Prerpints.org logo
Preprint
Article

This version is not peer-reviewed.

Symptoms of Digital Addiction of Estonian Primary School Students

A peer-reviewed article of this preprint also exists.

Submitted:

29 May 2024

Posted:

29 May 2024

You are already at the latest version

Abstract
The aim of the present work was to map Estonian primary school students' ratings of symptoms of digital addiction and to identify both class and gender differences. The sample consisted of 5493 primary school pupils from 46 schools in Estonia participating in the 2022 Student Survey conducted by the Tallinn University Centre for Educational Innovation (CIE). The results showed that the digital addiction symptoms scores were highest in 8th and 9th grades. Girls and those who did not disclose their gender, scored higher than boys on digital addiction symptom statements. Symptoms of digital addiction were moderately positively related to screen time during school days and weekends and to school burnout and weakly negatively related to feeling refreshed in the morning.
Keywords: 
Symptoms of digital addiction
;  
screen time
;  
school burnout
;  
refreshed from sleep in the morning
Subject: 
Social Sciences  -   Education

Introduction

Since the Covid-19 crisis, the use of digital devices and media has further increased in terms of time (Mohan et al., 2021). The results of a meta-analysis conducted by Meng and colleagues (2022) also showed that the crisis exacerbated the prevalence of digital addiction symptoms. In Estonia, during this period, schools implemented distance learning (HITSA, 2020) and students spent more time using screens, so this topic needs further research and mapping of the use of digital tools.
Using digital devices during the COVID-19 pandemic has been studied lately also in other studies. The European DigiGen project that looked for technological transformations on the Digital Generation (Eickelmann et al., 2022). Different digital tools, and in particular the internet, have transformed the way young people live, learn, interact and participate in society. As the constant use and carrying of digital devices has become a social norm, people often do not realise that they may be experiencing symptoms of digital device overuse (Turner et al., 2021). Studies have shown that excessive use of digital devices over a prolonged period of time can lead to behavioural addiction (Chen et al., 2020; Kesici & Tunç, 2018), similar to other addictive disorders (Joseph & Hamiliton-Ekeke, 2016), developing as a result of habit formation and self-control problems (Allcott et al., 2022).
The propensity to overuse devices increases with the age of students (Sipal & Bayhan, 2010), and it is the primary school age group, 15-16 year olds, who are the most prone to become addicted due to overuse (Karacic & Oreskovic, 2017). Girls have also been found to be the most vulnerable group of young people (Saikia et al., 2019; Seema et al., 2022).
Previous studies have shown that recreational screen time is associated with changes in children's mental health problems and health status (Babic et al., 2017). Prolonged use of digital devices affects students' sleep, leading to sleep disturbance (Li et al., 2017), and sleep deprivation in turn negatively affects students' physical and psychological health (Hale & Guan, 2015) and their ability to fully concentrate on learning at school (Medic et al., 2017). Furthermore, it has been found that online addiction can be one of the negative consequences of burnout in schools (Tomaszek & Muchacka-Cymerman, 2020). School burnout is associated with school dropout (Bask & Salmela-Aro, 2013) and predicts later excessive internet use (Salmela-Aro et al., 2017). Pupils with low academic performance have higher levels of digital addiction symptoms (Malak et al., 2017). At the same time, habitually frequent use of digital devices affects students' academic performance (Chung et al., 2019), disrupting students' attention and focus on learning (Chen et al., 2020). As a student's academic performance increases, the prevalence of digital addiction symptoms decreases (Turel & Torman, 2015).
The aim of the present study is to map Estonian primary school students' ratings of symptoms of digital addiction and to identify both class and gender differences. It also aims to identify the relationships of digital addiction symptoms with screen time, school burnout and feeling refreshed in the morning. The data of this study were collected with a larger e-survey across Estonian schools at the beginning of 2022, when periodic distance learning still took place.

The Concept of Digital Addiction

Digital addiction is a broad term that encompasses a range of digital devices and activities (Almourad et al., 2020) and is described as an addiction to the internet, social media, smartphones, digital devices, including video games, and other digital technologies (Karakose et al. 2022). Being preoccupied with the use of digital devices or media means being unable to reduce use and justifying ever-increasing use, which in turn has a negative impact on school life and relationships and sleep (Dresp-Langley & Hutt, 2022).
Digital addiction is a new concept in the field of mental disorders, which is not formally diagnosed as a disorder (Pies, 2009). To date, digital addiction has been officially recognised by the World Health Organisation as a global problem, citing that excessive online activity and internet use lead to an inability to manage time, energy and attention during the day and cause sleep disturbance and insomnia (International Classification of Diseases (ICD), 2020). Addiction to smartphones and social media among school students has been the subject of much research in recent years, and the prevalence of these addictions is also the fastest in recent years (Meng et al., 2022).
Digital addiction is described as the compulsive need to use devices to an extent that interferes with everyday life and prevents you from doing the things that matter (Time to Log Off, 2016). Often people fail to control their time use of digital devices and media, using devices more than they should (Dresp-Langley & Hutt, 2022). Thus, digital addiction refers to the compulsive, prolonged and uncontrolled use of digital devices and related activities, with detrimental effects on the user's psychological and physical health (Al-Khani et al., 2021; Meng et al., 2022).
Digital addiction is classified as a behavioural addiction (Kim et al., 2006). Similar to any other addiction (Weinstein, 2017), it can be classified as either passive (watching TV) or active (playing computer games) (Griffiths, 1995). It is also important to highlight that it is the characteristics of the technology that contribute to addictive tendencies (Griffiths, 1995), i.e. the more interactive the application, the greater the development of addictive behaviours (Young, 1998). Modern apps often contain algorithms designed to increase people's temporal use and liking of the app to a level that leads to addiction (Peper & Harvey, 2018). Overuse of the internet has an impact on the dopamine system (Hou et al., 2012), leading to repeated use, or compulsive overuse of digital tools. A recent meta-analysis concluded that a large proportion of people may be affected by at least one subtype of digital addiction, either internet, social media, smartphone or gaming addiction (Meng et al., 2022). Thus, the study of digital addiction is currently topical and relevant worldwide (Karakose et al., 2022).
Digital addiction is habit-forming and partly due to problems with self-control (Allcott et al., 2022) and excessive use of digital devices in turn has a negative impact on school life (Liao & Wu, 2022). In the case of students, the boundary between problematic and purposeful use has been found to cross at the point where the student becomes disengaged from relationships because of device use, and other areas of life suffer because device use dominates over other activities (Li et al., 2017). Students with symptoms of digital addiction who have longer screen time have lower subjective well-being scores than teens with lower symptoms of digital addiction who use digital devices for less time (Seema et al., 2022).
However, the overuse of digital devices and digital addiction are difficult to measure, as neither time criterion alone nor digital addiction scale scores are sufficient to define addictive tendencies (Seema et al., 2022). It can be defined through two interrelated components, i.e., emotional reactions to digital media and behavioural symptoms of addiction, i.e., compulsive use in different settings, and if the person experiences symptoms of addiction along with difficulties in living and coping at school (Seema et al., 2022).
One way of defining digital addiction is in terms of the time spent online and the fascination with the online world (Cash et al., 2012). For example, in China, students with addictive features use devices for 8 hours a day (Wang et al., 2011). Pupils with an online addiction may spend nearly three times as much time online as those without (Chou & Hsiao, 2000). In a recent study of digital addiction among Estonian teenagers, a cluster analysis found that students with digital addiction symptoms used digital devices for an average of 5 hours on weekdays and 7 hours on weekends (Seema et al., 2022). Time use of devices was also high in a study four years earlier, which found that only 33% of teenagers used screens at a healthy rate of 2 hours per day (Sukk & Soo, 2018). Compared to the study by Seema et al (2022), four years later the percentage of healthy use was already only 25%.

Relationship between School Burnout and Digital Addiction

Student burnout in school is linked to a number of different components, such as loss of interest in school activities, overwhelming learning demands, feelings of incompetence and pressure from teachers or parents to perform, and overload from homework (Apray, 2012). School burnout has been found to be related to problematic use of social media (Walburg et al., 2016) and with online addiction (Tomaszek & Muchacka-Cymerman, 2020). Internet addiction can be one of the negative consequences of school burnout, as it reduces commitment to studying for exams, doing homework and actively participating in school lessons (Tomaszek & Muchacka-Cymerman, 2020).
Also, Salmela-Aro et al. (2017) have found that school burnout is reciprocally related to excessive internet use, i.e. when a student feels burnout or exhaustion at school, excessive internet use increases and engagement in school decreases, but also vice versa - spending too much time online increases the three components of student burnout, i.e. feelings of incompetence, exhaustion and cynical attitudes towards school (Salmela-Aro et al., 2017). In addition, Seema and colleagues (2022) have found relations with digital addiction and school burnout and perceived learning difficulties of Estonian teenagers.

Associations of Sleep Quality with digital Addiction

The construct of sleep quality has a common core, however perceived good and poor sleep quality have distinct characteristics that have been measured with different scales. The most important sleep quality components for research and practice seems to be feeling (non)refreshed in the morning and nocturnal sleep continuity (Libman, et al., 2016).
Excessive use of digital devices over long periods of time is associated with excessive feelings of fatigue, increasing the risk of anxiety, sleep disturbance and symptoms of depression (Bener et al., 2016; Li et al., 2017; Saikia et al 2019). A link has also been found between social media use and sleep disturbance and inadequate sleep (Levenson et al., 2016). Sleep deprivation leads to problems with memory and performance, and in adolescents in particular, disrupted sleep can lead to poorer performance at school (Medic et al., 2017). Young (1996), a researcher of internet addiction, has also pointed out that excessive use of various internet applications and computer gaming addiction, leads to sleep deprivation, as people stay up later and feel tired in the morning. Similarly, the quality of sleep of young people is affected by the use of mobile phones and devices in the bedroom before bedtime with Internet use and turning-off time (Bruni et al., 2015). The consequences of excessive device use can also be highlighted by students in a recent DigiGen study, who reported that students are aware of and also experience physical problems such as fatigue, eye and headaches from excessive device use (Eickelmann et al., 2022).

Gender Differences in Digital Device Usage Patterns

Boys' and girls' interests and reasons for using the equipment are different (Santl et al., 2022). Girls are more likely to use digital devices for social media, socialising, listening to music and sending messages, i.e. mainly for socialising (Chang et al., 2019). For example, levels of smartphone absence anxiety or nomophobia are higher in girls (Santl et al., 2022). Boys prefer to play games using different apps, mainly using computers and tablets, and thus have higher levels of computer game addiction than girls (Chang et al., 2019). Compared to girls, they also rate internet use higher (Tomaszek & Muchacka-Cymerman, 2020). However, there are also opposite results, with girls having higher addiction rates compared to boys, using devices for 3-6 hours per day, mainly for social media (Saikia et al., 2019). The same result was found in a digital survey of Estonian teenagers, with girls showing more digital addiction symptoms than boys (Seema et al., 2022).

Objective and Research Questions

The aim of this study is to map Estonian primary school students' ratings of symptoms of digital addiction and to identify both class and gender differences. It also aims to find relationships of digital addiction, screen time, school burnout and assessment of feeling rested in the morning.
Research question 1: What are the average primary school pupils' perceptions of their use of digital devices in 2022?
We hypothesized (H1) that the mean score of digital addiction is similar or even higher than the result gained with the previous Estonian study at the beginning of Covid crises (Seema et al., 2021).
Research question 2: What are primary school pupils' perceptions of their use of digital devices by grade?
As a the second hypothesis (H2), it was hypothesized that students in grades 6-9 would have different ratings of their digital media use (Chang et al., 2019), and that with each higher grade level, the ratings of addiction symptoms would increase (Karacic & Oreskovic, 2017).
Research question 3: What are primary school pupils' perceptions of their use of digital devices by gender?
As a second hypothesis (H3), it was hypothesized that students' ratings will differ depending on gender (Meng et al., 2022), and girls' ratings of digital addiction symptoms will be higher (Saikia et al., 2019; Seema et al., 2022).
Research question 4: What are the associations between school burnout and feeling refreshed in the morning with their perceived digital addiction symptoms and screen time?
It was hypothesized (H4) that higher school burnout is associated with high ratings of digital addiction symptoms and screen time (Salmela-Aro et al., 2017; Seema et al. 2022) with feeling less refreshed in the morning (Bener et al., 2016; Li et al., 2017; Saikia et al 2019).

Methods

Sample

The initial sample of the survey included 5493 pupils from Estonian primary schools who participated in the 2022 Student Survey conducted by the Centre for Educational Innovation (CIE) of Tallinn University. A total of 46 schools from all over Estonia participated. Based on the survey questions and the purpose of the study, young people with non-responses and non-logical answers were eliminated from the sample, which totalled 301 pupils. The final sample consisted of 5192 students, of 2365 boys (45,6%) and 2526 girls (48,6%) and 301 students (5,8%) responded „other/do not want to disclose” to the gender question. Of these, 1256 pupils (24.2%) were in year 6, 1536 (29.6%) in year 7, 1310 (25.2%) in year 8 and 1090 (21.0%) in year 9. The mean age of the students was 13.8 years (SD = 1.19).

Data Collection

The study uses data from the Student Survey 2022 conducted by the Centre for Educational Innovation (CIE) of Tallinn University. The data collection took place between 17 January and 28 February 2022. All Estonian schools were invited to participate in the survey, and the participation was voluntary, but paid for by the school. Invitations and information on participation were sent to schools that had previously participated in the school survey or had cooperated with Education Innovation, and information on how to register was also made available on the CIE website. A link to a questionnaire was sent to each school, which took on average 30 minutes to complete. The questionnaire for the student survey consisted of 12 different thematic blocks, of which only one part has been used in this work: digital use and addiction and related topics. The questionnaire consisted of both open and closed questions. The survey was conducted using the Qualtrics survey environment. The students' responses were anonymous and the data are confidential. The current study is linked with a master thesis (Väljas, 2023).

Measuring Instruments

Digital Addiction Scale for Teenagers (DAST) (Seema et al., 2021) was used to assess the symptoms of digital addiction. The self-report scale has two names and can be used in two ways: as the DAST (Digital Addiction Scale for Teenagers) questionnaire to screen for symptoms of digital addiction and as the UDDS (Using Digital Devices Scale) questionnaire to distinguish between healthy and addictive digital device use and to use the questionnaire for reflection with students. The questionnaire used in this work describes a person's behaviour and self-perception in relation to the use of digital devices. The scale consists of ten statements, of which six statements assess emotional dependency (e.g. "I feel anxious when I don't know what my friends are saying on social media.") and four statements assess continuous or behavioural use habits (e.g. "I use my digital device to play games or chat in the evening before I go to bed."). The scale is used in this work as a single construct, i.e. all 10 statements together (Cronbach's in this sample α = 0.86). The statements were scored using the Likert 7-point frequency scale, where 1 = never, 2 = rarely, 3 = somewhat rarely, 4 = so-so, 5 = somewhat often, 6 = often, 7 = very often. Summary score of ten items or the total score up to 23 points are considered as a healthy using pattern while a summary score above 48 points shows digital addiction (Seema et., 2021).
School Burnout Inventory is an adapted and shortened Estonian version of the School Burnout Inventory (Salmela-Aro, Kiuru, Leskinen & Nurmi, 2009). In the current study we used five items on a 7-point Likert scale (completely disagree – completely agree). Sample item: ‘I feel a lack of motivation for my schoolwork and often think of giving up’. Alpha was α = .847.
To assess the sleep quality indicator “refreshed from sleep in the morning”, the following single question was asked: "Now think about the last two months. How often have you felt well rested in the morning after getting up?". The scale response options were as follows: 1 = Always or almost always; 2 = 5 to 6 times a week; 3 = 3 to 4 times a week; 4 = 1 to 2 times a week; 5 = Very rarely or never. The scale was in reverse to other scales, since smaller numbers indicated higher frequency. Therefore, the answers were reversed.
Two single questions were used to map time use of digital tools (Seema et al, 2021). "How many hours do you usually spend using digital devices on a school day (before and after school); How many hours do you usually spend using digital devices during a non-school day (weekend, school holidays)? These were the open-ended questions and the student had to write the answer in figures. There were students who gave some unrealistic answers like 24/7. We considered logical up to 17 h of screen time during school days and 20h during non-school days and we removed the outliers.

Data Analysis

Data were analysed using the statistical software IBM SPSS Statistics. Descriptive statistics were used to answer the first and second research question and analysis of variance (ANOVA) was used to compare differences between grade levels and genders of students.
To answer the fourth research question, correlational analysis was used to analyse relationships between variables of digital addiction, screen time, school burnout and feeling refreshed in the morning.

Results

Students' Ratings of Digital Addiction Symptom Statements

Primary school students' ratings of statements describing symptoms of digital addiction (see Table 1). Students rated the use of their digital devices to play games at night before going to bed as highest (M = 4.57, SD =1,95). Students also reported using their device for longer than they had originally planned (M = 4.45, SD = 1.63). The lowest rated use of digital devices was during school hours (M = 2.83, SD = 1.68). The arithmetic mean of the digital addiction symptom summary score (M = 35.02 SD = 11.28), revealed that the average students were estimated to experience digital addiction symptoms a little more than the average result on the scale, since the scale minimum was 10 points and maximum 70 points. In the current study 13% of student's (N=676) DAST scores were 48 points or higher, showing digital addiction, while only 15,7 % (N=814) were 23 points or less, and form the healthy range. The cut points are fixed in the original DAST development study (Seema et al., 2021).

Students' Ratings of Digital Addiction Symptom Statements by Grade and Gender

The highest digital addiction symptom ratings (Figure 1) were in grade 8 (M = 36.32, SD = 11.31) and grade 9 (M = 36.3, SD = 10.57), and the lowest digital addiction symptom ratings were in grade 6 (M = 33.01, SD = 11.4). Differences between grades in digital addiction symptom ratings were statistically significant, F (3, 5188) =24.395, p < .001. Bonferroni post hoc test showed that 6th grade ratings were statistically significantly different from other grades (p < .001). There was no statistically significant difference between 8th and 9th grades (p < 1.00). (Figure 1)
A comparison between genders (Figure 2) showed that boy's ratings of symptoms of digital addiction (M = 32.68, SD = 10.13) were statistically significantly (p < .001) lower than girls' (M = 37.05, SD = 11.84) and also the third group who chose „Other/do not want to answer “(M=36.43, SD=11.42).

Relations between Digital Addiction, School Burnout, Screen Time and Feeling Rested

Students' digital addiction summary scores were moderately positively related to school burnout and screen time during school days and weekends and weakly negatively related to feeling refreshed in the morning. All correlations were significant. Correlations between the DAST scores and other measures are in Table 2.

Discussion and Conclusions

The aim of the present study was to map Estonian primary school students' ratings of digital addiction symptoms and to identify both class and gender differences. It also aimed to find out the correlations between students’ perceived symptoms of digital addiction, screen time, school burnout and perceived feeling of being rested in the morning. The current study is a screening that does not diagnose an individual, but generalises the frequency of occurrence of the phenomenon in one population - Estonian primary school students. Digital addiction at an individual level cannot be defined solely on the basis of digital addiction scale ratings or time of use, but in combination with perceived low satisfaction with life or coping at school, or with the well-being scale (Seema et al., 2022).
The current study showed that students form a heterogeneous group with large differences in perceived symptoms of digital addiction measured with the DAST. The average Estonian student perceives symptoms of digital addiction a bit more than the scale average extent. Our study confirmed our hypotheses (H1) that the arithmetical mean score of digital addiction is similar or even higher than the result gained with the previous Estonian study at the beginning of Covid crises (Seema et al., 2021). The mean score of digital addiction was M = 34.8 points (SD =11.0) in 2020. The students’ mean score at the beginning of 2022 was only a bit higher than those reflected by the study two years earlier (M = 35.02 SD = 11.28).
In the current study 13% of students' DAST scores showed high symptoms of digital addiction and unhealthy use of digital devices (Seema et al., 2001) while only a small group of students showed a few symptoms of digital addiction, thus having a healthy pattern of using digital devices. The survey found that students are most likely to use their devices during out-of-school hours, with high ratings for using digital devices to play games or socialise with friends before going to bed. It was also found that digital devices are being used for longer than originally planned.
The results showed that students' ratings of digital addiction symptom statements increase with each grade level, confirming the hypothesis (H2). The highest digital addiction symptom ratings were found in 8th and 9th grade. Previous studies have shown that excessive use of digital devices and related behavioral addictions increases as students get older. Internet addiction (Sipal & Bayhan, 2010; Karacic & Oreskovic, 2017; Malak et al., 2017) and gaming addiction (Chang et al., 2019) prevalence is highest among 15 – 16 years old. This may be due to the fact that the symptoms of digital addiction increase over the years, as the symptoms develop over a longer period of time (Al-Khani et al., 2021), but it may also be caused by older children’s better ability to assess and recognise the symptoms of digital addiction due to their better metacognitive ability to accurately monitor own behaviour (Weil et al., 2013). By learning that the problem is becoming more severe in the final years of primary school, it is important that teachers are able to recognise the existence of the problem early and identify at-risk pupils in order to support them with preventive measures. In order to fulfil the fundamental process of educational institutions, i.e. to support the development and learning of the learner to the maximum, school leaders have an important role to play in ensuring that support systems are in place and implemented. In this context, it is also important to train support specialists, teachers and parents on this issue at national level (Chemnad, Aziz, & Abdelmoneium et al., 2023; Väljas, 2023).
In addition, it was found that there were differences of digital addiction scores between genders. Girls' ratings of digital addiction symptoms were higher than boys', which confirmed the hypothesis (H3) of this study, which suggested that gender differences exist (Meng et al., 2022). Some previous studies have shown that girls have higher Internet addiction scores (Saikia et al., 2019; Al-Khani et al., 2021). This is also in line with the results of a previous study that found higher prevalence of digital addiction symptoms in teenage girls (Seema et al., 2022). An interesting finding in our study was the fact that the third gender group had also significantly higher scores of digital addiction than boys.
Knowing that the symptoms of digital addiction are associated with self-regulation problems, occurring as a result of habit formation and in part due to self-control problems (Allcott et al., 2022), it is important to propose that teachers could teach self-regulation skills to young people and increase students' self-awareness and procedural knowledge for maintaining their health in a digitally rich environment (Seema et al., 2021). In light of the results, it is worth highlighting the suggestion to teachers that young people should be guided to use digital tools purposefully and to monitor the time spent on purposeful activities. Teachers can use the digital addiction scale used in this work, in their humanities or computer science classes to shape the development of digital competences through self-assessment of students. A further suggestion for policy makers is to develop possible prevention measures or programmes (Väljas, 2023). A literature review by Ding and Li, (2023) have suggested at least 5 different types of digital addiction prevention interventions, which could be implemented and studied.
The perceived well-being of students at school is a very important factor without which learning is difficult. The last research question aimed to find out which factors are related to digital addiction symptoms. Screen time, school burnout, and feeling refreshed in the morning were found to be associated with the presence of symptoms of digital addiction, which confirmed hypothesis (H4).
Similarly, Seema and colleagues (2022) found that the presence of symptoms of digital addiction was associated with poorer engagement in learning, higher levels of burnout due to learning, and the presence of learning disabilities (Seema et al., 2022; Seema & Varik-Maasik 2023). One of the reasons may be the habitual use of frequent digital devices, which diverts attention away from learning (Chen et al., 2020). At the same time, excessive academic challenges can cause students to become stressed, in turn leading them to spend more time online to increase their well-being (Cai et al., 2021). Adolescents who experience more stress and poor mental health may use screens to cope with stressors and negative feelings. Better coping behaviors and social support are associated with lower total screen usage (Nagata, Cortez, Cattle, 2022).
In our current study we measured only one important and positive sleep quality characteristic - feeling refreshed in the morning (Libman, et al., 2016) and by our knowledge no other studies have assessed the same sleep quality criteria in relation to students' digital addiction. However, a previous study has found connections between young adults' electronic media use in bed before going to sleep and insomnia symptoms and daytime sleepiness (Fossum, et al., 2014). Honmore (2023) showed that adolescents with high social media addiction suffer from poor sleep quality. Dresp-Langley and Hutt (2022) even provided a conceptual basis for understanding digital addiction as one of the major reasons why people, and adolescents in particular, sleep less and less well in the digital age.
However, knowing that digital technology plays a major role in today's society and that devices are an important part of young people's lives, agreements need to be put in place in schools to ensure effective learning. One suggestion is to consider the creation of smart-free school breaks, as implementing 45-minute school classes leave little time to ensure deep learning, as time spent on social media platforms during a break can take a sufficient amount of time before a student can switch to learning activities (Väljas, 2023). When creating a technology-free shift, it is important to provide students with substitute activities that allow them to develop creativity, playfulness and be active, so that students' desire to spend time on digital devices is reduced (Muidre & Raudkivi, 2022). In addition, students could be given the opportunity to have, for example, an outdoor lesson. This would require finding the necessary resources at school level and ensuring that the necessary arrangements are in place (Väljas, 2023).
Developing proper digital addiction literacy through educational initiatives is critical to preventing, detecting, and treating digital addiction. A proactive approach could help individuals understand and manage their digital device usage before it develops into problematic use requiring intervention (Ding & Li, 2023). Living in a digitally rich environment, it is important to draw young people's attention to the need to monitor the purpose of their use of digital tools and the time resources available to them, so that time spent on digital tools does not come at the expense of other important activities and interest in other activities is not lost. In conclusion, it is important for education leaders to be aware of the issue, by shaping the school culture and involving the network. It is important to raise the awareness of adults around teenagers, especially teachers and parents, of the dangers of excessive use and to provide the knowledge necessary for prevention in order to create the best possible school environment for development and learning (Väljas, 2023).

Limitations and Future Research Directions

In the light of the results of this work, the following limitations should be kept in mind. Firstly, the student survey 2022 was carried out during the corona period, when many children were in distance learning, so the result on the time spent by students using digital devices may be higher due to this. Secondly, there is the potential subjectivity of the results, as they are students' estimates of their own use habits and time, which may affect the reliability of the results. However, the students did not know that the questionnaire was a questionnaire of measuring their digital addiction symptoms, but mapping of usage behaviour, as the DAST scale has two names and purposes, and therefore the responses can be considered more reliable. Adolescents often multitask on screens therefore the computed total screen time is hard to calculate and could be under- or overestimated (Raney, et al., 2022; Seema & Varik-Maasik, 2022).
Another limitation is that the extensive questionnaire of the Student Survey is time-consuming to fill in and many children may not have responded with full seriousness, so many responses had to be deleted due to their unreliability. Sadly, students with learning difficulties may find answering a questionnaire difficult, therefore there may be students with learning difficulties whose answers were left out of analysis. Students with learning difficulties may need some extra attention and help with filling in any long questionnaire (Seema & Varik-Maasik, 2023).
This study was a cross-sectional survey, for better insight, a longitudinal research perspective could be used in the future. Future research could also focus on primary and secondary school pupils, using qualitative research methods and research designs that could explain cause and effect. Future studies could assess more students' sleep quality indicators in relation to digital addiction, screen time and school burnout. In addition, different school and classroom practices and interventions could be explored in terms of how agreements on the use of pupils' personal digital devices have been made, both during lessons and in class, and how this has changed pupils' behaviour and attitudes.

References

  1. Al-Khani, A. M., Saquib, J., Rajab, A. M., Khalifa, M. A., Almazrou, A., & Saquib, N. (2021). Internet addiction in Gulf countries: A systematic review and meta-analysis. Journal of Behavioral Addictions, 10(3), 601-610. [CrossRef]
  2. Allcott, H., Gentzkow, M., & Song, L. (2022).Digital addiction. American Economic Review, 112(7), 2424-2463. [CrossRef]
  3. B., McAlaney, J., Skinner, T., Pleya, M., & Ali, R. (2020). Defining digital addiction; Almourad, M. B., McAlaney, J., Skinner, T., Pleya, M., & Ali, R. (2020). Defining digital addiction: Key features from the literature. Psihologija, 53(3), 237-253.
  4. Apray, A. (2012). Secondary School Burnout Scale (SSBS). Educational Sciences: Theory and Practice, 12(2), 782-787.
  5. Babic, M. J., Smith, J. J., Morgan, P. J., Eather, N., Plotnikoff, R. C., & Lubans, D. R. (2017). Longitudinal associations between changes in screen-time and mental health outcomes in adolescents. Mental Health and Physical Activity, 12, 124-131. [CrossRef]
  6. Bask, M., & Salmela-Aro, K. (2013). Burned out to drop out: exploring the relationship between school burnout and school dropout. European journal of psychology of education, 28, 511-528. [CrossRef]
  7. Bener, A., Al-Mahdi, H., & Bhugra, D. (2016). Lifestyle Factors and Internet Addiction among School Children. International Journal of Community & Family Medicine, 1(2). [CrossRef]
  8. Bruni, O., Sette, S., Fontanesi, L., Baiocco, R., Laghi, F., & Baumgarten, E. (2015). Technology Use and Sleep Quality in Preadolescence and Adolescence. [CrossRef]
  9. Cai, H., Xi, H. T., An, F., Wang, Z., Han, L., Liu, S.,... & Xiang, Y. T. (2021). The association between Internet addiction and anxiety in nursing students: a network analysis. Frontiers in Psychiatry, 12, 723355. [CrossRef]
  10. Cash, H., D Rae, C., H Steel, A., & Winkler, A. (2012). Internet addiction: A brief summary of research and practice. current psychiatry reviews, 8(4), 292-298. [CrossRef]
  11. Chang, E., & Kim, B. (2020). School and individual factors on game addiction: A multilevel analysis. International Journal of Psychology, 55(5), 822-831. [CrossRef]
  12. Chang, F.-C., Chiu, C.-H., Chen, P.-H., Chiang, J.-T., Miao, N.-F., Chuang, H.-Y., & Liu, S. (2019). Children's use of mobile devices, smartphone addiction and parental mediation in Taiwan. Computers in Human Behavior, 93, 25-32. [CrossRef]
  13. Chen, L., Nath, R., & Tang, Z. (2020). Understanding the determinants of digital distraction: An automatic thinking behaviour perspective. Computers in Human Behavior, 104, 106195. [CrossRef]
  14. Chemnad, K., Aziz, M., Abdelmoneium, A. et al. (2023). Adolescents’ Internet addiction: Does it all begin with their environment? Child Adolesc Psychiatry Ment Health 17, 87 (2023). [CrossRef]
  15. Chou, C., & Hsiao, M. C. (2000) Internet addiction, usage, gratification, and pleasure. experience: The Taiwan college students' case. Computers & Education, 35(1), 65-80. [CrossRef]
  16. Chung, T. W., Sum, S. M., & Chan, M. W. (2019). Adolescent internet addiction in Hong Kong: Prevalence, psychosocial correlates, and prevention. Journal of Adolescent Health, 64(6), S34-S43. [CrossRef]
  17. Ding, K., & Li, H. (2023). Digital Addiction Intervention for Children and Adolescents: A Scoping Review. Int J Environ Res Public Health. 2023 Mar 8;20(6):4777. [CrossRef]
  18. Dresp-Langley, B., & Hutt, A. (2022). Digital Addiction and Sleep. International Journal of Environmental Research and Public Health, 19(11), Art. 11. [CrossRef]
  19. Eickelmann, B., Casamassima, Gianna, Labusch, Amelie, Drossel, Kerstin, Sisask, Merike, Teidla-Kunitsõn, Gertha, Karatzogianni, Athina, Parsanoglou, Dimitris, Symeonaki, Maria, Gudmundsdottir, Greta, Holmarsdottir, Halla Bjørk, Mifsud, Louise, & Barbovschi, Monica (2022). Children and young people's narratives and perceptions of ICT in education in selected European countries complemented by perspectives of teachers and further relevant stakeholders in the educational context. [CrossRef]
  20. Fossum, I. N., Nordnes, L. T., Storemark, S., S., Bjorvatn, B., & Pallesen, S. (2014). The association between use of electronic media in bed before going to sleep and insomnia symptoms, daytime sleepiness, morningness, and chronotype. Behavioural sleep medicine, 12(5), 343-357. [CrossRef]
  21. https://doi.org/10.1556/2006.8.2019.32. [CrossRef]
  22. Griffiths, M. (1995, February). Technological addictions. In Clinical psychology forum (pp. 14-14). Division of Clinical Psychology of the British Psychol Soc. [CrossRef]
  23. Hale, L., & Guan, S. (2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50-58. [CrossRef]
  24. HITSA. (2020). Schools in Estonia have switched to digital remote learning 21 March 2020. https://www. hitsa.ee/about-us/news/estonia-has-switched-to-digital-remote-learning.
  25. Hou, H., Jia, S., Hu, S., Fan, R., Sun, W., Sun, T., & Zhang, H. (2012). Reduced striatal dopamine transporters in people with internet addiction disorder. Journal of Biomedicine and Biotechnology, 2012. [CrossRef]
  26. Honmore, V. M. (2023). Social Media Addiction, Loneliness, Sleep Quality and Gender among Adolescents. Indian Journal of Health & Wellbeing, 14(4), 473–476.
  27. International Classification of Diseases (ICD). (2020). Available at https://www.who.int/standards/classifications/classification-of-diseases.
  28. Joseph, R., & Hamiliton-Ekeke, J.-T. (2016). A Review of Digital Addiction: A Call for Safety Education. Journal of Education and E-Learning Research, 3(1), Art. 1. [CrossRef]
  29. Karacic, S., & Oreskovic, S. (2017). Internet Addiction Through the Phase of Adolescence: A Questionnaire Study. JMIR Mental Health, 4(2), e5537. [CrossRef]
  30. Karakose, T., Tülübaş, T., & Papadakis, S. (2022). Revealing the Intellectual Structure and Evolution of Digital Addiction Research: An Integrated Bibliometric and Science Mapping Approach. International Journal of Environmental Research and Public Health, 19(22), 14883. [CrossRef]
  31. Kesici, A., & Tunç, N. F. (2018). Investigating the Digital Addiction Level of the University Students According to Their Purposes for Using Digital Tools. Universal Journal of Educational Research, 6(2), 235-241. [CrossRef]
  32. Kim, K., Ryu, E., Chon, M.-Y., Yeun, E.-J., Choi, S.-Y., Seo, J.-S., & Nam, B.-W. (2006). Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. International Journal of Nursing Studies, 43(2), 185-192. [CrossRef]
  33. Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventive medicine, 85, 36-41. [CrossRef]
  34. Li, J.-B., Lau, J. T. F., Mo, P. K. H., Su, X.-F., Tang, J., Qin, Z.-G., & Gross, D. L. (2017). insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China. Journal of Behavioral Addictions, 6(4), 554-563. [CrossRef]
  35. Liao, C. H., & Wu, J. Y. (2022). deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, 104599. [CrossRef]
  36. Libman E., Fichten C., Creti L., Conrod K., Tran D.L., Grad R., Jorgensen M., Amsel R., Rizzo D., Baltzan M., Pavilanis ABailes S. (2016). Refreshing Sleep and Sleep Continuity Determine Perceived Sleep Quality. Sleep Disord. 2016:7170610. [CrossRef]
  37. Malak, M. Z., Khalifeh, A. H., & Shuhaiber, A. H. (2017). Prevalence of Internet Addiction and associated risk factors in Jordanian school students. Computers in Human Behavior, 70, 556-563. [CrossRef]
  38. Medic, G., Wille, M., & Hemels, M. E. (2017). Short-and long-term health consequences of sleep disruption. Nature and science of sleep, 151-161. [CrossRef]
  39. Meng, S.-Q., Cheng, J.-L., Li, Y.-Y., Yang, X.-Q., Zheng, J.-W., Chang, X.-W., Shi, Y., Chen, Y., Lu, L., Sun, Y., Bao, Y.-P., & Shi, J. (2022). Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clinical Psychology Review, 92, 102128. [CrossRef]
  40. Mohan, A., Sen, P., Shah, C., Jain, E., & Jain, S. (2021). Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: Digital eye strain among kids (DESK study-1). Indian Journal of Ophthalmology, 69(1), 140-144. [CrossRef]
  41. Muidre, J. & Raudkivi, J. (2022). Use of digital tools in school in an inclusive education based on the example of 5th grade students in two primary schools. [Master thesis]. ETERA. https://www.etera.ee/s/ofIwwUK7Dk.
  42. Nagata JM, Cortez CA, Cattle & CJ, et al. (2022). Screen Time Use Among US Adolescents During the COVID-19 Pandemic: Findings from the Adolescent Brain Cognitive Development (ABCD) Study. JAMA Pediatr, 176(1), 94–96. [CrossRef]
  43. Park, S., Holloway, S. D., Arendtsz, A., Bempechat, J., & Li, J. (2012). engaged in learning? A time-use study of within-and between-individual predictors of emotional engagement in low-performing high schools. Journal of youth and adolescence, 41, 390-401. [CrossRef]
  44. Peper, E., & Harvey, R. (2018). digital addiction: increased loneliness, anxiety, and anxiety. depression. NeuroRegulation, 5(1), 3-3. [CrossRef]
  45. Pies, R. (2009). Should DSM-V designate "Internet addiction" a mental disorder? Psychiatry (Edgmont), 6(2), 31.
  46. Raney, H., Testa, A., Jackson, D. B. K, Ganson, K.T., & Nagata, J.M. (2022). Associations Between Adverse Childhood Experiences, Adolescent Screen Time and Physical Activity During the COVID-19 Pandemic, Academic Pediatrics, Volume 22 (8), Pages 1294-12.
  47. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67. [CrossRef]
  48. Saikia, A. M., Das, J., Barman, P., & Bharali, M. D. (2019). Internet addiction and its relationships with depression, anxiety, and stress in urban adolescents of Kamrup District, Assam. Journal of family & community medicine, 26(2), 108. [CrossRef]
  49. Salmela-Aro K., Kiuru N., Leskinen E., Nurmi J.-E. (2009). School Burnout Inventory (SBI): Reliability and validity. European Journal of Psychological Assessment, 25(1), 48–57. [CrossRef]
  50. Salmela-Aro, K., Upadyaya, K., Hakkarainen, K., Lonka, K., & Alho, K. (2017). The Dark Side of Internet Use: Two Longitudinal Studies of Excessive Internet Use, Depressive Symptoms, School Burnout and Engagement Among Finnish Early and Late Adolescents. Journal of Youth and Adolescence, 46(2), 343-357. [CrossRef]
  51. Santl, L., Brajkovic, L., & Kopilaš, V. (2022). Relationship between Nomophobia, Various Emotional Difficulties, and Distress Factors among Students. European Journal of Investigation in Health, Psychology and Education., 12(7), Art. 7. [CrossRef]
  52. Seema, R., Heidmets, M., Konstabel, K., & Varik-Maasik, E. (2022) Development and Validation of the Digital Addiction Scale for Teenagers (DAST). Journal of Psychoeducational Assessment, 40(2), 293-304. [CrossRef]
  53. Seema, R., & Varik-Maasik, E. (2023). Students' digital addiction and learning difficulties: shortcomings of surveys in inclusion. Frontiers in Education, 8, ARTN 1191817. [CrossRef]
  54. Sipal, R. F., & Bayhan, P. (2010). Preferred computer activities during school age: Indicators of internet addiction. Procedia-Social and Behavioral Sciences, 9, 1085-1089. [CrossRef]
  55. Sukk, M., Soo, K. (2018). Preliminary results of the EU Kids Online Estonia 2018 survey. Kalmus, V., Kurvits, R., Siibak, A. (eds). Tartu: University of Tartu, Institute of Social Sciences. Available at: https://sisu.ut.ee/sites/default/files/euko/files/eu_kids_online_eesti_2018_raport.pdf.
  56. Time to Log Off (2016). Digital Addiction. Retrieved from https://www.itstimetologoff.com/digital-addiction/.
  57. Tomaszek, K., & Muchacka-Cymerman, A. (2020). Examining the relationship between student school burnout and problematic internet use. Educational Sciences: Theory and Practice, 20(2), 16-31. [CrossRef]
  58. Turel, Y., & Toraman, M. (2015). The relationship between Internet addiction and academic success of secondary school students. Anthropologist, 20.
  59. Turner, L., Bewick, B. M., Kent, S., Khyabani, A., Bryant, L., & Summers, B. (2021) When Does a Lot Become Too Much? A Q Methodological Investigation of UK Student Perceptions of Digital Addiction. International Journal of Environmental Research and Public Health, 18(21), Art. 21. [CrossRef]
  60. Väljas, J. (2023). Digital devices usage habits of Estonian primary school students based on the results of the Student Survey 2022. Tallinn University. [Master thesis]. ETERA. https://www.etera.ee/zoom/200233/view?page=1&p=separate.
  61. Walburg, V., Mialhes, A., & Moncla, D. (2016). Does school-related burnout influence. problematic Facebook use? Children and Youth Services Review, 61, 327-331. [CrossRef]
  62. Wang, H., Zhou, X., Lu, C., Wu, J., Deng, X., & Hong, L. (2011). Problematic Internet Use in High School Students in Guangdong Province, China. PLOS ONE, 6(5), e19660. [CrossRef]
  63. Wang, Z., Yu, C., Zhang, W., Chen, Y., Zhu, J., & Liu, Q. (2017). School climate and adolescent aggression: A moderated mediation model involving deviant peer affiliation and sensation seeking. Personality and Individual Differences, 119, 301-306. [CrossRef]
  64. Weil L.G., Fleming S.M., Dumontheil I., Kilford E.J., Weil R.S., Rees G., Dolan R.J., Blakemore S.J. (2013). The development of metacognitive ability in adolescence. Conscious Cogn. 2013 Mar;22(1):264-71. [CrossRef]
  65. Weinstein, A. M. (2017). An update overview on brain imaging studies of internet gaming disorder. Frontiers in psychiatry, 8, 185. [CrossRef]
  66. Wiederhold, B. K., & Riva, G. (Eds.). (2012). Annual Review of Cybertherapy and Telemedicine 2012: Advanced Technologies in the Behavioral, Social and Neurosciences.
  67. Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237-244. [CrossRef]
Preprints 107768 g001
Figure 1. Digital addiction arithmetic mean score by grade.
Figure 1. Digital addiction arithmetic mean score by grade.
Preprints 107768 g001
Preprints 107768 g002
Figure 2. Digital addiction arithmetic mean score by gender.
Figure 2. Digital addiction arithmetic mean score by gender.
Preprints 107768 g002
Table 1. Descriptive statistics for student ratings of digital addiction symptoms.
Table 1. Descriptive statistics for student ratings of digital addiction symptoms.
Items Mean Std. Deviation
1. I feel bored if I cannot use my digital device 4.34 1.47
2. I feel uneasy when I do not know what my friends are saying on social media 2.93 1.65
3. I am grumpy if I cannot use digital devices 3.09 1.56
4. I end up spending more time using my digital device than initially planned 4.45 1.64
5. As soon as I put my device away, I feel the urge to use it again 3.38 1.61
6. I keep an eye on the digital device even when I talk to someone 3.12 1.70
7. I use a digital device while eating 3.36 1.84
8. I keep an eye on my digital device during lessons 2.83 1.68
9. I play or chat on my device while walking on the street 2.95 1.66
10. I play or chat on my device when in bed before falling asleep 4.57 1.95
Digital addiction summary score Mean 35.02 11.28
Note. N = 5192. The questionnaire was introduced as follows: „Below you will find statements describing the use of digital devices and the ways it makes the user feel. How often do these situations apply to you?” The seven points scale was: Never-1, rarely-2, somewhat rarely-3, so-so-4, often-5, somewhat often-6, very often-7.
Table 2. Correlations between (DAST) digital addiction symptoms and other characteristics.
Table 2. Correlations between (DAST) digital addiction symptoms and other characteristics.
Digital addiction School burnout Screen time on school days Screen time on weekends Feeling refreshed in the morning
Digital addiction 1 .45** .41** .40** -.24**
School burnout .45** 1 .25** .25** -.37**
Screen time on school days .41** .25** 1 .71** -.18**
Screen time on weekends .40** .25** .71** 1 -.18**
Feeling refreshed in the morning -.24** -.37** -.18** -.18** 1
Note: ** Correlation is significant, p < .001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
bsky
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

Privacy Settings

© 2025 MDPI (Basel, Switzerland) unless otherwise stated