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PLOS ONE logoLink to PLOS ONE
. 2020 Sep 11;15(9):e0238804. doi: 10.1371/journal.pone.0238804

Online activities as risk factors for Problematic internet use among students in Bahir Dar University, North West Ethiopia: A hierarchical regression model

Kerebih Asrese 1,*, Habtamu Muche 1
Editor: Alfonso Rosa Garcia2
PMCID: PMC7485847  PMID: 32915864

Abstract

Background

Problematic internet use (PIU) among youth has become a public health concern. Previous studies identified socio-demographic background risk factors for PIU. The effects of online activities on youth PIU behavior are not well investigated.

Methods

This cross-sectional study assessed the roles of online activities for PIU behavior of undergraduate students in Bahir Dar University, North West Ethiopia. Data were collected from 812 randomly selected regular program students recruited from 10 departments. Respondents completed a pre-tested structured questionnaire. Hierarchical logistic regression models were used for analyses.

Results

The results indicated that social networking (75.5%), entertainment (73.6%), academic works (70.9%), and online gaming (21.6%) are the important online activities students are engaging in the internet. About 33% and 1.8% of students showed symptoms of mild and severe PIU, respectively. Taking online activities into account improved the model explaining PIU behavior of students. Online activities explained 46% of the variance in PIU. Using the internet for social networking (AOR = 7.078; 95% CI: 3.913–12.804) and online gaming (AOR = 2.175; 95% CI: 1.419–3.335) were risk factors for PIU.

Conclusions

The findings revealed that more than a third of the respondents showed symptoms of PIU. Online activities improved the model explaining PIU behavior of students. Thus, university authorities need to be aware of the prevalence of PIU and introduce regulatory mechanisms to limit the usage of potentially addictive online activities and promoting responsible use of the internet.

Introduction

In the contemporary world, the internet is becoming an important part of peoples’ daily life [1]. It is widely used in diverse areas of life such as education, academic activities and research, information exchange, interpersonal communication, commerce, science, and entertainment [2, 3]. Internet becomes available and affordable at homes, schools, colleges, libraries, and internet cafes [4]. Recently, the prevalence of internet users has increased rapidly, with the current estimated world's number of internet users in 2019 is 4.3 billion [5]. In Ethiopia, internet users increased from 360,000 in 2008 forming .43% of the population [5] to 16,437,811 internet users in 2019 forming 14.9% of the country’s population [6].

While use of internet has enhanced the social and economic well-being of people [2], poor personal control over its use has become a concern [7] owing to increasing dependence on the internet for various aspects of lives [8]. Though debates on conceptualization and diagnosis are ongoing, the scientific community agreed that problematic use of digital technologies is associated with mental health problem [9].

Various terms and conceptualizations are used in the literature to label problematic use of digital technologies, such as “internet addiction” [10], “compulsive computer use” [11] or “problematic internet use” [12]. Though these competing definitions have various views and perspectives on the problem, the common concern for both is problematic use of digital technologies harms individuals [12]. For the sake of consistency, problematic internet use (PIU) defined as “excessive use of the internet that causes disturbances or harm to the individual” [12], is adopted in this study.

Problematic internet use is a compulsive behavior related to online activities in which individuals have inability to control internet usage despite its negative consequences [2]. It is reported that PIU leads to marked functional impairments in several aspects of life [13, 14], including social isolations [15, 16], unfriendly behavioral patterns [17, 18], attention deficit hyperactivity disorder [19], physical ill health [20], and performance and work difficulties [21].

PIU is a global phenomenon, especially among university students [2224]. Possible reasons suggested for this are: universities provide free and unlimited access to the internet, students are away from parental control and without anyone monitoring what they do online, and students are encouraged by faculty members to use different internet applications [16]. In addition, the universities’ settings foster a new student culture which necessitates the internet as a tool for communication, information sharing and community formation [25, 26].

With a broader education quality improvement initiative in higher institutions, the government of Ethiopia has deployed improved information communication technologies within the universities [27]. These institutions provide free and unlimited access to the internet; hence, students may use the internet more so than other population groups in the country [28]. Whether such context contributed for PIU behavior among university students in Ethiopia is not assessed.

To date, efforts to explain risk factors for PIU have identified a wide range of variables [29]. Being male [30, 31], adolescent ages [1], poor academic performance and family income [3234], anxiety and depression [32], and low self-esteem [3436], weak family support and low parental supervision [34, 37], peer pressure [38], and having free and easier internet access [33, 39] were noted contribute for developing PIU. Internet use variables as risk factors for PIU are also established. Age at first internet use [35] and online activities on the internet such as social networking [4042] online gaming [31, 33, 4244], frequency and length of internet use [45], online entertainments [46], and watching online pornography and online gambling [44] have been found more predictive of PIU.

The existing literature appeared that socio-demographic background factors and online activities people engaged in the internet are predictors of PIU. However, to the authors’ knowledge, no research has been conducted taking into consideration all of them simultaneously. In addition, as most studies on risk factors have been conducted in overseas, our understanding of the risk factors for PIU in Ethiopian context is limited. Hence, in this study, we estimated the prevalence of PIU and examined socio-demographic background factors and online activity variables as potential risk factors for PIU. The findings may assist the prevention and management of PIU among students. Based on the existing literature, we hypothesized that the types of online activities in which students are engaged significantly predict PIU when socio-demographic background factors are controlled.

Conceptual framework

We adopted Problem Behavior Theory [47] as a conceptual framework for the study. The theory is often used by researchers who investigated adolescents’ misbehaviors [12]. Problem Behavior Theory specifies three fundamental systems: personality, perceived environment, and behavior. The background and socialization variables affect the personality and perceived environment systems and have a distal impact on behavior. The personality and perceived environment systems have proximal impact on the behavior [47].

In this study, similar variables were adopted. The students’ socio-demographic background variables were grouped in one category as control variables and the online activity variables in other category as predictor variables and their hierarchical importance in predicting the PIU were assessed. Identifying the hierarchical importance of risk factors for PIU may help to design tailored intervention.

Methods and materials

Study setting, design and sample

An institution-based cross-sectional study was conducted in Bahir Dar University from February to March, 2018. The university has five campuses. Through education quality improvement initiative in higher institutions, free Wi-Fi is available in all campuses and private internet café services are also available around the campuses. At the time of the survey, the university enrolled more than 25,000 undergraduate students of which 14,884 are regular program students.

A single population proportion formula was used to estimate the sample size with 5% precision, 95% confidence, and a 10% non-response rate. There is no prior report about the estimated proportion of population with PIU in Ethiopian Universities. Therefore, we assumed this proportion to be 50% to increase the sample size of this study. The university is stratified by campuses and departments. And students in each department are stratified by years of study. We selected two departments randomly from each campus and students from year I to year IV were asked to participate in the study. To obtain representative sample, we used multi-stage stratified random sampling technique. Thus, to reduce the error that might incur during such multistage stratification, we multiplied the sample size by two (design effect). Therefore, the total sample size for the study was 844 students. Data obtained from 32 students were not complete, hence excluded.

The sample size was proportionally allocated to the selected departments, based on the number of regular students enrolled in the departments. Then systematic random sampling technique was used to select respondents from each selected department. Selected students were approached through their section mentors assigned by selected departments. The inclusion criteria included regular class undergraduate student, reside in the university dormitory, used internet at least for six months, and not diagnosed with anxiety or depression disorders for a year and above.

Study procedure

The study received ethical approval from Ethical Review Committee of Bahir Dar University. We initially developed the instrument in English and then translated into Amharic (national language) to ease understanding. Prior to the main study, a pretest was conducted on 25 regular undergraduate students (not included in the main survey) to check reliability and suitability of the instrument.

A week before data collection, we communicated selected departments with formal letter and we selected students who would participate in the study. Written consent of individual participants was obtained after being fully informed of the study purpose and procedures. It was also made clear to the respondents that participation was voluntary, and there would be no direct benefit or reward. We ensured confidentiality by removing all personal identities from the questionnaire. At each department, the questionnaires were self administered in a free lecture hall when students had free period. The investigators and two research assistants were available throughout the administration of the questionnaires to answer questions from individual students.

Measurement

Online activities

In our exploratory qualitative study, we learned that students mainly use the internet for academic works, social networking, and entertainment. A few informants also shared that they exercise online gaming. Significant association between these online activities and PIU behavior was reported [48]. Thus, in this study, students were asked whether they have been engaging in the internet for these online activities, responded as yes/no.

Socio-demography characteristics

Socio-demographic background characteristics such as age, sex, year (time spent in the university), field of study, and academic performance were also collected. These attributes are reported have important role for PIU behavior [32, 49].

Self-esteem

The tool was developed to measure what respondents feel about themselves and what they think others think of them. It was adapted from Rosenberg [50] self-esteem test questions. The tool consisted of 10 items (sample items: On the whole, I am satisfied with myself; At times, I think I am not good at all) with four point responses ranging from 1 (strongly disagree) to 4 (strongly agree). The value ranges from 10 to 40, higher value indicating higher self-esteem. The internal consistency of the items in this study was good (α = 0.88).

Parental support

The tool was adapted from Zimet and colleagues [51] developed to measure perceived social support. It was used to measure students’ perception of their parents’ support after they enrolled into the university. The instrument has seven items (sample items: I count on my parents’ when things go wrong; I get the emotional help and support I need from my parents) with four point responses ranging from 1 (strongly disagree) to 4 (strongly agree). In this study, the items have good internal consistency (α = 0.93). The score ranges from seven to 28, higher score indicating greater parental support.

Peer pressure

This tool was developed to assess the tendency of individuals to affiliate with like-minded friends. It was adapted from Steinberg and Monahan [52] developed to measure resistance to peer influence. The instrument has seven items (sample items: I often try what my friends do; I go along with my friends and do what they do to keep them happy of me) with four point responses ranging from 1 (strongly disagree) to 4 (strongly agree). The score ranges from seven to 28, with higher scores indicating greater peer pressure. The internal consistency of the items in this study was (α = 0.91).

Internet addiction test

The 20-item Internet Addiction Test developed by Young [10] was adopted to measure the level of internet use in this study. The test has been widely adopted globally to measure the internet addiction levels of individuals [53]. In assessing the degree to which respondents’ internet usage affected their daily routine, productivity, social life, psychological dependence, and time management [54], respondents were asked to rate items on a five-point Likert scale (1 = rarely and 5 = always). Item scores are added to create a final score between 20 and 100. Young suggested that a score of 20–49 points indicates an average online user who has complete control over their usage; a score of 50–79 reflects frequent problems due to Internet usage; and a score of 80–100 indicates that the internet is causing severe problems in the user’s life [10]. The tool was pilot tested and the internal consistency of the items was very good (α = 0.93).

Data analysis

All returned questionnaires were checked for completeness and consistency of responses manually. After cleaning, raw data were entered into SPSS for Windows versions 21 for analyses. Descriptive analysis was used to summarize the background characteristics of the respondents, their online activities, and to determine the prevalence of PIU. Between groups comparisons (non-PIU, mild PIU, and severe PIU) were performed using the Chi-square test of independence for categorical variables and one way ANOVA for continuous variables. Hierarchical multivariate logistic regression models were used to assess the relationship between independent variables and outcome variable. The socio-demographic background variables (controls) were entered into Model I. Model II added online activities variables (predictors) to Model I. Those variables with significant association to the outcome variable during bivariate analyses were entered during multivariate analyses.

Model I was nested into Model II. The overall fit of each logistic regression model was assessed by using its model Chi-square and goodness-of-fit indices (-2 log likelihood [-2LL]). We used model Chi-square and goodness-of-fit indices (-2 LL) to determine the improvement observed in Model II relative to Model I in explaining the dependent variable [18, 55, 56]. In addition to the indices of the overall model fit and model Chi-square, change in Nagelkerke’s R2 was evaluated as an approximate estimate of the amount of variance in the dependent variable accounted for by the models. Those variables with significant association to the dependent variable during bivariate analyses were entered into the multivariate logistic regression models. To test whether each individual factor had a significant relationship with PIU, Wald statistics were used. Multicollinearity among independent variables was checked using tolerance and variance inflation factor (VIF) values. The tolerance value ranged from 0.578 to 0.955 and the range of VIF was from 1.047 to 1.730.

Ethical consideration

The study received ethical approval from Ethical Review Committee of Bahir Dar University. A week before data collection, we communicated selected departments with formal letter and we selected students who would participate in the study. Written consent of individual participants was obtained after being fully informed of the study purpose and procedures. It was also made clear to the participants that participation was voluntary, and there would be no direct benefit or reward. We ensured confidentiality by removing all personal identities from the questionnaire.

Results

Background characteristics of respondents

Eight hundred twelve students (66% males and 34% females) participated in the study. Respondents’ age ranged from 18 to 27 years old with mean age of 21.38 years. Students’ fields of studies were social sciences (36%), law and Land Administration (21.6%), engineering (19%), textile (16%), and agriculture (7%). Majority of the respondents (30%) were 2nd year students followed by 3rd year (25%) and fourth year and freshman (22%). The four most important online activities of students were social networking (75.5%), entertainment (watching videos, sports, music, and news) (73.6%), academic works (72.5%), and online gaming (61%). Respondents’ cumulative grade point average ranged from 1.84 to 3.92 with mean grade point average 3.05. Respondents’ means scores on parental supervision, self-esteem, and peer pressure were 20.07, 30.84, and 12.95, respectively (Table 1).

Table 1. Distribution of respondents by background characteristics, online activities, and internet use status (n = 812).

Background characteristics n (%)
Sex
Male 535 (65.9)
Female 277 (34.1)
Use the internet first
Secondary school 567 (69.8)
At university 135 (16.8)
At elementary school 110 (13.5)
Field of study
Social sciences 293 (36.1)
Engineering 154 (19)
Law and land administration 175 (21.6)
Agriculture 59 (7.3)
Textile 131 (16.1)
Years spent at the university
1st year 177 (21.8)
2nd year 247 (30.4)
3rd year 207 (25.5)
4th year and above 181 (22.3)
Social networking use 613 (75.5)
Entertainment use 598 (73.6)
Academic works use 589 (70.9)
Online game use 175 (21.6)
Age 21.38 (1.70)*
Cumulative grade point average 3.05 (.45)*
Parental supervision 20.07 (5.15)*
Self-esteem 30.84 (5.52)*
Peer pressure 12.95 (4.13)*
Internet use status
Non- PIU 526 (64.8)
Mild PIU 271 (33.4)
Severe PIU 15 (1.8)

* = mean (standard deviation).

Prevalence of internet addiction

In this study, the prevalence of sever PIU was 1.8% (95% CI: 1% - 2.8%) and mild PIU was 33.4% (95% CI: 29.9% - 36.5%) (Table 1). The prevalence rate including mild and severe PIU was significantly higher for females than males (p < .05). Statistically significant relationship was found between years spent at the university and PIU indicating that senior students were more likely to have symptoms of PIU than juniors (p < .001). Greater proportions of students using the internet for social networking and for online gaming were more likely to have symptoms of PIU than their counterparts (p < .001). Lower proportion of students using the internet for academic works than those who did not use the medium for same purpose had PIU behaviors (p < .001). The one-way ANOVA results also demonstrated that the groups significantly differ in age (F (2,809) = 5.53, p < 0.05), grade point averages (F (2,809) = 65.67, p < 0.001), parental supervision (F (2,809) = 12.38, p < .001), self-esteem (F (2,809) = 38.37, p < .001), peer pressure (F (2,809) = 67.06, p < 0.001), and internet use score (F (2,809) = 145, P < .001) (Table 2).

Table 2. Respondents’ socio-economic background characteristics and online activities by internet use status (n = 812).

Background characteristics Internet use status χ2 Multiple comparisons
Non-PIU n (%) Mild PIU n (%) Severe PIU n (%)
Sex 7.88*
Male 335 (66.4) 175 (32.7) 5 (.9)
Female 171 (61.7) 96 (34.7) 10 (3.6)
Use the internet first .79
Before university 436 (64.4) 229 (33.8) 12 (1.8)
After university 90 (66.7) 42 (31.1) 3 (2.2)
Field of study 15.12
Social sciences 192 (65.5) 97 (33.1) 4 (1.4)
Engineering 92 (59.7) 61 (39.6) 1 (.6)
Law 123 (70.3) 48 (27.4) 4 (2.3)
Agriculture 42 (71.7) 17 (28.8) -
Textile 77 (58.8) 48 (36.9) 6 (4.6)
Years spent at the university 42.72***
1st year 147 (83.1) 28 (15.8) 2 (1.1)
2nd year 161 (65.2) 84 (34) 2 (.8)
3rd year 119 (57.5) 84 (40.6) 4 (1.9)
4th year and above 99 (54.7) 75 (41.4) 7 (3.9)
Social networking use 343 (56) 255 (41.6) 15 (2.4) 85.63***
No use 183 (92) 16 (8) -
Entertainment use 390 (65.2) 200 (33.4) 8 (1.3) 3.26
No use 136 (63.6) 71 (33.2) 7 (3.3)
Academic works use 404 (70.1) 163 (28.3) 9 (1.6) 24.96***
No use 122 (51.7) 108 (45.8) 6 (2.5)
Online game use 82 (46.9) 81 (46.3) 12 (6.9) 52.51***
No use 444 (69.7) 190 (29.8) 3 (.5)
F-test
Agea 21.23 (1.76) 21.64 (1.6) 21.80 (1.14) 5.53* NPIU vs. MPIU*
Grade point averagea 3.17 (.42) 2.86 (.42) 2.42 (.25) 65.67*** NPIU vs. MPIU; vs. SPIU***
MPIU vs. SPIU***
Parental supervisiona 20.67 (4.95) 19.10 (5.8) 16.46 (5.86) 12.38*** NPIU vs. MPIU; vs. SPIU**
Self-esteema 31.96 (4.96) 29.01 (5.6) 24.66 (7.06) 38.37*** NPIU vs. MPIU; vs. SPIU***
MPIU vs. SPIU**
Peer pressurea 11.88 (3.44) 14.70 (4.1) 18.93 (5.35) 67.05*** NPIU vs. MPIU; vs. SPIU***
MPIU vs. SPIU***
Sum of internet use 36.66 (7.80) 61.06 (8.2) 84.06 (3.06) 145.70*** NPIU vs. MPIU; vs. SPIU***
MPIU vs. SPIU***
Total 526 (64.8) 271 (33.4) 15 (1.8)

NPIU = non-PIU, MPIU = mild PIU, SPIU = severe PIU

a = mean (standard deviation)

*p < .05

**p < .01

***p< .001.

Risk factors for internet addiction

Table 3 presents the results of multivariate logistic regression analyses of the association between internet use status and various socio-demographic background and online activities of respondents. Because severe PIU rate was very low, we combined severe PIU and mild PIU into one group and computed the contributors of PIU based on two groups of respondents which were non-PIU and PIU users. As illustrated in the Table, the two models were significantly associated with PIU. Model II had smaller -2LL value than Model I (724.059 vs. 802.109) indicating that the inclusion of online activity variables significantly improved the goodness-of-fit of Model II as compared to Model I (χ2 (11) = 329.605, P <0.001). While Model I accounted for 36.70% of the variance in PIU (Nagelkerke R2 = .367), Model II accounted for 45.90% of the variance in PIU (Nagelkerke R2 = .459). The results indicated that there is statistically significant improvement in predicting PIU of students with the online activity variables after controlling the socio-demographic background variables.

Table 3. Hierarchical Logistic Regression analysis of students’ socio-demographic background characteristics and their online activities predicting PIU (N = 812).

Variables Model I Model II
B S.E. Wald AOR (95% CI) B S.E. Wald AOR (95% CI)
Sex
Male .092 .202 .209 1.097 (.738–1.631) -.005 .218 .001 .995 (.649–1.524)
FemaleR 1 1
Year of study
1st year -2.253 .375 36.184*** .105 (.050-.219) -2.060 .399 26.699*** .127 (.058-.278)
2nd year -.766 .273 10.263** .417 (.244-.712) -.699 .292 5.719* .497 (.280-.882)
3rd year -.623 .257 5.875* .536 (.324-.888) -.597 .272 4.828* .550 (.323-.938)
4th year and aboveR 1 1
Age -.017 .070 .056 .984 (.858–1.28) .037 .076 .241 1.038 (.895–1.2.44)
Grade point average -1.732 .215 65.037*** .177 (.116-.270) -.1.645 .226 53.234*** .193 (.124-.301)
Self-esteem -.051 .019 7.436** .951 (.917-.986) -.053 .020 7.209** .948 (.912-.986)
Parental support -.043 .017 6.239* .957 (.925-.991) -.047 .019 6.202* .954 (.919-.990)
Peer pressure .152 .025 36.677*** 1.167 (1.108–1.223) .125 .027 21.632*** 1.133 (1.075–1.194)
Social networking use 1.957 .303 41.825*** 7.080 (3.912–12.813)
Academic works use -.290 .206 1.975 .748 (.500–1.121)
Online gamind .777 .218 12.692*** 2.176 (1.419–3.337)
-2LL 801.900 724.059
Model χ2 251.764*** 329.606***
Degree of freedom 9 12
Nagelkerke R2 .367 .459
Δ R2 - .092

R = reference category

*p < .05

**p < .01

***p < .001, AOR (95% CI) = Adjusted odd ratio (95% confidence interval)- net effect of each independent variable on the dependent variables, Δ R2 = change in Nagelkerke R2.

Regarding online activity variables, use of internet for social networking was the strongest predictor of PIU. Students using the internet for social networking were more likely to have PIU than those who did not use the medium for such purpose (AOR = 7.078, 95% CI: 3.913–12.804). Compared to students who were not using the internet for online gaming, students using the medium for online gaming were more likely to be internet addicted (AOR = 2.175, 95% CI: 1.419–3.335) (Table 3).

The findings also indicated that socio-demographic background variables- seniority in the university, self-esteem, academic performance, parental support, and peer pressure were significant risk factors for PIU. Junior students were less likely to have symptoms of PIU behavior than senior students. With one point increase in grade point average students were .193 (95% CI: .125–.298, p < 0.001) times less likely to have symptoms of PIU. Increasing self-esteem (AOR = .948, 95% CI: .912-.985) and parental support (AOR = .954, 95% CI: .919-.990) were protective of PIU. On the other hand, students with increasing peer pressure were more likely to have symptoms of PIU (AOR = 1.133, 95% CI: 1.075–1.174) (Table 3).

Discussion

This study investigated the roles of online activities on PIU behaviors in a sample of undergraduate regular program students in Bahir Dar University. In this sample of students, 33.4% and 1.8% were classified as having mild PIU and severe PIU, respectively. Considering the similarity of measurement tools used, the results are comparable to the rates of 37.1% and 2.9% reported among university students in Jordan [57]. The results in the current study are higher than the rates of 10.4% and 0.8% reported among medical college students in India [58]. On the other hand, the results in this study are lower than the rates of 59.2% and 6.5% reported in Namibia and the rates of 70.3% and 4.7% reported in Uganda university students [59] and the rates of 56.5% and 7.8% reported for Malaysian undergraduate students [60]. The rate of severe PIU in the current study is also lower than the rate of 10.2% among Nigerian [61] university students. Variations in the prevalence of PIU could be due to cultural diversity among communities and the time frame when the research was conducted [30, 32]. Even so, the findings in our study has demonstrated that PIU is relatively higher in Ethiopia despite internet penetration is much more limited than other countries [27].

As we hypothesized, the analyses of factors for PIU revealed that students’ online activities significantly predicted students’ PIU behaviors when socio-demographic background variables are controlled. Compared to the Model that included socio-demographic background variables, the inclusion of online activities improved the fit of the Model predicting PIU behavior. The results corroborate previous studies that online activities people engaged in the internet are risk factors for internet addiction [33, 42, 62, 63].

Of all the online applications examined in this study, social networking was the strongest predictor of PIU. The finding corroborates previous research [4042] reporting that engagement in online social networking is most important risk factor for PIU. Social networking applications are used mainly to maintain and establish offline and online networks [40] that their excessive uses has a variety of negative consequences for the individual [63].

The other Internet online application that significantly increased the risks of PIU in this study was online gaming. Playing online games increased the risk of PIU on the internet by 117.5%. This finding is consistent to the existing literature [33, 4244, 63] documenting positive association between online gaming and PIU. Online gaming has been identified as potentially addictive as it requires large amount of commitment and time investment from the player which may in turn contribute to the development of maladaptive behaviors that reinforce gaming [64]. Our findings that engagements in social networking and online gaming have been increased the risk of PIU on the internet highlighted that the usage of some online activities are potentially problematic as overuse can result in a variety of negative consequences [40]. Thus, university officials and other concerned bodies may put regulatory mechanisms in place to limit the usage of potentially problematic internet applications such as social networking and online gaming.

This study also demonstrated that socio-demographic background variables such as self-esteem, academic performance, and number of years stayed in the university (seniority), parental support, and peer pressure significantly associated with PIU. The current finding revealed that higher level of self-esteem was protective of PIU. The result supports previous studies [34, 36, 62, 65] reporting that lower self-esteem was related to PIU. Consistent with previous studies [23, 31, 32], the finding in this study revealed a statistically significant negative relationship between academic grade point average and PIU. Gencer and Koc [66] noted that students with poor academic performance may experience stress and may develop low self-esteem; therefore, they use the internet as a way to cope these stressors. Others [16] argued that individuals with PIU often experienced lack of sleep since they stay awake during late-night hours in order to surf through various web pages. The lack of sleep causes lack of concentration and loss of interest in everyday lectures, resulting in reduced reading of the course material and eventually lower marks at exams. Our findings did not indicate whether poor academic performance or PIU is precursor for the observed relationship. Therefore, future longitudinal study is called for uncovering such causal relationships.

In this study, the proportion of students with PIU sequentially increased with increasing years spent in the university (freshman to fourth year). This finding is consistent with previous studies in Nigeria [61] reporting the likelihood of internet addiction among university students vary with respect to year of study in that senior students were more likely to show PIU symptoms than their juniors. This could be attributed to the fact that parents might have limited support for senior students. Students who successfully accomplished university’s expectations in each year will be seniors in turns. These students may have sufficed time to use internet excessively for different purposes. On the other hand, as seniority increases (second year and above), students may be expected to accomplish difficult tasks accordingly (e.g., assignments and research projects) that might cause stress, and students might use the internet as a method to cope the context [16]. Though these explanations are intuitive, the current result that senior students are significantly more likely to PIU behavior than their junior counterparts is informative for the university administrators to design interventions to inculcate responsive internet use behavior at the beginning of students’ university life.

This study identified inverse relationship between parental support and students’ PIU. Such negative association between parental support and adolescents’ PIU was repeatedly reported in studies conducted in different communities [33, 67, 68]. Good social support of families and friends buffers from psychological stress in individuals [69] and stimulates an individual to improve one's perceived efficacy to lessen the negative consequences of the stressful experience [70]. Wu et al. also suggested that lack of social support is both the cause and consequence of PIU [33]. Hence, consolidating familial ties and promoting helpful relationships might prevent PIU in adolescents [71].

On the other hand, positive significant relationship was found between peer pressure and the risk of PIU. Every unit increase in peer pressure score increased the odds of PIU risk by16%. Social norm theory assumes that adolescents’ beliefs about the norms that are prevalent among their peers influence their behavior [72] through descriptive and injunctive peer norms [73]. Descriptive peer norms are adolescents’ perceptions about the quantity and frequency of a certain behavior among peers. Injunctive peer norms are beliefs about the approval of a behavior among peers [73]. In this study, it is not clear whether the students’ perceptions of their peers’ internet use behavior or their approval of internet use that serve as factors for PIU. Regardless of the direction, the current findings highlighted the importance of peer pressure in students’ PIU behavior. Thus, programs designed to reduce PIU behavior may focus on students’ perception of normative behavior.

Limitations

This study has limitations that should be considered. We assessed internet use status of students using IAT, the most commonly used measure of Internet addiction [74]. Though strong internal reliability estimates of IAT have been established [75], researchers have reported different factor structures [76] which suggests a potential lack of construct validity of the instrument [74]. The absence of evaluation of the validity of the cut-off scores also limits its usefulness as a potential screening tool [77]. Some items also have been considered outdated or vague as that need to be removed or improved [78]. Future research may assess the concurrent validity of IAT with recent tools.

Of the 844 students, 812 students (96.21% response rate) fully completed the questionnaire. The participants were obtained from a single government university which might not be representative of the entire university students in Ethiopia. Data were collected using self administered structured questionnaire. Students may give responses which they believed to be expected or acceptable, thus there might be measurement bias. We also collected data only from students who participated in the study. The lack of data from significant others (e.g., parents and network members) is the limitation of the study. In this study, the purposes of internet use (students’ engagement in online activities) were measured categorically as yes/no. Such binary scoring did not indicate the amount of internet use (temporal dimensions) hence likely to lead to inflated prevalence of each activity. Future research that will consider the time spent online of each activity within a day/ a week will yield better evidence. Also, students may engage in the internet for more than one purpose or some online activities may have combined purposes. Such combined uses may be the greatest risk for PIU. Future research may discover such mingled proofs. Being informed by the exploratory qualitative results, we did not assess watching online pornography and online gambling activities as potential risks for PIU. Previous study [31, 46] reported that these online activities are risks for PIU. Hence, future research may incorporate these variables to understand what kinds of internet use cause the greatest risk.

In addition, the cross-sectional nature of the study limited the interpretation of the findings in terms of cause-effect relationships. There are also many factors this study did not assess, including environmental influences (university settings) and intrapersonal influences (e.g., anxiety, depression, and substance abuse). Future research may attempt to address these factors into consideration to predict risk behaviors for students’ PIU. Studies on factors contributing to responsible internet use behavior are also needed to assess the assets in and outside students so that interventions and programs which can foster such behavior can be developed and implemented.

Conclusion

This study assessed the influence of online activities as risk factors for PIU among undergraduate regular program students in Bahir Dar University. The findings indicated that social networking, entertainment, academic works, and online gaming are important activities students are doing online. More than a third of the students (35.2%) showed symptoms of PIU. The hierarchical logistic regression results revealed that students’ engagement into online activities improved the model explaining the PIU when socio-demographic background variables are controlled. Students who are engaging in the internet for social applications such as social networking and online gaming were more likely to show PIU. The findings in this study are important and timely as the internet has become the primary medium for information access in our universities. Thus, university authorities need to be aware of the prevalence of PIU and its antecedents so that interventions can be designed to prevent adverse outcomes. Interventions should focus on identifying students with PIU, creating awareness on the its negative effects, counseling services to develop students’ self image, introduce a regulatory mechanisms to limit the usage of potentially problematic internet applications, and promoting responsible use of the internet at the beginning of students’ university life.

Supporting information

S1 File. Survey questionnaire.

(DOCX)

S2 File. Survey data.

(SAV)

Acknowledgments

The authors would like to thank students who generously gave their time to complete the questionnaire. We would like also acknowledge the deans of the faculties who facilitated our data collection.

Data Availability

The data underlying the results are attached as Supporting Information.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Alfonso Rosa Garcia

17 Mar 2020

PONE-D-20-00961

Online activities as risk factors for internet addiction among students in Bahir Dar University, North West Ethiopia: Hierarchical regression model

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* Malak, Malakeh Z., Anas H. Khalifeh, and Ahmed H. Shuhaiber. "Prevalence of Internet Addiction and associated risk factors in Jordanian school students." Computers in Human Behavior 70 (2017): 556-563.

* Frangos, Christos C., Constantinos C. Frangos, and Ioannis Sotiropoulos. "Problematic internet use among Greek university students: an ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games." Cyberpsychology, Behavior, and Social Networking 14.1-2 (2011): 51-58.

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Reviewer #1: Thank you for the opportunity to review this manuscript on Internet addiction in Ethiopian university students. I have the following specific comments:

1. The introduction does not provide strong enough a reason to put individual-level, environmental, and online activities all into a single model for the prediction of Internet addiction. In theory, if each group of these variables could explain part of the variance in Internet addiction risks, it is acceptable to estimate them in separate models unless there are other causal relationships between them. The authors should explain why they think results may be different if they are put into the same model compared with separately estimated. Otherwise, there does not appear to be any new knowledge to anticipate judging from the introduction.

2. Also, although not all variables were considered, this study has tried to estimate a comprehensive explanatory model containing most of the variables you have mentioned for predicting Internet addiction: https://doi.org/10.1016/j.chb.2016.11.021

3. The method of calculation for the required sample size seems to be one used for prevalence studies (assuming 50%). I do not think this study is one of those. Authors may consider justifying their sample size using another method.

4. Was the Ethiopian IAT validated? If not, that is a limitation and needs to be mentioned.

5. How were the online activities reported? Frequency? Perceived amount of use? Need to state these more clearly. And all levels of all categorical variables being analyzed need to be explicitly stated.

6. In Table 1 it seems online activities are simply dichotomized as yes and no, and that is quite simplistic. I think a more specific frequency of use, e.g. once a week, twice a week,… for the particular online activities would capture more information than this.

7. The following statement is an incorrect interpretation of the result. You cannot say something is more important than the existing variables because it improves the fit of the model. I am sure if you put online activities into the model first, then add individual attributes and environmental factors the model fit would improve as well.

“Thus, the results revealed that students’ online activity variables are more risk factors for IA than their individual attributes and environmental factors.”

8. The authors need to understand that they didn’t ‘examined the relative importance’ of risk factors of IA. Interpretation needs to be revised thoroughly.

Reviewer #2: This investigated the relative contribution of the individual, environmental and content to problematic internet use.

Although it is an interesting study but a major issue is found for the statistical analysis. The authors must provide more details whether the required conditions were met for multiple regression.

Table 1

I strongly recommend the authors to reorganize the left column of the table to a single row to improve the readability.

e.g.

Online game -> Online game use

Use

Page 13

Better to present the significant digit in a unified way

32 – 38.7 -> 32.0-38.7

I think providing the t-score or chi-square number is too much of unneccessay information and suggest their omission from the text.

e internet addicted; what do you mean by e internet ?

Moreover, labeling addiction just by high scores on self-test score could be regarded as overstatement. I suggest changing the term to problematic internet use (PIU) instead of IA.

Compared to non-addicted, addicted students were significantly older (t (810) = -3.31, p < .05), had lower grade point averages (t (810) = 10.65, p < .001), had lower parental supervision (t (810) = 4.57, p < .001), had lower self-esteem (t (810) = 8.15, p < .001), and experience higher peer pressure

I think these are the main findings and should be highlighted by being included in the abstract!

When examing the risk factors by logistic regression, please add further explanation.

parental supervision or self-esteem

R2 = .459 -> 0.46 significant digit in a unified way; i.e. either three or two digits rather than mixture of both with exemption to p-value)

(AOR = 7.01, 95% CI: 290 3.91 – 12.083.91 – 12.08) ; seems like error. Please check!

p. 16

students were .19 (95% CI: .12 –.29, p < 0.001) times less likely to be internet addicted and with one point increase in self-esteem score students were .95 times (95% CI: .92 - .98, p < .01)

Please be consistent with the display of digits and I recommend against omitting zero. This applies to others for number presenations like Table 3.

one point increase in self-esteem

one point increase in parental supervision

In reality, self-esteem or parental supervision is not a subject to be actually manipulated by ‘one’ point. Please consider using plain words such as higher self-esteem or greater parental supervision.

Table 3

I again recommend the authors to reorganize the left column of the table to a single row to improve the readability.

Is sex included for the model I analysis?

University year and age may overlap. Please provide more data for collinearity check.

The Nagelkerke R2 for model I was 0.306, 0.367 for the model II and 0.459 for the model III.

I can see the difference between II and III (0.092) is larger than the difference between I and II (0.061). However, the R2 for model I was 0.306 in the first place, which is way more bigger than 0.092. Therefore, ‘Online activities better predict students’ addicted internet use behavior than individual and environmental attributes’ is a misleading statement. The data presented by the authors suggests contribution by the following orders; individual > online activity types > environment.

Reviewer #3: The manuscript presents an original study focused on (adolescent) Internet addiction (IA), which is the important and increasingly prevalent topic. The main aim was to analyze the effect of possible predictors (engagement in specific online activities, the amount of years spent at university, self-esteem and others) on IA in Ethiopian university students. The aim and study design are sound, but there are some issues that should be addressed before publishing the study. After addressing these issues, the study could be a valuable contribution to current knowledge, especially because it presents data on Internet addiction from Africa, which are currently scarce (the most studies within the area came from Asia, then Southern and Western Europe and Northern America).

Major issues

Introduction:

Introduction should be more focused on the main hypothesis, i.e., the effect of online activities on IA. Some sources dealing with this issue are missing. You might want to check at least these two:

Siomos, K., Floros, G., Fisoun, V., Evaggelia, D., Farkonas, N., Sergentani, E., Lamprou, M., & Geroukalis, D. (2012). Evolution of Internet addiction in Greek adolescent students over a two-year period: The impact of parental bonding. European Child & Adolescent Psychiatry, 21(4), 211–219. https://doi.org/10.1007/s00787-012-0254-0

Wu, X.-S., Zhang, Z.-H., Zhao, F., Wang, W.-J., Li, Y.-F., Bi, L., Qian, Z.-Z., Lu, S.-S., Feng, F., Hu, C.-Y., Gong, F.-F., & Sun, Y.-H. (2016). Prevalence of Internet addiction and its association with social support and other related factors among adolescents in China. Journal of Adolescence, 52, 103–111. https://doi.org/10.1016/j.adolescence.2016.07.012

Generally, the introduction is not very up to date with most sources published before 2015 (or even 2012). Given to the quick pace of scientific development within the area, this is a significant drawback. So please be sure to update your introduction thoroughly.

Few examples:

- Row 81: While defining IA, maybe you could use more relevant source or source with greater impact (from leading experts in the field) – see e.g. Fineberg, N., Demetrovics, Z., Stein, D., Ioannidis, K., Potenza, M., Grünblatt, E., Brand, M., Billieux, J., Carmi, L., King, D., Grant, J., Yücel, M., Dell’Osso, B., Rumpf, H., Hall, N., Hollander, E., Goudriaan, A., Menchon, J., Zohar, J., … Chamberlain, S. (2018). Manifesto for a European research network into Problematic Usage of the Internet. European Neuropsychopharmacology, 28(11), 1232–1246. https://doi.org/10.1016/j.euroneuro.2018.08.004 and Kuss, D. J., & Billieux, J. (2017). Technological addictions: Conceptualisation, measurement, etiology and treatment. Addictive Behaviors, 64, 231–233. https://doi.org/10.1016/j.addbeh.2016.04.005

- Row 81-84: While listing the negative outcomes of IA (which concerns your own study only marginally), it would be better to rely on available literature review or meta-analytic studies on the negative effects of IA (or similar concepts – e.g., Internet Gaming Disorder).

- Row 99-100: There are some recent and relevant sources concerning the effect of parental support and parental control on Internet addiction, that were not mentioned.

Aims:

The authors claimed they want to analyze whether the type of online activity (resp. the purpose of using internet) is better (stronger) predictor of IA than individual and environmental attributes (row 111-112). I find this to be problematic for several reasons.

- First, it is not possible to directly compare strength of predictors by hierarchical regression – you can only observe, whether predictors included in the initial model remain significant after the inclusion of other predictors; and whether new predictors enhance the overall explanatory power of the model. I would recommend reformulating the hypothesis in a manner similar to this: “The type of online activity is significant predictor of IA, even when other background variables (namely ….) are included.” If you really want to compare the strength of predictors, you should test two models (one with the main predicting variable – the online activities) and second with all other predicting variables and then compared their fit and explanatory power.

- Second, you claim to assess students’ individual and environmental attributes. This sounds very complex, but in fact you assessed some common sociodemographic variables (sex, age, field of study, year of study) and few other variables (grades, self-esteem, perceived parental support and peer pressure resistance). I do not claim that It is bad, it is similar to what we can find in other studies, but it is not so complex and comprehensive how implied by very general term “individual and environmental attributes”. Moreover, I am not sure whether it is possible to categorize your variables as being strictly “individual” or “environmental” (e.g., is parental support as perceived by an adolescent more environmental or individual?). I would recommend omitting the individual/environmental categories completely (from the whole manuscript, including results, table captions etc.). Instead, please clearly state what are your main predicting variables (online activities?) and what are the controlling (background) variables.

- Third, the term “internet addicted behavior” is not commonly used, I would recommend using “Internet addiction” or “addictive use” or “problematic internet use” throughout the whole manuscript.

Methods:

The design is described relatively clearly and in detail, but there are few points that should be addressed.

- First, when planning the sample size, how come you had expected IA to be prevalent in 50% of university students, when the worldwide prevalence is much lower? Or did I misunderstand? (row 124-125)

- Second, in two of your measures I see the obvious discrepancy between conceptualization (what you claimed to assess) and what you really assessed: (1) “parental supervision” is completely different phenomenon than “parental support”. They also have different associations with IA. Please consult the literature on parenting, parental control (demandingness, strictness) and parental warmth (responsiveness, support) and make sure that you use terms well. (2) You were using the term “peer pressure,” but measured “resistance to peer pressure” – this may be quite confusing, while these are basically “reversed” concepts, if I am reading it right.

- Third, could you specify whether you used the common cut-offs for Young’s Internet addiction test scores? You should be extra careful in this respect, while you are talking about IA prevalence (more about it also further).

- Fourth, you did not present the response rate and the amount of missing values and how they were addressed. Could you add this important information to the manuscript, please?

Results:

Results are quite clear and well-organized. Tables are standard. There are few points that should be addressed:

- First, there are significant associations between IA and both the age and years spent at university. While these two variables are most probably also associated, one of the two associations with IA could be artificial. Could you add another analysis that would shed light upon this? Or at least you should elaborate this while discussing these results.

- Second, when comparing predictive models (page 15), you should report not only R2 and its changes, but also F-value (with significance), that shows how much (and if significantly) models differed, especially when you stated that “there is statistically significant improvement in predicting IA”(row 283). The analysis does not imply the conclusion you made (row 285-286), it only showed that including the type of online activity significantly improved the prediction of IA. So please correct this (it relates to the point about hypothesis that I have made earlier).

- Third, the association between grades and IA (row 295-298). The impaired academic performance is a recognized negative outcome of IA. Therefore, it does not make much sense to test the reverse causality hypothesis without the supporting theory. However, you mentioned the theory in discussion – maybe you could elaborate it in introduction and perform the analysis with self-esteem as a mediating variable between grades and IA.

Discussion:

The discussion is standardly structured (the summary of results, discussion of results), but the content has several limitations.

- (1) The sources are not very recent and sometimes I missed the logic behind their inclusion – e.g., when discussing prevalence of IA, you reported prevalence in Hong Kong and Greece, but there are relatively recent paper with worldwide prevalence - e.g. Cheng, C., & Li, A. Y. (2014). Internet Addiction Prevalence and Quality of (Real) Life: A Meta-Analysis of 31 Nations Across Seven World Regions. Cyberpsychology, Behavior, and Social Networking, 17(12), 755–760. https://doi.org/10.1089/cyber.2014.0317. Moreover, the “Hong Kong” paper actually estimated the prevalence in six Asian countries… could you please clarify whether it is really prevalence for Hong Kong? And if so, then why you picked this one country?

- (2) Moreover, when discussing the prevalence, you should take into account the measurement of IA. You used screening test and based the prevalence on the presence of even “mild symptoms” of IA, which may be different approach then used in other studies. Please make the thorough comparison of your approach with your key source [46].

- (3) Discuss your results on the type of online activity with relevant studies (at least those two mentioned earlier in this review: Siomos et al., 2012; Wu et al., 2016).

- (4) Please be careful about the causality statements, especially in case of grades (as pointed out earlier) (row 343-345).

- (5) There are more recent sources dealing with IA and self-esteem. Please check at the very least this one: Dong, B., Zhao, F., Wu, X.-S., Wang, W.-J., Li, Y.-F., Zhang, Z.-H., & Sun, Y.-H. (2019). Social Anxiety May Modify the Relationship Between Internet Addiction and Its Determining Factors in Chinese Adolescents. International Journal of Mental Health and Addiction, 17(6), 1508–1520. https://doi.org/10.1007/s11469-018-9912-x

You could basically replicate their analysis, while they measured the associations between parent-child relationship (although in simplistic manner), self-esteem and IA.

- (6) The sentences on row 358-362 require clarification/rewriting. How findings can be “novel” and at the same time “consistent with previous studies”?

- (7) When you discuss the effect of years spent at university (“seniority of students”), you should take into account the associations between IA and age, rather than hypothesize about (i) the effect of parental supervision (that you did not measure), (ii) differences in online activities between junior and senior students (which you could analyze but you did not).

- (8) The paragraph on the effect of parental supervision (which is actually parental warmth/support) needs to be completely rewritten and accompanied by relevant sources.

Limitations:

- (1) You should state response rate (row 394).

- (2) Your main predictor (type of online activities) was measured in a quite simplistic manner – yes/no. It would be more advantageous to see the proportion of these activities for each student, or the time they spent by each activity. You should clearly acknowledge this in limitations. Also, some analyses that would show (1) whether some online activities tend to combine and (2) whether the combined use bear the greater risk of IA would strengthen the manuscript. Moreover, you did not assess two of three online activities with greatest risk of IA – watching online pornography and online gambling. This should be also mentioned in limitations.

The whole manuscript needs a language editing. You should also check some recommendations (e.g. by American Psychological Association) for authors, e.g. result should be described using past tense etc.

Minor issues

Please consider following adjustments:

[38] “students log in to the internet.” => “…students log in to the internet for.” Or reformulate (e.g., …students are engaging in.”)

[39] “had internet addicted behavior” => “showed symptoms of IA”

[47] and [321] “were internet addicted users” => “showed symptoms of IA”

[48] “Online activities” => “The type of online activity”

[230] “online game” => “online gaming”

[248] “duration in the university” => “years spent at the university”

[313] “predicti59ng” => “predicting”

[408] “students log in to the internet” => “students are doing online”

I hope you find the recommendations helpful.

Signed

Katerina Lukavska

**********

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PLoS One. 2020 Sep 11;15(9):e0238804. doi: 10.1371/journal.pone.0238804.r002

Author response to Decision Letter 0


30 Apr 2020

The comments given by both the editor and reviewers were very much constructive. We learned a lot from the comments and took all comments into consideration while revising the manuscript. Really, we appreciated their commitments and effort exerted to help us improve the manuscript.

Attachment

Submitted filename: Response to reviewers.docx

Decision Letter 1

Alfonso Rosa Garcia

3 Jun 2020

PONE-D-20-00961R1

Online activities as risk factors for Problematic internet use among students in Bahir Dar University, North West Ethiopia: A hierarchical regression model

PLOS ONE

Dear Dr. Asrese,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I consider that the manuscript has improved significantly in the current version, but it needs some important improvements, including a careful language editing. There are still some concepts, definitions and statistical issues that must be clarified. 

Please submit your revised manuscript by Jul 18 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have not addressed my comments fully (The numbering of the following points are in accordance with my previous reviewer report).

1. Adhering to a conceptual framework is good, but a specific explanation of why the included variables may interplay in relation to the outcome is still needed.

2. Addressed.

3. I was suggesting that this work was not a prevalence study. Why was a sample size calculation method for prevalence study used?

4. Addressed.

5. Addressed.

6. Addressed.

7. I was not questioning the sequence of variables being entered in the model. I was saying that neither the effect size nor the p-value should be taken as indicating relative importance between variables.

8. Same as above.

Reviewer #2: The authors revised their draft well, however, the term 'internet addiction' is still used in the draft. Please check the consistency of terminology.

Reviewer #3: I would like to thank authors for their responses on my concerns raised in the first review and for the changes they made.

In my view, the manuscript has been significantly improved, especially the introduction section, where authors are much clearer about their theoretical background (Problem Behavior Theory) and thus their aims/hypotheses are now better understandable. However, there are still issues that need to be addressed before considering publications. The main problems of manuscript concern methods – sampling procedure and Internet addiction test (and its cut-off scores). Also, the manuscript requires careful language editing. Detail description of major and minor issues follows.

Methods

Sampling

• Although sampling is described in detail, some important facts are still missing. Do authors know, how many students did not meet all inclusion criteria (and thus excluded/not invited to participate)? It is usual to report the number of “eligible” participants (in this case, the number of students in selected departments) and then to report how much of them were excluded (both before and after data collection). Given to the fact, that authors report PIU prevalence, it seems important to know at least how many students were excluded because they were not using internet regularly for at least six months (one of the inclusion criteria). If the number of these students is high, then the data on prevalence of PIU among university students are most certainly biased. At any case, authors should clearly state, that the data on prevalence concern “regular internet users”, not all students.

Measures

• The assessment of peer pressure is still not clear to me. Is it that the higher score indicates higher vulnerability to peer pressure? But originally, the tool measured the resistance to peer pressure, so the scoring is different (reversed) than in original?

• Internet addiction test: I still believe that the prevalence of PIU should be based rather on cut-off score 80, than 50, while 50-79 reflects “frequent problems” and PIU should reflect rather “severe problems”. If you want to use “the milder cut-off” please find a support for this in current studies. One study (Ref. no.48) is not enough. Moreover, even this study distinguished between “severe” and “mild” addiction: “The overall prevalence of Internet Addiction was 26.50%, with severe addiction being 0.96%.”). So, at the very least, you should also report “mild” and “severe” PIU by use of mild (50) severe (80) cut-offs. I totally understand that you want to use mild cut-off for the sake of statistical analyses (it works better to compare similarly large groups…) BUT it is problem when you report prevalence… So, I really think that you should at the very least report the prevalence of PIU on the basis of strict cut-off score (80 in IAT) alongside with the prevalence based on the mild cut-off score.

• Also, IAT is rather old instrument and does not reflect well all PIU symptoms. Please, provide detailed description of how symptoms measured by IAT differ from more developed current instruments.

Results

• What are AOR? Please explain in the text.

Discussion

• Prevalence of PIU – While prevalence of PIU is one of the key findings of this study, it is important while discussing the prevalence with other studies, to include also information about measurement tools (and cut-off scores) used in reported studies. Also, it is necessary to be extra cautious when assessing prevalence based on self-reports – see Maraz, A., Király, O., & Demetrovics, Z. (2015). The diagnostic pitfalls of surveys: if you score positive on a test of addiction, you still have a good chance not to be addicted. A response to Billieux et al. 2015. Journal of Behavioral Addictions, 4(3), 151-154. And again, the prevalence of severe PIU (using strict cut-off score) should be given alongside with the prevalence of mild PIU.

• The effect of online activities (applications) on PIU. You revealed that the strongest predictor is social networking and online gaming. I wonder, whether these effects are independent on gender. According to some scholars, social networks can be overused by females, while games by males (see e.g., Koning, I. M., Peeters, M., Finkenauer, C., & Van Den Eijnden, R. J. (2018). Bidirectional effects of Internet-specific parenting practices and compulsive social media and Internet game use. Journal of behavioral addictions, 7(3), 624-632.) Could you please provide the relevant analyses (at least chi-square test of association for the frequency of gaming and social networking in male and female students)?

Limitations

• Response rate is high enough, but I am not sure, whether is accurate – see earlier points (how many students were not included because they did not meet all inclusion criteria, e.g., not being regular user of internet for at least 6 months).

• Limitations of Young’s Internet addiction test should be mentioned, please consult the current sources on PIU measurement.

Minor issues

[105] …age… please be more specific – e.g., being between XX-XX years of age or something similar

[116] …as most studies on risk factors have been conducted in western societies… Actually, most studies on PIU were conducted in Asia.

[121] …online activity variables significantly predict PIU of students… Maybe this could be better explained… “the type of online activity in which students are engaged significantly predicts his/her PIU… or something similar

[177] Online internet application is a confusing term… Maybe stick to “online activities” as you use the term in introduction

[table 1] …Secondary school… -> At secondary school (same as “at university”…)

[332] …have PIU behaviors… -> showed symptoms of PIU (and I recommend to use …showed symptoms of mild PIU)

[334] …The result in the current study, on the other hand, is higher… -> The prevalence in the current study…

[343] …Mode… -> Model 1 (?)

[368] … (decease the chance by 5.20%) -> Please report odds ratio instead.

There are still many mistypes and grammar errors. The text should be edited by native English speaker. Also, authors should carefully check whether they use common scientific terms.

[112] …are found… -> have been found

[130] …categorization of variable… -> variables

[149] …were considered to participate in the study… -> were asked to participate

[150] …representative respondents… -> representative sample

[275] …online game… -> online gaming

[355] …PIUP… -> PIU

[382] …students were more likely have PIU behavior -> were more likely to show PIU symptoms (?)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Katerina Lukavska

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Sep 11;15(9):e0238804. doi: 10.1371/journal.pone.0238804.r004

Author response to Decision Letter 1


15 Jul 2020

Responses to Reviewers

To the Editor

Thank you for giving us this chance to revise and submit the manuscript. We have gone through all the comments of the reviewers. Many of the changes /improvements are highlighted yellow.

Reviewer 1

Thank you very much for the comments. We have gone through the comments and improved the document accordingly.

1. The interplay between independent variables and dependent variables needs to be explained.

Response: Variables entered in the analyses were selected based on the existing literature describing possible relationship with the dependent variable. Some authors reported that the individual and environmental factors (age, sex, academic achievement, parental support, peer pressure self-esteem) significantly related to PIU. Others differentiated between dependence to the internet and dependence on the internet and argued that majority of individuals with PIU use the internet as a medium of online activities, such as social networking, online entertainments, and gaming. And these variables are suggested as more important for PIU. These are explained in the manuscript (lines 104 – 112).

2. Concern related to sample size calculation: Why was a sample size calculation method for prevalence study used?

Response: The main purpose of the study was to learn the roles of online activities on the internet in predicting PIU. We need to know whether there was PIU and assess which variables (the control or predictor variables) better explain he variance of this phenomenon. Thus, we used the approach to estimate the sample size.

3. Either the effect size or the p-value should be taken as indicating relative importance between variables.

Response: As it is suggested, reporting effect size or lower p-value indicates the relative importance of the independent variables in explaining the dependent variable. In the current study, use of the internet for social networking, online gaming, peer pressure, and grade point average had lower p-vales. These variables are relatively important in explaining PIU among students. Nevertheless, the interest in this study was to assess whether socio-demographic variables (controls) or the online activities (predictors) better explain PIU among students. The interest was to compare the models in explaining the outcome variable. Our results revealed statistically significant improvement in predicting PIU of students with the online activity variables after controlling the socio-demographic background variables.

Reviewer 2

Thank you very much for the comments. We have gone through the comments and improved the document accordingly.

1. Concern related to consistency of using terminology: using PIU instead of internet addiction.

Response: The manuscript was edited as to the comment.

Reviewer 3.

Thank you very much for your critical review and thoughtful recommendations. We learned a lot from your suggestions.

Methods section

1. Sampling: Concern related to knowing eligible participants based on inclusion/exclusion criteria and the response rate

Response: There were 2010 students in the selected 10 departments. The sample size was proportionally allocated to number of students in each department and selected students were approached through section mentors. Written consent of individual participants was obtained after being fully informed of the study purpose and procedures. The inclusion criteria (reside in the university dormitory, used internet at least for six months, and not diagnosed with anxiety or depression disorders for a year) were communicated while students were completing consents. Fortunately, no student reported discordant information to be excluded from the study. Thus, all were eligible to participate in the study. During data cleaning, we identified that 32 students did not fully complete questionnaire, hence excluded from the study. Data obtained from 812 respondents (96.2% response rate) were reported in this study.

2. Measurement

A. Clarity on the assessment of peer pressure

Response: Originally, the tool was developed to measure resistance to peer pressure. In this study, the scoring is reversed: higher score indicated higher vulnerability to peer pressure.

B. Cut-offs used for Young’s Internet addiction test scores?

Response: The cut-offs for Internet addiction test scores were modified as to the reviewer’s comments.

Discussion section

A.Concern related to prevalence of PIU.

Response: In the revised manuscript, the prevalence of PIU was discussed in line previous research reports those used similar measurement tool (Internet Addiction Test) as suggested by the reviewer (lines 331 – 339).

B. Concern related to whether the effects of online activities on PIU was independent of gender (chi-square test of association for the frequency of gaming and social networking in male and female students)

Response: In the revised manuscript, bivariate analysis of our findings revealed that female students were more likely to show symptom of PIU than their male counterparts. When the effects of other variables are controlled in the multivariate analysis, such difference failed to reach significant. The main purpose of the study was to learn the roles of online activities on the internet in predicting PIU. Gender was one of the control variables entered first in the regression model and the improvement of the second model with online activity variables in explaining the variance in PIU was assessed. Below is the distribution of male and female students by online activities assessed in the study.

Online activities Sex

χ2

Male

n(%) Female

n(%)

Social networking use 406(75.9) 207(74.7) .132

No use 129(24.1) 70(25.3)

Entertainment use 408(76.3) 190(68.6) 5.531*

No use 127 (23.7) 87(31.4)

Academic works use 393(73.5) 183(66.1) 4.838*

No use 142(26.5) 94(33.9)

Online game use 119(22.2) 56(20.2) .443

No use 416(77.8) 221(79.8)

Limitation section

1. Concern related to limitations of Young’s Internet addiction test should be mentioned.

Response: The revised manuscript is improved as to the comments given by the reviewer (lines 416 – 423).

Minor issues

Language and editorial works

Response: A professor in English language teaching English as a foreign language edited the manuscript.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 2

Alfonso Rosa Garcia

12 Aug 2020

PONE-D-20-00961R2

Online activities as risk factors for Problematic internet use among students in Bahir Dar University, North West Ethiopia: A hierarchical regression model

PLOS ONE

Dear Dr. Asrese,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We ask you to review some minor points in the​ manuscript, that I will revise alone (I will not send the new version to reviewers). In particular, following the comments of reviewer 1, you should consider to eliminate the word "relative" in the first line in the "Conclusion". You also should try to fix the consistency mistakes identified by reviewer 3.

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We look forward to receiving your revised manuscript.

Kind regards,

Alfonso Rosa Garcia

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: (No Response)

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Listing the previously identified risk factors does not mean explaining the interplay between them.

2. The authors have conducted a logistic regression. Sample size calculation should be based on this.

3. I repeat: the way the authors do the analysis, the relative importance cannot be quantified. So claiming that they have assessed relative importance between the variables is misleading.

Reviewer #2: I recommend for the publication in the current form.

I hope that more interesting studies will come out from your region in regard to addictive behaviors related to new technologies.

Reviewer #3: Thanks for the opportunity to review this manuscript. I believe it is a solid piece of research in its current form. Only few minor and rather "technical" issues should be corrected (e.g. past sense while describing results - rows 251, 252 and elsewhere, consistency in replacement of IA by PIU - row 450).

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Katerina Lukavska

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PLoS One. 2020 Sep 11;15(9):e0238804. doi: 10.1371/journal.pone.0238804.r006

Author response to Decision Letter 2


12 Aug 2020

Responses to Reviewers

To the Editor

Thank you for giving us this chance to revise and submit the manuscript. We have gone through the comments of the reviewers. The changes /improvements are highlighted yellow.

Reviewer 1

Thank you very much for the comments. We have gone through the comments and improved the document accordingly.

1. You should consider eliminating the word "relative" in the first line in the "Conclusion

Response: we changed as to the comment.

Reviewer 3.

Thank you very much for your critical review and thoughtful recommendations. We learned a lot from your suggestions.

1. Technical issues should be corrected (e.g. past tense while describing results - rows 251, 252 and elsewhere, consistency in replacement of IA by PIU - row 450).

Response: Improvements are made as to the comments.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 3

Alfonso Rosa Garcia

25 Aug 2020

Online activities as risk factors for Problematic internet use among students in Bahir Dar University, North West Ethiopia: A hierarchical regression model

PONE-D-20-00961R3

Dear Dr. Asrese,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Alfonso Rosa Garcia

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Alfonso Rosa Garcia

31 Aug 2020

PONE-D-20-00961R3

Online activities as risk factors for Problematic internet use among students in Bahir Dar University, North West Ethiopia: A hierarchical regression model

Dear Dr. Asrese:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Alfonso Rosa Garcia

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. Survey questionnaire.

    (DOCX)

    S2 File. Survey data.

    (SAV)

    Attachment

    Submitted filename: Response to reviewers.docx

    Attachment

    Submitted filename: Response to Reviewers.docx

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    The data underlying the results are attached as Supporting Information.


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