Life Satisfaction and Academic Achievement among Tanzanian College Students: Mediated by Sleep Quality and Moderated by Smartphone Addiction

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KONE, JING TIAN, HONGDE LEI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7190828/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 Academic achievement is vital for socioeconomic advancement, particularly in developing countries like Tanzania. While cognitive factors of academic achievement have been widely studied, psychosocial and behavioral influences such as life satisfaction, sleep quality, and smartphone addiction remain underexplored. To address this research gap, this study examines how life satisfaction affects academic achievement through sleep quality, with smartphone addiction as a moderator. Data were collected from 513 Tanzanian college students and analyzed using SPSS 27 and PROCESS Macro Models 4 and 59, controlling for age, sponsorship, and occupation. Research results showed that life satisfaction was positively correlated with academic achievement ( r = 0.578) and sleep quality ( r = 0.638), while smartphone addiction showed negative correlations with these three variables. Sleep quality partially mediated the link between life satisfaction and academic achievement, accounting for 30.77% of the total effect. Moderated mediation analysis revealed that smartphone addiction strengthened the positive relationship between life satisfaction and sleep quality (effect sizes: 0.437 to 0.642, all p < 0.001), but weakened the link between sleep quality and academic achievement (effect sizes: 0.339 to 0.108, all p < 0.001). These findings highlight the dual moderating role of smartphone addiction and underscore the importance of promoting psychological well-being and responsible technology use in academic contexts. Academic achievement Life satisfaction Sleep quality Smartphone addiction College Students Figures Figure 1 Figure 2 Introduction Academic achievement represents a critical indicator of student success and a fundamental predictor of future career paths, socioeconomic mobility, and national development (Harpaz et al., 2023). Many scholars have explored the determinants of academic success, focusing primarily on cognitive aspects such as intelligence, memory, and pedagogical techniques. While these factors are undoubtedly important, they do not provide a complete explanation. Psychosocial and behavioral elements also significantly impact students' academic success, and recent scholarship has begun to highlight the role of these factors—such as life satisfaction, sleep quality, and smartphone usage—in shaping academic performance (Kone & Hongde, 2024; Stavrulaki et al., 2021a). However, there remains considerable debate concerning the influence of these psychosocial and behavioral factors. Some researchers argue that life satisfaction primarily enhances academic outcomes by fostering motivation and emotional resilience (Feraco, Casali, et al., 2023), while others underscore the mediating effects of health-related behaviors, including sleep patterns (Drapeau, 2022a). Concurrently, the issue of smartphone dependency has garnered increasing attention, with some studies indicating its adverse effects on sleep and concentration (Kibona & Mgaya, 2015), and others suggesting that its impact may be contingent upon contextual or personal factors such as coping strategies and self-regulation (Tu et al., 2023). These divergent perspectives highlight the need for integrative models that investigate not only whether but how and when these factors affect academic achievement. Tanzanian college students are a vital population for such investigation. As digital technology proliferates rapidly throughout the country—with smartphone penetration projected to reach 25.4 million users by the end of 2024 (Malima, 2025)—students are increasingly faced with the academic demands of higher education alongside the behavioral challenges posed by excessive digital consumption. Moreover, widespread infrastructural shortcomings and socioeconomic inequalities continue to mold students' educational experiences in ways that markedly differ from those in Western settings (Kibona & Mgaya, 2015; Digital, 2022). Against this backdrop, examining the interrelated effects of life satisfaction, sleep quality, and smartphone addiction can offer valuable insights into culturally sensitive approaches to improving both student well-being and academic outcomes. Life satisfaction and academic achievement Life satisfaction, which reflects an individual’s overall appraisal of life quality and personal fulfilment, is increasingly recognized as a critical factor in students' academic development and well-being (Feraco, Casali, et al., 2023). Research has shown that higher life satisfaction is often associated with greater enthusiasm for learning, more adaptive coping strategies when facing academic pressure, and a more optimistic outlook toward education (González Moreno et al., 2024). These psychological strengths—such as sustained focus, goal-setting, and perseverance—are believed to enhance academic achievement by promoting self-regulation and engagement. In addition, life satisfaction may contribute indirectly by fostering a supportive social environment, improving peer and teacher relationships, and facilitating emotionally grounded collaborative learning (Feraco, Resnati, et al., 2023). However, the mechanisms linking life satisfaction to academic achievement remain subject to debate. Some scholars argue that the relationship is primarily motivational, while others suggest that it is conditional upon contextual or behavioral mediators such as sleep, stress, or digital distraction (Stavrulaki et al., 2021a). Students with lower life satisfaction often face diminished motivation, reduced confidence, and weaker emotional resilience—factors that are central to academic underperformance. Moreover, elevated stress levels and negative affective states may erode students’ ability to sustain effort or recover from academic setbacks. The Broaden-and-Build Theory of Positive Emotions (Vacharkulksemsuk & Fredrickson, 2013) provides a useful lens for understanding this dynamic: it suggests that positive emotional states expand individuals’ cognitive and behavioral repertoires, facilitating the accumulation of personal and academic resources. Nevertheless, empirical evidence remains mixed regarding the direct versus indirect pathways through which life satisfaction influences academic achievement, particularly in non-Western or resource-constrained contexts (González Moreno et al., 2024). This raises an important empirical question: to what extent is life satisfaction a reliable predictor of academic success in settings where external challenges may override internal psychological strengths? Based on the theoretical and empirical considerations above, we propose the following hypothesis: H1 : Life satisfaction is positively associated with academic achievement. The mediating role of sleep quality Sleep quality is widely acknowledged as a key determinant of students’ cognitive functioning, emotional regulation, and academic performance (Drapeau, 2022a; Astridge et al., 2021). Sufficient and restorative sleep supports concentration, memory consolidation, and executive functioning—all of which are essential for learning. Students with higher life satisfaction typically report lower stress levels and better psychological well-being, which in turn facilitate healthier sleep patterns (Aldabbour et al., 2025). Conversely, dissatisfaction with life often manifests as anxiety and mental fatigue, which will affect sleep quality and result in poor academic outcomes (Orihuela et al., 2023). These patterns suggest that sleep quality may act as a bridge linking psychological well-being to academic achievement. Despite growing empirical support for the above view, the mediating role of sleep quality is not universally accepted. Some studies conceptualize sleep disturbances as outcomes of external pressures such as academic overload or digital overexposure, rather than internal states like life satisfaction (Evers et al., 2020a). Others point to bidirectional or even reciprocal relationships, where poor sleep not only results from psychological distress but also amplifies it, complicating causal interpretations. In this regard, the Restoration Theory of Sleep (Brinkman et al., 2025) offers a useful explanatory framework. It posits that sleep serves essential restorative functions, including the rebalancing of neurocognitive systems and the consolidation of emotional learning. From this perspective, adequate sleep is not merely a correlate of academic success but a functional mechanism through which psychological resources—such as those derived from life satisfaction—are translated into improved performance. In high-stress or resource-constrained environments, where external supports are limited, the quality of sleep may become an especially salient mediator. Yet the strength and consistency of this mediating role remain open questions, particularly in developing countries like Tanzania, where sleep hygiene may be compromised by environmental, economic, or behavioral factors (Gao et al., 2023). Clarifying the role of sleep in this pathway is therefore crucial for designing interventions that not only address academic performance directly, but also enhance upstream psychological and behavioral processes. Accordingly, we propose the following hypothesis: H2: Sleep quality mediates the relationship between life satisfaction and academic achievement. The moderating role of smartphone addiction As smartphones become increasingly integrated into students’ academic and social lives, concerns have grown regarding their excessive use and the potential consequences for psychological well-being and educational outcomes (Tu et al., 2023). Smartphone addiction, characterized by compulsive usage patterns and impaired self-regulation, has been linked to disrupted sleep, reduced academic focus, and heightened psychological distress (Safdar Bajwa et al., 2023). However, its role is likely more complex than a simple direct predictor of poor outcomes; rather, it may function as a moderating factor that alters the strength or direction of other established relationships, such as those between life satisfaction, sleep quality, and academic achievement. The current literature presents diverging perspectives on the issue mentioned above. On one hand, some studies suggest that smartphone addiction universally undermines psychological and behavioral benefits by reducing available cognitive and emotional resources (Feraco, Resnati, et al., 2023; Larsen et al., 2023a). Excessive smartphone use, particularly before bedtime, has been shown to disrupt circadian rhythms and reduce total sleep duration, which in turn impairs learning and memory functions (Gao et al., 2023). From this view, even students with high life satisfaction may fail to benefit from better sleep quality or improved academic achievement if smartphone addiction interferes with their capacity to regulate digital behavior. On the other hand, emerging research offers a more nuanced view, suggesting that the negative impact of smartphone addiction may depend on individuals’ psychological resources. According to Self-Regulation Theory (Hitcham et al., 2023a; Baumeister & Vohs, 2007), individuals with higher life satisfaction may develop stronger self-regulatory mechanisms, enabling them to maintain healthy sleep patterns or mitigate distraction despite high levels of smartphone use. In this sense, smartphone addiction may not uniformly weaken all beneficial pathways but may instead moderate them in context-specific ways—either amplifying or diminishing effects depending on the interplay of emotional, behavioral, and technological factors. This theoretical divergence points to the need for empirical models that test not only whether smartphone addiction affects academic achievement, but more importantly, when and how it alters the relationships among life satisfaction, sleep quality, and academic achievement. In high-usage contexts like Tanzania—where smartphone penetration is rapidly increasing (Malima, 2025), but digital literacy and boundary-setting may lag—the moderating role of smartphone addiction warrants focused investigation. Understanding this role is essential for developing interventions that go beyond screen-time limits to include emotional and behavioral regulation strategies. Based on this conceptual rationale, we propose the following hypotheses: H3a : Smartphone addiction moderates the relationship between life satisfaction and academic achievement. H3b : Smartphone addiction moderates the relationship between life satisfaction and sleep quality. H3c : Smartphone addiction moderates the relationship between sleep quality and academic achievement. In summary, drawing on the Broaden-and-Build Theory of Positive Emotions, the Restoration Theory of Sleep, and Self-Regulation Theory, this study develops an integrative conceptual model (see Fig. 1) to examine both the mechanisms (“how”) and conditions (“when”) under which life satisfaction influences academic achievement among college students. Specifically, the model is designed to address three core objectives: (1) to examine the nature and strength of the relationship between life satisfaction and academic achievement; (2) to assess whether sleep quality mediates the relationship between life satisfaction and academic achievement; (3) to investigate whether smartphone addiction moderates the direct and indirect pathways linking life satisfaction, sleep quality, and academic achievement. Methods Data collection and participants Data were collected between July and September 2024 in Arusha, Tanzania, from four colleges affiliated with the National Council for Technical and Vocational Education and Training. With institutional approval, questionnaires reviewed and validated by experts were administered to assess students’ life satisfaction, sleep quality, smartphone addiction, and academic achievement. All items were rated on 5-point Likert scales. Participation in the study was voluntary, anonymous, and based on informed consent. Of the total responses received, 63 incomplete questionnaires were excluded, resulting in a final sample of 513 valid cases (249 males and 264 females), yielding a response rate of 89.06%. The demographic characteristics of the participants are summarized in Table 1. Measures Life satisfaction Life satisfaction was assessed using a five-item scale developed specifically for this study to capture students’ overall sense of well-being. The scale covered key domains including personal experiences, family relationships, and social interactions, reflecting areas that may influence both sleep quality and academic performance. Each item was rated on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), yielding total scores between 5 and 25. Higher scores indicated higher levels of life satisfaction. Among the study sample, the scale demonstrated good internal consistency, with a Cronbach’s alpha of 0.798, supporting its reliability and appropriateness for use in this context. Table 1 Characteristics of college students ( n = 513) Variable s Category Frequency Percentage (%) Gender Female 264 51.46 Male 249 48.54 Age Groups 15 – 25 467 91.03 26 – 35 22 4.29 36 – 45 24 4.68 Years of Study (Education) First Year 284 55.36 Second Year 154 30.02 Third Year 75 14.62 Occupation (Working) Not Working 454 88.50 Working 59 11.50 Education Sponsorship Government Sponsored 129 25.15 Private Sponsored 384 74.85 Sleep quality Sleep quality was measured using a six-item scale specifically developed for this study to comprehensively assess students’ sleep-related experiences and their relevance to academic performance. The items captured perceptions of sleep sufficiency, daytime sleepiness, and beliefs regarding the role of sleep in supporting overall well-being and academic productivity. Each item was rated on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), producing total scores between 6 and 30. Higher scores indicated better sleep quality. The scale exhibited strong internal consistency, with a Cronbach’s alpha of 0.833, confirming its reliability for assessing sleep quality among college students in this context. Smartphone addiction Smartphone addiction was measured using an eight-item scale developed for this study, drawing on adapted elements from the Smartphone Addiction Scale (Hamamura et al., 2023) to capture a comprehensive profile of problematic smartphone use and its effects on academic and personal well-being. The items assessed excessive usage patterns, difficulties in self-regulation, and the perceived impact of smartphone habits on productivity and sleep quality. Each item was rated on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), yielding total scores between 8 and 40. Higher scores indicated greater levels of smartphone addiction. The scale demonstrated excellent internal consistency, with a Cronbach’s alpha of 0.916, confirming its reliability and robustness for measuring smartphone-related behavioral risks among college students. Academic achievement Academic achievement was assessed using a self-reported measure that included both last semester and cumulative GPA, rated on a five-point scale (1 = lowest, 5 = highest). In addition to GPA, students provided a subjective evaluation of their recent academic performance, allowing for a broader perspective on their perceived academic standing. This dual approach enabled the capture of both objective and perceived dimensions of academic success. The scale demonstrated good internal consistency, with a Cronbach’s alpha of 0.837, supporting its reliability for assessing academic achievement within the study sample. Covariates Potential covariates included students’ self-reported age group (1 = 15–25, 2 = 26–35, 3 = 36–45), sponsorship status (1 = Government Sponsored, 2 = Privately Sponsored), and occupational status (1 = No, 2 = Yes). These variables were included as control variables in all statistical models to adjust for their potential confounding effects. Statistical analysis In accordance with Hayes’ recommendations, all continuous variables were standardized prior to conducting the moderated mediation analyses (Hayes,2013). Statistical analyses were performed using the PROCESS Macro (version 3.5) for SPSS. To examine the mediating effect of sleep quality on the relationship between life satisfaction and academic achievement, Model 4 of the PROCESS Macro was applied. Subsequently, Model 59 was used to test a moderated mediation model, assessing the conditional effects of smartphone addiction on both the indirect path from life satisfaction to academic achievement via sleep quality and the direct path from life satisfaction to academic achievement. All mediation and moderation effects were tested using nonparametric bootstrapping with 5,000 resamples, generating bias-corrected 95% confidence intervals. Results Common method bias To minimize potential common method bias arising from the use of self-report measures, several procedural controls were implemented. These included the use of anonymous responses, the separation of measurement for different constructs, and the inclusion of reverse-coded items where appropriate. In addition, Harman’s single-factor test was conducted to statistically assess the extent of common method variance (Kock, 2021). Results showed that the first unrotated factor accounted for 35.5% of the total variance—well below the commonly accepted threshold of 40%. This suggests that common method bias was not a serious concern in this study. Correlation analysis Table 2 presents the bivariate correlations among all key study variables. Academic achievement was positively correlated with life satisfaction ( r = 0.578, p < 0.001) and sleep quality ( r = 0.532, p < 0.001), and negatively correlated with smartphone addiction ( r = -0.657, p < 0.001). Life satisfaction showed a strong positive correlation with sleep quality ( r = 0.638, p < 0.001), and a significant negative correlation with smartphone addiction ( r = -0.592, p < 0.001). Similarly, sleep quality was negatively associated with smartphone addiction ( r = -0.478, p < 0.001). These results indicate that higher life satisfaction and better sleep quality are linked to better academic outcomes, while higher levels of smartphone addiction are associated with poorer academic performance and reduced well-being. Table 2 Correlation analysis Variables M SD 1 2 3 4 1. Academic Achievement 3.22 0.75 1 2. Life Satisfaction 3.58 0.69 0.578 *** 1 3. Sleep Quality 3.61 0.65 0.532 *** 0.638 *** 1 4. Smartphone Addiction 3.32 0.96 -0.657 *** -0.592 *** -0.478 *** 1 Note: *** Correlation is significant at the 0.001 level (two-tailed). Mediation analys is Table 3 reveals that life satisfaction is significantly positively associated with both academic achievement in Model 1 ( β = 0.494, p < 0.001) and sleep quality in Model 2 ( β = 0.613, p < 0.001). In Model 3, life satisfaction ( β = 0.341, p < 0.001) and sleep quality ( β = 0.248, p < 0.001) both retain significant positive associations with academic achievement. The findings show that the 95% confidence intervals for both the direct effect of life satisfaction on academic achievement and the indirect effect through sleep quality exclude zero. This suggests that life satisfaction not only directly predicts academic achievement but also has an indirect effect through sleep quality. Specifically, the direct effect (0.341) and the indirect effect (0.152) account for 69.23% and 30.77% of the total effect (0.494), respectively. Table 3 Linear regression analysis Variables Model 1(AA) Model 2(SQ) Model 3(AA) β t 95%CI β t 95%CI β t 95%CI LS 0.494 13.655 *** [0.423,0.565] 0.613 16.975 *** [0.542,0.684] 0.341 7.781 *** [0.255,0.428] SQ 0.248 5.764 *** [0.164,0.333] Age 0.210 2.154 * [0.019,0.402] -0.099 -1.019 [-0.291,0.092] 0.235 2.480 * [0.049,0.421] Sponsorship -0.649 -7.814 *** [-0.812,-0.486] -0.187 -2.250 * [-0.350,-0.024] -0.603 -7.446 *** [-0.762,-0.444] Occupation 0.000 0.001 [-0.284,0.284] 0.214 1.482 [-0.070,0.498] -0.053 -0.377 [-0.329,0.223] R ² 0.413 0.415 0.449 F 89.492 *** 89.990 *** 82.779 *** Note: All continuous variables were standardized to z-scores and entered into PROCESS Macro Model 4, standardized coefficients are reported; * : p < 0.05, ** : p < 0.01, *** : p < 0.001; Age, sponsorship, and occupation were analyzed as control variables in Models 1 to 3; LS: Life Satisfaction, SQ: Sleep Quality, AA: Academic Achievement. Moderated mediation analys is Table 4 and Fig. 2 reveal a statistically significant interaction between life satisfaction and smartphone addiction in predicting sleep quality ( β = 0.102, p < 0.05, 95% CI [0.015, 0.190]), indicating that smartphone addiction moderates this relationship. Additionally, a significant interaction between sleep quality and smartphone addiction on academic achievement was found ( β = -0.115, p < 0.05, 95% CI [-0.209, -0.022]), suggesting that smartphone addiction also moderates this association. However, no significant interaction was observed between life satisfaction and smartphone addiction on academic achievement ( β = 0.080, p > 0.05, 95% CI [-0.010, 0.170]). In order to reveal how smartphone addiction moderates the relationship of “life satisfaction→sleep quality→academic achievement”, high and low groups (plus or minus one standard deviation) were grouped based on the value of smartphone addiction, and a simple slope test was conducted. The results showed that, in the relationship between life satisfaction and sleep quality, smartphone addiction had a significant positive moderating effect. As smartphone addiction increased, the association between life satisfaction and sleep quality strengthened: the effect was 0.437 (95% CI [0.316, 0.558]) at low smartphone addiction (M-1SD), 0.539 (95% CI [0.457, 0.622]) at moderate smartphone addiction (M), and 0.642 (95% CI [0.522, 0.761]) at high smartphone addiction (M+1SD). Table 4 Moderated mediation analysis Variables Model 1(SQ) Model 2(AA) β t 95%CI β t 95%CI LS 0.539 12.864 *** [0.457, 0.622] 0.167 3.842 *** [0.082, 0.252] SA -0.178 -3.841 *** [-0.269, -0.087] -0.382 -8.799 *** [-0.467,-0.296] SQ 0.224 5.275 *** [0.140, 0.307] LS×SA 0.102 2.299 * [0.015, 0.190] 0.080 1.756 [-0.010, 0.170] SQ×SA -0.115 -2.417 * [-0.209, -0.022] Age -0.116 -1.203 [-0.306,0.074] 0.147 1.696 [-0.023,0.318] Sponsorship -0.105 -1.228 [-0.272,0.063] -0.409 -5.351 *** [-0.560,-0.259] Occupation 0.152 1.058 [-0.130,0.433] -0.130 -1.012 [-0.384,0.123] R 2 0.433 0.546 F 64.300 *** 75.724 *** Note : All continuous variables were standardized to z-scores and entered into PROCESS Macro Model 59, standardized coefficients are reported; * p < 0.05, ** : p < 0.01, *** : p < 0.00; Age, sponsorship, occupation were analyzed as control variables; LS: Life Satisfaction, SQ: Sleep Quality, AA: Academic Achievement, SA: Smartphone Addiction. In contrast, smartphone addiction exhibited a significant negative moderating effect on the relationship between sleep quality and academic achievement. As smartphone addiction severity increased, the effect of sleep quality on academic achievement diminished: from 0.339 (95% CI [0.194, 0.485]) at low smartphone addiction (M-1SD), to 0.224 (95% CI [0.140, 0.307]) at moderate smartphone addiction (M), and to 0.108 (95% CI [0.007, 0.210]) at high smartphone addiction (M+1SD). These results suggest that while smartphone addiction strengthens the positive effect of life satisfaction on sleep quality, it simultaneously weakens the beneficial impact of sleep quality on academic achievement. Discussion This study explored the relationships between life satisfaction, sleep quality, and academic achievement, with a focus on the moderating role of smartphone addiction among Tanzanian college students. Drawing on the Broaden-and-Build Theory of Positive Emotions (Vacharkulksemsuk & Fredrickson, 2013 ), the Restoration Theory of Sleep (Adam, 1980 ), and Self-Regulation Theory (Feraco, Casali, et al., 2023 ), the findings contribute to a growing understanding of how psychosocial well-being and technology use interact to influence educational outcomes in resource-limited, digitally evolving settings. Life satisfaction and academic achievement This study supports H1 by confirming the critical role of life satisfaction in shaping academic achievement among Tanzanian college students, a result consistent with previous research (González Moreno et al., 2024 ; Harpaz et al., 2023 ). This relationship can be further understood through the Broaden-and-Build Theory of Positive Emotions (Fredrickson, 2001 ), which suggests that positive emotions—such as those associated with high life satisfaction—expand individuals’ cognitive and behavioral repertoires, while also cultivating lasting personal resources. Specifically, this theory argues that increased cognitive flexibility, psychological resilience, and intrinsic motivation enable students to engage more deeply with academic material, persist through challenges, and maintain goal-directed behaviors (Fredrickson & Branigan, 2005 ). The strong positive correlation between life satisfaction and academic achievement indicates that students who view their lives as fulfilling are better positioned to leverage these psychological resources, thus improving their academic outcomes. In the context of Tanzania, where students often face socioeconomic and structural challenges, life satisfaction may act as a compensatory mechanism that buffers against these adversities. According to the Broaden-and-Build Theory, positive emotions not only counteract negative states but also foster upward spirals of well-being and performance (Fredrickson, 2001 ). For Tanzanian students, maintaining high life satisfaction may help mitigate the stress caused by financial constraints or limited academic resources, allowing them to stay engaged and motivated in their studies. Additionally, the theory suggests that the cognitive broadening effect of positive emotions enhances creativity and problem-solving abilities, both of which are crucial for navigating the complexities of higher education (Fredrickson & Branigan, 2005 ). Therefore, the observed link between life satisfaction and academic achievement may reflect the cumulative benefits of these expanded cognitive and emotional resources. The mediating role of sleep quality The current findings support H2 by demonstrating a significant mediating effect of sleep quality in the relationship between life satisfaction and academic achievement, a result consistent with the Restoration Theory of Sleep (Adam, 1980 ). This theoretical framework explains how restorative sleep acts as a neurobiological mechanism that consolidates the cognitive and emotional benefits of life satisfaction, ultimately translating them into academic success. The mediation pathway identified in this study aligns with emerging neuroscientific evidence showing that sleep plays a vital role in synaptic pruning and memory consolidation—key processes for integrating learned material (Brinkman et al., 2025 ). When students experience higher life satisfaction, reduced stress and improved emotional regulation likely lead to better sleep quality, enhancing the restorative functions that are crucial for academic performance. Moreover, the mediating role of sleep quality resonates with cross-cultural studies highlighting its role in connecting psychological well-being and cognitive outcomes (Drapeau, 2022 b). The Restoration Theory posits that sleep acts as an equalizer, buffering the cognitive deficits caused by daytime stressors—an idea particularly relevant in the Tanzanian context, where students face significant socioeconomic pressures. This study extends prior research by showing that life satisfaction’s protective effects against academic underachievement are partially channeled through improved sleep efficiency. This suggests that interventions aimed at improving sleep hygiene could further enhance the academic benefits of psychological well-being programs. The moderating role of smartphone addiction The findings show that the interaction between life satisfaction and smartphone addiction did not significantly predict academic achievement; thus, H3a was not supported. This null finding suggests that, in the Tanzanian context, the direct association between life satisfaction and academic achievement is not contingent on students’ level of smartphone addiction. Instead, smartphone addiction exerts its influence primarily by moderating the indirect pathway via sleep quality, rather than by altering the direct link. In the relationship between life satisfaction and sleep quality, smartphone addiction demonstrated an unexpected enhancing effect, supporting H3b. This aligns with the compensatory regulation hypothesis of Self-Regulation Theory, which suggests that individuals with higher life satisfaction may develop adaptive coping strategies to counteract technology-related sleep disruptions (Hitcham et al., 2023 b; Larsen et al., 2023 b). The positive psychological resources associated with life satisfaction—such as emotional stability and future-oriented thinking—may help students compartmentalize smartphone use, allowing them to reserve pre-sleep periods for relaxation despite overall high usage levels (Feraco, Casali, et al., 2023 ). This finding challenges conventional assumptions about the universal negative impacts of smartphone overuse, instead highlighting the potential protective role of psychological well-being in digital environments. Conversely, smartphone addiction significantly weakened the beneficial relationship between sleep quality and academic achievement, supporting H3c. This aligns with the cognitive depletion component of Self-Regulation Theory, which posits that excessive smartphone use depletes the mental resources necessary for translating restorative sleep into academic success. The attentional fragmentation caused by compulsive smartphone checking creates cognitive "leakage" that persists even with adequate sleep duration, undermining the memory consolidation and emotional regulation typically supported by high-quality sleep (Tu et al., 2023 ). This effect was especially pronounced at high levels of addiction, suggesting a threshold beyond which even sufficient sleep cannot mitigate the cognitive impairments induced by technology overuse (Larsen et al., 2023 b; Mafla et al., 2021 b). The differential moderating effects observed across the above pathways highlight the multidimensional nature of smartphone addiction’s impact. While psychological resources may buffer its disruptive effects on sleep, its cognitive consequences seem more intractable once sleep processes are engaged. This duality aligns with recent developments in digital well-being research, which recognize that technology can both enhance and disrupt self-regulatory processes depending on the context (Larsen et al., 2023 ). Implication and limitations These findings discussed above have important implications for intervention design. Rather than advocating for blanket reductions in screen time, targeted strategies should focus on: (1) strengthening psychological resources to build resilience against sleep disruption, and (2) developing specific cognitive protection strategies to preserve sleep’s academic benefits. The Self-Regulation Theory framework suggests that interventions combining mindfulness training with personalized digital boundary-setting may be particularly effective in addressing both aspects of this complex issue. Future research should explore whether these moderating patterns hold across different cultural contexts and educational stages, especially in regions undergoing rapid digital transformation, such as Tanzania. Despite the valuable insights provided by this study, there are some limitations. The cross-sectional design, reliance on self-reported data, and geographic focus on Tanzania restrict the broader generalizability of our findings. Future research should adopt longitudinal methodologies to explore additional factors, such as stress and digital access, and expand to diverse African educational settings. This will require the development of culturally adapted strategies and integrated well-being programs that address student wellness alongside technology use, while advocating for balanced policies that mitigate smartphone-related risks without compromising learning outcomes. Ultimately, context-sensitive approaches are essential for further research and intervention in these complex environments. Conclusion This study contributes to our understanding of how psychological well-being, sleep patterns, and digital behaviors influence academic achievement among college students in developing countries like Tanzania. The findings suggest that life satisfaction enhances academic achievement both directly and indirectly by improving sleep quality, thus supporting the Broaden-and-Build Theory. Sleep quality acts as a key mediator, reinforcing the Restoration Theory of Sleep, particularly in high-stress environments. Notably, while smartphone addiction negatively affects sleep and academic achievement, it also moderates the relationships between these factors: it weakens the positive effects of sleep on academic achievement, yet strengthens the connection between life satisfaction and sleep quality at high levels of usage. These findings, framed by Self-Regulation Theory, suggest that digital overuse may not fully undermine psychological strengths if self-regulation is preserved. The study advocates for integrative, context-sensitive interventions in higher education that prioritize student well-being, promote sleep hygiene, and encourage responsible technology use. Future research should explore longitudinal and cross-cultural designs to further validate these findings. Declarations Competing interests The authors declare no competing interests. Acknowledgements We appreciate the support from the sample schools, especially the students who accepted the questionnaire survey. We also want to thank *** University for providing funding support for this research. Author’s contribution EK, JT, HDL had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of data analysis. Concept and design: EK, HDL; Acquisition, analysis of data: EK, JT, HDL; Drafting of manuscript: EK; Revision of manuscript: EK, JT, HDL; Supervisor: HDL. Funding This work was supported by the Think Tank Project (2025ZKJD07) funded by *** University. Ethical approval This study adhered to the Declaration of Helsinki and was approved by the Psychological Ethics Committee of *** University. Informed consent All the participants voluntarily and anonymously participated in the questionnaire survey, and informed consent to participate was obtained from all the participants in the study. Data availability There is a data set associated with this submission. The data set is deposited in https://www.scidb.cn/datalist, and its DOI is 10.57760/sciencedb.28236. Consent to publish Not applicable References Adam, K. (1980). Sleep as a restorative process and a theory to explain why. In P. S. McConnell, G. J. Boer, H. J. Romijn, N. E. Van de Poll, & M. A. 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American Psychologist, 56 (3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218 Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19 (3), 313–332. https://doi.org/10.1080/02699930441000238 Gao, W.-J., Hu, Y., Ji, J.-L., & Liu, X.-Q. (2023). Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic. World Journal of Psychiatry, 13 (6), 361–375. https://doi.org/10.5498/wjp.v13.i6.361 González Moreno, A., Simões, C., Santos, A. C., & Molero Jurado, M. del M. (2024). Creative self-efficacy and social skills in a Portuguese sample of university students: Links with self-esteem, academic achievement and life satisfaction. 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The relationship between smartphone use and smartphone addiction: An examination of logged and self-reported behavior in a pre-registered, two-wave sample. Computers in Human Behavior, 146 , Article 107822. https://doi.org/10.1016/j.chb.2023.107822 Jiang, W., Liu, S., Liu, M., Zhang, C., Chong, Z. Y., & Xu, W. (2024). The relationship between mindfulness and academic burnout in senior high school students during COVID-19 pandemic: The chain mediating role of social anxiety and smartphone addiction tendency. Current Psychology, 43 (43), 33658–33667. https://doi.org/10.1007/s12144-024-06101-6 Kibona, L., & Mgaya, G. (2015). Smartphones’ effects on academic performance of higher learning students. Journal of Multidisciplinary Engineering Science and Technology, 2 (4), 777–784. https://www.jmest.org/wp-content/uploads/JMESTN42350643.pdf Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. International Journal of e-Collaboration, 17 (4), 1–10. https://doi.org/10.4018/IJeC.2021100101 Kone, E. P., & Lei, H. (2024). The moderating effects of smartphone addiction on the relationship between life satisfaction, sleep quality and academic achievement among college students: A systematic review. International Journal of Higher Education, 13 (2), 56–68. https://doi.org/10.5430/ijhe.v13n2p56 Larsen, H., Wiers, R. W., Su, S., & Cousijn, J. (2023). Excessive smartphone use and addiction: When harms start outweighing benefits. Addiction, 118 (4), 586–588. https://doi.org/10.1111/add.16060 Mafla, A. C., Herrera-López, H. M., Eraso, T. F., Melo, M. A., Muñoz, N., & Schwendicke, F. (2021). Smartphones addiction associated with academic achievement among dental students: A cross-sectional study. Journal of Dental Education, 85 (11), 1802–1809. https://doi.org/10.1002/jdd.12728 Malima, W. (2025, January 19). Tanzania’s smartphone usage surges to 25.4 million. Daily News . https://dailynews.co.tz/tanzanias-smartphone-usage-surges-to-25-4-million/ Orihuela, C. A., Mrug, S., & Evans, R. R. (2023). Associations between sleepiness, sleep duration, and academic outcomes in early adolescence. Psychology in the Schools, 60 (6), 1652–1665. https://doi.org/10.1002/pits.22843 Safdar Bajwa, R., Abdullah, H., Zaremohzzabieh, Z., Wan Jaafar, W. M., & Abu Samah, A. (2023). Smartphone addiction and phubbing behavior among university students: A moderated mediation model by fear of missing out, social comparison, and loneliness. Frontiers in Psychology, 13 , Article 1072551. https://doi.org/10.3389/fpsyg.2022.1072551 Stavrulaki, E., Li, M., & Gupta, J. (2021). Perceived parenting styles, academic achievement, and life satisfaction of college students: The mediating role of motivation orientation. European Journal of Psychology of Education, 36 (3), 693–717. https://doi.org/10.1007/s10212-020-00493-2 Tu, Z., He, J., Li, Y., Wang, Z., Wang, C., Tian, J., & Tang, Y. (2023). Can restricting while-in-bed smartphone use improve sleep quality via decreasing pre-sleep cognitive arousal among Chinese undergraduates with problematic smartphone use? Longitudinal mediation analysis using parallel process latent growth curve modeling. Addictive Behaviors, 147 , 107825. https://doi.org/10.1016/j.addbeh.2023.107825 Vacharkulksemsuk, T., & Fredrickson, B. L. (2013). Looking back and glimpsing forward: The broaden-and-build theory of positive emotions as applied to organizations. In K. S. Cameron & G. M. Spreitzer (Eds.), Advances in positive organizational psychology (Vol. 1, pp. 45–60). Emerald Group Publishing. https://doi.org/10.1108/S2046-410X(2013)0000001005 Wu, H., Guo, Y., Yang, Y., Zhao, L., & Guo, C. (2021). A meta-analysis of the longitudinal relationship between academic self-concept and academic achievement. Educational Psychology Review, 33 (4), 1291–1319. https://doi.org/10.1007/s10648-021-09600-1 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7190828","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489425635,"identity":"4f54530a-ea53-4c9b-b8ce-299874564ef2","order_by":0,"name":"ESUPAT P. KONE","email":"","orcid":"","institution":"School of Education, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"ESUPAT","middleName":"P.","lastName":"KONE","suffix":""},{"id":489425774,"identity":"285edb39-e5d7-40a6-8fd9-8296ab59d1ba","order_by":1,"name":"JING TIAN","email":"","orcid":"","institution":"School of Education, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"JING","middleName":"","lastName":"TIAN","suffix":""},{"id":489425775,"identity":"27e49b54-526a-45a7-93c9-249953cc7feb","order_by":2,"name":"HONGDE LEI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACPgbGNhDN2MDeAxU6QEALG1wLzxkglUCUFhACaZHIIVYLe3Pbg487amX7Jd8ek/z5g0GO70YC4+cCfFp4DrYbzjxz3Hjm7Lw0aZ4EBmPJGwnM0jPwaZFIbJPmbTuWuOF2jpk00GGJG24ksDHz4NMi/xCiZf/NM2aSPxIY6glrkWAEaalJ3CDBYyYBdFiCAUEtPIltkjPbDhjPOJNjbM2TJgH02MNmaXxa+NmPP5P42FYn299+xvDmDxsbeb7jyQc/49MCBYdhDAkGUBwR1sDAUEeMolEwCkbBKBipAADmzElk+f6DPQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0860-2965","institution":"School of Education, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"HONGDE","middleName":"","lastName":"LEI","suffix":""}],"badges":[],"createdAt":"2025-07-23 00:19:53","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7190828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7190828/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87470942,"identity":"5794eb89-c79d-47be-8f81-48edd6ef730d","added_by":"auto","created_at":"2025-07-24 08:29:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25553,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7190828/v1/8ede5ce5dca6526e798b9d00.png"},{"id":87470770,"identity":"4526f89b-c1c7-41f9-9c91-ab8c40fbc72e","added_by":"auto","created_at":"2025-07-24 08:29:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33235,"visible":true,"origin":"","legend":"\u003cp\u003eModerated Mediation model (standardized coefficients are reported).\u003c/p\u003e\n\u003cp\u003eNote: ***: p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7190828/v1/ebc6e7cdd4653d90a27b570c.png"},{"id":87474729,"identity":"3a3e48fe-4295-4939-9bf6-7286feb1a0e5","added_by":"auto","created_at":"2025-07-24 08:55:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7190828/v1/9993dcc5-d1e6-4b09-9f0d-2834b49acef5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLife Satisfaction and Academic Achievement among Tanzanian College Students: Mediated by Sleep Quality and Moderated by Smartphone Addiction\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcademic achievement represents a critical indicator of student success and a fundamental predictor of future career paths, socioeconomic mobility, and national development (Harpaz et al., 2023). Many scholars have explored the determinants of academic success, focusing primarily on cognitive aspects such as intelligence, memory, and pedagogical techniques. While these factors are undoubtedly important, they do not provide a complete explanation. Psychosocial and behavioral elements also significantly impact students\u0026apos; academic success, and recent scholarship has begun to highlight the role of these factors\u0026mdash;such as life satisfaction, sleep quality, and smartphone usage\u0026mdash;in shaping academic performance (Kone \u0026amp; Hongde, 2024; Stavrulaki et al., 2021a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, there remains considerable debate concerning the influence of these psychosocial and behavioral factors. Some researchers argue that life satisfaction primarily enhances academic outcomes by fostering motivation and emotional resilience (Feraco, Casali, et al., 2023), while others underscore the mediating effects of health-related behaviors, including sleep patterns (Drapeau, 2022a). Concurrently, the issue of smartphone dependency has garnered increasing attention, with some studies indicating its adverse effects on sleep and concentration (Kibona \u0026amp; Mgaya, 2015), and others suggesting that its impact may be contingent upon contextual or personal factors such as coping strategies and self-regulation (Tu et al., 2023). These divergent perspectives highlight the need for integrative models that investigate not only whether but how and when these factors affect academic achievement.\u003c/p\u003e\n\u003cp\u003eTanzanian college students are a vital population for such investigation. As digital technology\u0026nbsp;proliferates\u0026nbsp;rapidly\u0026nbsp;throughout\u0026nbsp;the\u0026nbsp;country\u0026mdash;with smartphone\u0026nbsp;penetration projected to\u0026nbsp;reach\u0026nbsp;25.4 million users by the end of 2024 (Malima, 2025)\u0026mdash;students\u0026nbsp;are\u0026nbsp;increasingly\u0026nbsp;faced with\u0026nbsp;the academic\u0026nbsp;demands\u0026nbsp;of higher education\u0026nbsp;alongside\u0026nbsp;the behavioral challenges\u0026nbsp;posed by excessive\u0026nbsp;digital\u0026nbsp;consumption.\u0026nbsp;Moreover, widespread\u0026nbsp;infrastructural\u0026nbsp;shortcomings\u0026nbsp;and socioeconomic\u0026nbsp;inequalities\u0026nbsp;continue to\u0026nbsp;mold\u0026nbsp;students\u0026apos;\u0026nbsp;educational experiences in ways that\u0026nbsp;markedly\u0026nbsp;differ\u0026nbsp;from those in Western\u0026nbsp;settings\u0026nbsp;(Kibona \u0026amp; Mgaya, 2015; Digital, 2022).\u0026nbsp;Against\u0026nbsp;this\u0026nbsp;backdrop,\u0026nbsp;examining\u0026nbsp;the\u0026nbsp;interrelated\u0026nbsp;effects of life satisfaction, sleep quality, and smartphone\u0026nbsp;addiction\u0026nbsp;can\u0026nbsp;offer valuable\u0026nbsp;insights\u0026nbsp;into\u0026nbsp;culturally\u0026nbsp;sensitive\u0026nbsp;approaches\u0026nbsp;to\u0026nbsp;improving\u0026nbsp;both\u0026nbsp;student\u0026nbsp;well-being and academic\u0026nbsp;outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLife satisfaction and academic achievement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLife satisfaction, which reflects an individual\u0026rsquo;s overall appraisal of life quality and personal fulfilment, is increasingly recognized as a critical factor in students\u0026apos; academic development and well-being (Feraco, Casali, et al., 2023). Research has shown that higher life satisfaction is often associated with greater enthusiasm for learning, more adaptive coping strategies when facing academic pressure, and a more optimistic outlook toward education (Gonz\u0026aacute;lez Moreno et al., 2024). These psychological strengths\u0026mdash;such as sustained focus, goal-setting, and perseverance\u0026mdash;are believed to enhance academic achievement by promoting self-regulation and engagement. In addition, life satisfaction may contribute indirectly by fostering a supportive social environment, improving peer and teacher relationships, and facilitating emotionally grounded collaborative learning (Feraco, Resnati, et al., 2023).\u003c/p\u003e\n\u003cp\u003eHowever, the mechanisms linking life satisfaction to academic achievement remain subject to debate. Some scholars argue that the relationship is primarily motivational, while others suggest that it is conditional upon contextual or behavioral mediators such as sleep, stress, or digital distraction (Stavrulaki et al., 2021a). Students with lower life satisfaction often face diminished motivation, reduced confidence, and weaker emotional resilience\u0026mdash;factors that are central to academic underperformance. Moreover, elevated stress levels and negative affective states may erode students\u0026rsquo; ability to sustain effort or recover from academic setbacks. The Broaden-and-Build Theory of Positive Emotions (Vacharkulksemsuk \u0026amp; Fredrickson, 2013) provides a useful lens for understanding this dynamic: it suggests that positive emotional states expand individuals\u0026rsquo; cognitive and behavioral repertoires, facilitating the accumulation of personal and academic resources.\u003c/p\u003e\n\u003cp\u003eNevertheless, empirical evidence remains mixed regarding the direct versus indirect pathways through which life satisfaction influences academic\u0026nbsp;achievement, particularly in non-Western or resource-constrained contexts (Gonz\u0026aacute;lez Moreno et al., 2024). This raises an important empirical question: to what extent is life satisfaction a reliable predictor of academic success in settings where external challenges may override internal psychological strengths?\u0026nbsp;Based on the theoretical and empirical considerations above, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e: Life satisfaction is positively associated with academic achievement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe mediating role of sleep quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleep quality is widely acknowledged as a key determinant of students\u0026rsquo; cognitive functioning, emotional regulation, and academic performance (Drapeau, 2022a; Astridge et al., 2021). Sufficient and restorative sleep supports concentration, memory consolidation, and executive functioning\u0026mdash;all of which are essential for learning. Students with higher life satisfaction typically report lower stress levels and better psychological well-being, which in turn facilitate healthier sleep patterns (Aldabbour et al., 2025). Conversely, dissatisfaction with life often manifests as anxiety and mental fatigue, which\u0026nbsp;will affect\u0026nbsp;sleep\u0026nbsp;quality\u0026nbsp;and result in poor academic outcomes (Orihuela et al., 2023). These patterns suggest that sleep quality may act as a bridge linking psychological well-being to academic achievement.\u003c/p\u003e\n\u003cp\u003eDespite growing empirical support for the above\u0026nbsp;view, the mediating role of sleep quality is not universally accepted. Some studies conceptualize sleep disturbances as outcomes of external pressures such as academic overload or digital overexposure, rather than internal states like life satisfaction (Evers et al., 2020a). Others point to bidirectional or even reciprocal relationships, where poor sleep not only results from psychological distress but also amplifies it, complicating causal interpretations. In this regard, the Restoration Theory of Sleep (Brinkman et al., 2025) offers a useful explanatory framework. It posits that sleep serves essential restorative functions, including the rebalancing of neurocognitive systems and the consolidation of emotional learning. From this perspective, adequate sleep is not merely a correlate of academic success but a functional mechanism through which psychological resources\u0026mdash;such as those derived from life satisfaction\u0026mdash;are translated into improved performance.\u003c/p\u003e\n\u003cp\u003eIn high-stress or resource-constrained environments, where external supports are limited, the quality of sleep may become an especially salient mediator. Yet the strength and consistency of this mediating role remain open questions, particularly in developing countries\u0026nbsp;like Tanzania, where sleep hygiene may be compromised by environmental, economic, or behavioral factors (Gao et al., 2023). Clarifying the role of sleep in this pathway is therefore crucial for designing interventions that not only address academic performance directly, but also enhance upstream psychological and behavioral processes.\u0026nbsp;Accordingly, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Sleep quality mediates the relationship between life satisfaction and academic achievement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe moderating role of smartphone addiction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs smartphones become increasingly integrated into students\u0026rsquo; academic and social lives, concerns have grown regarding their excessive use and the potential consequences for psychological well-being and educational outcomes (Tu et al., 2023). Smartphone addiction, characterized by compulsive usage patterns and impaired self-regulation, has been linked to disrupted sleep, reduced academic focus, and heightened psychological distress (Safdar Bajwa et al., 2023). However, its role is likely more complex than a simple direct predictor of poor outcomes; rather, it may function as a moderating factor that alters the strength or direction of other established relationships, such as those between life satisfaction, sleep quality, and academic achievement.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;current\u0026nbsp;literature presents diverging perspectives on the\u0026nbsp;issue\u0026nbsp;mentioned above. On one hand, some studies suggest that smartphone addiction universally undermines psychological and behavioral benefits by reducing available cognitive and emotional resources (Feraco, Resnati, et al., 2023; Larsen et al., 2023a). Excessive smartphone use, particularly before bedtime, has been shown to disrupt circadian rhythms and reduce total sleep duration, which in turn impairs learning and memory functions (Gao et al., 2023). From this view, even students with high life satisfaction may fail to benefit from better sleep quality or improved academic\u0026nbsp;achievement\u0026nbsp;if smartphone addiction interferes with their capacity to regulate digital behavior.\u003c/p\u003e\n\u003cp\u003eOn the other hand, emerging research offers a more nuanced view, suggesting that the negative impact of smartphone addiction may depend on individuals\u0026rsquo; psychological resources. According to Self-Regulation Theory (Hitcham et al., 2023a; Baumeister \u0026amp; Vohs, 2007), individuals with higher life satisfaction may develop stronger self-regulatory mechanisms, enabling them to maintain healthy sleep patterns or mitigate distraction despite high levels of smartphone use. In this sense, smartphone addiction may not uniformly weaken all beneficial pathways but may instead moderate them in context-specific ways\u0026mdash;either amplifying or diminishing effects depending on the interplay of emotional, behavioral, and technological factors.\u003c/p\u003e\n\u003cp\u003eThis theoretical divergence points to the need for empirical models that test not only whether smartphone addiction affects academic\u0026nbsp;achievement, but more importantly, when and how it alters the relationships among life satisfaction, sleep quality, and academic achievement. In high-usage contexts like Tanzania\u0026mdash;where smartphone penetration is rapidly increasing (Malima, 2025), but digital literacy and boundary-setting may lag\u0026mdash;the moderating role of smartphone addiction warrants focused investigation. Understanding this role is essential for developing interventions that go beyond screen-time limits to include emotional and behavioral regulation strategies. Based on this conceptual rationale, we propose the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3a\u003c/strong\u003e: Smartphone addiction moderates the relationship between life satisfaction and academic achievement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3b\u003c/strong\u003e: Smartphone addiction moderates the relationship between life satisfaction and sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3c\u003c/strong\u003e: Smartphone addiction moderates the relationship between sleep quality and academic achievement.\u003c/p\u003e\n\u003cp\u003eIn summary, drawing on the Broaden-and-Build Theory of Positive Emotions, the Restoration Theory of Sleep, and Self-Regulation Theory, this study develops an integrative conceptual model (see Fig. 1) to examine both the mechanisms (\u0026ldquo;how\u0026rdquo;) and conditions (\u0026ldquo;when\u0026rdquo;) under which life satisfaction influences academic achievement among college students. Specifically, the model is designed to address three core objectives: (1) to examine the nature and strength of the relationship between life satisfaction and academic achievement; (2) to assess whether sleep quality mediates the relationship between life satisfaction and academic achievement; (3) to investigate whether smartphone addiction moderates the direct and indirect pathways linking life satisfaction, sleep quality, and academic achievement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected between July and September 2024 in Arusha, Tanzania, from four colleges affiliated with the National Council for Technical and Vocational Education and Training. With institutional approval, questionnaires\u0026nbsp;reviewed and validated by experts\u0026nbsp;were administered to assess students\u0026rsquo; life satisfaction, sleep quality, smartphone addiction, and academic achievement. All items were rated on 5-point Likert scales. Participation in the study was voluntary, anonymous, and based on informed consent. Of the total responses received, 63 incomplete questionnaires were excluded, resulting in a final sample of 513 valid cases (249 males and 264 females), yielding a response rate of 89.06%. The demographic characteristics of the participants are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLife satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLife satisfaction was assessed using a five-item scale developed specifically for this study to capture students\u0026rsquo; overall sense of well-being. The scale covered key domains including personal experiences, family relationships, and social interactions, reflecting areas that may influence both sleep quality and academic performance. Each item was rated on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;), yielding total scores between 5 and 25. Higher scores indicated higher levels of life satisfaction. Among the study sample, the scale demonstrated good internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.798, supporting its reliability and appropriateness for use in this context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics of college students (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 513)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e51.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e48.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAge Groups\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e15 \u0026ndash; 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e91.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e26 \u0026ndash; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e36 \u0026ndash; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eYears of Study (Education)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFirst Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e55.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSecond Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eThird Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eOccupation (Working)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eNot Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e88.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e11.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eEducation Sponsorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eGovernment Sponsored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003ePrivate Sponsored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e74.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSleep quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleep quality was measured using a six-item scale specifically developed for this study to comprehensively assess students\u0026rsquo; sleep-related experiences and their relevance to academic performance. The items captured perceptions of sleep sufficiency, daytime\u0026nbsp;sleepiness, and beliefs regarding the role of sleep in supporting overall well-being and academic productivity. Each item was rated on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;), producing total scores between 6 and 30. Higher scores indicated better sleep quality. The scale exhibited strong internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.833, confirming its reliability for assessing sleep quality among college students in this context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmartphone addiction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmartphone addiction was measured using an eight-item scale developed for this study, drawing on adapted elements from the Smartphone Addiction Scale (Hamamura et al., 2023) to capture a comprehensive profile of problematic smartphone use and its effects on academic and personal well-being. The items assessed excessive usage patterns, difficulties in self-regulation, and the perceived impact of smartphone habits on productivity and sleep quality. Each item was rated on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;), yielding total scores between 8 and 40. Higher scores indicated greater levels of smartphone addiction. The scale demonstrated excellent internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.916, confirming its reliability and robustness for measuring smartphone-related behavioral risks among college students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcademic achievement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcademic achievement was assessed using a self-reported measure that included both last semester and cumulative GPA, rated on a five-point scale (1 = lowest, 5 = highest). In addition to GPA, students provided a subjective evaluation of their recent academic performance, allowing for a broader perspective on their perceived academic standing. This dual approach enabled the capture of both objective and perceived dimensions of academic success. The scale demonstrated good internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.837, supporting its reliability for assessing academic achievement within the study sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential covariates included students\u0026rsquo; self-reported age group (1 = 15\u0026ndash;25, 2 = 26\u0026ndash;35, 3 = 36\u0026ndash;45), sponsorship status (1 = Government Sponsored, 2 = Privately Sponsored), and occupational status (1 = No, 2 = Yes). These variables were included as control variables in all statistical models to adjust for their potential confounding effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with Hayes\u0026rsquo; recommendations, all continuous variables were standardized prior to conducting the moderated mediation analyses (Hayes,2013). Statistical analyses were performed using the PROCESS Macro (version 3.5) for SPSS. To examine the mediating effect of sleep quality on the relationship between life satisfaction and academic achievement, Model 4 of the PROCESS Macro was applied. Subsequently, Model 59 was used to test a moderated mediation model, assessing the conditional effects of smartphone addiction on both the indirect path from life satisfaction to academic achievement via sleep quality and the direct path from life satisfaction to academic achievement. All mediation and moderation effects were tested using nonparametric bootstrapping with 5,000 resamples, generating bias-corrected 95% confidence intervals.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eCommon method bias\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo minimize potential common method bias arising from the use of self-report measures, several procedural controls were implemented. These included the use of anonymous responses, the separation of measurement for different constructs, and the inclusion of reverse-coded items where appropriate. In addition, Harman\u0026rsquo;s single-factor test was conducted to statistically assess the extent of common method variance (Kock, 2021). Results showed that the first unrotated factor accounted for 35.5% of the total variance\u0026mdash;well below the commonly accepted threshold of 40%. This suggests that common method bias was not a serious concern in this study.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCorrelation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTable 2 presents the bivariate correlations among all key study variables. Academic achievement was positively correlated with life satisfaction (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.578, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and sleep quality (\u003cem\u003er\u003c/em\u003e = 0.532, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and negatively correlated with smartphone addiction (\u003cem\u003er\u003c/em\u003e = -0.657, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Life satisfaction showed a strong positive correlation with sleep quality (\u003cem\u003er\u003c/em\u003e = 0.638, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and a significant negative correlation with smartphone addiction (\u003cem\u003er\u003c/em\u003e = -0.592, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Similarly, sleep quality was negatively associated with smartphone addiction (\u003cem\u003er\u003c/em\u003e = -0.478, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). These results indicate that higher life satisfaction and better sleep quality are linked to better academic outcomes, while higher levels of smartphone addiction are associated with poorer academic performance and reduced well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Correlation analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"92%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eM\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e1. Academic Achievement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e2. Life Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.578\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e3. Sleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.532\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.638\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4. Smartphone Addiction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.657\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.592\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.478\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003csup\u003e\u0026nbsp;***\u003c/sup\u003eCorrelation is significant at the 0.001 level (two-tailed).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMediation analys\u003c/strong\u003e\u003cstrong\u003eis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTable 3 reveals that life satisfaction is significantly positively associated with both academic achievement in Model 1 (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.494, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and sleep quality in Model 2 (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.613, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In Model 3, life satisfaction (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.341, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and sleep quality (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.248, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) both retain significant positive associations with academic achievement. The findings show that the 95% confidence intervals for both the direct effect of life satisfaction on academic achievement and the indirect effect through sleep quality exclude zero. This suggests that life satisfaction not only directly predicts academic achievement but also has an indirect effect through sleep quality. Specifically, the direct effect (0.341) and the indirect effect (0.152) account for 69.23% and 30.77% of the total effect (0.494), respectively.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eLinear regression analysis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"687\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1(AA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2(SQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3(AA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.655\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e[0.423,0.565]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16.975\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[0.542,0.684]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7.781\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[0.255,0.428]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.764\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[0.164,0.333]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.154\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e[0.019,0.402]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[-0.291,0.092]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.480\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[0.049,0.421]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSponsorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-7.814\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e[-0.812,-0.486]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-2.250\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[-0.350,-0.024]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-7.446\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[-0.762,-0.444]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e[-0.284,0.284]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[-0.070,0.498]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e[-0.329,0.223]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e\u0026sup2;\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e89.492\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e89.990\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e82.779\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003eAll continuous variables were standardized to z-scores and entered into PROCESS Macro Model 4, standardized coefficients are reported; \u003csup\u003e*\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05,\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Age, sponsorship, and occupation were analyzed as control variables in Models 1 to 3; LS: Life Satisfaction, SQ: Sleep Quality, AA: Academic Achievement.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eModerated mediation analys\u003c/strong\u003e\u003cstrong\u003eis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTable 4 and Fig. 2 reveal a statistically significant interaction between life satisfaction and smartphone addiction in predicting sleep quality (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.102, p \u0026lt; 0.05, 95% CI [0.015, 0.190]), indicating that smartphone addiction moderates this relationship. Additionally, a significant interaction between sleep quality and smartphone addiction on academic achievement was found (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.115, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, 95% CI [-0.209, -0.022]), suggesting that smartphone addiction also moderates this association. However, no significant interaction was observed between life satisfaction and smartphone addiction on academic achievement (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.080, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, 95% CI [-0.010, 0.170]).\u003c/p\u003e\n\u003cp\u003eIn order to reveal how smartphone addiction moderates the relationship of \u0026ldquo;life satisfaction\u0026rarr;sleep quality\u0026rarr;academic achievement\u0026rdquo;, high and low groups (plus or minus one standard deviation) were grouped based on the value of smartphone addiction, and a simple slope test was conducted. The results showed that, in the relationship between life satisfaction and sleep quality, smartphone addiction had a significant positive moderating effect. As smartphone addiction increased, the association between life satisfaction and sleep quality strengthened: the effect was 0.437 (95% CI [0.316, 0.558]) at low smartphone addiction (M-1SD), 0.539 (95% CI [0.457, 0.622]) at moderate smartphone addiction (M), and 0.642 (95% CI [0.522, 0.761]) at high smartphone addiction (M+1SD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e Moderated mediation analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1(SQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2(AA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12.864\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[0.457, 0.622]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.842\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[0.082, 0.252]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[-0.269, -0.087]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-8.799\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.467,-0.296]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5.275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[0.140, 0.307]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eLS\u0026times;SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.299\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[0.015, 0.190]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.010, 0.170]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSQ\u0026times;SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.417\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.209, -0.022]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[-0.306,0.074]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.023,0.318]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSponsorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[-0.272,0.063]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-5.351\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.560,-0.259]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e[-0.130,0.433]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e[-0.384,0.123]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 241px;\"\u003e\n \u003cp\u003e64.300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e75.724\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: All continuous variables were standardized to z-scores and entered into PROCESS Macro Model 59, standardized coefficients are reported; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e:\u003cem\u003e\u0026nbsp;p\u0026nbsp;\u003c/em\u003e\u0026lt; 0.00; Age, sponsorship, occupation were analyzed as control variables; LS: Life Satisfaction, SQ: Sleep Quality, AA: Academic Achievement, SA: Smartphone Addiction.\u003c/p\u003e\n\u003cp\u003eIn contrast, smartphone addiction exhibited a significant negative moderating effect on the relationship between sleep quality and academic achievement. As smartphone addiction severity increased, the effect of sleep quality on academic achievement diminished: from 0.339 (95% CI [0.194, 0.485]) at low smartphone addiction (M-1SD), to 0.224 (95% CI [0.140, 0.307]) at moderate smartphone addiction (M), and to 0.108 (95% CI [0.007, 0.210]) at high smartphone addiction (M+1SD). These results suggest that while smartphone addiction strengthens the positive effect of life satisfaction on sleep quality, it simultaneously weakens the beneficial impact of sleep quality on academic achievement.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the relationships between life satisfaction, sleep quality, and academic achievement, with a focus on the moderating role of smartphone addiction among Tanzanian college students. Drawing on the Broaden-and-Build Theory of Positive Emotions (Vacharkulksemsuk \u0026amp; Fredrickson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the Restoration Theory of Sleep (Adam, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), and Self-Regulation Theory (Feraco, Casali, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the findings contribute to a growing understanding of how psychosocial well-being and technology use interact to influence educational outcomes in resource-limited, digitally evolving settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLife satisfaction and academic achievement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study supports H1 by confirming the critical role of life satisfaction in shaping academic achievement among Tanzanian college students, a result consistent with previous research (Gonz\u0026aacute;lez Moreno et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Harpaz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This relationship can be further understood through the Broaden-and-Build Theory of Positive Emotions (Fredrickson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which suggests that positive emotions\u0026mdash;such as those associated with high life satisfaction\u0026mdash;expand individuals\u0026rsquo; cognitive and behavioral repertoires, while also cultivating lasting personal resources. Specifically, this theory argues that increased cognitive flexibility, psychological resilience, and intrinsic motivation enable students to engage more deeply with academic material, persist through challenges, and maintain goal-directed behaviors (Fredrickson \u0026amp; Branigan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The strong positive correlation between life satisfaction and academic achievement indicates that students who view their lives as fulfilling are better positioned to leverage these psychological resources, thus improving their academic outcomes.\u003c/p\u003e\u003cp\u003eIn the context of Tanzania, where students often face socioeconomic and structural challenges, life satisfaction may act as a compensatory mechanism that buffers against these adversities. According to the Broaden-and-Build Theory, positive emotions not only counteract negative states but also foster upward spirals of well-being and performance (Fredrickson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For Tanzanian students, maintaining high life satisfaction may help mitigate the stress caused by financial constraints or limited academic resources, allowing them to stay engaged and motivated in their studies. Additionally, the theory suggests that the cognitive broadening effect of positive emotions enhances creativity and problem-solving abilities, both of which are crucial for navigating the complexities of higher education (Fredrickson \u0026amp; Branigan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Therefore, the observed link between life satisfaction and academic achievement may reflect the cumulative benefits of these expanded cognitive and emotional resources.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe mediating role of sleep quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe current findings support H2 by demonstrating a significant mediating effect of sleep quality in the relationship between life satisfaction and academic achievement, a result consistent with the Restoration Theory of Sleep (Adam, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). This theoretical framework explains how restorative sleep acts as a neurobiological mechanism that consolidates the cognitive and emotional benefits of life satisfaction, ultimately translating them into academic success. The mediation pathway identified in this study aligns with emerging neuroscientific evidence showing that sleep plays a vital role in synaptic pruning and memory consolidation\u0026mdash;key processes for integrating learned material (Brinkman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When students experience higher life satisfaction, reduced stress and improved emotional regulation likely lead to better sleep quality, enhancing the restorative functions that are crucial for academic performance.\u003c/p\u003e\u003cp\u003eMoreover, the mediating role of sleep quality resonates with cross-cultural studies highlighting its role in connecting psychological well-being and cognitive outcomes (Drapeau, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003eb). The Restoration Theory posits that sleep acts as an equalizer, buffering the cognitive deficits caused by daytime stressors\u0026mdash;an idea particularly relevant in the Tanzanian context, where students face significant socioeconomic pressures. This study extends prior research by showing that life satisfaction\u0026rsquo;s protective effects against academic underachievement are partially channeled through improved sleep efficiency. This suggests that interventions aimed at improving sleep hygiene could further enhance the academic benefits of psychological well-being programs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe moderating role of smartphone addiction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings show that the interaction between life satisfaction and smartphone addiction did not significantly predict academic achievement; thus, H3a was not supported. This null finding suggests that, in the Tanzanian context, the direct association between life satisfaction and academic achievement is not contingent on students\u0026rsquo; level of smartphone addiction. Instead, smartphone addiction exerts its influence primarily by moderating the indirect pathway via sleep quality, rather than by altering the direct link.\u003c/p\u003e\u003cp\u003eIn the relationship between life satisfaction and sleep quality, smartphone addiction demonstrated an unexpected enhancing effect, supporting H3b. This aligns with the compensatory regulation hypothesis of Self-Regulation Theory, which suggests that individuals with higher life satisfaction may develop adaptive coping strategies to counteract technology-related sleep disruptions (Hitcham et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb; Larsen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb). The positive psychological resources associated with life satisfaction\u0026mdash;such as emotional stability and future-oriented thinking\u0026mdash;may help students compartmentalize smartphone use, allowing them to reserve pre-sleep periods for relaxation despite overall high usage levels (Feraco, Casali, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding challenges conventional assumptions about the universal negative impacts of smartphone overuse, instead highlighting the potential protective role of psychological well-being in digital environments.\u003c/p\u003e\u003cp\u003eConversely, smartphone addiction significantly weakened the beneficial relationship between sleep quality and academic achievement, supporting H3c. This aligns with the cognitive depletion component of Self-Regulation Theory, which posits that excessive smartphone use depletes the mental resources necessary for translating restorative sleep into academic success. The attentional fragmentation caused by compulsive smartphone checking creates cognitive \"leakage\" that persists even with adequate sleep duration, undermining the memory consolidation and emotional regulation typically supported by high-quality sleep (Tu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This effect was especially pronounced at high levels of addiction, suggesting a threshold beyond which even sufficient sleep cannot mitigate the cognitive impairments induced by technology overuse (Larsen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb; Mafla et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe differential moderating effects observed across the above pathways highlight the multidimensional nature of smartphone addiction\u0026rsquo;s impact. While psychological resources may buffer its disruptive effects on sleep, its cognitive consequences seem more intractable once sleep processes are engaged. This duality aligns with recent developments in digital well-being research, which recognize that technology can both enhance and disrupt self-regulatory processes depending on the context (Larsen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplication and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese findings discussed above have important implications for intervention design. Rather than advocating for blanket reductions in screen time, targeted strategies should focus on: (1) strengthening psychological resources to build resilience against sleep disruption, and (2) developing specific cognitive protection strategies to preserve sleep\u0026rsquo;s academic benefits. The Self-Regulation Theory framework suggests that interventions combining mindfulness training with personalized digital boundary-setting may be particularly effective in addressing both aspects of this complex issue. Future research should explore whether these moderating patterns hold across different cultural contexts and educational stages, especially in regions undergoing rapid digital transformation, such as Tanzania.\u003c/p\u003e\u003cp\u003eDespite the valuable insights provided by this study, there are some limitations. The cross-sectional design, reliance on self-reported data, and geographic focus on Tanzania restrict the broader generalizability of our findings. Future research should adopt longitudinal methodologies to explore additional factors, such as stress and digital access, and expand to diverse African educational settings. This will require the development of culturally adapted strategies and integrated well-being programs that address student wellness alongside technology use, while advocating for balanced policies that mitigate smartphone-related risks without compromising learning outcomes. Ultimately, context-sensitive approaches are essential for further research and intervention in these complex environments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study contributes to our understanding of how psychological well-being, sleep patterns, and digital behaviors influence academic achievement among college students in developing countries like Tanzania. The findings suggest that life satisfaction enhances academic achievement both directly and indirectly by improving sleep quality, thus supporting the Broaden-and-Build Theory. Sleep quality acts as a key mediator, reinforcing the Restoration Theory of Sleep, particularly in high-stress environments. Notably, while smartphone addiction negatively affects sleep and academic achievement, it also moderates the relationships between these factors: it weakens the positive effects of sleep on academic achievement, yet strengthens the connection between life satisfaction and sleep quality at high levels of usage. These findings, framed by Self-Regulation Theory, suggest that digital overuse may not fully undermine psychological strengths if self-regulation is preserved. The study advocates for integrative, context-sensitive interventions in higher education that prioritize student well-being, promote sleep hygiene, and encourage responsible technology use. Future research should explore longitudinal and cross-cultural designs to further validate these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e We appreciate the support from the sample schools, especially the students who accepted the questionnaire survey. We also want to thank *** University for providing funding support for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e EK, JT, HDL had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of data analysis. Concept and design: EK, HDL; Acquisition, analysis of data: EK, JT, HDL; Drafting of manuscript: EK; Revision of manuscript: EK, JT, HDL; Supervisor: HDL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Think Tank Project (2025ZKJD07) funded by *** University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study adhered to the Declaration of Helsinki and was approved by the Psychological Ethics Committee of *** University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e All the participants voluntarily and anonymously participated in the questionnaire survey, and informed consent to participate was obtained from all the participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e There is a data set associated with this submission. The data set is deposited in https://www.scidb.cn/datalist, and its DOI is 10.57760/sciencedb.28236.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdam, K. (1980). Sleep as a restorative process and a theory to explain why. In P. S. McConnell, G. J. Boer, H. J. Romijn, N. E. Van de Poll, \u0026amp; M. A. Corner (Eds.), \u003cem\u003eProgress in brain research\u003c/em\u003e (Vol. 53, pp. 289\u0026ndash;305). Elsevier. https://doi.org/10.1016/S0079-6123(08)60070-9\u003c/li\u003e\n\u003cli\u003eAldabbour, B., Jaradat, R., Aljbour, O., Abdo, M., Aljbour, J., Dayya, A. A., Dabbour Asad, M., \u0026amp; Abuabada, A. (2025). 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A meta-analysis of the longitudinal relationship between academic self-concept and academic achievement. \u003cem\u003eEducational Psychology Review, 33\u003c/em\u003e(4), 1291\u0026ndash;1319. https://doi.org/10.1007/s10648-021-09600-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"e6ccc747-afec-4366-9051-9d22e2b1fba7","identifier":"10.13039/501100003397","name":"Huazhong University of Science and Technology","awardNumber":"2025ZKJD07","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"School of Education, Huazhong University of Science and Technology","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":"Academic achievement, Life satisfaction, Sleep quality, Smartphone addiction, College Students","lastPublishedDoi":"10.21203/rs.3.rs-7190828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7190828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcademic achievement is vital for socioeconomic advancement, particularly in developing countries like Tanzania. While cognitive factors of academic achievement have been widely studied, psychosocial and behavioral influences such as life satisfaction, sleep quality, and smartphone addiction remain underexplored. To address this research gap, this study examines how life satisfaction affects academic achievement through sleep quality, with smartphone addiction as a moderator. Data were collected from 513 Tanzanian college students and analyzed using SPSS 27 and PROCESS Macro Models 4 and 59, controlling for age, sponsorship, and occupation. Research results showed that life satisfaction was positively correlated with academic achievement (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.578) and sleep quality (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.638), while smartphone addiction showed negative correlations with these three variables. Sleep quality partially mediated the link between life satisfaction and academic achievement, accounting for 30.77% of the total effect. Moderated mediation analysis revealed that smartphone addiction strengthened the positive relationship between life satisfaction and sleep quality (effect sizes: 0.437 to 0.642, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but weakened the link between sleep quality and academic achievement (effect sizes: 0.339 to 0.108, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings highlight the dual moderating role of smartphone addiction and underscore the importance of promoting psychological well-being and responsible technology use in academic contexts.\u003c/p\u003e","manuscriptTitle":"Life Satisfaction and Academic Achievement among Tanzanian College Students: Mediated by Sleep Quality and Moderated by Smartphone Addiction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 08:04:03","doi":"10.21203/rs.3.rs-7190828/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":"e7465b1e-7b84-4a95-90aa-00096581cf2d","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T08:04:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 08:04:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7190828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7190828","identity":"rs-7190828","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}