewing was associated to unhealthy food habits [36]. Furthermore, study among US children and adolescents revealed that increased screen time was associated with enlarged consumption of foods such as sugar-sweetened beverages, sweets, e.t.c. [37]. Moreover, it has been proposed that children exposed to increased TV viewing and advertising time were more prone to unhealthy food habits [38].

Also, the current data proposed that insufficient sleep duration (<8–9 h/d) is associated with higher probabilities (OR = 1.17–1.31, p < 0.001) of being a frequent sweet consumer, in both genders. In a cross sectional study of Danish students the authors speculated that shorter sleep duration is associated with increased consumption of energy-rich foods [19]. Moreover, in another study the authors concluded that sleep duration was negatively associated with energy density of the diet and added sugar [20]. Finally, a review study reported that there are significant associations between short sleep duration and higher total energy intake and lower quality diets [39].

Although the odds ratio of aerobic fitness to predict sweet consumers were small (0.99, p-value < 0.001), the current results proposed a trend that as performances of aerobic fitness were improved, odds of frequent sweet consumption were decreased. This findings are in line with those of a study among French students which shown a positive relationship between PF and eating habits [22]. Additional research is required to confirm this association. Enhanced aerobic fitness included in a healthy lifestyle and probably these children were more likely to avoid unhealthy dietary habits (e.g. sweet intake).

4.1 Strengths and limitations

This study was performed in a wide range of ages (8– to 17– year-old) and explored several covariates. In Greece, secondary and primary education is mandatory and, consequently, the study examined a large part of schoolchildren population. The methodology used allows the direct comparison of our results with results from other similarly large and representative studies.

Limitations include methodological issues, and the fact that prospective confounding factors, such as socio-economic status and availability of sweets that was possibly associated with sweet consumption have not been evaluated. In addition, this is a cross-sectional study so causality cannot be assigned. The current study restricted its analysis to the consumption of sweets and did not record other foods such as sweetened beverages, energy drinks e.t.c. whose frequent consumption can also interfere with the nutrition habits and health of schoolchildren. With respect to the original study design (school-based health survey) and the specific questions of KIDMED index, more options in terms of frequency (e.g. very high consumer or non-consumer of sweets) have not been evaluated. Moreover, the record of dietary habits, sleeping time and sedentary time were self-reported, therefore subject to desirable reporting bias. Nevertheless, participant responses were anonymous; as a result, they had no reason to misreport. Finally, because of the large sample size, statistical significance can easily be achieved.

5 Conclusions

The present study revealed that a significant proportion of Greek children and adolescents are frequent sweet consumers. Frequent sweet consumption is strongly associated with unhealthy dietary habits, such as skipping breakfast and fast food intake, and with a worse lifestyle profile (e.g. screen time, sleeping time e.t.c.). Healthy approaches to sweet consumption should be established in childhood, with the focus of preventing negative health effects in later life.

Funding

The authors reports no funding.

Disclosure

The authors declared no conflict of interest.

Footnotes

Acknowledgments

This study was supported by the Hellenic Ministry of Education and Religious Affairs, Secretariat General of Sports, OPAP S.A., Nestlé Hellas S.A., and the Department of Nutrition and Dietetics Graduate Program, Harokopio University of Athens.

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