Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Problematic smartphone and social media use among undergraduate students during the COVID-19 pandemic: In the case of southern Ethiopia universities

  • Nebiyu Mengistu ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    nebiyumen@gmail.com

    Affiliation Department of Psychiatry, Dilla University, Dilla, Ethiopia

  • Endashaw Habtamu,

    Roles Conceptualization, Data curation, Formal analysis, Software, Validation, Writing – review & editing

    Affiliation Department of Psychiatry, Dilla University, Dilla, Ethiopia

  • Chalachaw Kassaw,

    Roles Conceptualization, Data curation

    Affiliation Department of Psychiatry, Dilla University, Dilla, Ethiopia

  • Derebe Madoro,

    Roles Data curation, Formal analysis, Software, Writing – original draft, Writing – review & editing

    Affiliation Department of Psychiatry, Dilla University, Dilla, Ethiopia

  • Wondwosen Molla,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software

    Affiliation Department of Midwifery, Dilla University, Dilla, Ethiopia

  • Aregahegn Wudneh,

    Roles Conceptualization, Data curation, Methodology, Supervision, Visualization, Writing – original draft

    Affiliation Department of Midwifery, Dilla University, Dilla, Ethiopia

  • Lulu Abebe,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Resources, Software, Writing – original draft, Writing – review & editing

    Affiliation Department of Psychiatry, Dilla University, Dilla, Ethiopia

  • Bereket Duko

    Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing

    Affiliation Curtin School of Population Health, Curtin University, Perth, WA, Australia

Abstract

Background

Smartphone and social media use are supposed to be integral parts of university students’ daily lives. More specifically, smartphones and social media are frequently used for communication in daily life during the COVID-19 pandemic. Nonetheless, uninterrupted and persistent use of these technologies may lead to several psychological problems. Even though smartphones and social media were used more frequently during the pandemic, there is no evidence suggesting that the studies were not undertaken in low-income countries, including Ethiopia. Therefore, the current study aimed to assess problematic smartphone use and social media use among undergraduate university students in southern Ethiopia.

Methods

A cross-sectional study was carried out among 1,232 university students using a simple random sampling technique. The Bergen Social Media Addiction Scale and Smartphone Application-Based Addiction Scale were used to collect data on social media and smartphone use, respectively. The Beck Depression Inventory, Generalized Anxiety Assessment Tool, Rosenberg Self-Esteem Scale, and Pittsburg Sleep Quality Index were standardized tools used to measure other independent variables. To identify factors, simple and multiple linear regression analyses were performed. A p-value of 0.05 was used to determine statistical significance.

Results

The overall response rate was 95%. The mean scores for problematic smartphone and problematic social media use were 17 ± 3.3/36 and 12.7 ± 2.2/30, respectively. A linear regression model revealed that being female, first-year students and poor sleep quality were significantly associated with problematic smartphone use. Factors associated with problematic social media use (PSMU) were depression, substance use, and urban residence.

Conclusions

This study identified significant problems with smartphone and social media use among university students. Therefore, it is preferable to provide psychological counselling, educate students about safe, beneficial, and healthy internet use, and focus on recognized high-risk groups in order to give them special attention. It is also preferable to seek counselling about substance use. It is preferable to regularly screen and treat individuals with psychological problems in collaboration with stakeholders.

Introduction

Coronavirus disease (COVID-19) is a human-to-human communicable respiratory disease caused by a new strain of coronavirus associated with acute respiratory syndrome (SARS-CoV) [1]. After it was originally identified in December 2019 in Wuhan, China, as an emerging respiratory disease, it was abbreviated as COVID-19 [2].

Since the start of the COVID-19 pandemic (on August 20, 2020), 219.3 million people have been infected [3]. Since the outbreak started, Ethiopia’s government has taken a number of measures to stop the spread of COVID-19, including halting schools, enforcing spatial distance, enforcing the use of medical face masks, and banning travel to densely populated areas [4]. As a result of the nationwide actions to stop the spread of Covid-19, schools, institutions, and businesses have shifted to online platforms for virtual learning and employment [1, 4]. This new lifestyle, enforced by staying at home and under quarantine, has brought new challenges socially, economically, physiologically, and psychologically. The COVID-19 pandemic, as well as the accompanying home confinement and social isolation, have heightened fear and an unpleasant mood across society [59]. As a result of the pandemic’s various challenging social and psychological changes, daily use of a smart phone and social media becomes a repetitive activity on which every aspect of daily life in every part of the world depends [10, 11].

Problematic smartphone use is a type of behavioral or psychological dependence on mobile devices and is strongly related to other types of excessive use of digital media, like internet addiction disorder. Additionally, a theorized form of psychological or behavioral dependence on social media platforms, sometimes known as internet addiction disorder, is problematic social media use, also referred to as social media addiction or social media overuse [12]. It is a new and attractive subject considered as a behavior-based addiction in recent years [13].

Problematic uses can include preoccupation with mobile communication, excessive money or time spent on mobile phones, and use of mobile phones in socially or physically inappropriate situations such as driving an automobile. Increased use can also lead to adverse effects on relationships or mental or physical health and ensue anxiety if separated from a mobile phone or a sufficient signal. Preschool children and young adults are at the highest risk for problematic smartphone use [14, 15].

Currently, 60% of world population used internet via smart mobile phone and 6.7% of Ethiopian population has used social media [1618].

Excessive use of smartphones and social media websites, particularly among young adults, is likely to be exacerbated by the essential social-distancing measures of the pandemic [19].

Although smartphones with internet access could be useful for gathering information about the COVID-19 outbreak and communicating with others while under quarantine [16, 20], excessive use of smartphones can lead to maladaptive behaviors such as procrastination and skipping daily tasks, as well as undesirable health repercussions such as sleeplessness and neck/back pain [21].

The majority of studies have found that problematic use of smartphone and social media has a negative impact on one’s physical health and has associations with depression, [22] poor sleep quality, mood changes, and poor health outcomes such as obesity and low self-esteem [23]. Furthermore, the COVID-19 epidemic, as well as the accompanying home quarantine and social-distance measures, have boosted anxieties and negative emotions and felt across society [16]. Several people use smartphones and the internet as coping methods to cope with their emotions. However, the employment of such coping methods may have a number of negative implications, including functional deficits as a result of excessive use [24, 25].

According to a survey conducted in Asian countries like the Middle East, China, Japan, and Bangladesh, the mean scores for problematic smartphone and problematic social media use were 20.8 ± 6.8 and 14.7 ± 4.8 respectively. Younger age, poor sleep, watching television, anxiety, and depression were all associated with problematic smartphone and social media use. Moreover, problematic social media use was associated with being female, urban residence, and alcohol consumption [2629].

A cross-sectional survey with 425 participants and a median age of 19 years was conducted in South Africa, 59.5% of the participants were young women. Overall, 43.3% had likely depression and 22.4% of the students in a Zambian study reported having a social media addiction. The most problematic smartphone use risk profile is that of a female, under the age of 21, with low self-esteem who lives away from home, making her more vulnerable to problems and also to depression and anxiety [30, 31].

Another cross-sectional survey was conducted on the prevalence and relationship between depression, anxiety, and smart phone addiction among young people in Nigeria. It showed that the prevalence of probable smart phone addiction was 10.2% and 23.4% at the risk of smart phone addiction. Depression, anxiety, financial income level, and being married, using the smart phone for browsing social media and e-mail were the most important predictors of problematic smartphone use [32].

Several studies have been conducted to determine the prevalence of smartphone addiction risks in various countries among college students [33, 34]. However, there were limited studies done in Sub-Saharan African countries that focused on the various aspects of smartphone and social media usage, specifically during the COVID-19 pandemic.

To the best of the investigators’ knowledge, there has been no previous study focusing on problematic smart phone use (PSPU) and problematic social media use (PSMU) among undergraduate university students in Ethiopia during the COVID-19 pandemic. The current study also addressed important contributing factors that stakeholders could control to provide information for students, the need for education about the safe, beneficial, and healthy practices of using social media and smartphones, and the management of psychological issues among students.

Therefore, the aim of this study was to assess problematic smart phone use and problematic social media use and associated factors among undergraduate university students in Ethiopia during the COVID-19 pandemic.

Methods and materials

Study design and period

An institution based cross-sectional study was conducted from January 2021 to February 2021 at Dilla and Hawassa Universities.

Study setting

The study was carried out at the two selected universities in southern Ethiopia, Dilla and Hawassa. The distance between the capital city of Ethiopia and Dilla is 360 kilometers. Additionally, Hawassa University is located in Ethiopia, 278 kilometres south of Addis Ababa. At Dilla University and Hawassa University, respectively, the university had a total of 25,104 and 30,108 undergraduate students throughout the study period.

Sample size determination, sampling techniques and procedures

The minimum number of sample size required for this study was determined by using the formula to estimate the single population mean, n = (Z alpha/2)2(δ2)/d2, by using the following assumptions: standard deviation (SD) of the mean problematic smart phone score 12.08 [16], a 95% confidence interval (CI) of 1.96 (Z alpha/2 = 1.96), a 1% margin of error (d, 0.01), and a nonresponse rate of 10%. We applied the single population mean formula to give n = (1.96)2* (12.08)2/ (1)2 = 560. By considering a 10% non-response rate and design effects of 2, the final sample size becomes 1,232.

We used a multistage cluster sampling procedure to select a sample of undergraduate students. Initially, three colleges, and two schools were selected by using simple random sampling technique (lottery method) from both universities. In the second stage, the selected colleges and schools were stratified based on the departments.

Dilla University (8 departments) and Hawassa University (11 departments) each have nineteen (19) departments in the selected colleges and schools. All this departments with their level of academic years (batches) were included in this study and the design effect was used. The final sample size was allocated proportionally for each department based on the number of their students with their level academic years (batches). Finally, a simple random sampling technique was used to select participants by using their ID number as a sampling frame.

Study variables

The dependent variables in this study were Problematic smart phone use (PSPU) and problematic social media use (PSMU) and independent variables were socio-demographic factors (Age, Sex, Religion, Residence, marital status, Academic year, Financial support), Individual level factors (common mode of internet for smartphone and social media access and experience) and Psycho-social and Substance use factors (Depression, Anxiety, sleep quality, Social support, Self-esteem, Peer pressure and current substance use: chat, alcohol, cigarette and others).

Data collection instruments

The data were collected using self-administered, structured questionnaires. The questionnaire was divided into five(5) sections; It included socio-demographic factors, psycho-social and substance use factors, characteristics of common mode of internet for smartphone and social media access and experience, problematic smart phone use and social media use were used to collect the data. The questionnaire was written in English, translated into Amharic, and then retranslated back into English to ensure consistency.

The dependent variable was measured using the Bergen Social Media Addiction Scale (BSMAS), which was used to assess social media addiction. An advanced psychometric testing (e.g. IRT and network analysis) highlighted that the BSMAS is an easy-to-use, reliable, and valid instrument to assess the social media addiction. This tool was cross-culturally validated instrument with good sensitivity and specificity. It has a Cronbach’s alpha of 0.81. The tool has a five-likert scale ranging from 1 (very rarely) to 5 (very often). It was scored out of 30, and the highest score was considered a problematic social media use [35].

Another outcome variable was measured using the Smartphone Application Based Addiction Scale (SABAS), which was used to assess smart phone addiction. The internal reliability of the scale was good (Cronbach’s alpha 0.88). The SABAS appears to be a valid and reliable ultra-brief tool for a quick and easy assessment of smartphone application-based addiction symptoms. It contains six items and is scored out of 30. All items were rated from 1 (strongly disagree) to 6 (strongly agree). and the highest score was considered a problematic smartphone use [36].

Depression was measured using the Beck Depression Inventory (BDI). It is a standardized instrument that consists of a list of 21 sets of statements. Respondents are asked to choose the statement from each set that most closely describes them or their feelings. Total scores on the BDI were computed by summing the responses to each question. Higher scores indicate depressed mood. Scores were used as a continuous measure or a categorical variable; those scoring > 13 were considered depressed [37].

Anxiety was assessed using a GAD-7 assessment tool and contains seven items that can be responded to on a four-point Likert scale ranging from 0 (Not at all) to 3 (Nearly every day). The cut-off score ≥10 and had excellent reliability (Cronbach’s alpha = 0.85) [38].

The Rosenberg Self-Esteem Scale was used to assess the level self-esteem. It was a 10 item likert scale scored ranging 1 to 4. The highest score was considered as highest self-esteem [39].

Sleep quality was assed using a 19 item sleep Quality Index (PSQI), a self-report containing seven components of sleep. Each item has 0 to 3 scores. A total score was out 21 and those who scored > 5/21 was considered as poor sleep quality [40].

Data quality assurance

First, the questionnaire was prepared in English and translated into the local language (Amharic) and then back to English by senior English language expertise to check the accuracy. The questionnaire was pretested at Bulle Horra University among 5% of the calculated sample. During the pretest, the questionnaire was assessed for its clarity, readability, comprehensiveness, accuracy, and optimal time for completing the questioners. The optimal time to complete the questioners and the readability of the items were updated and revised based on the results of the pretest. Two days training were given for the data collectors and supervisors.

Data analysis and interpretation

The collected data were coded, entered in to EPiDATA version 3.1 and exported to SPSS version 24 for analysis. Simple and multiple linear regression analysis were used to assess the correlates of independent factors with problematic smartphone and social media use with a P-value of <0.25 were considered as candidates of multiple linear regressions. Variables with P- value less than 0.05 were considered as significantly correlated with smart phone and social media use and B coefficient was used to predict the strength of the correlations of variables with smart phone and social media use.

Ethics approval and consent to participation

The Institutional Review Board (IRB) of Dilla and Hawassa University’s College of Medicine and Health Sciences granted ethical approval. After the purpose and objectives of the study had been informed, oral and written consent was obtained from each study participant before the start of the data collection. To maintain the anonymity and confidentiality of information, similar data collection procedure was in place. And all necessary methods were carried out in accordance with the guidelines of institutional and Declaration of Helsinki.

Result

Socio demographic characteristics of respondents

A total of 1,232 study participants, Most of them 800(64.9%) were age 20-24 year old and 750(60.8%) males. Nearly two-thirds 860(69.7%) of them are originated from rural residence and 786(63.8%) of them were senior student (≥2nd year student) (Table 1).

thumbnail
Table 1. Socio demographic characteristics of Dilla and Hawassa university undergraduate students, Ethiopia, 2021 (N = 1,232).

https://doi.org/10.1371/journal.pone.0280724.t001

Characteristics of common mode of internet for smartphone and social media access and experience

Regarding the mode of internet access and its experience, most of the respondents 690(55.9%) were used internet service for above12 months internet use experience and 672(54.6%) of them were used ≥5 hours per day (Table 2).

thumbnail
Table 2. Characteristics of common mode of internet for smartphone and social media access and experience of Dilla and Hawassa university undergraduate students, Ethiopia, 2021 (N = 1,232).

https://doi.org/10.1371/journal.pone.0280724.t002

Psycho-social and substance use characteristics

According to psycho-social and substance use characteristics, out of all respondents 310(25.2%) were developed probable depression, 344(28%) poor sleep quality, 458(37.1%) anxiety and 534(43.4%) low self-esteem. The Current use of substances among 1,232 study participants, 314(25.5%) of them were used alcohol and 418(33.9%) were used khat (Table 3).

thumbnail
Table 3. Psycho-social and substance use characteristics of Dilla and Hawassa university undergraduate students, Ethiopia, 2021 (N = 1,232).

https://doi.org/10.1371/journal.pone.0280724.t003

Problematic smartphone and social media use and its associated factors among undergraduate students during the COVID-19 pandemic

The mean scores of problematic smartphone use (PSPU) and problematic social media use (PSMU) among undergraduate students at Dilla and Hawassa University were 17 ± 3.3/36 and 12.7 ± 2.2/30, respectively. Multiple linear regression revealed that being female, fresh man students and poor sleep quality were found to be statistically significant with problematic smartphone use (Table 4). Whereas, depression, current substance use, and urban residence were found to be statistically significant with problematic social media use (Table 5).

thumbnail
Table 4. Factors associated with problematic smartphone use among Dilla and Hawassa university students, Ethiopia, 2021, (N = 1,232).

https://doi.org/10.1371/journal.pone.0280724.t004

thumbnail
Table 5. Factors associated with problematic social media use among Dilla and Hawassa university students, Ethiopia, 2021, (N = 1,232).

https://doi.org/10.1371/journal.pone.0280724.t005

Discussion

The use of smart phones and social media has increased substantially all around the world since the era of the pandemic [16]. According to the current study findings, more than half of the respondents, scored above the mean for problematic smart phone use, while one third of them scored above the mean for problematic social media use. Females, freshmen students, and poor sleep quality were shown to be characteristics linked with problematic smartphone use, whereas depression, substance use, and urban living were found to be factors associated with problematic social media use (PSMU). This study finding was lower than the studies done in Lebanon [41], Zambia [42] and Bangladesh [43]. This variation may be due to the accessibility, knowledge and attitude difference towards smart phone use and social media use.

This study found that being females increase the problematic smart phone use score by 3.474 unit as compared to their counterpart which was supported by the studies conducted in Jordan [27], china [28], and japan [29]. The possible justification for this strong association could be due to the fact most female respondents accessed the internet on their smartphones to search for relevant information [28], and the majority of them said they used their smartphones for accessing academic information, reading news, entertainment, and listening to music.

Freshman students were showed an increment on problematic smart phone use score by 2.78 units as compared to senior students (≥ 2nd year students). The finding was similar with the studies conducted in Ghana [44], China [45]. This might be due to the fact that smartphones provide the ability to get answers really fast. In some situations, a student may not ask for clarification to a question he or she has in an open classroom because they can use their smartphone to get the answer they’re looking for. Audio and video can bring learning to life [46]. And another possible explanation is that new campus students encounter problems such as being separated from their families, adjusting to a new setting, making new acquaintances, and learning a new culture, all of which encourages them to stay glued to their smartphones [47].

Those respondents with poor sleep quality showed that a 5.83 unit increases in problematic smart phone use score as compared to their challengers which was supplemented by Saudi Arabia [48], Belgium [49] and United states [50]. This may be explained due to the fact that smart phone causes abnormal sleep inducing physiological process such as melatonin production associated with difficulty of sleep initiation and maintenance [51].

According to the current study finding result, respondents who had depression increase 2.45 units on problematic social media use which was similar with the study finding done in United states [52], Nigeria [30], South Africa [31]. The possible reason for this may be that people who are depressed may be more likely to utilize problematic social media platforms like Facebook, Twitter, and YouTube. A depressive state can make it difficult to manage stress, and subjects may turn to social media to distract themselves. Because of this, students who experience depressed symptoms frequently turn to social media to connect with distant friends and find temporary comfort, which leads to their addiction to the social media use [53, 54].

Those respondents with current substance use history had increase on problematic social media score by a 3.67 units as those with no current substance use history. This finding was supported by the studies done in Norway [55], Canada [56] and united states [57].

The drug’s mechanism of action causes users to search for entertaining content online while they are intoxicated or going through withdrawal. This could be explained by the biological impact of the drugs on the brain, which makes them stimulants of the central nervous system that can improve focus and alertness, uplift the mood, increase motivation for work, and have addictive or compulsive effects that are also linked to symptoms of problematic internet use. As a result, many individuals may be readily persuaded or driven to use the internet [57].

Those with urban residence had a problematic social media use score increase by a 4.54 unit as compared with those in rural area which was similar with the study finding in Bangladesh [43], Hungary [58]. The possible reasons explained due to the fact that living in a city gave you access to a variety of social media and technology, which you can utilize in your day-to-day activities.

Implications

Since the pandemic era, social media and smartphone usage have grown significantly all across the world. Prior to the discovery of the covid-19 virus, it was not widely used in Sub-Saharan African countries. Users of smartphones have more options because they can improve their capabilities by downloading various mobile applications. Many university students’ life revolve around their smartphones. However, it can be harmful to only have access to a smartphone and social media without specific directed educational activities. That is, using technology in an excessive or problematic way can cause a variety of psychological and mental conditions, such as anxiety, depression, substance abuse, and poor sleep quality. However, there was little information available on the problems with social media and problematic smartphone use during the COVID-19 pandemic. In order to reduce misconceptions, stakeholders like psychiatrists and psychologists should improve psychoeducation by addressing problematic smartphone and social media use. Governments should also provide institutional-based mental health services in light of the significance of psychological education in addressing problematic smartphone and social media use among university students.

Limitation

The current study was limited to assessing students’ learning behaviors which could be modifiable determinants in problematic smart phone and social media use. Another limitation of this study is that, due to the cross-sectional nature of the study design, it does not show any cause-effect relationship. There may be a social desirability bias, where students may not have provided exact web browsing statistics in order to impress the investigator.

Conclusion

There was significant, problematic smart phone and social media use among university students. This study revealed the psychosocial and sociodemographic characteristics that require treatment. The results suggest that in order to combat the expected increase in smart phone and social media use, it is better to counsel on substance use and its effects, educate on safe, valuable, and healthy smartphone or internet use, and give special emphasis to identified high-risk groups. Additionally, students need to be educated about safe, valuable, and healthy internet use. Furthermore, it is better to have routine screening and treatment of individuals having such psychological problems through collaboration with stakeholders.

Acknowledgments

First of all, we would like to acknowledge Dilla and Hawassa University, for giving this golden opportunity. We would like to express our deepest gratitude to also Dilla and Hawassa university student service director and registrar office for their cooperation to provide the necessary data about the study area.

References

  1. 1. Aregu MB, Kanno GG, Ashuro Z, Alembo A, Alemayehu A. Safe water supply challenges for hand hygiene in the prevention of COVID-19 in Southern Nations, Nationalities, and People’s Region (SNNPR), Ethiopia. Heliyon. 2021;7(11):e08430. pmid:34841117
  2. 2. Erdem H, Lucey DR. Healthcare worker infections and deaths due to COVID-19: A survey from 37 nations and a call for WHO to post national data on their website. International Journal of Infectious Diseases. 2021;102:239. pmid:33130210
  3. 3. Elhadi M, Msherghi A, Alkeelani M, Zorgani A, Zaid A, Alsuyihili A, et al. Assessment of healthcare workers’ levels of preparedness and awareness regarding COVID-19 infection in low-resource settings. The American journal of tropical medicine and hygiene. 2020;103(2):828. pmid:32563273
  4. 4. Baye K. COVID-19 prevention measures in Ethiopia: current realities and prospects: Intl Food Policy Res Inst; 2020.
  5. 5. Sahu P. Closure of universities due to coronavirus disease 2019 (COVID-19): Impact on education and mental health of students and academic staff. Cureus. 2020; 12 (4): e7541. (Eng) pmid:32377489
  6. 6. Brooks S, Webster R, Smith L, Woodland L, Wessely S, Greenberg N, et al. (2020). The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The Lancet. 2020;395(10227):912–20.
  7. 7. Mattioli AV, Puviani MB, Nasi M, Farinetti A. COVID-19 pandemic: the effects of quarantine on cardiovascular risk. European journal of clinical nutrition. 2020;74(6):852–5. pmid:32371988
  8. 8. Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Iosifidis C, et al. Las implicaciones socioeconómicas de la pandemia de coronavirus (COVID-19): una revisión. Revista internacional de cirugía (Londres, Inglaterra). 2020;78:185–93.
  9. 9. Tsehay A, Hareru HE, Molla W, Mengistu N, Kaso AW, Ashuro Z, et al. Factors associated with preventive practices of COVID-19 among health care workers in Dilla University Hospital, Southern Ethiopia. Environmental Challenges. 2021;5:100368.
  10. 10. Zou Z, Wang H, d’Oleire Uquillas F, Wang X, Ding J, Chen H. Definition of substance and non-substance addiction. Substance and Non-substance Addiction. 2017:21–41. pmid:29098666
  11. 11. Mengistu N, Tarekegn D, Molla W, Shumye S. Internet Addiction and its Associated Factors Among Dilla University Undergraduate Students, Dilla, Ethiopia, 2019: A Cross-Sectional Study. 2021.
  12. 12. Arnetz BB, Hillert L, Åkerstedt T, Lowden A, Kuster N, Ebert S, et al. Effects from 884 MHz mobile phone radiofrequency on brain electrophysiology, sleep, cognition, and well-being. 2015.
  13. 13. Vizeshfar F. Assessment of the internet addiction between Larian net users. 2005.
  14. 14. Servidio R, Koronczai B, Griffiths MD, Demetrovics Z. Problematic Smartphone Use and Problematic Social Media Use: The Predictive Role of Self-Construal and the Mediating Effect of Fear Missing Out. Frontiers in Public Health. 2022;10. pmid:35284373
  15. 15. Csibi S, Griffiths MD, Demetrovics Z, Szabo A. Analysis of problematic smartphone use across different age groups within the ‘components model of addiction’. International Journal of Mental Health and Addiction. 2021;19(3):616–31.
  16. 16. Saadeh H, Al Fayez RQ, Al Refaei A, Shewaikani N, Khawaldah H, Abu-Shanab S, et al. Smartphone use among university students during COVID-19 quarantine: an ethical trigger. Frontiers in public health. 2021;9. pmid:34381747
  17. 17. Stork C, Calandro E, Gillwald A. Internet going mobile: internet access and use in 11 African countries. info. 2013.
  18. 18. Manyazewal T, Woldeamanuel Y, Blumberg HM, Fekadu A, Marconi VC. The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia. NPJ digital medicine. 2021;4(1):1–13.
  19. 19. King DL, Delfabbro PH, Billieux J, Potenza MN. Problematic online gaming and the COVID-19 pandemic. Journal of Behavioral Addictions. 2020;9(2):184–6. pmid:32352927
  20. 20. Wan J, Xing S, Ding L, Wang Y, Gu C, Wu Y, et al. Human-IgG-neutralizing monoclonal antibodies block the SARS-CoV-2 infection. Cell reports. 2020;32(3):107918. pmid:32668215
  21. 21. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The lancet. 2020;395(10227):912–20. pmid:32112714
  22. 22. Lai C, Mak K, Watanabe H, Jeong J, Kim D, Bahar N, et al. The mediating role of Internet addiction in depression, social anxiety, and psychosocial well-being among adolescents in six Asian countries: a structural equation modelling approach. Public health. 2015;129(9):1224–36. pmid:26343546
  23. 23. Kim JH, Lau C, Cheuk K-K, Kan P, Hui HL, Griffiths SM. Brief report: Predictors of heavy Internet use and associations with health-promoting and health risk behaviors among Hong Kong university students. Journal of adolescence. 2010;33(1):215–20. pmid:19427030
  24. 24. Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, et al. Mental health problems and social media exposure during COVID-19 outbreak. Plos one. 2020;15(4):e0231924. pmid:32298385
  25. 25. Qiu J, Shen B, Zhao M, Wang Z, Xie B, Xu Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations. General psychiatry. 2020;33(2). pmid:32215365
  26. 26. Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, et al. Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: the role of psychological well-being and pandemic related factors. Frontiers in psychiatry. 2021;12:647386. pmid:33935834
  27. 27. Ameen N, Willis R, Shah MH. An examination of the gender gap in smartphone adoption and use in Arab countries: A cross-national study. Computers in Human Behavior. 2018;89:148–62.
  28. 28. Yang S-Y, Lin C-Y, Huang Y-C, Chang J-H. Gender differences in the association of smartphone use with the vitality and mental health of adolescent students. Journal of American college health. 2018;66(7):693–701. pmid:29565784
  29. 29. Tangmunkongvorakul A, Musumari PM, Tsubohara Y, Ayood P, Srithanaviboonchai K, Techasrivichien T, et al. Factors associated with smartphone addiction: A comparative study between Japanese and Thai high school students. PLoS One. 2020;15(9):e0238459. pmid:32898191
  30. 30. Ayandele O, Popoola O, Obosi A, Busari A. Depression, Anxiety and Smart phone Addiction among Young People in South West Nigeria. Covenant International Journal of Psychology. 2019;4(2).
  31. 31. Dietrich JJ, Otwombe K, Pakhomova TE, Horvath KJ, Hornschuh S, Hlongwane K, et al. High cellphone use associated with greater risk of depression among young women aged 15–24 years in Soweto and Durban, South Africa. Global Health Action. 2021;14(1):1936792. pmid:34431754
  32. 32. Ayandele O, Popoola O, Obosi A, Busari A. Depression, anxiety and smart phone addiction among young people in South West Nigeria. Covenant International Journal of Psychology. 2019.
  33. 33. Burkle FM Jr. Population‐based triage management in response to surge‐capacity requirements during a large‐scale bioevent disaster. Academic Emergency Medicine. 2006;13(11):1118–29. pmid:17015415
  34. 34. Lopez-Fernandez O. Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use. Addictive behaviors. 2017;64:275–80. pmid:26685805
  35. 35. Monacis L, De Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. Journal of Behavioral Addictions. 2017;6(2):178–86. pmid:28494648
  36. 36. Csibi S, Griffiths MD, Cook B, Demetrovics Z, Szabo A. The psychometric properties of the smartphone application-based addiction scale (SABAS). International journal of mental health and addiction. 2018;16(2):393–403. pmid:29670500
  37. 37. Abubakar A, Kalu RB, Katana K, Kabunda B, Hassan AS, Newton CR, et al. Adaptation and latent structure of the Swahili version of Beck Depression Inventory-II in a low literacy population in the context of HIV. PloS one. 2016;11(6):e0151030. pmid:27258530
  38. 38. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of internal medicine. 2006;166(10):1092–7. pmid:16717171
  39. 39. Martín-Albo J, Núñez JL, Navarro JG, Grijalvo F. The Rosenberg Self-Esteem Scale: translation and validation in university students. The Spanish journal of psychology. 2007;10(2):458–67. pmid:17992972
  40. 40. Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry research. 1989;28(2):193–213. pmid:2748771
  41. 41. Matar Boumosleh J, Jaalouk D. Depression, anxiety, and smartphone addiction in university students-A cross sectional study. PloS one. 2017;12(8):e0182239. pmid:28777828
  42. 42. Akakandelwa A, Walubita G. Students’ social media use and its perceived impact on their social life: A case study of the University of Zambia. The International Journal of Multi-Disciplinary Research. 2018;5(3):1–14.
  43. 43. Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, et al. Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: the role of psychological well-being and pandemic related factors. Frontiers in psychiatry. 2021;12. pmid:33935834
  44. 44. Darko-Adjei N. The use and effect of smartphones in students’ learning activities: Evidence from the University of Ghana, Legon. 2019.
  45. 45. Atas AH, Çelik B. Smartphone use of university students: Patterns, purposes, and situations. Malaysian Online Journal of Educational Technology. 2019;7(2):59–70.
  46. 46. Li S, Yamaguchi S, Takada J-i. Understanding factors affecting primary school teachers’ use of ICT for student-centered education in Mongolia. International Journal of Education and Development using ICT. 2018;14(1).
  47. 47. Jesse GR, editor Smartphone and app usage among college students: Using smartphones effectively for social and educational needs. Proceedings of the EDSIG Conference; 2015.
  48. 48. Ibrahim NK, Baharoon BS, Banjar WF, Jar AA, Ashor RM, Aman AA, et al. Mobile phone addiction and its relationship to sleep quality and academic achievement of medical students at King Abdulaziz University, Jeddah, Saudi Arabia. Journal of research in health sciences. 2018;18(3):e00420. pmid:30270211
  49. 49. Amez S, Vujić S, Soffers P, Baert S. Yawning while scrolling? Examining gender differences in the association between smartphone use and sleep quality. Journal of sleep research. 2020;29(6):e12971. pmid:31919946
  50. 50. Vhaduri S, Poellabauer C, editors. Impact of different pre-sleep phone use patterns on sleep quality. 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN); 2018: IEEE.
  51. 51. Patel RRS, FAPA O. Smartphone use before bedtime might impact sleep, and daytime tiredness.
  52. 52. Elhai JD, Yang H, Fang J, Bai X, Hall BJ. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addictive behaviors. 2020;101:105962. pmid:31030950
  53. 53. Akin A, Iskender M. Internet addiction and depression, anxiety and stress. International online journal of educational sciences. 2011;3(1):138–48.
  54. 54. Panicker J, Sachdev R. Relations among loneliness, depression, anxiety, stress and problematic internet use. International Journal of Research in Applied, Natural and Social Sciences. 2014;2(9):1–10.
  55. 55. Brunborg GS, Andreas JB, Kvaavik E. Social media use and episodic heavy drinking among adolescents. Psychological reports. 2017;120(3):475–90. pmid:28558617
  56. 56. Cénat JM, Blais M, Lavoie F, Caron P-O, Hébert M. Cyberbullying victimization and substance use among Quebec high schools students: The mediating role of psychological distress. Computers in Human Behavior. 2018;89:207–12.
  57. 57. Pokhrel P, Fagan P, Herzog TA, Laestadius L, Buente W, Kawamoto CT, et al. Social media e-cigarette exposure and e-cigarette expectancies and use among young adults. Addictive behaviors. 2018;78:51–8. pmid:29127784
  58. 58. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: Results from a large-scale nationally representative adolescent sample. PloS one. 2017;12(1):e0169839. pmid:28068404