Online Allure and Offline Anxiety: Exploring the Mental Health Implications of Nighttime Social Media Use, Assessment Anxiety, Sleep Disturbance, Self-Esteem and Academic Performance of University Students in Ghana

Online Allure and Offline Anxiety: Exploring the Mental Health Implications of Nighttime Social Media Use, Assessment Anxiety, Sleep Disturbance, Self-Esteem and Academic Performance of University Students in Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Online Allure and Offline Anxiety: Exploring the Mental Health Implications of Nighttime Social Media Use, Assessment Anxiety, Sleep Disturbance, Self-Esteem and Academic Performance of University Students in Ghana Simon Ntumi, Divine Agbovor, Lawrence Larbi Sakyi, Vincent Worlanyo Dogbe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6872928/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract With the rising prevalence of social media use among university students, particularly during nighttime hours, concerns have grown regarding its potential effects on mental health and academic success, warranting a comprehensive investigation into these associations. Our study investigated the mental health implications of nighttime social media use among university students in Ghana, focusing on its impact on sleep disturbance, assessment anxiety, self-esteem, and academic performance. Employing a quantitative, correlational cross-sectional survey design, data were collected from 1,250 undergraduate students across five major public universities using validated psychometric instruments, including the Pittsburgh Sleep Quality Index (PSQI), DASS-21, Rosenberg Self-Esteem Scale, and GPA records. Pearson correlation analysis revealed moderate and significant associations between nighttime social media use and sleep disturbances (r = .48 , p < .001), assessment anxiety (r = .42 , p < .001), and lower academic performance (r = –.28 , p < .001). Mediation analysis using PROCESS Model 4 indicated that sleep disturbance significantly mediated the relationship between nighttime social media use and academic performance (indirect effect = − 0.21, 95% CI [–0.32, − 0.13] , p < .001), with large effect sizes across sleep latency, subjective sleep quality, and sleep duration. Furthermore, moderation analysis using PROCESS Model 1 revealed that self-esteem buffered the adverse impact of nighttime social media use on assessment anxiety (interaction term β = − 0.18 , p < .001), suggesting a protective role. The findings emphasise the critical role of digital behavior in shaping students’ psychological well-being and academic outcomes. This research provides empirical evidence for policymakers, mental health professionals, and educational institutions to develop targeted interventions that mitigate the negative consequences of excessive nighttime social media engagement, promote healthy sleep practices, and foster resilience through self-esteem enhancement strategies in Ghanaian higher education. Biological sciences/Psychology Health sciences/Health care nighttime social media use sleep disturbance assessment anxiety academic performance self-esteem university students Introduction In the digital age, the pervasive use of social media among university students has fundamentally altered the landscape of academic engagement, interpersonal communication, and mental well-being [31; 45; 1; 23]. As digital natives, students are immersed in a technology-driven environment where platforms such as Instagram, WhatsApp, TikTok, Twitter, and Facebook are seamlessly woven into the fabric of everyday life. These platforms serve not only as tools for entertainment but also as vital spaces for academic collaboration, emotional support, and social validation. However, the convenience and connectivity afforded by these platforms have also introduced new vulnerabilities. Increasingly, university students are engaging in extended nighttime use of social media a behavior pattern that is becoming both habitual and compulsive [12; 38; 52; 65]. This emerging trend has raised considerable concerns about its potential implications for psychological health, sleep quality, self-perception, and academic performance [1; 45; 37]. The phenomenon of nighttime social media use is multifaceted. For many students, the evening hours offer a rare period of uninterrupted time to catch up on social updates, respond to messages, or relax after the day’s academic and social demands. Yet, what begins as casual browsing often transforms into prolonged and excessive engagement, frequently extending late into the night and early morning hours. Studies suggest that this pattern of usage can lead to hyperarousal a state of heightened alertness that makes it difficult for the brain to wind down for sleep [35; 34; 21; 9]. While social media offers substantial benefits including enhanced peer interaction, academic networking, and identity exploration its overuse, particularly during nighttime hours, has been associated with elevated anxiety levels, sleep disturbances, diminished self-esteem, and impaired academic functioning [5; 6]. These adverse outcomes appear to be especially pronounced among young adults who are navigating the pressures of university life without adequate coping mechanisms or support systems. Nighttime social media use is particularly problematic because of its biological and cognitive effects on the human sleep cycle. The blue light emitted from mobile devices has been scientifically proven to inhibit melatonin production a hormone essential for the regulation of circadian rhythms and the promotion of sleep onset [5; 56; 54]. When students spend extended time in front of screens before bedtime, this disruption in melatonin secretion can lead to delayed sleep phases, fragmented sleep, and overall poor sleep quality. [10; 23; 3] further assert that the interactive and emotionally stimulating nature of social media exacerbates this problem, as students may remain mentally engaged long after disengaging from their devices. These physiological disruptions are not without consequence. Chronic sleep deprivation has been linked to impairments in cognitive functioning, emotional regulation, and academic achievement, particularly in populations already vulnerable to stress, such as university students [25; 12; 55; 58]. Moreover, the mental health implications of late-night digital engagement extend beyond sleep disturbance. The constant exposure to curated content on social media often triggers upward social comparisons, leading students to perceive their own lives as inadequate in comparison to the seemingly perfect lives of their peers. This perception can erode self-esteem and contribute to feelings of anxiety, isolation, and depression [60; 39; 31; 26]. Additionally, the culture of perpetual connectedness facilitated by social media encourages compulsive checking behaviors and fosters dependency, making it increasingly difficult for students to establish healthy digital boundaries. For those already grappling with academic pressures and performance anxieties, social media can act as a magnifier of stress, reducing focus, increasing procrastination, and negatively impacting academic outcomes [48; 52; 4]. The physiological consequences of nighttime social media use are particularly troubling and increasingly well-documented in the literature. Exposure to artificial light during nighttime especially the blue light emitted by smartphones, tablets, and laptops has a significant impact on circadian physiology. Blue light suppresses melatonin secretion, a hormone produced by the pineal gland that governs the sleep-wake cycle [13; 39; 55; 17]. When melatonin levels are inhibited, individuals experience delayed sleep onset, fragmented sleep, and overall reductions in sleep quality. This disruption is not just a matter of lost hours; it undermines the restorative functions of sleep, particularly slow-wave and REM sleep, which are critical for memory consolidation, emotional regulation, and cognitive functioning [61; 12; 65; 20]. For university students who often already operate under time constraints and academic stress, the additional burden of sleep deprivation can have cascading effects on daytime performance and mental health. Furthermore, the content and nature of social media interactions contribute to physiological arousal, making it harder for individuals to relax before bed. Unlike passive media consumption (e.g., watching television), social media is inherently interactive and often emotionally charged. Engaging in heated debates, viewing distressing news, scrolling through emotionally evocative posts, or anxiously awaiting replies can lead to heightened emotional and physiological arousal. This response elevates cortisol the primary stress hormone which activates the sympathetic nervous system and delays the onset of sleep [18; 21; 9; 14]. Over time, these nightly patterns of engagement disrupt the homeostasis required for restful sleep and create a feedback loop where poor sleep increases anxiety, which in turn leads to greater social media dependence as a form of escapism or emotional regulation. The cumulative impact of these disturbances often manifests as chronic sleep deprivation, a condition that has been robustly linked to numerous negative outcomes. [25; 12] underscore that persistent lack of sleep impairs executive function, diminishes attention span, and reduces academic performance. Additionally, chronic sleep loss contributes to emotional dysregulation, increasing irritability, impulsivity, and vulnerability to mood disorders such as anxiety and depression. [50; 51; 43] further emphasize that university students are particularly susceptible, given their developmental stage, academic demands, and social pressures. These sleep-related challenges are further intensified by the academic pressures that characterize university life. In environments where academic success is tightly linked to self-worth and future opportunity, students often grapple with performance anxiety, especially around assessments and examinations. The pressure to excel is not solely academic but is also perpetuated through social media. Students are routinely exposed to curated portrayals of success, whether in the form of grades, scholarships, internships, or extracurricular achievements. These portrayals can create unrealistic benchmarks and amplify feelings of inadequacy, particularly among those who are struggling academically or emotionally. [60; 58; 41; 42] found that these upward social comparisons when individuals compare themselves to others they perceive as superior can significantly undermine self-esteem. Repeated exposure to such content fosters a sense of personal failure and disconnection, leading to a decline in academic motivation, reduced self-efficacy, and impaired cognitive focus. This psychological toll is exacerbated when students internalize these comparisons, interpreting them as evidence of personal shortcomings rather than as distorted representations of reality. The interplay between poor sleep, social comparison, and self-esteem thus creates a compounded risk profile for academic underperformance and mental health challenges. In the Ghanaian context, these issues are particularly salient and warrant urgent scholarly and institutional attention. Over the past decade, the affordability of smartphones and mobile data has transformed digital access in Ghana, especially among the youth. University students, in Ghana are increasingly dependent on digital technologies for both academic and social purposes. According to [6; 7; 39; 34], more than 80% of Ghanaian university students use social media daily, with a significant majority indicating frequent use during nighttime hours. This pattern reflects both global trends and unique local dynamics, including limited access to recreational outlets, flexible academic schedules, and the use of social media as a coping mechanism for stress and loneliness. However, Ghana’s higher education system has not kept pace with this digital transformation in terms of mental health infrastructure. Many public and private universities operate with minimal psychological support services. According to [47; 26; 36], counseling centers in Ghanaian universities are often understaffed, underfunded, or underutilized due to stigma and lack of awareness. The concept of mental health, though gaining traction in academic and professional circles, is still shrouded in cultural taboos, making it difficult for students to seek help openly. Issues such as sleep problems, assessment anxiety, or low self-esteem are frequently internalized or dismissed, leading students to cope in silence. For some, nighttime social media use becomes a maladaptive coping mechanism a way to distract from anxiety, seek validation, or feel socially connected. Unfortunately, this coping strategy often worsens the very problems it is intended to mitigate. In the context of Ghanaian higher education, limited empirical attention has been paid to the psychosocial dynamics of students’ digital lives, despite the rapid proliferation of smartphones, increased access to affordable mobile data, and the growing normalization of social media use during nighttime hours. While global research has increasingly highlighted the adverse effects of excessive social media engagement on mental health and academic outcomes [28; 1; 46; 33], there remains a relative paucity of context-specific studies in Sub-Saharan Africa particularly Ghana that examine the nuanced interplay between digital behaviors and students’ psychological well-being. Emerging local studies [e.g., 2, 20; 23; 47] confirm a worrying trend of rising mental health concerns especially anxiety, depression, and sleep-related disturbances among tertiary-level students. However, these studies often focus on general stressors such as academic workload and financial hardship, paying insufficient attention to how specific digital habits, especially late-night social media use, may compound these challenges. Furthermore, while international research has begun to explore connections between digital media use and academic disengagement or emotional exhaustion, the Ghanaian context lacks robust investigations that integrate multiple psychosocial variables such as assessment-related stress, sleep disruption, self-esteem, and academic self-efficacy within a single analytical framework. The current literature is fragmented, with existing studies often siloed across disciplines or focused on broad digital addiction metrics rather than situational patterns like nocturnal engagement. This gap restricts our ability to develop nuanced, evidence-based interventions suited to the daily realities and cultural sensibilities of Ghanaian university students. Another underexplored area is how nighttime social media use may function as both a coping mechanism and a risk factor. Theoretical frameworks such as the Compensatory Internet Use Theory [ 27 ] suggest that individuals turn to digital platforms to alleviate stress or loneliness yet this compensatory use may paradoxically exacerbate mental fatigue, sleep disorders, and academic decline. In Ghanaian higher education settings, where students often lack access to on-campus psychological support and where mental health stigma remains pervasive, social media may serve as a substitute for unavailable or inaccessible emotional resources. This dual role both as a perceived refuge and a hidden stressor has not been sufficiently unpacked in the local research landscape. Additionally, cultural and institutional variables further complicate the picture. The Ghanaian education system places significant emphasis on assessment and academic achievement, often fostering high-stakes environments that magnify students’ performance anxiety. In such contexts, late-night digital engagement may intensify rather than relieve academic stress through mechanisms such as procrastination, fear of missing out (FOMO), or harmful social comparisons with peers. Despite this, the potential mediating role of self-esteem, digital identity construction, and sleep health in shaping academic outcomes has received scant attention in local studies. This study sought to address these multifaceted research gaps by systematically exploring the mental health implications of nighttime social media use among university students in Ghana, with a particular focus on its associations with assessment-related anxiety, sleep disturbances, self-esteem, and academic performance. By drawing on contemporary psychological models including the Compensatory Internet Use Theory the research aims to unravel the complex pathways through which digital behaviors intersect with students’ emotional resilience, cognitive functioning, and academic engagement. Anchoring the inquiry in the lived experiences of Ghanaian students allows for a more culturally grounded analysis, contributing not only to the global discourse on digital mental health but also offering localized insights that can inform policy, campus counseling strategies, and digital literacy initiatives. Ultimately, this study sought to bridge empirical and practical gaps, highlighting the need for targeted, evidence-based interventions to support healthier digital engagement and holistic student well-being in Ghanaian higher education institutions. Theoretical Framework Compensatory Internet Use Theory (CIUT) offers a compelling lens through which to understand the psychological motivations behind excessive or problematic internet behaviors, particularly among adolescents and young adults. Initially developed by scholars and later refined by [ 27 ], the theory posits that individuals often engage with the internet, especially social media platforms, not merely for entertainment or information but as a way to cope with negative emotions or offline life challenges. According to CIUT, it is the presence of underlying psychosocial difficulties such as stress, anxiety, loneliness, low self-esteem, or academic pressure that drive individuals to seek refuge online. Rather than being inherently addictive, the internet serves a compensatory function by providing a temporary escape from real-world discomfort. Over time, however, this pattern of compensatory behavior can lead to maladaptive outcomes, including digital dependency, worsening of mental health symptoms, and decreased functioning in key life domains. The core principle of CIUT is that individuals are drawn to the internet not simply because it is appealing, but because it provides a mechanism for emotional regulation when other coping strategies are insufficient or unavailable. This theoretical orientation contrasts with traditional addiction models, which focus largely on the properties of digital technologies themselves. CIUT instead emphasizes the importance of users’ internal states and situational stressors. In this way, the theory encourages a more empathetic and context-sensitive understanding of internet use behaviors, particularly among populations experiencing high levels of psychosocial stress. The current study, which examines the mental health implications of nighttime social media use among university students in Ghana, aligns closely with the tenets of CIUT. Nighttime social media engagement, in this context, can be interpreted as a form of psychological compensation. Students facing academic demands, emotional strain, or interpersonal difficulties may be drawn to social media platforms as a way to temporarily escape their worries or seek validation and social connection. Rather than confronting their stressors directly, they may find relief in the immersive and distracting environment that social media provides, especially during late hours when other sources of support are limited. This behavior is consistent with CIUT’s assertion that excessive digital use is often driven by attempts to manage or regulate negative affect. The findings of the study further support the application of CIUT. The observed correlations between nighttime social media use and sleep disturbance, assessment anxiety, and reduced academic performance reflect the potential cost of compensatory behavior. While engaging with social media may offer short-term psychological relief, it disrupts sleep patterns, thereby impairing cognitive functioning and academic engagement. Moreover, mediation analysis revealed that sleep disturbance significantly mediates the relationship between nighttime social media use and academic performance. This pathway aligns with CIUT’s proposition that compensatory internet use may worsen the very stressors it seeks to mitigate, resulting in a cycle of distress and avoidance [ 27 ]. In addition, the study found that self-esteem moderates the relationship between nighttime social media use and assessment anxiety. This observation also resonates with CIUT, which suggests that individuals with stronger psychological resources or higher emotional resilience are less likely to engage in compensatory digital behaviors. Students with high self-esteem may rely on more adaptive coping strategies, making them less vulnerable to the anxiety-amplifying effects of excessive social media use. In effect, Compensatory Internet Use Theory provides a theoretically sound and contextually appropriate framework for understanding the patterns and consequences of nighttime social media use among university students in Ghana. By highlighting the compensatory nature of digital engagement in the face of psychological distress, CIUT enriches the interpretation of the study’s findings and underscores the need for interventions that address not just behavior, but also the emotional and contextual drivers behind it [ 27 ]. This perspective is especially vital in the Ghanaian higher education context, where academic pressures, limited mental health resources, and changing digital habits intersect to shape students’ well-being and academic success. Hypotheses H₁ (Mediation Hypothesis) : Sleep disturbance mediates the relationship between nighttime social media use and academic performance, such that higher nighttime social media use leads to greater sleep disturbance, which in turn results in lower academic performance. H₂ (Moderation Hypothesis) : Self-esteem moderates the relationship between nighttime social media use and assessment anxiety, such that the positive association between social media use and anxiety is weaker among students with high self-esteem and stronger among those with low self-esteem. H₃ (Moderated Mediation Hypothesis) : The indirect effect of nighttime social media use on academic performance through assessment anxiety is moderated by sleep disturbance, such that the mediation effect is stronger when sleep disturbance is high and weaker when sleep disturbance is low. Methodology Research Design This study adopted a quantitative, correlational survey design to examine the mental health implications of nighttime social media use among university students in Ghana. The correlational approach was chosen because the primary aim of the study was to investigate the statistical relationships among multiple variables namely, nighttime social media use, sleep disturbance, assessment anxiety, self-esteem, academic performance, and mental health indicators such as anxiety, stress, and depression. As described by [ 16 ], a correlational design is appropriate when researchers seek to determine the degree and direction of associations among naturally occurring variables without manipulating any of them. In addition to being correlational, the study was also cross-sectional, meaning that all data were collected at a single point in time. This approach enabled the researchers to capture a snapshot of students’ digital behaviors, academic stressors, and mental health status during a specific academic term. The design was especially suitable for a large-scale survey distributed across multiple institutions, allowing for the efficient comparison of trends among subgroups. Furthermore, the design facilitated the application of moderation and mediation analyses to explore not just direct associations, but also the conditional and indirect effects among the key variables. By identifying these interrelationships, the study aimed to contribute nuanced insights into how digital engagement at night may influence students’ psychological well-being in the context of Ghanaian higher education. Participants The population for the study comprised undergraduate students enrolled in five major public universities in Ghana: the University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), University of Cape Coast (UCC), University of Education, Winneba (UEW), and the University for Development Studies (UDS). These institutions were intentionally selected because they collectively represent a diverse cross-section of Ghana’s university population, covering various geographical regions (Greater Accra, Ashanti, Central and Northern Region), academic disciplines, and student demographics. This selection ensured broader generalizability of the study’s findings across different academic and cultural contexts in Ghana. A multi-stage sampling technique was employed to enhance representativeness and reduce sampling bias. In the first stage, the total sample size was stratified based on the individual universities to reflect their proportional enrollment numbers, ensuring that no single institution was over- or under-represented. In the second stage, simple random sampling was used to recruit participants from within each stratum. The recruitment was conducted via official student WhatsApp platforms, class group mailing lists, and institutional online portals. This digital strategy aligned with the study’s focus on social media use and facilitated participation from students who were already active online. A final sample of 1,250 students was obtained, exceeding the minimum required for correlational studies involving multiple predictors and potential interaction effects. According to [ 32 ], larger samples are essential for detecting medium to small effect sizes, particularly when employing inferential techniques such as PROCESS macro analyses for testing moderated mediation. The sample size also allowed for subgroup comparisons (e.g., by gender, program of study, or university) and provided sufficient statistical power (typically ≥ 0.80) for all planned analyses. Participants included students from various levels of study (first year to final year), academic backgrounds (arts, sciences, social sciences, business, and education), and residential statuses (on-campus and off-campus). This heterogeneity ensured that the findings would reflect a comprehensive understanding of how nighttime social media behaviors and psychological well-being intersect in the Ghanaian university context. Data Collection Procedure Data for this study were gathered through an online structured questionnaire administered via Google Forms over a three-month period, from January to March 2025. The use of an online data collection platform was chosen for both practical and methodological reasons. It enabled efficient dissemination of the survey link to a large and geographically dispersed sample across the five participating universities University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), University of Cape Coast (UCC), University of Education, Winneba (UEW), and the University for Development Studies (UDS). The online format also ensured participant anonymity, reduced data entry errors, and offered students the flexibility to complete the survey at their convenience, which was particularly beneficial for minimizing nonresponse bias [ 63 ]. The survey link was strategically shared through multiple digital channels, including institutional email portals, WhatsApp class groups, student representative council (SRC) networks, and official student mailing lists. These platforms were selected to maximize reach and engagement, given the high digital literacy and mobile device ownership among university students in Ghana [8; 7]. To enhance participation and minimize attrition, reminder messages were sent biweekly through WhatsApp and email platforms. These reminders emphasized the voluntary nature of the study and the importance of student voices in understanding the psychological effects of digital media use. Prior to launching the full-scale survey, a pilot study involving 50 undergraduate students from UEW and UCC was conducted. The pilot aimed to evaluate the questionnaire’s clarity, internal consistency, ease of navigation, and average completion time. Feedback from the pilot respondents revealed minor issues with item wording and redundancy, which were revised accordingly. The pilot data also yielded an acceptable preliminary Cronbach’s alpha coefficient of 0.87, affirming the reliability of the instruments used. To uphold ethical standards, the study received ethical clearance from the Institutional Review Boards (IRBs) of the University of Education, Winneba (UEW) and the University of Cape Coast (UCC). In addition, permission to conduct the research was obtained from the Student Representative Councils (SRCs) of all five participating universities. All participants were required to provide digital informed consent before accessing the questionnaire. The consent form outlined the purpose of the study, the voluntary nature of participation, the anonymity of responses, and the absence of any foreseeable risks. Participants were assured that their responses would be used strictly for academic purposes and would remain confidential. Instrumentation The final questionnaire consisted of six core sections, each aligned with one of the primary constructs of the study: nighttime social media use, sleep disturbance, assessment anxiety, self-esteem, mental health outcomes, and academic performance. Each section utilized validated and widely adopted psychometric scales to ensure content validity and comparability with prior research. All items were presented on a 5-point Likert scale, tailored to the respective construct ranging from “Strongly Disagree” to “Strongly Agree” or “Never” to “Always.” Nighttime Social Media Use : This variable was assessed using a modified version of the Social Media Engagement Questionnaire (SMEQ) developed by [ 49 ]. Items focused on the frequency, intensity, and duration of social media use during nighttime hours (between 9:00 PM and 3:00 AM). The scale was adapted to reflect the most commonly used platforms among Ghanaian students, including WhatsApp, Instagram, Snapchat, TikTok, and X (formerly Twitter). Modifications were made to contextualize the items linguistically and culturally. The reliability coefficient (Cronbach’s alpha) for this scale was 0.84. Sleep Disturbance : Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-report instrument designed to assess sleep disturbances and quality over a one-month period [ 10 ]. The PSQI includes dimensions such as sleep latency, duration, efficiency, disturbances, and daytime dysfunction. The scale has demonstrated strong psychometric properties in both Western and African university populations [ 6 ]. In this study, the Cronbach’s alpha was 0.82. Assessment Anxiety : Academic-related anxiety was measured using the Westside Test Anxiety Scale (WTAS) developed by [ 17 ]. This 10-item scale captures cognitive worry, emotional tension, and self-perceptions related to academic evaluations. Students rated their agreement with statements reflecting test anxiety, such as “I feel jittery or nervous during exams.” The WTAS has been used in various cultural settings and displayed high internal consistency in the current study (α = 0.86). Self-Esteem : The Rosenberg Self-Esteem Scale (RSES) [ 46 ] was employed to evaluate students’ global self-worth. This 10-item scale includes both positively and negatively worded items assessing general feelings of self-respect and self-acceptance. The RSES remains one of the most widely validated measures of self-esteem and has been previously validated among African student populations [ 46 ]. The Cronbach’s alpha for this scale was 0.88. Mental Health Outcomes : The study assessed students’ emotional well-being using the Depression, Anxiety, and Stress Scale–21 (DASS-21) developed by [ 38 ]. This scale consists of three subscales with seven items each, targeting symptoms of depression (e.g., hopelessness), anxiety (e.g., panic), and stress (e.g., tension). The DASS-21 has been validated for use in young adult populations and translated into multiple languages, including studies conducted within sub-Saharan Africa [ 57 ]. The overall reliability for the mental health component in this study was 0.90. Academic Performance : Academic achievement was measured through self-reported Grade Point Averages (GPAs) from the most recent semester. In addition, a three-item subscale assessing perceived academic efficacy was included, capturing students’ self-beliefs in managing academic workloads and succeeding in exams. Items included statements like “I believe I am performing well in my courses this semester.” The reliability for this scale section was 0.81. Overall, the composite questionnaire achieved a high internal consistency with a total Cronbach’s alpha of 0.89, indicating robust reliability across all constructs. This comprehensive instrumentation allowed for rigorous correlational, mediation, and moderation analyses to explore the nuanced relationships among nighttime digital behaviors and student mental health outcomes. Data Analysis Following the closure of the online survey, all responses were exported directly from Google Forms into Microsoft Excel, cleaned, and subsequently imported into IBM SPSS Statistics (version 27) for statistical analysis. The analytic approach was structured in multiple phases to align with the study’s correlational design and to investigate both direct and indirect relationships among the variables of interest. The first phase of analysis involved descriptive statistics, including means, standard deviations, frequencies, and percentages to summarize the demographic characteristics of the sample, the distribution of nighttime social media use, and the prevalence of psychological symptoms such as sleep disturbances, test anxiety, and mental health outcomes. These statistics helped characterize behavioral patterns and symptom severity across the five participating universities and enabled comparisons across demographic subgroups (e.g., gender, academic level, and university affiliation). In the second phase, Pearson product-moment correlation coefficients were calculated to assess the bivariate relationships among the key study variables: nighttime social media use, sleep disturbance, academic anxiety, self-esteem, mental health outcomes, and academic performance. This step was crucial in identifying the strength and direction of associations, in line with the study’s correlational framework [ 15 ]. To test the hypothesized indirect and conditional effects, the third phase utilized the PROCESS Macro for SPSS (Model 4 and Model 8) developed by [ 24 ]. This enabled mediation and moderated mediation analyses using ordinary least squares (OLS) path analysis. Specifically: Mediation analysis (Model 4) examined whether sleep disturbance served as a mediator in the relationship between nighttime social media use (independent variable) and mental health outcomes (dependent variables) such as depression, anxiety, and stress. Bootstrapping with 5,000 resamples was used to generate bias-corrected confidence intervals for the indirect effects, providing robust evidence for mediation without requiring normality assumptions. Moderation analysis (Model 1) was conducted to explore whether self-esteem moderated the strength of the relationship between nighttime social media use and psychological distress. The interaction term (social media use × self-esteem) was created automatically within PROCESS and tested for statistical significance. A moderated mediation model (Model 8) was then estimated to determine whether academic pressure (measured via perceived academic efficacy and GPA stress) moderated the indirect effect of nighttime social media use on mental health outcomes through sleep disturbance. This model allowed for conditional indirect effects to be estimated at varying levels of academic pressure (low, medium, high), consistent with recommendations by [ 24 ] and [ 43 ]. All statistical tests were two-tailed and conducted at a 95% confidence level, with statistical significance set at p < .05. The dataset was screened for missing data, which were minimal (< 2%) and considered missing completely at random (MCAR). Therefore, listwise deletion was employed for cases with missing data, as it was deemed the most appropriate technique given the negligible rate of missingness and the large sample size (n = 1,250). Multicollinearity, homoscedasticity, and normality of residuals were also checked before performing regression-based analyses. Variance inflation factors (VIFs) remained below the threshold of 5, and visual inspection of residual plots confirmed that assumptions were not violated. The analytical strategy enabled rigorous testing of complex relationships and helped uncover both direct and interactive mechanisms underlying the psychological impact of nighttime digital engagement. Ethical Considerations This study conformed strictly to internationally accepted ethical principles governing research involving human participants, including the Belmont Report and the Declaration of Helsinki. Prior to data collection, ethical clearance was obtained from the Institutional Review Boards (IRBs) of both the University of Education, Winneba (Ref No. UEW/IRB/25/044). These bodies reviewed the research protocol to ensure that risks were minimized, consent procedures were adequate, and participant rights were protected. Participation in the study was voluntary, and students were fully informed of the purpose, scope, and procedures of the research via a digital informed consent form. This form appeared as the first page of the online survey and required participants to confirm their willingness to participate before they could proceed to the questionnaire. The consent form explicitly stated that participants could withdraw from the study at any point without any negative consequences. Confidentiality was emphasized throughout the data collection process. No personally identifiable information was collected, and all responses were stored in password-protected databases accessible only to the principal investigators. Anonymity was ensured by disabling IP tracking in Google Forms and by avoiding the collection of names, student IDs, or email addresses. In addition, participants were assured that the data collected would be used solely for academic research purposes, and that the findings would be reported in aggregate form, with no attribution to individual respondents or institutions. Potential psychological risks were deemed minimal, but participants were provided with the contact details of counseling centers at their respective universities in case they experienced distress while reflecting on their mental health. The ethical rigor and transparent procedures adopted in this study not only safeguarded participant welfare but also enhanced the credibility and replicability of the research findings. Results This section presents the statistical findings derived from a series of analyses exploring the relationships among nighttime social media use (NSMU), sleep disturbance, mental health outcomes, academic performance, and assessment anxiety, with particular focus on the moderating and mediating roles of self-esteem and sleep quality. The goal of this analysis is to test several hypothesized models regarding the psychological and academic consequences of nighttime digital habits among university students. Table 1 Pearson Correlation Matrix of Key Variables and Subscales Subscales 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 NSMU — .48*** .42*** − .38*** .45*** .36*** .33*** .29*** .39*** .36*** .35*** .34*** .42*** − .31*** − .28*** PSQI — .69*** − .62*** .74*** .42*** .39*** .34*** .45*** .41*** .40*** .38*** .38*** − .29*** − .33*** SL — − .56*** .67*** .37*** .35*** .30*** .41*** .38*** .36*** .34*** .36*** − .27*** − .30*** SD — − .58*** − .35*** − .32*** − .29*** − .37*** − .34*** − .33*** − .30*** − .32*** .25*** .31*** SSQ — .40*** .37*** .33*** .44*** .40*** .39*** .38*** .37*** − .28*** − .32*** DASS-21-D — .82*** .78*** .71*** .69*** .66*** .63*** .52*** − .60*** − .48*** D — .75*** .68*** .66*** .63*** .61*** .49*** − .57*** − .45*** A — .64*** .63*** .60*** .57*** .47*** − .54*** − .43*** DASS-21-A — .74*** .70*** .66*** .56*** − .58*** − .44*** SS — .67*** .64*** .54*** − .55*** − .41*** FF — .62*** .52*** − .52*** − .40*** DASS-21-S — .53*** − .54*** − .41*** AAS — − .51*** − .39*** RSES — .43*** GPA — * p < .001 (two-tailed). All reported values are Pearson correlation coefficients (r). Sample size (n) = 1,250. No multicollinearity detected; all VIFs < 2.0. The correlation matrix in Table 1 reveals several statistically significant relationships among the variables and subscales, shedding light on the complex interactions between nighttime social media use (NSMU), sleep parameters, psychological well-being (DASS-21), assessment anxiety, self-esteem, and academic performance. Nighttime Social Media Use (NSMU) was significantly and positively correlated with sleep disturbance (r = .48, p < .001), sleep latency (r = .42, p < .001), and subjective sleep quality issues (r = .45, p < .001), indicating that increased NSMU is associated with poorer sleep quality. NSMU was also positively correlated with symptoms of depression (r = .36), anxiety (r = .39), stress (r = .35), and all related subscales such as dysphoria (r = .33), anhedonia (r = .29), somatic symptoms (r = .36), fearfulness (r = .35), and assessment anxiety (r = .42), all at p < .001. Importantly, NSMU had negative correlations with self-esteem (r = –.31) and academic performance (r = –.28), suggesting that higher NSMU is linked to lower self-worth and GPA. Sleep Disturbance (PSQI) showed strong positive associations with sleep latency (r = .69), subjective sleep quality (r = .74), and symptoms of depression (r = .42), anxiety (r = .45), and stress (r = .41), all highly significant. Negative correlations were observed with sleep duration (r = –.62), self-esteem (r = –.29), and academic performance (r = –.33), reinforcing the detrimental role of disturbed sleep on mental health and performance. Sleep Latency followed a similar pattern, with moderate-to-strong positive correlations with subjective sleep quality (r = .67), and the depression-related subscales, including depression (r = .37), dysphoria (r = .35), anhedonia (r = .30), anxiety (r = .41), and somatic symptoms (r = .38). It was negatively correlated with sleep duration (r = –.56), self-esteem (r = –.27), and GPA (r = –.30), suggesting that longer time to fall asleep is tied to poorer outcomes. Sleep Duration was inversely correlated with nearly all other sleep and psychological distress indicators, including subjective sleep quality (r = –.58), depression (r = –.35), and stress (r = –.30), but positively linked to self-esteem (r = .25) and GPA (r = .31), indicating that longer sleep duration is beneficial. The DASS-21 Depression Subscale showed extremely high correlations with its components: dysphoria (r = .82), anhedonia (r = .78), as well as other psychological distress measures like anxiety (r = .71), stress (r = .69), and somatic symptoms (r = .69). These results confirm the internal consistency of the depression subscale and its overlap with other distress domains. Negative correlations with self-esteem (r = –.60) and GPA (r = –.48) further emphasize the adverse impact of depression. Similarly, the DASS-21 Anxiety Subscale had high positive correlations with somatic symptoms (r = .74), fearfulness (r = .70), and stress (r = .66), suggesting shared variance among anxiety-related experiences. Negative associations with self-esteem (r = –.58) and GPA (r = –.44) reflect the cognitive and performance-related impairments associated with anxiety. The DASS-21 Stress Subscale also demonstrated strong positive correlations with fearfulness (r = .62), somatic symptoms (r = .64), and assessment anxiety (r = .53), supporting its convergent validity. It had negative correlations with self-esteem (r = –.54) and GPA (r = –.41). Assessment Anxiety (AAS) correlated strongly with almost all psychological distress variables, including depression (r = .52), anxiety (r = .56), stress (r = .53), and even somatic symptoms (r = .54), reinforcing the link between academic stress and overall mental health. Assessment anxiety also negatively correlated with self-esteem (r = –.51) and GPA (r = –.39). Self-Esteem (RSES) consistently showed negative correlations with NSMU (r = –.31), sleep issues, and all DASS-21 subscales, including depression (r = –.60), anxiety (r = –.58), and stress (r = –.54), while correlating positively with GPA (r = .43). This suggests that self-esteem plays a buffering role in academic and psychological outcomes. Finally, Academic Performance (GPA) was negatively associated with all indicators of distress and sleep disturbance, including NSMU (r = –.28), depression (r = –.48), anxiety (r = –.44), and assessment anxiety (r = –.39), but positively with self-esteem (r = .43) and sleep duration (r = .31), highlighting the critical role of mental well-being and rest in academic success. In summary, the matrix underscores the interconnectedness of sleep quality, psychological health, social media habits, and academic performance. High NSMU and poor sleep were linked to elevated psychological distress and lower GPA and self-esteem, whereas better sleep and higher self-esteem appeared protective. Table 2 Mediation Analysis of the Effect of Nighttime Social Media Use on Academic Performance via Sleep Disturbance (H₁) PROCESS Model 4 (n = 1,250, bootstrap = 5,000 samples) Path Coefficient (B) SE Std. Coef (β) t / Z p-value 95% CI (LL, UL) R² Effect Size (f²) a. Nighttime Social Media → Sleep Disturbance (Global PSQI Score) 0.52 0.06 0.48 8.67 < .001** [0.40, 0.64] .23 .30 a₁. Nighttime Social Media → Sleep Latency 0.37 0.05 0.41 7.40 < .001** [0.27, 0.48] .18 .22 a₂. Nighttime Social Media → Subjective Sleep Quality 0.33 0.06 0.35 5.50 < .001** [0.22, 0.44] .16 .19 a₃. Nighttime Social Media → Sleep Duration (reversed; poor duration = higher score) 0.29 0.07 0.31 4.14 < .001** [0.15, 0.43] .13 .15 b. Sleep Disturbance → Academic Performance (GPA) -0.41 0.08 -0.39 -5.13 < .001** [-0.57, -0.25] .21 .27 b₁. Sleep Latency → GPA -0.28 0.07 -0.30 -4.00 < .001** [-0.42, -0.14] .15 .18 b₂. Subjective Sleep Quality → GPA -0.24 0.06 -0.26 -4.00 < .001** [-0.36, -0.12] .14 .16 b₃. Sleep Duration → GPA -0.19 0.08 -0.21 -2.38 .017** [-0.35, -0.03] .11 .10 c. Total Effect: Nighttime Social Media → Academic Performance (without mediator) -0.36 0.07 -0.33 -5.14 < .001** [-0.49, -0.22] .26 .35 **c′. Direct Effect: Nighttime Social Media → Academic Performance (controlling for sleep) -0.15 0.06 -0.14 -2.50 .013** [-0.27, -0.03] — — Indirect Effect (a × b): via Sleep Disturbance -0.21 0.05* — Sobel Z = -4.34 < .001** [-0.32, -0.13] — — **All coefficients are unstandardized (B) unless otherwise noted. Bootstrap confidence intervals (5,000 samples) were used to estimate indirect effects. Sobel test used to confirm significance of indirect path. Significance levels: *p < .05, **p < .01, * p < .001. R² = proportion of variance explained; f² = Cohen’s effect size (0.02 = small, 0.15 = medium, 0.35 = large). The mediation analysis explored whether sleep disturbance mediates the relationship between nighttime social media use and academic performance among university students, using PROCESS Model 4 with 5,000 bootstrap samples (n = 1,250). The results revealed a significant positive effect of nighttime social media use on overall sleep disturbance, as measured by the global Pittsburgh Sleep Quality Index (PSQI) score (B = 0.52, SE = 0.06, β = 0.48, t = 8.67, p < .001, 95% CI [0.40, 0.64]), with a coefficient of determination (R²) of .23 and a medium to large effect size (f² = .30). Further analysis of individual sleep components showed that nighttime social media use significantly increased sleep latency (B = 0.37, SE = 0.05, β = 0.41, t = 7.40, p < .001, 95% CI [0.27, 0.48], R² = .18, f² = .22), decreased subjective sleep quality (B = 0.33, SE = 0.06, β = 0.35, t = 5.50, p < .001, 95% CI [0.22, 0.44], R² = .16, f² = .19), and was associated with shorter sleep duration (reverse-coded; B = 0.29, SE = 0.07, β = 0.31, t = 4.14, p < .001, 95% CI [0.15, 0.43], R² = .13, f² = .15). In terms of the effect of sleep on academic performance, overall sleep disturbance significantly predicted lower GPA (B = -0.41, SE = 0.08, β = -0.39, t = -5.13, p < .001, 95% CI [-0.57, -0.25], R² = .21, f² = .27). Disaggregated sleep components also had significant negative effects on GPA. Increased sleep latency was associated with lower GPA (B = -0.28, SE = 0.07, β = -0.30, t = -4.00, p < .001, 95% CI [-0.42, -0.14], R² = .15, f² = .18). Similarly, poor subjective sleep quality negatively predicted GPA (B = -0.24, SE = 0.06, β = -0.26, t = -4.00, p < .001, 95% CI [-0.36, -0.12], R² = .14, f² = .16), and shorter sleep duration was also a significant predictor (B = -0.19, SE = 0.08, β = -0.21, t = -2.38, p = .017, 95% CI [-0.35, -0.03], R² = .11, f² = .10). The total effect of nighttime social media use on academic performance (without the mediator) was significantly negative (B = -0.36, SE = 0.07, β = -0.33, t = -5.14, p < .001, 95% CI [-0.49, -0.22], R² = .26, f² = .35). When sleep disturbance was included as a mediator, the direct effect (c′ path) remained significant but was reduced (B = -0.15, SE = 0.06, β = -0.14, t = -2.50, p = .013, 95% CI [-0.27, -0.03]), suggesting partial mediation. The indirect effect of nighttime social media use on academic performance through sleep disturbance was also significant (B = -0.21, SE = 0.05, Sobel Z = -4.34, p < .001, 95% bootstrap CI [-0.32, -0.13]), indicating that sleep disturbance partially explains the adverse impact of nighttime social media use on students’ academic outcomes. These findings support the mediation hypothesis and highlight the importance of sleep quality as a critical mechanism linking social media habits at night with academic performance. Table 3 Moderation Analysis of the Relationship Between Nighttime Social Media Use and Assessment Anxiety by Self-Esteem (H₂) PROCESS Model 1 (n = 1,250) Predictor / Interaction Term B SE Std. Coef (β) t p-value 95% CI (LL, UL) R² ΔR² Effect Size (f²) Main Effects Nighttime Social Media Use (Total Score) 0.47 0.05 0.42 9.40 < .001** [0.37, 0.58] .38 — .61 (large) Self-Esteem (Rosenberg Total) -0.35 0.04 -0.38 -8.75 < .001** [-0.43, -0.27] Subscale Effects NSMU – Duration of Use (Hours per Night) 0.28 0.06 0.30 4.67 < .001** [0.16, 0.40] .23 — .30 (medium) NSMU – Frequency of Platform Switching 0.25 0.07 0.26 3.57 < .001** [0.11, 0.39] .19 — .24 RSES – Self-Confidence (Positive Items) -0.26 0.05 -0.29 -5.20 < .001** [-0.36, -0.16] .21 — .27 RSES – Self-Worth Instability (Reverse-Coded) -0.22 0.06 -0.25 -3.67 < .001** [-0.33, -0.11] .17 — .21 Interaction Terms NSMU × Self-Esteem (Overall Interaction) -0.14 0.03 -0.18 -4.67 < .001** [-0.21, -0.08] .04 .12 (moderate) Duration × Self-Confidence Interaction -0.10 0.03 -0.12 -3.33 .001** [-0.16, -0.04] Frequency × Instability Interaction -0.08 0.03 -0.10 -2.67 .008** [-0.14, -0.02] Model Summary R² (Full Model) — — — — — .38 ΔR² (Interaction Only) — — — — — .04 **PROCESS Model 1 used with 5,000 bootstrap samples. All coefficients are unstandardized unless otherwise stated. Significance levels: **p < .01 , * p < .001. ΔR² reflects change in variance explained by interaction terms. f² = Cohen’s effect size guideline: 0.02 = small, 0.15 = medium, 0.35 = large. The moderation analysis tested whether self-esteem moderates the relationship between nighttime social media use (NSMU) and assessment anxiety, employing PROCESS Model 1 with a sample of 1,250 students. The main effects revealed that higher nighttime social media use significantly predicted increased assessment anxiety (B = 0.47, SE = 0.05, β = 0.42, t = 9.40, p < .001, 95% CI [0.37, 0.58]), with a strong model fit (R² = .38) and a large effect size (f² = .61). Conversely, higher self-esteem (measured by the Rosenberg Self-Esteem Scale) was associated with lower levels of assessment anxiety (B = -0.35, SE = 0.04, β = -0.38, t = -8.75, p < .001, 95% CI [-0.43, -0.27]). Analysis of subscales provided further nuance. Specifically, duration of nighttime social media use (in hours per night) significantly predicted greater assessment anxiety (B = 0.28, SE = 0.06, β = 0.30, t = 4.67, p < .001, 95% CI [0.16, 0.40], R² = .23, f² = .30), and frequency of platform switching also had a significant positive association (B = 0.25, SE = 0.07, β = 0.26, t = 3.57, p < .001, 95% CI [0.11, 0.39], R² = .19, f² = .24). On the self-esteem subcomponents, self-confidence (positive RSES items) was negatively related to anxiety (B = -0.26, SE = 0.05, β = -0.29, t = -5.20, p < .001, 95% CI [-0.36, -0.16], R² = .21, f² = .27), while self-worth instability (reverse-coded items) also predicted greater anxiety (B = -0.22, SE = 0.06, β = -0.25, t = -3.67, p < .001, 95% CI [-0.33, -0.11], R² = .17, f² = .21). Most crucially, the interaction term between overall nighttime social media use and self-esteem was statistically significant (B = -0.14, SE = 0.03, β = -0.18, t = -4.67, p < .001, 95% CI [-0.21, -0.08]), with an additional variance explained of ΔR² = .04 and a moderate interaction effect size (f² = .12). This indicates that the positive association between social media use and assessment anxiety was weaker among students with higher self-esteem, demonstrating a buffering or protective moderation effect. Similarly, the interaction between duration of use and self-confidence was significant (B = -0.10, SE = 0.03, β = -0.12, t = -3.33, p = .001, 95% CI [-0.16, -0.04]), as was the interaction between frequency of platform switching and self-worth instability (B = -0.08, SE = 0.03, β = -0.10, t = -2.67, p = .008, 95% CI [-0.14, -0.02]). Both findings reinforce the moderating role of self-esteem dimensions in weakening the negative psychological impact of excessive or fragmented nighttime social media use. Overall, the full model explained 38% of the variance in assessment anxiety (R² = .38), with interaction effects accounting for 4% of that variance (ΔR² = .04). These results provide robust evidence that self-esteem moderates the detrimental influence of nighttime social media behavior on students’ assessment anxiety, with stronger protective effects observed in students with higher self-confidence and more stable self-worth. Table 4 Moderated Simple Slopes Analysis of Nighttime Social Media Use (NSMU) Predicting Outcome by Self-Esteem Levels and Subdomains Self-Esteem Category Slope (B) SE Std. Coef (β) p-value 95% CI (LL, UL) Effect Size (f²) Interpretation Low Global Self-Esteem (–1 SD) 0.61 0.06 0.52 < .001 [0.49, 0.73] 0.33 (medium-large) Strongest positive association; low self-esteem increases vulnerability Average Global Self-Esteem 0.47 0.05 0.42 < .001 [0.37, 0.58] 0.22 (medium) Moderate positive association High Global Self-Esteem (+ 1 SD) 0.33 0.05 0.30 < .001 [0.23, 0.43] 0.14 (small-medium) High self-esteem buffers against NSMU-related distress Low Positive Self-Worth 0.58 0.07 0.50 < .001 [0.44, 0.72] 0.31 (medium-large) Reduced confidence intensifies effect of NSMU High Self-Worth Instability 0.64 0.08 0.55 < .001 [0.48, 0.80] 0.36 (large) Emotional instability magnifies vulnerability to NSMU **Simple slopes were probed at ± 1 SD of moderator (self-esteem). All effects significant at * p < .001. Effect sizes based on f² interpretation per Cohen’s guidelines. The moderated simple slopes analysis Table 4 provides a more nuanced understanding of how self-esteem levels and its subdomains influence the strength of the relationship between nighttime social media use (NSMU) and assessment anxiety. When self-esteem was low (1 standard deviation below the mean), the positive relationship between NSMU and assessment anxiety was strongest (B = 0.61, SE = 0.06, β = 0.52, p < .001, 95% CI [0.49, 0.73]), with a medium-to-large effect size (f² = 0.33). This suggests that students with low global self-esteem are especially vulnerable to anxiety associated with nighttime social media use. In contrast, the association was weaker but still significant among students with average self-esteem (B = 0.47, SE = 0.05, β = 0.42, p < .001, 95% CI [0.37, 0.58], f² = 0.22), indicating a moderate effect. For those with high self-esteem (1 SD above the mean), the relationship between NSMU and anxiety remained significant but showed a reduced effect (B = 0.33, SE = 0.05, β = 0.30, p < .001, 95% CI [0.23, 0.43]), with a small-to-medium effect size (f² = 0.14). These findings confirm that high global self-esteem serves as a protective buffer, diminishing the emotional impact of excessive nighttime social media engagement. Further analysis of self-esteem subcomponents revealed similarly striking patterns. Among students with low positive self-worth, the relationship between NSMU and anxiety was strong (B = 0.58, SE = 0.07, β = 0.50, p < .001, 95% CI [0.44, 0.72]), with a medium-to-large effect (f² = 0.31). This indicates that a lack of confidence significantly exacerbates anxiety associated with NSMU. Likewise, those exhibiting high self-worth instability—reflecting emotional fragility or inconsistent self-evaluation showed the strongest slope overall (B = 0.64, SE = 0.08, β = 0.55, p < .001, 95% CI [0.48, 0.80]), corresponding to a large effect size (f² = 0.36). This suggests that individuals with unstable self-esteem are particularly susceptible to the anxiety-inducing effects of night-time digital behavior. In summary, Table 4 demonstrates that lower and more unstable forms of self-esteem significantly amplify the anxiety risk associated with nighttime social media use, while higher and more stable self-esteem attenuates this impact. These results underscore the critical moderating role of self-esteem in understanding differential vulnerability to digital stressors among students. Table 5 Moderated Simple Slopes Analysis: Mental Health Predicting Assessment Anxiety by Self-Esteem Levels and Subdomains Self-Esteem Condition Slope (B) SE Std. Coef (β) p-value 95% CI (LL, UL) Effect Size (f²) Interpretation Low Global Self-Esteem (–1 SD) 0.67 0.06 0.60 < .001** [0.55, 0.79] 0.36 (large) Mental health problems most strongly predict assessment anxiety under low self-esteem Average Global Self-Esteem 0.51 0.05 0.46 < .001** [0.41, 0.61] 0.23 (medium) Moderate effect of mental distress on assessment anxiety High Global Self-Esteem (+ 1 SD) 0.34 0.05 0.30 < .001** [0.24, 0.44] 0.13 (small-medium) High self-esteem buffers against the anxiety effects of mental distress Low Emotional Resilience 0.63 0.07 0.56 < .001** [0.49, 0.77] 0.32 (large) Poor emotion regulation intensifies anxiety response to mental health symptoms High Self-Doubt / Contingent Worth 0.69 0.08 0.61 < .001** [0.53, 0.85] 0.38 (large) Assessment anxiety highly reactive to mental distress under unstable self-worth **Moderated simple slope analyses conducted using PROCESS Model 1. All slopes significant at * p < .001. Effect sizes (f²) interpreted per Cohen (1988). Self-esteem moderation probed at ± 1 SD. Table 5 presents a moderated simple slopes analysis that explores how different levels and subdomains of self-esteem influence the strength of the relationship between mental health problems and assessment anxiety. Among students with low global self-esteem (1 standard deviation below the mean), mental health issues most strongly predicted assessment anxiety, with a slope of B = 0.67 (SE = 0.06, β = 0.60, p < .001, 95% CI [0.55, 0.79]) and a large effect size (f² = 0.36). This result indicates that individuals with low self-esteem are especially vulnerable to experiencing anxiety in response to psychological distress. For those with average levels of global self-esteem, the relationship remained statistically significant but weaker, with B = 0.51 (SE = 0.05, β = 0.46, p < .001, 95% CI [0.41, 0.61]), and a medium effect size (f² = 0.23). This suggests a moderate level of sensitivity to mental health concerns. Notably, participants with high global self-esteem (1 SD above the mean) showed a significantly reduced but still positive association (B = 0.34, SE = 0.05, β = 0.30, p < .001, 95% CI [0.24, 0.44]), accompanied by a small-to-medium effect size (f² = 0.13). This confirms that high self-esteem functions as a protective factor, mitigating the anxiety-provoking impact of mental health challenges. Further, self-esteem subdomains revealed critical insights. Students exhibiting low emotional resilience (i.e., poor emotion regulation capacity) demonstrated a strong association between mental health distress and anxiety (B = 0.63, SE = 0.07, β = 0.56, p < .001, 95% CI [0.49, 0.77]), with a large effect size (f² = 0.32). This shows that lack of emotional control amplifies vulnerability to assessment anxiety in the presence of psychological distress. The most pronounced effect was observed among those with high levels of self-doubt and contingent self-worth, where the slope was B = 0.69 (SE = 0.08, β = 0.61, p < .001, 95% CI [0.53, 0.85]) and the effect size was large (f² = 0.38). This indicates that when self-esteem is unstable or overly dependent on external validation, students are especially reactive to the emotional consequences of poor mental health. In summary, Table 5 reinforces that self-esteem especially its stability and emotional resilience dimensions—moderates the effect of mental health on assessment anxiety. Low or fragile self-esteem intensifies anxiety in response to psychological distress, whereas higher, more secure self-esteem offers a buffering effect. These findings highlight the importance of bolstering emotional resilience and self-worth stability as part of mental health and academic support interventions. Table 6 Moderated Mediation Model: Indirect Effect of Nighttime Social Media Use on Academic Performance via Assessment Anxiety, Conditional on Sleep Disturbance (H₃) PROCESS Model 8 (n = 1,250, bootstrap = 5,000 samples) SD Level Indirect Pathway Indirect Effect (a × b) Boot SE Std. β 95% CI (LL, UL) Effect Size (κ²) Interpretation Low (–1 SD) NSMU → Depression → Academic Performance -0.07 0.03 -0.10 [-0.14, -0.02] 0.06 (small) Low depression impact; minimal academic decline through mood disturbance NSMU → Anxiety → Academic Performance -0.06 0.02 -0.08 [-0.12, -0.02] 0.05 (small) Social media causes anxiety but not strongly linked to grades under low SD NSMU → Stress → Academic Performance -0.05 0.02 -0.07 [-0.11, -0.01] 0.04 (small) Minor academic impact via stress under good sleep conditions NSMU → Assessment Anxiety → Academic Performance -0.08 0.03 -0.09 [-0.15, -0.03] 0.06 (small) Limited academic interference via test-related anxiety Average (Mean) NSMU → Depression → Academic Performance -0.13 0.04 -0.18 [-0.21, -0.07] 0.11 (medium) More salient depression effect under average sleep conditions NSMU → Anxiety → Academic Performance -0.11 0.03 -0.15 [-0.18, -0.06] 0.09 (medium) Anxiety moderately mediates NSMU–GPA link NSMU → Stress → Academic Performance -0.09 0.03 -0.13 [-0.16, -0.04] 0.08 (medium) Stress plays a more prominent mediating role NSMU → Assessment Anxiety → Academic Performance -0.14 0.04 -0.17 [-0.22, -0.08] 0.12 (medium) Assessment anxiety increasingly harms performance High (+ 1 SD) NSMU → Depression → Academic Performance -0.20 0.05 -0.26 [-0.31, -0.11] 0.18 (large) Depression severely impairs GPA under high sleep disturbance NSMU → Anxiety → Academic Performance -0.17 0.05 -0.22 [-0.28, -0.09] 0.15 (large) Anxiety strongly drives academic decline NSMU → Stress → Academic Performance -0.15 0.04 -0.20 [-0.25, -0.07] 0.13 (large) Stress mediates the steepest academic impairment NSMU → Assessment Anxiety → Academic Performance -0.21 0.06 -0.25 [-0.33, -0.12] 0.19 (large) Test-related anxiety is a major indirect pathway to poor academic performance **Moderated mediation tested using PROCESS Model 14 (Hayes, 2018), n = 1,250, with 5,000 bootstrap samples. All indirect effects are significant at *p < .001. Confidence intervals (95%) reported are bias-corrected. Conditional indirect effects examined at ± 1 SD levels of moderators. Table 6 presents the results of a moderated mediation analysis exploring how sleep disturbance (SD) conditions the indirect effects of NSMU on academic performance through four psychological mediators: depression, anxiety, stress, and assessment anxiety. The analysis used 5,000 bootstrap samples and included 1,250 participants. At low levels of sleep disturbance (–1 SD), all indirect effects were statistically significant but small in magnitude. Specifically, NSMU predicted modest decreases in academic performance via depression (indirect effect = − 0.07, 95% CI [–0.14, − 0.02], κ² = 0.06), anxiety (–0.06, CI [–0.12, − 0.02], κ² = 0.05), stress (–0.05, CI [–0.11, − 0.01], κ² = 0.04), and assessment anxiety (–0.08, CI [–0.15, − 0.03], κ² = 0.06). These results suggest that even when sleep quality is relatively good, NSMU still contributes to emotional distress, which in turn mildly undermines academic outcomes though the overall effect remains limited under such conditions. Under average sleep disturbance (mean level), the indirect effects were stronger across all pathways, reaching medium effect sizes. Depression (–0.13, CI [–0.21, − 0.07], κ² = 0.11), anxiety (–0.11, CI [–0.18, − 0.06], κ² = 0.09), and stress (–0.09, CI [–0.16, − 0.04], κ² = 0.08) each significantly mediated the relationship between NSMU and academic decline. Notably, assessment anxiety had a more pronounced role (–0.14, CI [–0.22, − 0.08], κ² = 0.12), indicating that as sleep becomes moderately impaired, NSMU-induced test anxiety increasingly contributes to reduced academic performance. At high levels of sleep disturbanc e (+ 1 SD), the indirect effects became most substantial, with large effect sizes across all four mediators. Depression exhibited the strongest indirect pathway (–0.20, CI [–0.31, − 0.11], κ² = 0.18), suggesting that when students are severely sleep-deprived, NSMU intensifies depressive symptoms that heavily impair academic functioning. Anxiety (–0.17, CI [–0.28, − 0.09], κ² = 0.15) and stress (–0.15, CI [–0.25, − 0.07], κ² = 0.13) also emerged as strong mediators. The most detrimental pathway, however, was through assessment anxiety, with an indirect effect of − 0.21 (CI [–0.33, − 0.12], κ² = 0.19), highlighting that test-related anxiety is especially potent under poor sleep conditions. In sum, these findings demonstrate that sleep disturbance significantly moderates the indirect effects of NSMU on academic performance through emotional distress mechanisms. As sleep quality deteriorates, the psychological costs of NSMU rise sharply, leading to more severe academic consequences. The data underscores the importance of healthy sleep patterns as a protective buffer and suggests that interventions aimed at reducing NSMU or enhancing sleep hygiene could mitigate the negative academic impacts mediated by emotional and test-related anxiety. Discussion of Results The current study investigated the pathways through which nighttime social media use (NSMU) influences academic performance, focusing on the mediating role of mental health factors (depression, anxiety, stress, and assessment anxiety) and the moderating effects of self-esteem and sleep disturbance. The findings from the series of moderated mediation models offer critical insights into the psychological mechanisms and boundary conditions that shape the NSMU–academic performance link. The first model revealed that increased NSMU significantly predicted higher levels of depression, anxiety, stress, and assessment anxiety. These results corroborate a growing body of evidence showing the detrimental impact of excessive or poorly regulated social media usage on adolescents’ and university students’ mental health. For instance, [28; 29], in a meta-analysis, found strong positive correlations between social media use and psychological distress, especially anxiety and depression among youth. Similarly, [62; 65; 12; 64] emphasized that nighttime social media engagement disrupts emotional regulation and sleep hygiene, both of which contribute to poor mental health outcomes. The stronger predictive effect of NSMU on assessment anxiety is particularly noteworthy. This aligns with [11; 20; 21; 31], who documented that compulsive checking of social platforms before sleep increases anticipatory worry and negative academic self-appraisal key contributors to test-related anxiety. Moreover, poor sleep hygiene caused by late-night screen exposure exacerbates stress reactivity [39; 36; 58; 55], providing a possible physiological pathway through which NSMU undermines students’ emotional equilibrium. Table 4 presents compelling evidence that self-esteem functions as a significant moderator in the relationship between nighttime social media use (NSMU) and various negative emotional outcomes, including depression, anxiety, stress, and assessment anxiety. Specifically, the data reveal that the adverse emotional effects of NSMU are significantly intensified among individuals with low levels of global self-esteem and heightened self-worth instability. In other words, students who generally view themselves negatively or whose self-perceptions fluctuate dramatically depending on external feedback are more psychologically vulnerable to the consequences of excessive social media engagement during nighttime hours. This finding aligns closely with the conclusions of [53; 9; 12; 2], whose longitudinal meta-analysis demonstrated that low self-esteem is a robust risk factor for depression and other internalizing disorders. Their study also emphasized that individuals with low self-esteem are more likely to interpret social information especially criticism or exclusion negatively, which is especially relevant in the context of social media platforms where users are frequently exposed to idealized portrayals of others and evaluative feedback (likes, shares, comments). The moderating role of self-worth instability a personality trait reflecting fluctuations in one’s sense of value and self-confidence adds a critical layer to our understanding. Students with unstable self-worth appear particularly reactive to the perceived social judgments and peer comparisons facilitated by NSMU. These individuals may experience intense emotional highs and lows based on social media interactions, leading to greater levels of distress. This observation is consistent with the theoretical framework developed by [ 30 ], who posited that fragile self-esteem characterized by contingency, instability, and defensiveness renders individuals more emotionally reactive and less able to regulate negative affect in the face of social threat or rejection, which is commonplace on platforms like Instagram, Snapchat, and TikTok. Moreover, the interactive effect of low global self-esteem and high self-worth instability suggests that it is not merely a low opinion of oneself that predisposes students to harm, but rather the instability and reactivity of that self-opinion over time. This reflects a vulnerability-stress interaction, wherein students with an unstable self-concept are more likely to internalize the stressors triggered by nighttime social media use, leading to greater symptoms of depression, anxiety, and stress. For example, [59; 65; 55] found that adolescents with low self-concept clarity experienced more depressive symptoms in response to negative social media feedback, particularly when using these platforms at night when cognitive and emotional resources are depleted. These insights underscore the critical need for psychological interventions that promote self-esteem stability, rather than simply boosting global self-esteem. Interventions such as self-compassion training [40; 31; 34], resilience-building programs, and cognitive-behavioral strategies focused on decoupling self-worth from social validation may be particularly effective. By helping students cultivate a more stable, intrinsic, and non-contingent sense of self, institutions can mitigate the emotional toll of NSMU and reduce the likelihood of downstream effects on academic performance and well-being. In sum, the moderated effects identified in Table 4 not only reinforce existing theoretical models of self-esteem vulnerability but also highlight a critical intervention point: the stabilization of self-worth as a protective mechanism in the digital age. Given the ubiquitous nature of social media in students’ lives especially during nighttime hours promoting psychological resilience through self-concept clarity could be a key strategy for improving emotional health and academic success. Further analysis showed that mental health symptoms robustly predicted assessment anxiety, but this relationship was again contingent upon self-esteem levels. As with earlier findings, students with low global self-esteem and high self-doubt were most susceptible to experiencing elevated test anxiety in response to poor mental health. These results are in line with [44; 39; 38; 22], who found that students with low self-worth are more likely to internalize academic stressors, leading to heightened anxiety during exams. Interestingly, the buffering effect of high self-esteem in this model supports protective-factor theories of psychological resilience [42; 27; 28; 30], suggesting that students with a strong sense of self are better equipped to manage negative emotions and performance pressure. These findings extend the literature by highlighting self-esteem as not only a predictor of emotional well-being but also a critical moderator of academic stress processing. The final moderated mediation model (PROCESS Model 8) revealed that sleep disturbance significantly moderated the indirect relationship between NSMU and academic performance through depression, anxiety, stress, and assessment anxiety. Notably, the indirect effects were weakest under low sleep disturbance and strongest when sleep was highly disturbed, indicating that sleep quality plays a central role in shaping the academic consequences of NSMU. These results converge with evidence from [31; 36; 20], who showed that sleep disruption exacerbates the impact of emotional stress on cognitive performance. Under high sleep disturbance, the largest indirect effect was through assessment anxiety, suggesting that test-related anxiety serves as a critical pathway by which NSMU especially in poor sleep contexts translates into diminished academic success. This pattern affirms the Triple Vulnerability Model proposed by [ 5 ], which posits that biological (e.g., sleep disruption), psychological (e.g., emotional dysregulation), and environmental (e.g., NSMU) vulnerabilities interact to intensify the risk of poor outcomes. Additionally, it supports cognitive interference theory [ 19 ], which argues that anxiety consumes attentional resources necessary for optimal academic functioning an effect made worse by sleep loss. Conclusion This study set out to investigate the mental health implications of nighttime social media use (NSMU) among university students in Ghana, with a particular focus on its associations with assessment anxiety, sleep disturbance, self-esteem, and academic performance. Against the backdrop of a digitally connected yet psychologically strained generation of students, the research contributes to a critical and timely understanding of how online behaviors during nocturnal hours translate into real-world academic and emotional outcomes. The findings reveal that NSMU is not merely a leisure activity or a benign technological habit but a significant psychosocial determinant with far-reaching consequences. Students who engage in prolonged social media use during nighttime hours report increased levels of sleep disturbance. Disrupted sleep, in turn, was found to heighten assessment anxiety a form of academic stress that is particularly acute during examinations or graded evaluations. The compounding effects of poor sleep and anxiety impair not only cognitive functioning but also emotional regulation, undermining students’ capacity to perform well academically. Further, the study identifies self-esteem as both a mediating and moderating variable. Nighttime social media use was found to erode dimensions of self-esteem, particularly in areas related to emotional stability and academic competence. In addition, students with lower self-esteem were more susceptible to the negative psychological effects of NSMU. This points to a cyclical dynamic: diminished self-esteem leads to greater emotional vulnerability, which in turn exacerbates the negative impacts of digital overuse. Students with fragile self-concepts may seek validation online, only to become further entangled in a pattern of dependence that undermines their offline well-being and academic outcomes. The mediation models in this study support the conclusion that the relationship between NSMU and academic performance is not direct, but channeled through psychological distress particularly assessment anxiety and sleep disturbance. Similarly, moderation analyses confirm that individual differences, such as levels of self-esteem, influence the strength of this relationship. These results affirm the need for nuanced, multi-dimensional interventions that go beyond simple calls to reduce screen time. Mental health support, psychoeducation on sleep hygiene, and digital literacy campaigns must be combined with efforts to nurture students’ self-worth and emotional resilience. This research also draws attention to a broader paradox: while social media offers community, entertainment, and informational value, its excessive use especially at night can disconnect students from themselves. The digital world becomes a coping mechanism, yet it simultaneously contributes to the very anxieties it seeks to soothe. The allure of constant connection creates a false sense of productivity and social engagement, often at the expense of restorative sleep, emotional well-being, and academic achievement. The results of this study reinforce the conceptual framework that late-night digital engagement functions as both a symptom and a source of deeper psychological unease. From a policy perspective, the implications are profound. University administrators, counselors, educators, and national mental health advocates must acknowledge NSMU as a significant risk behavior in campus health assessments. Interventions should target both individual behavior (e.g., time management, mindfulness, sleep tracking) and systemic support (e.g., digital wellness programs, counseling services, peer mentorship initiatives). Given the rapid pace of technological immersion in young people’s lives, the time to act is now. In conclusion, this study offers a critical contribution to understanding the offline psychological and academic costs of nighttime social media use. It confirms that the lure of late-night online engagement carries measurable risks to students’ mental health, emotional regulation, and academic success. Ultimately, fostering a generation of digitally mindful, emotionally grounded, and academically resilient students requires addressing not only how they use technology but also why they turn to it especially in the quiet, vulnerable hours of the night. Recommendations Based on the findings of this study, a multi-level and context-specific set of interventions is recommended to address the growing concern of nighttime social media use (NSMU) and its adverse effects on university students in Ghana. These recommendations target students, educational institutions, mental health professionals, and policymakers. Firstly, there is a critical need for universities to integrate structured digital wellness programs into their student support services. These programs should aim at increasing students’ awareness of the psychological risks associated with excessive nighttime social media use. Such initiatives could include seminars, workshops, and peer-led campaigns that educate students on sleep hygiene, digital boundaries, and the neuroscience of screen time. Incorporating these themes into orientation activities for first-year students would be particularly impactful, given their heightened vulnerability to both academic and social pressures. Secondly, counseling and psychological services on campuses must be strengthened and tailored to address the specific challenges associated with technology-related anxiety and sleep disorders. Trained professionals should be equipped to screen for digital overuse, poor sleep patterns, and assessment-related stress during routine check-ins. Further, counselors should help students develop personalized coping strategies that involve healthier bedtime routines, emotional regulation, and self-esteem building exercises. Cognitive Behavioral Therapy (CBT)-based interventions that focus on anxiety and insomnia may be particularly useful in addressing the mediating effects identified in the study. Thirdly, academic departments and faculty must be sensitized to the mental health realities of their students, particularly during high-stakes assessment periods. Faculty can contribute to student well-being by offering flexible timelines, promoting formative assessment practices that reduce test anxiety, and ensuring that grading policies do not disproportionately heighten academic pressure. Instructors can also use technology in constructive ways, for instance, by embedding wellness prompts in their online learning platforms or using learning analytics to flag disengagement potentially linked to psychological distress. In addition, students themselves must be empowered to take agency over their digital habits. Student unions and peer mentoring groups can play an instrumental role in cultivating a campus culture that normalizes unplugging during nighttime hours. Student-led initiatives, such as “digital detox” challenges or dorm-based support circles, can reinforce communal accountability and provide alternative offline engagements that promote relaxation, interpersonal connection, and sleep readiness. Such bottom-up efforts can complement institutional policies and help students shift from compulsive social media use to intentional, balanced digital interaction. At the policy level, the Ghana Tertiary Education Commission (GTEC), in collaboration with the Ministry of Education and the Mental Health Authority of Ghana, should consider incorporating digital wellness as a component of national tertiary education and youth development frameworks. A national guideline on promoting sleep-friendly campus environments could include recommended curfews on campus Wi-Fi usage, mental health literacy campaigns, and the inclusion of digital self-regulation modules in general education curricula. Finally, future research is encouraged to explore the intersectionality of NSMU with other variables such as gender, socioeconomic status, and specific social media platforms. Longitudinal studies would also help in understanding the causal directions and long-term effects of nighttime social media use on psychological well-being and academic achievement. Qualitative explorations could further uncover the subjective narratives behind students’ motivations for NSMU, thereby informing more empathetic and effective interventions. Implications: Social Media and Mental Health The findings of this study present significant implications for understanding how social media use, particularly at night, intersects with the mental health and academic life of university students in Ghana. As social media becomes a dominant mode of communication, entertainment, and self-expression, its psychological and emotional impacts cannot be underestimated especially when its use occurs during late hours, disrupting sleep patterns and cognitive functioning. One of the central implications is that excessive nighttime social media use serves as a catalyst for mental health challenges such as anxiety, poor self-esteem, sleep disturbances, and academic underperformance. Students often rely on social media to escape academic stress or feelings of loneliness; however, this reliance inadvertently creates a cycle of over-engagement, emotional overstimulation, and poor sleep hygiene. The dopamine-driven design of these platforms encourages prolonged usage, leading to reduced sleep quality, which in turn exacerbates daytime fatigue, concentration difficulties, and psychological distress. Moreover, the impact of social comparison on platforms like Instagram, Snapchat, and TikTok contributes to feelings of inadequacy and diminished self-worth, especially when students compare their lives to curated, idealized representations of others. This study suggests that such comparisons can erode self-esteem and foster a sense of academic and social failure among students who already face the pressure of competitive academic environments. Another implication is the need to reposition digital behavior as a core factor in mental health assessment and promotion. University counseling centers and mental health practitioners must be equipped to discuss digital habits as part of regular interventions. Current mental health frameworks in tertiary institutions should be updated to incorporate guidance on managing screen time, particularly before bedtime, and on developing digital boundaries that promote well-being. The results also highlight the importance of digital literacy campaigns that go beyond technical skills to include critical awareness of emotional and psychological consequences. Students need structured opportunities to reflect on how their online interactions affect their mental states, sleep patterns, and academic functioning. Initiatives that promote mindful social media use such as "digital detox" weeks, peer-led workshops, and social media fasts during exams could foster healthier habits and create a more balanced lifestyle. From a policy perspective, this study implies that higher education institutions must adopt proactive digital wellness strategies. These strategies could include integrating digital well-being modules into orientation programs, monitoring signs of digital burnout among students, and advocating for national-level policies that regulate the addictive design features of social media applications. Lastly, the findings call for a national conversation on youth mental health in the digital age, particularly in developing countries like Ghana where mental health services are still evolving. Social media use is not inherently negative, but without appropriate support structures and awareness, it can exacerbate existing mental health vulnerabilities. A multi-sectoral approach involving educators, mental health professionals, parents, and tech developers is necessary to develop safeguards and interventions that prioritize the well-being of students in our increasingly digital society. Abbreviations • NSMU Nighttime Social Media Use • PSQI Pittsburgh Sleep Quality Index (Global Sleep Disturbance Score) • SL Sleep Latency • SD Sleep Duration • SSQ Subjective Sleep Quality • DASS 21 –Depression Anxiety Stress Scale–21 • D Depression (DASS–21 Subscale) • A Anxiety (DASS–21 Subscale) • SS Stress (DASS–21 Subscale) • FF Fearfulness (Anxiety Symptom Subscale) • SOM Somatic Symptoms • DYS Dysphoria • ANH Anhedonia • AAS Assessment Anxiety Scale • RSES Rosenberg Self–Esteem Scale • GPA Grade Point Average Declarations Ethics Approval and Consent to Participate This study was conducted in full compliance with the ethical standards of the Declaration of Helsinki and aligned with international research ethics guidelines. Ethical clearance was obtained from the Institutional Review Board (IRB) of the University of Education, Winneba. Additional institutional permissions were secured where applicable. Prior to data collection, all participants were provided with detailed information regarding the purpose, procedures, potential risks and benefits of the study, as well as the voluntary nature of their participation. Written informed consent was obtained from each participant, with assurances that they could withdraw from the study at any time without penalty. Participant confidentiality and anonymity were rigorously protected throughout the research process. All data were securely encrypted and stored on password-protected devices. Consent for Publication Not applicable. This study did not involve the collection or dissemination of personally identifiable information, images, or multimedia content. Availability of Data and Materials The datasets generated and analyzed during this study focusing on social media use, screen time, depression, mental health, assessment integrity, and academic performance among tertiary students in Ghana are available from the corresponding author, Simon Ntumi, upon reasonable request. To maintain participant confidentiality, raw data will not be publicly archived. Data access requests will be subject to institutional ethical review and approval procedures. Competing Interests The authors declare no competing interests. The research was independently conceptualized, conducted, and reported, without any external influence on the study’s design, data collection, analysis, interpretation, or dissemination. Funding This study was entirely self-funded by the authors. No external grants, sponsorship, or financial assistance were received, ensuring the objectivity and independence of the research process and outcomes. Acknowledgments The authors express their sincere gratitude to all the university students who participated in this study. Appreciation is also extended to the research assistants and data analysts for their contributions. Special thanks go to the Department of Educational Foundations and the Department of Counselling Psychology at the University of Education, Winneba, for their continuous academic and institutional support. Clinical Trial Number Not applicable. This study Author Contributions Simon Ntumi¹ led the conceptualization and design of the study, supervised data collection, performance the analysis, and drafted the initial manuscript. Divine Agbovor¹ assisted in instrument development, coordinated field data collection, and contributed to the literature review and data cleaning. Lawrence Larbi Sakyi² supported ethical review processes, contributed to the methodological framework, and reviewed statistical analysis outputs. Vincent Worlanyo Dogbe³ contributed to data interpretation and provided critical revisions of the manuscript for theoretical and psychological coherence. Collins Addo³ assisted in administering psychological scales, managed participant recruitment, and supported the proofreading process. Dortuo Daniel Kuupile⁴ reviewed the final manuscript for academic rigor, ensured alignment with educational policy implications, and contributed to referencing and formatting. All authors reviewed and approved the final manuscript prior to submission. References Alimoradi, Z. et al. Internet addiction and sleep problems: A systematic review and meta-analysis. Sleep Med. Rev. 47 , 51–61. https://doi.org/10.1016/j.smrv.2019.06.004 (2019). Asare, M., Osei, A. & Adu-Gyamfi, K. Digital behavior and mental health among Ghanaian university students: The role of social media use and perceived stress. J. Afr. Educational Res. Netw. 23 (2), 87–102 (2023). Atique, M. Digitally Hijacked: The Age of Influence: How Social Media and AI Are Reshaping Our Reality (Wipf and Stock, 2024). Bailey, C. Relationship Between Addictive Social Media Usage, Narcissistic Characteristics, and Self-Esteem in Adults (Master's thesis, Houston Baptist University). (2024). Barlow, D. H. Unraveling the mysteries of anxiety and its disorders from the perspective of emotion theory. Am. Psychol. 55 (11), 1247–1263. https://doi.org/10.1037/0003-066X.55.11.1247 (2000). Becker, S. P., Ramsay, J. R. & Willcutt, E. G. Sleep in adolescents and young adults with ADHD: Implications for functioning and treatment. Curr. Psychiatry Rep. 20 (12), 114. https://doi.org/10.1007/s11920-018-0972-0 (2018). Blankson, H., Adomako, B. & Amoako, M. Social media use among university students in Ghana: Patterns and implications. Ghana. J. Educ. Pract. 12 (3), 65–78 (2021). Blankson, H., Asare, K. B. & Osei, C. Digital media consumption and mental health among Ghanaian university students: Trends, triggers, and tensions. Afr. J. Psychol. Behav. Sci. 5 (2), 65–82 (2021). Burrell, D. N. Exploring the Cyberpsychology of Social Media Addiction and Public Health Risks among Black American Women in the USA. Health Econ. Manage. Rev. 5 (2), 14–31 (2024). Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 28 (2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4 (1989). Cain, N. Sleep and adolescent depression: A systematic review. Sleep Med. Rev. 42 , 1–9. https://doi.org/10.1016/j.smrv.2018.03.006 (2018). Campbell, C. Pseudoreality and Technology: Smartphone-Related Factors Impacting Mental Health. Psychodynamic Psychiatry . 52 (3), 345–357 (2024). Chellappa, S. L. et al. Acute exposure to evening blue-enriched light impacts on human sleep. J. Sleep Res. 20 (2), 258–265. https://doi.org/10.1111/j.1365-2869.2010.00883.x (2011). Chen, Y., Li, S., Tian, Y., Li, D. & Yin, H. Problematic Social Media Use may be Ruining Our Sleep: A Meta-Analysis on the Relationship Between Problematic Social Media Use and Sleep Quality. International J. Mental Health Addiction , 1–36. (2024). Cohen, J., Cohen, P., West, S. G. & Aiken, L. S. Applied multiple regression/correlation analysis for the behavioral sciences 3rd edn (Lawrence Erlbaum Associates, 2003). Creswell, J. W. & Creswell, J. D. Research design: Qualitative, quantitative, and mixed methods approaches 5th edn (SAGE, 2018). Driscoll, R. Westside Test Anxiety Scale Validation (ERIC Document Reproduction Service No. ED495968, 2007). Exelmans, L. & Van den Bulck, J. Bedtime mobile phone use and sleep in adults. Soc. Sci. Med. 148 , 93–101. https://doi.org/10.1016/j.socscimed.2015.11.037 (2016). Eysenck, M. W., Derakshan, N., Santos, R. & Calvo, M. G. Anxiety and cognitive performance: Attentional control theory. Emotion 7 (2), 336–353. https://doi.org/10.1037/1528-3542.7.2.336 (2007). Fatuma, D. The relationship between social media and anxiety. Res. Inventıon J. Curr. Issues Arts Manage. 3 (2), 102–107 (2024). Firth, J. et al. From online brains to online lives: understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions. World Psychiatry . 23 (2), 176–190 (2024). Garcia, M. A. Examining the Relationships Between Social Media Use Constructs and Mental and Sleep Health in Hispanic University Emerging Adults (Master's thesis, The University of Texas at El Paso). (2024). Gunarathne, N. & Walpola, A. S. A Psychological Study of the Impact of Social Media Addiction on Sleep Patterns. Int. J. Latest Technol. Eng. Manage. Appl. Sci. 13 (4), 140–145 (2024). Hayes, A. F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach 2nd edn (Guilford Press, 2018). Hirshkowitz, M. et al. National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep. Health . 1 (1), 40–43. https://doi.org/10.1016/j.sleh.2014.12.010 (2015). Jameel, A., Guo, W., Hussain, A., Kanwel, S. & Sahito, N. Exploring the mediating role of insomnia on the nexus between social media addiction and mental health among university students. Sci. Rep. 15 (1), 1–11 (2025). Kardefelt-Winther, D. A conceptual and methodological critique of Internet addiction research: Towards a model of compensatory Internet use. Comput. Hum. Behav. 31 , 351–354. https://doi.org/10.1016/j.chb.2013.10.059 (2014). Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolescence Youth . 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020). Keles, B., Grealish, A. & Leamy, M. The beauty and the beast of social media: an interpretative phenomenological analysis of the impact of adolescents' social media experiences on their mental health during the Covid-19 pandemic. Curr. Psychol. 43 (1), 96–112 (2024). Kernis, M. H. & Goldman, B. M. A multicomponent conceptualization of authenticity: Theory and research. In M. P. Zanna (Ed.), Advances in Experimental Social Psychology (Vol. 38, pp. 283–357). Academic Press. (2006). https://doi.org/10.1016/S0065-2601(06)38006-9 Khadka, C. Internet Addiction and Its Impact on Student Mental Health. Dristikon: Multidisciplinary J. 15 (1), 41–58 (2025). Kline, R. B. Principles and practice of structural equation modeling 4th edn (Guilford Press, 2016). Kuss, D. J. & Griffiths, M. D. Social networking sites and addiction: Ten lessons learned. Int. J. Environ. Res. Public Health . 12 (3), 1286–1306. https://doi.org/10.3390/ijerph120301286 (2015). Landa-Blanco, M. et al. Exploring suicide ideation in university students: sleep quality, social media, self-esteem, and barriers to seeking psychological help. Front. Psychiatry . 15 , 1352889 (2024). Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B. & Primack, B. A. The association between social media use and sleep disturbance among young adults. Prev. Med. 85 , 36–41. https://doi.org/10.1016/j.ypmed.2016.11.007 (2017). Li, X., Li, H. & Luo, J. Cross-lagged panel analysis of the relationship between social networking sites use (SNSU) and sleep problems among university students. BMC Public. Health . 24 (1), 2283 (2024). Liu, Z., Wei, Y., Yang, Y. & Kong, L. Unveiling a novel clinical risk assessment model for identifying non-suicidal self-injury risks in depressed Chinese adolescents amidst the COVID-19 pandemic: insights from low self-esteem, internet use, and sleep disturbance. Front. Psychiatry . 14 , 1259909 (2024). Lovibond, P. F. & Lovibond, S. H. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 33 (3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-U (1995). Mallary, J. Can We Live Without Our Phones? Smartphone Dependence, Impulsivity, and the Physiological Correlates of Resisting Smartphone Use (Doctoral dissertation, University of Hartford). (2025). Neff, K. D. Self-compassion, self-esteem, and well-being. Soc. Pers. Psychol. Compass . 5 (1), 1–12. https://doi.org/10.1111/j.1751-9004.2010.00330.x (2011). Oppong Asante, K. & Andoh-Arthur, J. Prevalence and determinants of depressive symptoms among university students in Ghana. J. Affect. Disord. 256 , 457–462. https://doi.org/10.1016/j.jad.2019.06.001 (2019). Orth, U. & Robins, R. W. The development of self-esteem. Curr. Dir. Psychol. Sci. 23 (5), 381–387. https://doi.org/10.1177/0963721414547414 (2014). Preacher, K. J., Rucker, D. D. & Hayes, A. F. Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivar. Behav. Res. 42 (1), 185–227. https://doi.org/10.1080/00273170701341316 (2007). Putwain, D. W., Daly, A. L., Chamberlain, S. & Sadreddini, S. Academic buoyancy and test anxiety: The buffering effect of self-worth. School Psychol. Int. 41 (2), 162–180. https://doi.org/10.1177/0143034319893267 (2020). Rao, K., John, S., Trivedi, G. & Gupta, S. Exploring The Relationship Between Nocturnal Social Media Usage, Quality of Sleep, Indecisiveness and Irritability in Emerging Adults. Int. J. Interdisciplinary Approaches Psychol. 2 (11), 9–26 (2024). Rosenberg, M. Society and the adolescent self-image (Princeton University Press, 1965). Saleem, N., Young, P. & Yousuf, S. Exploring the Relationship Between Social Media Use and Symptoms of Depression and Anxiety Among Children and Adolescents: A Systematic Narrative Review. Cyberpsychology Behav. Social Netw. 27 (11), 771–797 (2024). Samaha, M. & Hawi, N. S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput. Hum. Behav. 57 , 321–325. https://doi.org/10.1016/j.chb.2015.12.045 (2016). Savci, M. & Aysan, F. Technological addictions and social connectedness: Predictor effect of Internet addiction, social media addiction, digital game addiction and smartphone addiction on social connectedness. Dusunen Adam: J. Psychiatry Neurol. Sci. 30 (3), 202–216. https://doi.org/10.5350/DAJPN2017300303 (2017). Scott, H. & Woods, H. C. Understanding links between social media use, sleep and mental health: Recent progress and current challenges. Curr. Sleep. Med. Rep. 4 (4), 141–149. https://doi.org/10.1007/s40675-018-0122-1 (2018). Scott, H., Biello, S. M. & Woods, H. C. Social media use and adolescent sleep patterns: Cross-sectional findings from the UK Millennium Cohort Study. BMJ Open. 9 (9), e031161. https://doi.org/10.1136/bmjopen-2019-031161 (2019). Sheth, J. & Sharma, S. K. Digital Shock: Seven Shocks that are Shaping the Future (Vibrant, 2025). Sowislo, J. F. & Orth, U. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychol. Bull. 139 (1), 213–240. https://doi.org/10.1037/a0028931 (2013). Sun, C. et al. Exploring the interconnections of anxiety, depression, sleep problems and health-promoting lifestyles among Chinese university students: a comprehensive network approach. Front. Psychiatry . 15 , 1402680 (2024). Taddi, V. V., Kohli, R. K. & Puri, P. Perception, use of social media, and its impact on the mental health of Indian adolescents: A qualitative study. World J. Clin. Pediatr. 13 (3), 97501 (2024). Tirado, R. & Najmaei, M. The Interplay between Social Media Addiction and Psychological Needs: Exploring the Relationship & Implication amongst Gen Z. Asian Res. J. Arts Social Sci. 23 (6), 143–162 (2025). Topp, C. W., Østergaard, S. D., Søndergaard, S. & Bech, P. The WHO-5 Well-Being Index: A systematic review of the literature. Psychother. Psychosom. 84 (3), 167–176. https://doi.org/10.1159/000376585 (2015). Turner, K. E. The Effects of TikTok Use on Mental Health and Personal Well-Being Among Undergraduate Students (Doctoral dissertation, Alliant International University). (2025). Valkenburg, P. M., Beyens, I., Pouwels, J. L., van Driel, I. I. & Keijsers, L. Social media and adolescents’ self-esteem: Heading for a person-specific media effects paradigm. J. Communication . 71 (1), 56–78. https://doi.org/10.1093/joc/jqaa039 (2017). Vogel, E. A., Rose, J. P., Roberts, L. R. & Eckles, K. Social comparison, social media, and self-esteem. Psychol. Popular Media Cult. 3 (4), 206–222. https://doi.org/10.1037/ppm0000047 (2014). Walker, M. P. Why We Sleep: Unlocking the Power of Sleep and Dreams (Scribner, 2017). Woods, H. C. & Scott, H. #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J. Adolesc. 51 , 41–49. https://doi.org/10.1016/j.adolescence.2016.05.008 (2016). Wright, K. B. Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. J. Computer-Mediated Communication . 10 (3), JCMC1034. https://doi.org/10.1111/j.1083-6101.2005.tb00259.x (2005). Yu, D. J., Wing, Y. K., Li, T. M. & Chan, N. Y. The impact of Social Media Use on Sleep and Mental Health in Youth: a scoping review. Curr. Psychiatry Rep. 26 (3), 104–119 (2024). Yusoff, S. H. et al. Cultivating consciousness: Unveiling TikTok’s detrimental effects on youth mindset and behaviour. SEARCH J. Media Communication Res. (SEARCH) , 87. (2025). Additional Declarations No competing interests reported. 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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-6872928","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495538928,"identity":"f442ddda-45a6-4a6e-9034-0c9f60fc5574","order_by":0,"name":"Simon Ntumi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYNACAxsIzQNiE1bODFKWRrIWhsMkaJGf3X9M6kbB+cR+iQTGB2/bGOy2E9JicOcwm3SOwe3EmTMSmA3ntjEk72wgpEUiGaJlw+0ENmleoBaDA4QcNgOs5Vzi/tsJ7L+J0sJwA6zlQOIG6QQ2ZqAWO4JaDG4kG1vnGCQbz7j/sFlyzjmJBCIclvjwds4fO9n+nsMHP7wps7En7DAEYGwAEhKJDcTrgAJ7knWMglEwCkbBsAcAdVc9xofsEDAAAAAASUVORK5CYII=","orcid":"","institution":"University of Education, Winneba (UEW)","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"","lastName":"Ntumi","suffix":""},{"id":495538930,"identity":"8eb901ae-e19e-4663-a68a-8515b52fea1f","order_by":1,"name":"Divine Agbovor","email":"","orcid":"","institution":"University of Education, Winneba (UEW)","correspondingAuthor":false,"prefix":"","firstName":"Divine","middleName":"","lastName":"Agbovor","suffix":""},{"id":495538932,"identity":"f5157d94-4970-468f-8ded-314d3c814a7e","order_by":2,"name":"Lawrence Larbi Sakyi","email":"","orcid":"","institution":"Presbyterian College of Education","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"Larbi","lastName":"Sakyi","suffix":""},{"id":495538936,"identity":"d491d6e6-5bb2-40e0-9c30-9a10a8122712","order_by":3,"name":"Vincent Worlanyo Dogbe","email":"","orcid":"","institution":"University of Education, Winneba (UEW)","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"Worlanyo","lastName":"Dogbe","suffix":""},{"id":495538938,"identity":"4b33b5dd-4876-4d84-ba5e-5202dd2fa067","order_by":4,"name":"Collins Addo","email":"","orcid":"","institution":"University of Education, Winneba (UEW)","correspondingAuthor":false,"prefix":"","firstName":"Collins","middleName":"","lastName":"Addo","suffix":""},{"id":495538940,"identity":"f6d98f39-3ae9-4445-b107-669805ef2a0c","order_by":5,"name":"Dortuo Daniel Kuupile","email":"","orcid":"","institution":"University of Education, Winneba (UEW)","correspondingAuthor":false,"prefix":"","firstName":"Dortuo","middleName":"Daniel","lastName":"Kuupile","suffix":""}],"badges":[],"createdAt":"2025-06-11 14:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6872928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6872928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88387758,"identity":"d1c13e26-216f-4891-a589-5dd4419e262f","added_by":"auto","created_at":"2025-08-06 03:21:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1804452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6872928/v1/520fd011-cacd-4bf5-9ec8-c4af4b152362.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Online Allure and Offline Anxiety: Exploring the Mental Health Implications of Nighttime Social Media Use, Assessment Anxiety, Sleep Disturbance, Self-Esteem and Academic Performance of University Students in Ghana","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the digital age, the pervasive use of social media among university students has fundamentally altered the landscape of academic engagement, interpersonal communication, and mental well-being [31; 45; 1; 23]. As digital natives, students are immersed in a technology-driven environment where platforms such as Instagram, WhatsApp, TikTok, Twitter, and Facebook are seamlessly woven into the fabric of everyday life. These platforms serve not only as tools for entertainment but also as vital spaces for academic collaboration, emotional support, and social validation. However, the convenience and connectivity afforded by these platforms have also introduced new vulnerabilities. Increasingly, university students are engaging in extended nighttime use of social media a behavior pattern that is becoming both habitual and compulsive [12; 38; 52; 65]. This emerging trend has raised considerable concerns about its potential implications for psychological health, sleep quality, self-perception, and academic performance [1; 45; 37]. The phenomenon of nighttime social media use is multifaceted. For many students, the evening hours offer a rare period of uninterrupted time to catch up on social updates, respond to messages, or relax after the day’s academic and social demands. Yet, what begins as casual browsing often transforms into prolonged and excessive engagement, frequently extending late into the night and early morning hours. Studies suggest that this pattern of usage can lead to hyperarousal a state of heightened alertness that makes it difficult for the brain to wind down for sleep [35; 34; 21; 9]. While social media offers substantial benefits including enhanced peer interaction, academic networking, and identity exploration its overuse, particularly during nighttime hours, has been associated with elevated anxiety levels, sleep disturbances, diminished self-esteem, and impaired academic functioning [5; 6]. These adverse outcomes appear to be especially pronounced among young adults who are navigating the pressures of university life without adequate coping mechanisms or support systems.\u003c/p\u003e\u003cp\u003eNighttime social media use is particularly problematic because of its biological and cognitive effects on the human sleep cycle. The blue light emitted from mobile devices has been scientifically proven to inhibit melatonin production a hormone essential for the regulation of circadian rhythms and the promotion of sleep onset [5; 56; 54]. When students spend extended time in front of screens before bedtime, this disruption in melatonin secretion can lead to delayed sleep phases, fragmented sleep, and overall poor sleep quality. [10; 23; 3] further assert that the interactive and emotionally stimulating nature of social media exacerbates this problem, as students may remain mentally engaged long after disengaging from their devices. These physiological disruptions are not without consequence. Chronic sleep deprivation has been linked to impairments in cognitive functioning, emotional regulation, and academic achievement, particularly in populations already vulnerable to stress, such as university students [25; 12; 55; 58]. Moreover, the mental health implications of late-night digital engagement extend beyond sleep disturbance. The constant exposure to curated content on social media often triggers upward social comparisons, leading students to perceive their own lives as inadequate in comparison to the seemingly perfect lives of their peers. This perception can erode self-esteem and contribute to feelings of anxiety, isolation, and depression [60; 39; 31; 26]. Additionally, the culture of perpetual connectedness facilitated by social media encourages compulsive checking behaviors and fosters dependency, making it increasingly difficult for students to establish healthy digital boundaries. For those already grappling with academic pressures and performance anxieties, social media can act as a magnifier of stress, reducing focus, increasing procrastination, and negatively impacting academic outcomes [48; 52; 4].\u003c/p\u003e\u003cp\u003eThe physiological consequences of nighttime social media use are particularly troubling and increasingly well-documented in the literature. Exposure to artificial light during nighttime especially the blue light emitted by smartphones, tablets, and laptops has a significant impact on circadian physiology. Blue light suppresses melatonin secretion, a hormone produced by the pineal gland that governs the sleep-wake cycle [13; 39; 55; 17]. When melatonin levels are inhibited, individuals experience delayed sleep onset, fragmented sleep, and overall reductions in sleep quality. This disruption is not just a matter of lost hours; it undermines the restorative functions of sleep, particularly slow-wave and REM sleep, which are critical for memory consolidation, emotional regulation, and cognitive functioning [61; 12; 65; 20]. For university students who often already operate under time constraints and academic stress, the additional burden of sleep deprivation can have cascading effects on daytime performance and mental health. Furthermore, the content and nature of social media interactions contribute to physiological arousal, making it harder for individuals to relax before bed. Unlike passive media consumption (e.g., watching television), social media is inherently interactive and often emotionally charged. Engaging in heated debates, viewing distressing news, scrolling through emotionally evocative posts, or anxiously awaiting replies can lead to heightened emotional and physiological arousal. This response elevates cortisol the primary stress hormone which activates the sympathetic nervous system and delays the onset of sleep [18; 21; 9; 14]. Over time, these nightly patterns of engagement disrupt the homeostasis required for restful sleep and create a feedback loop where poor sleep increases anxiety, which in turn leads to greater social media dependence as a form of escapism or emotional regulation.\u003c/p\u003e\u003cp\u003eThe cumulative impact of these disturbances often manifests as chronic sleep deprivation, a condition that has been robustly linked to numerous negative outcomes. [25; 12] underscore that persistent lack of sleep impairs executive function, diminishes attention span, and reduces academic performance. Additionally, chronic sleep loss contributes to emotional dysregulation, increasing irritability, impulsivity, and vulnerability to mood disorders such as anxiety and depression. [50; 51; 43] further emphasize that university students are particularly susceptible, given their developmental stage, academic demands, and social pressures. These sleep-related challenges are further intensified by the academic pressures that characterize university life. In environments where academic success is tightly linked to self-worth and future opportunity, students often grapple with performance anxiety, especially around assessments and examinations. The pressure to excel is not solely academic but is also perpetuated through social media. Students are routinely exposed to curated portrayals of success, whether in the form of grades, scholarships, internships, or extracurricular achievements. These portrayals can create unrealistic benchmarks and amplify feelings of inadequacy, particularly among those who are struggling academically or emotionally. [60; 58; 41; 42] found that these upward social comparisons when individuals compare themselves to others they perceive as superior can significantly undermine self-esteem. Repeated exposure to such content fosters a sense of personal failure and disconnection, leading to a decline in academic motivation, reduced self-efficacy, and impaired cognitive focus. This psychological toll is exacerbated when students internalize these comparisons, interpreting them as evidence of personal shortcomings rather than as distorted representations of reality. The interplay between poor sleep, social comparison, and self-esteem thus creates a compounded risk profile for academic underperformance and mental health challenges.\u003c/p\u003e\u003cp\u003eIn the Ghanaian context, these issues are particularly salient and warrant urgent scholarly and institutional attention. Over the past decade, the affordability of smartphones and mobile data has transformed digital access in Ghana, especially among the youth. University students, in Ghana are increasingly dependent on digital technologies for both academic and social purposes. According to [6; 7; 39; 34], more than 80% of Ghanaian university students use social media daily, with a significant majority indicating frequent use during nighttime hours. This pattern reflects both global trends and unique local dynamics, including limited access to recreational outlets, flexible academic schedules, and the use of social media as a coping mechanism for stress and loneliness. However, Ghana’s higher education system has not kept pace with this digital transformation in terms of mental health infrastructure. Many public and private universities operate with minimal psychological support services. According to [47; 26; 36], counseling centers in Ghanaian universities are often understaffed, underfunded, or underutilized due to stigma and lack of awareness. The concept of mental health, though gaining traction in academic and professional circles, is still shrouded in cultural taboos, making it difficult for students to seek help openly. Issues such as sleep problems, assessment anxiety, or low self-esteem are frequently internalized or dismissed, leading students to cope in silence. For some, nighttime social media use becomes a maladaptive coping mechanism a way to distract from anxiety, seek validation, or feel socially connected. Unfortunately, this coping strategy often worsens the very problems it is intended to mitigate.\u003c/p\u003e\u003cp\u003eIn the context of Ghanaian higher education, limited empirical attention has been paid to the psychosocial dynamics of students’ digital lives, despite the rapid proliferation of smartphones, increased access to affordable mobile data, and the growing normalization of social media use during nighttime hours. While global research has increasingly highlighted the adverse effects of excessive social media engagement on mental health and academic outcomes [28; 1; 46; 33], there remains a relative paucity of context-specific studies in Sub-Saharan Africa particularly Ghana that examine the nuanced interplay between digital behaviors and students’ psychological well-being. Emerging local studies [e.g., 2, 20; 23; 47] confirm a worrying trend of rising mental health concerns especially anxiety, depression, and sleep-related disturbances among tertiary-level students. However, these studies often focus on general stressors such as academic workload and financial hardship, paying insufficient attention to how specific digital habits, especially late-night social media use, may compound these challenges. Furthermore, while international research has begun to explore connections between digital media use and academic disengagement or emotional exhaustion, the Ghanaian context lacks robust investigations that integrate multiple psychosocial variables such as assessment-related stress, sleep disruption, self-esteem, and academic self-efficacy within a single analytical framework. The current literature is fragmented, with existing studies often siloed across disciplines or focused on broad digital addiction metrics rather than situational patterns like nocturnal engagement. This gap restricts our ability to develop nuanced, evidence-based interventions suited to the daily realities and cultural sensibilities of Ghanaian university students. Another underexplored area is how nighttime social media use may function as both a coping mechanism and a risk factor. Theoretical frameworks such as the Compensatory Internet Use Theory [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] suggest that individuals turn to digital platforms to alleviate stress or loneliness yet this compensatory use may paradoxically exacerbate mental fatigue, sleep disorders, and academic decline. In Ghanaian higher education settings, where students often lack access to on-campus psychological support and where mental health stigma remains pervasive, social media may serve as a substitute for unavailable or inaccessible emotional resources. This dual role both as a perceived refuge and a hidden stressor has not been sufficiently unpacked in the local research landscape.\u003c/p\u003e\u003cp\u003eAdditionally, cultural and institutional variables further complicate the picture. The Ghanaian education system places significant emphasis on assessment and academic achievement, often fostering high-stakes environments that magnify students’ performance anxiety. In such contexts, late-night digital engagement may intensify rather than relieve academic stress through mechanisms such as procrastination, fear of missing out (FOMO), or harmful social comparisons with peers. Despite this, the potential mediating role of self-esteem, digital identity construction, and sleep health in shaping academic outcomes has received scant attention in local studies. This study sought to address these multifaceted research gaps by systematically exploring the mental health implications of nighttime social media use among university students in Ghana, with a particular focus on its associations with assessment-related anxiety, sleep disturbances, self-esteem, and academic performance. By drawing on contemporary psychological models including the Compensatory Internet Use Theory the research aims to unravel the complex pathways through which digital behaviors intersect with students’ emotional resilience, cognitive functioning, and academic engagement. Anchoring the inquiry in the lived experiences of Ghanaian students allows for a more culturally grounded analysis, contributing not only to the global discourse on digital mental health but also offering localized insights that can inform policy, campus counseling strategies, and digital literacy initiatives. Ultimately, this study sought to bridge empirical and practical gaps, highlighting the need for targeted, evidence-based interventions to support healthier digital engagement and holistic student well-being in Ghanaian higher education institutions.\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eCompensatory Internet Use Theory (CIUT) offers a compelling lens through which to understand the psychological motivations behind excessive or problematic internet behaviors, particularly among adolescents and young adults. Initially developed by scholars and later refined by [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the theory posits that individuals often engage with the internet, especially social media platforms, not merely for entertainment or information but as a way to cope with negative emotions or offline life challenges. According to CIUT, it is the presence of underlying psychosocial difficulties such as stress, anxiety, loneliness, low self-esteem, or academic pressure that drive individuals to seek refuge online. Rather than being inherently addictive, the internet serves a compensatory function by providing a temporary escape from real-world discomfort. Over time, however, this pattern of compensatory behavior can lead to maladaptive outcomes, including digital dependency, worsening of mental health symptoms, and decreased functioning in key life domains. The core principle of CIUT is that individuals are drawn to the internet not simply because it is appealing, but because it provides a mechanism for emotional regulation when other coping strategies are insufficient or unavailable. This theoretical orientation contrasts with traditional addiction models, which focus largely on the properties of digital technologies themselves. CIUT instead emphasizes the importance of users’ internal states and situational stressors. In this way, the theory encourages a more empathetic and context-sensitive understanding of internet use behaviors, particularly among populations experiencing high levels of psychosocial stress. The current study, which examines the mental health implications of nighttime social media use among university students in Ghana, aligns closely with the tenets of CIUT. Nighttime social media engagement, in this context, can be interpreted as a form of psychological compensation. Students facing academic demands, emotional strain, or interpersonal difficulties may be drawn to social media platforms as a way to temporarily escape their worries or seek validation and social connection. Rather than confronting their stressors directly, they may find relief in the immersive and distracting environment that social media provides, especially during late hours when other sources of support are limited. This behavior is consistent with CIUT’s assertion that excessive digital use is often driven by attempts to manage or regulate negative affect.\u003c/p\u003e\u003cp\u003eThe findings of the study further support the application of CIUT. The observed correlations between nighttime social media use and sleep disturbance, assessment anxiety, and reduced academic performance reflect the potential cost of compensatory behavior. While engaging with social media may offer short-term psychological relief, it disrupts sleep patterns, thereby impairing cognitive functioning and academic engagement. Moreover, mediation analysis revealed that sleep disturbance significantly mediates the relationship between nighttime social media use and academic performance. This pathway aligns with CIUT’s proposition that compensatory internet use may worsen the very stressors it seeks to mitigate, resulting in a cycle of distress and avoidance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, the study found that self-esteem moderates the relationship between nighttime social media use and assessment anxiety. This observation also resonates with CIUT, which suggests that individuals with stronger psychological resources or higher emotional resilience are less likely to engage in compensatory digital behaviors. Students with high self-esteem may rely on more adaptive coping strategies, making them less vulnerable to the anxiety-amplifying effects of excessive social media use. In effect, Compensatory Internet Use Theory provides a theoretically sound and contextually appropriate framework for understanding the patterns and consequences of nighttime social media use among university students in Ghana. By highlighting the compensatory nature of digital engagement in the face of psychological distress, CIUT enriches the interpretation of the study’s findings and underscores the need for interventions that address not just behavior, but also the emotional and contextual drivers behind it [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This perspective is especially vital in the Ghanaian higher education context, where academic pressures, limited mental health resources, and changing digital habits intersect to shape students’ well-being and academic success.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHypotheses\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH₁ (Mediation Hypothesis)\u003c/b\u003e: Sleep disturbance mediates the relationship between nighttime social media use and academic performance, such that higher nighttime social media use leads to greater sleep disturbance, which in turn results in lower academic performance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH₂ (Moderation Hypothesis)\u003c/b\u003e: Self-esteem moderates the relationship between nighttime social media use and assessment anxiety, such that the positive association between social media use and anxiety is weaker among students with high self-esteem and stronger among those with low self-esteem.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH₃ (Moderated Mediation Hypothesis)\u003c/b\u003e: The indirect effect of nighttime social media use on academic performance through assessment anxiety is moderated by sleep disturbance, such that the mediation effect is stronger when sleep disturbance is high and weaker when sleep disturbance is low.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study adopted a quantitative, correlational survey design to examine the mental health implications of nighttime social media use among university students in Ghana. The correlational approach was chosen because the primary aim of the study was to investigate the statistical relationships among multiple variables namely, nighttime social media use, sleep disturbance, assessment anxiety, self-esteem, academic performance, and mental health indicators such as anxiety, stress, and depression. As described by [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a correlational design is appropriate when researchers seek to determine the degree and direction of associations among naturally occurring variables without manipulating any of them. In addition to being correlational, the study was also cross-sectional, meaning that all data were collected at a single point in time. This approach enabled the researchers to capture a snapshot of students’ digital behaviors, academic stressors, and mental health status during a specific academic term. The design was especially suitable for a large-scale survey distributed across multiple institutions, allowing for the efficient comparison of trends among subgroups. Furthermore, the design facilitated the application of moderation and mediation analyses to explore not just direct associations, but also the conditional and indirect effects among the key variables. By identifying these interrelationships, the study aimed to contribute nuanced insights into how digital engagement at night may influence students’ psychological well-being in the context of Ghanaian higher education.\u003c/p\u003e\u003ch3\u003eParticipants\u003c/h3\u003e\u003cp\u003eThe population for the study comprised undergraduate students enrolled in five major public universities in Ghana: the University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), University of Cape Coast (UCC), University of Education, Winneba (UEW), and the University for Development Studies (UDS). These institutions were intentionally selected because they collectively represent a diverse cross-section of Ghana’s university population, covering various geographical regions (Greater Accra, Ashanti, Central and Northern Region), academic disciplines, and student demographics. This selection ensured broader generalizability of the study’s findings across different academic and cultural contexts in Ghana. A multi-stage sampling technique was employed to enhance representativeness and reduce sampling bias. In the first stage, the total sample size was stratified based on the individual universities to reflect their proportional enrollment numbers, ensuring that no single institution was over- or under-represented. In the second stage, simple random sampling was used to recruit participants from within each stratum. The recruitment was conducted via official student WhatsApp platforms, class group mailing lists, and institutional online portals. This digital strategy aligned with the study’s focus on social media use and facilitated participation from students who were already active online. A final sample of 1,250 students was obtained, exceeding the minimum required for correlational studies involving multiple predictors and potential interaction effects. According to [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], larger samples are essential for detecting medium to small effect sizes, particularly when employing inferential techniques such as PROCESS macro analyses for testing moderated mediation. The sample size also allowed for subgroup comparisons (e.g., by gender, program of study, or university) and provided sufficient statistical power (typically ≥ 0.80) for all planned analyses. Participants included students from various levels of study (first year to final year), academic backgrounds (arts, sciences, social sciences, business, and education), and residential statuses (on-campus and off-campus). This heterogeneity ensured that the findings would reflect a comprehensive understanding of how nighttime social media behaviors and psychological well-being intersect in the Ghanaian university context.\u003c/p\u003e\u003ch3\u003eData Collection Procedure\u003c/h3\u003e\u003cp\u003eData for this study were gathered through an online structured questionnaire administered via Google Forms over a three-month period, from January to March 2025. The use of an online data collection platform was chosen for both practical and methodological reasons. It enabled efficient dissemination of the survey link to a large and geographically dispersed sample across the five participating universities University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), University of Cape Coast (UCC), University of Education, Winneba (UEW), and the University for Development Studies (UDS). The online format also ensured participant anonymity, reduced data entry errors, and offered students the flexibility to complete the survey at their convenience, which was particularly beneficial for minimizing nonresponse bias [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The survey link was strategically shared through multiple digital channels, including institutional email portals, WhatsApp class groups, student representative council (SRC) networks, and official student mailing lists. These platforms were selected to maximize reach and engagement, given the high digital literacy and mobile device ownership among university students in Ghana [8; 7]. To enhance participation and minimize attrition, reminder messages were sent biweekly through WhatsApp and email platforms. These reminders emphasized the voluntary nature of the study and the importance of student voices in understanding the psychological effects of digital media use. Prior to launching the full-scale survey, a pilot study involving 50 undergraduate students from UEW and UCC was conducted. The pilot aimed to evaluate the questionnaire’s clarity, internal consistency, ease of navigation, and average completion time. Feedback from the pilot respondents revealed minor issues with item wording and redundancy, which were revised accordingly. The pilot data also yielded an acceptable preliminary Cronbach’s alpha coefficient of 0.87, affirming the reliability of the instruments used. To uphold ethical standards, the study received ethical clearance from the Institutional Review Boards (IRBs) of the University of Education, Winneba (UEW) and the University of Cape Coast (UCC). In addition, permission to conduct the research was obtained from the Student Representative Councils (SRCs) of all five participating universities. All participants were required to provide digital informed consent before accessing the questionnaire. The consent form outlined the purpose of the study, the voluntary nature of participation, the anonymity of responses, and the absence of any foreseeable risks. Participants were assured that their responses would be used strictly for academic purposes and would remain confidential.\u003c/p\u003e\u003ch3\u003eInstrumentation\u003c/h3\u003e\u003cp\u003eThe final questionnaire consisted of six core sections, each aligned with one of the primary constructs of the study: nighttime social media use, sleep disturbance, assessment anxiety, self-esteem, mental health outcomes, and academic performance. Each section utilized validated and widely adopted psychometric scales to ensure content validity and comparability with prior research. All items were presented on a 5-point Likert scale, tailored to the respective construct ranging from “Strongly Disagree” to “Strongly Agree” or “Never” to “Always.”\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNighttime Social Media Use\u003c/b\u003e: This variable was assessed using a modified version of the Social Media Engagement Questionnaire (SMEQ) developed by [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Items focused on the frequency, intensity, and duration of social media use during nighttime hours (between 9:00 PM and 3:00 AM). The scale was adapted to reflect the most commonly used platforms among Ghanaian students, including WhatsApp, Instagram, Snapchat, TikTok, and X (formerly Twitter). Modifications were made to contextualize the items linguistically and culturally. The reliability coefficient (Cronbach’s alpha) for this scale was 0.84.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSleep Disturbance\u003c/b\u003e: Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-report instrument designed to assess sleep disturbances and quality over a one-month period [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The PSQI includes dimensions such as sleep latency, duration, efficiency, disturbances, and daytime dysfunction. The scale has demonstrated strong psychometric properties in both Western and African university populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this study, the Cronbach’s alpha was 0.82.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAssessment Anxiety\u003c/b\u003e: Academic-related anxiety was measured using the Westside Test Anxiety Scale (WTAS) developed by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This 10-item scale captures cognitive worry, emotional tension, and self-perceptions related to academic evaluations. Students rated their agreement with statements reflecting test anxiety, such as “I feel jittery or nervous during exams.” The WTAS has been used in various cultural settings and displayed high internal consistency in the current study (α = 0.86).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSelf-Esteem\u003c/b\u003e: The Rosenberg Self-Esteem Scale (RSES) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] was employed to evaluate students’ global self-worth. This 10-item scale includes both positively and negatively worded items assessing general feelings of self-respect and self-acceptance. The RSES remains one of the most widely validated measures of self-esteem and has been previously validated among African student populations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The Cronbach’s alpha for this scale was 0.88.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMental Health Outcomes\u003c/b\u003e: The study assessed students’ emotional well-being using the Depression, Anxiety, and Stress Scale–21 (DASS-21) developed by [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This scale consists of three subscales with seven items each, targeting symptoms of depression (e.g., hopelessness), anxiety (e.g., panic), and stress (e.g., tension). The DASS-21 has been validated for use in young adult populations and translated into multiple languages, including studies conducted within sub-Saharan Africa [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The overall reliability for the mental health component in this study was 0.90.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAcademic Performance\u003c/b\u003e: Academic achievement was measured through self-reported Grade Point Averages (GPAs) from the most recent semester. In addition, a three-item subscale assessing perceived academic efficacy was included, capturing students’ self-beliefs in managing academic workloads and succeeding in exams. Items included statements like “I believe I am performing well in my courses this semester.” The reliability for this scale section was 0.81.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003eOverall, the composite questionnaire achieved a high internal consistency with a total Cronbach’s alpha of 0.89, indicating robust reliability across all constructs. This comprehensive instrumentation allowed for rigorous correlational, mediation, and moderation analyses to explore the nuanced relationships among nighttime digital behaviors and student mental health outcomes.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eFollowing the closure of the online survey, all responses were exported directly from Google Forms into Microsoft Excel, cleaned, and subsequently imported into IBM SPSS Statistics (version 27) for statistical analysis. The analytic approach was structured in multiple phases to align with the study’s correlational design and to investigate both direct and indirect relationships among the variables of interest. The first phase of analysis involved descriptive statistics, including means, standard deviations, frequencies, and percentages to summarize the demographic characteristics of the sample, the distribution of nighttime social media use, and the prevalence of psychological symptoms such as sleep disturbances, test anxiety, and mental health outcomes. These statistics helped characterize behavioral patterns and symptom severity across the five participating universities and enabled comparisons across demographic subgroups (e.g., gender, academic level, and university affiliation). In the second phase, Pearson product-moment correlation coefficients were calculated to assess the bivariate relationships among the key study variables: nighttime social media use, sleep disturbance, academic anxiety, self-esteem, mental health outcomes, and academic performance. This step was crucial in identifying the strength and direction of associations, in line with the study’s correlational framework [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To test the hypothesized indirect and conditional effects, the third phase utilized the PROCESS Macro for SPSS (Model 4 and Model 8) developed by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This enabled mediation and moderated mediation analyses using ordinary least squares (OLS) path analysis. Specifically: Mediation analysis (Model 4) examined whether sleep disturbance served as a mediator in the relationship between nighttime social media use (independent variable) and mental health outcomes (dependent variables) such as depression, anxiety, and stress. Bootstrapping with 5,000 resamples was used to generate bias-corrected confidence intervals for the indirect effects, providing robust evidence for mediation without requiring normality assumptions. Moderation analysis (Model 1) was conducted to explore whether self-esteem moderated the strength of the relationship between nighttime social media use and psychological distress. The interaction term (social media use × self-esteem) was created automatically within PROCESS and tested for statistical significance.\u003c/p\u003e\u003cp\u003eA moderated mediation model (Model 8) was then estimated to determine whether academic pressure (measured via perceived academic efficacy and GPA stress) moderated the indirect effect of nighttime social media use on mental health outcomes through sleep disturbance. This model allowed for conditional indirect effects to be estimated at varying levels of academic pressure (low, medium, high), consistent with recommendations by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. All statistical tests were two-tailed and conducted at a 95% confidence level, with statistical significance set at p \u0026lt; .05. The dataset was screened for missing data, which were minimal (\u0026lt; 2%) and considered missing completely at random (MCAR). Therefore, listwise deletion was employed for cases with missing data, as it was deemed the most appropriate technique given the negligible rate of missingness and the large sample size (n = 1,250). Multicollinearity, homoscedasticity, and normality of residuals were also checked before performing regression-based analyses. Variance inflation factors (VIFs) remained below the threshold of 5, and visual inspection of residual plots confirmed that assumptions were not violated. The analytical strategy enabled rigorous testing of complex relationships and helped uncover both direct and interactive mechanisms underlying the psychological impact of nighttime digital engagement.\u003c/p\u003e\u003ch3\u003eEthical Considerations\u003c/h3\u003e\u003cp\u003eThis study conformed strictly to internationally accepted ethical principles governing research involving human participants, including the Belmont Report and the Declaration of Helsinki. Prior to data collection, ethical clearance was obtained from the Institutional Review Boards (IRBs) of both the University of Education, Winneba (Ref No. UEW/IRB/25/044). These bodies reviewed the research protocol to ensure that risks were minimized, consent procedures were adequate, and participant rights were protected. Participation in the study was voluntary, and students were fully informed of the purpose, scope, and procedures of the research via a digital informed consent form. This form appeared as the first page of the online survey and required participants to confirm their willingness to participate before they could proceed to the questionnaire. The consent form explicitly stated that participants could withdraw from the study at any point without any negative consequences. Confidentiality was emphasized throughout the data collection process. No personally identifiable information was collected, and all responses were stored in password-protected databases accessible only to the principal investigators. Anonymity was ensured by disabling IP tracking in Google Forms and by avoiding the collection of names, student IDs, or email addresses. In addition, participants were assured that the data collected would be used solely for academic research purposes, and that the findings would be reported in aggregate form, with no attribution to individual respondents or institutions. Potential psychological risks were deemed minimal, but participants were provided with the contact details of counseling centers at their respective universities in case they experienced distress while reflecting on their mental health. The ethical rigor and transparent procedures adopted in this study not only safeguarded participant welfare but also enhanced the credibility and replicability of the research findings.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the statistical findings derived from a series of analyses exploring the relationships among nighttime social media use (NSMU), sleep disturbance, mental health outcomes, academic performance, and assessment anxiety, with particular focus on the moderating and mediating roles of self-esteem and sleep quality. The goal of this analysis is to test several hypothesized models regarding the psychological and academic consequences of nighttime digital habits among university students.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson Correlation Matrix of Key Variables and Subscales\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubscales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSMU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.48***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.42***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e− .38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.45***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.36***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.29***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.39***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.36***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.35***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.42***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .31***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .28***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSQI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.69***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e− .62***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.74***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.42***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.39***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.45***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.41***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.40***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .29***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .33***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e− .56***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.67***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.35***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.30***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.41***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.36***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.36***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .27***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .30***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e− .58***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e− .35***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e− .32***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e− .29***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e− .37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e− .34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e− .33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e− .30***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e− .32***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e.25***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.31***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.40***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.44***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.40***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.39***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .28***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .32***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21-D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.82***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.78***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.71***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.69***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.66***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.63***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.52***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .60***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .48***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.75***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.68***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.66***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.63***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.61***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.49***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .57***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .45***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.64***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.63***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.60***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.57***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.47***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .54***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .43***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.74***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.66***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.56***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .58***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .44***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.67***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.64***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.54***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .55***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .41***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.62***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.52***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .52***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .40***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21-S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.53***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .54***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .41***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e− .51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e− .39***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.43***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003cem\u003ep \u0026lt; .001 (two-tailed). All reported values are Pearson correlation coefficients (r). Sample size (n) = 1,250. No multicollinearity detected; all VIFs \u0026lt; 2.0.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe correlation matrix in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveals several statistically significant relationships among the variables and subscales, shedding light on the complex interactions between nighttime social media use (NSMU), sleep parameters, psychological well-being (DASS-21), assessment anxiety, self-esteem, and academic performance. Nighttime Social Media Use (NSMU) was significantly and positively correlated with \u003cem\u003esleep disturbance\u003c/em\u003e (r = .48, p \u0026lt; .001), \u003cem\u003esleep latency\u003c/em\u003e (r = .42, p \u0026lt; .001), and \u003cem\u003esubjective sleep quality\u003c/em\u003e issues (r = .45, p \u0026lt; .001), indicating that increased NSMU is associated with poorer sleep quality. NSMU was also positively correlated with symptoms of \u003cem\u003edepression\u003c/em\u003e (r = .36), \u003cem\u003eanxiety\u003c/em\u003e (r = .39), \u003cem\u003estress\u003c/em\u003e (r = .35), and all related subscales such as \u003cem\u003edysphoria\u003c/em\u003e (r = .33), \u003cem\u003eanhedonia\u003c/em\u003e (r = .29), \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .36), \u003cem\u003efearfulness\u003c/em\u003e (r = .35), and \u003cem\u003eassessment anxiety\u003c/em\u003e (r = .42), all at p \u0026lt; .001. Importantly, NSMU had negative correlations with \u003cem\u003eself-esteem\u003c/em\u003e (r = –.31) and \u003cem\u003eacademic performance\u003c/em\u003e (r = –.28), suggesting that higher NSMU is linked to lower self-worth and GPA. Sleep Disturbance (PSQI) showed strong positive associations with \u003cem\u003esleep latency\u003c/em\u003e (r = .69), \u003cem\u003esubjective sleep quality\u003c/em\u003e (r = .74), and symptoms of \u003cem\u003edepression\u003c/em\u003e (r = .42), \u003cem\u003eanxiety\u003c/em\u003e (r = .45), and \u003cem\u003estress\u003c/em\u003e (r = .41), all highly significant. Negative correlations were observed with \u003cem\u003esleep duration\u003c/em\u003e (r = –.62), \u003cem\u003eself-esteem\u003c/em\u003e (r = –.29), and \u003cem\u003eacademic performance\u003c/em\u003e (r = –.33), reinforcing the detrimental role of disturbed sleep on mental health and performance. Sleep Latency followed a similar pattern, with moderate-to-strong positive correlations with \u003cem\u003esubjective sleep quality\u003c/em\u003e (r = .67), and the depression-related subscales, including \u003cem\u003edepression\u003c/em\u003e (r = .37), \u003cem\u003edysphoria\u003c/em\u003e (r = .35), \u003cem\u003eanhedonia\u003c/em\u003e (r = .30), \u003cem\u003eanxiety\u003c/em\u003e (r = .41), and \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .38). It was negatively correlated with \u003cem\u003esleep duration\u003c/em\u003e (r = –.56), \u003cem\u003eself-esteem\u003c/em\u003e (r = –.27), and \u003cem\u003eGPA\u003c/em\u003e (r = –.30), suggesting that longer time to fall asleep is tied to poorer outcomes. Sleep Duration was inversely correlated with nearly all other sleep and psychological distress indicators, including \u003cem\u003esubjective sleep quality\u003c/em\u003e (r = –.58), \u003cem\u003edepression\u003c/em\u003e (r = –.35), and \u003cem\u003estress\u003c/em\u003e (r = –.30), but positively linked to \u003cem\u003eself-esteem\u003c/em\u003e (r = .25) and \u003cem\u003eGPA\u003c/em\u003e (r = .31), indicating that longer sleep duration is beneficial. The DASS-21 Depression Subscale showed extremely high correlations with its components: \u003cem\u003edysphoria\u003c/em\u003e (r = .82), \u003cem\u003eanhedonia\u003c/em\u003e (r = .78), as well as other psychological distress measures like \u003cem\u003eanxiety\u003c/em\u003e (r = .71), \u003cem\u003estress\u003c/em\u003e (r = .69), and \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .69). These results confirm the internal consistency of the depression subscale and its overlap with other distress domains. Negative correlations with \u003cem\u003eself-esteem\u003c/em\u003e (r = –.60) and \u003cem\u003eGPA\u003c/em\u003e (r = –.48) further emphasize the adverse impact of depression. Similarly, the DASS-21 Anxiety Subscale had high positive correlations with \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .74), \u003cem\u003efearfulness\u003c/em\u003e (r = .70), and \u003cem\u003estress\u003c/em\u003e (r = .66), suggesting shared variance among anxiety-related experiences. Negative associations with \u003cem\u003eself-esteem\u003c/em\u003e (r = –.58) and \u003cem\u003eGPA\u003c/em\u003e (r = –.44) reflect the cognitive and performance-related impairments associated with anxiety.\u003c/p\u003e\u003cp\u003eThe DASS-21 Stress Subscale also demonstrated strong positive correlations with \u003cem\u003efearfulness\u003c/em\u003e (r = .62), \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .64), and \u003cem\u003eassessment anxiety\u003c/em\u003e (r = .53), supporting its convergent validity. It had negative correlations with \u003cem\u003eself-esteem\u003c/em\u003e (r = –.54) and \u003cem\u003eGPA\u003c/em\u003e (r = –.41). Assessment Anxiety (AAS) correlated strongly with almost all psychological distress variables, including \u003cem\u003edepression\u003c/em\u003e (r = .52), \u003cem\u003eanxiety\u003c/em\u003e (r = .56), \u003cem\u003estress\u003c/em\u003e (r = .53), and even \u003cem\u003esomatic symptoms\u003c/em\u003e (r = .54), reinforcing the link between academic stress and overall mental health. Assessment anxiety also negatively correlated with \u003cem\u003eself-esteem\u003c/em\u003e (r = –.51) and \u003cem\u003eGPA\u003c/em\u003e (r = –.39). Self-Esteem (RSES) consistently showed negative correlations with NSMU (r = –.31), sleep issues, and all DASS-21 subscales, including \u003cem\u003edepression\u003c/em\u003e (r = –.60), \u003cem\u003eanxiety\u003c/em\u003e (r = –.58), and \u003cem\u003estress\u003c/em\u003e (r = –.54), while correlating positively with \u003cem\u003eGPA\u003c/em\u003e (r = .43). This suggests that self-esteem plays a buffering role in academic and psychological outcomes. Finally, Academic Performance (GPA) was negatively associated with all indicators of distress and sleep disturbance, including NSMU (r = –.28), \u003cem\u003edepression\u003c/em\u003e (r = –.48), \u003cem\u003eanxiety\u003c/em\u003e (r = –.44), and \u003cem\u003eassessment anxiety\u003c/em\u003e (r = –.39), but positively with \u003cem\u003eself-esteem\u003c/em\u003e (r = .43) and \u003cem\u003esleep duration\u003c/em\u003e (r = .31), highlighting the critical role of mental well-being and rest in academic success. In summary, the matrix underscores the interconnectedness of sleep quality, psychological health, social media habits, and academic performance. High NSMU and poor sleep were linked to elevated psychological distress and lower GPA and self-esteem, whereas better sleep and higher self-esteem appeared protective.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Analysis of the Effect of Nighttime Social Media Use on Academic Performance via Sleep Disturbance (H₁) PROCESS Model 4 (n = 1,250, bootstrap = 5,000 samples)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Coef (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et / Z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI (LL, UL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR²\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eEffect Size (f²)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea. Nighttime Social Media → Sleep Disturbance (Global PSQI Score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.40, 0.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea₁. Nighttime Social Media → Sleep Latency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.27, 0.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea₂. Nighttime Social Media → Subjective Sleep Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.22, 0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ea₃. Nighttime Social Media → Sleep Duration (reversed; poor duration = higher score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.15, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eb. Sleep Disturbance → Academic Performance (GPA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.57, -0.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eb₁. Sleep Latency → GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.42, -0.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eb₂. Subjective Sleep Quality → GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.36, -0.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eb₃. Sleep Duration → GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.017**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.35, -0.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec. Total Effect: Nighttime Social Media → Academic Performance (without mediator)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.49, -0.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e**c′. Direct Effect: Nighttime Social Media → Academic Performance (controlling for sleep)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.013**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.27, -0.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Effect (a × b): via Sleep Disturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSobel Z = -4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.32, -0.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e**All coefficients are unstandardized (B) unless otherwise noted. Bootstrap confidence intervals (5,000 samples) were used to estimate indirect effects. Sobel test used to confirm significance of indirect path. Significance levels: *p \u0026lt; .05, **p \u0026lt; .01, *\u003c/b\u003e\u003cem\u003ep \u0026lt; .001. R² = proportion of variance explained; f² = Cohen’s effect size (0.02 = small, 0.15 = medium, 0.35 = large).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe mediation analysis explored whether sleep disturbance mediates the relationship between nighttime social media use and academic performance among university students, using PROCESS Model 4 with 5,000 bootstrap samples (n = 1,250). The results revealed a significant positive effect of nighttime social media use on overall sleep disturbance, as measured by the global Pittsburgh Sleep Quality Index (PSQI) score (B = 0.52, SE = 0.06, β = 0.48, \u003cem\u003et\u003c/em\u003e = 8.67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.40, 0.64]), with a coefficient of determination (R²) of .23 and a medium to large effect size (f² = .30). Further analysis of individual sleep components showed that nighttime social media use significantly increased sleep latency (B = 0.37, SE = 0.05, β = 0.41, \u003cem\u003et\u003c/em\u003e = 7.40, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.27, 0.48], R² = .18, f² = .22), decreased subjective sleep quality (B = 0.33, SE = 0.06, β = 0.35, \u003cem\u003et\u003c/em\u003e = 5.50, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.22, 0.44], R² = .16, f² = .19), and was associated with shorter sleep duration (reverse-coded; B = 0.29, SE = 0.07, β = 0.31, \u003cem\u003et\u003c/em\u003e = 4.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.15, 0.43], R² = .13, f² = .15). In terms of the effect of sleep on academic performance, overall sleep disturbance significantly predicted lower GPA (B = -0.41, SE = 0.08, β = -0.39, \u003cem\u003et\u003c/em\u003e = -5.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.57, -0.25], R² = .21, f² = .27). Disaggregated sleep components also had significant negative effects on GPA. Increased sleep latency was associated with lower GPA (B = -0.28, SE = 0.07, β = -0.30, \u003cem\u003et\u003c/em\u003e = -4.00, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.42, -0.14], R² = .15, f² = .18). Similarly, poor subjective sleep quality negatively predicted GPA (B = -0.24, SE = 0.06, β = -0.26, \u003cem\u003et\u003c/em\u003e = -4.00, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.36, -0.12], R² = .14, f² = .16), and shorter sleep duration was also a significant predictor (B = -0.19, SE = 0.08, β = -0.21, \u003cem\u003et\u003c/em\u003e = -2.38, \u003cem\u003ep\u003c/em\u003e = .017, 95% CI [-0.35, -0.03], R² = .11, f² = .10). The total effect of nighttime social media use on academic performance (without the mediator) was significantly negative (B = -0.36, SE = 0.07, β = -0.33, \u003cem\u003et\u003c/em\u003e = -5.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.49, -0.22], R² = .26, f² = .35). When sleep disturbance was included as a mediator, the direct effect (c′ path) remained significant but was reduced (B = -0.15, SE = 0.06, β = -0.14, \u003cem\u003et\u003c/em\u003e = -2.50, \u003cem\u003ep\u003c/em\u003e = .013, 95% CI [-0.27, -0.03]), suggesting partial mediation. The indirect effect of nighttime social media use on academic performance through sleep disturbance was also significant (B = -0.21, SE = 0.05, Sobel \u003cem\u003eZ\u003c/em\u003e = -4.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% bootstrap CI [-0.32, -0.13]), indicating that sleep disturbance partially explains the adverse impact of nighttime social media use on students’ academic outcomes. These findings support the mediation hypothesis and highlight the importance of sleep quality as a critical mechanism linking social media habits at night with academic performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eModeration Analysis of the Relationship Between Nighttime Social Media Use and Assessment Anxiety by Self-Esteem (H₂)\u003c/b\u003e PROCESS Model 1 (n = 1,250)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor / Interaction Term\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Coef (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI (LL, UL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR²\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔR²\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEffect Size (f²)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNighttime Social Media Use (Total Score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.37, 0.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.61 (large)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Esteem (Rosenberg Total)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.43, -0.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubscale Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSMU – Duration of Use (Hours per Night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.16, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.30 (medium)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSMU – Frequency of Platform Switching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[0.11, 0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSES – Self-Confidence (Positive Items)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.36, -0.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSES – Self-Worth Instability (Reverse-Coded)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.33, -0.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteraction Terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSMU × Self-Esteem (Overall Interaction)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.21, -0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.12 (moderate)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration × Self-Confidence Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.16, -0.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency × Instability Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.008**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e[-0.14, -0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Summary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR² (Full Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔR² (Interaction Only)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**PROCESS Model 1 used with 5,000 bootstrap samples. All coefficients are unstandardized unless otherwise stated. Significance levels: **p \u0026lt; .01\u003c/em\u003e, \u003cb\u003e*\u003c/b\u003e\u003cem\u003ep \u0026lt; .001. ΔR² reflects change in variance explained by interaction terms. f² = Cohen’s effect size guideline: 0.02 = small, 0.15 = medium, 0.35 = large.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe moderation analysis tested whether self-esteem moderates the relationship between nighttime social media use (NSMU) and assessment anxiety, employing PROCESS Model 1 with a sample of 1,250 students. The main effects revealed that higher nighttime social media use significantly predicted increased assessment anxiety (B = 0.47, SE = 0.05, β = 0.42, \u003cem\u003et\u003c/em\u003e = 9.40, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.37, 0.58]), with a strong model fit (R² = .38) and a large effect size (f² = .61). Conversely, higher self-esteem (measured by the Rosenberg Self-Esteem Scale) was associated with lower levels of assessment anxiety (B = -0.35, SE = 0.04, β = -0.38, \u003cem\u003et\u003c/em\u003e = -8.75, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.43, -0.27]). Analysis of subscales provided further nuance. Specifically, duration of nighttime social media \u003cb\u003euse\u003c/b\u003e (in hours per night) significantly predicted greater assessment anxiety (B = 0.28, SE = 0.06, β = 0.30, \u003cem\u003et\u003c/em\u003e = 4.67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.16, 0.40], R² = .23, f² = .30), and frequency of platform switching also had a significant positive association (B = 0.25, SE = 0.07, β = 0.26, \u003cem\u003et\u003c/em\u003e = 3.57, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.11, 0.39], R² = .19, f² = .24). On the self-esteem subcomponents, self-confidence (positive RSES items) was negatively related to anxiety (B = -0.26, SE = 0.05, β = -0.29, \u003cem\u003et\u003c/em\u003e = -5.20, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.36, -0.16], R² = .21, f² = .27), while self-worth instability (reverse-coded items) also predicted greater anxiety (B = -0.22, SE = 0.06, β = -0.25, \u003cem\u003et\u003c/em\u003e = -3.67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.33, -0.11], R² = .17, f² = .21). Most crucially, the interaction term between overall nighttime social media use and self-esteem was statistically significant (B = -0.14, SE = 0.03, β = -0.18, \u003cem\u003et\u003c/em\u003e = -4.67, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [-0.21, -0.08]), with an additional variance explained of ΔR² = .04 and a moderate interaction effect size (f² = .12). This indicates that the positive association between social media use and assessment anxiety was weaker among students with higher self-esteem, demonstrating a buffering or protective moderation effect. Similarly, the interaction between duration of use and self-confidence was significant (B = -0.10, SE = 0.03, β = -0.12, \u003cem\u003et\u003c/em\u003e = -3.33, \u003cem\u003ep\u003c/em\u003e = .001, 95% CI [-0.16, -0.04]), as was the interaction between frequency of platform switching and self-worth instability (B = -0.08, SE = 0.03, β = -0.10, \u003cem\u003et\u003c/em\u003e = -2.67, \u003cem\u003ep\u003c/em\u003e = .008, 95% CI [-0.14, -0.02]). Both findings reinforce the moderating role of self-esteem dimensions in weakening the negative psychological impact of excessive or fragmented nighttime social media use. Overall, the full model explained 38% of the variance in assessment anxiety (R² = .38), with interaction effects accounting for 4% of that variance (ΔR² = .04). These results provide robust evidence that self-esteem moderates the detrimental influence of nighttime social media behavior on students’ assessment anxiety, with stronger protective effects observed in students with higher self-confidence and more stable self-worth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated Simple Slopes Analysis of Nighttime Social Media Use (NSMU) Predicting Outcome by Self-Esteem Levels and Subdomains\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Esteem Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Coef (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (LL, UL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect Size (f²)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Global Self-Esteem (–1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.49, 0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33 (medium-large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStrongest positive association; low self-esteem increases vulnerability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Global Self-Esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.37, 0.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModerate positive association\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Global Self-Esteem (+ 1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.23, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14 (small-medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh self-esteem buffers against NSMU-related distress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Positive Self-Worth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.44, 0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31 (medium-large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReduced confidence intensifies effect of NSMU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Self-Worth Instability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.48, 0.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEmotional instability magnifies vulnerability to NSMU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e**Simple slopes were probed at ± 1 SD of moderator (self-esteem). All effects significant at *\u003c/b\u003e\u003cem\u003ep \u0026lt; .001. Effect sizes based on f² interpretation per Cohen’s guidelines.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe moderated simple slopes analysis Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a more nuanced understanding of how self-esteem levels and its subdomains influence the strength of the relationship between nighttime social media use (NSMU) and assessment anxiety. When self-esteem was low (1 standard deviation below the mean), the positive relationship between NSMU and assessment anxiety was strongest (B = 0.61, SE = 0.06, β = 0.52, p \u0026lt; .001, 95% CI [0.49, 0.73]), with a medium-to-large effect size (f² = 0.33). This suggests that students with low global self-esteem are especially vulnerable to anxiety associated with nighttime social media use. In contrast, the association was weaker but still significant among students with average self-esteem (B = 0.47, SE = 0.05, β = 0.42, p \u0026lt; .001, 95% CI [0.37, 0.58], f² = 0.22), indicating a moderate effect. For those with high self-esteem (1 SD above the mean), the relationship between NSMU and anxiety remained significant but showed a reduced effect (B = 0.33, SE = 0.05, β = 0.30, p \u0026lt; .001, 95% CI [0.23, 0.43]), with a small-to-medium effect size (f² = 0.14). These findings confirm that high global self-esteem serves as a protective buffer, diminishing the emotional impact of excessive nighttime social media engagement. Further analysis of self-esteem subcomponents revealed similarly striking patterns. Among students with low positive self-worth, the relationship between NSMU and anxiety was strong (B = 0.58, SE = 0.07, β = 0.50, p \u0026lt; .001, 95% CI [0.44, 0.72]), with a medium-to-large effect (f² = 0.31). This indicates that a lack of confidence significantly exacerbates anxiety associated with NSMU. Likewise, those exhibiting high self-worth instability—reflecting emotional fragility or inconsistent self-evaluation showed the strongest slope overall (B = 0.64, SE = 0.08, β = 0.55, p \u0026lt; .001, 95% CI [0.48, 0.80]), corresponding to a large effect size (f² = 0.36). This suggests that individuals with unstable self-esteem are particularly susceptible to the anxiety-inducing effects of night-time digital behavior. In summary, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that lower and more unstable forms of self-esteem significantly amplify the anxiety risk associated with nighttime social media use, while higher and more stable self-esteem attenuates this impact. These results underscore the critical moderating role of self-esteem in understanding differential vulnerability to digital stressors among students.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerated Simple Slopes Analysis: Mental Health Predicting Assessment Anxiety by Self-Esteem Levels and Subdomains\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Esteem Condition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Coef (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (LL, UL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect Size (f²)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Global Self-Esteem (–1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.55, 0.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMental health problems most strongly predict assessment anxiety under low self-esteem\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Global Self-Esteem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.41, 0.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModerate effect of mental distress on assessment anxiety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Global Self-Esteem (+ 1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.24, 0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13 (small-medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh self-esteem buffers against the anxiety effects of mental distress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Emotional Resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.49, 0.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.32 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePoor emotion regulation intensifies anxiety response to mental health symptoms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Self-Doubt / Contingent Worth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; .001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.53, 0.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.38 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAssessment anxiety highly reactive to mental distress under unstable self-worth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**Moderated simple slope analyses conducted using PROCESS Model 1. All slopes significant at *\u003c/em\u003e\u003cb\u003ep \u0026lt; .001. Effect sizes (f²) interpreted per Cohen (1988). Self-esteem moderation probed at ± 1 SD.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a moderated simple slopes analysis that explores how different levels and subdomains of self-esteem influence the strength of the relationship between mental health problems and assessment anxiety. Among students with low global self-esteem (1 standard deviation below the mean), mental health issues most strongly predicted assessment anxiety, with a slope of B = 0.67 (SE = 0.06, β = 0.60, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.55, 0.79]) and a large effect size (f² = 0.36). This result indicates that individuals with low self-esteem are especially vulnerable to experiencing anxiety in response to psychological distress. For those with average levels of global self-esteem, the relationship remained statistically significant but weaker, with B = 0.51 (SE = 0.05, β = 0.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.41, 0.61]), and a medium effect size (f² = 0.23). This suggests a moderate level of sensitivity to mental health concerns. Notably, participants with high global self-esteem (1 SD above the mean) showed a significantly reduced but still positive association (B = 0.34, SE = 0.05, β = 0.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.24, 0.44]), accompanied by a small-to-medium effect size (f² = 0.13). This confirms that high self-esteem functions as a protective factor, mitigating the anxiety-provoking impact of mental health challenges. Further, self-esteem subdomains revealed critical insights. Students exhibiting low emotional resilience (i.e., poor emotion regulation capacity) demonstrated a strong association between mental health distress and anxiety (B = 0.63, SE = 0.07, β = 0.56, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.49, 0.77]), with a large effect size (f² = 0.32). This shows that lack of emotional control amplifies vulnerability to assessment anxiety in the presence of psychological distress. The most pronounced effect was observed among those with high levels of self-doubt and contingent self-worth, where the slope was B = 0.69 (SE = 0.08, β = 0.61, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [0.53, 0.85]) and the effect size was large (f² = 0.38). This indicates that when self-esteem is unstable or overly dependent on external validation, students are especially reactive to the emotional consequences of poor mental health. In summary, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reinforces that self-esteem especially its stability and emotional resilience dimensions—moderates the effect of mental health on assessment anxiety. Low or fragile self-esteem intensifies anxiety in response to psychological distress, whereas higher, more secure self-esteem offers a buffering effect. These findings highlight the importance of bolstering emotional resilience and self-worth stability as part of mental health and academic support interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"−\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eModerated Mediation Model: Indirect Effect of Nighttime Social Media Use on Academic Performance via Assessment Anxiety, Conditional on Sleep Disturbance (H₃)\u003c/b\u003e PROCESS Model 8 (n = 1,250, bootstrap = 5,000 samples)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndirect Pathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndirect Effect (a × b)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBoot SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (LL, UL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect Size (κ²)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow (–1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Depression → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.14, -0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06 (small)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow depression impact; minimal academic decline through mood disturbance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.12, -0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05 (small)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSocial media causes anxiety but not strongly linked to grades under low SD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Stress → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.11, -0.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04 (small)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMinor academic impact via stress under good sleep conditions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Assessment Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.15, -0.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06 (small)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLimited academic interference via test-related anxiety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage (Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Depression → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.21, -0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMore salient depression effect under average sleep conditions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.18, -0.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAnxiety moderately mediates NSMU–GPA link\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Stress → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.16, -0.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStress plays a more prominent mediating role\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Assessment Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.22, -0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12 (medium)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAssessment anxiety increasingly harms performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh (+ 1 SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Depression → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.31, -0.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDepression severely impairs GPA under high sleep disturbance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.28, -0.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAnxiety strongly drives academic decline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Stress → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.25, -0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStress mediates the steepest academic impairment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNSMU → Assessment Anxiety → Academic Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"−\" colname=\"c6\"\u003e\u003cp\u003e[-0.33, -0.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19 (large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTest-related anxiety is a major indirect pathway to poor academic performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e**Moderated mediation tested using PROCESS Model 14 (Hayes, 2018), n = 1,250, with 5,000 bootstrap samples. All indirect effects are significant at *p \u0026lt; .001. Confidence intervals (95%) reported are bias-corrected. Conditional indirect effects examined at ± 1 SD levels of moderators.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of a moderated mediation analysis exploring how sleep disturbance (SD) conditions the indirect effects of NSMU on academic performance through four psychological mediators: depression, anxiety, stress, and assessment anxiety. The analysis used 5,000 bootstrap samples and included 1,250 participants. At low levels of sleep disturbance (–1 SD), all indirect effects were statistically significant but small in magnitude. Specifically, NSMU predicted modest decreases in academic performance via depression (indirect effect = − 0.07, 95% CI [–0.14, − 0.02], κ² = 0.06), anxiety (–0.06, CI [–0.12, − 0.02], κ² = 0.05), stress (–0.05, CI [–0.11, − 0.01], κ² = 0.04), and assessment anxiety (–0.08, CI [–0.15, − 0.03], κ² = 0.06). These results suggest that even when sleep quality is relatively good, NSMU still contributes to emotional distress, which in turn mildly undermines academic outcomes though the overall effect remains limited under such conditions. Under average sleep disturbance (mean level), the indirect effects were stronger across all pathways, reaching medium effect sizes. Depression (–0.13, CI [–0.21, − 0.07], κ² = 0.11), anxiety (–0.11, CI [–0.18, − 0.06], κ² = 0.09), and stress (–0.09, CI [–0.16, − 0.04], κ² = 0.08) each significantly mediated the relationship between NSMU and academic decline. Notably, assessment anxiety had a more pronounced role (–0.14, CI [–0.22, − 0.08], κ² = 0.12), indicating that as sleep becomes moderately impaired, NSMU-induced test anxiety increasingly contributes to reduced academic performance. At high levels of sleep disturbanc\u003cb\u003ee\u003c/b\u003e (+ 1 SD), the indirect effects became most substantial, with large effect sizes across all four mediators. Depression exhibited the strongest indirect pathway (–0.20, CI [–0.31, − 0.11], κ² = 0.18), suggesting that when students are severely sleep-deprived, NSMU intensifies depressive symptoms that heavily impair academic functioning. Anxiety (–0.17, CI [–0.28, − 0.09], κ² = 0.15) and stress (–0.15, CI [–0.25, − 0.07], κ² = 0.13) also emerged as strong mediators. The most detrimental pathway, however, was through assessment anxiety, with an indirect effect of − 0.21 (CI [–0.33, − 0.12], κ² = 0.19), highlighting that test-related anxiety is especially potent under poor sleep conditions. In sum, these findings demonstrate that sleep disturbance significantly moderates the indirect effects of NSMU on academic performance through emotional distress mechanisms. As sleep quality deteriorates, the psychological costs of NSMU rise sharply, leading to more severe academic consequences. The data underscores the importance of healthy sleep patterns as a protective buffer and suggests that interventions aimed at reducing NSMU or enhancing sleep hygiene could mitigate the negative academic impacts mediated by emotional and test-related anxiety.\u003c/p\u003e"},{"header":"Discussion of Results","content":"\u003cp\u003eThe current study investigated the pathways through which nighttime social media use (NSMU) influences academic performance, focusing on the mediating role of mental health factors (depression, anxiety, stress, and assessment anxiety) and the moderating effects of self-esteem and sleep disturbance. The findings from the series of moderated mediation models offer critical insights into the psychological mechanisms and boundary conditions that shape the NSMU–academic performance link. The first model revealed that increased NSMU significantly predicted higher levels of depression, anxiety, stress, and assessment anxiety. These results corroborate a growing body of evidence showing the detrimental impact of excessive or poorly regulated social media usage on adolescents’ and university students’ mental health. For instance, [28; 29], in a meta-analysis, found strong positive correlations between social media use and psychological distress, especially anxiety and depression among youth. Similarly, [62; 65; 12; 64] emphasized that nighttime social media engagement disrupts emotional regulation and sleep hygiene, both of which contribute to poor mental health outcomes. The stronger predictive effect of NSMU on assessment anxiety is particularly noteworthy. This aligns with [11; 20; 21; 31], who documented that compulsive checking of social platforms before sleep increases anticipatory worry and negative academic self-appraisal key contributors to test-related anxiety. Moreover, poor sleep hygiene caused by late-night screen exposure exacerbates stress reactivity [39; 36; 58; 55], providing a possible physiological pathway through which NSMU undermines students’ emotional equilibrium.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents compelling evidence that self-esteem functions as a significant moderator in the relationship between nighttime social media use (NSMU) and various negative emotional outcomes, including depression, anxiety, stress, and assessment anxiety. Specifically, the data reveal that the adverse emotional effects of NSMU are significantly intensified among individuals with low levels of global self-esteem and heightened self-worth instability. In other words, students who generally view themselves negatively or whose self-perceptions fluctuate dramatically depending on external feedback are more psychologically vulnerable to the consequences of excessive social media engagement during nighttime hours. This finding aligns closely with the conclusions of [53; 9; 12; 2], whose longitudinal meta-analysis demonstrated that low self-esteem is a robust risk factor for depression and other internalizing disorders. Their study also emphasized that individuals with low self-esteem are more likely to interpret social information especially criticism or exclusion negatively, which is especially relevant in the context of social media platforms where users are frequently exposed to idealized portrayals of others and evaluative feedback (likes, shares, comments). The moderating role of self-worth instability a personality trait reflecting fluctuations in one’s sense of value and self-confidence adds a critical layer to our understanding. Students with unstable self-worth appear particularly reactive to the perceived social judgments and peer comparisons facilitated by NSMU. These individuals may experience intense emotional highs and lows based on social media interactions, leading to greater levels of distress. This observation is consistent with the theoretical framework developed by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], who posited that fragile self-esteem characterized by contingency, instability, and defensiveness renders individuals more emotionally reactive and less able to regulate negative affect in the face of social threat or rejection, which is commonplace on platforms like Instagram, Snapchat, and TikTok.\u003c/p\u003e\u003cp\u003eMoreover, the interactive effect of low global self-esteem and high self-worth instability suggests that it is not merely a low opinion of oneself that predisposes students to harm, but rather the instability and reactivity of that self-opinion over time. This reflects a vulnerability-stress interaction, wherein students with an unstable self-concept are more likely to internalize the stressors triggered by nighttime social media use, leading to greater symptoms of depression, anxiety, and stress. For example, [59; 65; 55] found that adolescents with low self-concept clarity experienced more depressive symptoms in response to negative social media feedback, particularly when using these platforms at night when cognitive and emotional resources are depleted. These insights underscore the critical need for psychological interventions that promote self-esteem stability, rather than simply boosting global self-esteem. Interventions such as self-compassion training [40; 31; 34], resilience-building programs, and cognitive-behavioral strategies focused on decoupling self-worth from social validation may be particularly effective. By helping students cultivate a more stable, intrinsic, and non-contingent sense of self, institutions can mitigate the emotional toll of NSMU and reduce the likelihood of downstream effects on academic performance and well-being. In sum, the moderated effects identified in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e not only reinforce existing theoretical models of self-esteem vulnerability but also highlight a critical intervention point: the stabilization of self-worth as a protective mechanism in the digital age. Given the ubiquitous nature of social media in students’ lives especially during nighttime hours promoting psychological resilience through self-concept clarity could be a key strategy for improving emotional health and academic success.\u003c/p\u003e\u003cp\u003eFurther analysis showed that mental health symptoms robustly predicted assessment anxiety, but this relationship was again contingent upon self-esteem levels. As with earlier findings, students with low global self-esteem and high self-doubt were most susceptible to experiencing elevated test anxiety in response to poor mental health. These results are in line with [44; 39; 38; 22], who found that students with low self-worth are more likely to internalize academic stressors, leading to heightened anxiety during exams. Interestingly, the buffering effect of high self-esteem in this model supports protective-factor theories of psychological resilience [42; 27; 28; 30], suggesting that students with a strong sense of self are better equipped to manage negative emotions and performance pressure. These findings extend the literature by highlighting self-esteem as not only a predictor of emotional well-being but also a critical moderator of academic stress processing. The final moderated mediation model (PROCESS Model 8) revealed that sleep disturbance significantly moderated the indirect relationship between NSMU and academic performance through depression, anxiety, stress, and assessment anxiety. Notably, the indirect effects were weakest under low sleep disturbance and strongest when sleep was highly disturbed, indicating that sleep quality plays a central role in shaping the academic consequences of NSMU. These results converge with evidence from [31; 36; 20], who showed that sleep disruption exacerbates the impact of emotional stress on cognitive performance. Under high sleep disturbance, the largest indirect effect was through assessment anxiety, suggesting that test-related anxiety serves as a critical pathway by which NSMU especially in poor sleep contexts translates into diminished academic success. This pattern affirms the Triple Vulnerability Model proposed by [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which posits that biological (e.g., sleep disruption), psychological (e.g., emotional dysregulation), and environmental (e.g., NSMU) vulnerabilities interact to intensify the risk of poor outcomes. Additionally, it supports cognitive interference theory [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which argues that anxiety consumes attentional resources necessary for optimal academic functioning an effect made worse by sleep loss.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study set out to investigate the mental health implications of nighttime social media use (NSMU) among university students in Ghana, with a particular focus on its associations with assessment anxiety, sleep disturbance, self-esteem, and academic performance. Against the backdrop of a digitally connected yet psychologically strained generation of students, the research contributes to a critical and timely understanding of how online behaviors during nocturnal hours translate into real-world academic and emotional outcomes. The findings reveal that NSMU is not merely a leisure activity or a benign technological habit but a significant psychosocial determinant with far-reaching consequences. Students who engage in prolonged social media use during nighttime hours report increased levels of sleep disturbance. Disrupted sleep, in turn, was found to heighten assessment anxiety a form of academic stress that is particularly acute during examinations or graded evaluations. The compounding effects of poor sleep and anxiety impair not only cognitive functioning but also emotional regulation, undermining students\u0026rsquo; capacity to perform well academically. Further, the study identifies self-esteem as both a mediating and moderating variable. Nighttime social media use was found to erode dimensions of self-esteem, particularly in areas related to emotional stability and academic competence. In addition, students with lower self-esteem were more susceptible to the negative psychological effects of NSMU. This points to a cyclical dynamic: diminished self-esteem leads to greater emotional vulnerability, which in turn exacerbates the negative impacts of digital overuse. Students with fragile self-concepts may seek validation online, only to become further entangled in a pattern of dependence that undermines their offline well-being and academic outcomes. The mediation models in this study support the conclusion that the relationship between NSMU and academic performance is not direct, but channeled through psychological distress particularly assessment anxiety and sleep disturbance. Similarly, moderation analyses confirm that individual differences, such as levels of self-esteem, influence the strength of this relationship. These results affirm the need for nuanced, multi-dimensional interventions that go beyond simple calls to reduce screen time. Mental health support, psychoeducation on sleep hygiene, and digital literacy campaigns must be combined with efforts to nurture students\u0026rsquo; self-worth and emotional resilience.\u003c/p\u003e\u003cp\u003eThis research also draws attention to a broader paradox: while social media offers community, entertainment, and informational value, its excessive use especially at night can disconnect students from themselves. The digital world becomes a coping mechanism, yet it simultaneously contributes to the very anxieties it seeks to soothe. The allure of constant connection creates a false sense of productivity and social engagement, often at the expense of restorative sleep, emotional well-being, and academic achievement. The results of this study reinforce the conceptual framework that late-night digital engagement functions as both a symptom and a source of deeper psychological unease. From a policy perspective, the implications are profound. University administrators, counselors, educators, and national mental health advocates must acknowledge NSMU as a significant risk behavior in campus health assessments. Interventions should target both individual behavior (e.g., time management, mindfulness, sleep tracking) and systemic support (e.g., digital wellness programs, counseling services, peer mentorship initiatives). Given the rapid pace of technological immersion in young people\u0026rsquo;s lives, the time to act is now. In conclusion, this study offers a critical contribution to understanding the offline psychological and academic costs of nighttime social media use. It confirms that the lure of late-night online engagement carries measurable risks to students\u0026rsquo; mental health, emotional regulation, and academic success. Ultimately, fostering a generation of digitally mindful, emotionally grounded, and academically resilient students requires addressing not only \u003cem\u003ehow\u003c/em\u003e they use technology but also \u003cem\u003ewhy\u003c/em\u003e they turn to it especially in the quiet, vulnerable hours of the night.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003eBased on the findings of this study, a multi-level and context-specific set of interventions is recommended to address the growing concern of nighttime social media use (NSMU) and its adverse effects on university students in Ghana. These recommendations target students, educational institutions, mental health professionals, and policymakers. Firstly, there is a critical need for universities to integrate structured digital wellness programs into their student support services. These programs should aim at increasing students\u0026rsquo; awareness of the psychological risks associated with excessive nighttime social media use. Such initiatives could include seminars, workshops, and peer-led campaigns that educate students on sleep hygiene, digital boundaries, and the neuroscience of screen time. Incorporating these themes into orientation activities for first-year students would be particularly impactful, given their heightened vulnerability to both academic and social pressures. Secondly, counseling and psychological services on campuses must be strengthened and tailored to address the specific challenges associated with technology-related anxiety and sleep disorders. Trained professionals should be equipped to screen for digital overuse, poor sleep patterns, and assessment-related stress during routine check-ins. Further, counselors should help students develop personalized coping strategies that involve healthier bedtime routines, emotional regulation, and self-esteem building exercises. Cognitive Behavioral Therapy (CBT)-based interventions that focus on anxiety and insomnia may be particularly useful in addressing the mediating effects identified in the study. Thirdly, academic departments and faculty must be sensitized to the mental health realities of their students, particularly during high-stakes assessment periods. Faculty can contribute to student well-being by offering flexible timelines, promoting formative assessment practices that reduce test anxiety, and ensuring that grading policies do not disproportionately heighten academic pressure. Instructors can also use technology in constructive ways, for instance, by embedding wellness prompts in their online learning platforms or using learning analytics to flag disengagement potentially linked to psychological distress.\u003c/p\u003e\u003cp\u003eIn addition, students themselves must be empowered to take agency over their digital habits. Student unions and peer mentoring groups can play an instrumental role in cultivating a campus culture that normalizes unplugging during nighttime hours. Student-led initiatives, such as \u0026ldquo;digital detox\u0026rdquo; challenges or dorm-based support circles, can reinforce communal accountability and provide alternative offline engagements that promote relaxation, interpersonal connection, and sleep readiness. Such bottom-up efforts can complement institutional policies and help students shift from compulsive social media use to intentional, balanced digital interaction. At the policy level, the Ghana Tertiary Education Commission (GTEC), in collaboration with the Ministry of Education and the Mental Health Authority of Ghana, should consider incorporating digital wellness as a component of national tertiary education and youth development frameworks. A national guideline on promoting sleep-friendly campus environments could include recommended curfews on campus Wi-Fi usage, mental health literacy campaigns, and the inclusion of digital self-regulation modules in general education curricula. Finally, future research is encouraged to explore the intersectionality of NSMU with other variables such as gender, socioeconomic status, and specific social media platforms. Longitudinal studies would also help in understanding the causal directions and long-term effects of nighttime social media use on psychological well-being and academic achievement. Qualitative explorations could further uncover the subjective narratives behind students\u0026rsquo; motivations for NSMU, thereby informing more empathetic and effective interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImplications: Social Media and Mental Health\u003c/h2\u003e\u003cp\u003eThe findings of this study present significant implications for understanding how social media use, particularly at night, intersects with the mental health and academic life of university students in Ghana. As social media becomes a dominant mode of communication, entertainment, and self-expression, its psychological and emotional impacts cannot be underestimated especially when its use occurs during late hours, disrupting sleep patterns and cognitive functioning. One of the central implications is that excessive nighttime social media use serves as a catalyst for mental health challenges such as anxiety, poor self-esteem, sleep disturbances, and academic underperformance. Students often rely on social media to escape academic stress or feelings of loneliness; however, this reliance inadvertently creates a cycle of over-engagement, emotional overstimulation, and poor sleep hygiene. The dopamine-driven design of these platforms encourages prolonged usage, leading to reduced sleep quality, which in turn exacerbates daytime fatigue, concentration difficulties, and psychological distress. Moreover, the impact of social comparison on platforms like Instagram, Snapchat, and TikTok contributes to feelings of inadequacy and diminished self-worth, especially when students compare their lives to curated, idealized representations of others. This study suggests that such comparisons can erode self-esteem and foster a sense of academic and social failure among students who already face the pressure of competitive academic environments. Another implication is the need to reposition digital behavior as a core factor in mental health assessment and promotion. University counseling centers and mental health practitioners must be equipped to discuss digital habits as part of regular interventions. Current mental health frameworks in tertiary institutions should be updated to incorporate guidance on managing screen time, particularly before bedtime, and on developing digital boundaries that promote well-being.\u003c/p\u003e\u003cp\u003eThe results also highlight the importance of digital literacy campaigns that go beyond technical skills to include critical awareness of emotional and psychological consequences. Students need structured opportunities to reflect on how their online interactions affect their mental states, sleep patterns, and academic functioning. Initiatives that promote mindful social media use such as \"digital detox\" weeks, peer-led workshops, and social media fasts during exams could foster healthier habits and create a more balanced lifestyle. From a policy perspective, this study implies that higher education institutions must adopt proactive digital wellness strategies. These strategies could include integrating digital well-being modules into orientation programs, monitoring signs of digital burnout among students, and advocating for national-level policies that regulate the addictive design features of social media applications. Lastly, the findings call for a national conversation on youth mental health in the digital age, particularly in developing countries like Ghana where mental health services are still evolving. Social media use is not inherently negative, but without appropriate support structures and awareness, it can exacerbate existing mental health vulnerabilities. A multi-sectoral approach involving educators, mental health professionals, parents, and tech developers is necessary to develop safeguards and interventions that prioritize the well-being of students in our increasingly digital society.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eNSMU\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNighttime Social Media Use\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePSQI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePittsburgh Sleep Quality Index (Global Sleep Disturbance Score)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSleep Latency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSleep Duration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSSQ\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSubjective Sleep Quality\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eDASS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u0026ndash;Depression Anxiety Stress Scale\u0026ndash;21\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDepression (DASS\u0026ndash;21 Subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnxiety (DASS\u0026ndash;21 Subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStress (DASS\u0026ndash;21 Subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eFF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFearfulness (Anxiety Symptom Subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSOM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSomatic Symptoms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eDYS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDysphoria\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eANH\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnhedonia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eAAS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAssessment Anxiety Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eRSES\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRosenberg Self\u0026ndash;Esteem Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eGPA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGrade Point Average\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics Approval and Consent to Participate\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in full compliance with the ethical standards of the Declaration of Helsinki and aligned with international research ethics guidelines. Ethical clearance was obtained from the Institutional Review Board (IRB) of the University of Education, Winneba. Additional institutional permissions were secured where applicable. Prior to data collection, all participants were provided with detailed information regarding the purpose, procedures, potential risks and benefits of the study, as well as the voluntary nature of their participation. Written informed consent was obtained from each participant, with assurances that they could withdraw from the study at any time without penalty. Participant confidentiality and anonymity were rigorously protected throughout the research process. All data were securely encrypted and stored on password-protected devices.\u003c/p\u003e\n\u003ch3\u003eConsent for Publication\u003c/h3\u003e\n\u003cp\u003eNot applicable. This study did not involve the collection or dissemination of personally identifiable information, images, or multimedia content.\u003c/p\u003e\n\u003ch3\u003eAvailability of Data and Materials\u003c/h3\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study focusing on social media use, screen time, depression, mental health, assessment integrity, and academic performance among tertiary students in Ghana are available from the corresponding author, Simon Ntumi, upon reasonable request. To maintain participant confidentiality, raw data will not be publicly archived. Data access requests will be subject to institutional ethical review and approval procedures.\u003c/p\u003e\n\u003ch3\u003eCompeting Interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests. The research was independently conceptualized, conducted, and reported, without any external influence on the study\u0026rsquo;s design, data collection, analysis, interpretation, or dissemination.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was entirely self-funded by the authors. No external grants, sponsorship, or financial assistance were received, ensuring the objectivity and independence of the research process and outcomes.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors express their sincere gratitude to all the university students who participated in this study. Appreciation is also extended to the research assistants and data analysts for their contributions. Special thanks go to the Department of Educational Foundations and the Department of Counselling Psychology at the University of Education, Winneba, for their continuous academic and institutional support.\u003c/p\u003e\n\u003ch3\u003eClinical Trial Number\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eNot applicable. This study\u003c/p\u003e\n\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003eSimon Ntumi\u0026sup1; led the conceptualization and design of the study, supervised data collection, performance the analysis, and drafted the initial manuscript.\u003cbr\u003eDivine Agbovor\u0026sup1; assisted in instrument development, coordinated field data collection, and contributed to the literature review and data cleaning.\u003cbr\u003eLawrence Larbi Sakyi\u0026sup2; supported ethical review processes, contributed to the methodological framework, and reviewed statistical analysis outputs.\u003cbr\u003eVincent Worlanyo Dogbe\u0026sup3; contributed to data interpretation and provided critical revisions of the manuscript for theoretical and psychological coherence.\u003cbr\u003eCollins Addo\u0026sup3; assisted in administering psychological scales, managed participant recruitment, and supported the proofreading process.\u003cbr\u003eDortuo Daniel Kuupile⁴ reviewed the final manuscript for academic rigor, ensured alignment with educational policy implications, and contributed to referencing and formatting.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript prior to submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlimoradi, Z. et al. Internet addiction and sleep problems: A systematic review and meta-analysis. \u003cem\u003eSleep Med. Rev.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 51\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.smrv.2019.06.004\u003c/span\u003e\u003cspan address=\"10.1016/j.smrv.2019.06.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsare, M., Osei, A. \u0026amp; Adu-Gyamfi, K. Digital behavior and mental health among Ghanaian university students: The role of social media use and perceived stress. \u003cem\u003eJ. Afr. Educational Res. Netw.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (2), 87\u0026ndash;102 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtique, M. \u003cem\u003eDigitally Hijacked: The Age of Influence: How Social Media and AI Are Reshaping Our Reality\u003c/em\u003e (Wipf and Stock, 2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBailey, C. \u003cem\u003eRelationship Between Addictive Social Media Usage, Narcissistic Characteristics, and Self-Esteem in Adults\u003c/em\u003e (Master's thesis, Houston Baptist University). (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarlow, D. H. Unraveling the mysteries of anxiety and its disorders from the perspective of emotion theory. \u003cem\u003eAm. Psychol.\u003c/em\u003e \u003cb\u003e55\u003c/b\u003e (11), 1247\u0026ndash;1263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0003-066X.55.11.1247\u003c/span\u003e\u003cspan address=\"10.1037/0003-066X.55.11.1247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker, S. P., Ramsay, J. R. \u0026amp; Willcutt, E. G. Sleep in adolescents and young adults with ADHD: Implications for functioning and treatment. \u003cem\u003eCurr. Psychiatry Rep.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (12), 114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11920-018-0972-0\u003c/span\u003e\u003cspan address=\"10.1007/s11920-018-0972-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlankson, H., Adomako, B. \u0026amp; Amoako, M. Social media use among university students in Ghana: Patterns and implications. \u003cem\u003eGhana. J. Educ. Pract.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (3), 65\u0026ndash;78 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlankson, H., Asare, K. B. \u0026amp; Osei, C. Digital media consumption and mental health among Ghanaian university students: Trends, triggers, and tensions. \u003cem\u003eAfr. J. Psychol. Behav. Sci.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), 65\u0026ndash;82 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurrell, D. N. Exploring the Cyberpsychology of Social Media Addiction and Public Health Risks among Black American Women in the USA. \u003cem\u003eHealth Econ. Manage. Rev.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), 14\u0026ndash;31 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. \u0026amp; Kupfer, D. J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. \u003cem\u003ePsychiatry Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (2), 193\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0165-1781(89)90047-4\u003c/span\u003e\u003cspan address=\"10.1016/0165-1781(89)90047-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1989).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCain, N. Sleep and adolescent depression: A systematic review. \u003cem\u003eSleep Med. Rev.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.smrv.2018.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.smrv.2018.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell, C. Pseudoreality and Technology: Smartphone-Related Factors Impacting Mental Health. \u003cem\u003ePsychodynamic Psychiatry\u003c/em\u003e. \u003cb\u003e52\u003c/b\u003e (3), 345\u0026ndash;357 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChellappa, S. L. et al. Acute exposure to evening blue-enriched light impacts on human sleep. \u003cem\u003eJ. Sleep Res.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (2), 258\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2869.2010.00883.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2869.2010.00883.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, Y., Li, S., Tian, Y., Li, D. \u0026amp; Yin, H. Problematic Social Media Use may be Ruining Our Sleep: A Meta-Analysis on the Relationship Between Problematic Social Media Use and Sleep Quality. \u003cem\u003eInternational J. Mental Health Addiction\u003c/em\u003e, 1\u0026ndash;36. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, J., Cohen, P., West, S. G. \u0026amp; Aiken, L. S. \u003cem\u003eApplied multiple regression/correlation analysis for the behavioral sciences\u003c/em\u003e 3rd edn (Lawrence Erlbaum Associates, 2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreswell, J. W. \u0026amp; Creswell, J. D. \u003cem\u003eResearch design: Qualitative, quantitative, and mixed methods approaches\u003c/em\u003e 5th edn (SAGE, 2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDriscoll, R. \u003cem\u003eWestside Test Anxiety Scale Validation\u003c/em\u003e (ERIC Document Reproduction Service No. ED495968, 2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eExelmans, L. \u0026amp; Van den Bulck, J. Bedtime mobile phone use and sleep in adults. \u003cem\u003eSoc. Sci. Med.\u003c/em\u003e \u003cb\u003e148\u003c/b\u003e, 93\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.socscimed.2015.11.037\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2015.11.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEysenck, M. W., Derakshan, N., Santos, R. \u0026amp; Calvo, M. G. Anxiety and cognitive performance: Attentional control theory. \u003cem\u003eEmotion\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (2), 336\u0026ndash;353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/1528-3542.7.2.336\u003c/span\u003e\u003cspan address=\"10.1037/1528-3542.7.2.336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatuma, D. The relationship between social media and anxiety. \u003cem\u003eRes. Inventıon J. Curr. Issues Arts Manage.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (2), 102\u0026ndash;107 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFirth, J. et al. From online brains to online lives: understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions. \u003cem\u003eWorld Psychiatry\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e (2), 176\u0026ndash;190 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia, M. A. \u003cem\u003eExamining the Relationships Between Social Media Use Constructs and Mental and Sleep Health in Hispanic University Emerging Adults\u003c/em\u003e (Master's thesis, The University of Texas at El Paso). (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunarathne, N. \u0026amp; Walpola, A. S. A Psychological Study of the Impact of Social Media Addiction on Sleep Patterns. \u003cem\u003eInt. J. Latest Technol. Eng. Manage. Appl. Sci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (4), 140\u0026ndash;145 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayes, A. F. \u003cem\u003eIntroduction to mediation, moderation, and conditional process analysis: A regression-based approach\u003c/em\u003e 2nd edn (Guilford Press, 2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirshkowitz, M. et al. National Sleep Foundation\u0026rsquo;s sleep time duration recommendations: Methodology and results summary. \u003cem\u003eSleep. Health\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (1), 40\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sleh.2014.12.010\u003c/span\u003e\u003cspan address=\"10.1016/j.sleh.2014.12.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJameel, A., Guo, W., Hussain, A., Kanwel, S. \u0026amp; Sahito, N. Exploring the mediating role of insomnia on the nexus between social media addiction and mental health among university students. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 1\u0026ndash;11 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKardefelt-Winther, D. A conceptual and methodological critique of Internet addiction research: Towards a model of compensatory Internet use. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 351\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2013.10.059\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2013.10.059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeles, B., McCrae, N. \u0026amp; Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. \u003cem\u003eInt. J. Adolescence Youth\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e (1), 79\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02673843.2019.1590851\u003c/span\u003e\u003cspan address=\"10.1080/02673843.2019.1590851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeles, B., Grealish, A. \u0026amp; Leamy, M. The beauty and the beast of social media: an interpretative phenomenological analysis of the impact of adolescents' social media experiences on their mental health during the Covid-19 pandemic. \u003cem\u003eCurr. Psychol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (1), 96\u0026ndash;112 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKernis, M. H. \u0026amp; Goldman, B. M. A multicomponent conceptualization of authenticity: Theory and research. In M. P. Zanna (Ed.), \u003cem\u003eAdvances in Experimental Social Psychology\u003c/em\u003e (Vol. 38, pp. 283\u0026ndash;357). Academic Press. (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0065-2601(06)38006-9\u003c/span\u003e\u003cspan address=\"10.1016/S0065-2601(06)38006-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhadka, C. Internet Addiction and Its Impact on Student Mental Health. \u003cem\u003eDristikon: Multidisciplinary J.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 41\u0026ndash;58 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKline, R. B. \u003cem\u003ePrinciples and practice of structural equation modeling\u003c/em\u003e 4th edn (Guilford Press, 2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuss, D. J. \u0026amp; Griffiths, M. D. Social networking sites and addiction: Ten lessons learned. \u003cem\u003eInt. J. Environ. Res. Public Health\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (3), 1286\u0026ndash;1306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph120301286\u003c/span\u003e\u003cspan address=\"10.3390/ijerph120301286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLanda-Blanco, M. et al. Exploring suicide ideation in university students: sleep quality, social media, self-esteem, and barriers to seeking psychological help. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1352889 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B. \u0026amp; Primack, B. A. The association between social media use and sleep disturbance among young adults. \u003cem\u003ePrev. Med.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e, 36\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ypmed.2016.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ypmed.2016.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, X., Li, H. \u0026amp; Luo, J. Cross-lagged panel analysis of the relationship between social networking sites use (SNSU) and sleep problems among university students. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e (1), 2283 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Z., Wei, Y., Yang, Y. \u0026amp; Kong, L. Unveiling a novel clinical risk assessment model for identifying non-suicidal self-injury risks in depressed Chinese adolescents amidst the COVID-19 pandemic: insights from low self-esteem, internet use, and sleep disturbance. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 1259909 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLovibond, P. F. \u0026amp; Lovibond, S. H. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. \u003cem\u003eBehav. Res. Ther.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (3), 335\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0005-7967(94)00075-U\u003c/span\u003e\u003cspan address=\"10.1016/0005-7967(94)00075-U\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMallary, J. \u003cem\u003eCan We Live Without Our Phones? Smartphone Dependence, Impulsivity, and the Physiological Correlates of Resisting Smartphone Use\u003c/em\u003e (Doctoral dissertation, University of Hartford). (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeff, K. D. Self-compassion, self-esteem, and well-being. \u003cem\u003eSoc. Pers. Psychol. Compass\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (1), 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1751-9004.2010.00330.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1751-9004.2010.00330.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOppong Asante, K. \u0026amp; Andoh-Arthur, J. Prevalence and determinants of depressive symptoms among university students in Ghana. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cb\u003e256\u003c/b\u003e, 457\u0026ndash;462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2019.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2019.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrth, U. \u0026amp; Robins, R. W. The development of self-esteem. \u003cem\u003eCurr. Dir. Psychol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (5), 381\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0963721414547414\u003c/span\u003e\u003cspan address=\"10.1177/0963721414547414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePreacher, K. J., Rucker, D. D. \u0026amp; Hayes, A. F. Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. \u003cem\u003eMultivar. Behav. Res.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (1), 185\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273170701341316\u003c/span\u003e\u003cspan address=\"10.1080/00273170701341316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutwain, D. W., Daly, A. L., Chamberlain, S. \u0026amp; Sadreddini, S. Academic buoyancy and test anxiety: The buffering effect of self-worth. \u003cem\u003eSchool Psychol. Int.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (2), 162\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0143034319893267\u003c/span\u003e\u003cspan address=\"10.1177/0143034319893267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRao, K., John, S., Trivedi, G. \u0026amp; Gupta, S. Exploring The Relationship Between Nocturnal Social Media Usage, Quality of Sleep, Indecisiveness and Irritability in Emerging Adults. \u003cem\u003eInt. J. Interdisciplinary Approaches Psychol.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (11), 9\u0026ndash;26 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberg, M. \u003cem\u003eSociety and the adolescent self-image\u003c/em\u003e (Princeton University Press, 1965).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaleem, N., Young, P. \u0026amp; Yousuf, S. Exploring the Relationship Between Social Media Use and Symptoms of Depression and Anxiety Among Children and Adolescents: A Systematic Narrative Review. \u003cem\u003eCyberpsychology Behav. Social Netw.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (11), 771\u0026ndash;797 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamaha, M. \u0026amp; Hawi, N. S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 321\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2015.12.045\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2015.12.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSavci, M. \u0026amp; Aysan, F. Technological addictions and social connectedness: Predictor effect of Internet addiction, social media addiction, digital game addiction and smartphone addiction on social connectedness. \u003cem\u003eDusunen Adam: J. Psychiatry Neurol. Sci.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (3), 202\u0026ndash;216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5350/DAJPN2017300303\u003c/span\u003e\u003cspan address=\"10.5350/DAJPN2017300303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott, H. \u0026amp; Woods, H. C. Understanding links between social media use, sleep and mental health: Recent progress and current challenges. \u003cem\u003eCurr. Sleep. Med. Rep.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (4), 141\u0026ndash;149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40675-018-0122-1\u003c/span\u003e\u003cspan address=\"10.1007/s40675-018-0122-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott, H., Biello, S. M. \u0026amp; Woods, H. C. Social media use and adolescent sleep patterns: Cross-sectional findings from the UK Millennium Cohort Study. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (9), e031161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2019-031161\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2019-031161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheth, J. \u0026amp; Sharma, S. K. \u003cem\u003eDigital Shock: Seven Shocks that are Shaping the Future\u003c/em\u003e (Vibrant, 2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSowislo, J. F. \u0026amp; Orth, U. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. \u003cem\u003ePsychol. Bull.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e (1), 213\u0026ndash;240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0028931\u003c/span\u003e\u003cspan address=\"10.1037/a0028931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, C. et al. Exploring the interconnections of anxiety, depression, sleep problems and health-promoting lifestyles among Chinese university students: a comprehensive network approach. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1402680 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaddi, V. V., Kohli, R. K. \u0026amp; Puri, P. Perception, use of social media, and its impact on the mental health of Indian adolescents: A qualitative study. \u003cem\u003eWorld J. Clin. Pediatr.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (3), 97501 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTirado, R. \u0026amp; Najmaei, M. The Interplay between Social Media Addiction and Psychological Needs: Exploring the Relationship \u0026amp; Implication amongst Gen Z. \u003cem\u003eAsian Res. J. Arts Social Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (6), 143\u0026ndash;162 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTopp, C. W., \u0026Oslash;stergaard, S. D., S\u0026oslash;ndergaard, S. \u0026amp; Bech, P. The WHO-5 Well-Being Index: A systematic review of the literature. \u003cem\u003ePsychother. Psychosom.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e (3), 167\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000376585\u003c/span\u003e\u003cspan address=\"10.1159/000376585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurner, K. E. \u003cem\u003eThe Effects of TikTok Use on Mental Health and Personal Well-Being Among Undergraduate Students\u003c/em\u003e (Doctoral dissertation, Alliant International University). (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValkenburg, P. M., Beyens, I., Pouwels, J. L., van Driel, I. I. \u0026amp; Keijsers, L. Social media and adolescents\u0026rsquo; self-esteem: Heading for a person-specific media effects paradigm. \u003cem\u003eJ. Communication\u003c/em\u003e. \u003cb\u003e71\u003c/b\u003e (1), 56\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/joc/jqaa039\u003c/span\u003e\u003cspan address=\"10.1093/joc/jqaa039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogel, E. A., Rose, J. P., Roberts, L. R. \u0026amp; Eckles, K. Social comparison, social media, and self-esteem. \u003cem\u003ePsychol. Popular Media Cult.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (4), 206\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/ppm0000047\u003c/span\u003e\u003cspan address=\"10.1037/ppm0000047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker, M. P. \u003cem\u003eWhy We Sleep: Unlocking the Power of Sleep and Dreams\u003c/em\u003e (Scribner, 2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoods, H. C. \u0026amp; Scott, H. #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. \u003cem\u003eJ. Adolesc.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 41\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.adolescence.2016.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.adolescence.2016.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, K. B. Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. \u003cem\u003eJ. Computer-Mediated Communication\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (3), JCMC1034. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1083-6101.2005.tb00259.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1083-6101.2005.tb00259.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, D. J., Wing, Y. K., Li, T. M. \u0026amp; Chan, N. Y. The impact of Social Media Use on Sleep and Mental Health in Youth: a scoping review. \u003cem\u003eCurr. Psychiatry Rep.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (3), 104\u0026ndash;119 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusoff, S. H. et al. Cultivating consciousness: Unveiling TikTok\u0026rsquo;s detrimental effects on youth mindset and behaviour. \u003cem\u003eSEARCH J. Media Communication Res. (SEARCH)\u003c/em\u003e, 87. (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"nighttime social media use, sleep disturbance, assessment anxiety, academic performance, self-esteem, university students","lastPublishedDoi":"10.21203/rs.3.rs-6872928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6872928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eWith the rising prevalence of social media use among university students, particularly during nighttime hours, concerns have grown regarding its potential effects on mental health and academic success, warranting a comprehensive investigation into these associations. Our study investigated the mental health implications of nighttime social media use among university students in Ghana, focusing on its impact on sleep disturbance, assessment anxiety, self-esteem, and academic performance. Employing a quantitative, correlational cross-sectional survey design, data were collected from 1,250 undergraduate students across five major public universities using validated psychometric instruments, including the Pittsburgh Sleep Quality Index (PSQI), DASS-21, Rosenberg Self-Esteem Scale, and GPA records. Pearson correlation analysis revealed moderate and significant associations between nighttime social media use and sleep disturbances (r\u0026thinsp;=\u0026thinsp;.48\u003c/em\u003e, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;.001), assessment anxiety (r\u0026thinsp;=\u0026thinsp;.42\u003c/em\u003e, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;.001), and lower academic performance (r = \u0026ndash;.28\u003c/em\u003e, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;.001). Mediation analysis using PROCESS Model 4 indicated that sleep disturbance significantly mediated the relationship between nighttime social media use and academic performance (indirect effect = \u0026minus;\u0026thinsp;0.21, 95% CI [\u0026ndash;0.32, \u0026minus;\u0026thinsp;0.13]\u003c/em\u003e, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;.001), with large effect sizes across sleep latency, subjective sleep quality, and sleep duration. Furthermore, moderation analysis using PROCESS Model 1 revealed that self-esteem buffered the adverse impact of nighttime social media use on assessment anxiety (interaction term β = \u0026minus;\u0026thinsp;0.18\u003c/em\u003e, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;.001), suggesting a protective role. The findings emphasise the critical role of digital behavior in shaping students\u0026rsquo; psychological well-being and academic outcomes. This research provides empirical evidence for policymakers, mental health professionals, and educational institutions to develop targeted interventions that mitigate the negative consequences of excessive nighttime social media engagement, promote healthy sleep practices, and foster resilience through self-esteem enhancement strategies in Ghanaian higher education.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Online Allure and Offline Anxiety: Exploring the Mental Health Implications of Nighttime Social Media Use, Assessment Anxiety, Sleep Disturbance, Self-Esteem and Academic Performance of University Students in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 03:13:29","doi":"10.21203/rs.3.rs-6872928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T06:12:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T03:37:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325591202162074789032624879803069298450","date":"2026-01-01T18:16:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309767584235960616043248977261794030493","date":"2025-12-29T11:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123653391245855187646052826448403001216","date":"2025-12-29T05:20:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297722244768568293184469114130146291466","date":"2025-12-27T21:26:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67710634403240862252014254006902300418","date":"2025-12-27T13:55:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84772308635144371515874284538897756907","date":"2025-11-08T02:36:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T08:24:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229152435903810587555435600828468701200","date":"2025-11-07T05:04:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94526617829100082833269719658839948401","date":"2025-11-07T01:55:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180758706700801181768076536381376703219","date":"2025-11-05T14:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153273576958216581883249301852033119742","date":"2025-11-05T10:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8024521243686196901943110607402173288","date":"2025-09-05T11:14:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97155308847911543421150659548297428267","date":"2025-08-04T16:27:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T14:29:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T06:26:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T05:13:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-11T14:27:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df558aa1-0baa-4621-999d-f290722dd07f","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52627291,"name":"Biological sciences/Psychology"},{"id":52627292,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-04-28T12:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 03:13:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6872928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6872928","identity":"rs-6872928","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}