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Gadgets and their impact on child development  †.

1. Introduction

2. past studies on related subjects, 2.1. issue of gadget use among children, 2.2. technology and the use of gadgets among children, 2.3. the future of gadgets for children, 2.4. the impacts of excessive usage of gadgets among children, 2.4.1. effects on socialization, 2.4.2. effects on health, 2.4.3. effects on speech development, 2.4.4. effects on cognitive skills, 3. methodology, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Zain, Z.M.; Jasmani, F.N.N.; Haris, N.H.; Nurudin, S.M. Gadgets and Their Impact on Child Development. Proceedings 2022 , 82 , 6. https://doi.org/10.3390/proceedings2022082006

Zain ZM, Jasmani FNN, Haris NH, Nurudin SM. Gadgets and Their Impact on Child Development. Proceedings . 2022; 82(1):6. https://doi.org/10.3390/proceedings2022082006

Zain, Zarina Mohd, Fatin Nur Najidah Jasmani, Nurul Hadirah Haris, and Suzei Mat Nurudin. 2022. "Gadgets and Their Impact on Child Development" Proceedings 82, no. 1: 6. https://doi.org/10.3390/proceedings2022082006

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Effects of Electronic Gadgets in the Academic Perfomance of Senior High School Students

23 Pages Posted: 2 Apr 2021

Lowie Borlado Balbaguio

DepEd-Ardemil National High School, Ardemil, Sara, Iloilo, Philippines

Kenneth Ron Articulo

Ardemil national high school, department of education, mary may bantillo, noli john magabolo, ella borres, marlon capuslanan, renaleen jabagat, jerson m. panes, mary rose panes, vincent ian b. muyco.

Date Written: November 24, 2020

Technology has played a vital role in educational innovations, providing both teachers and learners options and flexibility in their teaching and learning practice. This descriptive research was designed to find out the effect of electronic gadgets on the academic performance of senior high school (SHS) learners, study habit and level of proficiency in the use of electronic gadgets. This was conducted at four small implementers of SHS in the municipality of Sara, Iloilo. Eighty (80) randomly selected SHS responded on questionnaire-checklist. Mean, Pearson-r and t-test were used to analyze data. Results of the study showed that the effect of electronic gadgets on academic performance of SHS learners was very effective, level of proficiency on use of electronic gadgets was highly proficient, study habit of SHS learners was very good. There were no significant differences in effect of electronic gadgets on the academic performance, level of proficiency on the use of electronic gadgets and study habit of SHS. There was no significant relationships between the effect of electronic gadgets, level of proficiency in the use of electronic gadgets and study habit on the academic performance. Learners are aware on effects, yet responsive and educated about ill effects of gadgets. Educational use of gadgets develops a foundation of good/strong/progressive and effective citizens in the future. Electronic gadgets are beneficial in the schools. Therefore, the principals/school heads should allow learners and teachers use gadgets in class. However, constant reminder should be instigated in the various negative effects of these gadgets.

Keywords: Academic Performance, Educational Innovations, Electronic Gadgets, Senior High School Learners, Study Habit, Teaching and Learning Practice

JEL Classification: I21, I26, I30

Suggested Citation: Suggested Citation

Lowie Borlado Balbaguio (Contact Author)

Deped-ardemil national high school, ardemil, sara, iloilo, philippines ( email ).

Brgy. Ardemil Sara, Iloilo, Philippines Iloilo City, Iloilo 5014 Philippines

Philippines

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Demographic, gadget and internet profiles as determinants of disease and consequence related COVID-19 anxiety among Filipino college students

• Published: 04 April 2021
• Volume 26 , pages 6771–6786, ( 2021 )

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• Jerome V. Cleofas   ORCID: orcid.org/0000-0001-9203-0212 1 &
• Ian Christopher N. Rocha   ORCID: orcid.org/0000-0002-8775-6876 2  

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In the context of the nationwide shift to online learning due to the COVID-19 pandemic and its possible effect on mental health, this study investigated the relationship between demographic, gadget and Internet profiles, and disease and consequence related COVID-19 anxiety among Filipino college students. This is a quantitative cross-sectional study. A total of 952 students participated in the online survey. Descriptive and inferential statistics were used to draw insights from the data. Findings suggest that majority of the student respondents had high levels of disease and consequence related COVID-19 anxiety. Students from poorer households, who do not own laptops and desktop computers, and those with limited Internet connection exhibited higher levels of disease-related COVID-19 anxiety. Younger, poorer, female students who were enrolled in lower year levels, do not own laptops or tablets, and have limited or borrowed Internet connectivity demonstrated higher levels of consequence-related COVID-19 anxiety.

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1 Introduction

Education is one of the sectors most badly hit by the coronavirus disease 2019 (COVID-19) pandemic. As of this writing, there are more than 119 million cases of COVID-19 all over the world (World Health Organization, 2021 ). Because of the need for social distancing to combat the spread of the virus, campuses in many countries have been closed. Many schools and universities have shifted to online learning mode. In the Philippines, this abrupt change in the educational landscape in the midst of the pandemic has been challenging. In October 2020, the country ranked 20th in the world in terms of the number of coronavirus cases and deaths (ABS-CBN News, 2020 ). This public emergency has placed a strain on students, teachers, administrators and other stakeholders (Tria, 2020 ).

An important student outcome monitored by COVID-19 studies is mental health. Many reports suggest the increase of psychological distress states among students such as anxiety, depression and suicidal ideation among many others during this outbreak (Cao et al., 2020 ; Islam et al., 2020 ; Lasheras et al., 2020 ; Olaimat et al., 2020 ; Rogowska et al., 2020 ; Tasnim et al., 2020 ). Consequently, the students experiencing stress have been found to have lower self-directed learning readiness during online classes (Heo & Han, 2017 ). Aside from dealing with the challenging nature of the new face of academics, Filipino college students have to cope with the coronavirus public health emergency and its consequences in their social lives, while being in a country placed in the longest quarantine in the world (ABS-CBN News, 2020 ). This study specifically focuses on COVID-19 anxiety, which can be related to the fear of contracting the disease (disease-related COVID-19 anxiety) and fear of the negative effects of the disease in social life (consequence-related COVID-19 anxiety) as suggested by McElroy et al. ( 2020 ).

Disparities in mental health outcomes in a society are influenced by social inequality. Multiple studies link social background and anxiety among students (Lederer et al., 2020 ; Myhr et al., 2020 ; Shadmi et al., 2020 ). More specifically, COVID-19 related anxiety has been associated with demographic variables such as age (Moghanibashi-Mansourieh, 2020 ; Shevlin et al., 2020 ; Solomou & Constantinidou, 2020 ; Tee et al., 2020 ), gender (Hou et al., 2020 ; McElroy et al., 2020 ; Tee et al., 2020 ), and economic status (Ettman et al., 2020 ; Poudel & Subedi, 2020 ; Wang & Tang, 2020 ).

Another form of social disadvantage that can increase anxiety is lack of access to digital resources such as computing gadgets and Internet connectivity (Poudel & Subedi, 2020 ). Especially among students, the availability of these digital resources is vital for an effective participation in online college education and coping with many other challenges in the new normal (Kapasia et al., 2020 ). Increased gadget use during the pandemic has been suggested to improve social and cognitive health among students (Beng et al., 2020 ), while the lack of devices and connectivity for e-learning has been linked to increased stress among learners (Baticulon et al., 2021 ). Educational policies that design programs and provide human and financial resources to improve the information communication technology (ICT) integration in post-primary education have been emphasized (Alghamdi & Holland, 2020 ), and are now indispensable necessities in this era of lockdown and remote learning.

Clearly, COVID-19 magnifies the already inherent social and digital inequalities in society. In a developing country with one of the worst Internet connections in the South East Asian (ASEAN) region (Barreiro, 2017 ), the educational and mental health gaps between privileged and disadvantaged Filipino students are only expected to widen during this period of quarantine.

In the context of the educational shift due to the ongoing pandemic, and cognizant of the influences of social background and digital resources to student mental health, this study sought to determine the relationship between demographic, gadget and Internet profiles to disease and consequence related COVID-19 anxiety among Filipino college students.

2.1 Research goal and design

The present study measures COVID-19 anxiety among Filipino college students in terms of fear of contracting the disease (disease-related COVID-19 anxiety) and fear of the negative impacts of the disease to social life (consequence-related COVID-19 anxiety), and test their relationships with demographic, gadget and Internet profiles. This study uses a quantitative, cross-sectional, correlational design.

2.2 Instrumentation

There are three independent variables in the study: demographic characteristics, gadget ownership, and Internet connectivity. Demographic variables that were considered are as follows: (1) age, which was measured in years; (2) gender, which was measured as male [1] or female [0]; (3) year level, which included first to fifth year; (4) monthly family income, which was measured based on the National Economic Development Association brackets; and (5) type of institution, which was measured as private [1] or public [0]. In the Philippines, there are certain undergraduate degrees that have a five-year curriculum, such as in the fields of physical therapy and speech language pathology (Commission on Higher Education, 2015 ).

For gadget ownership, the participants were asked whether they owned one or more of the following electronic gadgets for regular and academic use: (1) smartphone; (2) laptop; (3) tablet; and (4) desktop computer. Also, the number of types of gadgets owned were measured in the study.

For Internet connectivity, two sub variables were included. First is the type of Internet connection they had at home. The answers were categorized into three: broadband or digital subscriber line (DSL), cellular service or mobile data, and connectivity that was borrowed from another household, or rented through a computer shop. The second sub variable is the duration of their connectivity on a daily basis, which was categorized as limited access (1–2 h only), moderate access (3–4 h only) and unlimited access. Please refer to Appendix Fig. 1 for the sample survey for demographic, gadget and Internet profiles.

The dependent variable is COVID-19 anxiety. This was measured using the Pandemic Anxiety Scale (PAS) developed by McElroy et al. ( 2020 ). The PAS is a 5-point likert scale that measures two dimensions of COVID-19 anxiety. First is disease anxiety composed of four items (e.g. “I am worried that I will catch COVID-19”), with scores ranging from 4 to 20. and Second is consequence anxiety with three items (e.g. “I’m worried about missing school/work”), with scores ranging from 3 to 15. The scale has a satisfactory reliability score (Cronbach’s alpha = 0.74). We determined the individual levels of the summated scores as low, moderate and high through establishing cut-offs (Harpe, 2015 ) by dividing the range into three equal intervals and then rounding off. Information on the PAS can be accessed through McElroy et al. ( 2020 ).

2.3 Sample and data collection

The target population for this study are Filipino undergraduate students studying in higher education institutions in the Philippines, between the age 18 to 22, years old. This age range comprises the majority of undergraduate students in the country. We employed convenience sampling. The recruitment was done via our social media accounts through posting of the survey link. We asked our personal networks to also share it on their respective timelines and schools. Google forms was the platform used for to create the survey and collect the data. Postgraduate students were not included in the study. Ultimately, we were able to gather a total of 952 qualified student respondents for this study.

Our study complied with the ethical standards enshrined in the Helsinki Declaration. Full study details and informed consent were presented and secured respectively in the first page of the form. Privacy and confidentiality of the answers were maintained.

2.4 Analysis of data

To determine the profile of the respondents and the prevalence of the levels of COVID-19 anxiety, frequency, percentage, mean and standard deviation were used. To test the significant associations between the independent and dependent variables, Pearson R correlation and one-way ANOVA were used. Jamovi version 1.2 software for Mac was used for analysis.

3.1 Demographic profile of the respondents

Table  1 shows that the majority of the participants are within the 18 to 19-year-old age bracket (f = 522; 54.8%), female (f = 746; 78.4%), first year students (f = 491; 51.6%), have an estimated monthly income of less than PHP 10,000 (USD 208) (f = 339; 35.6%), and are enrolled in a private university or college (f = 527; 55.4%).

3.2 Gadget and internet profiles of the respondents

Table 2 presents the gadget and Internet profiles of the respondents. In terms of gadgets, the majority of the respondents own a smartphone (f = 894; 93.9%) and own only one type of gadget (f = 476; 50.0%). In terms of Internet connectivity, the majority of the participants have their own subscription to a cellular or mobile service (f = 498; 52.3%) and have unlimited access (f = 447; 47.0%).

3.3 COVID-19 anxiety levels

Table 3 presents the distribution of the respondents based on COVID-19 anxiety levels. Findings suggest that the majority of the respondents have high levels of disease-related (f = 814; 85.5%) and consequence-related (f = 737; 77.4%) COVID-19 anxiety. The overall means and standard deviations for disease and consequence related anxiety are 17.28 ± 3.25 and 13.57 ± 2.04 respectively, both interpreted as high level.

3.4 Relationship between demographic profile and COVID-19 anxiety

Findings shown in Table 4 suggest that disease-related COVID-19 anxiety is significantly negatively correlated with monthly family income ( p  < 0.05). Students from families in lower income brackets are more anxious about the disease compared to their richer counterparts.

Furthermore, consequence-related COVID-19 anxiety was found to be significantly negatively correlated with age, gender, year level and family income ( p  < 0.05). Younger, female students from lower year levels and poorer families are more anxious about the consequences of the pandemic. Type of institution is not significantly related to the two domains.

3.5 Relationship between gadget profile and COVID-19 anxiety

Results shown in Table  5 suggest that disease-related COVID-19 anxiety is significantly negatively correlated with laptop and desktop ownership and the number of types of gadgets owned ( p  < 0.05). Higher disease anxiety scores are observed among those who do not own laptops or desktop computers, and have lesser types of gadgets owned.

On the other hand, consequence related COVID-19 anxiety was found to be significantly, negatively correlated with laptop and tablet ownership, and the number of gadget types owned (p < 0.05). Those who do not own laptops and tablets, and have a lesser variety of gadgets exhibit higher levels of anxiety related to COVID-19 consequences. Smartphone ownership was not significantly related to the two domains of COVID-19 anxiety.

3.6 Relationship between internet profile and COVID-19 anxiety

As seen in Table  6 , ANOVA test results suggest significant differences in COVID-19 anxiety based on Internet profile (p < 0.05). Tukey’s post hoc test was used to identify the specific groups with marked differences.

Broadband/DSL subscribers were seen to have significantly lower disease-related COVID-19 anxiety compared to those who only had cellular data subscriptions, and lower consequence-related COVID-19 anxiety compared to those subscribed to cellular service and those who rented/borrowed their Internet connectivity.

Respondents who had limited daily access to the Internet were seen to have significantly higher disease-related COVID-19 anxiety compared to those with unlimited access; and higher consequence-related COVID-19 anxiety compared to those with medium and unlimited access.

4 Discussion

The aim of this present study is to test the relationship between demographic, gadget and Internet profiles, and disease and consequence related COVID-19 anxiety among Filipino college students. As of this writing, this is the first large scale study done in the young adult, undergraduate student population in the country that looked into these determinants of COVID-19 anxiety. Our research suggests that high levels COVID-19 anxiety are prevalent among Filipino college students. Similarly, high rates of generalized and COVID-19-specific anxiety had been noted by previous studies in student populations in the Philippines (Baloran, 2020 ) and elsewhere (Cao et al., 2020 ; Islam et al., 2020 ; Lasheras et al., 2020 ; Olaimat et al., 2020 ; Rogowska et al., 2020 ) during the period of pandemic. The disruptive effects of the pandemic on the education, social life and future plans of these college students have truly negatively impacted their psycho-emotional status as emerging adults (Cleofas, 2020 ).

4.1 Demographic, gadget and internet profiles, and disease-related COVID-19 anxiety

Our present research demonstrates that students from poorer households have significantly higher disease-related COVID-19 anxiety. This correlation reflects the worry of those from the lower income bracket about having higher risks of getting infected. As the essential workers during quarantine are usually the poor, they are more exposed to the virus and may transmit it to other members of the household. Students with parents who are working outside the home during the pandemic, compared to those who are in a work-from-home scheme, have perceived increased susceptibility to the disease (Cleofas, 2020 ). Furthermore, the poor are less likely to afford testing and hospitalization related to COVID-19 (Shadmi et al., 2020 ).

In terms of gadget profile, findings suggest that students who do not own laptops and desktop computers, and have a lesser variety of gadgets owned experience significantly higher disease-related COVID-19 anxiety. This association is expected since in the time of quarantine, the main source of information about the disease can be accessed via electronic gadgets (Ansari & Anjali, 2020 ). Moreover, a study has suggested that compared to mobile devices, laptop and desktop searches provide better information regarding health (Boyd & Wilson, 2018 ).

As for Internet profile, our present study shows that type of Internet connection and daily duration of Internet access are significant determinants of disease-related COVID-19 anxiety. Students whose main Internet connection is cellular service have higher levels of disease-related anxiety compared to those subscribed to broadband/DSL. Also, those with limited duration of Internet access have significantly higher levels of anxiety related to COVID-19 disease compared to those with unlimited access. Because of some limitations of cellular data plans to allow users access to certain social networking sites, the students may have more exposure to misinformation and non-contextualized news headlines that are found about COVID-19 in free sites, and may not have the means to verify. This exposure to false information may be the cause for increased anxiety towards COVID-19 as suggested by previous studies (Lee et al., 2020 ; Shabahang et al., 2020 ).

4.2 Demographic, gadget and internet profiles, and consequence-related COVID-19 anxiety

Our present study suggests that younger students and those enrolled in lower year levels exhibit higher levels of consequence-related COVID-19 anxiety compared to older counterparts. This finding is congruent with the study of Wang et al. ( 2020 ) that suggested that freshmen and sophomores scored higher in terms of anxiety, which reflects the uncertainties younger students have in terms of coping with the online pedagogy, while also transitioning into college life, both of which are affected because of the pandemic (Tria, 2020 ).

As regards to gender, females were found to have higher scores of consequence-related COVID-19 anxiety compared to their male counterparts. This finding is reflective of how quarantine and online schooling due to the pandemic can increase the home making and caretaking responsibilities of women in the household (Wenham et al., 2020 ), which causes the strain and anxiety among females as seen in other COVID-19 studies as well (McElroy et al., 2020 ; Wang et al., 2020 ; Tee et al., 2020 ).

Moreover, our research suggests that students from poorer households demonstrate higher levels of consequence-related COVID-19 anxiety, which confirms that people of lower socioeconomic status experience the social and economic impacts of the pandemic disproportionately (Shadmi et al., 2020 ). Anxieties among poor students have been related to the economic consequences of the pandemic such as financial distress because of parents losing employment, lack of basic needs and the need of the student to work to gain more income (Baticulon et al., 2021 ).

In terms of gadget profile, our findings show that those who do not own laptops and tablets, and those with a lesser variety of gadgets owned are significantly more anxious about the consequences of COVID-19. Owning computing devices with required specifications are important to successfully engage in distance learning as a consequence of the pandemic, and not owning gadgets that can properly run learning management systems is considered a barrier for online education (Baticulon et al., 2021 ; Cedeño et al., 2021 ), which can be a factor contributing to students’ anxiety (Baloran, 2020 ; Pastor, 2020 ).

As for Internet profile, results suggest that students whose main Internet connection is cellular service, and those who borrow Internet from other households, have higher levels of consequence-related COVID-19 anxiety compared to those subscribed to broadband/DSL. Moreover, students who have less than five hours of daily Internet access are more anxious in terms of the consequences of COVID-19. Since online learning is highly dependent on Internet connection, which the Philippines still lacks in terms of national coverage (Barreiro, 2017 ), students with limited or unreliable connectivity will not be able to fully engage in class, which can be the source of anxiety for them (Baticulon et al., 2021 ; Pastor, 2020 ).

The findings on the negative relationships of family income, gadget ownership and quality of Internet connectivity with COVID-19 anxiety run parallel with each other. In order to effectively engage in online classes, obtain information on COVID-19 and access essential digital services, students must own computing devices connected to reliable Internet connection (Baloran, 2020 ; Pastor, 2020 ). These are digital resources needed in order to participate in education and other facets of life in the new normal that require finances, which students from poorer households may lack (Baticulon et al., 2021 ). These explain the high level of anxiety observed in both economically and digitally challenged sectors of the undergraduate student population.

5 Conclusions and recommendations

Based on the findings, we conclude that demographic characteristics, gadget ownership and Internet access of college students are significant determinants of COVID-19 anxiety. This study provides empirical evidence to support that socioeconomic gaps and the digital divide run parallel to each other and may lead to poor educational and mental health outcomes among students, especially in a time of pandemic.

Educational and psychological support for students during this online-mode period of education must focus on those who may experience higher levels of anxiety during the pandemic: the young, the females, the freshmen and the poor. School administrators must design less stressful digitized curricula and school experiences, conduct COVID-19 awareness campaigns, and implement counseling and mental health programs to care for students at risk for anxiety because of the pandemic.

Since gadget ownership is a protective factor for COVID-19 anxiety, government and civil society organizations may create programs to pool resources to provide computing devices to students from lower income brackets in order for them to successfully engage in online schooling and access to other digital services. Likewise, connection to a reliable Internet service is also a protective factor for COVID-19 anxiety. Local government units may provide financial support to students from poor households to be able to subscribe to stronger Internet connections. Internet service providers are enjoined to expand their coverage and introduce cheaper plans for students to help decrease the students’ worries about being able to participate meaningfully in school.

The Philippines can learn from the strategies employed by the education sector of its ASEAN neighbors. For instance, students and teachers were provided financial support to obtain computing devices and dongles for internet connectivity in Singapore (Yip et al., 2021 ), and use of broadcast media and low-bandwidth communication apps were promoted in Thailand and Vietnam (Chang & Yano, 2020 ). Through their Ministries of Education, the governments of Brunei Darussalam and Indonesia have forged partnerships with telecommunication companies to provide consistent Internet connectivity, free access to online learning management systems and platforms, and subsidized rates for students and teachers (Gupta & Khairina, 2020 ; Shahrill et al., 2021 ). In Malaysia, the government provided RM270 (65 USD) to students studying in higher education institutions, and certain universities have placed support systems in order to provide technical assistance to students who have difficulty accessing reliable Internet services (Sia & Adamu, 2020 ). In Lao PDR, the Ministry of Education and Sports have worked with civil society organizations in order to create applications for learning, equip schools with low-cost ICT equipment and provide trainings to teachers (Redmond, 2021 ).

6 Limitations

Despite a large sample size, it must be noted that the respondents were selected via convenience sampling and were limited to our social networks. Moreover, the current sample is predominantly female. Also, since the survey was administered online, the sample may not fairly represent students who completely do not have access to the Internet at all. These factors may affect the generalizability of the results. Also, due to the provisions of the Data Privacy Act of the Philippines (Official Gazette of the Philippines, 2012 ), we were not able to collect data on the institutions and locations of the students that voluntarily participated in the study, as these are considered personal and private information. Thus, we were not able to provide insight on the number of schools and regions that were included in this study. Future researchers may consider doing a similar study that will recruit a sample that aptly represents students in terms of gender, and also include those with entirely no Internet connectivity.

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Cleofas, J.V., Rocha, I.C.N. Demographic, gadget and internet profiles as determinants of disease and consequence related COVID-19 anxiety among Filipino college students. Educ Inf Technol 26 , 6771–6786 (2021). https://doi.org/10.1007/s10639-021-10529-9

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Prevalence and impact of the use of electronic gadgets on the health of children in secondary schools in Bangladesh: A cross-sectional study

Affiliations.

• 1 Department of Genetic Engineering & Biotechnology University of Dhaka Dhaka Bangladesh.
• 2 Disease Biology & Molecular Epidemiology Research Group Chattogram Bangladesh.
• 3 Institute of Statistical Research and Training University of Dhaka Dhaka Bangladesh.
• 4 Department of Endocrinology Chittagong Medical College Chattogram Bangladesh.
• 5 Department of Geography and Environmental Studies, Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh.
• 6 Department of Genetic Engineering & Biotechnology, Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh.
• PMID: 34622022
• PMCID: PMC8485597
• DOI: 10.1002/hsr2.388

Background and aims: Use of technological gadgets has rapidly been increasing among adolescents, which may result in health issues and technology addiction. This study focuses on the prevalence of usage of technological gadgets and health-related complications among secondary school-going children of Bangladesh.

Methods: A total of 1803 secondary school students from 21 different districts of Bangladesh participated in the study. The children were asked questions relating to their access to electronic gadgets, time spent on outdoor activities, and whether they experienced any health-complications as an after-effect of the usage. A binary logistic regression model was adapted considering time spent on gadgets as an independent variable and health problems (physical and mental) as the dependent variable.

Results: Among all the gadgets, 67.11% of the participants were reported to use mobile phones on a daily basis. Due to the ongoing COVID-19 pandemic, 24.48% of respondents used electronic gadgets for attending online classes. The participants were reported to use gadgets significantly more ( P < .05) in 2020 as compared to 2019. Children showed less tendency to spend time in outdoor activities. More than 50% of the participants spend time doing outdoor activities for less than 1 hour daily. An association between gadget use and health problems like headache, backache, visual disturbance, and sleeping disturbance has been observed in our study.

Conclusion: This study demonstrates that different socio-demographic factors have influence on the use of gadgets by children, and this use has greatly been affecting both the physical and mental health of the secondary school-going students of Bangladesh.

Keywords: Bangladesh; gadgets; health complications; secondary school students.

© 2021 The Authors. Health Science Reports published by Wiley Periodicals LLC.

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Conflict of interest statement

The authors declare there is no conflict of interest.

Types and purposes of use…

Types and purposes of use of gadgets by students; (A) Percentage of participants…

Pattern and total time spending…

Pattern and total time spending on gadgets and outdoor activities. (A) A comparison…

Types of physical problems for…

Types of physical problems for excessive use of gadgets

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Table of Contents

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Main findings

Many devices have become popular across generations, with a majority now owning cell phones, laptops and desktop computers. Younger adults are leading the way in increased mobility, preferring laptops to desktops and using their cell phones for a variety of functions, including internet, email, music, games, and video.

Among the findings:

• Cell phones are by far the most popular device among American adults, especially for adults under the age of 65. Some 85% of adults own cell phones overall. Taking pictures (done by 76% of cell owners) and text messaging (done by 72% of cell owners) are the two non-voice functions that are widely popular among all cell phone users.
• Desktop computers are most popular with adults ages 35-65, with 69% of Gen X, 65% of Younger Boomers and 64% of Older Boomers owning these devices.
• Millennials are the only generation that is more likely to own a laptop computer or netbook than a desktop:  70% own a laptop, compared with 57% who own a desktop.
• While almost half of all adults own an mp3 player like an iPod, this device is by far the most popular with Millennials, the youngest generation—74% of adults ages 18-34 own an mp3 player, compared with 56% of the next oldest generation, Gen X (ages 35-46).
• Game consoles are significantly more popular with adults ages 18-46, with 63% owning these devices.
• 5% of all adults own an e-book reader ; they are least popular with adults age 75 and older, with 2% owning this device.
• Tablet computers , such as the iPad, are most popular with American adults age 65 and younger. 4% of all adults own this device.

Additionally, about one in 11 (9%) adults do not own any of the devices we asked about, including 43% of adults age 75 and older.

In terms of generations, Millennials are by far the most likely group not only to own most of the devices we asked about, but also to take advantage of a wider range of functions. For instance, while cell phones have become ubiquitous in American households, most cell phone owners only use two of the main non-voice functions on their phones: taking pictures and text messaging.  Among Millennials, meanwhile, a majority use their phones also for going online, sending email, playing games, listening to music, and recording videos.

However, Gen X is also very similar to Millennials in ownership of certain devices, such as game consoles. Members of Gen X are also more likely than Millennials to own a desktop computer.

e-Book readers and tablet computers so far have not seen significant differences in ownership between generations, although members of the oldest generation (adults age 75 and older) are less likely than younger generations to own these devices.

These findings are based on a survey of 3,001 American adults (ages 18 and older) conducted between August 9 and September 13, 2010. The margin of error is +/- 3 percentage points. Interviews were conducted in English and Spanish, and the survey included 1,000 cell phone interviews. (More information is availabe in the Methodology section .)

In this chart, the dips in tech ownership registered in the September 2010 survey are mostly a result of the fact that Spanish interviews were added to the survey. Most of the Pew Internet surveys before 2010 were only conducted in English. The Project has added Spanish to this survey and that knocked down the overall tech-ownership numbers in some instances because respondents who wanted to be interviewed in Spanish were somewhat less likely than others to be tech non-users.

Background: Generations defined

This is part of a series of reports by the Pew Research Center’s Internet & American Life Project exploring how different generations use technology (previous reports: 2010 , 2009 , 2006 ). All the generation labels used in these reports, with the exceptions of “Younger Boomers” and “Older Boomers,” are the names conventionalized by William Strauss and Neil Howe in their book, Generations: The History of America’s Future, 1584 to 2069 (Perennial, 1992). The Pew Internet Project’s “Generations” reports make the distinction between Younger Boomers and Older Boomers because enough research has been done to suggest that the two decades of Baby Boomers are different enough to merit being divided into distinct generational groups.

The Pew Research Center recently published a series of reports that more closely examined the values, attitudes and experiences of the Millennial generation . 1  These reports are available in full at pewresearch.org/millennials . Many of these reports also compare this younger generation to older cohorts.

The primary adult data in this report come from a Pew Internet Project survey conducted from August 9-September 13, 2010, with some data from a survey conducted April 29 to May 30, 2010. For more information about these surveys, please see the  Methodology section at the end of this report.

Cell phones

Eighty-five percent of Americans age 18 and older own a cell phone, making it by far the most popular device among adults. Mobile phones are especially popular with adults under the age of 66, although the largest drop-off is for adults in the oldest generation (those age 75 and older), of whom 48% own a cell phone.

When asked further about the presence of mobile phones in their households, one-third (33%) of those who do not own a cell phone live in a household with at least one working mobile phone. This means that overall, 90% of all adults—including 62% of those age 75 and older—live in a household with at least one working cell phone.

As the proportion of households with at least one working cell phone rises, many are doing without a landline phone connection at all. In the first half of 2010, roughly one in four (25%) American adults lived in households that were “wireless only” in that they had at least one cell phone, but no landline. This includes more than half (51%) of young adults ages 25-29. 2

Though cell phones are now ubiquitous in American homes, the level of engagement with the phones does vary widely between generations. As shown in the above table, our May 2010 survey found that while roughly the same proportion of adults in the Millennial generation and Generation X own cell phones, Millennials are significantly more likely to use their phones for a variety of purposes. A majority of Millennials use their phones for taking photos, texting, going online, sending email, playing games, listening to music, and recording videos—making them significantly more likely than any other generation to engage in all of these activities.

In fact, the only two activities that are widely popular for all cell phone owners are taking pictures and sending text messages. Taking pictures is the most popular function on Americans’ phones, with more than half of all cell phone owners under the age of 75 using their phones for this purpose (only 16% of adults age 75 and older take photos with their phones). Text messaging, though also widely adopted, is less popular with adults over age 56.

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Desktop and laptop computers

As noted in previous reports , desktop computer ownership has fallen slightly since 2006, as laptops have gained in popularity. 3 Currently 59% of all adults own a desktop computer, and 52% own a laptop (76% own a computer overall).

Millennials are the only generation that is more likely to own a laptop or netbook (70%) than a desktop computer (57%). While 69% of adults in Generation X own a desktop, a close 61% own a laptop. While roughly six in ten adults ages 47-65 own a desktop, only 49% of Younger Boomers and 43% of Older Boomers own a laptop.

Only  45% of adults over age 65 have a computer of any kind (40% of adults in that age group use the internet), and they are increasingly likely to use a desktop: 28% of adults age 75 and older use a desktop, and 10% use a laptop. 4

Mp3 players

Almost half—47%—of adults own an iPod or other mp3 player. However, among the devices examined in this report, mp3 players saw the widest range in ownership rates between generations. While 74% of Millennials own an mp3 player, only 56% of members of Gen X do—and adoption rates continue to drop for each of the older generations. Only 3% of adults age 75 and older own this type of device.

Game consoles

Overall, 42% percent of all adults age 18 and older own a game console, and it is especially popular with members of the Millennial Generation and Generation X.  Sixty-three percent of all adults ages 18-46 own a game console like an Xbox or Play Station, as well as 38% of those ages 47-56. Ownership rates continue to drop off, to 19% of Older Boomers (ages 56-64), 8% of the Silent Generation (ages 66-74), and only 3% of the G.I. Generation (age 75 and older).

Additionally, as previously reported in “ Americans and Their Gadgets ,” parents with children living at home are nearly twice as likely as non-parents to own a game console—64% of parents own one,  vs 33% of non-parents. 5

e-Book Readers and Tablet Computers

As of September 2010, 5% of American adults own an electronic book reader such as a Kindle or Sony Digital Book, up from 2% of adults the first time the question was asked in April 2009.

Statistically, there is very little variation between the different generations, although the G.I. Generation is slightly less likely than younger generations to own such a device. Though age is not a strong predictor of e-book use,  our previous “Gadgets” report noted that ownership is more likely among college graduates and those with relatively high household incomes. 6

Though there have been several incarnations of tablet-like computers over the years, 7 they had not gained widespread attention until Apple introduced the iPad in early 2010. 8

As of September 2010, 4% of American adults own a tablet computer such as an iPad. Though education and household income are high predictors for owning a tablet computer, as with e-book readers, they are also more popular with adults age 56 and under (who are significantly more likely to own a tablet computer than adults age 66 and older).

In a previous May 2010 survey, when 3% of all adults said they owned a tablet computer,  roughly six in ten of tablet owners said they use their device to access the internet. However, given the small number of tablet owners these findings are not reported in detail here. 9

Infographic: Summary of gadget ownership

Click for a larger version

• Scott Keeter and Paul Taylor, “The Millennials.” Pew Research Center, December 11, 2009. http://pewresearch.org/pubs/1437/millennials-profile ↩
• Stephen J. Blumberg and Julian V Luke, “Wireless Substitution: Early Release of Estimates from the National health Interview Survey, January-June 2010.” National Center for Health Statistics, December 2010. http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201012.htm ↩
• See “Americans and their gadgets” (2010): https://www.pewresearch.org/internet/Reports/2010/Gadgets/Report/Desktop-and-Laptop-Computers.aspx ↩
• See “Americans and their gadgets” (2010): https://www.pewresearch.org/internet/Reports/2010/Gadgets/Report/Game-consoles.aspx ↩
• See “Americans and their gadgets” (2010): https://www.pewresearch.org/internet/Reports/2010/Gadgets/Report/eBook-Readers-and-Tablet-Computers.aspx ↩
• Brad Stone and Ashlee Vance, “Just a Touch Away, the Elusive Tablet PC.” The New York Times, October 4, 2009. http://www.nytimes.com/2009/10/05/technology/05tablet.html ↩
• The iPad was introduced in January 27, 2010 and went on sale April 3. ↩
• See “Mobile Access” (2010): https://www.pewresearch.org/internet/Reports/2010/Mobile-Access-2010/Part-3/Mobile-access-using-laptops-and-other-devices.aspx ↩

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Original research

Gadget addiction among school-going children and its association to cognitive function: a cross-sectional survey from bangladesh, mowshomi mannan liza.

1 Department of Public Health, North South University, Dhaka, Bangladesh

2 Department of Public Health, School of Research, Chattogram, Bangladesh

Mohammad Azmain Iktidar

Simanta roy, musa jallow.

3 Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Banjul, Gambia

Sreshtha Chowdhury

Mustari nailah tabassum.

4 Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh

Tarannum Mahmud

Associated data.

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

People are becoming more dependent on technology than ever before. Today’s children and adults are heavily plugged into electronics, which raises concerns for their physical and cognitive development. This cross-sectional study was conducted to assess the relationship between media usage and cognitive function among school-going children.

This cross-sectional study was conducted in 11 schools in 3 of Bangladesh’s most populous metropolitan areas: Dhaka, Chattogram and Cumilla. A semistructured questionnaire with three sections was used to obtain data from the respondents: (1) background information, (2) PedsQL Cognitive Functioning Scale and (3) Problematic Media Use Measure Short Form. Stata (V.16) was used for statistical analysis. Mean and SD were used to summarise quantitative variables. Qualitative variables were summarised using frequency and percentage. The χ 2 test was used to explore bivariate association between categorical variables, and a binary logistic regression model was fit to investigate the factors associated with the cognitive function of the study participants after adjusting for confounders.

The mean age of total of 769 participants was 12.0±1.8 years, and the majority (67.31%) were females. The prevalence of high gadget addiction and poor cognitive function was 46.9% and 46.5%, respectively, among the participants. After adjusting the factors, this study found a statistically significant relationship (adjusted OR 0.4, 95% CI 0.3 to 0.7) between gadget addiction and cognitive function. In addition, the duration of breast feeding was a predictor of cognitive function as well.

This study found digital media addiction as a predictor of decreased cognitive performance in children who use digital gadgets regularly. Although the cross-sectional design of the study precludes causal relationships from being determined, the study finding deserves further examination via longitudinal research.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• School age is a time of rapid physical and mental growth for children.
• Both children and adults are excessively immersed in electronic gadgets in today’s times.
• Digital addiction has a detrimental effect on ’students' performance in the classroom.
• Boys have a higher score of addiction to gadgets (66.3%).

WHAT THIS STUDY ADDS

• This study found a significant proportion of school-going children are addicted to digital gadgets. Gadget addiction has a statistically significant relationship with the cognitive function of school-going children.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• This study recommends regular screening of gadget addictions among school-going children and future interventions and policies on daily recommended time limits of digital media device usage in children.

Introduction

Around the world, people are increasing their reliance on technology devices at a rate that has never been seen before. 1 Not only adults but also children are excessively immersed in electronic gadgets in today’s times, which generates issues and worries regarding the effects these devices have on children in terms of their physical and cognitive development. 2 3 Regarding the situation in Asia, a prior study that was carried out in six Asian nations concluded that children aged from 12 to 18 years held ownership of smartphones at a rate of 62% overall. 3

Numerous developments have taken place in the public sphere of the modern period, leading to an explosion of new forms of data transmission, social interaction and leisure time activities. As technology continues to grow on a global scale, it is nearly impossible to live without any digital screen. 4 Technological progress brings about inevitable lifestyle changes, particularly in children. These changes include the habit of playing with gadgets, eating habits, physical activity levels and the impacts of these changes. 5 There are identified benefits of digital device use, such as helping children acquire new vocabulary, languages and stay engaged in the classroom. 6 However, the possible negative impact of digital device use and its problematic usage is also common. A study has shown that digital addiction has a detrimental effect on students’ performance in the classroom. 7 Children who spend an excessive amount of time in front of screens may have decreased levels of productivity. 3 Above-mentioned studies indicate that there are a variety of advantages as well as drawbacks associated with the use of the various forms of the digital screen.

A cognitive function is any psychological process that is involved in the process of acquiring knowledge, the manipulation of information or the logical derivation of conclusions. 8 The capabilities of perceiving, remembering, learning, paying attention, deliberating and communicating are all included in the cognitive processes. 8 People who use digital screens for prolonged periods have been reported to have impaired cognitive regulation and cognitive inflexibility. 9 According to the findings of another study, digital addiction is connected with an increased number of reported cognitive failures. 10

School age is a time of rapid physical and mental growth for children. 11 There are increasing concerns about the effects of children’s excessive screen usage on their growth and development. 12 According to the results of a survey, around two-thirds of students use the digital screen while they should be paying attention in class, studying or completing assignments. 7 The distraction that is resulted from this multitasking is one of the factors that has been proven to have a negative impact on students’ academic performance. 7 There are limited evidences of digital addiction among children and its correlates in this geographic area. Therefore, this cross-sectional study was carried out to determine the extent of media use, and its association with cognitive function among school-going children in the study region.

Study design, setting and sample

This cross-sectional study was carried out among children aged 8–14 enrolled in grades 4–7 at five private schools, five public schools and one madrasah (a specially adapted institution for Islamic education and culture) in Bangladesh. The study locations were chosen using convenient sampling. A printed questionnaire with instructions was used to obtain information from the parent, while trained volunteers performed face-to-face interviews with the participant.

Participants in the selected schools were sent informational pamphlets, parental consent forms and questionnaires. In addition, the pamphlets included a contact number for any more inquiries. Cognitive function assessment interviews were conducted with (n=769) children who provided written parental consent and completed the questionnaire within 1 week.

A semistructured questionnaire with three sections was used for data collection. Section 1 included questions on sociodemographic factors (age, gender, residence, family type, family income and parental education status), birth order (the order in which the child is born in comparison to other sibling), method of delivery (how the child was given birth: normal vaginal delivery or caesarean section), Expanded Programme on Immunisation (EPI) vaccination status (If the child received all vaccination according to the EPI schedule), duration of breast feeding (for how long the child was breastfed) and deworming status (The interval at which the child received deworming medication: never, occasionally or regularly). Sections 2 and 3 included two validated questionnaires (PedsQL Cognitive Functioning Scale and Problematic Media Use Measure Short Form (PMUM-–SF)) for measuring cognitive function and gadget addiction, respectively. The parents received sections 1 and 3 with precise instructions for completion. The remainder of the questionnaire (section 2: PedsQL Cognitive Functioning Scale) was completed by a trained volunteer after the participant’s face-to-face interview.

PedsQL Cognitive Functioning Scale

The PedsQL Cognitive Functioning Scale consists of six questions (‘It is hard for me to keep my attention on things;’ ‘It is hard for me to remember what people tell me;’ ‘It is hard for me to remember what I just heard;’ ‘It is hard for me to think quickly;’ ‘I have trouble remembering what I was just thinking;’ ‘I have trouble remembering more than one thing at a time.’). This scale was developed through focus group discussions, cognitive interviews, pretesting and field-testing measurement development techniques. 13 A five-point Likert scale was used to assess this scale, with 0 denoting never, 1 denoting nearly never, 2 denoting sometimes, 3 denoting often and 4 denoting almost always. All responses were reverse-scored and then linearly translated to a 0–100 scale (0=100, 1=75, 2=50, 3=25, 4=0), in accordance with established scoring protocols. Any score below the mean was considered as poor cognitive functioning and higher scores indicated higher functioning.

Problematic Media Use Measure Short Form

The PMUM–SF was used to determine the level of screen addiction among all of the children in our study cohort. It includes nine components. Each answer was based on a five-point Likert scale: (1) never, (2) seldom, (3) sometimes, (4) often and (5) always. Children who scored 3 or higher on at least five questions were deemed to have a high level of device addiction.

A pretesting was done on 20 participants from government and private schools to check the feasibility and reliability of the study. Necessary modifications were made to simplify the data collection without affecting the data quality. The inclusion of a helpline number in leaflets was considered on the suggestions of the pilot participants.

Statistical analysis

All analyses were performed using Stata (V.16). Descriptive statistics were calculated as mean and SD for quantitative variables or frequency and relative frequency for categorical variables. The bivariate association of two categorical variables was explored using the χ 2 test. A binary logistic regression model was fitted to assess the association between cognitive function and gadget addiction. Variables with a p≤0.2 in the bivariate analysis entered in the multivariate model in a forward stepwise selection method. A two-tailed p<0.05 was considered statistically significant.

Public involvement

Members of the public were involved in several stages of the study including design and conduct. We received input from children and their parents and implemented them in our study design. We intend to disseminate the main results to study participants and will seek public involvement in the development of an appropriate method of dissemination.

Of the 836 questionnaires and consent forms provided to the participants, 67 were ineligible (30 did not meet inclusion criteria and 37 did not consent), resulting in 769 potential responders. A total of 769 responses out of 836 amounted to a response rate of 91.9%.

Background information of the study participants is presented in table 1 . Among the 769 participants, 67.3% were female and hailed from urban areas. About 78% of the participants were from nuclear families, and most of the participants’ birth orders were second or more. Most of the participants’ family income was in between BDT10 000 and BDT20 000 (42.4%). Regarding parental education, 40.9% of parents had 8–12 years of schooling. In terms of birth, 26.3% of participants’ modes of delivery were by caesarean section, and 67.8% were normal vaginal delivery. Most of the participants (90.6%) were EPI vaccinated. 10.8% of participants’ duration of breast feeding was less than 6 months, whereas 47.8% of participants were more than 24 months. About 3% of participants were never dewormed, whereas 49.08% were occasionally and 48.1% were regularly. The prevalence of high gadget addiction and poor cognitive function were 46.9% and 46.5%, respectively, among the participants ( figure 1 ).

Background information of study participants (n=769)

BDT, Bangladeshi Taka; C/S, caesarean section; EPI, Expanded Programme on Immunisation; NVD, normal vaginal delivery.

Prevalence of gadget addiction and cognitive function among school-going children (n=769)

Table 2 includes all the potential variables and demonstrates the adjusted result. After adjusting for age, gender, residence, family type, birth order, family income, parental education, mode of delivery, EPI vaccination status, duration of breast feeding and deworming status, participants with high gadget addiction had 56% less chance of good cognitive function than those with low gadget addiction. Also, participants whose duration of breast feeding was 6–12 months (adjusted OR, AOR 2.5, 95% CI 1.1 to 5.4, p=0.02), 12–24 months (AOR 2.0, 95% CI 1.0 to 4.2, p=0.05) and more than 24 months (AOR 2.4, 95% CI 1.0 to 4.7, p=0.01) had a higher chance of having good cognitive function than those who were breastfed for less than 6 months. Responses regarding the PMUM questionnaire are presented in table 3 .

Cognitive function of the study participants and associated factors (n=769)

p<0.05 is in bold.

AOR, adjusted OR; C/S, caesarean section; EPI, Expanded Programme on Immunisation; NVD, normal vaginal delivery.

Problematic media use measure questionnaire and responses of the participant

The objective of this study was to determine the prevalence of gadget addiction and its association with cognitive functions among school-going children in Bangladesh. Using a semistructured questionnaire, data were collected on background information, and data estimating cognitive functions and gadget addictions via the PedsQL Cognitive Functioning Scale and PMUM-SF, respectively. In this study, a high gadget addiction score (46.9%) was found in the participants; this result is similar to other studies reporting the growing prevalence of gadget addiction in different parts of the world. Similarly, previous research consisting of two systematic reviews and meta-analysis 2 14 confirm the increasing prevalence trend of gadget addiction over time in children and children. An Indian study among school-going children, where 57.55% were female, found that 10.69% of technology users were addicted, with 8.91% addicted solely to their phones. 15

The PMUM-SF scale is a validated and reliable tool used to estimate screen media addiction in children by measuring child screen time and psychosocial functioning. 16–18 The high gadget addiction score estimated by PMUM was found to be across all age groups, and of the total participants in this study, the median age was 12.0 years with females being the majority (67%). This is in contrast to a study conducted in India, which reported boys as having a higher gadget addiction score (66.3%) because they had longer screen time than girls. 19 Other studies suggest that the prevalence of problematic media use or gadget addiction among children and young adults often varies (ranging from 5% to 50%). 16 20

Although the significance could not be established, it was observed that majority of the participants were from urban areas, belonged to nuclear families, had family income ≥BDT15 000/month, and had parents with some level of education. These elements could potentially be indicative of higher socioeconomic status and, therefore, children born from such families are more at risk of excessive screen exposure and gadget addiction. A few studies have demonstrated the link between high family income and screen or internet addiction, thus confirming our theory. 21 22

Using the PedsQL Cognitive Functioning Scale which is a reliable and valid measure of cognitive functioning in children, 13 23 we estimated the cognitive function of all participants in the study and determine their association with children with gadget addiction. Overall, it was found that 53.5% of the children had a good cognitive function score, and children identified to have high gadget addiction scores had 57% less chance (AOR 0.4, 95% CI 0.30 to 0.6, p<0.001) of having a good cognitive function compared with those with low gadget addiction. The adjusted logistic regression analysis showed that as gadget addiction increases the level of poor cognitive function increases as well. A previous study conducted on children under 12 years of age in India, found that gadget media addiction has a close association with decreased cognitive function. 19 The study findings indicated that increased screen time and gadget addiction were significantly associated with parental concerns in some cognitive elements such as problem-solving, communication and personal-social development. 19 Previous research further supports this, reporting the significant association between increased screen time and delays in cognition, language and developmental motor milestones. 24 Similarly, there is evidence to show that parents who frequently use digital media devices to calm upset children lead to increase concerns in socialemotional development in toddlers. 25 A few studies observed increased ADHD problems in children with excessive televison (TV) use, 26 27 while the cognitive development of children was found to improve when screen time was reduced to less than 2 hours per day. 28 It was reported that the use of electronic media in preschool-age children was associated with behavioural difficulties over time. 29 Hyperactivity or inattention problems were associated with baseline use of mobile phones, while emotional and conduct problems were associated with internet or computer usage. 29

To the best of our knowledge, this is the first study to examine gadget addiction and its association with cognitive function in children in Bangladesh, using the PMUM-SF and PedsQL Cognitive Functioning Scales. The measurement of cognitive function may not be accurate considering the absence of clinical test. Still, the questionnaire used in this study was developed from validated scales, thus, enhancing the strength of our research. Another strength of this study is the large sample size used, which allows for greater precision and generalisability of the findings. One of the limitations of this study is that we could only present the association between gadget addiction and cognitive function, rather than causality due to our research methodology. Due to convenience sampling methods employed in this study, there may be sampling bias, however, we attempted to minimise this by sampling 769 children from 11 schools in three of Bangladesh’s most populous metropolitan areas of Bangladesh (Dhaka, Chattogram and Cumilla). Recall and social desirability bias are likely to have occurred since part of the data was drawn from parental reports. Future research is needed to establish cause and effect on this topic and, therefore, draw definitive conclusions.

We conclude that there is a positive association between gadget addiction and poor cognitive function among children who use digital devices frequently. Therefore, interventions and education programmes should be developed to increase public awareness of harmful gadget addictions in children. However, additional longitudinal research is required to obtain a clearer data.

Supplementary Material

Acknowledgments.

The authors would like to thank Dr. Azaz bin sharif (North South University), and Dr. Sanjana Zaman (North South University) for their assistance and time with this article.

Contributors: MML conceived the need for the survey, participated in its design, contributed to the interpretation of the results and is responsible for the overall content as guarantor. SR and SC participated in the design. MML, MAI and SR participated in data analysis of the study. MJ, SC, MAI, TM and MNT collaborated in data collection and writing up the manuscript. All authors read and approved the final manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Consent obtained from parent(s)/guardian(s).

Ethics approval

Ethical approval for this study was obtained from the Institutional Review Board, North South University (Approval no-2022/0R-NSU/IRB/1005). All the participants were explained in detail about the aims and process of this study and informed consent was taken before data collection.

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Early-Childhood Tablet Use and Outbursts of Anger

• 1 Department of Preschool and Elementary School Education, Université de Sherbrooke, Sherbrooke, Québec, Canada
• 2 Department of Childhood Education, University of Johannesburg, Johannesburg, South Africa
• 3 LiNC, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
• 4 Département des Sciences de l’éducation, Université Sainte-Anne, Church Point, Canada
• 5 Department of Psychoeducation, Université de Sherbrooke, Sherbrooke, Québec, Canada

Question   Do higher levels of early-childhood tablet use undermine emotional regulation or is it the other way around?

Findings   In this study, child tablet use at age 3.5 years was associated with more expressions of anger and frustration by the age of 4.5 years. Child proneness to anger/frustration at age 4.5 years was then associated with more use of tablets by age 5.5 years.

Meaning   These results suggest that early-childhood tablet use may contribute to a cycle that is deleterious for emotional regulation.

Importance   Tablet use continues to increase in preschool-aged children. The use of mobile devices has been linked to child emotional dysregulation. However, few studies have been able to show a clear direction of association between child tablet use and the development of self-regulation skills. In addition, few studies have modeled within-person associations over time.

Objective   To estimate how child tablet use contributes to expressions of anger and frustration across the ages of 3.5 to 5.5 years at the within-person level. The study team also examined the extent to which associations are bidirectional to clarify the direction of the correlations.

Design, Setting, and participants   This prospective, community-based convenience sample of 315 parents of preschool-aged children from Nova Scotia, Canada, was studied repeatedly at the ages of 3.5 (2020), 4.5 (2021), and 5.5 years (2022) during the COVID-19 pandemic. All analyses were conducted between October 5, 2023, and December 15, 2023.

Exposure   Parent-reported tablet use at the ages of 3.5, 4.5, and 5.5 years.

Main outcome and measures   Parents reported child expressions of anger/frustration at the ages of 3.5, 4.5, and 5.5 years using the Children’s Behavior Questionnaire.

Results   The sample was equally distributed across child sex (171 were identified by parents as being born boys [54%] and 144 as girls [46%]). Most reported being Canadian (287 [91.0%]) and married (258 [82.0%]). A random-intercept cross-lagged panel model revealed that a 1-SD increase in tablet use at 3.5 years (corresponding to 1.15 hours per day) was associated with a 22% SD scale increase in anger/frustration at age 4.5 years (standardized coefficient = 0.22; 95% CI, 0.01-0.44). A 1 SD scale increase in anger and frustration at 4.5 years was associated with a 22% SD (corresponding to 0.28 hours per day) increase in tablet use at 5.5 years (standardized coefficient = 0.22; 95% CI, 0.01-0.43).

Conclusion and relevance   In this study, child tablet use at age 3.5 years was associated with more expressions of anger and frustration by the age of 4.5 years. Child proneness to anger/frustration at age 4.5 years was then associated with more use of tablets by age 5.5 years. These results suggest that early-childhood tablet use may contribute to a cycle that is deleterious for emotional regulation.

Read More About

Fitzpatrick C , Pan PM , Lemieux A , Harvey E , Rocha FDA , Garon-Carrier G. Early-Childhood Tablet Use and Outbursts of Anger. JAMA Pediatr. Published online August 12, 2024. doi:10.1001/jamapediatrics.2024.2511

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