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European Journal of Sport Science logoLink to European Journal of Sport Science
. 2024 Feb 5;24(2):239–248. doi: 10.1002/ejsc.12070

Sleep duration and screen time in children and adolescents: Simultaneous moderation role in the relationship between waist circumference and cardiometabolic risk according to physical activity

Ana Paula Sehn 1, Caroline Brand 2, Luciana Tornquist 3, Debora Tornquist 3, João Francisco de Castro Silveira 4, Anelise Reis Gaya 4, Carlos Cristi‐Montero 2, Ryan Donald Burns 5, Jane Dagmar Pollo Renner 6, Cézane Priscila Reuter 7,✉
PMCID: PMC11235641

Abstract

To evaluate the simultaneous moderating role of sleep duration and screen time in the relationship between waist circumference (WC) and clustered cardiometabolic risk score (cMetS) according to children and adolescents' physical activity. A cross‐sectional study was conducted on 3072 children and adolescents (aged 6–17 years, 57.5% girls). Physical activity, sleep duration, and screen time were assessed through a self‐report questionnaire. The cMetS was determined by averaging the z‐scores of risk factors and dividing it by four. Moderation analyses were tested through multiple linear regression models. Among physically active individuals, sleep duration (p = 0.85) and screen time (p = 0.96) had no influence on the relationship between WC and cMetS. However, a positive interaction between WC x screen time and cMetS (p = 0.04) was observed for physically inactive participants. Concerning sleep duration, there was no interaction with WC. Participants who spent 60 min of screen time presented lower cMetS, even presenting high WC, compared to the higher tertiles of screen time (180 and 360 min). However, although the interaction between sleep duration and WC was not significant, it was observed that the lowest tertile of sleep duration (482 min) combined with 60 min of screen time presented lower cMetS even with the presence of high WC. Our findings encourage compliance with physical activity guidelines associated with the adoption of adequate screen time to minimize the influence of waist circumference on cMetS.

Keywords: behavior, cardiovascular health, exercise, lifestyle, obesity

Highlights

  • Sleep duration and screen time influence adiposity and cardiometabolic risk in physically inactive children and adolescents;

  • Meeting the PA guidelines seems crucial in preventing cardiometabolic risk factors;

  • In inactive individuals, the screen has a deleterious effect on cardiometabolic health.

1. INTRODUCTION

Cardiometabolic risk has become more prevalent in children and adolescents primarily due to an increase in central and total adiposity observed in this population (Caprio et al., 2020; Faienza et al., 2020). Studies have pointed out that waist circumference (WC), an indicator of central adiposity, could detect cardiometabolic alterations in children and adolescents (Arellano‐Ruiz et al., 2020; Perona et al., 2017, 2019) as WC is an indicator of visceral fat (Jabłonowska‐Lietz et al., 2017). In this sense, it is well known that high WC influences the early development of cardiometabolic diseases (de Quadros et al., 2019; Quadros et al., 2017), in which an excess of abdominal fat increases the odds of presenting cardiometabolic risk factors at an early age. Indeed, central and total adiposity exert deleterious effects on cardiometabolic health (de Quadros et al., 2019).

Physical inactivity and sedentary behavior (SB) are well‐established and independent determinants of increased WC and cardiometabolic risk in the pediatric population (Carson, Tremblay, et al., 2016; Tremblay et al., 2011). However, less is known about the relationship between sleep and cardiometabolic health (Chaput et al., 2014). Physical activity (PA) shows an inverse relationship with cardiometabolic risk and greater WC (Carson, Tremblay, et al., 2016; Janssen et al., 2010; Poitras et al., 2016). Studies indicate a dose‐response relationship between PA and cardiometabolic risk with even small amounts of PA providing benefits (Janssen et al., 2010; Poitras et al., 2016). Current PA guidelines recommend that children and adolescents perform at least 60 min of daily moderate‐to‐vigorous PA and reduce the time spent in SB, particularly in front of screens (Brasil. Ministério da Saúde, 2021; WHO. World Health Organization, 2020). Furthermore, studies have pointed to a consistent relationship between increased SB time and unfavorable health outcomes (Tremblay et al., 2011). In the pediatric population, there is a specific emphasis on screen time, given the rising trends and evidence of its detrimental effects on cardiometabolic health and adiposity (Barnett et al., 2018).

Regarding time outside of waking hours, studies have indicated an inverse relationship between sleep duration and adiposity (Carson, Tremblay, et al., 2016; Chaput et al., 2016) with similar evidence found concerning cardiometabolic biomarkers (Chaput et al., 2016). For this reason, the consistent and rapid decline in sleep duration observed in children and adolescents over the last century (Matricciani et al., 2012), attributed to modern lifestyle habits, is a cause of concern (Gruber et al., 2014). Recommendations for the child and adolescent population indicate the need for children to achieve 9–11 h of sleep and for adolescents to get 8–10 h of sleep per night (Hirshkowitz et al., 2015). It is worth noting that both short and long sleep durations are a risk to the health of children and adolescents (Krittanawong et al., 2017; Liu et al., 2017).

Recent studies emphasize the importance of considering three crucial behaviors–PA, sedentary time, and sleep–collectively, addressing the concept of 24‐h movement behavior (Carson, Tremblay, et al., 2016; Chaput et al., 2014; Katzmarzyk et al., 2017). These behaviors are intrinsically collinear and codependent (Ž, 2014). Evidence has indicated that children and youth who have a combination of high PA, adequate sleep duration, and low SB tend to achieve more favorable outcomes in terms of adiposity and cardiometabolic health (Saunders et al., 2016). Researchers caution against the isolated evaluation of these behaviors as it may limit insights into their cumulative health benefits with each behavior moderating the impact on health relative to the others (Chaput et al., 2014). Therefore, we intend to fill a gap in the literature by investigating the simultaneous role of 24‐h movement behaviors in the development of cardiometabolic risk among Brazilian children and adolescents. In this sense, this study aims to evaluate the simultaneous moderating role of sleep duration and screen time in the relationship between WC and clustered cardiometabolic risk score (cMetS) according to children and adolescents' PA.

2. METHODS

2.1. Participants

This cross‐sectional study included 3072 children and adolescents of both sexes (57.5% girls) aged six to 17 years from public and private schools in a city in southern Brazil. The study was conducted meeting Resolution 466/2012 of the National Health Council of Brazil and received approval from the local ethics committee (n.° 4,278,679). Free and informed consent forms were signed by the parents or legal guardians of the schoolchildren.

In this study, participants aged seven to 17 years who underwent blood collection were included. Exclusion criteria comprised incomplete or inconsistent lifestyle data. The sample selection is presented in Figure 1. The 25 schools were randomly selected by conglomerates considering the different regions of the city (north, south, east, and west) as well as urban and rural zones. All students between the ages of 7 and 17 from these selected schools were invited to participate in all phases of the research.

FIGURE 1.

FIGURE 1

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A selected sample of the present study.

The sample size calculation for the present study was conducted using G*Power version 3.1 software. Multiple linear regression via post hoc power analysis was used. The following equation was considered to calculate the observed effect size (f2) within the built models: f2 = r2 adjusted/(1 ‐ r2 adjusted). To calculate r2 adjusted, the proportion of variance in the dependent variable that can be explained by the independent, moderator, and interaction variables within the models was considered. A significance level of alpha (type I error rate) of 0.05 and eight predictors were considered. A test power for each group was established: in the active group, the test power was 0.98; in the inactive group, the test power was ≥0.99.

2.2. Collection instruments

All evaluations were conducted by a trained research team at the University of Santa Cruz do Sul.

2.3. Evaluation of PA, sleep duration, and screen time

PA, sleep duration, and screen time were assessed through a self‐report questionnaire adapted from Barros and Nahas (Barros et al., 2003). The questionnaires were filled out at home by children aged seven to 10 years old with the help of their parents. The adolescents filled out the questionnaires at schools with the help of researchers. PA was evaluated using the following questions: “Do you usually practice any sport/PA?” (yes or not); “How many times a week and hours/minutes per day do you practice this sport/PA?” Then, the sum of the total time (minutes per week) spent in sports or PA was calculated. The PA classification was performed based on the PA guidelines of the World Health Organization, which recommends that children and adolescents perform at least 60 min of PA per day (WHO. World Health Organization, 2020). Children and adolescents were classified based on weekly PA practice into two categories: Met PA guidelines (participants who spent ≥420 min/week in PA) or did not meet PA guidelines (those with ≤419 min/week). PA was limited to leisure activities and did not consider physical education classes, daily commuting, or domestic or occupational activities.

Sleep duration was evaluated through the following questions: “What time do you go to sleep during the week and the weekend?” and “What time do you get up during the week and weekend?”. The total minutes of sleeping per week were calculated, and the mean sleep duration per day was determined. Screen time was obtained through the following question: “How much time in minutes do you spend in front of the TV, computer, and videogame per day?”. Subsequently, the sum of the total time in front of screens (TV, computer, and videogame) was calculated. The time spent with mobile devices, such as smartphones and tablets, was not considered.

2.4. Sexual maturation

Sexual maturation was determined according to Tanner's criteria (Tanner, 1986). The participants were presented with figures depicting the various stages of maturation, and they indicated the figure corresponding to their current stage. Boys' development was assessed based on genital maturation, while girls' development was based on the breast stage with both genders considered for pubic hair development. This process established five sexual maturational stages, which were subsequently grouped into four categories: prepubertal (stage I), initial development (stage II), continuous maturation (stages III and IV), and matured (stage V).

2.5. Waist circumference

The measurement of WC was taken at the narrowest point of the trunk, between the last rib and the iliac crest, using an inelastic tape with a resolution of 1 mm (Cardiomed®).

2.6. Cardiometabolic risk factors

To assess systolic blood pressure (SBP), an auscultatory method was used with a sphygmomanometer and a stethoscope in accordance with the VI Guidelines of the Brazilian Society of Cardiology (Sociedade Brasileira de Cardiologia, 2016). Two measures were performed on the left arm using a cuff appropriate for the participant's arm circumference after 5 minutes of resting. The lowest of these measurements was considered as the recorded SBP value. For the evaluation of lipid and glycemic profile [triglycerides (TG), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), and glucose], participants were required to fast for 12 h prior to blood collection, which was carried out in the morning. Serum samples were collected using the Miura 200 automated equipment (I.S.E., Rome, Italy) and analyzed with commercial kits (DiaSys Diagnostic Systems, Germany).

The cMetS was determined by summing z‐scores for SBP, TG, TC/HDL‐C ratio, and glucose and then dividing it by four. To calculate sex‐ and age‐specific standardized z‐scores, an international reference was used for each risk factor following the equation: z score = ([=([X ‐□]/SD), where X is the continuous value observed for the risk factor and □ is the predicted mean calculated for the risk factor. For this, regression equations and standard deviations (SDs) of the international reference were considered (Stavnsbo et al., 2018). Prior to analysis, skewed variables (TC/HDL‐C ratio and TG) were transformed by the natural logarithm.

2.7. Statistical analysis

Descriptive analysis was presented through means and standard deviations or as absolute and relative frequencies. Differences between continuous variables were tested using the bootstrapping resampling procedure for the independent two‐tailed t test. This procedure used a resampling of 1000 bootstrap samples and the bias corrected accelerated (BCa) method. For the categorical variables, the difference was tested through the chi‐squared test.

The moderation analysis tested the following associations: the direct relationship of the independent variable (WC) on cMetS adjusted for moderator variables (sleep duration and screen time); the direct relationship of moderator 1 (sleep duration) on cMetS adjusted for WC and screen time; the interaction between WC and sleep duration on cMetS; the direct relationship of moderator 2 (screen time) on cMetS adjusted for WC and sleep duration; and the interaction between WC and screen time on cMetS. All models were adjusted for sex, age, and sexual maturation. These models were tested according to meeting PA guidelines (Met PA guidelines or did not meet PA guidelines). The pick‐a‐point approach was applied to probe the interactions and for a better comprehension of the simultaneous moderator role of sleep duration and screen time on the relationship between WC and cMetS. This technique allows the determination of the relationship between WC and cMetS across three different levels of the moderator variables (low–16th percentile, middle–50th percentile, and high–84th percentiles of sleep duration and screen time, respectively) (Hayes, 2022). For all analyses, statistical significance was defined as p < 0.05. Moderation analyses were tested through multiple linear regression models using the PROCESS macro for the Statistical Package for Social Sciences (SPSS) version 23.0 (IBM Corp).

3. RESULTS

The characteristics of the children and adolescents are presented in Table 1. On average, participants spent 217 min per day in front of screens and slept for 560 min with only 8.4% meeting the recommended weekly PA levels. Girls present lower screen time and WC and a higher prevalence of physical inactivity compared to boys. Regarding the biochemical variables, girls presented higher total cholesterol values and TC/HDL‐C ratio and boys demonstrated higher values of HDL‐C and glucose.

TABLE 1.

Characteristics of children and adolescents according to sex.

Mean (SD)
All (n = 3068) Boys (n = 1305) Girls (n = 1763)
Age (years) 12.17 (2.77) 12.04 (2.84) 12.26 (2.71)*
Weight (kg) 47.34 (15.31) 47.76 (16.73) 47.04 (14.16)
Height (m) 1.51 (0.15) 1.52 (0.17) 1.50 (0.13)*
Body mass index (kg/m2) 20.34 (4.11) 20.11 (3.91) 20.51 (4.25)*
Sleep duration (minutes/day) 560.20 (85.06) 559.37 (85.90) 560.81 (85.90)
Screen time (minutes/day) 217.71 (159.71) 236.63 (166.48) 203.70 (153.06)*
Waist circumference (cm) 66.38 (9.67) 67.34 (10.09) 65.68 (9.28)*
cMetS (z score) −0.08 (0.69) −0.17 (0.67) −0.02 (0.70)
Systolic blood pressure (mmHg) 105.76 (13.98) 106.03 (14.26) 105.56 (13.76)
Triglycerides (mg/dl) 74.11 (163.61) 64.88 (32.79) 80.95 (213.75)
Total cholesterol (mg/dl) 160.88 (31.79) 157.62 (32.11) 163.29 (31.34)*
High density lipoprotein (mg/dl) 59.22 (11.38) 59.85 (11.85) 58.76 (10.99)*
TC/HDL‐C 2.79 (0.67) 2.70 (0.65) 2.85 (0.68)*
Glucose (mg/dl) 88.68 (9.13) 89.85 (9.40) 87.81 (8.83)*
n (%)
Physical activity
Met PA guidelines 259 (8.4) 152 (11.6) 107 (6.1)
Did not meet PA guidelines 2809 (91.6) 1153 (88.4) 1656 (93.9)**
Sexual maturation
Prepubertal 650 (21.2) 320 (24.5) 330 (18.70)
Initial development 719 (23.4) 285 (21.8) 434 (24.6)
Continuous maturation (stage III and IV) 1420 (46.3) 568 (43.5) 852 (48.3)
Maturated 279 (9.1) 132 (10.1) 147 (8.3)**
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Note: SD, Standard deviation; n, Absolute frequency; %, Relative frequency; and cMetS: Clustered cardiometabolic risk score.*Statistically difference between sex using the bootstrapping resampling procedure for the independent two‐tailed t test (p < 0.05). **Statistically difference between sex using the chi‐square test (p < 0.05).

Table 2 presents the simultaneous moderation role of sleep duration and screen time in the relationship between WC and cMetS in children and adolescents who met PA guidelines or did not meet PA guidelines. The results indicated that for those who met PA guidelines, sleep duration and screen time did not significantly affect this relationship. However, for those who did not meet PA guidelines, a positive interaction term was found between WC and screen time with cMetS. For both groups, no significant interaction terms between CC and sleep duration were observed.

TABLE 2.

Interaction term of behaviors in the relationship between WC and cMetS considering physical activity guidelines.

B Se t 95% CI p
cMetS
Met PA guidelines WC 0.017 0.032 0.526 −0.046; 0.079 0.60
Sleep duration −0.001 0.004 −0.205 −0.009; 0.007 0.84
WC x sleep duration 0.001 0.001 0.196 −0.001; 0.001 0.85
Screen time 0.001 0.002 0.142 −0.003; 0.004 0.89
WC x screen time 0.001 0.001 −0.044 −0.001; 0.001 0.96
Constant −1.220 2.237 −0.545 −5.625; 3.185 0.59
Sex 0.162 0.082 1.980 0.001; 0.324 0.04
Age −0.028 0.020 −1.346 −0.068; 0.013 0.18
Sexual maturation 0.043 0.056 0.768 −0.068; 0.154 0.44
Did not meet PA guidelines WC 0.041 0.009 4.632 0.023; 0.058 0.01
Sleep duration 0.001 0.001 0.523 −0.001; 0.002 0.60
WC x sleep duration −0.001 0.001 −0.757 −0.001; 0.001 0.44
Screen time 0.001 0.001 1.997 0.001; 0.002 0.04
WC x screen time −0.001 0.001 −1.963 −0.001; ‐0.001 0.04
Constant −2.715 0.596 −4.553 −3.885; −1.546 0.01
Sex 0.214 0.025 8.730 0.166; 0.262 0.01
Age −0.028 0.006 −4.683 −0.040; −0.016 0.01
Sexual maturation 0.021 0.017 1.276 −0.011; 0.054 0.20
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Note: Met PA guidelines: r2 adjusted = 0.09; f 2 = 0.10; and test power = 0.98; Did not meet PA guidelines: r2 adjusted = 0.18; f 2 = 0.22; and test power ≥0.99; All models were adjusted for sex, age, and sexual maturation. Bold values mean p < 0.05.

Abbreviation: B, linear regression coefficient; CI, confidence interval; cMetS, Clustered cardiometabolic risk score; SE, Standard error; WC, waist circumference.

To provide a better comprehension of interactions, we intend to establish how screen time influences the relationship between WC and cMetS in children and adolescents who did not meet PA guidelines (Figure 2). It was observed that participants who spent 60 min of screen time had lower cMetS compared to the highest tertiles of screen time (180 and 360 min), and this also occurred among adolescents with high WC. Additionally, it appears that extended screen time (≥360 min) might have a detrimental impact on the relationship between WC and cMetS as higher WC and cMetS were observed in this group. However, although the interaction between sleep duration and WC was not significant, it was observed that the lowest tertile of sleep duration (482 min) combined with 60 min of screen time presented lower cMetS even in the presence of high WC.

FIGURE 2.

FIGURE 2

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Simultaneous moderation role in the relationship between WC and cMetS in participants who did not meet PA guidelines. Legend: cMetS: clustered cardiometabolic risk score; WC: waist circumference; β: linear regression coefficient; 95% CI: confidence interval; and SD: standard deviation. All models were adjusted for sex, age, and sexual maturation.

4. DISCUSSION

When considering the simultaneous role of sleep duration and screen time in the relationship between WC and cMetS, we observed that screen time plays a moderating role but only in children and adolescents who did not meet PA guidelines. Thus, meeting the PA guidelines seems crucial in preventing cardiometabolic risk factors because, for physically active participants, both screen time and sleep duration did not influence children's and adolescents' relationship between abdominal adiposity and cMetS. In cases where children and adolescents did not meet PA guidelines, screen time had an additional deleterious effect on the relationship between WC and cMetS. Additionally, it was observed that sleep duration presented a similar tendency, indicating that individuals in the highest tertile of sleep (647 min) combined with 360 min of screen time presented high cMetS and WC, although it was not statistically significant.

A review of the evidence on combined PA, sleep duration, and SB has shown that youth who engage in high PA and sufficient sleep and low SB tend to have more favorable cardiometabolic health and adiposity compared to those with low PA and sleep along with high SB (Saunders et al., 2016). It has also been shown that high PA and sleep or high PA and low SB are associated with lower adiposity and lower risk when compared to the opposite combinations (Saunders et al., 2016). Additionally, the study revealed that among the three behaviors, exhibited the most consistent association with desirable health indicators (Saunders et al., 2016). This aligns with our findings as we observed that among children and adolescents who met PA guidelines, there was no significant moderation of sleep and screen time behaviors on the relationship between WC and cMetS.

The present study emphasizes the importance of meeting PA guidelines to enhance the cardiometabolic health of children and adolescents. It is recommended that the pediatric population spend at least 60 min of moderate to vigorous PA daily (WHO. World Health Organization, 2020). In this sense, a meta‐analysis has revealed that individuals who present high sitting time but demonstrate 60–75 min/day of moderate or vigorous PA have a lower mortality risk. This suggests that PA may have a mitigating effect on the consequences of prolonged time Ekelund et al., (2016). In addition, a systematic review found an inverse relationship between PA and cMetS, in which individuals with high PA levels present lower cMetS (Poitras et al., 2016). In this sense, our results indicated that girls present a higher prevalence of physical inactivity compared to boys. This finding align with existing literature which reports that girls tend to be less physically active than boys (Dumith et al., 2011; Guthold et al., 2020). Variations in PA levels between sex may be attributed to several factors. These include biological aspects, such as growth spurts and pubertal maturity (Rodrigues et al., 2010; Staiano et al., 2012), behavioral elements, such as the adoption of unhealthy lifestyles among girls (Chaves et al., 2021; Ricardo et al., 2019a, 2019b), Ricardo, da Silva, et al., 2019 and sociocultural influences. In the sociocultural context, social roles assigned to genders contribute to these differences with girls experiencing less freedom, facing greater family restrictions, and heightened concerns about safety (Farias Júnior et al., 2012; Seabra et al., 2008).

Given the established impact of adiposity, particularly central adiposity, on the development of cardiometabolic risk factors, we intended to understand how the adoption of healthy lifestyle habits could intervene in this relationship. In this context, existing evidence suggests that maintaining appropriate screen time and sleep habits may be associated with a lower WC in adolescents, even in the presence of a genetic predisposition to obesity (Brand et al., 2021). Indeed, we observed that screen time moderates the relationship between WC and cMetS, and data regarding sleep duration showed a similar tendency.

Moreover, it was observed that screen time seems to be more deleterious than sleep duration. Thus, in children and adolescents who do not adhere to PA guidelines, a stronger influence was noted of WC on cMetS in individuals with higher sleep duration (647 min) and screen time (360 min) than in those with 60 min of screen and 482 min of sleep duration. It is important to mention that all tertiles of sleep duration aligned with the recommended guidelines (Hirshkowitz et al., 2015); however, the lowest tertile (482 min) exhibited the least influence on the relationship between WC and cMetS, which is a finding that has not been extensively explored in previous studies.

The aforementioned results agree with the recommendations of screen time for the pediatric population; once in the WHO Guidelines on PA and SB, it is suggested for only reducing sedentary time (WHO. World Health Organization, 2020), and we provide an objective recommendation on this matter. Thus, the present study also suggests that for children and adolescents who did not meet PA guidelines, reducing screen time is important for the prevention of cardiometabolic diseases. It is essential to note that in our study, children and adolescents did not meet PA guidelines. This designation does not imply that they do not engage in any PA, but rather, they are less active than the recommended level.

Indeed, our findings are in line with existing literature, which suggests that PA is the most important lifestyle habit for promoting cardiometabolic health (Cristi‐Montero et al., 2019), followed by SB and sleep duration (Carson, Tremblay, et al., 2016; Xiong et al., 2021). In this sense, the evidence indicating that reducing screen time can impact the relationship between adiposity and cardiometabolic risk represents a significant finding, which can serve as a valuable guide for promoting other health‐related behaviors, especially among adolescents who do not have access to structured practices during leisure time. It is worth noting that extracurricular physical activities have been associated with better health indicators in schoolchildren (Jägerbrink et al., 2022). Thus, it is important to encourage individuals with limited access to PA to maintain healthy lifestyle habits, thereby mitigating the deleterious effects of adiposity and cMetS. Other studies have demonstrated the necessity of increasing PA to reduce SB time (Saunders et al., 2016; Xiong et al., 2021). Evidence also indicates that considering all 24‐h movement behaviors, including PA, screen time, and sleep duration, is essential for the health of children and adolescents (Carson et al., 2017; Katzmarzyk et al., 2017).

In addition, it was observed that central adiposity is an important risk factor in the present study as in combination with inadequate lifestyle habits, it led to a higher cMetS. Van Hulst et al. (Van Hulst et al., 2020) indicated that a high risk of developing cardiometabolic risk factors is observed in children who increase their central adiposity levels as they enter puberty. Moreover, it is known that there are differences in the distribution of adipose tissue related to sex. In general, greater adiposity is observed in women. However, men generally have a greater central distribution of adipose tissue (android), while women have a peripheral distribution of adipose tissue (gynoid), and these differences become evident from adolescence with the onset of puberty (Muscogiuri et al., 2023).

The results of this study may be partially explained by the effects of exercise on several molecular changes within target tissues, leading to functional changes, such as cardiovascular and metabolic adaptations (Widmann et al., 2019). Indeed, regular practice of physical exercise reduces the risk of developing various common health issues. In contrast, an inactive lifestyle is recognized as a major risk factor for health (Carson, Hunter, et al., 2016). Thus, physical exercise is an important strategy to attenuate the detrimental association of SB with mortality Ekelund et al., (2016).

Some limitations of the present study should be considered. First, cause and effect inferences are not possible due to the cross‐sectional design. Second, self‐reported measures were used to evaluate lifestyle habits assessed through a questionnaire not previously validated among the study population. Concerning PA, the questionnaire refers only to structured PA during leisure time, potentially leading to an underestimation of the time spent on this behavior. Activities, such as physical education at school, commuting, and active play, were not taken into account, although they can significantly impact adiposity and cardiometabolic risk reduction. Third, there is a lack of information regarding the time spent on mobile devices, such as cell phones and tablets, which are frequently used by children and adolescents. Additionally, the distinction between academic and nonacademic screen usage in school settings was not considered. Fourth, we must consider that some unmeasured variables, such as genetics and dietary intake, could intervene in the observed associations.

On the other hand, the present study presents significant strengths, such as investigating the simultaneous role of lifestyle habits in the relationship between WC and cMetS and mainly determining objective recommendations about how long children and adolescents can remain in SB and sleep duration with no detrimental influence on health. This study used cMetS to evaluate metabolic syndrome. Recent research has indicated that assessing cardiometabolic disease through the clustered cMetS score, which consolidates risk factors into a single continuous measure, offers advantages over evaluating individual cardiometabolic risk factors (Andersen et al., 2015; Reuter et al., 2021). In addition, it is highlighted that the present study was conducted within a local middle‐income country where studies featuring representative samples are scarce.

In conclusion, screen time plays a moderating role in the relationship between WC and cMetS only in children and adolescents who did not meet PA guidelines. Therefore, this study highlights the importance of meeting PA guidelines for the cardiometabolic health of the pediatric population. In addition, diminishing screen time duration needs to be encouraged, especially in children and adolescents who did not meet PA guidelines, to minimize the influence of WC on cMetS. Thus, public health strategies must encourage the pediatric population to adopt a combination of healthy lifestyle habits for the prevention of cardiometabolic diseases.

CONFLICT OF INTEREST STATEMENT

The authors report that there are no competing interests to declare.

ACKNOWLEDGMENTS

The authors thank the participating schools, their research group from the Health Research Laboratory (LAPES), and the support of the University of Santa Cruz do Sul—UNISC. This work was supported by the Higher Education Personnel Improvement Coordination–Brazil (CAPES) ‐ Financing Code 001.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Associated Data

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Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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