Prevalence and Predictors of Problematic Smartphone Use among High School Students in Addis Ababa, Ethiopia

Prevalence and Predictors of Problematic Smartphone Use among High School Students in Addis Ababa, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prevalence and Predictors of Problematic Smartphone Use among High School Students in Addis Ababa, Ethiopia Mebratu Abraha Kebede, Enas Imran Abdullahi, Mihiretu Wondimu Kebede, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8613405/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Students can use their smartphone for different purposes: to take notes, read PDFs, documents, and articles and as source of news. But due to excessive engagement with social media and entertainment, students are more prone to experience problems. Because there is no published research on the topic, the aim of the current study is to fill the research gap by determining the prevalence and predictors of problematic smartphone use among high school students in Addis Ababa, Ethiopia. A cross sectional study was conducted at randomly selected four government and four private high schools found in Arada and Gulele Sub Cities of Addis Ababa from May of 1st to 30th of 2025 among 422 high school students. Systematic random sampling technique was applied to select the study participants. We conducted data analyses using independent samples t-test, one-way ANOVA, linear regression analyses and multinomial logistic regression. The results indicated that about 41.2% of the students had problematic smartphone use. Students from government schools (AOR 1.76, 95%CI (1.13, 2.74)), those who used their smartphone every day (AOR 95%CI; 2.64 (1.55, 4.49) and those who didn’t use it for educational purpose (AOR 1.88 (95%CI (1.18, 3.02)) showed significant association with problematic smartphone use. Therefore, awareness creation on the PSU and the risk factors is warranted. Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors problematic smartphone use prevalence high school students Figures Figure 1 Figure 2 Introduction The development of mobile communication technology has been a long journey of innovation, constantly evolving and updating to meet the needs and preferences of users ( 1 ). Fifth-generation phones are expected to enhance performance and introduce a wide range of new applications, including advancements in e-Health ( 2 ). It is equipped with different functionalities like email services, office programs, and multimedia features like high-definition cameras, video players, audio players, and sound recording that facilitate more than the traditional mobile phones ( 3 ). By the end of 2018, around 239 million people, which is equivalent to 23% of the Sub-Saharan Africa population, used mobile internet on a regular basis ( 4 ). Students can use their smartphone to take notes, read PDFs, PowerPoints, documents, and articles or anything related to school ( 5 ) as well as a source of news ( 6 ). But due to their excessive engagement with social media and entertainment, students are more prone to experience problematic smartphone behaviors ( 7 ). The prevalence of problematic smartphone use (PSU) among high school students in the United States is 22% ( 8 ). The prevalence somewhat varies by gender; 23% of male and 21% of female high school students have PSU ( 9 ). In Southeast Asia, the prevalence of severe problematic internet use (PIU)/internet addiction ranged from 0 to 47.4%, though internet overuse/possible internet addiction ranged from 7.4% to 46.4% ( 10 ). In Nigeria, 46.1% of young people aged 10 to 24 years experienced moderate to high problematic phone use ( 11 ). Smartphone addiction is more prevalent in younger adolescents (15–16 years), students, males, persons reporting lower physical activity, extraversion, and low conscientiousness personalities, and those reporting higher stress ( 11 , 12 ). According to the Ikeda and Nakamura report, PSU is associated with female students. Further, the study identified nonparticipation in sports club activities, early mobile phone use, and fewer hours spent sleeping as correlates of PSU ( 13 ). Smartphone overuse can negatively affect users’ daily lives and activities ( 14 ) as well as their behavior, thoughts, and well-being ( 15 ). It is therefore conceivable that implementing actions aimed at reducing students’ PSU, particularly at the elementary and middle school levels, may translate into improved academic performance ( 16 ). Students are supposed to use the internet to enhance their academic performance, but they need to restrict themselves from nonacademic purposes. Teachers could also have a remarkable contribution to guide the extent of Internet usage to avert over dependency among students ( 17 ). However, to the knowledge of the researchers, no study as yet has addressed predictors of PSU among high school students in Addis Ababa. Therefore, the current study aims to narrow the research gap by addressing the magnitude and predictors of problematic smartphone use among high school students in Addis Ababa, Ethiopia. Method and materials Study design, period and population A cross-sectional study was conducted at eight randomly selected high schools (four from each of the government and private schools) found in Arada and Gullele sub-cities of Addis Ababa from May of 1st to 30th of 2025. The study schools were selected using lottery method. All students attending both the selected government and private high schools are the target population, and those who were available during the data collection period at these schools were the study population. All high school students who used smartphones for at least a month before the data collection period were included in the study. Sample size and sampling technique , The minimum number of sample participants required for this study was determined by using single population proportion formula considering the following assumptions: \(\:ni=\frac{Z{\alpha\:⁄2}^{2}pq}{{d}^{2}}\) . Because we found no study conducted among high school students in Ethiopia, we assumed 50% of the students experience problematic smartphone use. Based on the above formula, the calculated sample size was 384. By adding 10% for possible non-response, the final sample size was 422. The two sub-cities were selected purposely because large number of both private and government schools are located in the sub-cities. Then eight schools were selected using lottery method with equal allocation of government and private schools. Around 10880 and 2059 high school students were attending their education at the government and private schools, respectively, during the 2024/25 academic year. Then students from each school were selected by using stratified random sampling technique, by proportional allocation from government and private schools. That is, two-thirds and one-third of the sample students were selected from the government and private schools. The proportion of natural and social science classes was also taken into consideration when selecting the sample students. Then the students were selected randomly. But for those from the private schools, the sampling frame (roster), which had the list of students from each school, was taken from the schools. Once the students were stratified based on their grade level, the sample was selected using systematic random sampling technique. Data collection method, tools, and analysis Data was collected by using interviewer administered written questionnaire in the class once after getting permission from the teacher. The data had three sections; these are socio-demographic factors, smartphone use-related factors, and smartphone use addiction. The data was collected on consecutive days from May of 1st to 30th of 2025 until the desired sample size was achieved from each school by two trained data collectors and the first author. Smartphone addiction-related information was collected by the Smartphone Addiction Scale-Short Version (SAS-SV). It is a self-assessment tool designed to identify individuals with smartphone use addiction ( 18 ). The current version was preceded by an in-depth validation of the scale in a previous study, during which the final version was developed, consisting of 33 items grouped into 6 distinct subscales. These subscales assess different aspects of smartphone use addiction: disruptions in daily life, positive anticipation, withdrawal, cyberspace orientation, excessive use, and tolerance. Therefore participants rated the items on a 6-point scale [strongly disagree ( 1 ), disagree ( 2 ), slightly disagree ( 3 ), slightly agree ( 4 ), agree ( 5 ), and strongly agree ( 6 )] ( 19 – 21 ). In general, the SAS-SV is a shortened version of the original SAS. It contains 10 items evaluated on a scale similar to that of the original version (1: “strongly disagree” to 6: “strongly agree”), and it has strong (reliability and validity) psychometric characteristics. The total SAS-SV score ranges from 10 to 60, with higher scores indicating a predisposition to “smartphone use addiction.” Then distinguishing problematic smartphone users can be achieved using a score threshold of ≥ 33 for females and ≥ 31 for males ( 22 ). Data was directly entered and analyzed using SPSS-27. We computed descriptive statistics for both continuous (mean and standard deviation) and categorical (frequency and percentage) variables. We analyzed the data using Independent samples t-test, one-way ANOVA, and logistic regression analysis considering the AOR and 95% CI concomitantly with the p-value. Ethical Consideration Support letters were obtained from the School of Psychology, College of Education and Language Studies, Addis Ababa University and the education offices of the two sub-cities. The proposal was reviewed and approved by the St. Paulo’s Hospital Millennium Medical College Institutional Research Ethics Review Committee (IRERC) with the protocol number of pm23/632 on 25/04/2025 . The purpose of the study was briefly explained to each study participant/student and their parents for those who were at the age category of 16–17 years. Hence, data was collected after obtaining written informed consent from the study participants. Additionally, for those who were between the age of 16 and 17, parental verbal assent was taken through phone interview in addition to the written consent that was taken from the students. The verbal consent was takes placed by using of phone interview due to difficulty to address the parents in physical. Each consent and assent was taken by the data collectors prior to the data collection process started. Any student who was not willing to participate was not obligated to provide information for the study. Information obtained from the study was kept confidential just by left any personal identifier. Participating in the study had no any risk on the study participants as well as their family (for those who were 16 to 17 years of old students) except the 30 to 40 minute spending to fill the questionnaire. All study participants had a full write to withdraw from the study at all or skipping of some questions in case if they weren’t interested to answer. The data was collected at the place where the students could felt safe to keep their privacy. In general, the research was implemented in accordance with the Declaration of Helsinki protocol. Results Socio-demographic characteristics of the study participants A total of 417 high school students were included for the current study, yielding a response rate of 98.8%. The mean (± SD) age of the students was 17.04 (± 1.05) years. Female students were slightly greater (51.3%). About 64.5% of the students were from government schools (see Table 1 ). Table 1 ; Socio-demographic related responses of high school students, Addis Ababa, Ethiopia, 2024/2025 Variable Category Frequency Percent Age 16–17 years 281 67.4 18 years and above 136 32.6 Sex Male 203 48.7 Female 214 51.3 Grade level grade 9 123 29.5 grade 10 102 24.5 grade 11 101 24.2 grade 12 91 21.8 School type private school 148 35.5 government school 269 64.5 Numbers of brothers None 109 26.1 1–2 brothers 242 58.0 3 or more 66 15.8 Numbers of sisters None 116 27.8 1–2 sisters 255 61.2 3 or more 46 11.0 Smartphone utilization-related responses Regarding students' smartphone utilization, the mean (± SD) duration since starting smartphone usage was 4.13 (± 2.38) years. Besides, 55.6% of the students started using smartphones four years prior to the data collection period. More than three-fourths (88.0%) of the students were using their own phones. Home WiFi was the source of internet for 56.4% of the students. More than three-fourth (75.5%) of them were using smartphones every day. Further, students spent a mean (± SD) of 5:08 (± 2:54) hours with their smartphone and a minimum and maximum of 30 minutes and twelve hours and fifty-nine minutes per day, respectively. Besides, 43.4% of the students prefer the night period to use their smartphone (see Table 2 ). Table 2 ; Smartphone utilization-related responses of high school students, Addis Ababa, Ethiopia, 2024/2025 Variable Category Frequency Percent Duration of smartphone use ≤ 4 years 232 55.6 > 4 years 185 44.4 Whose phone do you use Mine 367 88.0 My father's phone 8 1.9 My mother’s phone 19 4.6 My sibling's phone 12 2.9 Other*1 11 2.6 Source of internet Wifi at home 235 56.4 Wifi other than home 9 2.2 Mobile data 89 21.3 Wifi at home, other than home and data 10 2.4 Wifi at home & mobile data 64 15.3 Other*2 10 2.4 Number of days using smartphone per week ≤ 3 days 38 9.1 4–6 days 64 15.3 7 days 315 75.5 Place of using smartphone at home 314 75.3 at school 1 .2 both at home and school 44 10.6 other area*3 58 13.9 Other*1 (grandmom's phone, both fathers' and mothers' phones); Other*2 (hotspot and data) ; Other area*3 (other than school everywhere, refreshment areas) As Fig. 1 shows, more than half of the students were using their smartphone for accessing educational documents and doing their group assignment with the frequency of 63.5% and 58.8%, respectively. *Other purpose = (for religious issues) Problematic smartphone use The current study found that 172 (41.2% (95% CI; 36.5, 46.0)) of the students had problematic smartphone use with a mean (± SD) score of 29.31 (± 11.90) (see Fig. 2 ). Normality of the distribution was assessed using skewness, kurtosis and a histogram. Accordingly, skewness (0.44) and kurtosis (-0.52) of the data were in the acceptable range while the histogram showed a bell-shaped distribution. Further, no outliers were detected. Predictors of problematic smartphone use Bivariate logistic regression analysis For each explanatory variable, bivariate analysis was done. Accordingly grade level, school type, owner of the phone, frequency of using SP, and using SP for educational purposes were the variables that fulfilled the minimum requirement (at p-value < 0.2) to be included in the multivariate logistic regression analysis (see Table 3 ). Multivariable logistic regression analysis Those variables that had a p-value of less than 0.2 in the bivariate logistic analysis with PSU were entered into multivariable analysis to check their true association with PSU. During the multivariable logistic regression analysis, school type, frequency of using SP, and using SP for educational purposes had statistically significant associations with PSU at an alpha level of < 0.05. Specifically, students from government school (AOR 1.76, 95% CI (1.13, 2.74)) and those who used their smartphone every day (AOR 95% CI; 2.64 (1.55, 4.49)) had a statistically significant association with PSU. Also, those who didn’t use it for educational purposes (AOR 1.88 (95% CI (1.18, 3.02))) showed significant association with problematic smartphone use (p < .05) (see Table 3 ). Table 3 ; Factors associated with problematic smartphone use among high school students in Addis Ababa, Ethiopia, 2024/25 Variable Category Problematic smartphone use COR (95% CI) AOR (95% CI) P-value No Yes Grade level Grade 9 79 44 1 1 1 Grade 10 63 39 1.11 (0.65, 1.91) 0.89 (0.51, 1.59) 0.712 Grade 11 58 43 1.33 (0.77, 2.28) 1.05 (0.59, 1.87) 0.861 Grade 12 45 46 1.84 (1.06, 3.19)* 1.42 (0.79, 2.55) 0.237 School type Government School 144 125 1.87 (1.23, 2.84)* 1.76 (1.13, 2.74)** 0.013 Private School 101 47 1 1 1 Owner of the phone Other 36 14 1 1 1 The Student 209 158 1.94 (1.01, 3.73)* 1.37 (0.68, 2.73) 0.378 Frequency of using SP Sometimes 78 24 1 1 1 Every day 167 148 2.88 (1.73, 4.79)* 2.64 (1.55, 4.49)** < 0.001 Using SP for educational purposes No 49 62 2.26 (1.45, 3.51)* 1.88 (1.18, 3.02)** 0.008 Yes 196 110 1 1 1 * = variables fulfil the minimum requirement at p-value < 0.2 during bivariate analysis ** = variables that have a statistically significant association at p < 0.05 during multivariable analysis Further, the current study identified the presence of a statistically significant mean difference in problematic smartphone use due to duration of smartphone use. Those who had used smartphone for more than 4 years (M = 31.88, SD = 12.41) reported a higher PSU mean score than those who had used it for 4 years or lower (M = 27.26, SD = 11.10) [t (415) = -4.01; p-value < 0.001; d = -0.395]. Relationship between duration, frequency and time spent on smartphone use and problematic smartphone use Based on the linear regression analysis result, the duration since the students started using smartphones, the number of days using them, and the time spent on them per day had a weak but positive correlation with experiencing problematic smartphone use at Pearson correlation coefficient (r) values of 0.16, 0.27, and 0.37, respectively, at a p-value of < 0.001 (Table 4 ) . Table 4 ; Multiple Linear regression statistics of duration, frequency and time spent on smartphone with respect to problematic smartphone use among high school students in Addis Ababa, 2025 Model Unstandardized Coefficients SD Coef* t p 95.0% CI for B Collinearity Statistics B Std. Error β LB* UB* Tolerance VIF 1 (Constant) 13.055 2.383 5.479 0.000 8.371 17.738 Total duration of smartphone use .240 .233 .048 1.030 0.304 − .218 .697 .924 1.082 Numbers of days smartphone used per week 1.381 .381 .171 3.625 0.000 .632 2.129 .901 1.110 Time spent per day using smartphone .000 .000 .315 6.630 0.000 .000 .000 .892 1.121 Model summary R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change Durbin-Watson .413 a .171 .165 10.879 .171 28.352 3 413 .000 1.705 a. Predictors: (Constant), time spent per day using smartphone, total duration of smartphone use, numbers of days smartphone used per week b. Dependent Variable: Problematic smartphone use LB* (lower border); UB* (upper border) ; SD Coef* (standardized coefficients) As Table 4 shows, the three variables explain 17.1% of the variance in problematic smartphone use which is statistically significant (p < 0.001). Also, the tolerance and VIF (variance inflation factor) values of the three variables indicate no multicollinearity risk. Discussion The current study found that 41.2% of the students had problematic smartphone use, which is in line with studies in Japan (45.3%) ( 23 ) and the United Kingdom (38.9%) ( 24 ). Similar to the results of this study, a study found that 30–45% of Korean teenagers exhibited problematic smartphone behaviors ( 25 ). In addition, somewhat similar findings were reported by a study which found a prevalence of 36.9% among a Turkish high-school sample ( 26 ). Another study reported a prevalence of 45.5% in a metropolitan secondary-school cohort ( 27 ). Furthermore, a study reported approximately 36% PSU among Jordanian adolescents, which further supports that excessive smartphone use has become a widespread occurrence among students worldwide ( 28 ). But the recent finding is slightly higher than that of the United States study, where 23% and 21% of male and female high school students had problematic smartphone use ( 9 ). The prevalence found in the current study is also higher than a systematic review report that reported a median prevalence of 23.3% ( 29 ). This figure is also higher than the prevalence of PSU found in other countries, such as Switzerland, where 16.9% of the 1,519 vocational school students were engaged in heavy internet use ( 12 ), and Thailand, where 35.3% were similarly engaged in heavy internet use ( 30 ). In contrast, our finding is lower than the Bangladeshi finding, where about 86.9% of the students experienced PSU ( 31 ). About 65.8%, 58.1%, and 52.8% of students in China reported PSU at three waves, which is higher than the recent result ( 32 , 33 ). The reason for the discrepancy in the findings might be the result of the difference in study design and study population. The other observed difference is sample size and data collection method. Different scholars forward various reasons for why high school students are particularly vulnerable to problematic smartphone use. For instance, according to one explanation, adolescence is a developmental period marked by increased social sensitivity and peer orientation, making smartphones an appealing tool for maintaining social connection and self-presentation ( 26 ). Besides, many adolescents use their smartphones for entertainment and social networking activities that expose them to higher addictive potential compared to purely informational use ( 34 ). Furthermore, smartphones often become a default means of coping with stress or boredom; qualitative studies of younger adolescents show that “drivers of excessive use” include social factors, family/friends modelling, and nighttime habits that displace other activities ( 35 ). Moreover, persuasive designs embedded in apps (e.g., notifications, infinite scroll, reward loops) have been shown to prolong screen engagement and reinforce checking behaviors, increasing the risk of problematic use ( 36 ). With respect to the correlates/predictors of PSU, we found that students from government schools are 1.76 times more likely to experience problematic smartphone use than their counterparts from private schools. Comparable findings are reported in studies from India and Bangladesh, where adolescents in public schools demonstrated significantly higher odds of smartphone addiction than their private school peers ( 31 , 37 ). This could be due to less parental engagement and monitoring and lower awareness of digital health ( 37 ). There is evidence to support that parental supervision contributes significantly to disparities in smartphone use behaviors among adolescents ( 38 ). Students from lower- to middle-class families frequently attend government schools, using smartphones as a learning and entertainment tool ( 39 ). In contrast, private school students may have greater access to structured extracurricular activities and parental monitoring, factors known to buffer against problematic smartphone use ( 36 , 40 ). Moreover, educational infrastructure and digital policies in private institutions often emphasize balanced technology use and digital literacy, whereas government schools may lack similar strong regulatory frameworks or counseling programs to address excessive screen behavior ( 41 , 42 ). We also found that those students who used their smartphone every day are 2.64 times more likely to experience PSU than those who use it sometimes. This result is consistent with prior research indicating that the frequency of smartphone use are strong predictors of addictive behaviors among adolescents ( 14 , 25 ). Daily use increases exposure to social networking, gaming, and multimedia content, which can reinforce habitual checking and reduce self-regulatory capacity ( 22 , 43 ). Similarly, studies in both high- and low-income countries have reported that students with continuous daily access to smartphones exhibit higher levels of dependence, as repeated reinforcement strengthens usage routines and creates compulsive patterns ( 29 , 37 ). In addition, the current study found that those students who didn’t use it for educational purposes are 1.88 times more likely to have problematic smartphone use than those who use it for academic purposes. This finding aligns with previous research suggesting that purposeful, goal-directed use of technology, such as for learning or school assignments, can mitigate the risk of addictive patterns by promoting structured engagement and reducing idle screen time ( 37 , 41 ). In contrast, recreational use without educational intent such as excessive social networking, gaming, or video streaming has been consistently associated with higher levels of smartphone dependency among adolescents ( 25 , 43 ). By focusing on academic-related activities, students may develop greater self-regulation and purposeful routines that limit impulsive checking and prolonged nonessential use, highlighting the protective role of educational engagement in reducing problematic smartphone behaviors ( 14 , 44 ). According to the Thailand study report, students who spent on average 5.3 hours per day on the internet are engaged in heavy internet use problems ( 30 ). This is supported by our finding that the more time spent on a smartphone per day, the more likely that the students experience problematic smartphone use (r = 0.37, p < 0.001). Further, based on our linear regression analysis, duration since starting smartphone use and number of days using smartphone have a positive correlation with the outcome variable (r = 0.16). This is supported by previous study findings where a higher frequency of smartphone use ( 40 ), a higher duration of daily usage ( 22 , 25 ), and a higher habitual use ( 22 ) have all been found to be related to problematic smartphone usage. Limitation of the study Because the study focused exclusively on high school students, generalizability of the findings may be limited to other populations, such as primary and undergraduate students. The other limitation of this study is its use of a cross-sectional design, which collects data at a single point in time, making it impossible to determine causal relationships between the explanatory and outcome variables. Conclusion and implication The current study found that more than 40% of the high school students are experiencing PSU. Key predictors of PSU were identified; these are attending government schools, using smartphones every day, and non-educational use. The finding also highlights the significant relationship between smartphone usage patterns such as duration, time spent, and time of using and problematic smartphone use (PSU). Overall, these findings suggest that both environmental factors (such as school type and access to structured activities) and behavioral patterns (frequency and purpose of smartphone use) play significant roles in the development of PSU among adolescents. The study findings suggest that there is a need to address smartphone use as a critical aspect of adolescent health and daily functioning. Therefore, to address the challenges associated with PSU, it is suggested that the Ministry of Education take the lead in creating national guidelines on healthy digital practices, incorporating digital well-being education into the curriculum, and making sure that schools, particularly government institutions, have the tools necessary for awareness and counseling programs. Also, enforcing explicit school policies that restrict needless smartphone use during class hours and encouraging technology used primarily for academic purposes appear to be beneficial. By encouraging open communication and establishing limits that promote responsible use, parents can keep an eye on and mentor their children's smartphone habits. In the meantime, raising the students’ awareness on the consequences of PSU may help them to control their behavior, give priority to educational and purposeful smartphone activities, and partake in other recreational activities that lessen their reliance on electronic gadgets. When combined, these initiatives can produce a nurturing atmosphere that promotes a healthy digital learning teaching environment. Finally, conducting further research (e.g., longitudinal and experimental designs) will be useful to address the cause-effect relationship and long-term impact of PSU. Declarations Conflict of interest: The authors declare that they have no competing interests Author Contribution Both MAK and SZ conceived the study and were involved in the study design, reviewing the manuscript, data analysis, and drafting the manuscript. EI, MW, and AT involved in drafting the manuscript. All authors read and approved the final manuscript. Data Availability we have attached the word document based information sheet and consent form, questionnaire/tool that we used to collect the primary data and the raw data SPSS based filled data as a supplementary document. References Mokhlis S, Yaakop A (2012) Consumer Choice Criteria in Mobile Phone Selection: An Investigation of Malaysian University Students. Int Rev Social Sci Humanit 2:203–212 WHO, Radiation 5G mobile networks and health, 27 February 2020 [ Gadzama W, Joseph B, Maigana N (2019) Global Smartphone Ownership, Internet Usage And Their Impacts On Humans GSMA The Mobile Economy Sub-Saharan Africa 2019 2019 [Available from: www.gsma.com Desserie L, Aloquin KC, Navarro, Mahilum DZ, EXPLORING, THE LIVED EXPERIENCES ON SMARTPHONE USAGE AT TONGANTONGAN NATIONAL HIGH SCHOOL (2024) IJARW 6(6):12 García-Santillán A (2021) Addiction to the Smartphone in High School Students: How It’s in Daily Life? Contemp Educational Technol. ;13 (2) Rania Islambouli S, Ingram JC, Farah Z, Charlesworth R, Aouad A, Delabays et al (2025) Exploring the Negative Impact of Smartphone Usage on Students’ Digital Wellbeing: A Systematic Review of Empirical Studies. International Journal of Human–Computer Interaction Donaldson SI, Strong D, Zhu S-H (2021) A brief measure of problematic smartphone use among high school students: Psychometric assessment using item response theory. Computers Hum Behav Rep 3:100091 Kiva S, Paul R (2023) Problematic Smartphone Use Among High School Students Journal of School Administration Research and Development. ;8 Balhara YPS, Mahapatra A, Sharma P, Bhargava R (2018) Problematic internet use among students in South-East Asia: Current state of evidence. Indian J Public Health 62(3):197–210 Balogun FM, Olatunde OE (2020) Prevalence and predictors of problematic smart phone use among pre-varsity young people in Ibadan, Nigeria. Pan Afr Med J 36:285 Haug SCRR, Kwon M, Filler A, Kowatsch T, Schaub MP (2015) Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict 4(4):299–307 Ikeda KNK (2014) Association between mobile phone use and depressed mood in Japanese adolescents: a cross-sectional study. Environ Health Prev Med 19(3):187–193 Kadir D, Akgönül M, Abdullah A (2015) Relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety in University Students. J Behav addictions 4:85–92 Alhassan A, Alqadhib E, Taha N, Alahamri R, Salam M, Almutairi A (2018) The relationship between addiction to smartphone usage and depression among adults: A cross sectional study. BMC Psychiatry. ;18 Paterna A, A-I M, Aguilar-Parra JM, Salavera C, Demetrovics Z, Griffiths MD (2024) Problematic smartphone use and academic achievement: A systematic review and meta-analysis. Journal of Behavioral Addictions. 2024;13(2):313 – 26 Ogheneakoke Clifford BJ, Sunday OBRO, INTERNET ACCESS AND USAGE: IMPACT ON STUDENTS’ ACADEMIC PERFORMANCE IN SOCIAL STUDIES. European Journal of Education Studies, [Sl],. 2018(ISSN 25011111) Sfendla A, Laita M, Nejjar B, Souirti Z, Touhami AAO, Senhaji M (2018) Reliability of the Arabic Smartphone Addiction Scale and Smartphone Addiction Scale-Short Version in Two Different Moroccan Samples. Cyberpsychol Behav Soc Netw 21(5):325–332 Kwon M, Kim D-J, Cho H, Yang S (2014) The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS ONE 8(12):e83558 Meskini Nadia Chakit MLMF, Aameri Z, Sfendla M (2024) Anis Ouahidi Moulay. School score and Depression in Moroccan adolescents. ;21:308 – 18 Meskini Nadia Lamtai MS, Madhi AE, Ahami Y, Ouahidi AOT, Laarbi M (2023) Prevalence of stress, anxiety and depression in the context of climate change among newly recruited contract teachers in Morocco. E3S Web Conf 412:01073 Jeewon L, Sung M, Sook-Hyung S, Lee Y, Je-Jung L, Cho S-M et al (2016) Psychological Factors Associated With Smartphone Addiction in South Korean Adolescents. J Early Adolescence. ;38 Tateno M, Teo AR, Ukai W, Kanazawa J, Katsuki R, Kubo H et al (2019) Internet Addiction, Smartphone Addiction, and Hikikomori Trait in Japanese Young Adult: Social Isolation and Social Network. Front Psychiatry 10:455 Sohn SY, Krasnoff L, Rees P, Kalk NJ, Carter B (2021) The Association Between Smartphone Addiction and Sleep: A UK Cross-Sectional Study of Young Adults. Front Psychiatry 12:629407 Cha S-S, Seo B-K (2018) Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychol Open 5(1):2055102918755046 Çağan Ö, Koca B (2020) Evaluation of High School Students’ Smartphone Addiction and Insomnia Level. J Turkish Sleep Med 7:45–51 QureshiK Q, ChavhanS (2024) BishnoiD The Prevalence of Smartphone Addiction and Its Associated Risk Factors among School Students of a Metropolitan City: A Cross-Sectional Study. JCommunityHealthResearch 13:172–179 Tarawneh F, Abuattallah A, Dardas L Maladaptive Smartphone use among Jordanian Adolescents: Prevalence, Correlations, and the Role of Cognitive Emotion Regulation. Health Psychol Res. 2025;X:2025. Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B (2019) Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 19(1):356 Peltzer KPS (2014) Heavy Internet use and its associations with health risk and health-promoting behaviours among Thai university students. Int J Adolesc Med Health 26(2):187–194 Hosen I, Al Mamun F, Sikder MT, Abbasi AZ, Zou L, Guo T et al (2021) Prevalence and Associated Factors of Problematic Smartphone Use During the COVID-19 Pandemic: A Bangladeshi Study. Risk Manag Healthc Policy 14:3797–3805 Wang CJ, Chang FC, Chiu CH (2017) Smartphone addiction and related factors among elementary school students in New Taipei City. Res Educational Commun Technol 117:67–87 Wang A, Wang Z, Zhu Y, Shi X (2022) The Prevalence and Psychosocial Factors of Problematic Smartphone Use Among Chinese College Students: A Three-Wave Longitudinal Study. Front Psychol 13:877277 Wölfling K, Beutel ME, Dreier M, Müller KW (2022) Adolescent media use and addiction: Risk and protective factors. J Behav Addictions J Behav Addictions 11:90–103 Mahmud A, Islam M (2023) Understanding the drivers of smartphone addiction among university students: a perspective from Bangladesh. SN Social Sci. ;3 Chen YLRW (2021) X. Persuasive app design and the reinforcement of habitual smartphone checking behavior among youth. Comput Hum Behav. ;122(106839) Azizi S, Mohammadi H, Etemad F (2024) Differences in smartphone addiction between public and private school students: The role of family supervision and digital literacy. Asian J Educ Psychol 9(1):44–53 Yogesh H, Ranjith R, Divya G (2024) Parental supervision and smartphone addiction among adolescents: A comparative study between school types. Indian J Mental Health Educ 15:58–67 Nazir A, Sewani R, Zahid A, THE USE OF SMARTPHONES, IN SHAPING OF STUDENTS’ ACADEMIC PERFORMANCE AT SECONDARY SCHOOL LEVEL (2024) J Social Sci Dev 3:128–141 Bae S-M (2017) The relationship between the type of smartphone use and smartphone dependence of Korean adolescents: National survey study. Child Youth Serv Rev 81:207–211 Nguyen T-V, Nguyen Q-A, Nguyen N, Uyên B (2024) Smartphone use, nomophobia, and academic achievement in Vietnamese high school students. Computers Hum Behav Rep 14:100418 Tomczyk Ł. PK, Velagić Z (2024) Digital well-being and educational policies: Toward balanced technology integration in schools. Education and Information Technologies. ;29(7):7349-63 Jeong S-H, Kim H, Yum J-Y, Hwang Y (2016) What type of content are smartphone users addicted to? SNS vs. games. Comput Hum Behav 54:10–17 Gao QSR, Fu E, Jia G, Xiang Y (2022) Smart interventions for smartphone addiction: A meta-analysis of randomized controlled trials. J Behav Addictions 11(1):150–156 Additional Declarations No competing interests reported. Supplementary Files QuestionniareusedforPSU.docx smartphoneuseamonghighschoolstudentsfinal.sav Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 18 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8613405","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616735637,"identity":"3224f1cf-3ce2-46bb-a358-72ac2246c835","order_by":0,"name":"Mebratu Abraha Kebede","email":"data:image/png;base64,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","orcid":"","institution":"St. Paul's Hospital Millennium Medical College","correspondingAuthor":true,"prefix":"","firstName":"Mebratu","middleName":"Abraha","lastName":"Kebede","suffix":""},{"id":616735638,"identity":"4033d207-7832-4986-89ff-2304a8a30c61","order_by":1,"name":"Enas Imran Abdullahi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Enas","middleName":"Imran","lastName":"Abdullahi","suffix":""},{"id":616735639,"identity":"f043515e-0e2c-4f71-a65b-c972866270da","order_by":2,"name":"Mihiretu Wondimu Kebede","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Mihiretu","middleName":"Wondimu","lastName":"Kebede","suffix":""},{"id":616735640,"identity":"2419ba6b-6beb-4542-865e-938ecd0afcb4","order_by":3,"name":"Adema Tema Lota","email":"","orcid":"","institution":"Werabe University","correspondingAuthor":false,"prefix":"","firstName":"Adema","middleName":"Tema","lastName":"Lota","suffix":""},{"id":616735641,"identity":"a7ab4204-dc56-437a-b71b-f7eff2264b65","order_by":4,"name":"Ambaye Biyadu Negeri","email":"","orcid":"","institution":"Mizanetepi University","correspondingAuthor":false,"prefix":"","firstName":"Ambaye","middleName":"Biyadu","lastName":"Negeri","suffix":""},{"id":616735642,"identity":"cc5f8917-22bf-4dcc-bd66-6d5ba1ce42c5","order_by":5,"name":"Seleshi Zeleke","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Seleshi","middleName":"","lastName":"Zeleke","suffix":""}],"badges":[],"createdAt":"2026-01-15 20:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8613405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8613405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106190026,"identity":"3244812e-609d-4b46-8456-d48c9f81228f","added_by":"auto","created_at":"2026-04-05 17:13:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epurpose of smartphone use according to high school students in Addis Ababa, Ethiopia, 2024/2025\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e*Other purpose = (for religious issues)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8613405/v1/fb301dc137d8365360baa4d6.png"},{"id":106402626,"identity":"3a87ad73-0411-45d9-8b20-16d3f592adcd","added_by":"auto","created_at":"2026-04-08 09:12:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrevalence of Problematic Smartphone use among high school students, Addis Ababa, Ethiopia, 2024/2025\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8613405/v1/f0eb1160dcb341dfde01b13d.png"},{"id":106405632,"identity":"ba277132-71d1-4b11-8643-03cdb3e76cf6","added_by":"auto","created_at":"2026-04-08 09:27:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8613405/v1/efb5d709-e2a2-4743-af87-505f2fa371c6.pdf"},{"id":106402406,"identity":"3c124b0f-991b-46ed-a743-345d1749c005","added_by":"auto","created_at":"2026-04-08 09:11:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22516,"visible":true,"origin":"","legend":"","description":"","filename":"QuestionniareusedforPSU.docx","url":"https://assets-eu.researchsquare.com/files/rs-8613405/v1/315f318c93cee41e7b67e96f.docx"},{"id":106190029,"identity":"e4688bab-dc11-4764-bbd0-7ffc1fc845e4","added_by":"auto","created_at":"2026-04-05 17:13:00","extension":"sav","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":176271,"visible":true,"origin":"","legend":"","description":"","filename":"smartphoneuseamonghighschoolstudentsfinal.sav","url":"https://assets-eu.researchsquare.com/files/rs-8613405/v1/a98423a9b1427c0a8037aff6.sav"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Predictors of Problematic Smartphone Use among High School Students in Addis Ababa, Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe development of mobile communication technology has been a long journey of innovation, constantly evolving and updating to meet the needs and preferences of users (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Fifth-generation phones are expected to enhance performance and introduce a wide range of new applications, including advancements in e-Health (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is equipped with different functionalities like email services, office programs, and multimedia features like high-definition cameras, video players, audio players, and sound recording that facilitate more than the traditional mobile phones (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy the end of 2018, around 239\u0026nbsp;million people, which is equivalent to 23% of the Sub-Saharan Africa population, used mobile internet on a regular basis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Students can use their smartphone to take notes, read PDFs, PowerPoints, documents, and articles or anything related to school (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) as well as a source of news (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). But due to their excessive engagement with social media and entertainment, students are more prone to experience problematic smartphone behaviors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of problematic smartphone use (PSU) among high school students in the United States is 22% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The prevalence somewhat varies by gender; 23% of male and 21% of female high school students have PSU (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In Southeast Asia, the prevalence of severe problematic internet use (PIU)/internet addiction ranged from 0 to 47.4%, though internet overuse/possible internet addiction ranged from 7.4% to 46.4% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In Nigeria, 46.1% of young people aged 10 to 24 years experienced moderate to high problematic phone use (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSmartphone addiction is more prevalent in younger adolescents (15\u0026ndash;16 years), students, males, persons reporting lower physical activity, extraversion, and low conscientiousness personalities, and those reporting higher stress (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). According to the Ikeda and Nakamura report, PSU is associated with female students. Further, the study identified nonparticipation in sports club activities, early mobile phone use, and fewer hours spent sleeping as correlates of PSU (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Smartphone overuse can negatively affect users\u0026rsquo; daily lives and activities (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) as well as their behavior, thoughts, and well-being (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is therefore conceivable that implementing actions aimed at reducing students\u0026rsquo; PSU, particularly at the elementary and middle school levels, may translate into improved academic performance (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Students are supposed to use the internet to enhance their academic performance, but they need to restrict themselves from nonacademic purposes. Teachers could also have a remarkable contribution to guide the extent of Internet usage to avert over dependency among students (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, to the knowledge of the researchers, no study as yet has addressed predictors of PSU among high school students in Addis Ababa. Therefore, the current study aims to narrow the research gap by addressing the magnitude and predictors of problematic smartphone use among high school students in Addis Ababa, Ethiopia.\u003c/p\u003e"},{"header":"Method and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, period and population\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted at eight randomly selected high schools (four from each of the government and private schools) found in Arada and Gullele sub-cities of Addis Ababa from May of 1st to 30th of 2025. The study schools were selected using lottery method. All students attending both the selected government and private high schools are the target population, and those who were available during the data collection period at these schools were the study population. All high school students who used smartphones for at least a month before the data collection period were included in the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample size and sampling technique\u003c/b\u003e,\u003c/p\u003e \u003cp\u003eThe minimum number of sample participants required for this study was determined by using single population proportion formula considering the following assumptions:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:ni=\\frac{Z{\\alpha\\:\u0026frasl;2}^{2}pq}{{d}^{2}}\\)\u003c/span\u003e \u003c/span\u003e. Because we found no study conducted among high school students in Ethiopia, we assumed 50% of the students experience problematic smartphone use. Based on the above formula, the calculated sample size was 384. By adding 10% for possible non-response, the final sample size was 422.\u003c/p\u003e \u003cp\u003eThe two sub-cities were selected purposely because large number of both private and government schools are located in the sub-cities. Then eight schools were selected using lottery method with equal allocation of government and private schools. Around 10880 and 2059 high school students were attending their education at the government and private schools, respectively, during the 2024/25 academic year. Then students from each school were selected by using stratified random sampling technique, by proportional allocation from government and private schools. That is, two-thirds and one-third of the sample students were selected from the government and private schools. The proportion of natural and social science classes was also taken into consideration when selecting the sample students. Then the students were selected randomly. But for those from the private schools, the sampling frame (roster), which had the list of students from each school, was taken from the schools. Once the students were stratified based on their grade level, the sample was selected using systematic random sampling technique.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection method, tools, and analysis\u003c/h3\u003e\n\u003cp\u003eData was collected by using interviewer administered written questionnaire in the class once after getting permission from the teacher. The data had three sections; these are socio-demographic factors, smartphone use-related factors, and smartphone use addiction. The data was collected on consecutive days from May of 1st to 30th of 2025 until the desired sample size was achieved from each school by two trained data collectors and the first author. Smartphone addiction-related information was collected by the Smartphone Addiction Scale-Short Version (SAS-SV). It is a self-assessment tool designed to identify individuals with smartphone use addiction (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The current version was preceded by an in-depth validation of the scale in a previous study, during which the final version was developed, consisting of 33 items grouped into 6 distinct subscales. These subscales assess different aspects of smartphone use addiction: disruptions in daily life, positive anticipation, withdrawal, cyberspace orientation, excessive use, and tolerance. Therefore participants rated the items on a 6-point scale [strongly disagree (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), disagree (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), slightly disagree (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), slightly agree (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), agree (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and strongly agree (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)] (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In general, the SAS-SV is a shortened version of the original SAS. It contains 10 items evaluated on a scale similar to that of the original version (1: \u0026ldquo;strongly disagree\u0026rdquo; to 6: \u0026ldquo;strongly agree\u0026rdquo;), and it has strong (reliability and validity) psychometric characteristics. The total SAS-SV score ranges from 10 to 60, with higher scores indicating a predisposition to \u0026ldquo;smartphone use addiction.\u0026rdquo; Then distinguishing problematic smartphone users can be achieved using a score threshold of \u0026ge;\u0026thinsp;33 for females and \u0026ge;\u0026thinsp;31 for males (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData was directly entered and analyzed using SPSS-27. We computed descriptive statistics for both continuous (mean and standard deviation) and categorical (frequency and percentage) variables. We analyzed the data using Independent samples t-test, one-way ANOVA, and logistic regression analysis considering the AOR and 95% CI concomitantly with the p-value.\u003c/p\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003e Support letters were obtained from the School of Psychology, College of Education and Language Studies, Addis Ababa University and the education offices of the two sub-cities. The proposal was reviewed and approved by the St. Paulo\u0026rsquo;s Hospital Millennium Medical College Institutional Research Ethics Review Committee (IRERC) with the protocol number of \u003cb\u003epm23/632 on 25/04/2025\u003c/b\u003e. The purpose of the study was briefly explained to each study participant/student and their parents for those who were at the age category of 16\u0026ndash;17 years. Hence, data was collected after obtaining written informed consent from the study participants. Additionally, for those who were between the age of 16 and 17, parental verbal assent was taken through phone interview in addition to the written consent that was taken from the students. The verbal consent was takes placed by using of phone interview due to difficulty to address the parents in physical. Each consent and assent was taken by the data collectors prior to the data collection process started. Any student who was not willing to participate was not obligated to provide information for the study. Information obtained from the study was kept confidential just by left any personal identifier. Participating in the study had no any risk on the study participants as well as their family (for those who were 16 to 17 years of old students) except the 30 to 40 minute spending to fill the questionnaire. All study participants had a full write to withdraw from the study at all or skipping of some questions in case if they weren\u0026rsquo;t interested to answer. The data was collected at the place where the students could felt safe to keep their privacy. In general, the research was implemented in accordance with the Declaration of Helsinki protocol.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics of the study participants\u003c/h2\u003e \u003cp\u003eA total of 417 high school students were included for the current study, yielding a response rate of 98.8%. The mean (\u0026plusmn;\u0026thinsp;SD) age of the students was 17.04 (\u0026plusmn;\u0026thinsp;1.05) years. Female students were slightly greater (51.3%). About 64.5% of the students were from government schools (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e; Socio-demographic related responses of high school students, Addis Ababa, Ethiopia, 2024/2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;17 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrade 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrade 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrade 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egrade 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprivate school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egovernment school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers of brothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 brothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers of sisters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 sisters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSmartphone utilization-related responses\u003c/h2\u003e \u003cp\u003eRegarding students' smartphone utilization, the mean (\u0026plusmn;\u0026thinsp;SD) duration since starting smartphone usage was 4.13 (\u0026plusmn;\u0026thinsp;2.38) years. Besides, 55.6% of the students started using smartphones four years prior to the data collection period. More than three-fourths (88.0%) of the students were using their own phones. Home WiFi was the source of internet for 56.4% of the students. More than three-fourth (75.5%) of them were using smartphones every day. Further, students spent a mean (\u0026plusmn;\u0026thinsp;SD) of 5:08 (\u0026plusmn;\u0026thinsp;2:54) hours with their smartphone and a minimum and maximum of 30 minutes and twelve hours and fifty-nine minutes per day, respectively. Besides, 43.4% of the students prefer the night period to use their smartphone (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e; Smartphone utilization-related responses of high school students, Addis Ababa, Ethiopia, 2024/2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of smartphone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhose phone do you use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy father's phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy mother\u0026rsquo;s phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy sibling's phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther*1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of internet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWifi at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWifi other than home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMobile data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWifi at home, other than home and data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWifi at home \u0026amp; mobile data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther*2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of days using smartphone per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;6 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of using smartphone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eat home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eat school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eboth at home and school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eother area*3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOther*1 (grandmom's phone, both fathers' and mothers' phones); Other*2 (hotspot and data) ; Other area*3 (other than school everywhere, refreshment areas)\u003c/p\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows, more than half of the students were using their smartphone for accessing educational documents and doing their group assignment with the frequency of 63.5% and 58.8%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Other purpose = (for religious issues)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProblematic smartphone use\u003c/h3\u003e\n\u003cp\u003eThe current study found that 172 (41.2% (95% CI; 36.5, 46.0)) of the students had problematic smartphone use with a mean (\u0026plusmn;\u0026thinsp;SD) score of 29.31 (\u0026plusmn;\u0026thinsp;11.90) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Normality of the distribution was assessed using skewness, kurtosis and a histogram. Accordingly, skewness (0.44) and kurtosis (-0.52) of the data were in the acceptable range while the histogram showed a bell-shaped distribution. Further, no outliers were detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePredictors of problematic smartphone use\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eFor each explanatory variable, bivariate analysis was done. Accordingly grade level, school type, owner of the phone, frequency of using SP, and using SP for educational purposes were the variables that fulfilled the minimum requirement (at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2) to be included in the multivariate logistic regression analysis (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression analysis\u003c/h2\u003e \u003cp\u003eThose variables that had a p-value of less than 0.2 in the bivariate logistic analysis with PSU were entered into multivariable analysis to check their true association with PSU. During the multivariable logistic regression analysis, school type, frequency of using SP, and using SP for educational purposes had statistically significant associations with PSU at an alpha level of \u0026lt;\u0026thinsp;0.05. Specifically, students from government school (AOR 1.76, 95% CI (1.13, 2.74)) and those who used their smartphone every day (AOR 95% CI; 2.64 (1.55, 4.49)) had a statistically significant association with PSU. Also, those who didn\u0026rsquo;t use it for educational purposes (AOR 1.88 (95% CI (1.18, 3.02))) showed significant association with problematic smartphone use (p \u0026lt;\u0026thinsp;.05) (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e; Factors associated with problematic smartphone use among high school students in Addis Ababa, Ethiopia, 2024/25\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProblematic smartphone use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGrade level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.65, 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.51, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (0.77, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.59, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84 (1.06, 3.19)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (0.79, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSchool type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87 (1.23, 2.84)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 (1.13, 2.74)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOwner of the phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94 (1.01, 3.73)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 (0.68, 2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency of using SP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvery day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88 (1.73, 4.79)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64 (1.55, 4.49)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUsing SP for educational purposes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26 (1.45, 3.51)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88 (1.18, 3.02)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e* = variables fulfil the minimum requirement at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 during bivariate analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** = variables that have a statistically significant association at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 during multivariable analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther, the current study identified the presence of a statistically significant mean difference in problematic smartphone use due to duration of smartphone use. Those who had used smartphone for more than 4 years (M\u0026thinsp;=\u0026thinsp;31.88, SD\u0026thinsp;=\u0026thinsp;12.41) reported a higher PSU mean score than those who had used it for 4 years or lower (M\u0026thinsp;=\u0026thinsp;27.26, SD\u0026thinsp;=\u0026thinsp;11.10) [t (415) = -4.01; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; d = -0.395].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between duration, frequency and time spent on smartphone use and problematic smartphone use\u003c/h2\u003e \u003cp\u003eBased on the linear regression analysis result, the duration since the students started using smartphones, the number of days using them, and the time spent on them per day had a weak but positive correlation with experiencing problematic smartphone use at Pearson correlation coefficient (r) values of 0.16, 0.27, and 0.37, respectively, at a p-value of \u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e; Multiple Linear regression statistics of duration, frequency and time spent on smartphone with respect to problematic smartphone use among high school students in Addis Ababa, 2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" morerows=\"1\" nameend=\"c4\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSD Coef*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c11\" namest=\"c10\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c13\" namest=\"c12\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003e95.0% CI for B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eLB*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eUB*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e8.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e17.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTotal duration of smartphone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.304\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumbers of days smartphone used per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e2.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTime spent per day using smartphone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"20\" nameend=\"c20\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel summary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eR Square Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eF Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003eSig. F Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eDurbin-Watson\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e.413\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e28.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"20\" nameend=\"c20\" namest=\"c1\"\u003e \u003cp\u003ea. Predictors: (Constant), time spent per day using smartphone, total duration of smartphone use, numbers of days smartphone used per week\u003c/p\u003e \u003cp\u003eb. Dependent Variable: Problematic smartphone use\u003c/p\u003e \u003cp\u003e\u003cb\u003eLB* (lower border); UB* (upper border)\u003c/b\u003e; \u003cb\u003eSD Coef* (standardized coefficients)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows, the three variables explain 17.1% of the variance in problematic smartphone use which is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Also, the tolerance and VIF (variance inflation factor) values of the three variables indicate no multicollinearity risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study found that 41.2% of the students had problematic smartphone use, which is in line with studies in Japan (45.3%) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and the United Kingdom (38.9%) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Similar to the results of this study, a study found that 30\u0026ndash;45% of Korean teenagers exhibited problematic smartphone behaviors (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In addition, somewhat similar findings were reported by a study which found a prevalence of 36.9% among a Turkish high-school sample (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Another study reported a prevalence of 45.5% in a metropolitan secondary-school cohort (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Furthermore, a study reported approximately 36% PSU among Jordanian adolescents, which further supports that excessive smartphone use has become a widespread occurrence among students worldwide (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBut the recent finding is slightly higher than that of the United States study, where 23% and 21% of male and female high school students had problematic smartphone use (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The prevalence found in the current study is also higher than a systematic review report that reported a median prevalence of 23.3% (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This figure is also higher than the prevalence of PSU found in other countries, such as Switzerland, where 16.9% of the 1,519 vocational school students were engaged in heavy internet use (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and Thailand, where 35.3% were similarly engaged in heavy internet use (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, our finding is lower than the Bangladeshi finding, where about 86.9% of the students experienced PSU (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). About 65.8%, 58.1%, and 52.8% of students in China reported PSU at three waves, which is higher than the recent result (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The reason for the discrepancy in the findings might be the result of the difference in study design and study population. The other observed difference is sample size and data collection method.\u003c/p\u003e \u003cp\u003eDifferent scholars forward various reasons for why high school students are particularly vulnerable to problematic smartphone use. For instance, according to one explanation, adolescence is a developmental period marked by increased social sensitivity and peer orientation, making smartphones an appealing tool for maintaining social connection and self-presentation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Besides, many adolescents use their smartphones for entertainment and social networking activities that expose them to higher addictive potential compared to purely informational use (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Furthermore, smartphones often become a default means of coping with stress or boredom; qualitative studies of younger adolescents show that \u0026ldquo;drivers of excessive use\u0026rdquo; include social factors, family/friends modelling, and nighttime habits that displace other activities (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Moreover, persuasive designs embedded in apps (e.g., notifications, infinite scroll, reward loops) have been shown to prolong screen engagement and reinforce checking behaviors, increasing the risk of problematic use (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith respect to the correlates/predictors of PSU, we found that students from government schools are 1.76 times more likely to experience problematic smartphone use than their counterparts from private schools. Comparable findings are reported in studies from India and Bangladesh, where adolescents in public schools demonstrated significantly higher odds of smartphone addiction than their private school peers (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This could be due to less parental engagement and monitoring and lower awareness of digital health (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). There is evidence to support that parental supervision contributes significantly to disparities in smartphone use behaviors among adolescents (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Students from lower- to middle-class families frequently attend government schools, using smartphones as a learning and entertainment tool (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In contrast, private school students may have greater access to structured extracurricular activities and parental monitoring, factors known to buffer against problematic smartphone use (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Moreover, educational infrastructure and digital policies in private institutions often emphasize balanced technology use and digital literacy, whereas government schools may lack similar strong regulatory frameworks or counseling programs to address excessive screen behavior (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also found that those students who used their smartphone every day are 2.64 times more likely to experience PSU than those who use it sometimes. This result is consistent with prior research indicating that the frequency of smartphone use are strong predictors of addictive behaviors among adolescents (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Daily use increases exposure to social networking, gaming, and multimedia content, which can reinforce habitual checking and reduce self-regulatory capacity (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Similarly, studies in both high- and low-income countries have reported that students with continuous daily access to smartphones exhibit higher levels of dependence, as repeated reinforcement strengthens usage routines and creates compulsive patterns (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the current study found that those students who didn\u0026rsquo;t use it for educational purposes are 1.88 times more likely to have problematic smartphone use than those who use it for academic purposes. This finding aligns with previous research suggesting that purposeful, goal-directed use of technology, such as for learning or school assignments, can mitigate the risk of addictive patterns by promoting structured engagement and reducing idle screen time (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In contrast, recreational use without educational intent such as excessive social networking, gaming, or video streaming has been consistently associated with higher levels of smartphone dependency among adolescents (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). By focusing on academic-related activities, students may develop greater self-regulation and purposeful routines that limit impulsive checking and prolonged nonessential use, highlighting the protective role of educational engagement in reducing problematic smartphone behaviors (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the Thailand study report, students who spent on average 5.3 hours per day on the internet are engaged in heavy internet use problems (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This is supported by our finding that the more time spent on a smartphone per day, the more likely that the students experience problematic smartphone use (r\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further, based on our linear regression analysis, duration since starting smartphone use and number of days using smartphone have a positive correlation with the outcome variable (r\u0026thinsp;=\u0026thinsp;0.16). This is supported by previous study findings where a higher frequency of smartphone use (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), a higher duration of daily usage (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and a higher habitual use (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) have all been found to be related to problematic smartphone usage.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eBecause the study focused exclusively on high school students, generalizability of the findings may be limited to other populations, such as primary and undergraduate students. The other limitation of this study is its use of a cross-sectional design, which collects data at a single point in time, making it impossible to determine causal relationships between the explanatory and outcome variables.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and implication","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cp\u003eThe current study found that more than 40% of the high school students are experiencing PSU. Key predictors of PSU were identified; these are attending government schools, using smartphones every day, and non-educational use. The finding also highlights the significant relationship between smartphone usage patterns such as duration, time spent, and time of using and problematic smartphone use (PSU). Overall, these findings suggest that both environmental factors (such as school type and access to structured activities) and behavioral patterns (frequency and purpose of smartphone use) play significant roles in the development of PSU among adolescents. The study findings suggest that there is a need to address smartphone use as a critical aspect of adolescent health and daily functioning. Therefore, to address the challenges associated with PSU, it is suggested that the Ministry of Education take the lead in creating national guidelines on healthy digital practices, incorporating digital well-being education into the curriculum, and making sure that schools, particularly government institutions, have the tools necessary for awareness and counseling programs. Also, enforcing explicit school policies that restrict needless smartphone use during class hours and encouraging technology used primarily for academic purposes appear to be beneficial. By encouraging open communication and establishing limits that promote responsible use, parents can keep an eye on and mentor their children's smartphone habits. In the meantime, raising the students\u0026rsquo; awareness on the consequences of PSU may help them to control their behavior, give priority to educational and purposeful smartphone activities, and partake in other recreational activities that lessen their reliance on electronic gadgets. When combined, these initiatives can produce a nurturing atmosphere that promotes a healthy digital learning teaching environment. Finally, conducting further research (e.g., longitudinal and experimental designs) will be useful to address the cause-effect relationship and long-term impact of PSU.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBoth MAK and SZ conceived the study and were involved in the study design, reviewing the manuscript, data analysis, and drafting the manuscript. EI, MW, and AT involved in drafting the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ewe have attached the word document based information sheet and consent form, questionnaire/tool that we used to collect the primary data and the raw data SPSS based filled data as a supplementary document.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMokhlis S, Yaakop A (2012) Consumer Choice Criteria in Mobile Phone Selection: An Investigation of Malaysian University Students. Int Rev Social Sci Humanit 2:203\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, Radiation 5G mobile networks and health, 27 February 2020 [\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadzama W, Joseph B, Maigana N (2019) Global Smartphone Ownership, Internet Usage And Their Impacts On Humans\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGSMA The Mobile Economy Sub-Saharan Africa 2019 2019 [Available from: www.gsma.com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesserie L, Aloquin KC, Navarro, Mahilum DZ, EXPLORING, THE LIVED EXPERIENCES ON SMARTPHONE USAGE AT TONGANTONGAN NATIONAL HIGH SCHOOL (2024) IJARW 6(6):12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Santill\u0026aacute;n A (2021) Addiction to the Smartphone in High School Students: How It\u0026rsquo;s in Daily Life? Contemp Educational Technol. ;13 (2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRania Islambouli S, Ingram JC, Farah Z, Charlesworth R, Aouad A, Delabays et al (2025) Exploring the Negative Impact of Smartphone Usage on Students\u0026rsquo; Digital Wellbeing: A Systematic Review of Empirical Studies. International Journal of Human\u0026ndash;Computer Interaction\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonaldson SI, Strong D, Zhu S-H (2021) A brief measure of problematic smartphone use among high school students: Psychometric assessment using item response theory. Computers Hum Behav Rep 3:100091\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiva S, Paul R (2023) Problematic Smartphone Use Among High School Students Journal of School Administration Research and Development. ;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalhara YPS, Mahapatra A, Sharma P, Bhargava R (2018) Problematic internet use among students in South-East Asia: Current state of evidence. Indian J Public Health 62(3):197\u0026ndash;210\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalogun FM, Olatunde OE (2020) Prevalence and predictors of problematic smart phone use among pre-varsity young people in Ibadan, Nigeria. Pan Afr Med J 36:285\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaug SCRR, Kwon M, Filler A, Kowatsch T, Schaub MP (2015) Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict 4(4):299\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkeda KNK (2014) Association between mobile phone use and depressed mood in Japanese adolescents: a cross-sectional study. Environ Health Prev Med 19(3):187\u0026ndash;193\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKadir D, Akg\u0026ouml;n\u0026uuml;l M, Abdullah A (2015) Relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety in University Students. J Behav addictions 4:85\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhassan A, Alqadhib E, Taha N, Alahamri R, Salam M, Almutairi A (2018) The relationship between addiction to smartphone usage and depression among adults: A cross sectional study. BMC Psychiatry. ;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaterna A, A-I M, Aguilar-Parra JM, Salavera C, Demetrovics Z, Griffiths MD (2024) Problematic smartphone use and academic achievement: A systematic review and meta-analysis. Journal of Behavioral Addictions. 2024;13(2):313\u0026thinsp;\u0026ndash;\u0026thinsp;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgheneakoke Clifford BJ, Sunday OBRO, INTERNET ACCESS AND USAGE: IMPACT ON STUDENTS\u0026rsquo; ACADEMIC PERFORMANCE IN SOCIAL STUDIES. European Journal of Education Studies, [Sl],. 2018(ISSN 25011111)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSfendla A, Laita M, Nejjar B, Souirti Z, Touhami AAO, Senhaji M (2018) Reliability of the Arabic Smartphone Addiction Scale and Smartphone Addiction Scale-Short Version in Two Different Moroccan Samples. Cyberpsychol Behav Soc Netw 21(5):325\u0026ndash;332\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwon M, Kim D-J, Cho H, Yang S (2014) The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS ONE 8(12):e83558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeskini Nadia Chakit MLMF, Aameri Z, Sfendla M (2024) Anis Ouahidi Moulay. School score and Depression in Moroccan adolescents. ;21:308\u0026thinsp;\u0026ndash;\u0026thinsp;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeskini Nadia Lamtai MS, Madhi AE, Ahami Y, Ouahidi AOT, Laarbi M (2023) Prevalence of stress, anxiety and depression in the context of climate change among newly recruited contract teachers in Morocco. E3S Web Conf 412:01073\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeewon L, Sung M, Sook-Hyung S, Lee Y, Je-Jung L, Cho S-M et al (2016) Psychological Factors Associated With Smartphone Addiction in South Korean Adolescents. J Early Adolescence. ;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTateno M, Teo AR, Ukai W, Kanazawa J, Katsuki R, Kubo H et al (2019) Internet Addiction, Smartphone Addiction, and Hikikomori Trait in Japanese Young Adult: Social Isolation and Social Network. Front Psychiatry 10:455\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohn SY, Krasnoff L, Rees P, Kalk NJ, Carter B (2021) The Association Between Smartphone Addiction and Sleep: A UK Cross-Sectional Study of Young Adults. Front Psychiatry 12:629407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCha S-S, Seo B-K (2018) Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychol Open 5(1):2055102918755046\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ccedil;ağan \u0026Ouml;, Koca B (2020) Evaluation of High School Students\u0026rsquo; Smartphone Addiction and Insomnia Level. J Turkish Sleep Med 7:45\u0026ndash;51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshiK Q, ChavhanS (2024) BishnoiD The Prevalence of Smartphone Addiction and Its Associated Risk Factors among School Students of a Metropolitan City: A Cross-Sectional Study. JCommunityHealthResearch 13:172\u0026ndash;179\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTarawneh F, Abuattallah A, Dardas L Maladaptive Smartphone use among Jordanian Adolescents: Prevalence, Correlations, and the Role of Cognitive Emotion Regulation. Health Psychol Res. 2025;X:2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohn SY, Rees P, Wildridge B, Kalk NJ, Carter B (2019) Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 19(1):356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeltzer KPS (2014) Heavy Internet use and its associations with health risk and health-promoting behaviours among Thai university students. Int J Adolesc Med Health 26(2):187\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosen I, Al Mamun F, Sikder MT, Abbasi AZ, Zou L, Guo T et al (2021) Prevalence and Associated Factors of Problematic Smartphone Use During the COVID-19 Pandemic: A Bangladeshi Study. Risk Manag Healthc Policy 14:3797\u0026ndash;3805\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang CJ, Chang FC, Chiu CH (2017) Smartphone addiction and related factors among elementary school students in New Taipei City. Res Educational Commun Technol 117:67\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang A, Wang Z, Zhu Y, Shi X (2022) The Prevalence and Psychosocial Factors of Problematic Smartphone Use Among Chinese College Students: A Three-Wave Longitudinal Study. Front Psychol 13:877277\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW\u0026ouml;lfling K, Beutel ME, Dreier M, M\u0026uuml;ller KW (2022) Adolescent media use and addiction: Risk and protective factors. J Behav Addictions J Behav Addictions 11:90\u0026ndash;103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmud A, Islam M (2023) Understanding the drivers of smartphone addiction among university students: a perspective from Bangladesh. SN Social Sci. ;3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YLRW (2021) X. Persuasive app design and the reinforcement of habitual smartphone checking behavior among youth. Comput Hum Behav. ;122(106839)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzizi S, Mohammadi H, Etemad F (2024) Differences in smartphone addiction between public and private school students: The role of family supervision and digital literacy. Asian J Educ Psychol 9(1):44\u0026ndash;53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYogesh H, Ranjith R, Divya G (2024) Parental supervision and smartphone addiction among adolescents: A comparative study between school types. Indian J Mental Health Educ 15:58\u0026ndash;67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazir A, Sewani R, Zahid A, THE USE OF SMARTPHONES, IN SHAPING OF STUDENTS\u0026rsquo; ACADEMIC PERFORMANCE AT SECONDARY SCHOOL LEVEL (2024) J Social Sci Dev 3:128\u0026ndash;141\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae S-M (2017) The relationship between the type of smartphone use and smartphone dependence of Korean adolescents: National survey study. Child Youth Serv Rev 81:207\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen T-V, Nguyen Q-A, Nguyen N, Uy\u0026ecirc;n B (2024) Smartphone use, nomophobia, and academic achievement in Vietnamese high school students. Computers Hum Behav Rep 14:100418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomczyk Ł. PK, Velagić Z (2024) Digital well-being and educational policies: Toward balanced technology integration in schools. Education and Information Technologies. ;29(7):7349-63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong S-H, Kim H, Yum J-Y, Hwang Y (2016) What type of content are smartphone users addicted to? SNS vs. games. Comput Hum Behav 54:10\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao QSR, Fu E, Jia G, Xiang Y (2022) Smart interventions for smartphone addiction: A meta-analysis of randomized controlled trials. J Behav Addictions 11(1):150\u0026ndash;156\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"problematic smartphone use, prevalence, high school students","lastPublishedDoi":"10.21203/rs.3.rs-8613405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8613405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudents can use their smartphone for different purposes: to take notes, read PDFs, documents, and articles and as source of news. But due to excessive engagement with social media and entertainment, students are more prone to experience problems. Because there is no published research on the topic, the aim of the current study is to fill the research gap by determining the prevalence and predictors of problematic smartphone use among high school students in Addis Ababa, Ethiopia. A cross sectional study was conducted at randomly selected four government and four private high schools found in Arada and Gulele Sub Cities of Addis Ababa from May of 1st to 30th of 2025 among 422 high school students. Systematic random sampling technique was applied to select the study participants. We conducted data analyses using independent samples t-test, one-way ANOVA, linear regression analyses and multinomial logistic regression. The results indicated that about 41.2% of the students had problematic smartphone use. Students from government schools (AOR 1.76, 95%CI (1.13, 2.74)), those who used their smartphone every day (AOR 95%CI; 2.64 (1.55, 4.49) and those who didn\u0026rsquo;t use it for educational purpose (AOR 1.88 (95%CI (1.18, 3.02)) showed significant association with problematic smartphone use. Therefore, awareness creation on the PSU and the risk factors is warranted.\u003c/p\u003e","manuscriptTitle":"Prevalence and Predictors of Problematic Smartphone Use among High School Students in Addis Ababa, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:12:56","doi":"10.21203/rs.3.rs-8613405/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-18T13:40:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197725173056570098030681659190359929984","date":"2026-04-05T16:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T12:10:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T00:50:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T21:32:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T13:55:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-18T13:48:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b25f07dd-a9ad-4a72-9d2f-5177c3be195c","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65630784,"name":"Health sciences/Health care"},{"id":65630785,"name":"Health sciences/Medical research"},{"id":65630786,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-05T17:12:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:12:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8613405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8613405","identity":"rs-8613405","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}