The Mediating Role of Compulsive Internet Use in the Relationship Between Depressive Symptoms and Sleep Quality Among Qatar University Students: A Path Analysis

The Mediating Role of Compulsive Internet Use in the Relationship Between Depressive Symptoms and Sleep Quality Among Qatar University Students: A Path Analysis | 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 Research Article The Mediating Role of Compulsive Internet Use in the Relationship Between Depressive Symptoms and Sleep Quality Among Qatar University Students: A Path Analysis Taha R. Adawi, Summayya Waseem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6692326/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Sleep quality and mental health play pivotal role in overall well-being, especially for university students who frequently encounter academic, social, and personal challenges. This study aims to explore how compulsive internet use mediates the relationship between depressive symptoms and sleep quality among Qatar University students. Method: This study utilized a cross-sectional design to examine the relationship between depressive symptoms, compulsive internet use, and sleep quality among Qatar University students. 750 participants were recruited through convenient sampling, and data were collected via an online survey. Validated psychological scales were used to assess depressive symptoms, compulsive internet use, and sleep quality. The collected date were analyzed using descriptive tests, Pearson correlation, and path analysis. Results : Findings revealed that compulsive internet use significantly impacts both sleep quality and depressive symptoms. Furthermore, the results highlighted its critical mediating role in the relationship between sleep quality and depressive symptoms, underscoring its influence on overall well-being. Conclusion : These findings underscore the pivotal role of compulsive internet use as a mediator, highlighting the necessity of targeted interventions to regulate internet use behaviors, enhance sleep quality, and mitigate depressive symptoms among university students. compulsive internet use depressive symptoms sleep quality Figures Figure 1 Figure 2 Introduction Over the past two decades, internet use has become essential to daily life. As the number of internet users continues to grow, health-related issues such as internet addiction have become widespread [1] . Qatar's swift digital growth has notably increased in compulsive internet use (CIU) among its younger population. With one of the highest internet access rates in the world, almost all young Qataris have uninterrupted internet connectivity, mainly via smartphones [2] . This significant connectivity, combined with global trends of excessive internet use among teenagers, encourages compulsive online behavior. Qatar’s youth often use social media for communication, self-expression, and exploring their identities. However, this frequent engagement can lead to compulsive behaviors driven by the desire to stay perpetually connected online. Studies indicate that excessive internet use is associated with various adverse effects, including sleep problems and a higher likelihood of mental health concerns [3] [4] . Factors in the environment, such as family interactions and school environments, also influence this situation, underlining the complex interplay between behavioral habits and contextual factors [5] . Recent research in Qatar has revealed that a considerable number of adolescents are experiencing symptoms of CIU [6] [7] . One notable study conducted by Al-Harahsheh et al. (2023) indicated that many Qatari teenagers reported spending excessive time online, which has negatively affected various aspects of their daily lives and general well-being. The research conducted by Abdelmoneium et al. (2023) and Chemnad et al. (2023) on parental perspectives in Qatar concerning adolescents’ excessive engagement with technology indicates that parents view the overuse of digital devices as an escalating issue, as 29.64% of adolescents exhibited signs of addiction. They report various adverse outcomes associated with this phenomenon, including social isolation, diminished family interactions, health complications such as sleep disturbances and eye strain, and a decline in academic performance [8] . The number of internet-addicted adolescents in Qatar has gradually increased over time, with an addiction rate of 86.6% [9] . Moreover, the use of internet, particularly through social media, has become a significant means for individuals to express themselves and explore their identities, especially among teenagers and young adults. However, this online participation is often linked to the quest for self-affirmation. People commonly turn to likes, comments, and shares as external measures of their self-worth, leading to a reliance on digital feedback for validation. This cycle of seeking approval can result in compulsive online behavior, where individuals feel an urgency to stay connected to sustain their perceived social importance. Over time, such dependence on digital validation can warp self-image, heighten sensitivity to online criticism, and contribute to mental health issues like anxiety, diminished self-esteem, and depressive symptoms [10] [11] . Sleep quality is an important precursor that may be linked to internet addiction. Due to the ubiquity and easy access to technological products, adolescents engage in various online activities to help them sleep. However, the likelihood of replacing online activities directly with sleep, thus shortening sleep duration, increases with the time adolescents spend online. In this vicious cycle, the risk of internet addiction among adolescents increases. Sleep problems are considered an indicator of internet addiction [12] [13] [14] . The phenomenon of CIU often involves prolonged screen time, particularly during late-night hours, which interferes with circadian rhythms, delays the onset of sleep, and decreases total sleep duration. These sleep issues further add to mood fluctuations, increased emotional sensitivity, and a heightened vulnerability to depressive states. Studies have consistently indicated that inadequate sleep quality is a key factor contributing to mental health challenges, especially depression. Evidence supports this mediating role. For instance, individuals with depressive symptoms may resort to online activities—such as social networking or gaming—for temporary relief. Although these online interactions may provide brief distraction, they ultimately worsen sleep issues and emotional discomfort over time, creating a negative cycle [15] [16] . Consequently, CIU does not only stem from poor mental health or disrupted sleep; it actively mediates the relationship between sleep disturbances and depression. An increasing amount of research highlights the complex connections between sleep quality, CIU, and symptoms of depression. CIU, which is characterized by the inability to manage online activities despite negative consequences, has become a crucial element affecting sleep and mental health outcomes. Research shows that CIU can mediate the relationship between poor sleep quality and symptoms of depression, intensifying the negative effects that sleep disturbances have on mental well-being [17] . Research supporting this mediating role posits that individuals with poor sleep may seek online activities as a means of distraction or stress relief, but this coping mechanism often becomes compulsive [18] [19] [20] . CIU not only contribute to reduced sleep quality but also intensified depressive symptoms, creating a cycle where individuals oscillate between internet use and sleep deprivation. In this way, CIU amplifies the effects of poor sleep on mental health, as individuals increasingly turn to internet use for relief, yet experience worsened mood and sleep outcomes. Apart from the negative impact of internet addiction on the risk of depression, many studies have reported significant associations between internet addiction and sleep problems [21] , which may be mediated by depressive symptoms [22] . Bhandari et al. (2017) found that 35.4% of students experienced poor sleep quality, another 35.4% exhibited signs of internet addiction, and 21.2% reported symptoms of depression. The study identified significant interrelations between sleep quality and internet addiction, with each construct partially mediating the effects of the other on depressive symptoms. This underscores the intricate interplay between these factors and their collective impact on the mental health of students. Research indicates that those with CIU frequently face irregular sleep patterns, such as trouble falling asleep, waking up often, and shorter sleep duration. These sleep issues further intensify depressive symptoms, leading to a harmful cycle. Additionally, CIU plays a direct role in increasing depressive symptoms through factors like social isolation, decreased physical activity, and engagement with negative online material [23] . Sun et al. (2024) [24] conducted a study to investigate the prevalence of internet addiction, depression, and sleep quality, as well as the various relationships among these variables. The results indicate increasing rates of internet addiction and depression, along with decreasing levels of sleep quality. A study by Huang et al. (2023) [25] on first-year university students in Taiwan, found that the prevalence of internet addiction among first-year university students was 21.2%. The indirect effects revealed that depression fully mediated the relationship between sleep quality and internet addiction. For example, research conducted by Wang et al. (2020) [26] revealed that CIU substantially mediated the association between poor sleep quality and symptoms of depression among Chinese university students. Similarly, findings from Tan et al. (2016) [27] indicated that problematic internet use and depression partially mediated sleep disturbances in young Chinese individuals. Mediation analysis conducted by Zou et al. (2019) [28] demonstrated that sleep quality partially mediated the link between problematic mobile phone use and depressive symptoms, highlighting how sleep issues intensify the effects of excessive internet use on mental health. Additionally, the addictive quality of internet use accumulates effects on both sleep and emotional health. A meta-analysis by Demirci et al. (2015) [29] showed that night-time CIU was linked to increased sleep latency and higher levels of depression across different age groups. This reinforces the understanding that CIU exacerbates the effects of poor sleep on depression by disrupting restorative processes and perpetuating maladaptive behavior patterns. Rufino et al. (2024) [30] found that social media addiction and poor sleep quality mediate the link between time spent on social media and depressive symptoms in Brazilian university students. In a longitudinal study conducted on university students, internet addiction was identified as a significant risk factor for depression [31] . Wang et al., (2025) [32] found that Social network site addiction in adolescents was positively linked to poor sleep quality, depression, and difficulty describing feelings. Depression partially mediated the link between addiction and sleep, while difficulty describing feelings strengthened the connection between addiction and depression. A systematic review and meta-analysis conducted by Al-Khani et al. (2021) [33] on internet addiction in the Gulf countries indicated an overall prevalence rate of 33% based on the studies reviewed. The findings also revealed that females are more likely to be addicted to the internet compared to males. In summary, the role of compulsive internet use as a mediator in the connection between sleep and depression is well-established, with research indicating its harmful effects on both sleep quality and mental well-being. The interaction among compulsive internet use, sleep, and depressive symptoms forms a self-perpetuating cycle that amplifies the influence of sleep issues on depression, highlighting the necessity of tackling internet use patterns in programs designed to enhance sleep and mental health. Despite increasing concerns about the effects of compulsive internet use (CIU) on mental health and sleep patterns, research investigating its specific influence on the interplay between sleep quality and depressive symptoms remains scarce, particularly among university students in the Gulf region. To date, the available literature addressing this issue within the Qatari population is limited, particularly in light of the country’s high internet penetration and distinctive sociocultural context. Conducting empirical studies is crucial for elucidating the extent to which CIU impacts psychological well-being. This research aims to bridge this knowledge gap by exploring the mediating role of compulsive internet use in the relationship between sleep quality and depressive symptoms among students at Qatar University. The theoretical model is shown in Fig. 1 Methods Sample This research was conducted with a total of 750 university students in Qatar, including 121 males and 629 females. The participants' ages ranged between 17 and 44 years, with a mean age of 22.46 years ( SD = 4.44). Students from both the humanities and applied sciences disciplines were included in the sample. The majority of respondents were unmarried, accounting for 72.9% of the total sample. The demographic distribution was reflective of the student population commonly observed in higher education settings across the region. Instruments Compulsive Internet Use Scale (CIUS) Developed by Meerkerk and colleagues (2009) [34] , the Compulsive Internet Use Scale (CIUS) is a 14-item self-report tool that assesses compulsive patterns of internet use. It evaluates core aspects such as preoccupation with online activities, withdrawal symptoms, loss of control, and use of the internet as a coping strategy. Items are scored using a 5-point Likert scale ranging from 0 (“Never”) to 4 (“Very Often”), with higher scores signifying greater compulsivity in internet use. The CIUS has shown excellent internal reliability, with a Cronbach’s alpha coefficient of 0.882. stability over time was confirmed through split-half reliability, yielding a Spearman-Brown coefficient of 0.851. Furthermore, removing any individual item did not significantly affect the overall consistency, as alpha values remained between 0.868 and 0.879. The instrument’s structure was validated through confirmatory factor analysis (CFA), which supported a single-factor model. Standardized regression weights were statistically significant, ranging from 0.441 to 0.789 (p < .01). Model fit indices further indicated a well-fitting model (CFI = 0.953, RMSEA = 0.059, SRMR = 0.044), confirming the scale’s robustness in measuring problematic internet behavior. Sleep Quality Scale (SQS) The Sleep Quality Scale (SQS), introduced by Yi, Shin, and Shin (2006) [35] , is a multidimensional 28-item instrument designed to evaluate overall sleep quality. It addresses various dimensions such as sleep disturbance, satisfaction with sleep, sleep efficiency, daytime dysfunction, and somnolence. Respondents rate items on a 4-point Likert scale, with higher scores reflecting poorer sleep quality. Psychometric evaluation indicates the scale has strong internal consistency, demonstrated by a Cronbach’s alpha of 0.879. Its reliability is also supported by split-half testing, which yielded a Spearman-Brown coefficient of 0.760. CFA results validated the multidimensional structure, with standardized regression weights falling between 0.414 and 0.789 (p < .01). Model fit indicators were within acceptable thresholds (CFI = 0.928, RMSEA = 0.049, SRMR = 0.056), affirming the scale’s construct validity. The SQS has been widely used in both clinical and academic settings to assess sleep-related concerns comprehensively. Patient Health Questionnaire-9 (PHQ-9) The Patient Health Questionnaire-9 (PHQ-9) is a brief self-assessment tool developed as part of the PHQ series. It consists of 9 items aligned with the diagnostic criteria for major depressive disorder from the DSM-IV. Respondents indicate how frequently they experienced symptoms over the past two weeks, using a 4-point Likert scale ranging from 0 (“Not at all”) to 3 (“Nearly every day”). Total scores range from 0 to 27, with higher scores indicating greater symptom severity. Severity categories include minimal, mild, moderate, moderately severe, and severe. The PHQ-9 has demonstrated solid reliability, with a Cronbach’s alpha of 0.818. Split-half reliability produced a Spearman-Brown coefficient of 0.738, further confirming its internal stability. Confirmatory factor analysis supported a unidimensional structure, with significant item loadings ranging from 0.475 to 0.644 (p < .01). The model showed good fit, as reflected in the indices (CFI = 0.958, RMSEA = 0.061, SRMR = 0.044). These findings affirm the PHQ-9 as a dependable instrument for detecting and monitoring depressive symptoms across diverse populations. Procedures This research utilized a cross-sectional design to explore the interrelationships among depressive symptoms, compulsive internet use (CIU), and sleep quality. Data were collected through an online self-administered questionnaire distributed across various faculties, focusing on students from both humanities and applied sciences. Participation was entirely voluntary and anonymous, with informed consent obtained from all participants prior to their involvement. A university-wide announcement was made to invite students from Qatar University to take part, and data collection was carried out via Google Forms over a one-month. All procedures were approved by Qatar University’s institutional review board (QUIRB) The SQS and CIUS were translated from English into Arabic for use in the present study. The translation process followed established guidelines for cross-cultural adaptation [36] [37] [38] [39] [40] incorporating the committee translation method and forward and back-translation, and pretesting to ensure both linguistic and conceptual equivalence.. Initially, two independent translations were produced—one by the first author (TRA) and another by a bilingual research assistant who had no prior exposure to the questionnaires. These two versions were then reviewed and compared with the original English version to create a unified Arabic draft. In the second phase, a bilingual researcher unfamiliar with the original questionnaires conducted a back-translation of the Arabic items into English. A British scholar with a strong background in psychology evaluated the back-translated version, and any feedback provided was incorporated into the Arabic version. In the final phase, the first author revised some of the items to enhance readability for university students. These revisions were then compared with the original English version to produce the final Arabic version of the questionnaire. Data analysis Data analysis was conducted using IBM SPSS version v24.0 and AMOS v24 for Path Analysis. Descriptive statistics were calculated to determine the mean, standard deviation, skewness, and kurtosis of each variable. Pearson correlation analysis was performed to assess the bivariate relationships among depressive symptoms, CIU, and sleep quality. The data that support the findings of this study are available upon reasonable request first author. Results Table 1 Demographic characteristics of the study sample (N = 750) Variable Category Frequency (%) Sex Males 121 (16.1) Females 629 (83.9) Specialization Humanities 566 (75.5) Applied 184 (24.5) Marital status Single 547 (72.9) Married 203 (27.1) Table 1 depicted that the sample comprised 750 university students, with a majority being female (83.9%) and 16.1% male. Most students (75.5%) were enrolled in humanities programs, while 24.5% were from applied disciplines. In terms of marital status, 72.9% were single and 27.1% were married. Table 2 Descriptive statistics for research variables and correlation coefficient between them ( N = 750) Variable 1 2 3 M SD Skewness Kurtosis Depressive Symptoms - 19.17 5.53 0.570 0.182 Compulsive Internet Use 0.314 ** - 40.96 11.61 0.113 0.538 Sleep Quality 0.224 ** 0.321 ** - 66.02 14.42 0.153 0.261 Note. **= p < .01. Table 2 shows the mean score for compulsive internet use was 40.96 (SD = 11.61), depressive symptoms averaged 19.17 (SD = 5.53), and sleep quality scored a mean of 66.02 (SD = 14.42). All variables showed positive and significant correlations. Compulsive internet use correlated positively with both depressive symptoms ( r = .314, p < .01) and poor sleep quality ( r = .321, p < .01). Similarly, depressive symptoms were significantly associated with poor sleep quality ( r = .224, p < .01). Figure 2 illustrated the statistical model which demonstrates a strong fit with the data, supporting the robustness of these pathways. Table 3 Path coefficients for direct, indirect and total relationships ( N = 750) Independent Variable Outcome Variable Standardized β Unstandardized B t -value CR p Depressive Symptoms Compulsive Internet Use 0.314 0.660 0.073 9.064 <.001 Compulsive Internet Use Sleep Quality 0.137 0.170 0.054 3.795 <.001 Depressive Symptoms (Direct) Sleep Quality 0.278 0.725 0.094 7.701 <.001 Depressive Symptoms (Indirect) Sleep Quality 0.043 0.112 — — <.001 Depressive Symptoms (Total) Sleep Quality 0.321 0.837 — — <.001 Table 3 indicate the path analysis findings offer valuable insight into the interrelationships among depressive symptoms, compulsive internet use, and sleep quality in a university student population. The direct effects demonstrate that depressive symptoms significantly predict both compulsive internet use and poor sleep quality. Specifically, the standardized path coefficient from depressive symptoms to compulsive internet use (β = 0.314, p < .01) indicates that individuals with elevated depressive symptoms are more likely to engage in compulsive internet behaviors. This aligns with previous research suggesting that individuals experiencing emotional distress may turn to the internet for distraction or emotional relief, potentially reinforcing maladaptive use patterns. The path from compulsive internet use to sleep quality (β = 0.137, p < .01) further supports its role as a significant mediator. This relationship implies that compulsive internet engagement contributes to disrupted sleep, possibly due to extended screen exposure before bedtime, interference with circadian rhythms, or psychological stimulation from digital content, which delays sleep onset and impairs restfulness. Moreover, the direct path between depressive symptoms and sleep quality (β = 0.278, p < .01) reflects a strong negative association, suggesting that depressive symptoms independently reduce sleep quality. This may occur through cognitive mechanisms such as rumination, heightened physiological arousal, or emotional dysregulation that interferes with the ability to fall or stay asleep. The presence of an indirect effect from depressive symptoms to sleep quality via compulsive internet use (β = 0.043, p < .01) confirms a partial mediation effect. While the direct association remains significant, the mediating role of compulsive internet use indicates that it exacerbates the impact of depressive symptoms on sleep disturbances. The total effect (β = 0.321, p < .01) reflects the combined influence of both direct and indirect pathways, underscoring the substantial overall impact of depressive symptoms on sleep quality. These findings reveal a nuanced, interdependent relationship between the constructs, suggesting that interventions addressing compulsive internet use may help buffer the negative influence of depressive symptoms on sleep. Table 4 The goodness of Fit Indices for the Models Table 4 shows that the model exhibited strong fit indices (CFI = 1.00, RMSEA = 0.049, SRMR = 0.00), providing robust support for the hypothesized mediation model. Collectively, the results highlight the need for integrated approaches targeting both emotional distress and problematic internet behaviors to promote better sleep outcomes among university students. Discussion The findings from the path analysis offer meaningful insights into the interrelations between depressive symptoms, compulsive internet use (CIU), and sleep quality among university students. The direct effects reveal that depressive symptoms significantly influence both CIU and sleep quality. Specifically, the standardized path coefficient for the relationship between depressive symptoms and CIU (β = 0.314, p < .01) indicates that individuals with higher levels of depressive symptoms are more prone to engaging in compulsive internet use. This is consistent with prior research suggesting that individuals with depressive tendencies often rely on internet use as an avoidant coping mechanism, which may reinforce problematic online behaviors [41] [42] . Furthermore, the association between CIU and sleep quality (β = 0.137, p < .01) supports the mediating role of CIU. Excessive internet use has been shown to impair sleep through several pathways, including prolonged screen time that suppresses melatonin production and disrupts circadian rhythms, as well as increased cognitive and emotional stimulation from online interactions, which may delay sleep onset [43] [44] . These effects collectively contribute to sleep disturbances, which are particularly concerning among student populations already vulnerable to mental health challenges. The direct relationship between depressive symptoms and sleep quality (β = 0.278, p < .01) underscores the independent role of depression in sleep impairment. Symptoms such as persistent rumination, heightened physiological arousal, and emotional dysregulation are known to interfere with the ability to initiate and maintain restful sleep [45] [46] . Importantly, the indirect effect of depressive symptoms on sleep quality through CIU (β = 0.043, p < .01) demonstrates that CIU partially mediates this association, suggesting that while depression alone can impair sleep, the impact is exacerbated when coupled with problematic internet use. The total effect of depressive symptoms on sleep quality (β = 0.321, p < .01) illustrates the cumulative influence of both direct and indirect pathways. These results point to a complex and interconnected relationship between emotional distress, digital behavior, and physiological well-being. The model’s excellent fit indices (CFI = 1.00, RMSEA = 0.049, SRMR = 0.00) lend strong empirical support to these hypothesized relationships and reinforce the need for integrated interventions that address both depressive symptoms and internet use patterns to enhance sleep quality among university students [41] [43]. Excessive screen time, particularly during late hours, exposes individuals to blue light, which interferes with the natural sleep-wake cycle and reduces both the duration and quality of sleep. Additionally, the emotional engagement elicited by online activities—such as social media browsing or gaming—may increase psychological arousal, thereby delaying sleep onset and contributing to fragmented rest [47] . This ongoing interference with healthy sleep routines can severely affect emotional regulation, making individuals more susceptible to mood disturbances and depressive episodes. The connection between sleep quality and depression is well-established. Sleep disruption impairs cognitive and emotional processing, increasing the likelihood of experiencing symptoms like sadness, hopelessness, and anxiety [46]. This relationship is bidirectional: depression often leads to sleep problems, and poor sleep in turn intensifies depressive symptoms. Such a feedback loop indicates that addressing one component—either sleep or mood—can potentially lead to improvements in the other. Beyond sleep disturbances, CIU contributes to depression through behavioral and psychosocial mechanisms. It may lead to social withdrawal, reduced physical activity, academic or occupational neglect, and lower self-worth. However, empirical evidence suggests that CIU’s effect on depressive symptoms is largely mediated through its disruption of sleep. Thus, the pathway from CIU to depression frequently operates via its impact on sleep quality rather than through direct psychological effects [29]. The interaction among depressive symptoms, CIU, and sleep quality is cyclical in nature. For instance, a student experiencing emotional distress may turn to late-night internet use as a form of relief. While this may offer temporary distraction, it disrupts the sleep cycle and results in increased daytime fatigue and emotional instability, further worsening the depressive state. Without proper intervention, this cycle can become self-perpetuating. Overall, the study highlights the importance of early identification and integrated treatment strategies that address both emotional and behavioral contributors to sleep disturbances. Interventions focusing on reducing compulsive digital behavior, improving sleep hygiene, and managing depressive symptoms may offer a more holistic approach to improving student well-being. Limitations While the study provides valuable insights into the relationships between depressive symptoms, compulsive internet use (CIU), and sleep quality, several limitations should be acknowledged. First, the use of a cross-sectional research design restricts the ability to draw causal inferences. Although significant associations were observed, the directionality of these relationships remains uncertain. Future longitudinal studies are needed to explore temporal and causal links among these variables [48] . Second, the data were collected through self-report measures, which may be subject to biases such as social desirability and recall inaccuracies. These biases could potentially influence the validity of the responses and the overall reliability of the findings [49] . Third, the sample was predominantly female (83.9%), limiting the generalizability of the results to male students or more gender-balanced populations. Prior research suggests that gender may influence patterns of internet use and psychological responses, highlighting the need for gender-specific analyses in future investigations [50] . Fourth, the study was conducted within the cultural and educational context of Qatar, which may have unique sociocultural dynamics affecting both CIU and mental health. Therefore, the findings may not be fully applicable to students in other countries or cultural settings, and caution should be taken when attempting to generalize the results. Lastly, the model examined in this study included only CIU as a mediating variable. Other potentially influential factors—such as academic stress, physical activity, and perceived social support—were not included but may play important roles in the relationship between depressive symptoms and sleep quality. Incorporating these variables in future models may offer a more comprehensive understanding of the pathways linking emotional distress and sleep disturbances [51] . Taken together, these limitations underscore the need for more diverse, longitudinal, and culturally sensitive research to further clarify the mechanisms through which psychological and behavioral factors influence sleep health among university students. Declarations Ethical approval and consent to participate This study involves human participants. Ethical procedures followed the Declaration of Helsinki and were approved by the Institutional Review Board (IRB) at Qatar University [QU-IRB 1902-E/23]. Participants were informed about the purpose and scope of the study and were assured that their participation was entirely voluntary and that their responses would remain confidential. Informed consent was obtained in writing prior to participation. Each participant received a survey packet containing three instruments along with an information sheet detailing the study procedures and their rights as participants. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions The concept and study design were formed by T.R.T. Data acquisition was conducted T.R.T. Data analysis and explanation was conducted by TRT and S.W. Drafting of the manuscript and figures was contributed TRT and S.W. Declaration of general artificial intelligence and AI-supported writing tools The authors employed "ChatGPT," and " Grammarly " to help with language and readability and cite pertinent literature throughout the development of this paper. The writers checked and corrected the material as necessary following the use of this instrument or service, therefore assuming all responsibility for the publication's content References Kuss, D. J., Griffiths, M. D., Karila, L., & Billieux, J. (2014). Internet addiction: a systematic review of epidemiological research for the last decade. Current pharmaceutical design, 20(25), 4026–4052. https://doi.org/10.2174/13816128113199990617 Statista. (2023). Internet penetration rate in Qatar 2023. https://www.statista.com/map/asia/qatar/internet Aziz, M., Chemnad, K., Al-Harahsheh, S., Abdelmoneium, A. O., Baghdady, A., & Ali, R. (2024a). Depression, stress, and anxiety versus internet addiction in early and middle adolescent groups: The mediating roles of family and school environments. BMC Psychology, 12(1), 184. https://doi.org/10.1186/s40359-024-01659-z Aziz, M., Chemnad, K., Al-Harahsheh, S., Abdelmoneium, A. O., Baghdady, A., Hassan, D. A., & Ali, R. (2024b). The influence of adolescents’ essential and non-essential use of technology and Internet addiction on their physical and mental fatigues. Scientific Reports, 14(1), 1745. https://doi.org/10.1038/s41598-024-51655-x Chemnad, K., Aziz, M., Abdelmoneium, A. O., Al-Harahsheh, S., Baghdady, A., Al Motawaa, F. Y., ... & Ali, R. (2023). Adolescents’ internet addiction: Does it all begin with their environment? Child and Adolescent Psychiatry and Mental Health, 17(1), 87. https://doi.org/10.1186/s13034-023-00626-7 Wise (2021). Technology Overuse Amongst Adolescents in Qatar. https://www.wise-qatar.org/technology-overuse-amongst-adolescents-in-qatar/ Ajlouni, A., & Rawadieh, S. (2022). Technophobia and technophilia among undergraduates: Cross-national research in Jordan, Qatar, and Egypt. Journal of Social Studies Education Research, 13(4), 24-55. Abdelmoneium, A. O., Al Fara, H., Motawaa, F., Al Sultan, A., Al-Harahsheh, S., & Baghdady, A. (2023). Parental perspectives on adolescents’ excessive use of technology in Qatar: challenges and coping strategies. Doha International Family Institute Journal, 2023(2), 3-13. Khan, H. U., & Awan, M. A. (2017). Possible factors affecting internet addiction: A case study of higher education students of Qatar. International Journal of Business Information Systems, 26(2), 199-218 Popat, A., & Tarrant, C. (2023). Exploring adolescents' perspectives on social media and mental health and well-being - A qualitative literature review. Clinical child psychology and psychiatry, 28(1), 323–337. https://doi.org/10.1177/13591045221092884 Digennaro, S., & Iannaccone, A. (2025). Imagining Another Self: The Use of Social Media Among Preadolescents and Its Body-Related Consequences. An Exploratory Study. SAGE Open, 15(1). https://doi.org/10.1177/21582440251321364 Chen, Y. L., & Gau, S. S. F. (2016). Sleep problems and internet addiction among children and adolescents: a longitudinal study. Journal of sleep research, 25(4), 458-465. Tavernier, R., & Willoughby, T. (2014). Sleep problems: predictor or outcome of media use among emerging adults at university?. Journal of sleep research, 23(4), 389–396. https://doi.org/10.1111/jsr.12132 Younes, F., Halawi, G., Jabbour, H., El Osta, N., Karam, L., Hajj, A., & Rabbaa Khabbaz, L. (2016). Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study. PloS one, 11(9), e0161126. https://doi.org/10.1371/journal.pone.0161126 Cheng, C., Lau, Y. C., & Chan, L. (2021). The mediating role of internet addiction between sleep quality and depressive symptoms among adolescents. Journal of Behavioral Addictions, 10(2), 287–295. https://doi.org/10.1556/2006.2021.00027 Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059–1068. https://doi.org/10.5665/sleep.2810 Ma, Y., Li, J., Zhang, M., Zuo, T., Kong, L., & Yang, Y. (2024). Relationship between social anxiety and sleep quality in depressed adolescents: The mediating role of internet addiction. Frontiers in Psychiatry, 15, 1416130. https://doi.org/10.3389/fpsyt.2024.1416130 Stanković, M., Nešić, M., Čičević, S., & Shi, Z. (2021). Association of smartphone use with depression, anxiety, stress, sleep quality, and internet addiction. Empirical evidence from a smartphone application. Personality and individual differences, 168, 110342. https://doi.org/10.1016/j.paid.2020.110342 Yu, D. J., Wing, Y. K., Li, T. M. H., & Chan, N. Y. (2024). The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review. Current psychiatry reports, 26(3), 104–119. https://doi.org/10.1007/s11920-024-01481-9 Pirdehghan, A., Khezmeh, E., & Panahi, S. (2021). Social Media Use and Sleep Disturbance among Adolescents: A Cross-Sectional Study. Iranian journal of psychiatry, 16(2), 137–145. https://doi.org/10.18502/ijps.v16i2.5814 Alimoradi, Z., Lin, C. Y., Broström, A., Bülow, P. H., Bajalan, Z., Griffiths, M. D., Ohayon, M. M., & Pakpour, A. H. (2019). Internet addiction and sleep problems: A systematic review and meta-analysis. Sleep medicine reviews, 47, 51–61. https://doi.org/10.1016/j.smrv.2019.06.004 Bhandari, P. M., Neupane, D., Rijal, S., Thapa, K., Mishra, S. R., & Poudyal, A. K. (2017). Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC psychiatry, 17(1), 106. https://doi.org/10.1186/s12888-017-1275-5 Li, T., Xie, Y., Tao, S., Yang, Y., Xu, H., Zou, L., ... & Wu, X. (2020). Chronotype, sleep, and depressive symptoms among Chinese college students: A cross-sectional study. Frontiers in Neurology, 11, 592825. https://doi.org/10.3389/fneur.2020.592825 Sun, H. L., Chen, P., Zhang, Q., Si, T. L., Li, Y. Z., Zhu, H. Y., Zhang, E., Chen, M., Zhang, J., Su, Z., Cheung, T., Ungvari, G. S., Jackson, T., Xiang, Y. T., & Xiang, M. (2024). Prevalence and network analysis of internet addiction, depression and their associations with sleep quality among commercial airline pilots: A national survey in China. Journal of affective disorders, 356, 597–603. https://doi.org/10.1016/j.jad.2024.03.022 Huang, I. L., Liu, C. Y., & Chung, M. H. (2023). Sleep quality and internet addiction among junior college students; The mediating role of depression: A cross-sectional study. Archives of psychiatric nursing, 46, 1–7. https://doi.org/10.1016/j.apnu.2023.06.011 Wang, W., Wang, Y., Zhang, Y., Zhang, Z., Liu, H., & Yang, J. (2020). Problematic internet use mediates the association between sleep quality and depressive symptoms among Chinese college students. Current Psychology, 39, 1878–1885. https://doi.org/10.1007/s12144-018-9871-8 Tan, Y., Chen, Y., Lu, Y., & Li, L. (2016). Exploring Associations between Problematic Internet Use, Depressive Symptoms and Sleep Disturbance among Southern Chinese Adolescents. International journal of environmental research and public health, 13(3), 313. https://doi.org/10.3390/ijerph13030313 Zou, L., Wu, X., Tao, S., Xu, H., Xie, Y., Yang, Y., & Tao, F. (2019). Mediating effect of sleep quality on the relationship between problematic mobile phone use and depressive symptoms in college students. Frontiers in Psychiatry, 10, 822. https://doi.org/10.3389/fpsyt.2019.00822 Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4(2), 85–92. https://doi.org/10.1556/2006.4.2015.010 Rufino, J. V., Rodrigues, R., Mesas, A. E., & Guidoni, C. M. (2024). O papel mediador da dependência de mídia social e da qualidade do sono na associação entre tempo de uso de mídia social e sintomas depressivos em universitários [The mediating role of social media addiction and sleep quality in the association between social media usage and depressive symptoms in university students]. Cadernos de saude publica, 40(5), e00097423. https://doi.org/10.1590/0102-311XPT097423 Yang, X., Guo, W. J., Tao, Y. J., Meng, Y. J., Wang, H. Y., Li, X. J., Zhang, Y. M., Zeng, J. K., Tang, W. J., Wang, Q., Deng, W., Zhao, L. S., Ma, X. H., Li, M. L., Xu, J. J., Li, J., Liu, Y. S., Tang, Z., Du, X. D., Hao, W., … Li, T. (2022). A bidirectional association between internet addiction and depression: A large-sample longitudinal study among Chinese university students. Journal of affective disorders, 299, 416–424. https://doi.org/10.1016/j.jad.2021.12.013 Wang, J., Wang, N., Liu, P., & Liu, Y. (2025). Social network site addiction, sleep quality, depression and adolescent difficulty describing feelings: a moderated mediation model. BMC psychology, 13(1), 57. https://doi.org/10.1186/s40359-025-02372-1 Al-Khani, A. M., Saquib, J., Rajab, A. M., Khalifa, M. A., Almazrou, A., & Saquib, N. (2021). Internet addiction in Gulf countries: A systematic review and meta-analysis. Journal of behavioral addictions, 10(3), 601–610. https://doi.org/10.1556/2006.2021.00057 Meerkerk, G. J., Van Den Eijnden, R. J., Vermulst, A. A., & Garretsen, H. F. (2009). The compulsive internet use scale (CIUS): some psychometric properties. Cyberpsychology & behavior, 12(1), 1-6. Yi, H., Shin, K., & Shin, C. (2006). Development of the sleep quality scale. Journal of sleep research, 15(3), 309-316. Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014 Sousa, V. D., & Rojjanasrirat, W. (2011). Translation, adaptation and validation of instruments or scales for use in cross-cultural health care research: A clear and user-friendly guideline. Journal of Evaluation in Clinical Practice, 17(2), 268–274. https://doi.org/10.1111/j.1365-2753.2010.01434.x Gjersing, L., Caplehorn, J. R. M., & Clausen, T. (2010). Cross-cultural adaptation of research instruments: Language, setting, time and statistical considerations. BMC Medical Research Methodology, 10, Article 13. https://doi.org/10.1186/1471-2288-10-13 Harkness, J., Van de Vijver, F. J., & Mohler, P. P. (Eds.). (2003). Cross-cultural survey methods. Wiley. Hambleton, R. K. (2005).Issues, designs, and technical guidelines for adapting tests into multiple languages and cultures. In R. K. Hambleton, P. F. Merenda, & C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 3–38). Mahwah, NJ: Lawrence Erlbaum Associates. Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic internet use. Cyber Psychology & Behavior, 10(2), 234–242. https://doi.org/10.1089/cpb.2006.9963 Kuss, D. J., & Griffiths, M. D. (2015). Internet addiction in psychologists: A study of its prevalence and impact on social functioning. International Journal of Social Psychiatry, 61(4), 307–313. https://doi.org/10.1177/0020764014567939 Cain, N., & Gradisar, M. (2010). Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Medicine, 11(8), 735–742. https://doi.org/10.1016/j.sleep.2010.02.006 Cheng, C., Li, A. Y., Zeng, H., & Hao, J. (2020). Screen time and sleep among young adults: Moderating effects of gender and chronotype. Health Psychology Open, 7(1), 1–8. https://doi.org/10.1177/2055102920904788 Ford, D. E., & Kamerow, D. B. (1989). Epidemiologic study of sleep disturbances and psychiatric disorders: An opportunity for prevention? JAMA, 262(11), 1479–1484. https://doi.org/10.1001/jama.1989.03430110069030 Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., & Riemann, D. (2011). Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders, 135(1–3), 10–19. https://doi.org/10.1016/j.jad.2011.01.011 Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventive medicine, 85, 36-41. Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23 Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 van Rooij, A. J., Ferguson, C. J., Van de Mheen, D., & Schoenmakers, T. M. (2014). Time to abandon internet addiction? Predicting problematic internet use without the internet. Computers in Human Behavior, 40, 195–200. https://doi.org/10.1016/j.chb.2014.08.024 Chang, S. P., Ford, D. E., Mead, L. A., Cooper-Patrick, L., & Klag, M. J. (2014). Insomnia in young men and subsequent depression: The Johns Hopkins Precursors Study. American Journal of Epidemiology, 146(2), 105–114. https://doi.org/10.1093/oxfordjournals.aje.a009245 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6692326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474258116,"identity":"e85b2a54-0189-4f7a-9b3d-053d25e6381f","order_by":0,"name":"Taha R. Adawi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYHACAyCyAbMkiFLPA9GSRrIWhsMkaLFnP7zxM0/BedkNB5gP3uZhsJYnbAtPWrE0j8Ft4w0H2JKteRjSDRsIOyzHQDrH4HbihgM8ZtI8DIcZCWvhf2P8O8fgHFAL/zeQFnvCWiRyzIC2HADZwgbSkkhYy41nZdZ/DJKNZx5mM7acY5CeTFALe3/y5psz/tjJ9h1vfnjjTYW1LUEtMMDYwAyiDJiJ1QDSAqFJ0DIKRsEoGAUjBgAACo43TK6mGPIAAAAASUVORK5CYII=","orcid":"","institution":"College of Education Qatar University, Doha, Qatar","correspondingAuthor":true,"prefix":"","firstName":"Taha","middleName":"R.","lastName":"Adawi","suffix":""},{"id":474258117,"identity":"39ffa78f-f143-49f9-8583-3f7b8920af88","order_by":1,"name":"Summayya Waseem","email":"","orcid":"","institution":"Lahore School of Behavioural Sciences The University of Lahore Sargodha, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Summayya","middleName":"","lastName":"Waseem","suffix":""}],"badges":[],"createdAt":"2025-05-18 14:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6692326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6692326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346941,"identity":"59ceb6f3-5545-40cd-b48f-ab0486063d92","added_by":"auto","created_at":"2025-06-25 02:15:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49645,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6692326/v1/2718759222a7949bf7c8ecd6.png"},{"id":85346943,"identity":"3db8efa5-7407-4d96-aab6-4a0a463b003a","added_by":"auto","created_at":"2025-06-25 02:16:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37424,"visible":true,"origin":"","legend":"\u003cp\u003eResult of path model analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6692326/v1/30a67fb4b911c41f0ad4ba78.png"},{"id":97896574,"identity":"5b6359c4-b5e7-44d7-a787-65cf94205006","added_by":"auto","created_at":"2025-12-10 15:36:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":731003,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6692326/v1/6b3c564a-a6bd-45a2-a8f3-d97753d3f9bd.pdf"}],"financialInterests":"","formattedTitle":"The Mediating Role of Compulsive Internet Use in the Relationship Between Depressive Symptoms and Sleep Quality Among Qatar University Students: A Path Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the past two decades, internet use has become essential to daily life. As the number of internet users continues to grow, health-related issues such as internet addiction have become widespread \u003csup\u003e[1]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eQatar's swift digital growth has notably increased in compulsive internet use (CIU) among its younger population. With one of the highest internet access rates in the world, almost all young Qataris have uninterrupted internet connectivity, mainly via smartphones \u003csup\u003e[2]\u003c/sup\u003e. This significant connectivity, combined with global trends of excessive internet use among teenagers, encourages compulsive online behavior.\u003c/p\u003e\n\u003cp\u003eQatar’s youth often use social media for communication, self-expression, and exploring their identities. However, this frequent engagement can lead to compulsive behaviors driven by the desire to stay perpetually connected online. Studies indicate that excessive internet use is associated with various adverse effects, including sleep problems and a higher likelihood of mental health concerns \u003csup\u003e[3]\u003c/sup\u003e\u003csup\u003e[4]\u003c/sup\u003e. Factors in the environment, such as family interactions and school environments, also influence this situation, underlining the complex interplay between behavioral habits and contextual factors \u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecent research in Qatar has revealed that a considerable number of adolescents are experiencing symptoms of CIU \u003csup\u003e[6]\u003c/sup\u003e \u003csup\u003e[7]\u003c/sup\u003e. One notable study conducted by Al-Harahsheh et al. (2023) indicated that many Qatari teenagers reported spending excessive time online, which has negatively affected various aspects of their daily lives and general well-being. The research conducted by Abdelmoneium et al. (2023) and Chemnad \u0026nbsp; et al. (2023) on parental perspectives in Qatar concerning adolescents’ excessive engagement with technology indicates that parents view the overuse of digital devices as an escalating issue, as 29.64% of adolescents exhibited signs of addiction. They report various adverse outcomes associated with this phenomenon, including social isolation, diminished family interactions, health complications such as sleep disturbances and eye strain, and a decline in academic performance \u003csup\u003e[8]\u003c/sup\u003e.\u0026nbsp;The number of internet-addicted adolescents in Qatar has gradually increased over time, with an addiction rate of 86.6%\u0026nbsp;\u003csup\u003e[9]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the use of internet, particularly through social media, has become a significant means for individuals to express themselves and explore their identities, especially among teenagers and young adults. However, this online participation is often linked to the quest for self-affirmation. People commonly turn to likes, comments, and shares as external measures of their self-worth, leading to a reliance on digital feedback for validation. This cycle of seeking approval can result in compulsive online behavior, where individuals feel an urgency to stay connected to sustain their perceived social importance. Over time, such dependence on digital validation can warp self-image, heighten sensitivity to online criticism, and contribute to mental health issues like anxiety, diminished self-esteem, and depressive symptoms \u003csup\u003e[10]\u003c/sup\u003e \u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSleep quality is an important precursor that may be linked to internet addiction. Due to the ubiquity and easy access to technological products, adolescents engage in various online activities to help them sleep. However, the likelihood of replacing online activities directly with sleep, thus shortening sleep duration, increases with the time adolescents spend online. In this vicious cycle, the risk of internet addiction among adolescents increases. Sleep problems are considered an indicator of internet addiction\u0026nbsp;\u003csup\u003e[12]\u003c/sup\u003e\u0026nbsp;\u003csup\u003e[13]\u003c/sup\u003e\u0026nbsp;\u003csup\u003e[14]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe phenomenon of CIU often involves prolonged screen time, particularly during late-night hours, which interferes with circadian rhythms, delays the onset of sleep, and decreases total sleep duration. These sleep issues further add to mood fluctuations, increased emotional sensitivity, and a heightened vulnerability to depressive states. Studies have consistently indicated that inadequate sleep quality is a key factor contributing to mental health challenges, especially depression. Evidence supports this mediating role. For instance, individuals with depressive symptoms may resort to online activities—such as social networking or gaming—for temporary relief. Although these online interactions may provide brief distraction, they ultimately worsen sleep issues and emotional discomfort over time, creating a negative cycle\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e. Consequently, CIU does not only stem from poor mental health or disrupted sleep; it actively mediates the relationship between sleep disturbances and depression.\u003c/p\u003e\n\u003cp\u003eAn increasing amount of research highlights the complex connections between sleep quality, CIU, and symptoms of depression. CIU, which is characterized by the inability to manage online activities despite negative consequences, has become a crucial element affecting sleep and mental health outcomes. Research shows that CIU can mediate the relationship between poor sleep quality and symptoms of depression, intensifying the negative effects that sleep disturbances have on mental well-being\u0026nbsp;\u003csup\u003e[17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResearch supporting this mediating role posits that individuals with poor sleep may seek online activities as a means of distraction or stress relief, but this coping mechanism often becomes compulsive\u0026nbsp;\u003csup\u003e[18]\u003c/sup\u003e\u0026nbsp;\u003csup\u003e[19]\u003c/sup\u003e\u0026nbsp;\u003csup\u003e[20]\u003c/sup\u003e. CIU not only contribute to reduced sleep quality but also intensified depressive symptoms, creating a cycle where individuals oscillate between internet use and sleep deprivation. In this way, CIU amplifies the effects of poor sleep on mental health, as individuals increasingly turn to internet use for relief, yet experience worsened mood and sleep outcomes.\u003c/p\u003e\n\u003cp\u003eApart from the negative impact of internet addiction on the risk of depression, many studies have reported significant associations between internet addiction and sleep problems\u0026nbsp;\u003csup\u003e[21]\u003c/sup\u003e, which may be mediated by depressive symptoms\u0026nbsp;\u003csup\u003e[22]\u003c/sup\u003e.\u0026nbsp;Bhandari et al. (2017) found that 35.4% of students experienced poor sleep quality, another 35.4% exhibited signs of internet addiction, and 21.2% reported symptoms of depression. The study identified significant interrelations between sleep quality and internet addiction, with each construct partially mediating the effects of the other on depressive symptoms. This underscores the intricate interplay between these factors and their collective impact on the mental health of students.\u003c/p\u003e\n\u003cp\u003eResearch indicates that those with CIU frequently face irregular sleep patterns, such as trouble falling asleep, waking up often, and shorter sleep duration. These sleep issues further intensify depressive symptoms, leading to a harmful cycle. Additionally, CIU plays a direct role in increasing depressive symptoms through factors like social isolation, decreased physical activity, and engagement with negative online material\u0026nbsp;\u003csup\u003e[23]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSun et al. (2024)\u0026nbsp;\u003csup\u003e[24]\u003c/sup\u003e\u0026nbsp;conducted a study to investigate the prevalence of internet addiction, depression, and sleep quality, as well as the various relationships among these variables. The results indicate increasing rates of internet addiction and depression, along with decreasing levels of sleep quality.\u003c/p\u003e\n\u003cp\u003eA study by Huang et al. (2023)\u0026nbsp;\u003csup\u003e[25]\u003c/sup\u003e\u0026nbsp;on first-year university students in Taiwan, found that the prevalence of internet addiction among first-year university students was 21.2%. The indirect effects revealed that depression fully mediated the relationship between sleep quality and internet addiction.\u003c/p\u003e\n\u003cp\u003eFor example, research conducted by Wang et al. (2020)\u0026nbsp;\u003csup\u003e[26]\u003c/sup\u003e\u0026nbsp;revealed that CIU substantially mediated the association between poor sleep quality and symptoms of depression among Chinese university students. Similarly, findings from Tan et al. (2016)\u0026nbsp;\u003csup\u003e[27]\u003c/sup\u003e\u0026nbsp;indicated that problematic internet use and depression partially mediated sleep disturbances in young Chinese individuals. Mediation analysis conducted by Zou et al. (2019)\u0026nbsp;\u003csup\u003e[28]\u003c/sup\u003e\u0026nbsp;demonstrated that sleep quality partially mediated the link between problematic mobile phone use and depressive symptoms, highlighting how sleep issues intensify the effects of excessive internet use on mental health. Additionally, the addictive quality of internet use accumulates effects on both sleep and emotional health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA meta-analysis by Demirci et al. (2015)\u0026nbsp;\u003csup\u003e[29]\u003c/sup\u003e\u0026nbsp;showed that night-time CIU was linked to increased sleep latency and higher levels of depression across different age groups. This reinforces the understanding that CIU exacerbates the effects of poor sleep on depression by disrupting restorative processes and perpetuating maladaptive behavior patterns.\u003c/p\u003e\n\u003cp\u003eRufino et al. (2024)\u0026nbsp;\u003csup\u003e[30]\u003c/sup\u003e\u0026nbsp;found that social media addiction and poor sleep quality mediate the link between time spent on social media and depressive symptoms in Brazilian university students. In a longitudinal study conducted on university students, internet addiction was identified as a significant risk factor for depression\u0026nbsp;\u003csup\u003e[31]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWang et al., (2025)\u0026nbsp;\u003csup\u003e[32]\u003c/sup\u003e\u0026nbsp;found that Social network site addiction in adolescents was positively linked to poor sleep quality, depression, and difficulty describing feelings. Depression partially mediated the link between addiction and sleep, while difficulty describing feelings strengthened the connection between addiction and depression.\u003c/p\u003e\n\u003cp\u003eA systematic review and meta-analysis conducted by Al-Khani et al. (2021)\u0026nbsp;\u003csup\u003e[33]\u003c/sup\u003e\u0026nbsp;on internet addiction in the Gulf countries indicated an overall prevalence rate of 33% based on the studies reviewed. The findings also revealed that females are more likely to be addicted to the internet compared to males.\u003c/p\u003e\n\u003cp\u003eIn summary, the role of compulsive internet use as a mediator in the connection between sleep and depression is well-established, with research indicating its harmful effects on both sleep quality and mental well-being. The interaction among compulsive internet use, sleep, and depressive symptoms forms a self-perpetuating cycle that amplifies the influence of sleep issues on depression, highlighting the necessity of tackling internet use patterns in programs designed to enhance sleep and mental health.\u003c/p\u003e\n\u003cp\u003eDespite increasing concerns about the effects of compulsive internet use (CIU) on mental health and sleep patterns, research investigating its specific influence on the interplay between sleep quality and depressive symptoms remains scarce, particularly among university students in the Gulf region. To date, the available literature addressing this issue within the Qatari population is limited, particularly in light of the country’s high internet penetration and distinctive sociocultural context. Conducting empirical studies is crucial for elucidating the extent to which CIU impacts psychological well-being. This research aims to bridge this knowledge gap by exploring the mediating role of compulsive internet use in the relationship between sleep quality and depressive symptoms among students at Qatar University. The theoretical model is shown in Fig. 1\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted with a total of 750 university students in Qatar, including 121 males and 629 females. The participants' ages ranged between 17 and 44 years, with a mean age of 22.46 years (\u003cem\u003eSD\u003c/em\u003e = 4.44). Students from both the humanities and applied sciences disciplines were included in the sample. The majority of respondents were unmarried, accounting for 72.9% of the total sample. The demographic distribution was reflective of the student population commonly observed in higher education settings across the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompulsive Internet Use Scale (CIUS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeveloped by Meerkerk and colleagues (2009)\u0026nbsp;\u003csup\u003e[34]\u003c/sup\u003e, the Compulsive Internet Use Scale (CIUS) is a 14-item self-report tool that assesses compulsive patterns of internet use. It evaluates core aspects such as preoccupation with online activities, withdrawal symptoms, loss of control, and use of the internet as a coping strategy. Items are scored using a 5-point Likert scale ranging from 0 (“Never”) to 4 (“Very Often”), with higher scores signifying greater compulsivity in internet use.\u003c/p\u003e\n\u003cp\u003eThe CIUS has shown excellent internal reliability, with a Cronbach’s alpha coefficient of 0.882. stability over time was confirmed through split-half reliability, yielding a Spearman-Brown coefficient of 0.851. Furthermore, removing any individual item did not significantly affect the overall consistency, as alpha values remained between 0.868 and 0.879. The instrument’s structure was validated through confirmatory factor analysis (CFA), which supported a single-factor model. Standardized regression weights were statistically significant, ranging from 0.441 to 0.789 (p \u0026lt; .01). Model fit indices further indicated a well-fitting model (CFI = 0.953, RMSEA = 0.059, SRMR = 0.044), confirming the scale’s robustness in measuring problematic internet behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSleep Quality Scale (SQS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sleep Quality Scale (SQS), introduced by Yi, Shin, and Shin (2006)\u0026nbsp;\u003csup\u003e[35]\u003c/sup\u003e, is a multidimensional 28-item instrument designed to evaluate overall sleep quality. It addresses various dimensions such as sleep disturbance, satisfaction with sleep, sleep efficiency, daytime dysfunction, and somnolence. Respondents rate items on a 4-point Likert scale, with higher scores reflecting poorer sleep quality.\u003c/p\u003e\n\u003cp\u003ePsychometric evaluation indicates the scale has strong internal consistency, demonstrated by a Cronbach’s alpha of 0.879. Its reliability is also supported by split-half testing, which yielded a Spearman-Brown coefficient of 0.760. CFA results validated the multidimensional structure, with standardized regression weights falling between 0.414 and 0.789 (p \u0026lt; .01). Model fit indicators were within acceptable thresholds (CFI = 0.928, RMSEA = 0.049, SRMR = 0.056), affirming the scale’s construct validity. The SQS has been widely used in both clinical and academic settings to assess sleep-related concerns comprehensively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Patient Health Questionnaire-9 (PHQ-9) is a brief self-assessment tool developed as part of the PHQ series. It consists of 9 items aligned with the diagnostic criteria for major depressive disorder from the DSM-IV. Respondents indicate how frequently they experienced symptoms over the past two weeks, using a 4-point Likert scale ranging from 0 (“Not at all”) to 3 (“Nearly every day”). Total scores range from 0 to 27, with higher scores indicating greater symptom severity. Severity categories include minimal, mild, moderate, moderately severe, and severe.\u003c/p\u003e\n\u003cp\u003eThe PHQ-9 has demonstrated solid reliability, with a Cronbach’s alpha of 0.818. Split-half reliability produced a Spearman-Brown coefficient of 0.738, further confirming its internal stability. Confirmatory factor analysis supported a unidimensional structure, with significant item loadings ranging from 0.475 to 0.644 (p \u0026lt; .01). The model showed good fit, as reflected in the indices (CFI = 0.958, RMSEA = 0.061, SRMR = 0.044). These findings affirm the PHQ-9 as a dependable instrument for detecting and monitoring depressive symptoms across diverse populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilized a cross-sectional design to explore the interrelationships among depressive symptoms, compulsive internet use (CIU), and sleep quality. Data were collected through an online self-administered questionnaire distributed across various faculties, focusing on students from both humanities and applied sciences. Participation was entirely voluntary and anonymous, with informed consent obtained from all participants prior to their involvement. A university-wide announcement was made to invite students from Qatar University to take part, and data collection was carried out via Google Forms over a one-month. All procedures were approved by Qatar University’s institutional review board (QUIRB)\u003c/p\u003e\n\u003cp\u003eThe SQS and CIUS were translated from English into Arabic for use in the present study. The translation process followed established guidelines for cross-cultural adaptation \u003csup\u003e[36]\u003c/sup\u003e \u003csup\u003e[37]\u003c/sup\u003e \u003csup\u003e[38]\u003c/sup\u003e \u003csup\u003e[39]\u003c/sup\u003e \u003csup\u003e[40]\u003c/sup\u003e\u0026nbsp;incorporating the committee translation method and forward and back-translation, and pretesting to ensure both linguistic and conceptual equivalence.. Initially, two independent translations were produced—one by the first author (TRA) and another by a bilingual research assistant who had no prior exposure to the questionnaires. These two versions were then reviewed and compared with the original English version to create a unified Arabic draft. In the second phase, a bilingual researcher unfamiliar with the original questionnaires conducted a back-translation of the Arabic items into English. A British scholar with a strong background in psychology evaluated the back-translated version, and any feedback provided was incorporated into the Arabic version. In the final phase, the first author revised some of the items to enhance readability for university students. These revisions were then compared with the original English version to produce the final Arabic version of the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was conducted using IBM SPSS version v24.0 and AMOS v24 for Path Analysis. Descriptive statistics were calculated to determine the mean, standard deviation, skewness, and kurtosis of each variable. Pearson correlation analysis was performed to assess the bivariate relationships among depressive symptoms, CIU, and sleep quality. The data that support the findings of this study are available upon reasonable request first author.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDemographic characteristics of the study sample (N = 750)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\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: 31px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e121 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e629 (83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSpecialization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHumanities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e566 (75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eApplied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e184 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e547 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e203 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1 depicted that the sample comprised 750 university students, with a majority being female (83.9%) and 16.1% male. Most students (75.5%) were enrolled in humanities programs, while 24.5% were from applied disciplines. In terms of marital status, 72.9% were single and 27.1% were married.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eDescriptive statistics for research variables and correlation coefficient between them\u0026nbsp;(\u003cem\u003eN\u003c/em\u003e = 750)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eDepressive Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCompulsive\u0026nbsp;\u003cbr\u003e\u0026nbsp;Internet Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.314\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e40.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.224\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.321\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e66.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e **= \u003cem\u003ep\u003c/em\u003e \u0026lt; .01.\u003c/p\u003e\n\u003cp\u003eTable 2 shows the mean score for compulsive internet use was 40.96 (SD = 11.61), depressive symptoms averaged 19.17 (SD = 5.53), and sleep quality scored a mean of 66.02 (SD = 14.42). All variables showed positive and significant correlations. Compulsive internet use correlated positively with both depressive symptoms (\u003cem\u003er\u003c/em\u003e = .314, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) and poor sleep quality (\u003cem\u003er\u003c/em\u003e = .321, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Similarly, depressive symptoms were significantly associated with poor sleep quality (\u003cem\u003er\u003c/em\u003e = .224, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01).\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrated the statistical model which demonstrates a strong fit with the data, supporting the robustness of these pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003ePath coefficients for direct, indirect and total relationships (\u003cem\u003eN\u003c/em\u003e = 750)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOutcome Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized \u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnstandardized B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\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\u003e\n \u003cp\u003eDepressive Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCompulsive Internet Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCompulsive Internet Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepressive Symptoms (Direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepressive Symptoms (Indirect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepressive Symptoms (Total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 indicate the path analysis findings offer valuable insight into the interrelationships among depressive symptoms, compulsive internet use, and sleep quality in a university student population. The direct effects demonstrate that depressive symptoms significantly predict both compulsive internet use and poor sleep quality. Specifically, the standardized path coefficient from depressive symptoms to compulsive internet use (\u0026beta; = 0.314, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) indicates that individuals with elevated depressive symptoms are more likely to engage in compulsive internet behaviors. This aligns with previous research suggesting that individuals experiencing emotional distress may turn to the internet for distraction or emotional relief, potentially reinforcing maladaptive use patterns. The path from compulsive internet use to sleep quality (\u0026beta; = 0.137, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) further supports its role as a significant mediator. This relationship implies that compulsive internet engagement contributes to disrupted sleep, possibly due to extended screen exposure before bedtime, interference with circadian rhythms, or psychological stimulation from digital content, which delays sleep onset and impairs restfulness. Moreover, the direct path between depressive symptoms and sleep quality (\u0026beta; = 0.278, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) reflects a strong negative association, suggesting that depressive symptoms independently reduce sleep quality. This may occur through cognitive mechanisms such as rumination, heightened physiological arousal, or emotional dysregulation that interferes with the ability to fall or stay asleep.\u003c/p\u003e\n\u003cp\u003eThe presence of an indirect effect from depressive symptoms to sleep quality via compulsive internet use (\u0026beta; = 0.043, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) confirms a partial mediation effect. While the direct association remains significant, the mediating role of compulsive internet use indicates that it exacerbates the impact of depressive symptoms on sleep disturbances. The total effect (\u0026beta; = 0.321, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) reflects the combined influence of both direct and indirect pathways, underscoring the substantial overall impact of depressive symptoms on sleep quality. These findings reveal a nuanced, interdependent relationship between the constructs, suggesting that interventions addressing compulsive internet use may help buffer the negative influence of depressive symptoms on sleep.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u0026nbsp; The goodness of Fit Indices for the Models\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"937\" height=\"88\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows that the model exhibited strong fit indices (CFI = 1.00, RMSEA = 0.049, SRMR = 0.00), providing robust support for the hypothesized mediation model. Collectively, the results highlight the need for integrated approaches targeting both emotional distress and problematic internet behaviors to promote better sleep outcomes among university students.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from the path analysis offer meaningful insights into the interrelations between depressive symptoms, compulsive internet use (CIU), and sleep quality among university students. The direct effects reveal that depressive symptoms significantly influence both CIU and sleep quality. Specifically, the standardized path coefficient for the relationship between depressive symptoms and CIU (β = 0.314, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) indicates that individuals with higher levels of depressive symptoms are more prone to engaging in compulsive internet use. This is consistent with prior research suggesting that individuals with depressive tendencies often rely on internet use as an avoidant coping mechanism, which may reinforce problematic online behaviors \u003csup\u003e[41]\u003c/sup\u003e \u003csup\u003e[42]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the association between CIU and sleep quality (β = 0.137, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) supports the mediating role of CIU. Excessive internet use has been shown to impair sleep through several pathways, including prolonged screen time that suppresses melatonin production and disrupts circadian rhythms, as well as increased cognitive and emotional stimulation from online interactions, which may delay sleep onset \u003csup\u003e[43]\u003c/sup\u003e \u003csup\u003e[44]\u003c/sup\u003e.\u0026nbsp;These effects collectively contribute to sleep disturbances, which are particularly concerning among student populations already vulnerable to mental health challenges.\u003c/p\u003e\n\u003cp\u003eThe direct relationship between depressive symptoms and sleep quality (β = 0.278, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) underscores the independent role of depression in sleep impairment. Symptoms such as persistent rumination, heightened physiological arousal, and emotional dysregulation are known to interfere with the ability to initiate and maintain restful sleep \u003csup\u003e[45]\u003c/sup\u003e \u003csup\u003e[46]\u003c/sup\u003e. Importantly, the indirect effect of depressive symptoms on sleep quality through CIU (β = 0.043, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) demonstrates that CIU partially mediates this association, suggesting that while depression alone can impair sleep, the impact is exacerbated when coupled with problematic internet use. The total effect of depressive symptoms on sleep quality (β = 0.321, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) illustrates the cumulative influence of both direct and indirect pathways. These results point to a complex and interconnected relationship between emotional distress, digital behavior, and physiological well-being. The model’s excellent fit indices (CFI = 1.00, RMSEA = 0.049, SRMR = 0.00) lend strong empirical support to these hypothesized relationships and reinforce the need for integrated interventions that address both depressive symptoms and internet use patterns to enhance sleep quality among university students [41] [43].\u003c/p\u003e\n\u003cp\u003eExcessive screen time, particularly during late hours, exposes individuals to blue light, which interferes with the natural sleep-wake cycle and reduces both the duration and quality of sleep. Additionally, the emotional engagement elicited by online activities—such as social media browsing or gaming—may increase psychological arousal, thereby delaying sleep onset and contributing to fragmented rest \u003csup\u003e[47]\u003c/sup\u003e.\u0026nbsp;This ongoing interference with healthy sleep routines can severely affect emotional regulation, making individuals more susceptible to mood disturbances and depressive episodes.\u003c/p\u003e\n\u003cp\u003eThe connection between sleep quality and depression is well-established. Sleep disruption impairs cognitive and emotional processing, increasing the likelihood of experiencing symptoms like sadness, hopelessness, and anxiety [46]. This relationship is bidirectional: depression often leads to sleep problems, and poor sleep in turn intensifies depressive symptoms. Such a feedback loop indicates that addressing one component—either sleep or mood—can potentially lead to improvements in the other.\u003c/p\u003e\n\u003cp\u003eBeyond sleep disturbances, CIU contributes to depression through behavioral and psychosocial mechanisms. It may lead to social withdrawal, reduced physical activity, academic or occupational neglect, and lower self-worth. However, empirical evidence suggests that CIU’s effect on depressive symptoms is largely mediated through its disruption of sleep. Thus, the pathway from CIU to depression frequently operates via its impact on sleep quality rather than through direct psychological effects [29].\u003c/p\u003e\n\u003cp\u003eThe interaction among depressive symptoms, CIU, and sleep quality is cyclical in nature. For instance, a student experiencing emotional distress may turn to late-night internet use as a form of relief. While this may offer temporary distraction, it disrupts the sleep cycle and results in increased daytime fatigue and emotional instability, further worsening the depressive state. Without proper intervention, this cycle can become self-perpetuating.\u003c/p\u003e\n\u003cp\u003eOverall, the study highlights the importance of early identification and integrated treatment strategies that address both emotional and behavioral contributors to sleep disturbances. Interventions focusing on reducing compulsive digital behavior, improving sleep hygiene, and managing depressive symptoms may offer a more holistic approach to improving student well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the study provides valuable insights into the relationships between depressive symptoms, compulsive internet use (CIU), and sleep quality, several limitations should be acknowledged. First, the use of a cross-sectional research design restricts the ability to draw causal inferences. Although significant associations were observed, the directionality of these relationships remains uncertain. Future longitudinal studies are needed to explore temporal and causal links among these variables \u003csup\u003e[48]\u003c/sup\u003e. Second, the data were collected through self-report measures, which may be subject to biases such as social desirability and recall inaccuracies. These biases could potentially influence the validity of the responses and the overall reliability of the findings \u003csup\u003e[49]\u003c/sup\u003e. Third, the sample was predominantly female (83.9%), limiting the generalizability of the results to male students or more gender-balanced populations. Prior research suggests that gender may influence patterns of internet use and psychological responses, highlighting the need for gender-specific analyses in future investigations \u003csup\u003e[50]\u003c/sup\u003e.\u0026nbsp;Fourth, the study was conducted within the cultural and educational context of Qatar, which may have unique sociocultural dynamics affecting both CIU and mental health. Therefore, the findings may not be fully applicable to students in other countries or cultural settings, and caution should be taken when attempting to generalize the results. Lastly, the model examined in this study included only CIU as a mediating variable. Other potentially influential factors—such as academic stress, physical activity, and perceived social support—were not included but may play important roles in the relationship between depressive symptoms and sleep quality. Incorporating these variables in future models may offer a more comprehensive understanding of the pathways linking emotional distress and sleep disturbances \u003csup\u003e[51]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTaken together, these limitations underscore the need for more diverse, longitudinal, and culturally sensitive research to further clarify the mechanisms through which psychological and behavioral factors influence sleep health among university students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves human participants. Ethical procedures followed the Declaration of Helsinki and were approved by the Institutional Review Board (IRB) at Qatar University [QU-IRB 1902-E/23]. Participants were informed about the purpose and scope of the study and were assured that their participation was entirely voluntary and that their responses would remain confidential. Informed consent was obtained in writing prior to participation. Each participant received a survey packet containing three instruments along with an information sheet detailing the study procedures and their rights as participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concept and study design were formed by T.R.T. Data acquisition was conducted T.R.T. Data analysis and explanation was conducted by TRT and S.W. Drafting of the manuscript and figures was contributed TRT and S.W.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of general artificial intelligence and AI-supported writing tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors employed \"ChatGPT,\" and \" Grammarly \" to help with language and readability and cite pertinent literature throughout the development of this paper. The writers checked and corrected the material as necessary following the use of this instrument or service, therefore assuming all responsibility for the publication's content\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKuss, D. J., Griffiths, M. D., Karila, L., \u0026amp; Billieux, J. (2014). Internet addiction: a systematic review of epidemiological research for the last decade. Current pharmaceutical design, 20(25), 4026–4052. https://doi.org/10.2174/13816128113199990617 \u003c/li\u003e\n \u003cli\u003eStatista. (2023). Internet penetration rate in Qatar 2023. https://www.statista.com/map/asia/qatar/internet\u003c/li\u003e\n \u003cli\u003eAziz, M., Chemnad, K., Al-Harahsheh, S., Abdelmoneium, A. O., Baghdady, A., \u0026amp; Ali, R. (2024a). Depression, stress, and anxiety versus internet addiction in early and middle adolescent groups: The mediating roles of family and school environments. BMC Psychology, 12(1), 184. https://doi.org/10.1186/s40359-024-01659-z\u003c/li\u003e\n \u003cli\u003eAziz, M., Chemnad, K., Al-Harahsheh, S., Abdelmoneium, A. O., Baghdady, A., Hassan, D. A., \u0026amp; Ali, R. (2024b). The influence of adolescents’ essential and non-essential use of technology and Internet addiction on their physical and mental fatigues. Scientific Reports, 14(1), 1745. https://doi.org/10.1038/s41598-024-51655-x\u003c/li\u003e\n \u003cli\u003eChemnad, K., Aziz, M., Abdelmoneium, A. O., Al-Harahsheh, S., Baghdady, A., Al Motawaa, F. Y., ... \u0026amp; Ali, R. (2023). Adolescents’ internet addiction: Does it all begin with their environment? Child and Adolescent Psychiatry and Mental Health, 17(1), 87. https://doi.org/10.1186/s13034-023-00626-7\u003c/li\u003e\n \u003cli\u003eWise (2021). Technology Overuse Amongst Adolescents in Qatar. https://www.wise-qatar.org/technology-overuse-amongst-adolescents-in-qatar/ \u003c/li\u003e\n \u003cli\u003eAjlouni, A., \u0026amp; Rawadieh, S. (2022). Technophobia and technophilia among undergraduates: Cross-national research in Jordan, Qatar, and Egypt. Journal of Social Studies Education Research, 13(4), 24-55.\u003c/li\u003e\n \u003cli\u003eAbdelmoneium, A. O., Al Fara, H., Motawaa, F., Al Sultan, A., Al-Harahsheh, S., \u0026amp; Baghdady, A. (2023). Parental perspectives on adolescents’ excessive use of technology in Qatar: challenges and coping strategies. Doha International Family Institute Journal, 2023(2), 3-13.\u003c/li\u003e\n \u003cli\u003eKhan, H. U., \u0026amp; Awan, M. A. (2017). Possible factors affecting internet addiction: A case study of higher education students of Qatar. International Journal of Business Information Systems, 26(2), 199-218\u003c/li\u003e\n \u003cli\u003ePopat, A., \u0026amp; Tarrant, C. (2023). Exploring adolescents' perspectives on social media and mental health and well-being - A qualitative literature review. Clinical child psychology and psychiatry, 28(1), 323–337. https://doi.org/10.1177/13591045221092884\u003c/li\u003e\n \u003cli\u003eDigennaro, S., \u0026amp; Iannaccone, A. (2025). Imagining Another Self: The Use of Social Media Among Preadolescents and Its Body-Related Consequences. An Exploratory Study. SAGE Open, 15(1). https://doi.org/10.1177/21582440251321364\u003c/li\u003e\n \u003cli\u003eChen, Y. L., \u0026amp; Gau, S. S. F. (2016). Sleep problems and internet addiction among children and adolescents: a longitudinal study. Journal of sleep research, 25(4), 458-465.\u003c/li\u003e\n \u003cli\u003eTavernier, R., \u0026amp; Willoughby, T. (2014). Sleep problems: predictor or outcome of media use among emerging adults at university?. Journal of sleep research, 23(4), 389–396. https://doi.org/10.1111/jsr.12132 \u003c/li\u003e\n \u003cli\u003eYounes, F., Halawi, G., Jabbour, H., El Osta, N., Karam, L., Hajj, A., \u0026amp; Rabbaa Khabbaz, L. (2016). Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study. PloS one, 11(9), e0161126. https://doi.org/10.1371/journal.pone.0161126 \u003c/li\u003e\n \u003cli\u003eCheng, C., Lau, Y. C., \u0026amp; Chan, L. (2021). The mediating role of internet addiction between sleep quality and depressive symptoms among adolescents. Journal of Behavioral Addictions, 10(2), 287–295. https://doi.org/10.1556/2006.2021.00027\u003c/li\u003e\n \u003cli\u003eAlvaro, P. K., Roberts, R. M., \u0026amp; Harris, J. K. (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059–1068. https://doi.org/10.5665/sleep.2810\u003c/li\u003e\n \u003cli\u003eMa, Y., Li, J., Zhang, M., Zuo, T., Kong, L., \u0026amp; Yang, Y. (2024). Relationship between social anxiety and sleep quality in depressed adolescents: The mediating role of internet addiction. Frontiers in Psychiatry, 15, 1416130. https://doi.org/10.3389/fpsyt.2024.1416130\u003c/li\u003e\n \u003cli\u003eStanković, M., Nešić, M., Čičević, S., \u0026amp; Shi, Z. (2021). Association of smartphone use with depression, anxiety, stress, sleep quality, and internet addiction. Empirical evidence from a smartphone application. Personality and individual differences, 168, 110342. https://doi.org/10.1016/j.paid.2020.110342 \u003c/li\u003e\n \u003cli\u003eYu, D. J., Wing, Y. K., Li, T. M. H., \u0026amp; Chan, N. Y. (2024). The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review. Current psychiatry reports, 26(3), 104–119. https://doi.org/10.1007/s11920-024-01481-9\u003c/li\u003e\n \u003cli\u003ePirdehghan, A., Khezmeh, E., \u0026amp; Panahi, S. (2021). Social Media Use and Sleep Disturbance among Adolescents: A Cross-Sectional Study. Iranian journal of psychiatry, 16(2), 137–145. https://doi.org/10.18502/ijps.v16i2.5814\u003c/li\u003e\n \u003cli\u003eAlimoradi, Z., Lin, C. Y., Broström, A., Bülow, P. H., Bajalan, Z., Griffiths, M. D., Ohayon, M. M., \u0026amp; Pakpour, A. H. (2019). Internet addiction and sleep problems: A systematic review and meta-analysis. Sleep medicine reviews, 47, 51–61. https://doi.org/10.1016/j.smrv.2019.06.004\u003c/li\u003e\n \u003cli\u003eBhandari, P. M., Neupane, D., Rijal, S., Thapa, K., Mishra, S. R., \u0026amp; Poudyal, A. K. (2017). Sleep quality, internet addiction and depressive symptoms among undergraduate students in Nepal. BMC psychiatry, 17(1), 106. https://doi.org/10.1186/s12888-017-1275-5\u003c/li\u003e\n \u003cli\u003eLi, T., Xie, Y., Tao, S., Yang, Y., Xu, H., Zou, L., ... \u0026amp; Wu, X. (2020). Chronotype, sleep, and depressive symptoms among Chinese college students: A cross-sectional study. Frontiers in Neurology, 11, 592825. https://doi.org/10.3389/fneur.2020.592825\u003c/li\u003e\n \u003cli\u003eSun, H. L., Chen, P., Zhang, Q., Si, T. L., Li, Y. Z., Zhu, H. Y., Zhang, E., Chen, M., Zhang, J., Su, Z., Cheung, T., Ungvari, G. S., Jackson, T., Xiang, Y. T., \u0026amp; Xiang, M. (2024). Prevalence and network analysis of internet addiction, depression and their associations with sleep quality among commercial airline pilots: A national survey in China. Journal of affective disorders, 356, 597–603. https://doi.org/10.1016/j.jad.2024.03.022\u003c/li\u003e\n \u003cli\u003eHuang, I. L., Liu, C. Y., \u0026amp; Chung, M. H. (2023). Sleep quality and internet addiction among junior college students; The mediating role of depression: A cross-sectional study. Archives of psychiatric nursing, 46, 1–7. https://doi.org/10.1016/j.apnu.2023.06.011\u003c/li\u003e\n \u003cli\u003eWang, W., Wang, Y., Zhang, Y., Zhang, Z., Liu, H., \u0026amp; Yang, J. (2020). Problematic internet use mediates the association between sleep quality and depressive symptoms among Chinese college students. Current Psychology, 39, 1878–1885. https://doi.org/10.1007/s12144-018-9871-8\u003c/li\u003e\n \u003cli\u003eTan, Y., Chen, Y., Lu, Y., \u0026amp; Li, L. (2016). Exploring Associations between Problematic Internet Use, Depressive Symptoms and Sleep Disturbance among Southern Chinese Adolescents. International journal of environmental research and public health, 13(3), 313. https://doi.org/10.3390/ijerph13030313\u003c/li\u003e\n \u003cli\u003eZou, L., Wu, X., Tao, S., Xu, H., Xie, Y., Yang, Y., \u0026amp; Tao, F. (2019). Mediating effect of sleep quality on the relationship between problematic mobile phone use and depressive symptoms in college students. Frontiers in Psychiatry, 10, 822. https://doi.org/10.3389/fpsyt.2019.00822\u003c/li\u003e\n \u003cli\u003eDemirci, K., Akgönül, M., \u0026amp; Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4(2), 85–92. https://doi.org/10.1556/2006.4.2015.010\u003c/li\u003e\n \u003cli\u003eRufino, J. V., Rodrigues, R., Mesas, A. E., \u0026amp; Guidoni, C. M. (2024). O papel mediador da dependência de mídia social e da qualidade do sono na associação entre tempo de uso de mídia social e sintomas depressivos em universitários [The mediating role of social media addiction and sleep quality in the association between social media usage and depressive symptoms in university students]. Cadernos de saude publica, 40(5), e00097423. https://doi.org/10.1590/0102-311XPT097423\u003c/li\u003e\n \u003cli\u003eYang, X., Guo, W. J., Tao, Y. J., Meng, Y. J., Wang, H. Y., Li, X. J., Zhang, Y. M., Zeng, J. K., Tang, W. J., Wang, Q., Deng, W., Zhao, L. S., Ma, X. H., Li, M. L., Xu, J. J., Li, J., Liu, Y. S., Tang, Z., Du, X. D., Hao, W., … Li, T. (2022). A bidirectional association between internet addiction and depression: A large-sample longitudinal study among Chinese university students. Journal of affective disorders, 299, 416–424. https://doi.org/10.1016/j.jad.2021.12.013\u003c/li\u003e\n \u003cli\u003eWang, J., Wang, N., Liu, P., \u0026amp; Liu, Y. (2025). Social network site addiction, sleep quality, depression and adolescent difficulty describing feelings: a moderated mediation model. BMC psychology, 13(1), 57. https://doi.org/10.1186/s40359-025-02372-1\u003c/li\u003e\n \u003cli\u003eAl-Khani, A. M., Saquib, J., Rajab, A. M., Khalifa, M. A., Almazrou, A., \u0026amp; Saquib, N. (2021). Internet addiction in Gulf countries: A systematic review and meta-analysis. Journal of behavioral addictions, 10(3), 601–610. https://doi.org/10.1556/2006.2021.00057 \u003c/li\u003e\n \u003cli\u003eMeerkerk, G. J., Van Den Eijnden, R. J., Vermulst, A. A., \u0026amp; Garretsen, H. F. (2009). The compulsive internet use scale (CIUS): some psychometric properties. Cyberpsychology \u0026amp; behavior, 12(1), 1-6.\u003c/li\u003e\n \u003cli\u003eYi, H., Shin, K., \u0026amp; Shin, C. (2006). Development of the sleep quality scale. Journal of sleep research, 15(3), 309-316.\u003c/li\u003e\n \u003cli\u003eBeaton, D. E., Bombardier, C., Guillemin, F., \u0026amp; Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014 \u003c/li\u003e\n \u003cli\u003eSousa, V. D., \u0026amp; Rojjanasrirat, W. (2011). Translation, adaptation and validation of instruments or scales for use in cross-cultural health care research: A clear and user-friendly guideline. Journal of Evaluation in Clinical Practice, 17(2), 268–274. https://doi.org/10.1111/j.1365-2753.2010.01434.x \u003c/li\u003e\n \u003cli\u003eGjersing, L., Caplehorn, J. R. M., \u0026amp; Clausen, T. (2010). Cross-cultural adaptation of research instruments: Language, setting, time and statistical considerations. BMC Medical Research Methodology, 10, Article 13. https://doi.org/10.1186/1471-2288-10-13 \u003c/li\u003e\n \u003cli\u003eHarkness, J., Van de Vijver, F. J., \u0026amp; Mohler, P. P. (Eds.). (2003). Cross-cultural survey methods. Wiley.\u003c/li\u003e\n \u003cli\u003eHambleton, R. K. (2005).Issues, designs, and technical guidelines for adapting tests into multiple languages and cultures. In R. K. Hambleton, P. F. Merenda, \u0026amp; C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 3–38). Mahwah, NJ: Lawrence Erlbaum Associates.\u003c/li\u003e\n \u003cli\u003eCaplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic internet use. Cyber Psychology \u0026amp; Behavior, 10(2), 234–242. https://doi.org/10.1089/cpb.2006.9963\u003c/li\u003e\n \u003cli\u003eKuss, D. J., \u0026amp; Griffiths, M. D. (2015). Internet addiction in psychologists: A study of its prevalence and impact on social functioning. International Journal of Social Psychiatry, 61(4), 307–313. https://doi.org/10.1177/0020764014567939\u003c/li\u003e\n \u003cli\u003eCain, N., \u0026amp; Gradisar, M. (2010). Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Medicine, 11(8), 735–742. https://doi.org/10.1016/j.sleep.2010.02.006\u003c/li\u003e\n \u003cli\u003eCheng, C., Li, A. Y., Zeng, H., \u0026amp; Hao, J. (2020). Screen time and sleep among young adults: Moderating effects of gender and chronotype. Health Psychology Open, 7(1), 1–8. https://doi.org/10.1177/2055102920904788\u003c/li\u003e\n \u003cli\u003eFord, D. E., \u0026amp; Kamerow, D. B. (1989). Epidemiologic study of sleep disturbances and psychiatric disorders: An opportunity for prevention? JAMA, 262(11), 1479–1484. https://doi.org/10.1001/jama.1989.03430110069030\u003c/li\u003e\n \u003cli\u003eBaglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., \u0026amp; Riemann, D. (2011). Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders, 135(1–3), 10–19. https://doi.org/10.1016/j.jad.2011.01.011\u003c/li\u003e\n \u003cli\u003eLevenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., \u0026amp; Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventive medicine, 85, 36-41.\u003c/li\u003e\n \u003cli\u003eMaxwell, S. E., \u0026amp; Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23\u003c/li\u003e\n \u003cli\u003ePodsakoff, P. M., MacKenzie, S. B., Lee, J. Y., \u0026amp; Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879\u003c/li\u003e\n \u003cli\u003evan Rooij, A. J., Ferguson, C. J., Van de Mheen, D., \u0026amp; Schoenmakers, T. M. (2014). Time to abandon internet addiction? Predicting problematic internet use without the internet. Computers in Human Behavior, 40, 195–200. https://doi.org/10.1016/j.chb.2014.08.024\u003c/li\u003e\n \u003cli\u003eChang, S. P., Ford, D. E., Mead, L. A., Cooper-Patrick, L., \u0026amp; Klag, M. J. (2014). Insomnia in young men and subsequent depression: The Johns Hopkins Precursors Study. American Journal of Epidemiology, 146(2), 105–114. https://doi.org/10.1093/oxfordjournals.aje.a009245\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"compulsive internet use, depressive symptoms, sleep quality","lastPublishedDoi":"10.21203/rs.3.rs-6692326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6692326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Sleep quality and mental health play pivotal role in overall well-being, especially for university students who frequently encounter academic, social, and personal challenges. This study aims to explore how compulsive internet use mediates the relationship between depressive symptoms and sleep quality among Qatar University students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This study utilized a cross-sectional design to examine the relationship between depressive symptoms, compulsive internet use, and sleep quality among Qatar University students. 750 participants were recruited through convenient sampling, and data were collected via an online survey. Validated psychological scales were used to assess depressive symptoms, compulsive internet use, and sleep quality. The collected date were analyzed using descriptive tests, Pearson correlation, and path analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Findings revealed that compulsive internet use significantly impacts both sleep quality and depressive symptoms. Furthermore, the results highlighted its critical mediating role in the relationship between sleep quality and depressive symptoms, underscoring its influence on overall well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: These findings underscore the pivotal role of compulsive internet use as a mediator, highlighting the necessity of targeted interventions to regulate internet use behaviors, enhance sleep quality, and mitigate depressive symptoms among university students.\u003c/p\u003e","manuscriptTitle":"The Mediating Role of Compulsive Internet Use in the Relationship Between Depressive Symptoms and Sleep Quality Among Qatar University Students: A Path Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:15:54","doi":"10.21203/rs.3.rs-6692326/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9be0b7b-6b0c-4c37-916c-6c5a3c2ae81e","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T08:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6692326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6692326","identity":"rs-6692326","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}