|Year : 2023 | Volume
| Issue : 1 | Page : 9
Internet addiction among undergraduate medical students in Myanmar: A cross-sectional study
Pa Pa Soe1, Khin May Oo2, Phoo Nay Chi3, Phyo Thet Naing Win4, Win Myint Oo5
1 Department of Preventive & Social Medicine, University of Medicine(2), Yangon 11031, Myanmr
2 Department of Public Health Laboratory, University of Public Health, Yangon 11131, Myanmar
3 Pasadena City College, Pasadena, California 91106-2003, USA
4 Freelance Researcher, Yangon 11091, Myanmar
5 ASEAN Institute for Health Development, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
|Date of Submission||24-Feb-2023|
|Date of Decision||14-Mar-2023|
|Date of Acceptance||10-Apr-2023|
|Date of Web Publication||23-May-2023|
Win Myint Oo
ASEAN Institute for Health Development, Mahidol University, Salaya, Nakhon Pathom 73170
Source of Support: This research is partially funded by IR grant (MOHS, 2020), and Internal Research grant from Ministry of Health and Sports, Myanmar, Conflict of Interest: None
Objective: To determine the prevalence of internet addiction and its associated factors among undergraduate students attending medical universities in Myanmar.
Methods: Internet addiction was assessed using Young’s internet addiction test. Multiple logistic regression analysis was applied to determine the factors associated with internet addiction. Altogether 950 students from all medical universities were included in the study voluntarily.
Result: The prevalence of internet addiction in the study population was 72.2% (95%CI: 69.3%, 75.0%). According to the results of multiple logistic regression analysis, age, percentage of pocket money spent for internet, time spent per day using the internet, peer pressure, health-related behaviours (irregular meals, sleep disturbances, and missing social gatherings), and academic performance (postponement of the study and inability to concentrate on studying) were significant predictors of internet addiction.
Conclusions: There is a high prevalence of internet addiction among Myanmar undergraduate medical students. Appropriate interventions, including promotion and strengthening of active and healthy lifestyles among students, should be implemented to prevent internet addiction and its adverse outcomes.
Keywords: Internet addiction; Medical students; Myanmar; Undergraduate students
|How to cite this article:|
Soe PP, Oo KM, Chi PN, Win PT, Oo WM. Internet addiction among undergraduate medical students in Myanmar: A cross-sectional study. One Health Bull 2023;3:9
|How to cite this URL:|
Soe PP, Oo KM, Chi PN, Win PT, Oo WM. Internet addiction among undergraduate medical students in Myanmar: A cross-sectional study. One Health Bull [serial online] 2023 [cited 2023 May 31];3:9. Available from: http://www.johb.info/text.asp?2023/3/1/9/376723
| 1. Introduction|| |
Over the last few decades, a technological breakthrough with the invention of internet has significantly changed people’s daily lives worldwide. There are over 400 million internet users in the South-East Asia region. As of June 2021, the internet penetration rate in the region was as high as more than 70% in all countries except for Laos, Myanmar and Timor-Leste. In Myanmar, the internet has been established since 2000. Although no more than half (i.e. 1000 people in 2000) were able to access the internet at first, the internet penetration rate in Myanmar has become 33.4% and 52.1% of the total population in 2018 and 2021, respectively. In recent years, the internet has become an important part of a student’s life due to various reasons. If used properly, the internet can yield fruitful results both academically and socioeconomically, whereas excessive or improper use can cause internet addiction,. Internet addiction is characterised by excessive and poorly controlled urges or behaviours to use internet that can cause distress or impairment,.
Moreover, the internet has become an essential medium for communication as well as learning, which has been switched from conventional face-to-face to virtual learning during the COVID-19 pandemic era, especially for university students,. This combined with psychological and environmental factors, such as developmental stressors and increased accessibility to internet service, could predispose the development of internet addiction among university students too,,. Internet addiction, also known as pathological internet use or problematic internet use, among university students, becomes a significant health and academic issue because it can create adverse consequences not only on physical and mental health, but also on academic performance. Previous studies also reported that it had become a major behavioural health problem in many countries, including Asian and South-East Asian countries,. Moreover, medical education itself is regarded as stressful, and there are many reports of psychological morbidity among undergraduate medical students. As little is known about the pattern and extent of the internet use among Myanmar medical students, this study was conducted to determine the prevalence of internet addiction and its associated factors among undergraduate medical students in Myanmar.
| 2. Subjects and methods|| |
Before commencing the study, ethical approval was granted by the Research and Ethical Committee of the University of Medicine, Yangon(1), Myanmar, with the approval number of 75(REC)/UM1/2020.
2.1. Study design
A cross-sectional study was conducted among undergraduate medical students from all medical universities in Myanmar during June to November 2020. All undergraduate medical students were considered the study population. Epi-info version 7.0 was used in calculating the sample size. The prevalence of internet addiction among undergraduate medical students was estimated at 44.2% and absolute precision was set at 5%. The minimum requirement of the sample size was 379 at a 95%CI. The anonymous online self-administered questionnaire was developed by investigators using Google forms and the English language. It was distributed using personal contacts and via word of mouth using emails and messenger apps such as Viber, Messenger, Telegram, and other social media, such as Facebook and Twitter. The present study utilised convenience sampling and online data collection because of the closure of the medical universities due to prohibitions against the use of random sampling and face-to-face interviews during COVID-19 in Myanmar.
The first part of the questionnaire was an informed consent form explaining the voluntary participation and confidentiality of the data. Data collection started on 25 June 2020. The participants were asked to respond only once, and the form settings allowed only one response per user. Inclusion criteria used in this study were any students currently attending in any University of Medicine in Myanmar at the time of data collection and being willing to participate in the study voluntarily. Therefore, informed consent was taken from the respondents for adults (≥18 years of age). For those who were under 18 years of age, informed consent was received from either their fathers, mothers, or guardians. If no consent was given, students were not included in the study. A total of 950 students responded voluntarily to this study.
Internet addiction was assessed using Young’s internet addiction test (YIAT), which consists of 20 items on a 6-point Likert scale, ranging from 0 to 5 (0 = never; 1 = seldom; 2 = occasionally; 3 = frequently; 4 = often, 5 = always) for each item. The total scores of a respondent ranged from 0 to 100. The levels of internet addiction were categorised as none (total scores between 0–30), mild (31–49), moderate (50–79), and severe (80–100)[l7],,. Respondents in the mild, moderate, and severe categories were considered as those with internet addiction. The internal consistency (or) reliability of the YIAT in this study was excellent (Cronbach’s alpha = 0.88).
The variables considered as the factors associated with internet addiction in the study were as follows:
- Socioeconomic and demographic variables, such as age, gender, pocket money provided per month, and percentage of pocket money used for internet per month;
- Factors related to the use of the internet, such as reasons for using the internet, duration of internet use, time spent (hours) a day to use the internet, frequency (daily or a few days in a week);
- Health-related factors, such as sleep problems (insomnia, hypersomnia, late sleep, sleep pattern changes, waking up at midnight), junk food, irregularity in having meals, reduction of regular physical exercise, and inability to participate in social gatherings;
- Factors related to academic performance, such as (inability to concentrate on studying, poor achievement on class tests, missing class, and poor time management or postponement of the study).
2.2. Statistical analysis
After checking any missing data, data entry and analysis were done using the Stata version 11.0 statistical package. Mean, median, mode, and standard deviation were measured for quantitative variables, and frequency tables were drawn for categorical variables. The prevalence of internet addiction and a 95%CI were estimated. Multiple logistic regression analysis with a backward deletion strategy was applied to determine the factors associated with internet addiction. Any variable with P≤0.25 in univariate analysis was chosen as a candidate variable to be included in the multivariate analysis.
| 3. Results|| |
The study included 950 students from five different medical universities, namely University of Medicine, Yangon(1), University of Medicine, Yangon(2), University of Medicine of Mandalay, University of Medicine, Magway, and University of Medicine, Taunggyi. Their general characteristics, including the name of the medical universities, are shown in [Table 1]. Most respondents were from the University of Medicine, Yangon(1) (53.1%). In contrast, the smallest number of students were from the University of Medicine, Taunggyi, the newest medical university in Myanmar, established in 2015. The majority of the participants were younger than 20 years old (50.4%), female (61.7%), with pocket money available per month of less than 100000 Myanmar kyats (MMK) (64.6%), and those who spent equal or less than 10% of their pocket money to access the internet (52.5%). Characteristics of the respondents including information related to internet use among them, and the exchange rate between MMK and the United States dollar (USD) are also shown in [Table 1]. Almost all respondents (99.2%) used the internet daily, and more than half (55.2%) used the internet for one to six hours a day. The top three reasons for using the internet were to access social media (96.4%), to watch movies (94.4%), and for educational purposes (88.3%); the tool or gadget commonly used to access the internet was mobile phones/smart phones (94.2%), and the average duration (standard deviation) of internet use among students was 6.2 (2.1) years.
|Table 1: Socioeconomic, demographic and baseline characteristics of the respondents.|
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Behaviours related to health and academic performance of the respondents are shown in [Table 2]. The prevalence (95%CI) of internet addiction among medical students was 72.2% (69.3%, 75.0%). Most students (43.2%) were in the mild addiction category. The severity of internet addiction is shown in [Table 3].
|Table 2: Behaviours related to health and academic performance of the respondents.|
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Multiple logistic regression analysis was done with a backward deletion strategy to assess the association between the characteristics of the respondents and internet addiction. Age group, percentage of pocket money spent on the internet, time spent per day, peer pressure, health-related behaviours (irregularity in having meals, late sleep, waking up at midnight, hypersomnia, and missing social gatherings), and academic performance (postponement of the study and inability to concentrate on studying), were found to have significant associations with internet addiction. On the other hand, frequency and duration of internet use, some health-related behaviours (having junk food, sleep pattern changes, insomnia), and some factors related to academic performance (missing class and poor achievement on class tests) did not show any significant association with internet addiction. although they have been chosen to be included in multiple logistic regression analysis [Table 4].
|Table 4: Results of multiple logistic regression to identify the factors associated with internet addiction.|
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| 4. Discussion|| |
The prevalence of internet addiction identified in this study was too high: 72.2% (95%CI: 69.3%, 75.0%). Previous studies have reported a varying prevalence of internet addiction among respondents. Similar studies conducted among medical students in Malaysia, Egypt and Iraq, have revealed the high prevalence of internet addiction (>80% in all studies). A separate survey among Malaysian medical students also reported that almost all participants suffered at least mild internet addiction. The World Health Organization has also disclosed that internet addiction has become an emerging behavioural public health problem. In a previous meta-analysis, however, the prevalence of internet addiction among adolescents/students was less than 50%. Similarly, the lower prevalence of internet addiction has been observed in studies carried out in Malaysia (28.9% among undergraduate students at a public university), Tanzania (31% among allied health sciences and medical students), India (44.2% among medical students), Saudi Arabia (<10% among medical students) and China (12.6% among medical students). The socio-demographic characteristics of the participants (age, gender, whether they are attending medical university or not, etc.) and time of the study (before or during COVID-19 pandemic period) would be responsible for these differences. Zhang et al. concluded that internet addiction was more prevalent among medical students compared to other undergraduate students and the general population. Moreover, different tools and/or cut-off points to determine the presence and severity of internet addiction should also be taken into consideration in comparing the prevalence of internet addiction across the different studies. The authors of two previous meta-analyses also concluded that the discrepancy in assessment tools and cut-off points in defining internet addiction would be the source of heterogeneity in comparing the findings across the studies,.
The most typical reason for using the internet among medical students identified in this study was to access social media or social networking. It is consistent with the findings of similar studies carried out in Malaysia and Iran,.
Based on the results of multiple logistic regression analysis, socio-demographic characteristics of the students, such as age, percentage of pocket money, time spent per day using the internet, and peer pressure, were identified as possible risk factors for internet addiction. It means that the younger the age or the longer the time spent using the internet, the higher the tendency for internet addiction among medical students. However, regarding age, contradictory findings were observed in previous studies. Some studies reported that the respondent’s age was not a determinant of internet addiction,,,, while others revealed a significant association between them,. The time spent per day using the internet was consistently found to be associated with internet addiction in previous studies,,,. Although there was no significant association between gender and internet addiction in the present study, the association was significant in some studies,,,. Prior studies in Malaysia and Iraq also found that gender was not associated with internet addiction. In this study, using the internet for educational purposes was not included in the final model of multiple logistic regression analysis; only peer pressure was found to be a significant factor [Table 4]. In contrast, using the internet for educational purposes was identified as a significant determinant of internet addiction in previous studies,,,,. It means that the rational use of the internet should be focused on minimising the burden of internet addiction. Similar to the results of the present study, a previous study done in Turkey also found that peer pressure significantly influenced the occurrence of internet addiction. A study done in China also discovered the significant relationship between monthly expenditure and internet addiction.
In this study, internet addiction was significantly associated with some health-risk behaviours (having meals irregularly, missing social gatherings, and sleep disturbances in the forms of late sleep, hypersomnia, and waking up at midnight), and the academic performance of the medical students (in terms of postponement of and failure to concentrate on studying). These findings were consistent with those of previous studies. Internet addiction was found to have a negative impact on the academic performance of the students in some studies,,,,, while others disclosed the presence of a significant relationship between health-risk behaviours (including eating and sleep disturbances) and internet addiction,,. Loneliness or failure to take part in social gatherings was also determined to be an essential determinant of internet addiction in a previous similar study conducted in Turkey.
Although other risk behaviours, such as having junk food, reduced physical activity, insomnia, changing sleep patterns, missing class, and poor achievement on class tests were found to have a significant association with internet addiction in the univariate analysis, the association disappeared in the multivariate analysis. A Malaysian study has reported the same finding.
| 5. Conclusions and recommendations|| |
There is a high prevalence of internet addiction among Myanmar undergraduate medical students. It is also associated with some health-risk behaviours (irregular meals and sleep disturbances) and poor academic performance (postponement and inability to concentrate on studying). University authorities, including lecturers, government officials concerned with medical education, and medical students themselves, should be aware of these issues and develop appropriate interventions, such as screening for early detection of internet addiction, awareness-raising campaigns on the proper use of the internet and negative effects of internet addiction, and promoting active and healthy lifestyles among students, especially younger age groups to prevent internet addiction and its adverse outcomes. Further prospective studies should also be implemented in order to assess the trajectories of such a disorder.
The present study relied on self-reported information; hence, the data may be subject to recall bias and reporting errors. A causal relationship cannot be established in this study due to the utilisation of a cross-sectional study design. Besides, using non-random (or convenience) sampling would affect the representativeness of the samples included in the study. Therefore, the present study may not have good generalisability. Lastly, this study collected the data using online questionnaires, which might cause selection bias because students without internet access or those who did not use internet during the data collection were unable to participate. That could lead to the overestimation of the prevalence of internet addiction.
Conflict of interest statement
The authors claim there is no conflict of interest.
The authors gratefully acknowledge both the members of research and ethical committee, University of Medicine, Yangon (1) for their ethical approval and the authorities from all Myanmar medical universities for their permission to conduct the study. We would also like to extend our sincere thanks to all students included in the study for their voluntary participation.
This research is partially funded by IR grant (MOHS, 2020), and Internal Research grant from Ministry of Health and Sports, Myanmar.
Soe PP, Oo KM and Oo WM designed the study. All authors took part in questionnaire development and data acquisition (collection and entry). Data analysis was done by Oo WM. All authors not only drafted the initial manuscript but also reviewed and edited the manuscript, and approved the final version.
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[Table 1], [Table 2], [Table 3], [Table 4]