Smartphone Addiction, Anxiety, Depression, and Academic Performance in University Students: A Cross-Sectional Study

Smartphone Addiction, Anxiety, Depression, and Academic Performance in University Students: A Cross-Sectional Study | 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 Smartphone Addiction, Anxiety, Depression, and Academic Performance in University Students: A Cross-Sectional Study Shazli Ezzat Ghazali, Ponnusamy Subramaniam, Hend Faye AL-shahrani, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252874/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In today's globalized world, technology significantly influences daily life. While it offers convenience, it also affects individuals in various ways. The increasing use of smartphones has raised concerns about smartphone addiction. This study seeks to examine the relationship between smartphone addiction, anxiety, depression, and academic performance among university students. A total of 1,846 students (1,362 females and 484 males; mean age = 19.62 ± 1.11) participated in the research. An online questionnaire was distributed, including the Smartphone Addiction Scale-M (SAS-M), the Beck Anxiety Inventory-M (BAI-M), and the Beck Depression Inventory-M (BDI-M). Descriptive analysis revealed mean scores of smartphone addiction, anxiety, and depression among respondents as 105.78 ± 22.38, 11.66 ± 10.93, and 7.28 ± 7.89, respectively. Further analysis through simple linear regression indicated a statistically significant positive relationship between smartphone addiction, anxiety, and depression (p < 0.001). Specifically, smartphone addiction was identified as a predictor of anxiety (b = 0.006, t = 12.084, p < 0.001) and depression (b = 0.005, t = 10.770, p < 0.001). However, the study found no statistically significant relationship between smartphone addiction and academic performance. However, it concluded that college students are particularly vulnerable to smartphone addiction, which can result in heightened anxiety and depression. Consequently, comprehensive intervention programs are essential to address smartphone addiction and enhance mental health among college students. Biological sciences/Psychology/Human behaviour Biological sciences/Psychology Health sciences/Health care Smartphone addictions Depression Anxiety Academic Achievement University Students Introduction In today's globalized world, the use of technology, especially smartphones, has been expanding across the nation. This rapid expansion following the new versions of smartphones and the extension of various application software has an undeniable positive benefit on the users as they can be employed to make calls and messages and connect to the world through social media. On the other hand, the applications that cover the different fields have supplemented the users' lives. In 2016, there were approximately 2.5 billion smartphone users worldwide, with projections of growth to 3.5 billion in 2020 and 3.8 billion in 2021 (Statista Research Department, 2020). In Malaysia, the number of smartphone users was 18.46 million in 2016, and this figure is expected to rise significantly, exceeding 30.41 million by 2020 and reaching over 33 million by 2024 (Statista Research Department, 2020). A survey by the Malaysian Communications and Multimedia Commission (2018) pointed out that the percentage of smartphone ownership grew marginally from 74.0–76.4%. The MCMC also found that 86.3% of those below 20 years old and 87% of those between 20–34 years old have higher smartphone usage. This explains the fact that about 95% of tertiary education students use smartphone (Malaysian Communications and Multimedia Commission, 2018). The 2015 'Smartphone User Persona Report' from Vserv indicates that, when compared to neighboring countries like Indonesia, the Philippines, and Thailand, smartphone users in Malaysia spend approximately 187 minutes or, to be precise, 3 hours 7 minutes with their devices (Ithnain, Ghazali & Jaafar, 2018 ; Smartphone User Persona Report, 2015). Practicality and social needs were among the main determining factors of smartphone usage among users. The usefulness of smartphones has increased drastically and are rapidly becoming part of our everyday lifestyle. However, despite the functionality of a smartphone, uncontrolled use can lead to smartphone addiction. Lin et al. ( 2016 ) found that there are four components of smartphone addiction which are; Obsessive phone use is characterized by behaviors such as repeatedly checking for messages or updates, developing a tolerance for longer and more intense usage, experiencing withdrawal symptoms like agitation or distress when without the phone, and facing functional impairment that interferes with daily activities and social relationships (Chen. et al, 2017; Hammad et al, 2024 ; Hammad & Al-Shahrani, 2024 ; Lin et al. 2016 ; Moattari et. al, 2017 ). Brod ( 1984 ) defines the inability to adapt to the latest technologies healthily as “Technostress’. The smartphone addiction incident has been a worldwide concern as it can contribute to poor mental conditions, especially among university students. This condition is known to affect both their personnel and professional life. Researchers have found a correlation between excessive use of smartphones for texting, social media, gaming, listening to music, emailing, and watching clips and depression. The obsessive usage and ‘technostress’ also lead to over-reliance on smartphones resulting in stress and compulsive usage of smartphone (Lee et al, 2014 ). Moattari et al. ( 2017 ) found a positive correlation between the increased ‘technostress’ and uncontrolled smartphone use with anxiety in social communication, behavioral control, materialism and a need to communicate and touch. Without a smartphone, irritation, frustration, and impatience could also damage relationships with others or result in psychological distress for users (Hammad & Awed, 2023 ; James & Drennan, 2005 ). The majority of the participants from a Malaysian private university agreed that smartphone usage can result in headache, sleeping disturbances, and causes loss of mental attention (Hammad, 2023 ; Kumar et al, 2011 ). A study by Zulkefly and Baharudin ( 2009 ) found that most of the students who have smartphone addiction issues have low self-esteem and spend a longer time with smartphones. They also found that those who tend to spend a longer time with a smartphone have psychological disturbances. Besides, higher usage of smartphone results in poor academic performance. Ng, Hassan et al ( 2017 ) mentioned that though used for educational purposes, smartphones negatively affected students’ academic performance. Students who deemed smartphones as favourable before use perceived that smartphones could affect their educational goals in a negative way (Tossell, Kortum, Shepard, Rahmati & Zhong, 2015 ). Ching et al. ( 2015 ) found that approximately 46.9% of the students were at risk of developing smartphone addiction. Similarly, a study by Ithnain et al. ( 2018 ) also found that about 47.7% of university students have high smartphone addiction. The increase in the percentage shows that smartphones are becoming a daily-life essential item in a student’s life. However, due to the limited studies conducted on this topic, the current research was undertaken to examine the relationship between smartphone addiction, anxiety, depression, and academic performance among undergraduates in Malaysia.. Methods Design and sample This cross-sectional study was conducted in September 2019 among newly enrolled undergraduate students at a local university in Malaysia. Respondents were recruited using purposive sampling. Full time, Malaysian undergraduates were included in this study, whereas those who were absent, non-Malaysian, and have submitted an incomplete or redundant questionnaire were excluded from the study. Data collection procedure and ethics Prior to the actual research commencement, a pilot study was administered on 69 undergraduates who were not part of the study. The pilot study respondents showed no difficulty in understanding and answering the self-administered online questionnaire. Hence, the actual research was carried out. A concise introduction to the study purpose was given to the students. The Google form link was then distributed among all the present students. Those who have consented to partake in the study were required to fill in the self-administered questionnaire via Google form. The study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Research Ethics Committee, the National University of Malaysia (UKM). Also, informed consent was obtained from all participants. Instrument A self-administered Google Form questionnaire was distributed to 2,181 participants. The questionnaire consisted of six sections: a) demographic characteristics, including information on age, race, gender, and family income; b) patterns of smartphone usage, covering daily usage duration (in hours), monthly expenses on smartphones, and primary smartphone functions; c) factors influencing smartphone use; d) the Malay version of the Smartphone Addiction Scale (SAS-M); e) the Malay version of the Beck Anxiety Inventory (BAI); and f) the Malay version of the Beck Depression Inventory (BDI). The pilot study demonstrated that the selected instruments were reliable for the actual study, with Cronbach's Alpha values ranging from 0.715 to 0.955. Smartphone Addiction Scale The Malay version of the ‘Smartphone Addiction Scale’ was adapted from Ching et al. ( 2015 ). The SAS-M consists of 33 items divided into six domains: daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance. Symptoms are assessed using a response scale from 1 to 6, where 1 indicates strong disagreement and 6 indicates strong agreement. The SAS-M has a minimum score of 33 and a maximum score of 198; higher scores indicate a greater risk of smartphone addiction. The Cronbach alpha value for the SAS-M is 0.922. Beck’s Anxiety Inventory The Becks Anxiety Inventory-Malay Version, developed by Firdaus and Nor Sheereen (2011) was used as an instrument to measure anxiety in this study. The inventory which comprises of 21 items with a Cronbach Alpha of 0.942 was scored using a four-point scale and measured in categories that are minimal anxiety (score 0–7), mild anxiety (score 8–15), moderate anxiety (score16-25) and severe anxiety (score 26–63)). Beck’s Depression Inventory Beck’s Depression Inventory-Malay version developed by Mukhtar and Oei (2008) was used to measure depression. Though the original version consists of 21 item, one item was omitted from the Malay version due to the cultural factors rounding to a total of 20 item. The inventory was further subdivided into four categories which are no mild depression (score 0–9), mild-moderate depression (score 10–18), moderate-severe depression (score 19–29) and severe depression (score 30–63) (Beck, Steer & Carbin, 1988 ). Statistical Analysis Data were entered and analyzed using SPSS software version 26. Descriptive statistical analysis was conducted to calculate the mean, standard deviation, frequency, and percentage. Pearson’s correlation assessed the strength of the relationship between the variables, while Simple Linear Regression was performed to evaluate the effect of smartphone addiction on anxiety and depression. Results An amount of 2181 questionnaires received, 69 (3.16%) respondents were removed due to being pilot study participants. Another 12.2% were removed due to respondents being non-Malaysian, submitted multiple responses, from different intake than 2019/2020 and outliers (PGNK less than 2.0). Hence, the final sample size left was 1846. Table 1 presents the demographic characteristics of the sample. The majority of respondents were female, comprising 1,362 individuals (73.8%). The ages of participants ranged from 19 to 33 years, with a mean age of 19.62 ± 1.11 years. Malay participants dominated the study at 77.6%, followed by Chinese at 11.7%, Indian at 7.0%, and others at 3.6%. Approximately 64.4% of the respondents were from the B40 family background and had around RM201-RM300 for their subsistence expenses. Upon university registration majority of the students were supported by their parents financially. Table 1 . Demographic data (n=1846) Variables (n) (%) Gender Male Female 484 1362 26.2 73.8 Age Category 18-19 20-21 22-23 24 & above 1054 710 62 20 57.1 38.5 3.4 1.1 Race Malay Chinese Indian Others 1433 216 130 67 77.6 11.7 7.0 3.6 Year of Study Year 1 Year 2 1844 2 99.9 0.1 Approximate family income per month (RM) Below RM 1000 RM1000-RM1999 RM2000-RM2999 RM3000-RM3999 RM4000-RM4999 RM5000-RM5999 RM6000-RM6999 RM7000-RM7999 RM8000-RM8999 RM9000-RM9999 RM10000 and above 187 356 297 196 153 149 81 86 64 44 233 10.1 19.3 16.1 10.6 8.3 8.1 4.4 4.7 3.5 2.4 12.6 Income based on T20, M40 and B40 category B40 M40 T20 1189 424 233 64.4 23 12.6 Income Sub-category B1 B2 B3 B4 M1 M2 M3 M4 T1 & T2 543 297 196 153 149 81 150 44 233 29.4 16.1 10.6 8.3 8.1 4.4 8.1 2.4 12.6 Subsistence expenses category (RM) RM0-RM100 RM101-RM200 RM201-RM300 RM301-RM400 RM401-RM500 RM501-RM600 RM601-RM700 RM701-RM800 RM801-RM900 RM901-RM1000 RM1001 and above 99 281 562 336 358 74 38 37 11 27 23 5.4 15.2 30.4 18.2 19.4 4 2.1 2 0.6 1.5 1.2 Sources of monthly subsistence Parents Yes No Loans (including PTPTN) Yes No Schorlarship Yes No Part time job (including online business) Yes No Others Yes No 1670 176 672 1174 103 1743 150 1696 54 1792 90.5 9.5 36.4 63.6 5.6 94.4 8.1 91.9 2.9 97.1 Table 2. The pattern of smartphone usage (n=1846) Variables (n) (%) Do you have a smartphone? Yes No 1844 2 99.9 0.1 Number of smartphones you own 1 2 3 1701 138 6 92.1 7.5 0.3 The main purpose of smartphone use Academic Yes No Games Yes No Social media Yes No Entertainment Yes No Communication Yes No Virtual Group Activities Yes No Others Yes No 1601 245 725 1121 1516 330 1233 613 1663 183 1605 241 37 1809 86.7 13.3 39.3 60.7 82.1 17.9 66.8 33.2 90.1 9.9 86.9 13.1 2 98 Type of telephone service taken Prepaid Postpaid Prepaid and postpaid 1088 730 28 58.9 39.5 1.5 Duration of smartphone use Less than a year One year More than a year 151 105 1590 8.2 5.7 86.1 Duration of use of smartphone in a day (hours) 20 hour 17 755 697 258 79 40 0.9 40.9 37.8 14 4.3 2.2 Monthly smartphone expenses (bill / credit payments) RM0-Rm30 RM31-RM60 RM61-RM90 RM91-RM120 RM121-RM150 >RM150 700 778 159 108 36 65 37.9 42.1 8.6 5.9 2 3.5 Table 2 shows that approximately 99.9% of the respondent have a smartphone of which 7.8% have more than a smartphone. Apple (25.7%) and Samsung (17.1%) were among the most use smartphone by the students. Academic, social media, entertainment, communication and virtual group activities were among the key factor of smartphone usage based on the student’s response. Majority of them (58.9%) were observed to use the prepaid service plans and about 80% of the students spend RM 60 and below for their monthly smartphone expense. 40.9% of the students used smartphone between 2-5 hours per day. Smartphone addiction, anxiety, and depression levels Table 3 . Comparison of total SAS-M mean score between males and females Variable M (SD) t (df) p * Male Female Total SAS-M score 106.5 (24.3) 105.5 (21.6) 0.752 (771) 0.453 Results from table 3 shows that the female’s total SAS-M mean score is not statistically different from the male’s total SAS-M mean score. Hence, there is no difference between the SAS-M score in terms of gender. Table 4 . Comparison of total SAS-M mean score between playing games or not Variable M (SD) t (df) p * No games Play games Total SAS-M score 103.7(22.2) 109.1(22.2) -5.098(1844) 0.000 However, the table 4 above shows that the gamers total SAS-M mean score is statistically different from the non-gamers total SAS-M mean score. Those who plays game have a higher means SAS-M score compared to those who do not. Table 5. Mean and Standard Deviation: smartphone addiction, anxiety, depression and academic performance (n=1846) Variable n (%) M(SD) Smartphone Addiction Scale Low smartphone addiction Medium smartphone addiction Extreme smartphone addiction 628 (34) 605 (32.8) 613 (33.2) 105.78 ± 22.38 Becks Anxiety Inventory Minimal anxiety Mild anxiety Moderate anxiety Severe anxiety 834 (45.2) 460 (24.9) 339 (18.4) 213 (11.5) 11.66 ± 10.93 Becks Depression Inventory No mild depression Mild-moderate depression Moderate-severe depression Severe depression 1334 (72.3) 336 (18.2) 130 (7) 46 (2.5) 7.28 ± 7.89 Academic performance (PNGK) - 3.4805 ± 0.29 Results from Table 5 indicate that the mean score for smartphone addiction in this study was 105.78 ± 22.38. This study utilized percentile values to categorize the scores, dividing smartphone addiction into three categories: low smartphone addiction (SAS-M score = 33-97) and medium smartphone addiction (SAS-M score = 98 - 115) and extreme smartphone addiction (SAS-M score = 116 – 183). Results showed that almost all the smartphone addiction categories have the similar percentage. For anxiety, results showed that 45.2% of the respondents experienced minimal anxiety, while 24.9%, 18.4%, and 11.5% of the respondents have mild, moderate and severe anxiety respectively. The mean anxiety score was 11.66 with a standard deviation of 10.93. Additionally, the results indicated that 72.3% of respondents reported no mild depression, while 18.2%, 7.0%, and 2.5% of the respondents have mild-moderate, moderate-severe and severe depression respectively. Mean ± Standard Deviation for depression score was 7.28 ± 7.89. As per the students’ academic performance, the respondents scored a pointer of 3.4805 ± 0.29 for their overall semester results. Table 6. Pearson correlation analysis of the relationship between smartphone addiction, anxiety, and depression (n=1846) Anxiety Depression PGNK Smartphone addiction r p-value r p-value r p-value 0.271 <0.001 0.243 <0.001 0.005 0.822 Table 6 displays the correlation between smartphone addiction, anxiety, depression, and students' academic performance. The results indicate a significant positive correlation between smartphone addiction and anxiety (r = 0.271; p<0.001) and depression (r=-0.243; p<0.001) respectively. However, no significant correlation was found between smartphone addiction and the students’ academic performance (PGNK). The correlation between the two variables is poor (r = 0.005). Table 7 . Analysis of Smartphone Addiction: A Simple Linear Regression Approach to Anxiety and Depression (n=1846) Anxiety Smartphone addiction b (95% CI) t p * r2 0.006 (0.005,0.007) 12.084 < 0.001 0.073 Depression Smartphone addiction b (95% CI) t p * r2 0.005 (0.004,0.006) 10.770 <0.001 0.059 Table 7 shows that smartphone addiction has a significant effect on anxiety, with smartphone addiction as predictor accounted 7.3% variance in anxiety (b=0.006, t=12.084, p<0.001). Results also found that 5.9% of variation of depression is explained by smartphone addiction (b=0.005, t=10.770, p<0.001). Table 8. Pearson correlation analysis between smartphone addiction and academic performance (n=1846) PGNK Smartphone addiction r p-value 0.005 0.822 Table 8 above illustrates the correlation between smartphone addiction and students' academic performance (PGNK). The results indicate a weak correlation between the two variables, with no significant relationship identified, as the p-value exceeds 0.05. Discussion Smartphone usage in the rise despite all the unfavourable implications it results in especially among the university students. Smartphones have been a life necessary until it was found that nearly all the university student owns a smartphone. Although smartphones have their beneficial purpose of use, over-usage could be detrimental. The results of this study uncovered that 40.9% of students use smartphones for 2–5 hours per day. This finding is supported by Ithnain et al, ( 2018 ), who reported that approximately 45.0% of students spend 4–6 hours a day on their smartphones. Similarly, Kahyaoglu, Kurt, Uzal, and Ozdilek (2016) found that about 40.1% of students also use their smartphones for 4 to 6 hours daily. Even more concerning is that 58.3% of students reportedly use smartphones for over 6 hours each day. A study by Ko, Song, and Yu ( 2019 ) indicated that while long-term smartphone usage can enhance concentration, it may also lead to increased muscle stiffness and decreased visual acuity if users do not incorporate stretching and eye movement during prolonged use. Additionally, Jamal et al, ( 2012 ) found that heavy smartphone usage could contribute to long-term memory impairment, prolonged sleep, insomnia, chronic headaches, and concentration issues among students. The findings of this study found no significant difference between gender and smartphone addiction. This is in line with the study by Alosaimi, Alyahya, Alshahwan, Al Mahyijari and Shaik (2016), who also found no significant difference between gender and addiction score. Dixit et al. ( 2010 ) found through their study that the addiction is prevalent irrespective of the students’ gender. However, a Korean study revealed that female students were more addicted to their smartphones compared to male students (Mok et. al, 2014 ). It was found that there is association between smartphone addiction with anxiety through this study. Those who are found to have high scores of smartphone addiction reported high scores of anxiety and depression as well. These findings are in line with similar earlier studies carried out (Hwang et al, 2012 ; Hammad & Awed, 2023 ; Ithnain et. al, 2018 ). Hawi and Samaha ( 2017 ) in their study found that those who are addicted to smartphones have greater odds to experience anxiety compared to those who are not. A study carried out among the Turkish university students identified that smartphone overuse could result in depression, anxiety, low sleep quality, and daytime dysfunction (Demirci et al, 2015 ). While most studies indicate a positive relationship between smartphone addiction and anxiety, a study by Lepp, Barkley, and Karpinski ( 2014 ) found a negative correlation between the two variables. In contrast, Aker, Şahin, Sezgin, and Oğuz (2017) suggested that smartphone addiction is influenced by depression, anxiety, insomnia, and familial social support. The study's findings indicate a relationship between smartphone addiction and depression. A systematic review by Elhai, Dvorak, Levine, and Hall ( 2017 ) identified a common link between depression and problematic smartphone usage. In a study involving 353 Korean college students, depression was found to be a significant predictor of smartphone addiction. Similar relationships have been reported in other research (Alhassan et al., 2018 ; Augner & Hacker, 2012 ; Hammad & Awed, 2020 ; Matar Boumosleh & Jaalouk, 2017 ). Additionally, a study by Bian and Leung ( 2015 ) highlighted that loneliness, which correlates positively with depression, emerged as a key predictor of smartphone addiction scores. While most studies identified a positive association between smartphone addiction and depression, Choi et al. (Mok et al., 2014 ) reported a negative association between the two variables. The study's results revealed no significant correlation between smartphone addiction and students' academic performance. This aligns with the findings of Matar Boumosleh and Jaalouk ( 2017 ), who also concluded that academic performance was not associated with smartphone addiction scores. Other studies, however, found that smartphone addiction negatively affects students’ academic performance (Alosaimi et. al, 2016 ; Junco & Cotton, 2012; Hammad & Awed, 2022 Samaha & Hawi, 2016 ). Nayak ( 2018 ) found that besides behavioural changes, smartphone addiction hardly affects female students. However, male students who are addicted to smartphones tend to neglect work, feel anxious and lose control of themselves. Kibona and Mgaya ( 2015 ) through their study on a Ruaha Catholic University, found that smartphones adversely affect students’ academic performance as they reported to be distracted from the lectures when someone texted or called them during a lecture. Nevertheless, Karuniawan and Cahyanti ( 2013 ) brought a new meaning on the association between smartphone addiction and academic stress. They found that the higher the academic stress, the greater the smartphone dependence on students and vice versa. Students tend to use smartphones as a stress reliever when feeling stressed. This situation could eventually lead to smartphone addiction if used uncontrollably (Chiu, 2014 ). In conclusion, the findings of this study align with other research that indicates a relationship between smartphone addiction, anxiety, depression, and academic performance among university students. While it is concerning that smartphone addiction is increasing, future studies should be conducted across universities in Malaysia to gain a more comprehensive understanding of this issue. Conclusion Smartphone addiction could result in harmful functional impairments which could interfere with college, work and family relationship. Hence, steps need to be taken in order to curb the addiction from its root cause. Possible programs and interventions should be thoroughly planned and executed among the targeted university students to improve their physical and mental well-being. Declarations Data availability : the datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Author contributions Author Contributions: Conceptualization: S.E.G., P.S., and H.F.A. Data Collection: R.R., N.A.A., Q.H and M.S. Data Analysis: S.W., S.E.G. and P.S. Resources: P.S, H.A., and R.R. Writing—Original Draft Preparation: N.A.A., Q.H., and M.S. Writing—Review & Editing: S.W., S.P.G., and P.S. Funding Acquisition: P.S. and H.F.A. Funding This work was supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R707), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Institutional Review Board Statement : The study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Research Ethics Committee, the National University of Malaysia (UKM). Also, informed consent was obtained from all participants. Competing interests The study is reported in accordance with ARRIVE guidelines. No conflicts of interest, financial or otherwise, are declared by the authors. Additional information: Correspondence and requests for materials should be addressed to author correspondence. Correspondence and requests for materials should be addressed to P.S. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Aker, S., Sahin, M. K., Sezgin, S., & Oguz, G. (2017). Psychosocial factors affecting smartphone addiction in university students. Journal of Addictions Nursing , 28 (4), 215-219. doi:10.1097/JAN.0000000000000197 Alhassan, A. A., Alqadhib, E. M., Taha, N. W., Alahmari, R. A., Salam, M., & Almutairi, A. F. (2018). The relationship between addiction to smartphone usage and depression among adults: a cross sectional study. BMC psychiatry , 18 (1), 1-8. doi:10.1186/s12888-018-1745-4 Alosaimi, F. D., Alyahya, H., Alshahwan, H., Al Mahyijari, N., & Shaik, S. A. (2016). 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Journal of Taibah University Medical Sciences , 7 (1), 45-49. doi:10.1016/j.jtumed.2012.07.001 James, D., & Drennan, J. (2005, December). Exploring addictive consumption of mobile phone technology. In Australian and New Zealand Marketing Academy conference, Perth, Australia . Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education , 59 (2), 505-514. doi:10.1016/j.compedu.2011.12.023 Kahyaoğlu-Süt, H., Kurt, S., Uzal, Ö., & Özdilek, S. (2016). Efects of smartphone addiction level on social and educational life ın health sciences students. Euras Journal Fam Med , 5 (1), 13-19. Karuniawan, A., & Cahyanti, I. Y. (2013). Hubungan antara academic stress dengan smartphone addiction pada mahasiswa pengguna smartphone. Jurnal psikologi klinis dan kesehatan mental , 2 (1), 16-21. Kibona, L., & Mgaya, G. (2015). Smartphones’ effects on academic performance of higher learning students. Journal of Multidisciplinary Engineering Science and Technology , 2 (4), 777-784. Ko, M. G., Song, C. H., & Yu, J. H. (2019). The Effects of Long-Term Smartphone Usage Time and of Stretching on Stiffness, Concentration, and Visual Acuity. PNF and Movement , 17 (1), 57-68. Kumar, L. R., Chii, K. D., Way, L. C., Jetly, Y., & Rajendaran, V. (2011). Awareness of mobile phone hazards among university students in a Malaysian medical school. Health , 3 (07), 406. doi:10.4236/health.2011.37068 Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Computers in human behavior , 31 , 373-383. doi:10.1016/j.chb.2013.10.047 Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in human behavior , 31 , 343-350. doi:10.1016/j.chb.2013.10.049 Lin, Y. H., Chiang, C. L., Lin, P. H., Chang, L. R., Ko, C. H., Lee, Y. H., & Lin, S. H. (2016). Proposed diagnostic criteria for smartphone addiction. PloS one , 11 (11), e0163010. doi:10.1371/journal.pone.0163010 Malaysian Communications and Multimedia Commission. Hand Phone Users Survey 2018 .; 2018. https://www.mcmc.gov.my/skmmgovmy/media/General/pdf/HPUS2018.pdf Matar Boumosleh, J., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students-A cross sectional study. PloS one , 12 (8), e0182239. doi:10.1371/journal.pone.0182239 Moattari, M., Moattari, F., Kaka, G., Kouchesfahani, H. M., Sadraie, S. H., & Naghdi, M. (2017). Smartphone addiction, sleep quality and mechanism. Int J Cogn Behav , 1 (002). doi:10.23937/ijcb-2017/1710002 Mok, J. Y., Choi, S. W., Kim, D. J., Choi, J. S., Lee, J., Ahn, H., ... & Song, W. Y. (2014). Latent class analysis on internet and smartphone addiction in college students. Neuropsychiatric disease and treatment , 10 , 817. doi:10.2147/NDT.S59293 Muhktar, F., & Oei, T. P. (2008). Exploratory and confirmatory factor validation and psychometric properties of the Beck Depression Inventory for Malays (BDI-Malay) in Malaysia. Malaysian Journal of Psychiatry , 17 (1). Nayak, J. K. (2018). Relationship among smartphone usage, addiction, academic performance and the moderating role of gender: A study of higher education students in India. Computers & Education , 123 , 164-173. doi:10.1016/j.compedu.2018.05.007 Ng, S. F., Hassan, N. S. I. C., Nor, N. H. M., & Malek, N. A. A. (2017). The Relationship between Smartphone Use and Academic Performance: A Case of Students in A Malaysian Tertiary Institution. Malaysian Online Journal of Educational Technology , 5 (4), 58-70. Number of smartphone users worldwide from 2016 to 2021. Statista Research Department. Published 2020. Accessed October 8, 2020. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in human behavior , 57 , 321-325. doi:10.1016/j.chb.2015.12.045 Smartphone User Persona Report (SUPR) 2015 for Malaysia. Vserv. Published 2016. Accessed October 8, 2020. https://www.vserv.com/infographic-smartphone-user-persona-report-supr-2015-malaysia/ Smartphone users in Malaysia 2015-2025. Statista Research Department. Published 2020. Accessed October 8, 2020. https://www.statista.com/statistics/494587/smartphone-users-in-malaysia/ Tossell, C. C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2015). You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. British Journal of Educational Technology , 46 (4), 713-724. doi:10.1111/bjet.12176 Zulkefly, S. N., & Baharudin, R. (2009). Mobile phone use amongst students in a university in Malaysia: its correlates and relationship to psychological health. European Journal of Scientific Research , 37 (2), 206-218. Additional Declarations No competing interests reported. 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This rapid expansion following the new versions of smartphones and the extension of various application software has an undeniable positive benefit on the users as they can be employed to make calls and messages and connect to the world through social media. On the other hand, the applications that cover the different fields have supplemented the users' lives. In 2016, there were approximately 2.5\u0026nbsp;billion smartphone users worldwide, with projections of growth to 3.5\u0026nbsp;billion in 2020 and 3.8\u0026nbsp;billion in 2021 (Statista Research Department, 2020). In Malaysia, the number of smartphone users was 18.46\u0026nbsp;million in 2016, and this figure is expected to rise significantly, exceeding 30.41\u0026nbsp;million by 2020 and reaching over 33\u0026nbsp;million by 2024 (Statista Research Department, 2020).\u003c/p\u003e \u003cp\u003eA survey by the Malaysian Communications and Multimedia Commission (2018) pointed out that the percentage of smartphone ownership grew marginally from 74.0\u0026ndash;76.4%. The MCMC also found that 86.3% of those below 20 years old and 87% of those between 20\u0026ndash;34 years old have higher smartphone usage. This explains the fact that about 95% of tertiary education students use smartphone (Malaysian Communications and Multimedia Commission, 2018). The 2015 'Smartphone User Persona Report' from Vserv indicates that, when compared to neighboring countries like Indonesia, the Philippines, and Thailand, smartphone users in Malaysia spend approximately 187 minutes or, to be precise, 3 hours 7 minutes with their devices (Ithnain, Ghazali \u0026amp; Jaafar, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smartphone User Persona Report, 2015). Practicality and social needs were among the main determining factors of smartphone usage among users. The usefulness of smartphones has increased drastically and are rapidly becoming part of our everyday lifestyle.\u003c/p\u003e \u003cp\u003eHowever, despite the functionality of a smartphone, uncontrolled use can lead to smartphone addiction. Lin et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that there are four components of smartphone addiction which are; Obsessive phone use is characterized by behaviors such as repeatedly checking for messages or updates, developing a tolerance for longer and more intense usage, experiencing withdrawal symptoms like agitation or distress when without the phone, and facing functional impairment that interferes with daily activities and social relationships (Chen. et al, 2017; Hammad et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hammad \u0026amp; Al-Shahrani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moattari et. al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Brod (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) defines the inability to adapt to the latest technologies healthily as \u0026ldquo;Technostress\u0026rsquo;.\u003c/p\u003e \u003cp\u003eThe smartphone addiction incident has been a worldwide concern as it can contribute to poor mental conditions, especially among university students. This condition is known to affect both their personnel and professional life. Researchers have found a correlation between excessive use of smartphones for texting, social media, gaming, listening to music, emailing, and watching clips and depression. The obsessive usage and \u0026lsquo;technostress\u0026rsquo; also lead to over-reliance on smartphones resulting in stress and compulsive usage of smartphone (Lee et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moattari et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found a positive correlation between the increased \u0026lsquo;technostress\u0026rsquo; and uncontrolled smartphone use with anxiety in social communication, behavioral control, materialism and a need to communicate and touch. Without a smartphone, irritation, frustration, and impatience could also damage relationships with others or result in psychological distress for users (Hammad \u0026amp; Awed, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; James \u0026amp; Drennan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe majority of the participants from a Malaysian private university agreed that smartphone usage can result in headache, sleeping disturbances, and causes loss of mental attention (Hammad, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kumar et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A study by Zulkefly and Baharudin (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that most of the students who have smartphone addiction issues have low self-esteem and spend a longer time with smartphones. They also found that those who tend to spend a longer time with a smartphone have psychological disturbances.\u003c/p\u003e \u003cp\u003eBesides, higher usage of smartphone results in poor academic performance. Ng, Hassan et al (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) mentioned that though used for educational purposes, smartphones negatively affected students\u0026rsquo; academic performance. Students who deemed smartphones as favourable before use perceived that smartphones could affect their educational goals in a negative way (Tossell, Kortum, Shepard, Rahmati \u0026amp; Zhong, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChing et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that approximately 46.9% of the students were at risk of developing smartphone addiction. Similarly, a study by Ithnain et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also found that about 47.7% of university students have high smartphone addiction. The increase in the percentage shows that smartphones are becoming a daily-life essential item in a student\u0026rsquo;s life. However, due to the limited studies conducted on this topic, the current research was undertaken to examine the relationship between smartphone addiction, anxiety, depression, and academic performance among undergraduates in Malaysia..\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and sample\u003c/h2\u003e \u003cp\u003e This cross-sectional study was conducted in September 2019 among newly enrolled undergraduate students at a local university in Malaysia. Respondents were recruited using purposive sampling. Full time, Malaysian undergraduates were included in this study, whereas those who were absent, non-Malaysian, and have submitted an incomplete or redundant questionnaire were excluded from the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection procedure and ethics\u003c/h3\u003e\n\u003cp\u003ePrior to the actual research commencement, a pilot study was administered on 69 undergraduates who were not part of the study. The pilot study respondents showed no difficulty in understanding and answering the self-administered online questionnaire. Hence, the actual research was carried out. A concise introduction to the study purpose was given to the students. The Google form link was then distributed among all the present students. Those who have consented to partake in the study were required to fill in the self-administered questionnaire via Google form. The study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Research Ethics Committee, the National University of Malaysia (UKM). Also, informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch3\u003eInstrument\u003c/h3\u003e\n\u003cp\u003eA self-administered Google Form questionnaire was distributed to 2,181 participants. The questionnaire consisted of six sections: a) demographic characteristics, including information on age, race, gender, and family income; b) patterns of smartphone usage, covering daily usage duration (in hours), monthly expenses on smartphones, and primary smartphone functions; c) factors influencing smartphone use; d) the Malay version of the Smartphone Addiction Scale (SAS-M); e) the Malay version of the Beck Anxiety Inventory (BAI); and f) the Malay version of the Beck Depression Inventory (BDI). The pilot study demonstrated that the selected instruments were reliable for the actual study, with Cronbach's Alpha values ranging from 0.715 to 0.955.\u003c/p\u003e\n\u003ch3\u003eSmartphone Addiction Scale\u003c/h3\u003e\n\u003cp\u003eThe Malay version of the \u0026lsquo;Smartphone Addiction Scale\u0026rsquo; was adapted from Ching et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The SAS-M consists of 33 items divided into six domains: daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance. Symptoms are assessed using a response scale from 1 to 6, where 1 indicates strong disagreement and 6 indicates strong agreement. The SAS-M has a minimum score of 33 and a maximum score of 198; higher scores indicate a greater risk of smartphone addiction. The Cronbach alpha value for the SAS-M is 0.922.\u003c/p\u003e\n\u003ch3\u003eBeck’s Anxiety Inventory\u003c/h3\u003e\n\u003cp\u003eThe Becks Anxiety Inventory-Malay Version, developed by Firdaus and Nor Sheereen (2011) was used as an instrument to measure anxiety in this study. The inventory which comprises of 21 items with a Cronbach Alpha of 0.942 was scored using a four-point scale and measured in categories that are minimal anxiety (score 0\u0026ndash;7), mild anxiety (score 8\u0026ndash;15), moderate anxiety (score16-25) and severe anxiety (score 26\u0026ndash;63)).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBeck\u0026rsquo;s Depression Inventory\u003c/h2\u003e \u003cp\u003eBeck\u0026rsquo;s Depression Inventory-Malay version developed by Mukhtar and Oei (2008) was used to measure depression. Though the original version consists of 21 item, one item was omitted from the Malay version due to the cultural factors rounding to a total of 20 item. The inventory was further subdivided into four categories which are no mild depression (score 0\u0026ndash;9), mild-moderate depression (score 10\u0026ndash;18), moderate-severe depression (score 19\u0026ndash;29) and severe depression (score 30\u0026ndash;63) (Beck, Steer \u0026amp; Carbin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered and analyzed using SPSS software version 26. Descriptive statistical analysis was conducted to calculate the mean, standard deviation, frequency, and percentage. Pearson\u0026rsquo;s correlation assessed the strength of the relationship between the variables, while Simple Linear Regression was performed to evaluate the effect of smartphone addiction on anxiety and depression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAn amount of 2181 questionnaires received, 69 (3.16%) respondents were removed due to being pilot study participants. Another 12.2% were removed due to respondents being non-Malaysian, submitted multiple responses, from different intake than 2019/2020 and outliers (PGNK less than 2.0). Hence, the final sample size left was 1846.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 presents the demographic characteristics of the sample. The majority of respondents were female, comprising 1,362 individuals (73.8%). The ages of participants ranged from 19 to 33 years, with a mean age of 19.62 \u0026plusmn; 1.11 years. Malay participants dominated the study at 77.6%, followed by Chinese at 11.7%, Indian at 7.0%, and others at 3.6%. Approximately 64.4% of the respondents were from the B40 family background and had around RM201-RM300 for their subsistence expenses. Upon university registration majority of the students were supported by their parents financially.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Demographic data (n=1846)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003cp\u003e1362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e18-19\u003c/p\u003e\n \u003cp\u003e20-21\u003c/p\u003e\n \u003cp\u003e22-23\u003c/p\u003e\n \u003cp\u003e24 \u0026amp; above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1054\u003c/p\u003e\n \u003cp\u003e710\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMalay\u003c/p\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1433\u003c/p\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of Study\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYear 1\u003c/p\u003e\n \u003cp\u003eYear 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1844\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99.9\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApproximate family income per month (RM)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBelow RM 1000\u003c/p\u003e\n \u003cp\u003eRM1000-RM1999\u003c/p\u003e\n \u003cp\u003eRM2000-RM2999\u003c/p\u003e\n \u003cp\u003eRM3000-RM3999\u003c/p\u003e\n \u003cp\u003eRM4000-RM4999\u003c/p\u003e\n \u003cp\u003eRM5000-RM5999\u003c/p\u003e\n \u003cp\u003eRM6000-RM6999\u003c/p\u003e\n \u003cp\u003eRM7000-RM7999\u003c/p\u003e\n \u003cp\u003eRM8000-RM8999\u003c/p\u003e\n \u003cp\u003eRM9000-RM9999\u003c/p\u003e\n \u003cp\u003eRM10000 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome based on T20, M40 and B40 category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eB40\u003c/p\u003e\n \u003cp\u003eM40\u003c/p\u003e\n \u003cp\u003eT20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Sub-category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003cp\u003eT1 \u0026amp; T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubsistence expenses category (RM)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRM0-RM100\u003c/p\u003e\n \u003cp\u003eRM101-RM200\u003c/p\u003e\n \u003cp\u003eRM201-RM300\u003c/p\u003e\n \u003cp\u003eRM301-RM400\u003c/p\u003e\n \u003cp\u003eRM401-RM500\u003c/p\u003e\n \u003cp\u003eRM501-RM600\u003c/p\u003e\n \u003cp\u003eRM601-RM700\u003c/p\u003e\n \u003cp\u003eRM701-RM800\u003c/p\u003e\n \u003cp\u003eRM801-RM900\u003c/p\u003e\n \u003cp\u003eRM901-RM1000\u003c/p\u003e\n \u003cp\u003eRM1001 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 373px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSources of monthly subsistence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eParents\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLoans (including PTPTN)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSchorlarship\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePart time job (including online business)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;1670\u003c/p\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003cp\u003e1174\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003cp\u003e1743\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003cp\u003e1696\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003cp\u003e1792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eThe pattern of smartphone usage (n=1846)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(n)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you have a smartphone?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1844\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99.9\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of smartphones you own\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1701\u003c/p\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe main purpose of smartphone use\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcademic\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGames\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial media\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEntertainment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommunication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVirtual Group Activities\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1601\u003c/p\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e725\u003c/p\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1516\u003c/p\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1233\u003c/p\u003e\n \u003cp\u003e613\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1663\u003c/p\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1605\u003c/p\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e1809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.1\u003c/p\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.9\u003c/p\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of telephone service taken\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePrepaid\u003c/p\u003e\n \u003cp\u003ePostpaid\u003c/p\u003e\n \u003cp\u003ePrepaid and postpaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1088\u003c/p\u003e\n \u003cp\u003e730\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of smartphone use\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than a year\u003c/p\u003e\n \u003cp\u003eOne year\u003c/p\u003e\n \u003cp\u003eMore than a year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003cp\u003e1590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of use of smartphone in a day (hours)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; 1 hour\u003c/p\u003e\n \u003cp\u003e2-5 hours\u003c/p\u003e\n \u003cp\u003e6-10 hours\u003c/p\u003e\n \u003cp\u003e11-15 hours\u003c/p\u003e\n \u003cp\u003e16-19 hours\u003c/p\u003e\n \u003cp\u003e\u0026gt; 20 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e755\u003c/p\u003e\n \u003cp\u003e697\u003c/p\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly smartphone expenses (bill / credit payments)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRM0-Rm30\u003c/p\u003e\n \u003cp\u003eRM31-RM60\u003c/p\u003e\n \u003cp\u003eRM61-RM90\u003c/p\u003e\n \u003cp\u003eRM91-RM120\u003c/p\u003e\n \u003cp\u003eRM121-RM150\u003c/p\u003e\n \u003cp\u003e\u0026gt;RM150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003cp\u003e778\u003c/p\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 shows that approximately 99.9%\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eof the respondent have a smartphone of which 7.8% have more than a smartphone. Apple (25.7%) and Samsung (17.1%) were among the most use smartphone by the students. Academic, social media, entertainment, communication and virtual group activities were among the key factor of smartphone usage based on the student\u0026rsquo;s response. Majority of them (58.9%) were observed to use the prepaid service plans and about 80% of the students spend RM 60 and below for their monthly smartphone expense. 40.9% of the students used smartphone between 2-5 hours per day.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSmartphone addiction, anxiety, and depression levels\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Comparison of total SAS-M mean score between males and females\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eM (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003et (df)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003ep *\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eTotal SAS-M score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e106.5 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e105.5 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.752 (771)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.453 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults from table 3 shows that the female\u0026rsquo;s total SAS-M mean score is not statistically different from the male\u0026rsquo;s total SAS-M mean score. Hence, there is no difference between the SAS-M score in terms of gender.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. Comparison of total SAS-M mean score between playing games or not\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cem\u003eM (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003et (df)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003ep *\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eNo games\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePlay games\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTotal SAS-M score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e103.7(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e109.1(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-5.098(1844)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHowever, the table 4 above shows that the gamers total SAS-M mean score is statistically different from the non-gamers total SAS-M mean score. Those who plays game have a higher means SAS-M score compared to those who do not.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Mean and Standard Deviation: smartphone addiction, anxiety, depression and academic performance (n=1846)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cem\u003eM(SD)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartphone Addiction Scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow smartphone addiction Medium smartphone addiction\u003c/p\u003e\n \u003cp\u003eExtreme smartphone addiction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e628 (34)\u003c/p\u003e\n \u003cp\u003e605 (32.8)\u003c/p\u003e\n \u003cp\u003e613 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e105.78 \u0026plusmn; 22.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBecks Anxiety Inventory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMinimal anxiety\u003c/p\u003e\n \u003cp\u003eMild anxiety\u003c/p\u003e\n \u003cp\u003eModerate anxiety\u003c/p\u003e\n \u003cp\u003eSevere anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e834 (45.2)\u003c/p\u003e\n \u003cp\u003e460 (24.9)\u003c/p\u003e\n \u003cp\u003e339 (18.4)\u003c/p\u003e\n \u003cp\u003e213 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e11.66 \u0026plusmn; 10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBecks Depression Inventory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo mild depression\u003c/p\u003e\n \u003cp\u003eMild-moderate depression\u003c/p\u003e\n \u003cp\u003eModerate-severe depression\u003c/p\u003e\n \u003cp\u003eSevere depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1334 (72.3)\u003c/p\u003e\n \u003cp\u003e336 (18.2)\u003c/p\u003e\n \u003cp\u003e130 (7)\u003c/p\u003e\n \u003cp\u003e46 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e7.28 \u0026plusmn; 7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic performance (PNGK)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e3.4805 \u0026plusmn; 0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults from Table 5 indicate that the mean score for smartphone addiction in this study was 105.78 \u0026plusmn; 22.38. This study utilized percentile values to categorize the scores, dividing smartphone addiction into three categories: low smartphone addiction (SAS-M score = 33-97) and medium smartphone addiction (SAS-M score = 98 - 115) and extreme smartphone addiction (SAS-M score = 116 \u0026ndash; 183). Results showed that almost all the smartphone addiction categories have the similar percentage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor anxiety, results showed that 45.2% of the respondents experienced minimal anxiety, while 24.9%, 18.4%, and 11.5% of the respondents have mild, moderate and severe anxiety respectively. The mean anxiety score was 11.66 with a standard deviation of 10.93. Additionally, the results indicated that 72.3% of respondents reported no mild depression, while 18.2%, 7.0%, and 2.5% of the respondents have mild-moderate, moderate-severe and severe depression respectively. Mean \u0026plusmn; Standard Deviation for depression score was 7.28 \u0026plusmn; 7.89. As per the students\u0026rsquo; academic performance, the respondents scored a pointer of 3.4805 \u0026plusmn; 0.29 for their overall semester results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e Pearson correlation analysis of the relationship between smartphone addiction, anxiety, and depression (n=1846)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ePGNK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eSmartphone addiction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 displays the correlation between smartphone addiction, anxiety, depression, and students\u0026apos; academic performance. The results indicate a significant positive correlation between smartphone addiction and anxiety (r = 0.271; p\u0026lt;0.001) and depression (r=-0.243; p\u0026lt;0.001) respectively. However, no significant correlation was found between smartphone addiction and the students\u0026rsquo; academic performance (PGNK). The correlation between the two variables is poor (r = 0.005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e. Analysis of Smartphone Addiction: A Simple Linear Regression Approach to Anxiety and Depression (n=1846)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartphone addiction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.005,0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmartphone addiction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.005 (0.004,0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 shows that smartphone addiction has\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ea significant effect on anxiety, with smartphone addiction as predictor accounted 7.3% variance in anxiety (b=0.006, t=12.084, p\u0026lt;0.001). Results also found that 5.9% of variation of depression is explained by smartphone addiction (b=0.005, t=10.770, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8.\u003c/strong\u003e Pearson correlation analysis between smartphone addiction and academic performance (n=1846)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 432px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePGNK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSmartphone addiction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8 above illustrates the correlation between smartphone addiction and students\u0026apos; academic performance (PGNK). The results indicate a weak correlation between the two variables, with no significant relationship identified, as the p-value exceeds 0.05.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSmartphone usage in the rise despite all the unfavourable implications it results in especially among the university students. Smartphones have been a life necessary until it was found that nearly all the university student owns a smartphone. Although smartphones have their beneficial purpose of use, over-usage could be detrimental.\u003c/p\u003e \u003cp\u003eThe results of this study uncovered that 40.9% of students use smartphones for 2\u0026ndash;5 hours per day. This finding is supported by Ithnain et al, (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported that approximately 45.0% of students spend 4\u0026ndash;6 hours a day on their smartphones. Similarly, Kahyaoglu, Kurt, Uzal, and Ozdilek (2016) found that about 40.1% of students also use their smartphones for 4 to 6 hours daily. Even more concerning is that 58.3% of students reportedly use smartphones for over 6 hours each day. A study by Ko, Song, and Yu (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicated that while long-term smartphone usage can enhance concentration, it may also lead to increased muscle stiffness and decreased visual acuity if users do not incorporate stretching and eye movement during prolonged use. Additionally, Jamal et al, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that heavy smartphone usage could contribute to long-term memory impairment, prolonged sleep, insomnia, chronic headaches, and concentration issues among students.\u003c/p\u003e \u003cp\u003eThe findings of this study found no significant difference between gender and smartphone addiction. This is in line with the study by Alosaimi, Alyahya, Alshahwan, Al Mahyijari and Shaik (2016), who also found no significant difference between gender and addiction score. Dixit et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found through their study that the addiction is prevalent irrespective of the students\u0026rsquo; gender. However, a Korean study revealed that female students were more addicted to their smartphones compared to male students (Mok et. al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was found that there is association between smartphone addiction with anxiety through this study. Those who are found to have high scores of smartphone addiction reported high scores of anxiety and depression as well. These findings are in line with similar earlier studies carried out (Hwang et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hammad \u0026amp; Awed, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ithnain et. al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hawi and Samaha (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in their study found that those who are addicted to smartphones have greater odds to experience anxiety compared to those who are not. A study carried out among the Turkish university students identified that smartphone overuse could result in depression, anxiety, low sleep quality, and daytime dysfunction (Demirci et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While most studies indicate a positive relationship between smartphone addiction and anxiety, a study by Lepp, Barkley, and Karpinski (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found a negative correlation between the two variables. In contrast, Aker, Şahin, Sezgin, and Oğuz (2017) suggested that smartphone addiction is influenced by depression, anxiety, insomnia, and familial social support.\u003c/p\u003e \u003cp\u003eThe study's findings indicate a relationship between smartphone addiction and depression. A systematic review by Elhai, Dvorak, Levine, and Hall (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified a common link between depression and problematic smartphone usage. In a study involving 353 Korean college students, depression was found to be a significant predictor of smartphone addiction. Similar relationships have been reported in other research (Alhassan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Augner \u0026amp; Hacker, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hammad \u0026amp; Awed, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matar Boumosleh \u0026amp; Jaalouk, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, a study by Bian and Leung (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) highlighted that loneliness, which correlates positively with depression, emerged as a key predictor of smartphone addiction scores. While most studies identified a positive association between smartphone addiction and depression, Choi et al. (Mok et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported a negative association between the two variables.\u003c/p\u003e \u003cp\u003eThe study's results revealed no significant correlation between smartphone addiction and students' academic performance. This aligns with the findings of Matar Boumosleh and Jaalouk (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who also concluded that academic performance was not associated with smartphone addiction scores. Other studies, however, found that smartphone addiction negatively affects students\u0026rsquo; academic performance (Alosaimi et. al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Junco \u0026amp; Cotton, 2012; Hammad \u0026amp; Awed, 2022 Samaha \u0026amp; Hawi, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nayak (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that besides behavioural changes, smartphone addiction hardly affects female students. However, male students who are addicted to smartphones tend to neglect work, feel anxious and lose control of themselves. Kibona and Mgaya (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) through their study on a Ruaha Catholic University, found that smartphones adversely affect students\u0026rsquo; academic performance as they reported to be distracted from the lectures when someone texted or called them during a lecture.\u003c/p\u003e \u003cp\u003eNevertheless, Karuniawan and Cahyanti (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) brought a new meaning on the association between smartphone addiction and academic stress. They found that the higher the academic stress, the greater the smartphone dependence on students and vice versa. Students tend to use smartphones as a stress reliever when feeling stressed. This situation could eventually lead to smartphone addiction if used uncontrollably (Chiu, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, the findings of this study align with other research that indicates a relationship between smartphone addiction, anxiety, depression, and academic performance among university students. While it is concerning that smartphone addiction is increasing, future studies should be conducted across universities in Malaysia to gain a more comprehensive understanding of this issue.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSmartphone addiction could result in harmful functional impairments which could interfere with college, work and family relationship. Hence, steps need to be taken in order to curb the addiction from its root cause. Possible programs and interventions should be thoroughly planned and executed among the targeted university students to improve their physical and mental well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: the datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor Contributions: Conceptualization: S.E.G., P.S., and H.F.A. Data Collection: R.R., N.A.A., Q.H and M.S. Data Analysis: S.W., S.E.G. and P.S. Resources: P.S, H.A., and R.R. Writing\u0026mdash;Original Draft Preparation: N.A.A., Q.H., and M.S. Writing\u0026mdash;Review \u0026amp; Editing: S.W., S.P.G., and P.S. Funding Acquisition: \u0026nbsp;P.S. and H.F.A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R707), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Research Ethics Committee, the National University of Malaysia (UKM). Also, informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is reported in accordance with ARRIVE guidelines. No conflicts of interest, financial or otherwise, are declared by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information:\u0026nbsp;\u003c/strong\u003eCorrespondence and requests for materials should be addressed to author correspondence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to P.S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u0026nbsp;\u003c/strong\u003eis available at www.nature.com/reprints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u0026nbsp;\u003c/strong\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAker, S., Sahin, M. K., Sezgin, S., \u0026amp; Oguz, G. (2017). 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Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. \u003cem\u003eComputers in human behavior\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e, 321-325. doi:10.1016/j.chb.2015.12.045\u003c/li\u003e\n\u003cli\u003eSmartphone User Persona Report (SUPR) 2015 for Malaysia. Vserv. Published 2016. Accessed October 8, 2020. https://www.vserv.com/infographic-smartphone-user-persona-report-supr-2015-malaysia/\u003c/li\u003e\n\u003cli\u003eSmartphone users in Malaysia 2015-2025. Statista Research Department. Published 2020. Accessed October 8, 2020. https://www.statista.com/statistics/494587/smartphone-users-in-malaysia/\u003c/li\u003e\n\u003cli\u003eTossell, C. C., Kortum, P., Shepard, C., Rahmati, A., \u0026amp; Zhong, L. (2015). You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. \u003cem\u003eBritish Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(4), 713-724. doi:10.1111/bjet.12176\u003c/li\u003e\n\u003cli\u003eZulkefly, S. N., \u0026amp; Baharudin, R. (2009). Mobile phone use amongst students in a university in Malaysia: its correlates and relationship to psychological health. \u003cem\u003eEuropean Journal of Scientific Research\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(2), 206-218.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smartphone addictions, Depression, Anxiety, Academic Achievement, University Students","lastPublishedDoi":"10.21203/rs.3.rs-6252874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn today's globalized world, technology significantly influences daily life. While it offers convenience, it also affects individuals in various ways. The increasing use of smartphones has raised concerns about smartphone addiction. This study seeks to examine the relationship between smartphone addiction, anxiety, depression, and academic performance among university students. A total of 1,846 students (1,362 females and 484 males; mean age\u0026thinsp;=\u0026thinsp;19.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11) participated in the research. An online questionnaire was distributed, including the Smartphone Addiction Scale-M (SAS-M), the Beck Anxiety Inventory-M (BAI-M), and the Beck Depression Inventory-M (BDI-M). Descriptive analysis revealed mean scores of smartphone addiction, anxiety, and depression among respondents as 105.78\u0026thinsp;\u0026plusmn;\u0026thinsp;22.38, 11.66\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93, and 7.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89, respectively. Further analysis through simple linear regression indicated a statistically significant positive relationship between smartphone addiction, anxiety, and depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, smartphone addiction was identified as a predictor of anxiety (b\u0026thinsp;=\u0026thinsp;0.006, t\u0026thinsp;=\u0026thinsp;12.084, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and depression (b\u0026thinsp;=\u0026thinsp;0.005, t\u0026thinsp;=\u0026thinsp;10.770, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the study found no statistically significant relationship between smartphone addiction and academic performance. However, it concluded that college students are particularly vulnerable to smartphone addiction, which can result in heightened anxiety and depression. Consequently, comprehensive intervention programs are essential to address smartphone addiction and enhance mental health among college students.\u003c/p\u003e","manuscriptTitle":"Smartphone Addiction, Anxiety, Depression, and Academic Performance in University Students: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 16:02:09","doi":"10.21203/rs.3.rs-6252874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T08:00:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T19:42:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287888023182037573842291550995221431049","date":"2025-04-03T12:29:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T12:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241902454073404002316377235765747289057","date":"2025-03-29T07:56:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T05:35:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-29T05:15:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T04:48:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T04:23:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T11:44:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41828c39-94d0-422f-ad4b-76fef0bc9e07","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46995405,"name":"Biological sciences/Psychology/Human behaviour"},{"id":46995406,"name":"Biological sciences/Psychology"},{"id":46995407,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-04-13T16:05:45+00:00","versionOfRecord":{"articleIdentity":"rs-6252874","link":"https://doi.org/10.1038/s41598-026-47811-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-11 15:58:02","publishedOnDateReadable":"April 11th, 2026"},"versionCreatedAt":"2025-04-17 16:02:09","video":"","vorDoi":"10.1038/s41598-026-47811-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47811-0","workflowStages":[]},"version":"v1","identity":"rs-6252874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252874","identity":"rs-6252874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}