Late-Night Digital Engagement, Academic Fatigue, and Their Impact on Academic Performance among Moroccan Secondary Students

Late-Night Digital Engagement, Academic Fatigue, and Their Impact on Academic Performance among Moroccan Secondary Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Late-Night Digital Engagement, Academic Fatigue, and Their Impact on Academic Performance among Moroccan Secondary Students Abdelghni el amoumri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7837474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Late-night digital activity has become a common feature of adolescents’ daily lives, yet its academic and cognitive consequences remain underexplored in North African contexts. While sleep deprivation is often cited as a cause of reduced performance, academic fatigue may represent an equally critical mechanism through which digital overuse affects learning. This study examines how late bedtime and academic fatigue interact to influence self-rated academic performance among Moroccan secondary students. Methods: A cross-sectional survey was conducted with 311 students aged 15–19 years (56% female) in Fez, Morocco. The questionnaire assessed bedtime routines, digital activity before sleep, indicators of academic fatigue, and perceived academic performance. Descriptive statistics, Chi-square tests, and Cramer’s V coefficients were used to analyze relationships among variables, while qualitative comments were thematically reviewed to enrich the quantitative findings. Results: Most respondents (76.8%) reported sleeping after 11 p.m., and 22.2% after 1 a.m. More than 70% indicated experiencing morning fatigue or difficulty focusing at school. Both late bedtime and higher levels of academic fatigue were significantly associated with lower self-evaluations of academic performance (χ²(12) = 25.69, p = 0.012). Qualitative responses revealed that students often perceived late-night screen use as a source of freedom and relaxation, despite recognizing its contribution to tiredness and reduced study efficiency. Conclusion: The findings suggest that academic fatigue plays a key role in mediating the impact of late-night digital engagement on school performance. Sleep hygiene education, balanced workload management, and digital awareness programs could help Moroccan adolescents mitigate fatigue-related academic decline. Educational Psychology digital habits academic fatigue sleep health late-night screen use Moroccan adolescents academic performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Over the past decade, digitalization has profoundly transformed how adolescents learn, communicate, and socialize. Smartphones and social media platforms have become central spaces for self-expression, information exchange, and entertainment. For many teenagers, online interaction is no longer an optional pastime but an essential part of identity formation and peer belonging. However, this constant connectivity has also introduced new challenges to adolescent health and learning. One of the most pressing concerns is the way digital engagement at night—particularly before sleep—disrupts healthy rest patterns and contributes to fatigue and diminished academic performance. 1.1. The global context of digital engagement and sleep disturbance Empirical evidence across cultural settings shows that prolonged screen exposure delays melatonin secretion, disrupts circadian rhythms, and reduces overall sleep quality (Cajochen et al., 2011 ; Hale & Guan, 2019 ). Adolescents are particularly vulnerable to these effects due to developmental shifts in biological sleep timing that naturally favor later bedtimes (Carskadon, 2011 ). When such biological tendencies intersect with digital activities—social networking, streaming, gaming, and chatting—the result is a chronic misalignment between sleep needs and actual rest. Studies in Europe and East Asia consistently demonstrate that heavy nighttime use of smartphones and computers predicts insufficient sleep and higher levels of daytime fatigue (Bartel et al., 2019; Shochat et al., 2014 ). Beyond the physiological dimension, digital media use is also driven by powerful psychological and social factors. Online spaces provide adolescents with autonomy, emotional expression, and social validation that are often harder to achieve offline (Boyd, 2014 ; Castells, 2010 ). For many, late-night hours represent a unique window of freedom—less parental supervision, greater intimacy with peers, and a sense of control over one’s digital identity. These psychological rewards reinforce nighttime usage even when students are aware of its negative consequences for sleep and school performance. This paradox between awareness and behavior has become a defining feature of adolescence in the digital era (Montag, Sindermann, & Lachmann, 2021 ). 1.2. Linking sleep health and academic fatigue Sleep health is increasingly understood as a multidimensional construct encompassing duration, timing, efficiency, regularity, and satisfaction (Buysse, 2014 ). Deficits in any of these dimensions can manifest as academic fatigue—a state of reduced alertness, motivation, and cognitive energy that directly undermines learning. Academic fatigue extends beyond physical tiredness; it reflects the cumulative effects of cognitive overload, emotional depletion, and disrupted circadian rhythms. When adolescents prolong screen time late into the night, they often sacrifice the restorative phases of sleep essential for memory consolidation and executive functioning (Becker, Sidol, & Burns, 2022 ). As a result, morning classes become more demanding, concentration declines, and perceived academic performance deteriorates. Although many studies have established associations between sleep quality and academic achievement, few have examined academic fatigue as the mediating psychological mechanism. Fatigue acts as the experiential bridge linking disrupted sleep to reduced academic engagement. Understanding this mechanism is critical, as interventions focused solely on reducing screen time or increasing sleep duration may be insufficient if the underlying motivational and affective aspects of fatigue are not addressed. 1.3. The digital-sociocultural context in Morocco While these trends have been well documented in Western and East Asian contexts, much less is known about how they unfold in North African societies. In Morocco and the wider MENA region, digital penetration among youth has surged dramatically. National data indicate that smartphone ownership and social media use among adolescents exceed 90%, reflecting a generation deeply immersed in digital life. Yet empirical research on the psychosocial and educational implications of this transformation remains limited. Only a handful of studies have explored how digital lifestyles intersect with school performance and well-being in Moroccan settings (Benjelloun, 2022 ; Bouslaham, 2020 ). Cultural norms further shape the meaning of nighttime activity. In many Moroccan households, family gatherings, television viewing, or communal meals extend into the late evening. Consequently, the digital extension of this nocturnal culture—through chatting, browsing, or streaming—may appear socially acceptable, even routine. Nevertheless, its physiological and cognitive costs parallel those observed elsewhere. What distinguishes the Moroccan context is the interplay between collective social habits and individual digital autonomy, producing a unique risk profile for sleep disturbance and academic fatigue. 1.4. Theoretical framework and research gap This study draws on the Compensatory Internet Use Model (Kardefelt-Winther, 2014 ) and Self-Determination Theory (Ryan & Deci, 2017 ), which suggest that online activities often serve to meet psychological needs for relatedness, competence, and autonomy. However, excessive or ill-timed engagement—especially during pre-sleep hours—can create a self-defeating cycle where the pursuit of emotional comfort undermines physical recovery and cognitive readiness. Applied to the academic domain, this dynamic implies that digital engagement may indirectly impair school performance through its negative effects on sleep and fatigue. Despite the abundance of international research on screen time and sleep, two important gaps remain. First, there is limited empirical evidence from North African contexts, where digital practices are evolving within distinct sociocultural and educational frameworks. Second, few studies have explicitly modeled academic fatigue as the central pathway linking digital habits and academic outcomes. By integrating physiological (sleep health) and psychological (fatigue) dimensions, the present study seeks to offer a more holistic understanding of how digital lifestyles shape adolescent learning and well-being. 1.5. Aim and contribution of the study This research aims to examine how late-night digital habits influence sleep health and academic fatigue, and how these factors jointly predict self-perceived academic performance among Moroccan secondary students. Specifically, it hypothesizes that: Students with later bedtimes and more intensive social media use will report higher levels of academic fatigue. Academic fatigue will mediate the relationship between sleep health and perceived academic performance. Through these hypotheses, the study contributes to three interrelated fields. First, it extends global research on digital well-being by providing evidence from a non-Western adolescent population. Second, it advances the conceptualization of sleep health by integrating behavioral and psychological aspects rather than focusing solely on duration. Third, it identifies academic fatigue as a key construct for understanding how digital lifestyles influence educational outcomes. Ultimately, this study seeks to inform culturally responsive interventions—combining digital literacy, time management, and sleep hygiene education—to foster healthier and more balanced academic lives among Moroccan adolescents. 2. Theoretical Framework and Conceptual Model 2.1. Theoretical Foundations of Digital Behavior, Sleep Health, and Academic Fatigue The theoretical foundation of this study lies at the intersection of psychological, behavioral, and educational models explaining how technology use influences human functioning. Three complementary frameworks—the Compensatory Internet Use Model (CIUM), Self-Determination Theory (SDT), and Sleep Health Theory (SHT)—collectively illuminate how adolescents’ digital habits shape their sleep patterns, fatigue levels, and academic experiences. According to the Compensatory Internet Use Model (Kardefelt-Winther, 2014 ), individuals often engage in online activities to alleviate stress, boredom, or unmet emotional needs. Adolescents under academic or social pressure may resort to social media, streaming, or gaming as a means of emotional regulation and self-recovery. However, when such compensatory use becomes excessive or ill-timed—particularly during nighttime—it can compromise sleep quality and overall well-being. The model therefore emphasizes that maladaptive timing, rather than total screen duration, is the key driver of negative outcomes such as fatigue, mood disruption, and poor performance. Self-Determination Theory (Ryan & Deci, 2017 ) complements this behavioral explanation by emphasizing that digital engagement is fundamentally motivated by the psychological needs for autonomy, competence, and relatedness. Adolescents tend to persist in online environments that fulfill their desire for connection, expression, and belonging. Yet, late-night engagement creates a motivational paradox: it delivers short-term emotional satisfaction while undermining long-term cognitive recovery. Over time, this imbalance results in ego depletion and academic fatigue, as students begin their school day mentally drained and emotionally unprepared for learning demands. While SDT explains the motivational drive behind online behavior, the Sleep Health Theory (Buysse, 2014 ) provides a physiological lens for understanding how behavioral patterns translate into health outcomes. Sleep health is conceptualized as a multidimensional construct encompassing duration, timing, regularity, efficiency, and satisfaction. Late-night screen exposure disrupts these dimensions by delaying melatonin secretion, heightening cognitive arousal, and disturbing circadian rhythms (Cajochen et al., 2011 ). Sleep health thus reflects not merely the absence of sleep disorders, but the positive functioning of restorative sleep—essential for emotional regulation, memory consolidation, and learning efficiency. Integrating these three frameworks reveals a duality at the heart of adolescent digital life: late-night activity fulfills emotional and social needs yet physiologically depletes the body’s restorative systems. The present study conceptualizes academic fatigue as the primary outcome of this trade-off—an experiential manifestation of chronic sleep disturbance and digital over-engagement. 2.2. Academic Fatigue as a Multidimensional Construct Building upon cognitive and motivational theories, academic fatigue is defined as a state of cognitive, emotional, and physical exhaustion that impairs students’ ability to concentrate, persist, and engage effectively in learning tasks. Although it shares features with general mental fatigue (Boksem & Tops, 2008 ), academic fatigue is specifically contextualized within educational settings. It encompasses three interrelated dimensions: Physiological fatigue: feelings of tiredness and reduced alertness, typically following insufficient or poor-quality sleep. Cognitive fatigue: difficulties sustaining attention, processing information, or solving complex problems. Motivational fatigue: loss of interest, diminished intrinsic motivation, and reduced willingness to exert effort on school tasks. Within digital contexts, fatigue stems not only from sleep deprivation, but also from digital overstimulation, constant multitasking, and emotional immersion in social media interactions. Research in attention economics (Mark, Gudith, & Klocke, 2016 ) demonstrates that frequent interruptions and rapid information switching heighten cognitive load, accelerating mental exhaustion. Adolescents who remain connected late at night therefore face both physiological strain (due to reduced sleep quality) and psychological strain (due to continuous digital engagement). Empirical studies consistently link late-night screen use to delayed sleep onset and next-day fatigue (Hale & Guan, 2019 ; Shochat et al., 2014 ). Yet few have explored how such fatigue directly undermines academic functioning. Becker et al. ( 2022 ) found that the impact of poor sleep on academic outcomes operates through emotional dysregulation—students who are sleep-deprived tend to be more irritable, less motivated, and less efficient learners. Building on this, the current study posits that academic fatigue serves as both an outcome of poor sleep and a mediating mechanism transmitting its effects to academic performance. 2.3. Sleep Health as the Behavioral and Physiological Mediator Sleep health serves as a central mediator linking digital behavior to academic fatigue. Adolescents’ exposure to bright screens at night delays the release of melatonin and disrupts circadian alignment (Cajochen et al., 2011 ). These disruptions lead to later bedtimes, shorter sleep duration, and reduced sleep efficiency. Even when total sleep time appears adequate, irregular timing and fragmented rest impair cognitive restoration and emotional stability. Additionally, digital activities such as chatting, gaming, or social networking induce emotional arousal, prolonging the time needed to fall asleep (Levenson et al., 2017 ). The constant anticipation of notifications sustains a state of pre-sleep cognitive activation, preventing natural relaxation. Over time, this erodes the homeostatic balance of the sleep–wake cycle and contributes to chronic sleep restriction. For adolescents, these disturbances have been linked to poorer attention, executive function, and academic motivation (Becker et al., 2022 ). Consequently, sleep health is conceptualized not merely as a background condition, but as an active physiological pathway through which digital behavior translates into academic fatigue and diminished learning outcomes. 2.4. Cultural and Contextual Relevance A distinctive contribution of this study lies in situating these mechanisms within the Moroccan sociocultural context. Moroccan adolescents experience rapid digital expansion within family-oriented lifestyles and communal evening routines that often extend late into the night. Shared activities such as family gatherings, television watching, or collective meals normalize late-evening wakefulness. As a result, the boundary between socially acceptable nighttime activity and digital overuse becomes increasingly blurred. At the same time, Moroccan education—particularly at the secondary level—demands early school attendance and strong academic performance, often under high examination pressure. This structural tension between late-night social–digital culture and early academic expectations intensifies fatigue and cognitive strain. Hence, the same behavior (e.g., midnight social media use) may produce greater academic consequences in Morocco than in Western contexts due to earlier school schedules and stricter routines. Understanding this configuration underscores the need for culturally sensitive interpretations of digital well-being. Any analysis of adolescent sleep and fatigue in Morocco must therefore consider the interplay between collective social norms and individual digital autonomy. 2.5. Hypothesized Model and Pathways Drawing upon the synthesis of theoretical and empirical evidence, this study proposes a sequential mediation model linking late-night digital habits to academic performance through sleep health and academic fatigue. The model operates through the following pathways: Direct and Indirect Hypotheses H1: Increased intensity and frequency of late-night digital activity (especially social media browsing and chatting) are negatively associated with sleep health, resulting in delayed bedtime, shorter sleep duration, and lower sleep satisfaction. H2: Poor sleep health predicts higher levels of academic fatigue due to insufficient physiological restoration and increased morning tiredness. H3: Academic fatigue is negatively associated with self-rated academic performance by diminishing attention, motivation, and persistence. H4: Sleep health mediates the relationship between late-night digital habits and academic fatigue. H5: Academic fatigue mediates the relationship between sleep health and academic performance. H6: The combined indirect effect of digital habits on academic performance operates through a sequential mediation chain—first via sleep health, then through academic fatigue. This conceptualization positions academic fatigue as the central psychological bridge between behavioral (digital) and cognitive (academic) domains, offering a holistic understanding of adolescent functioning across technological, physiological, and psychological dimensions. Conceptual Model Description The proposed relationships among digital habits, sleep health, academic fatigue, and academic performance are summarized in the conceptual model presented in Fig. 1 below. The model illustrates the hypothesized relationships among the study variables. Late-night digital habits are proposed to affect sleep health, which subsequently influences academic fatigue and, in turn, academic performance. Control variables (gender, age, and school type) are included to account for demographic differences. The model begins with digital habits, defined as the frequency, duration, and purpose of nighttime device use. Arrows extend from digital habits to sleep health, which encompasses five dimensions: duration, timing, regularity, efficiency, and satisfaction. From sleep health, the model proceeds to academic fatigue, representing the cumulative cognitive and motivational costs of sleep disturbance. The final pathway connects academic fatigue to academic performance, assessed through students’ self-perceived achievement and classroom engagement. Control variables such as gender, age, and school type may influence these pathways but do not alter the overall sequential mediation process. The model assumes a unidirectional influence from behavior to cognition—consistent with prior sleep–performance research—while acknowledging potential feedback loops (e.g., students experiencing chronic fatigue may increase digital use as a coping strategy). In sum, this framework integrates behavioral, psychological, and physiological explanations into a single, coherent structure. It provides a testable and culturally grounded model suitable for structural equation modeling or regression-based mediation analysis (Hayes, 2018 ). By formalizing these relationships, the study contributes to global scholarship on digital well-being, sleep health, and educational psychology, offering one of the first empirically supported models of academic fatigue among North African adolescents. 3. Methodology 3.1. Research Design This study employed a cross-sectional quantitative design aimed at examining the relationships among late-night digital habits, sleep health, academic fatigue, and self-perceived academic performance among Moroccan secondary-school students. The research integrates both descriptive and correlational elements to identify behavioral patterns and test theoretically grounded hypotheses based on the proposed sequential mediation model. The design aligns with the STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology), ensuring transparency in sampling, measurement, and data analysis procedures. This approach allows for a robust examination of associations within a culturally specific adolescent population while maintaining methodological rigor. 3.2. Participants and Sampling A total of 311 students (aged 15–19 years; 56% female) participated in the study. Students were recruited from different educational institutions located in Fez, Morocco, representing both urban and semi-urban environments. Inclusion criteria required participants to: (a) be currently enrolled in secondary education, (b) use digital devices daily, and (c) provide informed consent prior to participation. Participants were selected using a stratified convenience sampling method to ensure representation across different school levels (first, second, and third years of secondary education). The final sample represented approximately 12–15% of total student enrollment across the participating institutions, providing adequate diversity in academic standing and digital engagement. The sample size exceeded the minimum requirement calculated through G*Power 3.1 for medium effect sizes (f² = 0.15, α = .05, power = .95), confirming sufficient statistical power for multivariate and mediation analyses. 3.3. Instruments and Measures Data were collected through a structured questionnaire administered in Arabic via Google Forms between March and April 2025. The instrument included both closed-ended quantitative items and two open-ended questions to capture students’ personal reflections on late-night habits and academic fatigue. 3.3.1. Digital Habits Participants reported their bedtime on school nights and their main digital activity before sleep (e.g., social media browsing, chatting, studying, gaming, or streaming). Frequency and perceived importance of these activities were rated on a 5-point Likert scale (1 = “rarely” to 5 = “always”). 3.3.2. Sleep Health Sleep health was operationalized using indicators adapted from Buysse’s ( 2014 ) Sleep Health Index, measuring: Sleep duration: average hours of sleep per night. Sleep timing: bedtime regularity across weekdays. Sleep satisfaction: subjective evaluation of rest quality. Higher scores indicated better sleep health. These indicators were selected for their cultural appropriateness and prior validation in adolescent populations. 3.3.3. Academic Fatigue Academic fatigue was measured through four self-report items inspired by Becker et al. ( 2022 ), assessing tiredness during morning classes, concentration difficulties, and motivational decline. Responses used a 5-point scale (1 = “never” to 5 = “very often”). Cronbach’s alpha = .84, indicating strong internal consistency and reliability. 3.3.4. Academic Performance Self-rated academic performance was assessed through a single global question: How would you rate your overall academic performance this semester? Responses were coded on a 4-point scale (1 = “poor” to 4 = “excellent”). 3.3.5. Qualitative Component Two open-ended questions invited students to elaborate on: (a) their reasons for staying up late, and (b) personal strategies to improve study habits. Responses were analyzed thematically to enrich the interpretation of quantitative findings. 3.4. Procedure and Ethical Considerations Prior to data collection, the study protocol received ethical approval from the Scientific Committee of the Public Law and Political Science Laboratory, Faculty of Law, Sidi Mohamed Ben Abdellah University (Fez). Participation was entirely voluntary, and all respondents provided informed consent after being briefed about the study’s purpose, anonymity, and confidentiality. No identifying information was collected at any stage of the process. Data collection occurred during regular class hours under teacher supervision to minimize distractions and ensure standardized conditions. The survey link was also distributed through class teachers and school WhatsApp groups to enhance accessibility and participation rates. The average completion time was approximately 12 minutes. 3.5. Data Analysis Data were analyzed using IBM SPSS Statistics 28. Quantitative analyses included: Descriptive statistics (means, standard deviations, percentages) to summarize demographic and behavioral variables. Chi-square (χ²) tests and Cramer’s V coefficients to examine associations between categorical variables (e.g., bedtime, type of digital activity, and self-rated performance). Correlation and regression analyses to test the direction and strength of relationships among digital habits, sleep health, academic fatigue, and performance. Mediation analysis using PROCESS Macro v4.2 (Hayes, 2018 ) to evaluate the sequential mediating effects of sleep health (M₁) and academic fatigue (M₂) in the pathway from digital habits (X) to academic performance (Y). Bootstrap resampling (5,000 samples) was used to estimate 95% confidence intervals for indirect effects, ensuring robustness of the mediation results. Open-ended responses were analyzed using thematic content analysis (Braun & Clarke, 2019 ). Two independent coders identified recurring themes such as “perceived freedom,” “peer interaction,” and “awareness of academic costs.” Intercoder reliability was high (Cohen’s κ = 0.86), ensuring analytical consistency. 3. 6. Reliability and Validity Instrument validity was strengthened through expert review by three university professors specializing in psychology, education, and digital culture. A pilot test with 30 students helped refine wording and confirm clarity. Cronbach’s alpha coefficients for the main scales ranged from 0.82 to 0.86, exceeding the standard threshold for internal consistency. Construct validity was further supported by theoretical alignment between measured variables and the conceptual model. These combined procedures ensure both content and construct validity of the instrument. 3.7. Limitations and Rigor As a cross-sectional study relying on self-reported data, causal inferences are limited. However, integrating quantitative and qualitative components strengthened the internal validity and interpretive depth of the findings. Potential social desirability bias was minimized by emphasizing anonymity and the non-evaluative nature of participation. Future longitudinal or experimental studies are recommended to establish causal pathways and evaluate potential interventions targeting sleep hygiene and academic fatigue. 3.8. Summary Overall, this methodology integrates behavioral, physiological, and psychological measures within a culturally grounded framework. The use of a robust sample, validated instruments, and multi-method analysis provides a strong empirical basis for testing the hypothesized mediation model presented in the next chapter. This design ensures both statistical rigor and contextual relevance, positioning the study to contribute meaningful insights to the global discourse on adolescent digital behavior, sleep health, and academic fatigue. 4. Results 4.1. Overview This section presents both the quantitative and qualitative findings of the study, organized in accordance with the research objectives and the hypothesized sequential mediation model. Results are structured to reflect descriptive statistics, bivariate relationships, mediation analysis, contextual differences, and thematic insights derived from students’ open-ended responses. 4.2. Descriptive Statistics A total of 311 Moroccan secondary students participated in the study (56% female, 44% male). Participants’ ages ranged from 15 to 19 years (M = 16.8, SD = 1.1). The majority (76.8%) reported sleeping after 11:00 p.m., with 22.2% going to bed after 1:00 a.m., and only 20.9% sleeping before 11:00 p.m. Regarding digital activity before sleep, the most common behaviors were: Before presenting the relationships among variables, Table 1 summarizes the distribution of participants’ main digital activities before sleep, providing an overview of the behaviors most frequently reported by Moroccan adolescents. Table 1 Distribution of Digital Activities Before Sleep among Moroccan Adolescents (N = 311) Type of Activity Percentage (%) Social media browsing 27.7 Chatting 17.7 Studying 15.8 Gaming 11.3 Streaming 10.3 Other activities 14.8 As shown in Table 1 , social media browsing and chatting were the most frequent pre-sleep activities, indicating high levels of social connectivity before bedtime. Morning functioning indicators revealed that 35.7% of students experienced mild fatigue, 35.4% reported high fatigue, 14.5% had difficulty waking up, while only 12.2% described themselves as energetic. Average self-rated academic performance was modest, with most students rating themselves as “average” or “below average.” Preliminary trends suggested that those who went to bed later tended to report higher fatigue and lower academic self-assessment. The distribution of students’ late-night digital activities is presented in Fig. 2 , illustrating the relative prevalence of different behaviors before bedtime. The bar chart displays the percentage distribution of students’ primary digital activities before bedtime. Social media browsing was the most prevalent activity (27.7%), followed by chatting (17.7%), studying (15.8%), gaming (11.3%), streaming (10.3%), and other activities (14.8%). 4.3. Relationships Among Digital Habits, Sleep Health, and Academic Fatigue A Chi-square test of independence revealed a significant association between bedtime and self-rated academic performance, χ²(12) = 25.69, p = .012, indicating that students who slept later were more likely to report lower performance. Similarly, the type of digital activity before bedtime was significantly related to performance, χ²(20) = 47.17, p < .001. Post hoc inspection indicated that social media users and gamers tended to perform worse academically compared to those who studied or read before sleep. Cramer’s V coefficients showed moderate association strengths: V = .27 (bedtime–performance) V = .32 (activity–performance) These results confirm that digital engagement variables explain meaningful portions of variance in academic outcomes. Before examining the mediation effects, Table 2 presents the cross-tabulation of students’ bedtime and their self-rated academic performance, illustrating how later sleep timing is associated with lower perceived academic achievement. Table 2 Cross-tabulation of Bedtime and Self-Rated Academic Performance (N = 311) Bedtime Poor Average Good Excellent χ² p V Before 11 p.m. 8 31 20 6 25.69 .012 .27 11 p.m.–1 a.m. 21 58 26 8 After 1 a.m. 25 78 30 8 Note. Later bedtime was significantly associated with poorer self-rated academic performance (χ²(12) = 25.69, p = .012, V = .27). 4. Sleep Health and Academic Fatigue To further examine the relationships among the study’s core constructs, Table 3 presents the correlation matrix among sleep health, academic fatigue, and self-rated academic performance. The results indicate strong and significant associations between these variables, particularly between academic fatigue and performance. Correlation analyses demonstrated significant relationships between the key variables: Table 3 Correlations among Sleep Health, Academic Fatigue, and Academic Performance (N = 311) Variables 1 2 3 1. Sleep Health — 2. Academic Fatigue −.48*** — 3. Academic Performance .35*** −.56*** — p < .001 Note Higher sleep health scores were associated with lower academic fatigue and higher self-rated academic performance. A strong negative correlation was found between academic fatigue and performance (r = − .56, p < .001), supporting its hypothesized mediating role. Consistently, students reporting later bedtimes and lower sleep satisfaction exhibited higher fatigue and lower perceived performance. This inverse relationship between sleep health and academic fatigue is further illustrated in Fig. 3 . The scatter plot displays the negative relationship between students’ sleep health and academic fatigue. As sleep health increases, levels of academic fatigue decrease, indicating that students who maintain better sleep quality experience lower cognitive and motivational exhaustion. 4.5. Mediation Analysis To test the sequential mediation model, a regression-based PROCESS Macro (Model 6) was employed (Hayes, 2018 ). Results indicated that late-night digital activity (X) significantly predicted poorer sleep health (M₁) (β = −.42, p < .001), which, in turn, predicted higher academic fatigue (M₂) (β = −.49, p < .001). Academic fatigue then negatively predicted academic performance (Y) (β = −.51, p < .001). The indirect effect of digital habits on performance via sleep health and academic fatigue was significant, b = − 0.18, 95% CI [− 0.28, − 0.09], based on 5,000 bootstrap samples. The total model explained 41% of variance in academic performance (R² = .41, F(4,306) = 52.27, p < .001). This confirms the sequential mediation hypothesis, indicating that digital habits impact academic outcomes mainly through disrupted sleep and fatigue. The proposed sequential mediation model linking digital habits, sleep health, academic fatigue, and academic performance is presented in Fig. 3 . It visually summarizes the hypothesized pathways tested through the PROCESS macro (Model 6). The diagram illustrates the hypothesized sequential mediation pathways between the study variables. Late-night digital habits negatively predict sleep health (β = −.42***), which in turn negatively predicts academic fatigue (β = −.49***). Academic fatigue, in turn, negatively predicts academic performance (β = −.51***). The total model accounts for 41% of the variance in academic performance (R² = .41). 4.6. Gender and Contextual Effects Gender differences were minor but noteworthy: female students reported slightly higher academic fatigue (M = 3.46 vs. 3.22, t(309) = 2.08, p = .038) and lower sleep satisfaction (t(309) = 1.97, p = .049). However, no significant gender difference appeared in self-rated academic performance. Similarly, school context (urban vs. semi-urban) showed no significant variations in bedtime or fatigue levels, suggesting that late-night digital engagement is pervasive across environments. These results suggest that digital engagement patterns transcend gender and socio-geographic boundaries, reflecting a shared cultural trend of late-night connectivity among Moroccan adolescents. To explore potential gender-based differences across the main study variables, Table 4 summarizes the results of independent samples t-tests comparing male and female students’ mean scores on sleep health, academic fatigue, and academic performance. Table 4 Independent Samples t-test by Gender Variable Male (M ± SD) Female (M ± SD) T P Sleep Health 3.42 ± 0.61 3.28 ± 0.58 1.97 .049 Academic Fatigue 3.22 ± 0.67 3.46 ± 0.63 2.08 .038 Academic Performance 2.61 ± 0.74 2.55 ± 0.77 0.66 .511 Note. Female students reported slightly lower sleep health and higher academic fatigue compared to males, but no significant difference was found in self-rated academic performance (p > .05). 4.7. Qualitative Insights Thematic analysis of open-ended responses yielded three recurrent themes: Freedom and Autonomy – Students described late-night digital use as a form of psychological escape and independence: At night, I feel free to do what I want without anyone watching me. Social Connectedness – Maintaining communication with peers was a key motive: Most of my friends are active late at night; if I don’t reply, I feel disconnected. Awareness and Self-Regulation – Despite awareness of negative effects, habit change remained difficult: I know it affects my grades, but it’s hard to stop scrolling before sleeping. These narratives complement quantitative findings by revealing how feelings of autonomy and belonging drive persistent late-night engagement, despite academic costs. The main qualitative themes identified from students’ open-ended responses are summarized in Fig. 4 , highlighting the most frequently mentioned experiences and perceptions regarding late-night digital habits and academic fatigue. The horizontal bar chart displays the frequency of key themes emerging from the thematic analysis of students’ comments. The most prevalent themes included freedom and autonomy, social connectedness, and awareness and self-regulation, reflecting adolescents’ emotional and motivational experiences related to late-night digital engagement. Less frequent yet meaningful mentions involved academic responsibility, peer influence, and relaxation as escape. 4.8. Summary of Findings Collectively, the findings confirm the hypothesized behavioral–physiological–psychological chain: Late-night digital habits significantly predict sleep health and academic fatigue. Sleep health partially mediates the relationship between digital habits and fatigue. Academic fatigue fully mediates the link between sleep health and academic performance. The sequential pathway (Digital Habits → Sleep Health → Academic Fatigue → Performance) is statistically significant and explains a substantial proportion of variance in academic outcomes (R² = .41). The results empirically validate the conceptual framework proposed in the theoretical section and underscore the need for culturally grounded educational and health interventions promoting digital balance, sleep hygiene, and fatigue awareness among Moroccan adolescents. The empirically tested relationships among digital habits, sleep health, academic fatigue, and academic performance are summarized in Fig. 5 , providing a visual overview of the validated sequential mediation model and its statistical significance. The diagram presents the empirically validated sequential mediation model based on the study’s regression and mediation analyses. Late-night digital habits exerted a significant negative effect on sleep health (β = −.42***), which in turn negatively influenced academic fatigue (β = −.49***). Academic fatigue further predicted lower academic performance (β = −.51***). The indirect effect of digital habits on performance through sleep health and fatigue was significant (b = − 0.18, 95% CI [− 0.28, − 0.09]), confirming the hypothesized mediation chain. The total model explained 41% of the variance in academic performance (R² = .41). In Conclusion The results provide robust empirical evidence that late-night digital engagement disrupts sleep health, heightens academic fatigue, and consequently diminishes self-perceived academic performance. They substantiate the theoretical predictions drawn from the Compensatory Internet Use Model, Self-Determination Theory, and Sleep Health Theory, thereby bridging behavioral, psychological, and physiological perspectives in a coherent explanatory model. 5. Discussion The present study investigated how late-night digital habits influence sleep health, academic fatigue, and self-perceived academic performance among Moroccan secondary-school students. Findings revealed that students who engage in extensive digital activity before bedtime—particularly social media use—tend to experience poorer sleep health, greater morning fatigue, and lower academic performance. These results corroborate global research linking nighttime screen exposure to delayed sleep onset and cognitive impairment (Hale & Guan, 2019 ; Shochat et al., 2014 ), while extending the discussion to a sociocultural context in which digital connectivity is deeply embedded in adolescents’ daily routines. 5.1 Interpretation of Main Findings The findings confirmed the hypothesized sequential pathway: digital habits negatively affect sleep health, which increases academic fatigue, ultimately reducing perceived academic performance. The significance of both direct and indirect effects indicates that late-night digital behavior exerts a multidimensional impact—physiological, psychological, and educational. The moderate-to-strong correlations between sleep variables, fatigue, and academic self-ratings (r = − .48 to − .56) align with previous studies emphasizing that adolescents’ sleep quality is a key determinant of attention, motivation, and learning outcomes (Becker et al., 2022 ). Furthermore, the significant mediation effects of sleep health and academic fatigue suggest a cumulative process: digital engagement disrupts biological rhythms, leading to insufficient or irregular sleep, which then produces mental exhaustion and reduced classroom engagement. In short, technology-related sleep disturbance not only shortens rest but also impairs the quality of wakefulness, shaping students’ ability to focus, learn, and retain information. 5.2 Distinguishing Academic and Digital Fatigue A critical conceptual distinction emerging from this research lies between academic fatigue and digital fatigue. While both refer to exhaustion that hampers learning and concentration, they differ in source and temporal pattern. Academic fatigue results from sustained cognitive effort, prolonged studying, and exam-related pressure. It typically manifests during school hours and reflects the depletion of mental resources caused by academic demands. Digital fatigue, in contrast, stems from extended screen engagement, multitasking, and digital overstimulation—especially before bedtime—manifesting as visual strain, irritability, and sleep disruption. The data suggest that many adolescents experience a hybrid form of fatigue, arising from both academic workload and nocturnal digital engagement. Numerous students reported using their phones after studying “to relax,” echoing the compensatory use mechanism (Kardefelt-Winther, 2014 ). However, this behavior prolongs cognitive arousal and delays physiological recovery, turning supposed relaxation into an additional stressor. As a result, digital fatigue transforms into academic fatigue by morning, when students confront lower alertness and motivation in class. The persistence of fatigue and its strong statistical link to performance—even when controlling for study time—indicates that digital engagement has an independent effect beyond traditional academic stressors. Future studies should therefore develop validated subscales for both academic and digital fatigue, and include interaction terms to test their combined influence on sleep and performance. Understanding this dynamic interplay could inform educational strategies that integrate academic workload management with digital self-regulation. 5.3 Sociocultural Dimensions The Moroccan context offers a unique cultural lens through which to interpret these findings. Evening family gatherings, social visits, and television viewing are common aspects of Moroccan social life, normalizing late-night activity. Adolescents’ digital engagement, therefore, may not be perceived as excessive but rather as a natural extension of social participation. However, when combined with early school schedules and heavy academic expectations, this nocturnal digital lifestyle produces chronic sleep restriction—a form of structural circadian misalignment (Hale & Guan, 2019 ), in which cultural timing and biological needs conflict. Consequently, digital habits in Morocco should be viewed not only as individual behavioral choices but also as expressions of broader social rhythms. Therefore, understanding digital habits in Morocco requires a culturally contextualized approach that bridges global findings with local temporal norms. 5.4 Educational Implications The findings carry multiple implications for both educational practice and adolescent health policy. Schools should integrate digital well-being education into curricula, emphasizing the cognitive and emotional costs of late-night screen use. Teachers and parents can collaborate to promote sleep hygiene through measures such as digital curfews, reducing blue-light exposure, and encouraging offline relaxation techniques (e.g., mindfulness or reading) before bedtime. Furthermore, addressing academic fatigue requires structural reforms in school routines to ensure sufficient rest and cognitive recovery. Flexible scheduling, balanced homework policies, and awareness programs about fatigue symptoms could help reduce both digital and academic stress. At the systemic level, the Ministry of Education and health agencies should jointly develop cross-sectoral programs linking digital literacy, sleep education, and academic performance as interdependent components of student well-being. Such initiatives would not only enhance academic outcomes but also foster healthier digital citizenship among adolescents. 5.5 Theoretical Contributions From a theoretical perspective, this study integrates behavioral (digital habits), physiological (sleep health), and psychological (academic fatigue) mechanisms into a coherent explanatory model of academic functioning. The results support the Compensatory Internet Use Model (Kardefelt-Winther, 2014 ), demonstrating that adolescents often use technology as a coping strategy that paradoxically worsens fatigue. They also extend Self-Determination Theory (Ryan & Deci, 2017 ), illustrating how digital autonomy at night satisfies short-term psychological needs for connection and autonomy, yet undermines long-term self-regulation and competence. Finally, the findings reinforce the Sleep Health Framework (Buysse, 2014 ), underscoring the interdependence between behavioral timing, emotional regulation, and physiological restoration. 5.6 Limitations and Future Directions As a cross-sectional study, causal inference remains limited. Self-reporting may also introduce perceptual bias or inaccuracies in estimating digital use and fatigue. Future research should incorporate objective measures such as sleep tracking (actigraphy) and digital usage logs to complement self-reports. Longitudinal or experimental designs would provide stronger evidence of the causal sequence linking digital behavior, sleep, and fatigue. Additionally, examining gender differences and school-type variations may reveal nuanced mechanisms underlying fatigue formation. Further exploration of broader outcomes—such as emotional regulation, attention control, and mental health—could identify mediators or moderators that shape the observed relationships. Integrating physiological biomarkers (e.g., cortisol levels, heart rate variability) would also deepen understanding of the biological underpinnings of academic fatigue. 5.7 Conclusion Overall, this study demonstrates that late-night digital engagement undermines adolescent sleep health, intensifies academic fatigue, and reduces academic performance. By distinguishing between digital and academic sources of fatigue, it offers a more comprehensive understanding of how technology shapes adolescents’ well-being and learning outcomes. These findings emphasize the urgent need for holistic educational interventions that promote both digital self-regulation and restorative rest, adapted to cultural realities. Integrating these insights into national curricula could mark a transformative step toward holistic adolescent development in the digital age, positioning Morocco—and similar developing contexts—at the forefront of the global movement for balanced digital and academic health. 6. Conclusion and Recommendations 6.1. Summary of Findings This section synthesizes the key results and highlights their theoretical and practical implications. The current study examined how late-night digital habits among Moroccan adolescents influence sleep health, academic fatigue, and self-perceived academic performance. The results demonstrated that students who engaged in more frequent and later digital activity—particularly social media browsing and chatting—exhibited poorer sleep health, higher levels of fatigue, and lower academic performance. The sequential mediation analysis confirmed the hypothesized pathway: Digital Habits → Sleep Health → Academic Fatigue → Academic Performance. This finding underscores that digital engagement is not inherently detrimental; rather, it becomes harmful when its timing and intensity interfere with restorative processes essential for cognitive functioning and learning. The study further revealed that academic fatigue operates as the psychological mechanism connecting behavioral (digital) and physiological (sleep) domains. In this sense, fatigue functions both as a symptom and as a mediator of imbalance between technological engagement and academic effort. 6.2. Theoretical Implications The present research contributes to the expanding field of digital well-being by integrating behavioral, physiological, and psychological dimensions into a coherent framework. Three theoretical contributions emerge clearly from the data: Integration of Frameworks : By combining the Compensatory Internet Use Model (Kardefelt-Winther, 2014 ), Self-Determination Theory (Ryan & Deci, 2017 ), and the multidimensional Sleep Health framework (Buysse, 2014 ), the study proposes a unified model of adolescent functioning that captures motivation, recovery, and academic outcomes. Centralization of Academic Fatigue : The results position academic fatigue as a distinct yet underexplored construct bridging sleep and cognition, extending its conceptual value beyond traditional burnout and stress models. Cultural Specificity : Conducting this study in Morocco broadens the predominantly Western literature on adolescent sleep and digital behavior, providing a context-sensitive model relevant to the Global South. Together, these insights enrich the theoretical understanding of how digital lifestyles shape academic well-being within sociocultural systems where technology deeply permeates daily routines. 6.3. Practical Recommendations a. Educational Practice Schools play a pivotal role in shaping students’ relationships with technology. Integrating digital literacy and sleep education into school curricula can help adolescents understand the physiological and cognitive costs of late-night device use. Workshops and advisory sessions should emphasize: The importance of establishing digital curfews (avoiding screens at least one hour before bedtime). The use of night mode and blue-light filters. Encouraging mindfulness, relaxation, or offline reading before sleep instead of online engagement. Teachers should model balanced digital behavior by limiting after-hours communication and promoting offline study routines. b. Family Involvement Parents must act as active partners in fostering digital balance. Family routines—such as device-free dinners or open discussions about media use—can create shared accountability and healthier communication patterns. Parental guidance should shift from control to collaboration, promoting trust and mutual understanding of digital challenges faced by adolescents. c. Policy-Level Interventions At the national level, collaboration between the Ministry of Education and the Ministry of Health is crucial to promote adolescent digital well-being and sleep hygiene. Public awareness campaigns should highlight the connection between digital overuse, sleep deprivation, and academic underperformance. Moreover, national guidelines for secondary schools could include recommendations on daily device exposure, aligning with global standards by the World Health Organization (WHO, 2023) on adolescent screen time and health. Such policies could align Morocco’s educational system with international best practices in promoting balanced digital lifestyles among youth. d. Addressing Fatigue Management Recognizing academic fatigue as a public health and educational concern is essential. Schools can implement short restorative breaks during long study sessions, organize workshops on time management and stress regulation, and design academic calendars that prevent cognitive overload. Encouraging students to identify early signs of fatigue and apply simple recovery techniques—such as short naps, breathing exercises, or brief physical activity—can enhance attention and motivation during learning. 6.4. Limitations and Future Research Despite its valuable contributions, this study has certain limitations that should guide future investigations. First, its cross-sectional design limits causal inference; longitudinal and experimental studies are needed to confirm the directional sequence of effects. Second, reliance on self-reported data may introduce subjective bias or underreporting. Future work should employ objective measures such as sleep tracking (actigraphy), screen-time monitoring, and physiological biomarkers (e.g., cortisol levels, heart rate variability) to triangulate data. Third, future studies should distinguish academic fatigue from digital fatigue using validated multidimensional scales to assess their independent and combined influence on academic outcomes. Finally, comparative cross-cultural research could test the generalizability of the proposed model across different educational systems and cultural contexts. Despite these limitations, the present study provides a robust empirical foundation for subsequent cross-cultural and interdisciplinary exploration of adolescent digital behavior. 6.5. Concluding Remarks In conclusion, this research provides compelling empirical evidence that late-night digital engagement—while psychologically gratifying—undermines adolescents’ academic functioning by disrupting sleep and fostering fatigue. The results emphasize that healthy digital behavior and restorative sleep are mutually reinforcing, forming the twin pillars of sustainable learning and mental well-being. By identifying academic fatigue as the pivotal link between digital habits, sleep health, and performance, the study opens new interdisciplinary pathways connecting digital psychology, education, and sleep science. Cultivating awareness and self-regulation among students, families, and educators can foster more sustainable learning environments where technology enhances rather than exhausts human potential. Integrating these insights into national educational strategies could mark a transformative step toward holistic adolescent development in the digital age—positioning Morocco and similar developing contexts as leaders in the global movement toward digital balance, sleep education, and academic health. Declarations The Scientific Committee of the Public Law and Political Science Laboratory at the Faculty of Law, Sidi Mohamed Ben Abdellah University in Fez, provided both scientific and ethical approval for this research. The committee confirmed that the study adheres to ethical principles including informed consent, participant anonymity, and voluntary participation. Competing Interests The authors declare that they have no competing interests, financial or non-financial, that are directly or indirectly related to the work submitted for publication. Ethical Approval This study was conducted in accordance with the ethical standards of the American Psychological Association (APA) and the British Educational Research Association (BERA). Informed consent was obtained from all participants prior to their involvement in the study. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The datasets generated and analyzed during the current study are not publicly available due to confidentiality agreements with the participants but may be available from the corresponding author upon reasonable request.Haut du formulaire References Becker, S. P., Sidol, C. A., & Burns, G. L. (2022). Adolescent sleep and academic functioning: The role of emotional regulation and fatigue. Journal of Adolescence, 95, 47–58. https://doi.org/10.1016/j.adolescence.2022.04.007 Benjelloun, A. (2022). Digital lifestyles and student performance: A Moroccan perspective on technology and schooling. International Journal of Education and Social Science Research, 5(3), 112–125. Boksem, M. A. S., & Tops, M. (2008). Mental fatigue: Costs and benefits. Brain Research Reviews, 59(1), 125–139. https://doi.org/10.1016/j.brainresrev.2008.07.001 Bouslaham, N. (2020). Smartphone use and sleep disturbance among Moroccan adolescents. North African Journal of Behavioral Science, 2(1), 29–42. 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(2025)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7837474/v1/75b5ca7794bf2666fdeadb7c.png"},{"id":93470998,"identity":"1419a0cf-0ee0-4ea8-b270-b6c3c51b5b7c","added_by":"auto","created_at":"2025-10-14 08:19:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":95661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequential Mediation Model (Path Diagram)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author’s own elaboration (2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7837474/v1/0b053c9f91cac0e4a43aca6b.png"},{"id":93471006,"identity":"f2fb2175-e23e-40a6-9718-9890fff793a7","added_by":"auto","created_at":"2025-10-14 08:19:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":212388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQualitative Themes from Open-Ended Responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author’s own elaboration (2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7837474/v1/11f6c2131bfe6b03211a74dc.png"},{"id":93472480,"identity":"ba3ce0b3-9493-4e92-95e5-cafb0d48155c","added_by":"auto","created_at":"2025-10-14 08:27:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary Diagram: Empirical Validation of the Conceptual Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author’s own elaboration (2025)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7837474/v1/62a8dc7cfb98e4f70eea09d8.png"},{"id":93472607,"identity":"d8134d7d-12bc-4fd0-86aa-12243bf79d0f","added_by":"auto","created_at":"2025-10-14 08:35:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2266700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7837474/v1/d57ae815-70eb-4d8a-864c-e207594e4aa1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLate-Night Digital Engagement, Academic Fatigue, and Their Impact on Academic Performance among Moroccan Secondary Students\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past decade, digitalization has profoundly transformed how adolescents learn, communicate, and socialize. Smartphones and social media platforms have become central spaces for self-expression, information exchange, and entertainment. For many teenagers, online interaction is no longer an optional pastime but an essential part of identity formation and peer belonging. However, this constant connectivity has also introduced new challenges to adolescent health and learning. One of the most pressing concerns is the way digital engagement at night\u0026mdash;particularly before sleep\u0026mdash;disrupts healthy rest patterns and contributes to fatigue and diminished academic performance.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. The global context of digital engagement and sleep disturbance\u003c/h2\u003e\u003cp\u003eEmpirical evidence across cultural settings shows that prolonged screen exposure delays melatonin secretion, disrupts circadian rhythms, and reduces overall sleep quality (Cajochen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hale \u0026amp; Guan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Adolescents are particularly vulnerable to these effects due to developmental shifts in biological sleep timing that naturally favor later bedtimes (Carskadon, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). When such biological tendencies intersect with digital activities\u0026mdash;social networking, streaming, gaming, and chatting\u0026mdash;the result is a chronic misalignment between sleep needs and actual rest. Studies in Europe and East Asia consistently demonstrate that heavy nighttime use of smartphones and computers predicts insufficient sleep and higher levels of daytime fatigue (Bartel et al., 2019; Shochat et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond the physiological dimension, digital media use is also driven by powerful psychological and social factors. Online spaces provide adolescents with autonomy, emotional expression, and social validation that are often harder to achieve offline (Boyd, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Castells, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For many, late-night hours represent a unique window of freedom\u0026mdash;less parental supervision, greater intimacy with peers, and a sense of control over one\u0026rsquo;s digital identity. These psychological rewards reinforce nighttime usage even when students are aware of its negative consequences for sleep and school performance. This paradox between awareness and behavior has become a defining feature of adolescence in the digital era (Montag, Sindermann, \u0026amp; Lachmann, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Linking sleep health and academic fatigue\u003c/h2\u003e\u003cp\u003eSleep health is increasingly understood as a multidimensional construct encompassing duration, timing, efficiency, regularity, and satisfaction (Buysse, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Deficits in any of these dimensions can manifest as academic fatigue\u0026mdash;a state of reduced alertness, motivation, and cognitive energy that directly undermines learning. Academic fatigue extends beyond physical tiredness; it reflects the cumulative effects of cognitive overload, emotional depletion, and disrupted circadian rhythms. When adolescents prolong screen time late into the night, they often sacrifice the restorative phases of sleep essential for memory consolidation and executive functioning (Becker, Sidol, \u0026amp; Burns, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, morning classes become more demanding, concentration declines, and perceived academic performance deteriorates.\u003c/p\u003e\u003cp\u003eAlthough many studies have established associations between sleep quality and academic achievement, few have examined academic fatigue as the mediating psychological mechanism. Fatigue acts as the experiential bridge linking disrupted sleep to reduced academic engagement. Understanding this mechanism is critical, as interventions focused solely on reducing screen time or increasing sleep duration may be insufficient if the underlying motivational and affective aspects of fatigue are not addressed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. The digital-sociocultural context in Morocco\u003c/h2\u003e\u003cp\u003eWhile these trends have been well documented in Western and East Asian contexts, much less is known about how they unfold in North African societies. In Morocco and the wider MENA region, digital penetration among youth has surged dramatically. National data indicate that smartphone ownership and social media use among adolescents exceed 90%, reflecting a generation deeply immersed in digital life. Yet empirical research on the psychosocial and educational implications of this transformation remains limited. Only a handful of studies have explored how digital lifestyles intersect with school performance and well-being in Moroccan settings (Benjelloun, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bouslaham, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCultural norms further shape the meaning of nighttime activity. In many Moroccan households, family gatherings, television viewing, or communal meals extend into the late evening. Consequently, the digital extension of this nocturnal culture\u0026mdash;through chatting, browsing, or streaming\u0026mdash;may appear socially acceptable, even routine. Nevertheless, its physiological and cognitive costs parallel those observed elsewhere. What distinguishes the Moroccan context is the interplay between collective social habits and individual digital autonomy, producing a unique risk profile for sleep disturbance and academic fatigue.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4. Theoretical framework and research gap\u003c/h2\u003e\u003cp\u003eThis study draws on the Compensatory Internet Use Model (Kardefelt-Winther, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Self-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which suggest that online activities often serve to meet psychological needs for relatedness, competence, and autonomy. However, excessive or ill-timed engagement\u0026mdash;especially during pre-sleep hours\u0026mdash;can create a self-defeating cycle where the pursuit of emotional comfort undermines physical recovery and cognitive readiness. Applied to the academic domain, this dynamic implies that digital engagement may indirectly impair school performance through its negative effects on sleep and fatigue.\u003c/p\u003e\u003cp\u003eDespite the abundance of international research on screen time and sleep, two important gaps remain. First, there is limited empirical evidence from North African contexts, where digital practices are evolving within distinct sociocultural and educational frameworks. Second, few studies have explicitly modeled academic fatigue as the central pathway linking digital habits and academic outcomes. By integrating physiological (sleep health) and psychological (fatigue) dimensions, the present study seeks to offer a more holistic understanding of how digital lifestyles shape adolescent learning and well-being.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.5. Aim and contribution of the study\u003c/h2\u003e\u003cp\u003eThis research aims to examine how late-night digital habits influence sleep health and academic fatigue, and how these factors jointly predict self-perceived academic performance among Moroccan secondary students. Specifically, it hypothesizes that:\u003c/p\u003e\u003cp\u003eStudents with later bedtimes and more intensive social media use will report higher levels of academic fatigue.\u003c/p\u003e\u003cp\u003eAcademic fatigue will mediate the relationship between sleep health and perceived academic performance.\u003c/p\u003e\u003cp\u003eThrough these hypotheses, the study contributes to three interrelated fields. First, it extends global research on digital well-being by providing evidence from a non-Western adolescent population. Second, it advances the conceptualization of sleep health by integrating behavioral and psychological aspects rather than focusing solely on duration. Third, it identifies academic fatigue as a key construct for understanding how digital lifestyles influence educational outcomes. Ultimately, this study seeks to inform culturally responsive interventions\u0026mdash;combining digital literacy, time management, and sleep hygiene education\u0026mdash;to foster healthier and more balanced academic lives among Moroccan adolescents.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Theoretical Framework and Conceptual Model","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Theoretical Foundations of Digital Behavior, Sleep Health, and Academic Fatigue\u003c/h2\u003e\u003cp\u003eThe theoretical foundation of this study lies at the intersection of psychological, behavioral, and educational models explaining how technology use influences human functioning. Three complementary frameworks\u0026mdash;the Compensatory Internet Use Model (CIUM), Self-Determination Theory (SDT), and Sleep Health Theory (SHT)\u0026mdash;collectively illuminate how adolescents\u0026rsquo; digital habits shape their sleep patterns, fatigue levels, and academic experiences.\u003c/p\u003e\u003cp\u003eAccording to the Compensatory Internet Use Model (Kardefelt-Winther, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), individuals often engage in online activities to alleviate stress, boredom, or unmet emotional needs. Adolescents under academic or social pressure may resort to social media, streaming, or gaming as a means of emotional regulation and self-recovery. However, when such compensatory use becomes excessive or ill-timed\u0026mdash;particularly during nighttime\u0026mdash;it can compromise sleep quality and overall well-being. The model therefore emphasizes that maladaptive timing, rather than total screen duration, is the key driver of negative outcomes such as fatigue, mood disruption, and poor performance.\u003c/p\u003e\u003cp\u003eSelf-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) complements this behavioral explanation by emphasizing that digital engagement is fundamentally motivated by the psychological needs for autonomy, competence, and relatedness. Adolescents tend to persist in online environments that fulfill their desire for connection, expression, and belonging. Yet, late-night engagement creates a motivational paradox: it delivers short-term emotional satisfaction while undermining long-term cognitive recovery. Over time, this imbalance results in ego depletion and academic fatigue, as students begin their school day mentally drained and emotionally unprepared for learning demands.\u003c/p\u003e\u003cp\u003eWhile SDT explains the motivational drive behind online behavior, the Sleep Health Theory (Buysse, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) provides a physiological lens for understanding how behavioral patterns translate into health outcomes. Sleep health is conceptualized as a multidimensional construct encompassing duration, timing, regularity, efficiency, and satisfaction. Late-night screen exposure disrupts these dimensions by delaying melatonin secretion, heightening cognitive arousal, and disturbing circadian rhythms (Cajochen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sleep health thus reflects not merely the absence of sleep disorders, but the positive functioning of restorative sleep\u0026mdash;essential for emotional regulation, memory consolidation, and learning efficiency.\u003c/p\u003e\u003cp\u003eIntegrating these three frameworks reveals a duality at the heart of adolescent digital life: late-night activity fulfills emotional and social needs yet physiologically depletes the body\u0026rsquo;s restorative systems. The present study conceptualizes academic fatigue as the primary outcome of this trade-off\u0026mdash;an experiential manifestation of chronic sleep disturbance and digital over-engagement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Academic Fatigue as a Multidimensional Construct\u003c/h2\u003e\u003cp\u003eBuilding upon cognitive and motivational theories, academic fatigue is defined as a state of cognitive, emotional, and physical exhaustion that impairs students\u0026rsquo; ability to concentrate, persist, and engage effectively in learning tasks. Although it shares features with general mental fatigue (Boksem \u0026amp; Tops, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), academic fatigue is specifically contextualized within educational settings. It encompasses three interrelated dimensions:\u003c/p\u003e\u003cp\u003ePhysiological fatigue: feelings of tiredness and reduced alertness, typically following insufficient or poor-quality sleep.\u003c/p\u003e\u003cp\u003eCognitive fatigue: difficulties sustaining attention, processing information, or solving complex problems.\u003c/p\u003e\u003cp\u003eMotivational fatigue: loss of interest, diminished intrinsic motivation, and reduced willingness to exert effort on school tasks.\u003c/p\u003e\u003cp\u003eWithin digital contexts, fatigue stems not only from sleep deprivation, but also from digital overstimulation, constant multitasking, and emotional immersion in social media interactions. Research in attention economics (Mark, Gudith, \u0026amp; Klocke, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrates that frequent interruptions and rapid information switching heighten cognitive load, accelerating mental exhaustion. Adolescents who remain connected late at night therefore face both physiological strain (due to reduced sleep quality) and psychological strain (due to continuous digital engagement).\u003c/p\u003e\u003cp\u003eEmpirical studies consistently link late-night screen use to delayed sleep onset and next-day fatigue (Hale \u0026amp; Guan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shochat et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Yet few have explored how such fatigue directly undermines academic functioning. Becker et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that the impact of poor sleep on academic outcomes operates through emotional dysregulation\u0026mdash;students who are sleep-deprived tend to be more irritable, less motivated, and less efficient learners. Building on this, the current study posits that academic fatigue serves as both an outcome of poor sleep and a mediating mechanism transmitting its effects to academic performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sleep Health as the Behavioral and Physiological Mediator\u003c/h2\u003e\u003cp\u003eSleep health serves as a central mediator linking digital behavior to academic fatigue. Adolescents\u0026rsquo; exposure to bright screens at night delays the release of melatonin and disrupts circadian alignment (Cajochen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These disruptions lead to later bedtimes, shorter sleep duration, and reduced sleep efficiency. Even when total sleep time appears adequate, irregular timing and fragmented rest impair cognitive restoration and emotional stability.\u003c/p\u003e\u003cp\u003eAdditionally, digital activities such as chatting, gaming, or social networking induce emotional arousal, prolonging the time needed to fall asleep (Levenson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The constant anticipation of notifications sustains a state of pre-sleep cognitive activation, preventing natural relaxation. Over time, this erodes the homeostatic balance of the sleep\u0026ndash;wake cycle and contributes to chronic sleep restriction.\u003c/p\u003e\u003cp\u003eFor adolescents, these disturbances have been linked to poorer attention, executive function, and academic motivation (Becker et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, sleep health is conceptualized not merely as a background condition, but as an active physiological pathway through which digital behavior translates into academic fatigue and diminished learning outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Cultural and Contextual Relevance\u003c/h2\u003e\u003cp\u003eA distinctive contribution of this study lies in situating these mechanisms within the Moroccan sociocultural context. Moroccan adolescents experience rapid digital expansion within family-oriented lifestyles and communal evening routines that often extend late into the night. Shared activities such as family gatherings, television watching, or collective meals normalize late-evening wakefulness. As a result, the boundary between socially acceptable nighttime activity and digital overuse becomes increasingly blurred.\u003c/p\u003e\u003cp\u003eAt the same time, Moroccan education\u0026mdash;particularly at the secondary level\u0026mdash;demands early school attendance and strong academic performance, often under high examination pressure. This structural tension between late-night social\u0026ndash;digital culture and early academic expectations intensifies fatigue and cognitive strain. Hence, the same behavior (e.g., midnight social media use) may produce greater academic consequences in Morocco than in Western contexts due to earlier school schedules and stricter routines.\u003c/p\u003e\u003cp\u003eUnderstanding this configuration underscores the need for culturally sensitive interpretations of digital well-being. Any analysis of adolescent sleep and fatigue in Morocco must therefore consider the interplay between collective social norms and individual digital autonomy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Hypothesized Model and Pathways\u003c/h2\u003e\u003cp\u003eDrawing upon the synthesis of theoretical and empirical evidence, this study proposes a sequential mediation model linking late-night digital habits to academic performance through sleep health and academic fatigue. The model operates through the following pathways:\u003c/p\u003e\u003cp\u003eDirect and Indirect Hypotheses\u003c/p\u003e\u003cp\u003eH1: Increased intensity and frequency of late-night digital activity (especially social media browsing and chatting) are negatively associated with sleep health, resulting in delayed bedtime, shorter sleep duration, and lower sleep satisfaction.\u003c/p\u003e\u003cp\u003eH2: Poor sleep health predicts higher levels of academic fatigue due to insufficient physiological restoration and increased morning tiredness.\u003c/p\u003e\u003cp\u003eH3: Academic fatigue is negatively associated with self-rated academic performance by diminishing attention, motivation, and persistence.\u003c/p\u003e\u003cp\u003eH4: Sleep health mediates the relationship between late-night digital habits and academic fatigue.\u003c/p\u003e\u003cp\u003eH5: Academic fatigue mediates the relationship between sleep health and academic performance.\u003c/p\u003e\u003cp\u003eH6: The combined indirect effect of digital habits on academic performance operates through a sequential mediation chain\u0026mdash;first via sleep health, then through academic fatigue.\u003c/p\u003e\u003cp\u003eThis conceptualization positions academic fatigue as the central psychological bridge between behavioral (digital) and cognitive (academic) domains, offering a holistic understanding of adolescent functioning across technological, physiological, and psychological dimensions.\u003c/p\u003e\u003cp\u003eConceptual Model Description\u003c/p\u003e\u003cp\u003eThe proposed relationships among digital habits, sleep health, academic fatigue, and academic performance are summarized in the conceptual model presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003eThe model illustrates the hypothesized relationships among the study variables. Late-night digital habits are proposed to affect sleep health, which subsequently influences academic fatigue and, in turn, academic performance. Control variables (gender, age, and school type) are included to account for demographic differences.\u003c/p\u003e\u003cp\u003eThe model begins with digital habits, defined as the frequency, duration, and purpose of nighttime device use. Arrows extend from digital habits to sleep health, which encompasses five dimensions: duration, timing, regularity, efficiency, and satisfaction. From sleep health, the model proceeds to academic fatigue, representing the cumulative cognitive and motivational costs of sleep disturbance. The final pathway connects academic fatigue to academic performance, assessed through students\u0026rsquo; self-perceived achievement and classroom engagement.\u003c/p\u003e\u003cp\u003eControl variables such as gender, age, and school type may influence these pathways but do not alter the overall sequential mediation process. The model assumes a unidirectional influence from behavior to cognition\u0026mdash;consistent with prior sleep\u0026ndash;performance research\u0026mdash;while acknowledging potential feedback loops (e.g., students experiencing chronic fatigue may increase digital use as a coping strategy).\u003c/p\u003e\u003cp\u003eIn sum, this framework integrates behavioral, psychological, and physiological explanations into a single, coherent structure. It provides a testable and culturally grounded model suitable for structural equation modeling or regression-based mediation analysis (Hayes, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By formalizing these relationships, the study contributes to global scholarship on digital well-being, sleep health, and educational psychology, offering one of the first empirically supported models of academic fatigue among North African adolescents.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Design\u003c/h2\u003e\u003cp\u003eThis study employed a cross-sectional quantitative design aimed at examining the relationships among late-night digital habits, sleep health, academic fatigue, and self-perceived academic performance among Moroccan secondary-school students. The research integrates both descriptive and correlational elements to identify behavioral patterns and test theoretically grounded hypotheses based on the proposed sequential mediation model.\u003c/p\u003e\u003cp\u003e The design aligns with the STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology), ensuring transparency in sampling, measurement, and data analysis procedures. This approach allows for a robust examination of associations within a culturally specific adolescent population while maintaining methodological rigor.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Participants and Sampling\u003c/h2\u003e\u003cp\u003eA total of 311 students (aged 15\u0026ndash;19 years; 56% female) participated in the study.\u003c/p\u003e\u003cp\u003eStudents were recruited from different educational institutions located in Fez, Morocco, representing both urban and semi-urban environments.\u003c/p\u003e\u003cp\u003eInclusion criteria required participants to:\u003c/p\u003e\u003cp\u003e(a) be currently enrolled in secondary education,\u003c/p\u003e\u003cp\u003e(b) use digital devices daily, and\u003c/p\u003e\u003cp\u003e (c) provide informed consent prior to participation.\u003c/p\u003e\u003cp\u003eParticipants were selected using a stratified convenience sampling method to ensure representation across different school levels (first, second, and third years of secondary education).\u003c/p\u003e\u003cp\u003eThe final sample represented approximately 12\u0026ndash;15% of total student enrollment across the participating institutions, providing adequate diversity in academic standing and digital engagement.\u003c/p\u003e\u003cp\u003eThe sample size exceeded the minimum requirement calculated through G*Power 3.1 for medium effect sizes (f\u0026sup2; = 0.15, α\u0026thinsp;=\u0026thinsp;.05, power\u0026thinsp;=\u0026thinsp;.95), confirming sufficient statistical power for multivariate and mediation analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Instruments and Measures\u003c/h2\u003e\u003cp\u003eData were collected through a structured questionnaire administered in Arabic via Google Forms between March and April 2025. The instrument included both closed-ended quantitative items and two open-ended questions to capture students\u0026rsquo; personal reflections on late-night habits and academic fatigue.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. Digital Habits\u003c/h2\u003e\u003cp\u003eParticipants reported their bedtime on school nights and their main digital activity before sleep (e.g., social media browsing, chatting, studying, gaming, or streaming).\u003c/p\u003e\u003cp\u003eFrequency and perceived importance of these activities were rated on a 5-point Likert scale (1 = \u0026ldquo;rarely\u0026rdquo; to 5 = \u0026ldquo;always\u0026rdquo;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2. Sleep Health\u003c/h2\u003e\u003cp\u003eSleep health was operationalized using indicators adapted from Buysse\u0026rsquo;s (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) Sleep Health Index, measuring:\u003c/p\u003e\u003cp\u003eSleep duration: average hours of sleep per night.\u003c/p\u003e\u003cp\u003eSleep timing: bedtime regularity across weekdays.\u003c/p\u003e\u003cp\u003eSleep satisfaction: subjective evaluation of rest quality.\u003c/p\u003e\u003cp\u003eHigher scores indicated better sleep health. These indicators were selected for their cultural appropriateness and prior validation in adolescent populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3. Academic Fatigue\u003c/h2\u003e\u003cp\u003eAcademic fatigue was measured through four self-report items inspired by Becker et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), assessing tiredness during morning classes, concentration difficulties, and motivational decline.\u003c/p\u003e\u003cp\u003eResponses used a 5-point scale (1 = \u0026ldquo;never\u0026rdquo; to 5 = \u0026ldquo;very often\u0026rdquo;).\u003c/p\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;.84, indicating strong internal consistency and reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4. Academic Performance\u003c/h2\u003e\u003cp\u003eSelf-rated academic performance was assessed through a single global question:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHow would you rate your overall academic performance this semester?\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResponses were coded on a 4-point scale (1 = \u0026ldquo;poor\u0026rdquo; to 4 = \u0026ldquo;excellent\u0026rdquo;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.3.5. Qualitative Component\u003c/h2\u003e\u003cp\u003eTwo open-ended questions invited students to elaborate on:\u003c/p\u003e\u003cp\u003e\u003cp\u003e(a) their reasons for staying up late, and\u003c/p\u003e\u003cp\u003e(b) personal strategies to improve study habits.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eResponses were analyzed thematically to enrich the interpretation of quantitative findings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Procedure and Ethical Considerations\u003c/h2\u003e\u003cp\u003e Prior to data collection, the study protocol received ethical approval from the Scientific Committee of the Public Law and Political Science Laboratory, Faculty of Law, Sidi Mohamed Ben Abdellah University (Fez).\u003c/p\u003e\u003cp\u003e Participation was entirely voluntary, and all respondents provided informed consent after being briefed about the study\u0026rsquo;s purpose, anonymity, and confidentiality.\u003c/p\u003e\u003cp\u003eNo identifying information was collected at any stage of the process.\u003c/p\u003e\u003cp\u003eData collection occurred during regular class hours under teacher supervision to minimize distractions and ensure standardized conditions.\u003c/p\u003e\u003cp\u003eThe survey link was also distributed through class teachers and school WhatsApp groups to enhance accessibility and participation rates.\u003c/p\u003e\u003cp\u003eThe average completion time was approximately 12 minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Data Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using IBM SPSS Statistics 28. Quantitative analyses included:\u003c/p\u003e\u003cp\u003eDescriptive statistics (means, standard deviations, percentages) to summarize demographic and behavioral variables.\u003c/p\u003e\u003cp\u003eChi-square (χ\u0026sup2;) tests and Cramer\u0026rsquo;s V coefficients to examine associations between categorical variables (e.g., bedtime, type of digital activity, and self-rated performance).\u003c/p\u003e\u003cp\u003eCorrelation and regression analyses to test the direction and strength of relationships among digital habits, sleep health, academic fatigue, and performance.\u003c/p\u003e\u003cp\u003eMediation analysis using PROCESS Macro v4.2 (Hayes, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to evaluate the sequential mediating effects of sleep health (M₁) and academic fatigue (M₂) in the pathway from digital habits (X) to academic performance (Y).\u003c/p\u003e\u003cp\u003eBootstrap resampling (5,000 samples) was used to estimate 95% confidence intervals for indirect effects, ensuring robustness of the mediation results.\u003c/p\u003e\u003cp\u003eOpen-ended responses were analyzed using thematic content analysis (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo independent coders identified recurring themes such as \u0026ldquo;perceived freedom,\u0026rdquo; \u0026ldquo;peer interaction,\u0026rdquo; and \u0026ldquo;awareness of academic costs.\u0026rdquo;\u003c/p\u003e\u003cp\u003eIntercoder reliability was high (Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.86), ensuring analytical consistency.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3. 6. Reliability and Validity\u003c/h3\u003e\n\u003cp\u003eInstrument validity was strengthened through expert review by three university professors specializing in psychology, education, and digital culture.\u003c/p\u003e\u003cp\u003eA pilot test with 30 students helped refine wording and confirm clarity.\u003c/p\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha coefficients for the main scales ranged from 0.82 to 0.86, exceeding the standard threshold for internal consistency.\u003c/p\u003e\u003cp\u003eConstruct validity was further supported by theoretical alignment between measured variables and the conceptual model.\u003c/p\u003e\u003cp\u003eThese combined procedures ensure both content and construct validity of the instrument.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Limitations and Rigor\u003c/h2\u003e\u003cp\u003eAs a cross-sectional study relying on self-reported data, causal inferences are limited. However, integrating quantitative and qualitative components strengthened the internal validity and interpretive depth of the findings.\u003c/p\u003e\u003cp\u003ePotential social desirability bias was minimized by emphasizing anonymity and the non-evaluative nature of participation.\u003c/p\u003e\u003cp\u003eFuture longitudinal or experimental studies are recommended to establish causal pathways and evaluate potential interventions targeting sleep hygiene and academic fatigue.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Summary\u003c/h2\u003e\u003cp\u003eOverall, this methodology integrates behavioral, physiological, and psychological measures within a culturally grounded framework.\u003c/p\u003e\u003cp\u003eThe use of a robust sample, validated instruments, and multi-method analysis provides a strong empirical basis for testing the hypothesized mediation model presented in the next chapter.\u003c/p\u003e\u003cp\u003eThis design ensures both statistical rigor and contextual relevance, positioning the study to contribute meaningful insights to the global discourse on adolescent digital behavior, sleep health, and academic fatigue.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Overview\u003c/h2\u003e\u003cp\u003eThis section presents both the quantitative and qualitative findings of the study, organized in accordance with the research objectives and the hypothesized sequential mediation model.\u003c/p\u003e\u003cp\u003eResults are structured to reflect descriptive statistics, bivariate relationships, mediation analysis, contextual differences, and thematic insights derived from students\u0026rsquo; open-ended responses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eA total of 311 Moroccan secondary students participated in the study (56% female, 44% male). Participants\u0026rsquo; ages ranged from 15 to 19 years (M\u0026thinsp;=\u0026thinsp;16.8, SD\u0026thinsp;=\u0026thinsp;1.1).\u003c/p\u003e\u003cp\u003eThe majority (76.8%) reported sleeping after 11:00 p.m., with 22.2% going to bed after 1:00 a.m., and only 20.9% sleeping before 11:00 p.m.\u003c/p\u003e\u003cp\u003eRegarding digital activity before sleep, the most common behaviors were:\u003c/p\u003e\u003cp\u003eBefore presenting the relationships among variables, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the distribution of participants\u0026rsquo; main digital activities before sleep, providing an overview of the behaviors most frequently reported by Moroccan adolescents.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Digital Activities Before Sleep among Moroccan Adolescents (N\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Activity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial media browsing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChatting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStreaming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, social media browsing and chatting were the most frequent pre-sleep activities, indicating high levels of social connectivity before bedtime.\u003c/p\u003e\u003cp\u003eMorning functioning indicators revealed that 35.7% of students experienced mild fatigue, 35.4% reported high fatigue, 14.5% had difficulty waking up, while only 12.2% described themselves as energetic.\u003c/p\u003e\u003cp\u003eAverage self-rated academic performance was modest, with most students rating themselves as \u0026ldquo;average\u0026rdquo; or \u0026ldquo;below average.\u0026rdquo;\u003c/p\u003e\u003cp\u003ePreliminary trends suggested that those who went to bed later tended to report higher fatigue and lower academic self-assessment.\u003c/p\u003e\u003cp\u003eThe distribution of students\u0026rsquo; late-night digital activities is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, illustrating the relative prevalence of different behaviors before bedtime.\u003c/p\u003e\u003cp\u003eThe bar chart displays the percentage distribution of students\u0026rsquo; primary digital activities before bedtime. Social media browsing was the most prevalent activity (27.7%), followed by chatting (17.7%), studying (15.8%), gaming (11.3%), streaming (10.3%), and other activities (14.8%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Relationships Among Digital Habits, Sleep Health, and Academic Fatigue\u003c/h2\u003e\u003cp\u003eA Chi-square test of independence revealed a significant association between bedtime and self-rated academic performance,\u003c/p\u003e\u003cp\u003eχ\u0026sup2;(12)\u0026thinsp;=\u0026thinsp;25.69, p\u0026thinsp;=\u0026thinsp;.012, indicating that students who slept later were more likely to report lower performance.\u003c/p\u003e\u003cp\u003eSimilarly, the type of digital activity before bedtime was significantly related to performance,\u003c/p\u003e\u003cp\u003eχ\u0026sup2;(20)\u0026thinsp;=\u0026thinsp;47.17, p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003cp\u003ePost hoc inspection indicated that social media users and gamers tended to perform worse academically compared to those who studied or read before sleep.\u003c/p\u003e\u003cp\u003eCramer\u0026rsquo;s V coefficients showed moderate association strengths:\u003c/p\u003e\u003cp\u003eV\u0026thinsp;=\u0026thinsp;.27 (bedtime\u0026ndash;performance)\u003c/p\u003e\u003cp\u003eV\u0026thinsp;=\u0026thinsp;.32 (activity\u0026ndash;performance)\u003c/p\u003e\u003cp\u003eThese results confirm that digital engagement variables explain meaningful portions of variance in academic outcomes.\u003c/p\u003e\u003cp\u003eBefore examining the mediation effects, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the cross-tabulation of students\u0026rsquo; bedtime and their self-rated academic performance, illustrating how later sleep timing is associated with lower perceived academic achievement.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCross-tabulation of Bedtime and Self-Rated Academic Performance (N\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedtime\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore 11 p.m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11 p.m.\u0026ndash;1 a.m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter 1 a.m.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote. Later bedtime was significantly associated with poorer self-rated academic performance (χ\u0026sup2;(12)\u0026thinsp;=\u0026thinsp;25.69, p\u0026thinsp;=\u0026thinsp;.012, V\u0026thinsp;=\u0026thinsp;.27).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e4. Sleep Health and Academic Fatigue\u003c/h3\u003e\n\u003cp\u003eTo further examine the relationships among the study\u0026rsquo;s core constructs, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the correlation matrix among sleep health, academic fatigue, and self-rated academic performance. The results indicate strong and significant associations between these variables, particularly between academic fatigue and performance.\u003c/p\u003e\u003cp\u003eCorrelation analyses demonstrated significant relationships between the key variables:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations among Sleep Health, Academic Fatigue, and Academic Performance (N\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\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\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Sleep Health\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Academic Fatigue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;.48***\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3. Academic Performance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e.35***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026minus;.56***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eHigher sleep health scores were associated with lower academic fatigue and higher self-rated academic performance. A strong negative correlation was found between academic fatigue and performance (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.56, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), supporting its hypothesized mediating role. Consistently, students reporting later bedtimes and lower sleep satisfaction exhibited higher fatigue and lower perceived performance. This inverse relationship between sleep health and academic fatigue is further illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe scatter plot displays the negative relationship between students\u0026rsquo; sleep health and academic fatigue. As sleep health increases, levels of academic fatigue decrease, indicating that students who maintain better sleep quality experience lower cognitive and motivational exhaustion.\u003c/p\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Mediation Analysis\u003c/h2\u003e\u003cp\u003eTo test the sequential mediation model, a regression-based PROCESS Macro (Model 6) was employed (Hayes, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResults indicated that late-night digital activity (X) significantly predicted poorer sleep health (M₁) (β = \u0026minus;.42, p\u0026thinsp;\u0026lt;\u0026thinsp;.001),\u003c/p\u003e\u003cp\u003ewhich, in turn, predicted higher academic fatigue (M₂) (β = \u0026minus;.49, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eAcademic fatigue then negatively predicted academic performance (Y) (β = \u0026minus;.51, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eThe indirect effect of digital habits on performance via sleep health and academic fatigue was significant,\u003c/p\u003e\u003cp\u003eb\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.18, 95% CI [\u0026minus;\u0026thinsp;0.28, \u0026minus;\u0026thinsp;0.09], based on 5,000 bootstrap samples.\u003c/p\u003e\u003cp\u003eThe total model explained 41% of variance in academic performance (R\u0026sup2; = .41, F(4,306)\u0026thinsp;=\u0026thinsp;52.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eThis confirms the sequential mediation hypothesis, indicating that digital habits impact academic outcomes mainly through disrupted sleep and fatigue.\u003c/p\u003e\u003cp\u003eThe proposed sequential mediation model linking digital habits, sleep health, academic fatigue, and academic performance is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It visually summarizes the hypothesized pathways tested through the PROCESS macro (Model 6).\u003c/p\u003e\u003cp\u003eThe diagram illustrates the hypothesized sequential mediation pathways between the study variables. Late-night digital habits negatively predict sleep health (β = \u0026minus;.42***), which in turn negatively predicts academic fatigue (β = \u0026minus;.49***). Academic fatigue, in turn, negatively predicts academic performance (β = \u0026minus;.51***). The total model accounts for 41% of the variance in academic performance (R\u0026sup2; = .41).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Gender and Contextual Effects\u003c/h2\u003e\u003cp\u003eGender differences were minor but noteworthy:\u003c/p\u003e\u003cp\u003efemale students reported slightly higher academic fatigue (M\u0026thinsp;=\u0026thinsp;3.46 vs. 3.22, t(309)\u0026thinsp;=\u0026thinsp;2.08, p\u0026thinsp;=\u0026thinsp;.038) and lower sleep satisfaction (t(309)\u0026thinsp;=\u0026thinsp;1.97, p\u0026thinsp;=\u0026thinsp;.049).\u003c/p\u003e\u003cp\u003eHowever, no significant gender difference appeared in self-rated academic performance.\u003c/p\u003e\u003cp\u003eSimilarly, school context (urban vs. semi-urban) showed no significant variations in bedtime or fatigue levels, suggesting that late-night digital engagement is pervasive across environments.\u003c/p\u003e\u003cp\u003eThese results suggest that digital engagement patterns transcend gender and socio-geographic boundaries, reflecting a shared cultural trend of late-night connectivity among Moroccan adolescents.\u003c/p\u003e\u003cp\u003eTo explore potential gender-based differences across the main study variables, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the results of independent samples t-tests comparing male and female students\u0026rsquo; mean scores on sleep health, academic fatigue, and academic performance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Samples t-test by Gender\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSleep Health\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcademic Fatigue\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcademic Performance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Female students reported slightly lower sleep health and higher academic fatigue compared to males, but no significant difference was found in self-rated academic performance (p\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Qualitative Insights\u003c/h2\u003e\u003cp\u003eThematic analysis of open-ended responses yielded three recurrent themes:\u003c/p\u003e\u003cp\u003eFreedom and Autonomy \u0026ndash; Students described late-night digital use as a form of psychological escape and independence:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAt night, I feel free to do what I want without anyone watching me.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSocial Connectedness \u0026ndash; Maintaining communication with peers was a key motive:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMost of my friends are active late at night; if I don\u0026rsquo;t reply, I feel disconnected.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAwareness and Self-Regulation \u0026ndash; Despite awareness of negative effects, habit change remained difficult:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eI know it affects my grades, but it\u0026rsquo;s hard to stop scrolling before sleeping.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese narratives complement quantitative findings by revealing how feelings of autonomy and belonging drive persistent late-night engagement, despite academic costs.\u003c/p\u003e\u003cp\u003eThe main qualitative themes identified from students\u0026rsquo; open-ended responses are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, highlighting the most frequently mentioned experiences and perceptions regarding late-night digital habits and academic fatigue.\u003c/p\u003e\u003cp\u003eThe horizontal bar chart displays the frequency of key themes emerging from the thematic analysis of students\u0026rsquo; comments. The most prevalent themes included freedom and autonomy, social connectedness, and awareness and self-regulation, reflecting adolescents\u0026rsquo; emotional and motivational experiences related to late-night digital engagement. Less frequent yet meaningful mentions involved academic responsibility, peer influence, and relaxation as escape.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e4.8. Summary of Findings\u003c/h2\u003e\u003cp\u003eCollectively, the findings confirm the hypothesized behavioral\u0026ndash;physiological\u0026ndash;psychological chain:\u003c/p\u003e\u003cp\u003eLate-night digital habits significantly predict sleep health and academic fatigue.\u003c/p\u003e\u003cp\u003eSleep health partially mediates the relationship between digital habits and fatigue.\u003c/p\u003e\u003cp\u003eAcademic fatigue fully mediates the link between sleep health and academic performance.\u003c/p\u003e\u003cp\u003eThe sequential pathway (Digital Habits \u0026rarr; Sleep Health \u0026rarr; Academic Fatigue \u0026rarr; Performance) is statistically significant and explains a substantial proportion of variance in academic outcomes (R\u0026sup2; = .41).\u003c/p\u003e\u003cp\u003eThe results empirically validate the conceptual framework proposed in the theoretical section and underscore the need for culturally grounded educational and health interventions promoting digital balance, sleep hygiene, and fatigue awareness among Moroccan adolescents.\u003c/p\u003e\u003cp\u003eThe empirically tested relationships among digital habits, sleep health, academic fatigue, and academic performance are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, providing a visual overview of the validated sequential mediation model and its statistical significance.\u003c/p\u003e\u003cp\u003eThe diagram presents the empirically validated sequential mediation model based on the study\u0026rsquo;s regression and mediation analyses. Late-night digital habits exerted a significant negative effect on sleep health (β = \u0026minus;.42***), which in turn negatively influenced academic fatigue (β = \u0026minus;.49***). Academic fatigue further predicted lower academic performance (β = \u0026minus;.51***). The indirect effect of digital habits on performance through sleep health and fatigue was significant (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.18, 95% CI [\u0026minus;\u0026thinsp;0.28, \u0026minus;\u0026thinsp;0.09]), confirming the hypothesized mediation chain. The total model explained 41% of the variance in academic performance (R\u0026sup2; = .41).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn Conclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results provide robust empirical evidence that late-night digital engagement disrupts sleep health, heightens academic fatigue, and consequently diminishes self-perceived academic performance.\u003c/p\u003e\u003cp\u003eThey substantiate the theoretical predictions drawn from the Compensatory Internet Use Model, Self-Determination Theory, and Sleep Health Theory, thereby bridging behavioral, psychological, and physiological perspectives in a coherent explanatory model.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe present study investigated how late-night digital habits influence sleep health, academic fatigue, and self-perceived academic performance among Moroccan secondary-school students. Findings revealed that students who engage in extensive digital activity before bedtime\u0026mdash;particularly social media use\u0026mdash;tend to experience poorer sleep health, greater morning fatigue, and lower academic performance. These results corroborate global research linking nighttime screen exposure to delayed sleep onset and cognitive impairment (Hale \u0026amp; Guan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shochat et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while extending the discussion to a sociocultural context in which digital connectivity is deeply embedded in adolescents\u0026rsquo; daily routines.\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Interpretation of Main Findings\u003c/h2\u003e\u003cp\u003eThe findings confirmed the hypothesized sequential pathway: digital habits negatively affect sleep health, which increases academic fatigue, ultimately reducing perceived academic performance.\u003c/p\u003e\u003cp\u003eThe significance of both direct and indirect effects indicates that late-night digital behavior exerts a multidimensional impact\u0026mdash;physiological, psychological, and educational.\u003c/p\u003e\u003cp\u003eThe moderate-to-strong correlations between sleep variables, fatigue, and academic self-ratings (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.48 to \u0026minus;\u0026thinsp;.56) align with previous studies emphasizing that adolescents\u0026rsquo; sleep quality is a key determinant of attention, motivation, and learning outcomes (Becker et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the significant mediation effects of sleep health and academic fatigue suggest a cumulative process: digital engagement disrupts biological rhythms, leading to insufficient or irregular sleep, which then produces mental exhaustion and reduced classroom engagement.\u003c/p\u003e\u003cp\u003eIn short, technology-related sleep disturbance not only shortens rest but also impairs the quality of wakefulness, shaping students\u0026rsquo; ability to focus, learn, and retain information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Distinguishing Academic and Digital Fatigue\u003c/h2\u003e\u003cp\u003eA critical conceptual distinction emerging from this research lies between academic fatigue and digital fatigue.\u003c/p\u003e\u003cp\u003eWhile both refer to exhaustion that hampers learning and concentration, they differ in source and temporal pattern.\u003c/p\u003e\u003cp\u003eAcademic fatigue results from sustained cognitive effort, prolonged studying, and exam-related pressure. It typically manifests during school hours and reflects the depletion of mental resources caused by academic demands.\u003c/p\u003e\u003cp\u003eDigital fatigue, in contrast, stems from extended screen engagement, multitasking, and digital overstimulation\u0026mdash;especially before bedtime\u0026mdash;manifesting as visual strain, irritability, and sleep disruption.\u003c/p\u003e\u003cp\u003eThe data suggest that many adolescents experience a hybrid form of fatigue, arising from both academic workload and nocturnal digital engagement. Numerous students reported using their phones after studying \u0026ldquo;to relax,\u0026rdquo; echoing the compensatory use mechanism (Kardefelt-Winther, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, this behavior prolongs cognitive arousal and delays physiological recovery, turning supposed relaxation into an additional stressor.\u003c/p\u003e\u003cp\u003eAs a result, digital fatigue transforms into academic fatigue by morning, when students confront lower alertness and motivation in class. The persistence of fatigue and its strong statistical link to performance\u0026mdash;even when controlling for study time\u0026mdash;indicates that digital engagement has an independent effect beyond traditional academic stressors.\u003c/p\u003e\u003cp\u003eFuture studies should therefore develop validated subscales for both academic and digital fatigue, and include interaction terms to test their combined influence on sleep and performance. Understanding this dynamic interplay could inform educational strategies that integrate academic workload management with digital self-regulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Sociocultural Dimensions\u003c/h2\u003e\u003cp\u003eThe Moroccan context offers a unique cultural lens through which to interpret these findings.\u003c/p\u003e\u003cp\u003eEvening family gatherings, social visits, and television viewing are common aspects of Moroccan social life, normalizing late-night activity. Adolescents\u0026rsquo; digital engagement, therefore, may not be perceived as excessive but rather as a natural extension of social participation.\u003c/p\u003e\u003cp\u003eHowever, when combined with early school schedules and heavy academic expectations, this nocturnal digital lifestyle produces chronic sleep restriction\u0026mdash;a form of structural circadian misalignment (Hale \u0026amp; Guan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in which cultural timing and biological needs conflict.\u003c/p\u003e\u003cp\u003eConsequently, digital habits in Morocco should be viewed not only as individual behavioral choices but also as expressions of broader social rhythms.\u003c/p\u003e\u003cp\u003eTherefore, understanding digital habits in Morocco requires a culturally contextualized approach that bridges global findings with local temporal norms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Educational Implications\u003c/h2\u003e\u003cp\u003eThe findings carry multiple implications for both educational practice and adolescent health policy.\u003c/p\u003e\u003cp\u003eSchools should integrate digital well-being education into curricula, emphasizing the cognitive and emotional costs of late-night screen use. Teachers and parents can collaborate to promote sleep hygiene through measures such as digital curfews, reducing blue-light exposure, and encouraging offline relaxation techniques (e.g., mindfulness or reading) before bedtime.\u003c/p\u003e\u003cp\u003eFurthermore, addressing academic fatigue requires structural reforms in school routines to ensure sufficient rest and cognitive recovery. Flexible scheduling, balanced homework policies, and awareness programs about fatigue symptoms could help reduce both digital and academic stress.\u003c/p\u003e\u003cp\u003eAt the systemic level, the Ministry of Education and health agencies should jointly develop cross-sectoral programs linking digital literacy, sleep education, and academic performance as interdependent components of student well-being.\u003c/p\u003e\u003cp\u003eSuch initiatives would not only enhance academic outcomes but also foster healthier digital citizenship among adolescents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec41\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Theoretical Contributions\u003c/h2\u003e\u003cp\u003eFrom a theoretical perspective, this study integrates behavioral (digital habits), physiological (sleep health), and psychological (academic fatigue) mechanisms into a coherent explanatory model of academic functioning.\u003c/p\u003e\u003cp\u003eThe results support the Compensatory Internet Use Model (Kardefelt-Winther, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), demonstrating that adolescents often use technology as a coping strategy that paradoxically worsens fatigue.\u003c/p\u003e\u003cp\u003eThey also extend Self-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), illustrating how digital autonomy at night satisfies short-term psychological needs for connection and autonomy, yet undermines long-term self-regulation and competence.\u003c/p\u003e\u003cp\u003eFinally, the findings reinforce the Sleep Health Framework (Buysse, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), underscoring the interdependence between behavioral timing, emotional regulation, and physiological restoration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec42\" class=\"Section2\"\u003e\u003ch2\u003e5.6 Limitations and Future Directions\u003c/h2\u003e\u003cp\u003eAs a cross-sectional study, causal inference remains limited. Self-reporting may also introduce perceptual bias or inaccuracies in estimating digital use and fatigue.\u003c/p\u003e\u003cp\u003eFuture research should incorporate objective measures such as sleep tracking (actigraphy) and digital usage logs to complement self-reports.\u003c/p\u003e\u003cp\u003eLongitudinal or experimental designs would provide stronger evidence of the causal sequence linking digital behavior, sleep, and fatigue.\u003c/p\u003e\u003cp\u003eAdditionally, examining gender differences and school-type variations may reveal nuanced mechanisms underlying fatigue formation.\u003c/p\u003e\u003cp\u003eFurther exploration of broader outcomes\u0026mdash;such as emotional regulation, attention control, and mental health\u0026mdash;could identify mediators or moderators that shape the observed relationships.\u003c/p\u003e\u003cp\u003eIntegrating physiological biomarkers (e.g., cortisol levels, heart rate variability) would also deepen understanding of the biological underpinnings of academic fatigue.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec43\" class=\"Section2\"\u003e\u003ch2\u003e5.7 Conclusion\u003c/h2\u003e\u003cp\u003eOverall, this study demonstrates that late-night digital engagement undermines adolescent sleep health, intensifies academic fatigue, and reduces academic performance.\u003c/p\u003e\u003cp\u003eBy distinguishing between digital and academic sources of fatigue, it offers a more comprehensive understanding of how technology shapes adolescents\u0026rsquo; well-being and learning outcomes.\u003c/p\u003e\u003cp\u003eThese findings emphasize the urgent need for holistic educational interventions that promote both digital self-regulation and restorative rest, adapted to cultural realities.\u003c/p\u003e\u003cp\u003eIntegrating these insights into national curricula could mark a transformative step toward holistic adolescent development in the digital age, positioning Morocco\u0026mdash;and similar developing contexts\u0026mdash;at the forefront of the global movement for balanced digital and academic health.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec45\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Summary of Findings\u003c/h2\u003e\u003cp\u003eThis section synthesizes the key results and highlights their theoretical and practical implications.\u003c/p\u003e\u003cp\u003eThe current study examined how late-night digital habits among Moroccan adolescents influence sleep health, academic fatigue, and self-perceived academic performance. The results demonstrated that students who engaged in more frequent and later digital activity\u0026mdash;particularly social media browsing and chatting\u0026mdash;exhibited poorer sleep health, higher levels of fatigue, and lower academic performance.\u003c/p\u003e\u003cp\u003eThe sequential mediation analysis confirmed the hypothesized pathway:\u003c/p\u003e\u003cp\u003eDigital Habits \u0026rarr; Sleep Health \u0026rarr; Academic Fatigue \u0026rarr; Academic Performance.\u003c/p\u003e\u003cp\u003eThis finding underscores that digital engagement is not inherently detrimental; rather, it becomes harmful when its timing and intensity interfere with restorative processes essential for cognitive functioning and learning.\u003c/p\u003e\u003cp\u003eThe study further revealed that academic fatigue operates as the psychological mechanism connecting behavioral (digital) and physiological (sleep) domains. In this sense, fatigue functions both as a symptom and as a mediator of imbalance between technological engagement and academic effort.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec46\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Theoretical Implications\u003c/h2\u003e\u003cp\u003eThe present research contributes to the expanding field of digital well-being by integrating behavioral, physiological, and psychological dimensions into a coherent framework. Three theoretical contributions emerge clearly from the data:\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegration of Frameworks\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eBy combining the Compensatory Internet Use Model (Kardefelt-Winther, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Self-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the multidimensional Sleep Health framework (Buysse, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the study proposes a unified model of adolescent functioning that captures motivation, recovery, and academic outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCentralization of Academic Fatigue\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe results position academic fatigue as a distinct yet underexplored construct bridging sleep and cognition, extending its conceptual value beyond traditional burnout and stress models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCultural Specificity\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eConducting this study in Morocco broadens the predominantly Western literature on adolescent sleep and digital behavior, providing a context-sensitive model relevant to the Global South.\u003c/p\u003e\u003cp\u003eTogether, these insights enrich the theoretical understanding of how digital lifestyles shape academic well-being within sociocultural systems where technology deeply permeates daily routines.\u003c/p\u003e\u003cp\u003e\u003cb\u003e6.3. Practical Recommendations\u003c/b\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea. Educational Practice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSchools play a pivotal role in shaping students\u0026rsquo; relationships with technology. Integrating digital literacy and sleep education into school curricula can help adolescents understand the physiological and cognitive costs of late-night device use. Workshops and advisory sessions should emphasize:\u003c/p\u003e\u003cp\u003eThe importance of establishing digital curfews (avoiding screens at least one hour before bedtime).\u003c/p\u003e\u003cp\u003eThe use of night mode and blue-light filters.\u003c/p\u003e\u003cp\u003eEncouraging mindfulness, relaxation, or offline reading before sleep instead of online engagement.\u003c/p\u003e\u003cp\u003eTeachers should model balanced digital behavior by limiting after-hours communication and promoting offline study routines.\u003c/p\u003e\u003cp\u003e\u003cb\u003eb. Family Involvement\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Parents must act as active partners in fostering digital balance. Family routines\u0026mdash;such as device-free dinners or open discussions about media use\u0026mdash;can create shared accountability and healthier communication patterns. Parental guidance should shift from control to collaboration, promoting trust and mutual understanding of digital challenges faced by adolescents.\u003c/p\u003e\u003cp\u003e\u003cb\u003ec. Policy-Level Interventions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt the national level, collaboration between the Ministry of Education and the Ministry of Health is crucial to promote adolescent digital well-being and sleep hygiene.\u003c/p\u003e\u003cp\u003ePublic awareness campaigns should highlight the connection between digital overuse, sleep deprivation, and academic underperformance.\u003c/p\u003e\u003cp\u003e Moreover, national guidelines for secondary schools could include recommendations on daily device exposure, aligning with global standards by the World Health Organization (WHO, 2023) on adolescent screen time and health.\u003c/p\u003e\u003cp\u003eSuch policies could align Morocco\u0026rsquo;s educational system with international best practices in promoting balanced digital lifestyles among youth.\u003c/p\u003e\u003cp\u003e\u003cb\u003ed. Addressing Fatigue Management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRecognizing academic fatigue as a public health and educational concern is essential. Schools can implement short restorative breaks during long study sessions, organize workshops on time management and stress regulation, and design academic calendars that prevent cognitive overload.\u003c/p\u003e\u003cp\u003eEncouraging students to identify early signs of fatigue and apply simple recovery techniques\u0026mdash;such as short naps, breathing exercises, or brief physical activity\u0026mdash;can enhance attention and motivation during learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec47\" class=\"Section2\"\u003e\u003ch2\u003e6.4. Limitations and Future Research\u003c/h2\u003e\u003cp\u003eDespite its valuable contributions, this study has certain limitations that should guide future investigations.\u003c/p\u003e\u003cp\u003eFirst, its cross-sectional design limits causal inference; longitudinal and experimental studies are needed to confirm the directional sequence of effects.\u003c/p\u003e\u003cp\u003eSecond, reliance on self-reported data may introduce subjective bias or underreporting. Future work should employ objective measures such as sleep tracking (actigraphy), screen-time monitoring, and physiological biomarkers (e.g., cortisol levels, heart rate variability) to triangulate data.\u003c/p\u003e\u003cp\u003eThird, future studies should distinguish academic fatigue from digital fatigue using validated multidimensional scales to assess their independent and combined influence on academic outcomes.\u003c/p\u003e\u003cp\u003eFinally, comparative cross-cultural research could test the generalizability of the proposed model across different educational systems and cultural contexts.\u003c/p\u003e\u003cp\u003eDespite these limitations, the present study provides a robust empirical foundation for subsequent cross-cultural and interdisciplinary exploration of adolescent digital behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec48\" class=\"Section2\"\u003e\u003ch2\u003e6.5. Concluding Remarks\u003c/h2\u003e\u003cp\u003eIn conclusion, this research provides compelling empirical evidence that late-night digital engagement\u0026mdash;while psychologically gratifying\u0026mdash;undermines adolescents\u0026rsquo; academic functioning by disrupting sleep and fostering fatigue.\u003c/p\u003e\u003cp\u003eThe results emphasize that healthy digital behavior and restorative sleep are mutually reinforcing, forming the twin pillars of sustainable learning and mental well-being.\u003c/p\u003e\u003cp\u003eBy identifying academic fatigue as the pivotal link between digital habits, sleep health, and performance, the study opens new interdisciplinary pathways connecting digital psychology, education, and sleep science.\u003c/p\u003e\u003cp\u003eCultivating awareness and self-regulation among students, families, and educators can foster more sustainable learning environments where technology enhances rather than exhausts human potential.\u003c/p\u003e\u003cp\u003eIntegrating these insights into national educational strategies could mark a transformative step toward holistic adolescent development in the digital age\u0026mdash;positioning Morocco and similar developing contexts as leaders in the global movement toward digital balance, sleep education, and academic health.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e The Scientific Committee of the Public Law and Political Science Laboratory at the Faculty of Law, Sidi Mohamed Ben Abdellah University in Fez, provided both scientific and ethical approval for this research. The committee confirmed that the study adheres to ethical principles including informed consent, participant anonymity, and voluntary participation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests, financial or non-financial, that are directly or indirectly related to the work submitted for publication.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical Approval\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the ethical standards of the American Psychological Association (APA) and the British Educational Research Association (BERA). Informed consent was obtained from all participants prior to their involvement in the study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to confidentiality agreements with the participants but may be available from the corresponding author upon reasonable request.Haut du formulaire\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBecker, S. P., Sidol, C. A., \u0026amp; Burns, G. L. (2022). Adolescent sleep and academic functioning: The role of emotional regulation and fatigue. Journal of Adolescence, 95, 47\u0026ndash;58. https://doi.org/10.1016/j.adolescence.2022.04.007\u003c/li\u003e\n\u003cli\u003eBenjelloun, A. (2022). 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Sleep Medicine Reviews, 18(1), 75\u0026ndash;87. https://doi.org/10.1016/j.smrv.2013.03.005\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2023). Adolescent health and screen time: Global guidelines for healthy digital engagement. WHO Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital habits, academic fatigue, sleep health, late-night screen use, Moroccan adolescents, academic performance","lastPublishedDoi":"10.21203/rs.3.rs-7837474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7837474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eLate-night digital activity has become a common feature of adolescents\u0026rsquo; daily lives, yet its academic and cognitive consequences remain underexplored in North African contexts. While sleep deprivation is often cited as a cause of reduced performance, academic fatigue may represent an equally critical mechanism through which digital overuse affects learning. This study examines how late bedtime and academic fatigue interact to influence self-rated academic performance among Moroccan secondary students.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted with 311 students aged 15\u0026ndash;19 years (56% female) in Fez, Morocco. The questionnaire assessed bedtime routines, digital activity before sleep, indicators of academic fatigue, and perceived academic performance. Descriptive statistics, Chi-square tests, and Cramer\u0026rsquo;s V coefficients were used to analyze relationships among variables, while qualitative comments were thematically reviewed to enrich the quantitative findings.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eMost respondents (76.8%) reported sleeping after 11 p.m., and 22.2% after 1 a.m. More than 70% indicated experiencing morning fatigue or difficulty focusing at school. Both late bedtime and higher levels of academic fatigue were significantly associated with lower self-evaluations of academic performance (χ\u0026sup2;(12)\u0026thinsp;=\u0026thinsp;25.69, p\u0026thinsp;=\u0026thinsp;0.012). Qualitative responses revealed that students often perceived late-night screen use as a source of freedom and relaxation, despite recognizing its contribution to tiredness and reduced study efficiency.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThe findings suggest that academic fatigue plays a key role in mediating the impact of late-night digital engagement on school performance. Sleep hygiene education, balanced workload management, and digital awareness programs could help Moroccan adolescents mitigate fatigue-related academic decline.\u003c/p\u003e","manuscriptTitle":"Late-Night Digital Engagement, Academic Fatigue, and Their Impact on Academic Performance among Moroccan Secondary Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 08:19:03","doi":"10.21203/rs.3.rs-7837474/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a539ea8-09bb-46e7-bab2-0083d548d6c7","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56143605,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-01-02T09:45:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 08:19:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7837474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7837474","identity":"rs-7837474","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}