European Journal of Cardiovascular Medicine

Digital devices, including smartphones, tablets, and laptops, have become an integral part of everyday life for people worldwide. The use of these technologies in classrooms and at home has increased significantly, serving a wide range of purposes such as educational activities, gaming, video streaming, online communication, and web browsing. It has enabled the teachers and parents to pass more information in a shorter time than it was traditionally possible [1].

However, the widespread use of these devices has also raised concerns about their impact on children's health and overall well-being.

Digital device usage, commonly known as “screen time”, refers to activities done in front of a screen, such as watching TV, working on a computer, using smartphones, or playing video games [2].

Digital screen time can be classified into six types [3]

1. Educational screen time involves the use of digital devices for academic purposes.
2. Passive screen time refers to viewing content without active engagement.
3. Active screen time includes activities that promote learning, creativity, and problem-solving.
4. Interactive screen time requires user participation and feedback.
5. Social screen time involves digital communication and social interaction.
6. Creation screen time focuses on producing or expressing creative content using digital tools.

Excessive screen exposure, especially during adolescence has been associated with lower academic performance, increased sleep problems, obesity, behavioural problems, increased aggression, lower self-esteem and depression [4].

The American Academy of Pediatrics (AAP) recommends limiting screen time to no more than two hours per day for adolescents.

The Indian Psychiatric Society (2020–2021) issued guidelines on media use for children and adolescents up to 18 years of age, emphasizing the importance of active adult involvement during media exposure [5].

According to IAP guidelines, children under 2 years should have no screen exposure except occasional video calls. Screen time should be limited to ≤1 hour per day for 2–5-year-olds and <2 hours per day for children aged 5–10 years. In older children and adolescents, screen use should not replace essential activities such as physical activity, adequate sleep, schoolwork, meals, hobbies, or family time; displacement of these indicates excessive screen time [6].

This study aims to explore the association between screen time and behavioural health problems among adolescents in Hyderabad, India. Given the limited research available from this region, such studies are essential to highlight demographic trends, understand behavioural changes, and raise awareness on the issue.

AIM AND OBJECTIVES

AIM

To assess the association between digital device usage and behavioural health problems among government and private school-going children aged 11–16 years in Hyderabad, Telangana, India.

PRIMARY OBJECTIVE

To determine the association between excessive screen time using digital device and behavioural health problems using the Strengths and Difficulties Questionnaire (SDQ).

SECONDARY OBJECTIVE:

1. To estimate the screen time among children studying in sixth to tenth class in government and private school.
2. To compare screen time patterns between government and private school-going children.
3. To describe the demographic and socioeconomic profile of the participating adolescents.
4. To assess the association between screen time and academic performance in both school groups.
5. To explore parental practices related to screen use regulation at home.

The study was an analytical cross-sectional study conducted over one year (August 2023–September 2024) among 400 school-going children aged 11–16 years (200 each from a government and a private school) in Hyderabad, Telangana (India), selected using simple random sampling from grades six to ten.

The study received approval from the Institutional Ethics Committee.

The sample size was calculated using the following formula:

N = Z2 (1-α/2) (P) (1-P)

e 2

N= (1.96)2 (0.5) (0.5)

(0.05)2

N = 384.16 ≈ 400

N= Sample size

P= Expected Proportion

e= Marginal error

Z= Z value at 95% confidence level

INCLUSION CRITERIA

School-going children aged 11–16 years studying in Classes VI to X in selected government and private schools in Hyderabad, Telangana, India.

EXCLUSION CRITERIA

School-going children who answered less than 50% of questions and those with known ADHD, other behavioural disorders, or on medications affecting behaviour were excluded.

METHODOLOGY

Informed consent/assent was obtained from students, parents, and teachers after explaining the study objectives and questionnaire.

Students completed the sociodemographic proforma, screen-time questionnaire, and the English/Telugu version of the Strengths and Difficulties Questionnaire (SDQ) [7] independently in the classroom under research assistant supervision.

Teachers completed the SDQ on the same day, while parent SDQs were distributed in sealed envelopes and collected the following day.

The SDQ assesses five domains: emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behaviour.

The total difficulties score was calculated by summing the first four domain scores, excluding prosocial behaviour. A total SDQ score >15 was considered indicative of psychosocial problems.

The average screen time is calculated by the formula: average screen time = (WD ST x 5) + (WE ST x 2)]/7 [8].

WD = weekday; WE = weekend; ST = screen time.

SDQ SCORING

Note: This broad classification is based on information from the http://www.sdqinfo.org/ web site © R Goodman and is derived from British norms

 STATISTICAL ANALYSIS

The collected data were coded, entered into Microsoft Excel, and analyzed using IBM SPSS version 29. Qualitative variables were presented as percentages and tables, with comparisons made using the chi-square test. Quantitative variables were summarized as means, standard deviations. Associations were evaluated using independent sample t-tests, and correlations were assessed using Pearson’s correlation coefficient. A p-value of < 0.05 was considered statistically significant in the study.

The study included school-going children predominantly aged 11–16 years, with a mean age of 13.5 ± 3.8 years, and an equal gender distribution across government and private schools. Most participants belonged to nuclear families (90%). Fathers’ education was mainly at the primary (32.3%) and secondary (31.2%) levels, while 20.5% were degree holders; 11.8% were illiterate. Among mothers, secondary education was most common (57.8%), followed by primary education (20.3%), with 14% being illiterate. Most fathers were skilled workers, whereas the majority of mothers were unemployed, with others engaged in self-employment or professional work. (Table 1).

Table 1: Socio-demographic profile

Private school students had higher weekday and weekend screen time, with mean daily screen time exceeding recommended limits in both government (223.9 ± 155.6 minutes) and private (255.3 ± 108.4 minutes) schools. (Table 2)

Smartphone ownership was reported by 76% of government and 82.5% of private school students. (Figure 1)

Table 2: Screen time exposure

Table 3: Strength and Difficulties Questionnaire findings

The study shows that adherence to screen-time policies was low, with only 21 government and 30 private school students following such guidelines; screens were commonly used by parents for distraction or as rewards, despite general supervision. (Figure 2)

Table 4: Association between screen time and SDQ

Academic performance analysis showed that most students in both groups achieved grades within the A2, B1, and B2 categories. (Figure 3) However, no significant correlation was found between screen time and academic performance in either group. (Table 5).

Table 5: Correlation between screen time and academic score among government and private school children

A significant negative correlation was observed between screen time and prosocial behaviour among government school children (r = −0.224, p < 0.001). In contrast, private school children demonstrated an insignificant moderate negative correlation between screen time and prosocial behaviour (r = −0.072, p = 0.312). (Table 6).

Table 6: Correlation between screen time and prosocial scale

The current study explored the screen time patterns among government and private school students, their psychological well-being assessed via the Strengths and Difficulties Questionnaire (SDQ), and correlations with academic performance and prosocial behaviours. The findings reflect important trends and reinforce concerns about digital overexposure in adolescents.

DEMOGRAPHIC DISTRIBUTION

In our study, both government and private school groups showed comparable demographic profiles, with equal gender representation and a mean age of 13.5 years. These findings are consistent with the study by Singh et al. (2020, India) [9], which reported a mean age of 13.7 years and balanced gender distribution among adolescents in Delhi. Similarly, international evidence from Twenge and Campbell (2018, USA) [10] indicates that adolescents aged 12–16 years represent the group with the highest screen exposure, with minimal gender differences, reinforcing the representativeness of the current sample.

SOCIOECONOMIC AND EDUCATIONAL BACKGROUND

The current data showed that the majority of parents had secondary-level education, with more private school parents holding degrees. Parental education and socioeconomic status have been recognized as important determinants of children’s screen exposure. Hinkley et al. (2019, Australia) [11] observed that parental education was a significant determinant of screen exposure among children aged 5–17.

Studies from South-East Asia by Cao et al. [12] and Ravikaran et al. [13] similarly emphasized the role of socioeconomic factors in shaping access to digital devices and screen use behaviours.

In contrast, an Italian study [14] observed higher television viewing among children from lower socioeconomic backgrounds, suggesting that the type and mode of screen exposure may vary across cultural and economic contexts.

SCREEN TIME EXPOSURE AND PATTERNS:

Children from private schools demonstrated significantly higher screen time during weekdays and weekends, especially on mobile devices, computers, and social media platforms. Notably, mean screen time exceeded the recommended limit of 2 hours per day in both groups, as per the American Academy of Pediatrics guidelines [15].

Compared to the study by Golla Rajendra Prasad et al. [16], which found that only 13.5% of participants exceeded the recommended screen time of two hours per day, our findings indicate a significantly higher prevalence of excessive screen time—77.5% among government school students and 80.5% among private school students in Hyderabad, India. This stark contrast suggests a growing trend of screen overuse in our study population, possibly influenced by regional, socioeconomic, or educational differences.

Indian studies have reported variable prevalence rates, with 61.8% in rural North India [17] and 68% in urban adolescents before the COVID-19 pandemic [18], suggesting a temporal rise in screen exposure.

Consistent with Rosen et al. (2014) [19] and Twenge and Campbell (2018) [10], our findings demonstrate that high screen time correlates with easy access to personal digital devices and urban schooling environments. Notably, our study found that smartphone ownership was over 75% in both groups, which facilitates easy and unsupervised screen use.

Globally, Stiglic and Viner’s (2019, UK) meta-analysis [20] and Dana Markow et al. (2010, USA) [21] similarly documented widespread excessive screen exposure and its adverse health implications.

PSYCHOLOGICAL IMPACT

Higher screen time in this study was associated with increased SDQ total difficulties scores in both school groups, indicating potential emotional and behavioural concerns. This aligns with Przybylski and Weinstein (2017) [22], who described a curvilinear relationship between screen time and psychological well-being, with detrimental effects beyond moderate use.

A significant negative correlation between screen time and prosocial behaviour was observed among government school students (r = –0.224, p < 0.001), suggesting that excessive digital engagement may reduce opportunities for face-to-face social interaction. Similar findings have been reported by George et al. (2020) [24], Radesky et al. [25], and Boers et al. (2019, Canada) [26], all of whom linked high screen exposure with reduced social engagement and emotional well-being. Additionally, Pankaj Singh et al. [27] and Domoff et al. [28] reported associations between digital gaming and social media use with increased anxiety, behavioural problems, and reduced prosocial tendencies. Twenge et al. (2018) [10] further demonstrated links between high digital media use and depression and anxiety in adolescents.

ACADEMIC PERFORMANCE:

Despite concerns about digital distractions, this study found no significant correlation between screen time and academic performance in either group. This is consistent with findings from Lisa K Mundy et al. [29] and Przybylski & Weinstein (2017, UK) [22], both of which suggest that moderate screen use does not directly impact academic success.

This study concludes that excessive digital device use is significantly associated with behavioural health concerns among adolescents aged 11–16 years in both government and private schools in Hyderabad, highlighting its potential impact on adolescents’ emotional, social and behavioural well-being.

However, academic performance did not show a significant relationship with screen time, indicating that behavioural effects may manifest independently of scholastic outcomes.

Overall, the study emphasizes the need for targeted interventions, including the formulation of effective screen time policies, enhanced parental monitoring, and increased awareness regarding healthy digital habits. Addressing excessive screen use during adolescence is essential to promote balanced development and safeguard behavioural health in school-going children.

The study is strengthened by its adequate sample size and comprehensive multi-informant approach involving students, parents, and teachers. Use of the standardized SDQ tool enhances reliability, while a clearly defined age group and detailed demographic data allow precise interpretation. Inclusion of socioeconomic factors and practical screen-use behaviours adds real-world relevance and supports meaningful policy and intervention planning.

The study's limitations include reliance on self-reported data and lack of control for confounding variables.

RECOMMENDATIONS

1.      PROMOTE AWARENESS AMONG PARENTS AND TEACHERS: Conduct educational sessions to inform parents and educators about the potential behavioural impacts of excessive screen time and the importance of setting clear boundaries.

2.      ENCOURAGE ADHERENCE TO SCREEN-TIME GUIDELINES: Advocate for limiting screen time through school-based awareness campaigns and parental guidance materials.

3.      STRENGTHEN PARENTAL SUPERVISION AND INVOLVEMENT: Emphasize the role of active supervision and co-viewing during screen use, rather than using screens as a reward or distraction tool.

4.      INTEGRATE DIGITAL LITERACY AND RESPONSIBLE USE INTO SCHOOL CURRICULA: Teach children the value of balanced screen use and promote offline activities that foster social and emotional development.

5.      FOSTER ALTERNATIVES TO SCREEN-BASED RECREATION: Encourage physical, social, and creative activities in both school and home settings to reduce reliance on screens for entertainment.

6.      CONDUCT REGULAR BEHAVIOURAL SCREENINGS: Use tools like the SDQ in schools to monitor students’ emotional and behavioural health, allowing for early identification and intervention.

7.      ENGAGE STAKEHOLDERS IN POLICY-MAKING: Collaborate with educators, health professionals, and policymakers to develop local guidelines for screen use that reflect current behavioural research.

8.      TAILOR INTERVENTIONS BY SCHOOL TYPE: Since screen time and behavioural patterns varied between government and private school students, interventions should be adapted to the specific needs and context of each group.

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