Screen time exposure, sleep alterations, and their relationship with excess weight in children and adolescents

Abstract

BACKGROUND

The digital era has led to an unprecedented increase in screen time exposure among youth, creating a critical intersection between technology use, sleep hygiene, and metabolic health.

AIM

To evaluate the association between daily screen time, sleep quality, and the prevalence of overweight and obesity in a pediatric clinical population.

METHODS

A cross-sectional study was conducted on 70 patients (mean age 10.8 years). Subjects were categorized into obese (n = 44) and non-obese (n = 26) groups. Anthropometric data, body composition (perivisceral fat), biochemical parameters [insulin, Homeostasis Model Assessment (HOMA), lipid profile], and lifestyle habits (Krece Plus and KIDMED tests) were analyzed. Binary logistic regression was performed to estimate odds ratios (OR) for obesity and sleep disturbances.

RESULTS

Obesity was significantly associated with higher perivisceral fat (P < 0.001), systolic blood pressure (P = 0.009), insulin levels (P = 0.035), and HOMA index (P = 0.024), alongside lower high-density lipoprotein cholesterol (P < 0.001). Sociodemographic analysis revealed that maternal education level was significantly lower in the obese group (P = 0.006). Regarding lifestyle, obese children reported significantly higher daily screen time (2.9 hours vs 2.1 hours; P = 0.047) and shorter sleep duration (8.4 hours vs 9.1 hours; P = 0.021). The prevalence of chronic sleep disturbances was notably higher in obese subjects (39% vs 8.3%; P = 0.016). After adjusting for confounders, screen time was strongly associated with a higher risk of obesity (OR: 3.08; 95%CI: 1.53-7.90) and sleep problems (OR: 1.65; 95%CI: 0.96-2.99).

CONCLUSION

Excessive screen time and poor sleep quality are strongly associated with pediatric obesity and its metabolic comorbidities. Furthermore, lower maternal education level and family history of diabetes are significant compounding factors. Clinical interventions should address digital hygiene and sleep quality as essential pillars in the management of childhood excess weight.

Key Words: Pediatric obesity; Screen time; Sleep quality; Body composition; Mediterranean diet

Core Tip: Insights into the synergistic impact of screen time and sleep quality on pediatric body composition are limited. We quantified visceral fat area and body composition using advanced bioimpedance software and investigated lifestyle factors that affect childhood obesity. It was revealed that increased daily screen time and poor sleep quality are heavily associated with higher visceral adiposity, while a high KIDMED score in patients with obesity often reflects reactive dietary changes rather than historical habits. With this knowledge, pediatricians can design more effective, multi-behavioral intervention strategies targeting digital habits and sleep hygiene alongside traditional nutritional counseling.

INTRODUCTION

Childhood obesity is currently one of the most serious public health challenges. Its prevalence has increased significantly, reaching pandemic proportions in recent years[1,2]. Consequently, it is crucial to identify modifiable lifestyle factors involved in the development of this disease to design more effective interventions for its management and control[3]. Lifestyle is arguably the primary factor associated with the development of childhood obesity[4]. A priori, childhood is a life stage inherently characterized by daily physical activity, whether through play or structured exercise. However, this paradigm has shifted drastically over the last few years[5].

Since the emergence of new information technologies, the use of electronic devices such as smartphones and tablets has become generalized across the entire population[6,7]. Exposure to this type of technology is directly linked to sedentary behavior, which constitutes a well-established risk factor for obesity[5].

Beyond this direct effect of screen time exposure on weight in children, the use of these technologies is associated with disrupted sleep patterns, which in turn have been linked to a higher risk of developing obesity[8-12]. Thus, the negative effects of screen time during childhood and its potential repercussions on the development of obesity are multiple and multidirectional.

Sleep is vital for proper neurodevelopment in infants and young children; hence, an inadequate sleep pattern (in terms of both duration and quality) compromises physical and mental health[13]. The proposed mechanisms underlying this relationship include the displacement of rest time, psychological stimulation induced by screen content, and the suppression of melatonin caused by blue light emissions from devices[14]. Concurrently, sleep deprivation alters appetite-regulating hormones—decreasing leptin and increasing ghrelin—which elevates hunger and caloric intake, thereby closing a pro-obesogenic metabolic loop[15].

The American Academy of Pediatrics and the World Health Organization (WHO) recommend avoiding screen use entirely in children under 2 years of age and limiting its use to a maximum of 1 hour per day in children aged 2 years to 5 years[9,16]. Nevertheless, the vast majority of minors fail to meet these recommendations[17,18].

Given that 80% of children with obesity will become adults with obesity, and since the metabolic complications of this condition initiate during childhood, it is essential to investigate the interaction between lifestyle factors and the development of overweight from early childhood. However, to the best of our knowledge, studies analyzing these combined factors in the pediatric clinical population remain scarce.

The primary outcome of our study was the presence of sleep disturbances (concerning both sleep duration and quality) in children and adolescents, aiming to evaluate whether these characteristics differ significantly in individuals with obesity compared to their normal-weight peers. Additionally, as part of this primary evaluation, we assessed daily screen time exposure as an outcome to identify differences between the obesity and normal-weight groups.

As a secondary objective, we aimed to analyze the association between screen time exposure and sleep quality, as well as their joint relationship with metabolic parameters and body composition.

MATERIALS AND METHODS

Patients and study design

This was an analytical cross-sectional study conducted by the pediatric endocrinology section at a university hospital in Spain. This study is part of a larger research project evaluating the influence of various environmental factors on the development of childhood obesity and its associated complications.

Inclusion criteria: Subjects aged 3 years to 15 years whose parents or legal guardians provided voluntary written informed consent. In compliance with ethical regulations, assent was also obtained from minors whenever their age and maturity level warranted it.

Exclusion criteria: Patients with underlying chronic diseases (other than exogenous obesity) that could interfere with the study objectives; and subjects undergoing chronic pharmacological treatment with drugs known to affect sleep architecture, specifically those altering sleep induction or maintenance (such as psychostimulants, sedatives, or systemic corticosteroids).

Biochemical and metabolic assessments

A comprehensive evaluation of clinical, biochemical, and lifestyle variables was performed for all participants. For the metabolic evaluation, peripheral blood samples (3 mL) were obtained via venipuncture after a strict 12-hour overnight fast. All biochemical parameters were determined using standardized laboratory protocols:

Thyroid and hormonal function: Plasma thyroid-stimulating hormone (U/mL), fasting insulin (U/mL), and morning cortisol (μg/dL) levels were measured using chemiluminescent microparticle immunoassay techniques.

Lipid and metabolic profile: Total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, triglycerides, and fasting plasma glucose were quantified using automated enzymatic methods.

Glycemic control and liver function: Glycated hemoglobin (%) was measured via high-performance liquid chromatography. Liver function markers, including aspartate aminotransferase, alanine aminotransferase, and gamma-glutamyl transferase, were analyzed using enzymatic techniques, while serum uric acid levels were determined using the uricase method.

These variables and their technical specifications are described in detail in Supplementary Table 1.

Assessment of insulin homeostasis

Beta-cell function, insulin sensitivity, and insulin resistance were estimated from fasting glucose and fasting insulin concentrations using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR)[19]. The HOMA-IR index was calculated using the standard formula: HOMA-IR = [(fasting insulin × fasting glucose)/405]. This method provides an indirect but well-validated assessment of metabolic health and its relationship with excess weight in the pediatric clinical population.

Assessment of lifestyle habits

To evaluate the nutritional quality of dietary intake, the KIDMED index (Mediterranean Diet Quality Index for Children and Adolescents) was utilized[20]. To estimate physical activity levels and sedentary behaviors, the Krece Plus short questionnaire was utilized[21], which collects information regarding the number of weekly hours dedicated to sports activities outside of school hours. Each question includes six possible responses, scored on a scale from 0 to 5 points. The total score of the test ranges from a minimum of 0 to a maximum of 10 points. Based on the overall score, individuals are classified into three categories representing their physical activity level. Good: Total score of 9-10 for boys and 8-10 for girls. Moderate/regular: Total score of 6-8 for boys and 5-7 for girls. Poor: Total score ≤ 5 for boys and ≤ 4 for girls. This test serves as a rapid screening tool to assess an individual’s level of physical activity or inactivity and is specifically validated for children and adolescents aged 4 years to 14 years[21].

Additionally, this test evaluates daily leisure screen time exposure through questions focusing on the number of hours spent watching television, using smartphones or tablets, or playing computer games (with options capturing total daily exposure times ranging from 0 to 5 or more hours per day).

This measurement is crucial, as current scientific literature identifies recreational screen time exceeding 2 hours per day as a significant risk factor for increased body mass index (BMI) and the development of adiposity.

Clinical and anthropometric assessment

Body weight was measured using a SECA 799^® medical-grade electronic column scale (CE Class III approval, capacity up to 200 kg). Height was determined using a SECA Mod. 222^® mechanical wall-mounted stadiometer (measurement range: 6-230 cm; graduation: 1 mm). Blood pressure was measured on the left arm with the patient in a seated position using an age-appropriate cuff size and a Welch Allyn Connex Spot Monitor 7400^® sphygmomanometer. Blood pressure percentiles were subsequently calculated in accordance with the recommendations established by the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents[22]. Based on these criteria, hypertension was defined as a mean blood pressure value ≥ the 95^th percentile obtained on three or more separate occasions.

For a more comprehensive analysis of adiposity, body composition was assessed via bioelectrical impedance analysis using the InBody S10 multi-frequency analyzer (Microcaya^®). This method differentiates between lean mass and fat mass, providing an estimate of adipose tissue distribution that complements the clinical data derived from BMI.

Participants were classified according to their nutritional status into two distinct groups: Obesity and normal weight. For this stratification, the growth criteria from the Faustino Orbegozo Eizaguirre Foundation were applied. These references serve as the predominant national clinical standards in Spain, utilizing percentile curves of BMI-for-age and sex to ensure precise identification of excess weight within the pediatric population[23].

Evaluation of environmental factors and sleep habits

The assessment of exposure to environmental determinants was performed using a multidimensional questionnaire administered to the families, which integrated a total of 21 critical variables related to dietary habits, physical activity, recreational screen time exposure, and sleep hygiene. Within the specific domain of sleep, the instrument explored fundamental dimensions of sleep architecture and sleep-related morbidity:

Chronobiology and duration: Habitual sleep onset time and average daily sleep duration were recorded, allowing sleep to be categorized as sufficient or insufficient according to the international standards established by the National Sleep Foundation.

Sleep disorders: The presence of chronic sleep disorders (defined as a persistence of more than one year) was evaluated, and specific symptomatology was characterized, including prolonged sleep latency (difficulty falling asleep) and sleep fragmentation (nocturnal awakenings).

Chronological progression: The age of onset and resolution of sleep disturbances was documented to analyze their potential influence on the development of excess weight throughout childhood.

To enhance data validity and minimize social desirability or recall bias, the questionnaire was completed anonymously and exclusively during morning hours. Furthermore, data collection was strictly restricted to mid-week days (Tuesday to Thursday), deliberately avoiding Mondays, Fridays or extended school holiday weekends to mitigate variability in sleep patterns and the circadian shift typically observed on weekends and festive periods.

Statistical analysis

A descriptive analysis of sociodemographic and biochemical variables, as well as exposure parameters related to lifestyle factors and sleep quality disturbances, was performed for the overall sample and stratified according to the participants’ BMI status (normal weight vs obesity) based on the criteria of the Faustino Orbegozo Eizaguirre Foundation. Quantitative variables were expressed as mean, whereas qualitative variables were presented as absolute frequencies and percentages. Differences in sociodemographic, biochemical, and lifestyle exposure parameters, alongside sleep quality issues, between children with obesity and those with normal weight were evaluated. For quantitative variables, the Wilcoxon-Mann-Whitney test was utilized, while the χ² test was applied for qualitative variables. For all statistical analyses, the significance level was set at α = 0.05. Furthermore, logistic regression models were used to calculate odds ratios (OR) and their corresponding 95% confidence intervals for obesity—as defined by the Faustino Orbegozo Eizaguirre Foundation criteria[23] — as a function of daily screen time. These models were adjusted for potential confounding factors, including the child’s age (months) and sex (categorical), family socioeconomic status (three categories), the Krece Plus sports activities score, and the KIDMED Mediterranean diet adherence score. Similarly, the OR of experiencing sleep disturbances as a function of estimated screen time hours was calculated. Multivariable logistic regression was performed using a backward stepwise selection method (inclusion criteria: P < 0.10 in bivariate analysis). Multicollinearity was ruled out using variance inflation factors < 2.5. Model fit was verified via the Hosmer-Lemeshow goodness-of-fit test. Additionally, all regression assumptions were formally verified prior to the analyses; the linearity of independent continuous variables with the logit of the dependent variable was confirmed via the Box-Tidwell test, and influential outliers were ruled out using Cook’s distance. Missing data (< 5%) were handled using complete-case analysis.

Finally, a linear regression analysis was performed to evaluate the association between sleep duration and the Krece Plus screen time score, after confirming the assumptions of normality, homoscedasticity, and linearity of residuals. For these latter two analyses, adjustments were made for the same confounding factors mentioned above. All statistical analyses were conducted within the R statistical computing environment (R Core Team, 2023).

Ethical considerations

The design and conduct of this study strictly adhered to the ethical principles for medical research involving human subjects outlined in the Declaration of Helsinki. The research protocol was reviewed and received formal approval from the Institutional Review Board/Research Ethics Committee (Code: 23/052 and Code: CD/74/2022).

Prior to the commencement of data collection, written informed consent was obtained from the parents or legal guardians of all participating minors. To ensure the integrity and privacy of the subjects, the collected information was completely anonymized using a coding system, in compliance with current personal data protection regulations, thereby guaranteeing that the results are presented solely in an aggregated format.

RESULTS

Between January 1 and December 31, 2022, a total of 70 patients (35 boys and 35 girls) with a mean age of 10.8 years (range: 5.4-15.8 years) were enrolled in the study. Demographic and anthropometric characteristics are detailed in Table 1. Statistically significant differences were confirmed between children classified as normal weight and those with obesity; body weight, BMI, and body fat percentage were significantly higher in the obesity group. Similarly, the perivisceral fat percentage was significantly greater in children with obesity. Furthermore, statistically significant differences were also observed in the family history of diabetes mellitus between normal-weight children (35%) compared to children with obesity (67%).

Table 1 Demographic and anthropometric characteristics of the cohort, n (%).

	Total cohort, n = 70	Normal weight, n = 26	Obesity, n = 44	P value
Age (years)	10.8 (3.3)	10.9 (3.7)	10.7 (3.0)	0.61
Sex				0.82
Female	35 (50)	14 (54)	21 (48)
Male	35 (50)	12 (46)	23 (52)
Weight (kg)	60.6 (25.1)	47.5 (23.8)	68.4 (22.7)	< 0.0011
Height (cm)	148.7 (19.6)	147.2 (24.3)	149.6 (16.5)	0.921
BMI (kg/m2)	26.0 (6.5)	20.1 (4.8)	29.5 (4.5)	< 0.0011
BMI (SDS)	2.2 (1.8)	0.2 (1.0)	3.3 (0.9)	< 0.0011
VFA	92.5 (58.0)	44.9 (40.1)	119.1 (48.9)	< 0.0011
Body fat percentage (%)	33.0 (12.1)	20.4 (9.7)	40.0 (6.0)	< 0.0011
Family history of DM				0.0272
No	27 (43)	13 (65)	14 (33)
Yes	36 (57)	7 (35)	29 (67)
Paternal educational level				0.0562
No formal education or incomplete primary school	2 (3.8)	0 (0)	2 (6.5)
Primary or secondary	33 (62)	11 (50)	22 (71)
University degree	18 (34)	11 (50)	7 (23)
Maternal educational level				0.0062
No formal education or incomplete primary school	2 (3.4)	0 (0)	2 (6.2)
Primary or secondary	38 (66)	13 (50)	25 (78)
University degree	18 (31)	13 (50)	5 (16)

¹P value determined by the Wilcoxon-Mann-Whitney test.

²P value determined by the χ² test.

VFA: Visceral fat area; DM: Diabetes mellitus; BMI: Body mass index; SDS: Standard Deviation Score.

A statistically significant difference was detected regarding the maternal educational level between patients with and without obesity, with obesity being more prevalent among children of mothers without higher education degrees. Specifically, 78% of mothers in the obesity group had primary or secondary education, or had not completed their studies, whereas only 16% held a university degree. Conversely, only 50% of mothers in the non-obese participant group had an educational background limited to primary or secondary school, while the remaining 50% had completed university education.

Metabolic differences between the two study groups were analyzed based on biochemical parameters. Insulin levels, HOMA-IR values, and systolic blood pressure were significantly higher in the obesity group, whereas HDL cholesterol levels were significantly lower in this cohort (Table 2). No statistically significant differences were observed for the remaining biochemical variables.

Table 2 Biochemical and metabolic profiles by study group, n (%).

	Total cohort, n = 70	Normal weight, n = 26	Obesity, n = 44	P value1
Mean SBP (mmHg)	118.4 (15.8)	111.8 (12.3)	122.1 (16.4)	0.009
Mean DBP (mmHg)	66.4 (7.7)	64.3 (6.0)	67.5 (8.3)	0.14
SBP percentile	74.7 (25.6)	64.7 (26.7)	80.2 (23.6)	0.008
DBP percentile	62.3 (20.9)	57.2 (20.0)	65.0 (21.1)	0.10
SBP (SDS)	1.2 (1.4)	0.6 (1.1)	1.5 (1.4)	0.006
DBP (SDS)	0.4 (0.7)	0.2 (0.6)	0.5 (0.7)	0.10
TSH (μU/mL)	2.4 (1.1)	2.5 (0.9)	2.3 (1.1)	0.2
Total cholesterol (mg/dL)	145.4 (34.2)	153.4 (33.3)	141.3 (34.4)	0.3
HDL-C (mg/dL)	46.7 (9.9)	53.0 (9.4)	43.8 (8.8)	< 0.001
LDL-C (mg/dL)	88.2 (25.8)	90.2 (24.8)	87.3 (26.4)	0.7
Triglycerides (mg/dL)	77.3 (43.2)	74.3 (34.5)	78.9 (47.3)	0.8
Fasting glucose (mg/dL)	88.0 (7.2)	86.8 (7.5)	88.6 (7.1)	0.3
Fasting insulin (μU/dL)	12.7 (9.0)	9.7 (7.3)	14.1 (9.4)	0.035
HOMA-IR	2.8 (2.0)	2.1 (1.7)	3.1 (2.1)	0.024
HbA1c (%)	5.3 (0.3)	5.3 (0.3)	5.3 (0.2)	0.9
GOT (U/L)	27.4 (12.0)	30.9 (14.9)	25.7 (10.1)	0.2
GPT (U/L)	22.2 (11.5)	18.6 (7.5)	24.0 (12.9)	0.069
GGT (U/L)	15.6 (11.1)	13.2 (2.7)	16.7 (13.2)	0.5
Uric acid (mg/dL)	5.3 (1.3)	4.8 (1.4)	5.5 (1.3)	0.13
Cortisol	10.5 (3.6)	12.2 (3.0)	10.2 (3.7)	0.2

¹P value determined by the Wilcoxon-Mann-Whitney test.

HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; HOMA-IR: Homeostatic Model Assessment of Insulin Resistance; HbA1c: Hemoglobin A1C; GOT: Glutamic-oxaloacetic transaminase; GPT: Glutamic-pyruvic transaminase; GGT: Gamma-glutamyl transferase.

Regarding the prevalence of sleep disturbances among the study participants, 14 of the 70 children reported altered sleep quality, representing a prevalence of 20% in our sample (Table 3). Chronically disturbed sleep was present in 39% of patients with obesity compared to 8.3% of normal-weight patients (P = 0.016). The mean sleep duration was 8.7 hours, with a significantly lower mean in children with obesity compared to normal-weight children (8.4 hours vs 9.1 hours, respectively; P = 0.021). The adjusted binomial logistic regression model suggests that screen time exposure is associated with an OR of experiencing sleep disturbances of 1.654 (95%CI: 0.963-2.999).

Table 3 Lifestyle exposure parameters (diet, physical activity, and electronic screen time) and sleep quality disturbances, n (%).

	Total cohort, n = 70	Normal weight, n = 26	Obesity, n = 44	P value
KIDMED score categories				0.0292
Very low quality	20 (28.6)	5 (19.2)	15 (34.1)
Needs improvement	40 (57.1)	20 (77)	20 (45.4)
Optimal	10 (14.3)	1 (3.8)	9 (20.5)
Physical activity level				0.0212
Low	36 (51.4)	8 (30.8)	28 (63.6)
Moderate	16 (22.9)	6 (23.1)	10 (22.7)
High	14 (20)	9 (34.6)	5 (11.4)
Vigorous	3 (4.3)	2 (7.7)	1 (2.3)
Very vigorous	1 (1.4)	1 (3.8)	0 (0)
K-P total score	4.9 (2.6)	6.0 (2.3)	4.0 (2.6)	0.0041
Screen time hours (K-P)	2.6 (1.5)	2.1 (1.4)	2.9 (1.5)	0.0471
Sports hours (K-P)	2.9 (3.4)	4.1 (4.1)	2.0 (2.3)	0.0111
Mean sleep duration (hours)	8.7 (1.3)	9.1 (1.5)	8.4 (1.0)	0.0211
Presence of chronic sleep disturbances (> 1 year)				0.0162
No	41 (75)	22 (92)	19 (61)
Yes	14 (25)	2 (8.3)	12 (39)

¹P value determined by the Wilcoxon-Mann-Whitney test.

²P value determined by the χ² test.

K-P: Krece-Plus.

Parameters reflecting poor dietary quality and low physical activity levels were also significantly worse in the excess weight group, as detailed in Table 3.

The results of the KIDMED test showed that a higher percentage of children with obesity had a very low-quality diet (34%) compared to normal-weight children (19%). Conversely, the proportion of children with an optimal diet was higher in the obesity group (20%) than in the normal-weight group (4%). The majority of normal-weight children followed a diet that “needs improvement” (77%), compared to 45% of children with obesity. These differences—specifically the higher adherence to an optimal diet among children with obesity—might reflect the implementation of dietary regimens recommended by pediatricians and nutritionists aimed at weight reduction. According to the Krece Plus test scores, physical activity hours were significantly lower in the group of children with obesity (P = 0.011). This aligns with the information reported in the questionnaires, where 63% of children with obesity reported low physical activity levels, compared to 31% of normal-weight children. Concurrently, the majority of normal-weight participants (58%) reported engaging in moderate or high physical activity, whereas only 34% of children with obesity reported moderate or high physical activity levels.

Screen time exposure, as measured by the Krece Plus questionnaire, was statistically higher in the group of children with obesity (P = 0.047), averaging nearly 3 hours of daily screen time compared to the 2 hours reported by normal-weight children. Furthermore, the binomial logistic regression model adjusted for confounding variables—including diet and physical activity—indicated that the OR of developing obesity was 3.086 (95%CI: 1.538-7.907) as a function of the Krece Plus screen time score. Similarly, the OR for obesity remained significantly elevated at 3.38 (95%CI: 1.31-8.76) when evaluating the score of screen time hours.

DISCUSSION

The WHO Global Nutrition Targets 2025 explicitly include halting the rise in childhood obesity[24]. However, this pandemic continues to rise, with no current signs suggesting that we are close to achieving control.

Exposure to certain environmental factors plays a crucial role in its progression; therefore, it is mandatory to develop control strategies targeting its triggers. Electronic screen use, for instance, has become routine during childhood and adolescence. On the other hand, sleep is highly important for children’s neurodevelopment[17]. Inadequate sleep, in terms of both duration and quality, can compromise children’s physical and mental health, as well as their social functioning. Indeed, sleep disturbances have been linked to depression and suicidal behaviors in adolescents[25-28], and to attention-deficit/hyperactivity disorder, among others[13].

Our study includes data from a non-negligible number of boys and girls. The balanced proportion of male and female participants in our study ensures that the results are representative of both sexes. The mean age of our sample was 10.8 years, thereby reflecting data from a pediatric population, rather than exclusively adolescents. This carries significant value, as the majority of studies exploring screen time exposure and sleep have focused predominantly on adult or adolescent populations[9,12,29].

Furthermore, our study compares data from normal-weight and excess-weight populations, making our findings regarding metabolic and sleep disturbances, and their relationship with BMI, more demonstrative.

Regarding socioeconomic variables, our analysis reveals that the mother’s educational level is linked to childhood obesity, showing that a lower maternal educational attainment is associated with a higher BMI. This underscores children’s vulnerability to developing obesity in relation to parental education levels. Concurrently, it indicates that public health campaigns aimed at reducing childhood obesity should reinforce their message particularly within families where mothers have not attained a university education.

The prevalence of sleep disturbances in our sample was 20%. This prevalence is high and concerning given the mean age of our participants. Likewise, the mean nocturnal sleep duration in our study was under 9 hours. Sleep disturbances were significantly more prevalent in the excess-weight group, which aligns with existing literature data[7,12,13,30,31].

Regarding screen time exposure, the mean duration across the entire sample exceeded the 2-hour limit recommended by current guidelines. The American Academy of Pediatrics[32] and the WHO[16] recommend restricting daily screen time to less than 1 hour for children under 5 years, and less than 2 hours for older children and adolescents. However, the vast majority of minors fail to meet these recommendations, a trend that is particularly pronounced among children with obesity[33,34]. These findings align with previous literature reporting a daily mean screen time ranging from approximately 2.3 hours in early childhood up to 3 hours in adolescents[17].

Our analysis suggests that screen time exposure could be a factor associated with the development of obesity. Our results are consistent with findings from both national[35] and international[10,36] literature. Concurrently, this opens a window of opportunity for intervention to reduce childhood obesity by providing recommendations to families aimed at limiting children’s screen time.

The adjusted binomial logistic regression model suggests that longer screen time exposure is associated with an increased risk of sleep disturbances. This result is consistent with previous studies conducted in Spain by members of our research group[35], as well as with a recent literature review indicating that higher screen use is associated with shorter sleep duration[37].

A remarkable finding in our study was that 20.5% of children with obesity presented an optimal diet according to the KIDMED index, a proportion significantly higher than the 3.8% observed in the normal-weight group. While this paradoxical result could suggest that patients with higher BMI are under stricter dietary control or prescribed nutritional regimens, it must be interpreted with caution due to inherent methodological limitations. First, social desirability bias frequently leads individuals with obesity—and their parents—to over report healthy eating behaviors and under report the consumption of ultra-processed foods. This reporting bias may be further amplified by increased parental awareness and education inherent to patients followed in a specialized pediatric endocrinology clinic. Second, the cross-sectional design of this study precludes establishing a temporal sequence, making it highly susceptible to reverse causality. In this context, although the optimal diet scores captured in the obesity group might partially reflect recent dietary interventions initiated after the development of excess weight, this interpretation remains speculative. Instead, these scores are more likely a reflection of the aforementioned reporting biases and clinical monitoring, rather than the historical dietary patterns that led to their current clinical status. This is further evidenced by our body composition data, where the obesity group exhibited a profoundly greater visceral fat area compared to their normal-weight peers (119.1 vs 44.9, respectively), confirming a state of chronic, long-term positive energy balance that contradicts recent adherence to an optimal diet.

Low physical activity was significantly more prevalent in the obesity group. Crucially, these factors tend to co-occur and are exacerbated by high screen time exposure. Consequently, there is an inverse association between sleep duration and subsequent screen time; sleep deprivation induces daytime fatigue, which in turn leads to increased sedentary screen-viewing behavior[38], thereby demonstrating a bidirectional relationship between these factors. Previous studies by our research group have observed that electronic screen time exposure is associated with an increased incidence of metabolic pathologies, such as obesity[33].

Screen time affects sleep through multiple mechanisms: Exposure to the light emitted by these devices suppresses melatonin production[39], delays bedtime[6], and exposure to engaging or stimulating content increases alertness and sympathetic tone. Furthermore, screen use could be associated with obesity, as it displaces or even replaces time dedicated to physical activity. Passive exposure to these devices and violent content also disrupt sleep regulation[13].

Furthermore, using screens while eating could lead children to consume larger food portions due to prolonged meal duration; additionally, being less attentive to what they are eating delays internal satiety signals[40]. On the other hand, evidence suggests that longer screen time is associated with a higher intake of unhealthy foods[10,35,41,42]. In addition, screen time exposure has been shown to alter cortisol secretion[43] and decrease insulin sensitivity[44,45]. The existing association between higher caloric intake, consumption of unhealthy foods, and an increased prevalence of obesity is well established[35]. On the other hand, both circumstances are associated with heavier digestion, which increases the likelihood of experiencing sleep disturbances, thereby compounding the risk of obesity associated with poor sleep quality.

The main limitation of our study is the relatively small sample size that could limit the statistical power of the analyses. This study should be considered exploratory, and the findings need to be validated in larger cohorts. Additionally, differentiated information regarding the specific type of screen to which the children were exposed was unavailable. Furthermore, the cross-sectional design precludes the inference of causality. Lifestyle, sleep, and screen habits were reported via questionnaires by parents, which could introduce potential recall and reporting biases. The lack of objective measurements, such as actigraphy for sleep duration or automated screen-time tracking apps, may affect the precision of these variables. Additionally, we were unable to adjust for potential confounding variables such as pubertal status, parental BMI, socioeconomic status, and psychological factors, which are known to influence both sleep patterns and weight status. Finally, because participants were recruited from a tertiary pediatric endocrinology clinic, our sample represents a specific clinical population with potentially higher motivation or severity, meaning the findings cannot be directly generalized to the healthy, general pediatric population.

Despite these limitations, we were able to detect statistically significant differences across the majority of the evaluated environmental factors. Therefore, our study provides valuable insights into the broad field concerning the relationship between electronic screen use and metabolism, specifically highlighting the impact of screen time exposure on childhood obesity and sleep disturbances—two closely intertwined entities. Furthermore, it accounts for exposure to all types of screens, rather than focusing exclusively on television, as most classic studies have done.

The influence of electronic device use on host body composition represents a current and innovative area of research that could offer crucial information for developing future therapeutic strategies to curb the rising prevalence of obesity. Additionally, our work is highly relevant as it provides data on this interaction within a pediatric population, which is characterized by its high vulnerability to external factors. Moreover, our findings serve as a benchmark for future studies with larger sample sizes.

To propose adequate sleep hygiene and healthy habits, it is essential to understand the current reality of our population. Consequently, studies such as ours are of great interest. For children, non-digital play experiences remain the optimal approach to promote executive functions and higher-order thinking skills, such as impulse control, emotional regulation, and task persistence. To foster these habits, parental role modeling is paramount. Therefore, enhancing health education for families is crucial, as a lack of awareness regarding rest requirements and their implications can lead to an absence of parental boundaries surrounding sleep habits.

CONCLUSION

Excessive screen time and poor sleep quality are strongly associated with a higher prevalence of visceral adiposity in children, while the high adherence to a Mediterranean diet observed in this cohort likely reflects a reactive response to the obesity diagnosis. Although the cross-sectional design of this study precludes establishing definitive causal relationships or temporal precedence, these findings indicate that pediatric interventions must extend beyond traditional dietary counseling to actively integrate digital device restriction and sleep hygiene optimization.