The Effect of Mindfulness on Smartphone Addiction: The Mediating Role of Self-regulation Learning and the Moderating Role of Digital Detox

The Effect of Mindfulness on Smartphone Addiction: The Mediating Role of Self-regulation Learning and the Moderating Role of Digital Detox | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Mindfulness on Smartphone Addiction: The Mediating Role of Self-regulation Learning and the Moderating Role of Digital Detox Aamer Aldbyani, Zhang Chuanxia, Afnan Alhimaidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6405697/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2025 Read the published version in BMC Psychology → Version 1 posted 12 You are reading this latest preprint version Abstract Mindfulness has been extensively studied for its role in reducing addictive behaviors, including smartphone addiction. However, the underlying mechanisms involving self-regulation learning and digital detox in this association remain unclear. This study aimed to investigate the mediating role of self-regulation learning and the moderating role of digital detox in the relationship between mindfulness and smartphone addiction among 1,241 Chinese college students from Shandong Xiehe University. Participants completed the Mindful Attention Awareness Scale (MAAS), the Smartphone Addiction Scale (SAS), the Self-Regulation of Learning Self-Report Scale (SRL-SRS), and the Digital Detoxification Scale (DDS). The results found that mindfulness was negatively associated with smartphone addiction, and self-regulation learning partially played a mediating role in this association. Further analysis revealed that digital detox moderated the relationship between self-regulation learning and smartphone addiction. These results suggest a complex interplay where mindfulness reduces smartphone addiction through improved self-regulation learning, with digital detox further enhancing this effect. This study provides valuable insights into the mechanisms underlying the association between mindfulness and smartphone addiction, emphasizing the importance of promoting self-regulation learning and digital detox strategies. Mindfulness Smartphone Addiction Self-regulation Learning Digital Detox Figures Figure 1 Figure 2 Figure 3 Introduction Smartphones have become integral to university students’ daily routines, providing a wide range of academic and social functionalities (1). However, excessive smartphone use—often labeled “smartphone addiction”—raises significant concerns, as it disrupts academic performance, social interactions, and overall well-being (2,3). Mindfulness, characterized by a present-moment focus maintained without judgment, has emerged as a promising strategy to reduce compulsive smartphone use (4,5). By fostering self-awareness and emotional regulation, mindfulness-based interventions have been shown to mitigate detrimental patterns of technology engagement (6). In addition, self-regulation learning—entailing proactive management of one’s cognitive, emotional, and behavioral processes—appears critical in the relationship between mindfulness and smartphone addiction (7,8). Poor self-regulation has been linked to heightened addictive behavior, whereas mindfulness training can fortify self-regulatory capacities (9,10). Furthermore, digital detox, defined as a deliberate pause from digital device use, may buffer against excessive smartphone reliance by lessening distractions and reinforcing self-regulation efforts (11,12). The present study investigates whether self-regulation learning mediates the link between mindfulness and smartphone addiction and whether digital detox moderates this mediated pathway, aiming to provide a nuanced framework for addressing problematic smartphone use among university students. Mindfulness and Smartphone Addiction The use of smartphones has become integral to university students' daily lives, serving multiple purposes such as accessing the internet, engaging with educational resources, communicating with peers, and enjoying various forms of entertainment, including games and social media. Smartphones also facilitate academic learning by providing access to online libraries, research databases, and collaboration tools, thereby enhancing students’ learning experiences (1). However, the pervasive use of smartphones among students has raised concerns about their negative impacts, particularly when usage becomes excessive or addictive (2). Smartphone addiction, often classified as a non-chemical behavioral addiction, is characterized by compulsive usage patterns that interfere with daily life, social interactions, and academic performance (3). Recent studies indicate that university students are among the most affected demographic groups, as smartphones have become essential for both social engagement and academic activities (13,14). While smartphones offer various benefits, such as promoting social connectivity and providing learning resources, excessive use can lead to adverse psychological, social, and physical outcomes. Mindfulness, which is defined as the practice of maintaining present-moment awareness without judgment, serves as a powerful element to decrease addictive behaviors. These practices empower individuals to cultivate a more intentional and balanced relationship with technology. Mindfulness meditation, in particular, has emerged as an effective method for reducing reactivity to digital distractions, promoting mental clarity, and enhancing overall well-being (15). Techniques such as mindful breathing and body scan meditation help individuals anchor themselves in the present moment, fostering relaxation and a deepened sense of embodiment (16). Several studies emphasize the effect of mindfulness on smartphone addiction. Research has demonstrated that mindfulness negatively predicts smartphone addiction (5,17) and reduces smartphone addiction (4,18). Experimental evidence also supports the effectiveness of mindfulness interventions on smartphone addiction. For instance, a recent study investigated the effect of a brief online mindfulness-based intervention on mobile phone addiction (6). The findings revealed that the intervention effectively reduced mobile phone addiction. These results align with previous studies demonstrating that high mindfulness plays a crucial role in reducing smartphone addiction (19–21). Therefore, based on previous research consistently supporting the theory suggesting that higher levels of mindfulness are associated with lower levels of smartphone addiction, we propose the following hypothesis: H1: Mindfulness negatively correlates with smartphone addiction. Self-regulation Learning as a mediator Recent research has shown that mindfulness is negatively associated with smartphone addiction among university students. This means that higher levels of mindfulness correspond with lower levels of smartphone addiction. A key factor that might explain this relationship is self-regulation learning. Self-regulation learning refers to a student's ability to intentionally manage their thoughts, emotions, and behaviors to achieve educational goals. It involves strategies such as planning, observing one's actions, monitoring progress, and evaluating outcomes. Studies have demonstrated that mindfulness training significantly improves self-regulation learning skills. For example, mindfulness practices help students enhance their self-control, which is an essential part of self-regulation learning (7,8,22). Furthermore, mindfulness training positively impacts students' ability to regulate their behaviors and maintain a sense of control over their academic activities (10). On the other hand, poor self-regulation learning has been linked to increased smartphone addiction (9,23,24). Therefore, enhancing mindfulness could strengthen self-regulation learning skills, subsequently reducing problematic smartphone usage among university students, so, we propose the following hypothesis: H2: Self-regulation Learning may mediate the relationship between mindfulness and smartphone addiction. Digital Detox as a moderator Digital detox refers to the deliberate practice of abstaining temporarily from the use of digital devices such as smartphones, computers, and social media platforms, aiming to improve mental health, increase productivity, and enhance concentration (11,12,25). This intentional disengagement provides individuals with the opportunity to recover from overstimulation and information overload, conditions increasingly prevalent in the contemporary technological environment. Stepping away from digital devices has been associated with reductions in stress and anxiety, thereby offering the mind a restorative break(25). Furthermore, minimizing digital distractions enables individuals to regain cognitive control, thereby enhancing their ability to concentrate on meaningful and goal-oriented tasks. In educational contexts, digital detox acts as a moderating variable in the relationship between self-regulated learning and smartphone addiction. When individuals engage in digital detoxification, they experience decreased distractions resulting from excessive smartphone usage, allowing them to invest more effectively in self-regulated learning practices and thus achieve educational goals with greater efficiency. Recent research supports the notion that digital detox practices moderate the influence of self-regulated learning on smartphone addiction. Digital detox practices strengthen students' self-control, subsequently reducing excessive reliance on smartphones (26). Similarly, students regularly employing digital detox strategies demonstrated higher levels of self-regulated learning and lower tendencies toward smartphone addiction (27). Therefore, we propose the following hypothesis: H3: Digital Detox may moderate the relationship between self-regulation Learning and smartphone addiction. Current study Although it is plausible to hypothesize that self-regulation learning may mediate the effect of mindfulness on reducing addictive behaviors, this mediation is less evident when it comes to smartphone addiction—a behavior that is deeply integrated into the daily lives of university students. Therefore, the primary aim of this study is to investigate whether self-regulation learning mediates the relationship between mindfulness and smartphone addiction. Additionally, the study examines whether digital detox moderates the association between self-regulation learning and smartphone addiction. The proposed study model is presented in Figure 1. Method A cross-sectional study was conducted to investigate the relationship between mindfulness and smartphone addiction, while also exploring the potential mediating role of self-regulation and the moderating role of digital detox in these associations. Participants A total of 1,241 college students from Shandong Xiehe University, China, voluntarily participated in this study. Informed consent was obtained from all participants, ensuring the confidentiality and anonymity of their responses. The participants’ ages ranged from 18 to 22 years (M age = 21.12, SD = 2.86). None of the participants had prior experience with mindfulness training or related practices such as digital detox strategies or yoga. This study followed the ethical standards set by the Academic Committee of Shandong Xiehe University, the principles of the Declaration of Helsinki (1964), and comparable guidelines governing research involving human subjects. Detailed demographic characteristics of the participants are presented in Table 1. Table 1. Demographic characteristics of the participants Variable Frequency Percent% Gender Male 499 40. 2% Female 742 59.8% Family Income 3000-10000 RMB 741 59.71% 10000-20000 RMB 314 25.30% 20000-above RMB 186 14.99% Total 1241 100% RMB= The Chinese currency Measurements Mindfulness . The Chinese version of the Mindful Attention Awareness Scale (MAAS), consisting of 15 items (28), has been validated and demonstrated as a reliable instrument within Chinese contexts (29). Each item is rated on a 6-point Likert scale, ranging from 1 (Almost always) to 6 (Almost never). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach’s alpha of 0.82. Smartphone Addiction . The Chinese version of the Smartphone Addiction Scale (SAS), consisting of 10 items (21), has been validated and demonstrated as a reliable instrument within Chinese contexts. Each item is rated on a 6-point Likert scale, ranging from 1 (Strongly disagree) to 6 (Strongly agree). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach’s alpha of 0.86. Self-Regulation of Learning . The Chinese version of the Self-Regulation of Learning Self-Report Scale (SRL-SRS), consisting of 26 items (30), has been validated and demonstrated as a reliable instrument within Chinese contexts (29). Each item is rated on a 4-point Likert scale, ranging from 1 (Never true) to 4 (Always true). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach’s alpha of 0.90. Digital Detox . The Chinese version of the Digital Detoxification Scale (DDS), consists of 10 items (31). Each item is rated on a 5-point Likert scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach’s alpha of 0.78. Data analysis Pearson’s correlation coefficients were calculated to examine the relationships between the study variables. Mediation analyses were conducted using SPSS with the PROCESS macro (version 3.5). To assess the significance of the mediated effects, 95% confidence intervals were generated through 5,000 bootstrap resamples, following the recommendations of Hayes (32). Results Correlation among study variables The results (Table 2) show that all the study variables are significantly correlated with one another. Mindfulness demonstrated a negative correlation with smartphone addiction. Furthermore, mindfulness was positively correlated with both self-regulation learning and digital detox. Table 2. Correlation among the study variables Variables 1 2 3 4 1. MAAS 1 2. SAS -. 41 ** 1 3. SRL-SRS . 36 ** -. 31 ** 1 4. DD . 40 ** -. 60 ** * . 50 ** .1 M 9.90 4.42 7.17 8.88 SD 1.80 1.62 1.40 1.20 Note. *p < 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS= Self-regulation Learning and DD= Digital Detox Mediation Effects To investigate the influence of mindfulness on smartphone addiction, we employed the bias-corrected percentile bootstrap method (Model 4 in SPSS PROCESS; 5,000 bootstrap samples; 95% confidence interval) to test the mediating effect. Mindfulness was entered as the independent variable, smartphone addiction as the dependent variable, and self-regulation learning as the mediating variable. After controlling for gender, age, and family income (all of which were non-significant), the total effect of mindfulness on smartphone addiction was significant (β = −.41, p < .05), and the direct effect remained significant (β = −.29, p < .05). Further details are presented in Table 3. Table 3. Path Analysis Path Coefficient (β) Standard Error (SE) t-value (t) Significance a (MAAS → SRL-SRS) 0. 36 0.0 2 12. 86 Significant b (SRL-SRS → SAS) -0. 31 0.0 3 - 10 . 33 Significant c (Total Effect) -0. 41 0.02 - 14 . 46 Significant c' (Direct Effect) -0. 29 0.02 - 10 . 36 Significant Indirect Effect (a*b) -0. 11 0.04 -2. 80 Significant Note. *p < 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and DD= Digital Detox Mindfulness positively predicted self-regulation learning (see Model 1 in Table 4). In turn, self-regulation learning negatively predicted smartphone addiction (see Model 2 in Table 4). Furthermore, the bias-corrected percentile bootstrap method revealed a significant indirect effect of mindfulness on smartphone addiction through self-regulation learning (ab = −0.11, SE = 0.06, 95% CI [−0.162, −0.006]), indicating that self-regulation learning partially mediates this relationship. Refer to Table 4 and Figure 2 for further details. Table 4. Mediation Effects Predictors Model 1 (SRL-SRS) Model 2 (SAS) β SE t β SE t MAAS 0. 36 0.02 12. 86 -0. 29 0.02 - 10 . 36 SRL-SRS 0. 31 0.0 3 - 10 . 33 Note. *p < 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and DD= Digital Detox Moderation Effects The results (Table 5) show that both self-regulation learning and digital detox significantly predict smartphone addiction, such that higher levels of either correspond to lower levels of smartphone addiction. Moreover, digital detox moderates the relationship between self-regulation learning and smartphone addiction, as evidenced by a negative interaction term (Self-Regulation Learning × Digital Detox). This finding indicates that engaging in digital detox practices intensifies the protective effect of self-regulation learning against smartphone addiction. See Figure 3 for more details. Table 5. Moderation Effects Predictor Coefficient (β) Standard Error (SE) t-value (t) Significance SRL-SRS -0.31 0. 12 - 2 . 08 0. 05 DD -0. 60 0. 20 - 3 . 00 0. 00 SRL-SRS *DD -0. 12 0.0 3 - 4 . 00 0. 00 Note. *p < 0.05., SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and DD= Digital Detox Discussion The first aim of this study was to examine the relationship between mindfulness and smartphone addiction among a sample of 1,241 Chinese college students. The findings indicated a significant negative correlation between mindfulness and smartphone addiction, suggesting that higher levels of mindfulness are associated with reduced tendencies toward problematic smartphone use. These results are consistent with previous research demonstrating a negative relationship between mindfulness and smartphone addiction (4,5,17). Moreover, the findings of the present study revealed a positive correlation between mindfulness and both self-regulated learning and digital detox practices. These findings are partially aligned with prior studies indicating that mindfulness positively correlates with self-regulated learning (33,34) and digital detox (35–37). The underlying mechanism for these associations may be attributed to the capacity of mindfulness to enhance self-regulation learning skills and digital detox strategies, thereby mitigating the impact of problematic smartphone use. These findings contribute to the broader understanding of how mindfulness interventions may serve as effective approaches for promoting healthier digital habits through improved self-regulation and intentional technology use. The second aim of our study was to explore the potential mediating role of self-regulated learning in the relationship between mindfulness and smartphone addiction. Our findings supported this hypothesis, demonstrating that self-regulated learning partially mediated the association between mindfulness and smartphone addiction. This indicates that while self-regulated learning plays a significant role in this relationship, it does not fully explain the influence of mindfulness on smartphone addiction. Previous research has established that mindfulness is inversely related to smartphone addiction, with higher levels of mindfulness associated with decreased tendencies toward problematic smartphone use (5,17,18). Moreover, studies have highlighted that mindfulness enhances self-regulation capabilities, which in turn reduces problematic smartphone use (38,39). For instance, mindfulness significantly improved self-control and reduced rumination, thereby mitigating smartphone addiction among college students(5). Similarly, mindfulness and self-control partially mediated the relationship between emotion regulation and mobile phone addiction, indicating a critical pathway through which mindfulness influences technology use (39). Theoretical frameworks from positive psychology and self-regulation theories further support these findings, suggesting that mindfulness enhances self-regulation skills by promoting awareness, attention control, and emotional regulation (40,41). According to the Dual Systems Model, self-regulation serves as a reflective system that moderates impulsive behaviors associated with smartphone addiction, thereby providing a mechanism through which mindfulness exerts its beneficial effects (40). Furthermore, mindfulness-based interventions are known to enhance executive functioning and cognitive flexibility, which are essential components of self-regulation (42). Our findings align with these theoretical insights, demonstrating that enhancing mindfulness can significantly improve self-regulation learning skills, which in turn reduces problematic smartphone usage among university students. This emphasizes the importance of incorporating mindfulness-based interventions in educational settings to foster healthier digital habits and improve overall well-being. The third aim of this study was to examine whether digital detox moderates the association between self-regulated learning and smartphone addiction. The results confirmed our hypothesis, demonstrating that digital detox practices significantly moderate this relationship. This suggests that engaging in digital detox activities can enhance the positive effects of self-regulated learning on reducing smartphone addiction. These findings are consistent with previous research emphasizing the importance of digital detox practices in improving self-control and reducing smartphone addiction. For instance, higher levels of self-regulated learning were associated with a lower risk of smartphone addiction, especially when accompanied by effective digital detox strategies (43). Furthermore, participating in a one-day digital detox camp improved students’ self-awareness and interpersonal relationships, ultimately reducing their smartphone addiction (44). Additionally, the use of digital detox applications significantly decreased the negative impacts of smartphone use by promoting conscious disconnection, which supports the positive effects of self-regulated learning on managing smartphone addiction (45). This evidence underscores the importance of integrating digital detox strategies to enhance the effectiveness of self-regulated learning in reducing problematic smartphone use. This study, like many others relying on self-report measures, is subject to certain limitations that may impact the validity and generalizability of the findings. Self-reported data can introduce biases, such as social desirability bias, wherein participants may respond in ways they perceive as favorable or socially acceptable. This tendency can artificially inflate or distort the results, thereby compromising the accuracy of the findings. Although we sought to minimize such biases by ensuring participant anonymity and emphasizing the importance of honest responses during data collection, these measures alone may not be sufficient. Future research should consider integrating multi-method approaches, including behavioral assessments, peer evaluations, and objective measures (e.g., digital usage tracking), to enhance the robustness and validity of the data. Another limitation concerns the restricted scope of the study population, as the sample consisted exclusively of students from Shandong Xiehe University. This limited scope constrains the generalizability of the findings to broader populations. To enhance external validity, future research should aim to include participants from a more diverse array of educational institutions, academic disciplines, and cultural backgrounds. Expanding the study to incorporate participants from various geographical regions and ethnicities would provide a more comprehensive understanding of the phenomena under investigation and enhance the applicability of the findings across different contexts. Furthermore, the cross-sectional design employed in this study limits the ability to establish causal relationships among the variables examined. While associations were identified, causality cannot be conclusively determined due to the lack of temporal sequencing inherent in cross-sectional studies. To address this limitation, future research should employ longitudinal designs or experimental methodologies that allow for the observation of changes over time and the establishment of causative links. Additionally, incorporating objective measures, such as digital tracking tools or physiological assessments, would further reduce reliance on self-reported data and enhance the precision of the findings. Finally, future studies should adopt a multi-method, longitudinal approach encompassing diverse participant populations to strengthen the reliability, validity, and generalizability of findings. Such improvements will contribute to a more comprehensive understanding of the complex relationships between mindfulness, self-regulated learning, digital detox practices, and smartphone addiction. Conclusion This study provides evidence that mindfulness is negatively associated with smartphone addiction among Chinese college students, highlighting that higher mindfulness levels correspond with reduced problematic smartphone use. Furthermore, mindfulness positively correlates with self-regulated learning and digital detox practices, which further contribute to mitigating smartphone addiction. Self-regulated learning partially mediates the relationship between mindfulness and smartphone addiction, emphasizing its role in enhancing mindfulness's effectiveness. Moreover, digital detox practices significantly moderate the association between self-regulated learning and smartphone addiction, enhancing its positive impact. Collectively, these findings underscore the potential of mindfulness-based interventions and digital detox strategies to promote healthier digital habits through improved self-regulation and intentional technology use. Abbreviations MAAS - Mindful Attention Awareness Scale SAS - Smartphone Addiction Scale SRL-SRS - Self-Regulation of Learning Self-Report Scale DDS - Digital Detoxification Scale RMB - Renminbi (Chinese currency) Declarations Acknowledgments The authors extend their sincere appreciation to Princess Nourah bint Abdulrahman University (PNURSP2025R553) for their invaluable participation in this research. Funding The authors gratefully acknowledge the financial support provided by Shandong Xiehe University (SDXIEHE) for this study. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research. Consent to Participate All participants provided informed consent after being briefed on the study’s purpose and procedures. Participation was voluntary, and anonymity and confidentiality were assured. Consent for Publication Not Applicable. Human Ethics This study was approved by the Academic Committee of the College of Humanities, Arts, and Education at Shandong Xiehe University and conducted following the Declaration of Helsinki. Data Availability Statement Upon a reasonable request, the corresponding author (Dr. Aamer Aldbyani) will provide the data supporting the study’s conclusions. For further inquiries regarding data access, please contact the corresponding author at [email protected] . References Kumban W, Cetthakrikul S, Santiworakul A. Smartphone Addiction, Screen Time, and Physical Activity of Different Academic Majors and Study Levels in University Students. International Journal of Environmental Research and Public Health. 2025;22(2):237. Al-Barashdi HS, Bouazza A, Jabur NH. Smartphone addiction among university undergraduates: a literature review. J Sci Res Rep. 2015;4(3):210–25. Elamin NO, Almasaad JM, Busaeed RB, Aljafari DA, Khan MA. Smartphone addiction, stress, and depression among university students. Clin Epidemiol Glob Health. 2024; 25:101487. Jeong JH, Bae SM. The relationship between perceived stress and smartphone addiction: the mediating effect of rumination and the mediated moderating effect of mindfulness. Psychiatry Investigation. 2024;21(4):340. Cheng SS, Zhang CQ, Wu JQ. Mindfulness and smartphone addiction before going to sleep among college students: The mediating roles of self-control and rumination. Clocks & sleep. 2020;2(3):354–63. Zhang Z, Wu L, Lu C, Guan T. Effectiveness of a brief online mindfulness-based intervention on different types of mobile phone addiction: mechanisms of influence of trait mindfulness. Frontiers in Psychology. 2025; 16:1400327. Olia F, Saeedmanesh M. The effectiveness of mindfulness training on self-control among university students in Yazd. Mediterranean Journal of Social Sciences. 2016;7. Rowland Z, Wenzel M, Kubiak T. Effects of an ultra-brief computer-based mindfulness training on mindfulness and self-control: A randomized controlled trial using a 40-day ecological momentary assessment. Mindfulness. 2019; 10:2312–26. Al-Abyadh MHA, Alatawi MA, Emara EAM, Almasoud SA, Alsetoohy O, Ali ARM. Do Smartphone Addiction and Self-Regulation Failures Affect Students’ Academic Life Satisfaction? The Role of Students’ Mind Wandering and Cognitive Failures. Psychology Research and Behavior Management. 2024;1231–53. Darabi F. The effectiveness of mindfulness training on self-regulation and perceived academic control of girl students. Research in Science and Social Justice. 2021;2(2):128–40. Mirbabaie M, Stieglitz S, Marx J. Digital detox. Business & Information Systems Engineering. 2022;64(2):239–46. Syvertsen T. Digital detox: The politics of disconnecting. Emerald Publishing Limited; 2020. Abed SN, Abd RK, Salim ID, Jamal NAR. Prevalence of mobile phone addiction among students in Institute Technical of Kut. Mosul Journal of Nursing. 2017;5(1):33–8. Achangwa C, Ryu HS, Lee JK, Jang JD. Adverse effects of smartphone addiction among university students in South Korea: a systematic review. In: Healthcare. MDPI; 2022. p. 14. Barua R. Harmonizing Body and Mind: Investigating the Combined Impact of Yoga and Mindfulness Meditation on Depression, Anxiety, Stress. In: Global Innovations in Physical Education and Health. IGI Global; 2025. p. 203–32. Linso S. Mindfulness 101: Integrating Mindfulness into the First-Year College Curriculum. 2025; Kayiş AR. Mindfulness, impulsivity, and psychological distress: the mediation role of smartphone addiction. British Journal of Guidance & Counselling. 2022;50(5):791–804. Gwak Ji-hye, Seong Go-eun, Park Jin-young, Jeong Yeong-cheol, Jeong Yeong-cheol. The relationship between smartphone addiction, mindfulness and impulsivity. Addiction psychiatry. 2022;26(2):70–7. Arpaci I. Relationships between early maladaptive schemas and smartphone addiction: The moderating role of mindfulness. Int J Ment Health Addict. 2021;19(3):778–92. Chen WY, Yan L, Yuan YR, Zhu XW, Zhang YH, Lian SL. Preference for solitude and Mobile phone addiction among Chinese college students: the mediating role of psychological distress and the moderating role of mindfulness. Front Psychol. 2021; 12:750511. Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013;8(12): e83558. Yeh Y chu, Peng YY. When smartphones meet mindful learning: the cluster profiles of passion toward smartphone use, creativity mindsets, and creativity self-efficacy. International Journal of Mobile Learning and Organisation. 2023;17(4):574–94. Zhang MX, Wu AMS. Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addictive Behaviors. 2020; 111:106552. Wibowo MRS, Wahidah FRN, Agil HM. Self-regulation and tendency of smartphone addiction among college students. Jurnal Ilmiah Psikologi Terapan. 2024;12(1):33–8. Wilcockson TDW, Osborne AM, Ellis DA. Digital detox: The effect of smartphone abstinence on mood, anxiety, and craving. Addictive behaviors. 2019; 99:106013. Li X, Chen W, Wen J. The impact of digital detox on self-regulated learning and smartphone addiction among university students. Journal of Educational Psychology. 2023;115(4):789–803. Zhang Y, Wang P. The role of digital detox in moderating the relationship between self-regulated learning and smartphone addiction. Computers in Human Behavior. 2022;130–213. Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84(4):822. Chen S yi, Cui H, Zhou R Lai, Jia Y Yan. Revision of mindful attention awareness scale (MAAS). Chinese Journal of Clinical Psychology. 2012; Toering T, Elferink-Gemser MT, Jordet G, Visscher C. Self-regulation of learning and performance level of elite youth soccer players. Journal of Sports Sciences. 2012;30(16):1609–17. Imran H, Akhtar S, Mehmood T. Development and Psychometric Properties of Digital Detoxification Scale. Journal of Development and Social Sciences. 2023;4(3):1113–25. Hayes AF. PROCESS: A versatile computational tool for observed variable moderation, mediation, and conditional process modeling. 2012. Liebherr M, Brandtner A, Brand M, Tang Y. Digital mindfulness training and cognitive functions: A preregistered systematic review of neuropsychological findings. Ann N Y Acad Sci. 2024;1532(1):37–49. MacDonald HZ. Associations of five facets of mindfulness with self-regulation in college students. Psychol Rep. 2021;124(3):1202–19. Aini DK, Bukhori B, Bakar ZA. The role of mindfulness and digital detox to adolescent nomophobia. 2021. Remskar M, Western MJ, Ainsworth B. Mindfulness improves psychological health and supports health behavior cognitions: Evidence from a pragmatic RCT of a digital mindfulness‐based intervention. British journal of health psychology. 2024;29(4):1031–48. Achangwa C, Ryu HS, Lee JK, Jang JD. Adverse effects of smartphone addiction among university students in South Korea: a systematic review. In: Healthcare. MDPI; 2022. p. 14. Han Song, Park Jeong-mi. The mediating effect of mindfulness in the relationship between smartphone addiction, difficulty in emotional regulation, and college life adaptation of students majoring in emergency medical care. Crisisonomy. 2024;20(2):103–14. Gülden Ç, Polat K. Mobile Phone Addiction, Emotion Regulation, Mindfulness, and Self-Control Among Adolescents: A Serial Mediation Analysis. Erzincan Üniversitesi Eğitim Fakültesi Dergisi. 2024;26(4). Tan KA, Nik Jaafar NR, Bahar N, Ibrahim N, Baharudin A, Wan Ismail WS, et al. The dual systems model—impulsivity and narcissism as the reflexive System and self-regulation as the reflective system—of smartphone addiction. Cyberpsychology, Behavior, and Social Networking. 2024;27(2):156–62. Darmayanti N, Surbakti A. Does Self-Regulated Learning Mediate the Effect of Smartphone Addiction on Academic Procrastination? A SEM Analysis. Journal of Educational, Health & Community Psychology (JEHCP). 2024;13(4). Uniyal R, Shahnawaz* MG. Wellbeing and problematic smartphone use: serial mediation of mindfulness and self-compassion. Psychological Reports. 2024;127(4):1705–26. Gezgin DM, Türk Kurtça T, Mihci C, Lin C, Griffiths MD. The role of self‐regulated learning in modeling the relationships between learning approaches, FoMO, and smartphone addiction among university students. British Journal of Educational Technology. 2025; Balasubramanian N. Impact of smartphone abstinence: A digital detox study among college students in Chennai. Journal of Management and Science. 2024;14(4):25–32. Schmuck D. Does digital detox work? Exploring the role of digital detox applications for problematic smartphone use and well-being of young adults using multigroup analysis. Cyberpsychology, Behavior, and Social Networking. 2020;23(8):526–32. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2025 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 20 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers invited by journal 06 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 14 Apr, 2025 Submission checks completed at journal 12 Apr, 2025 First submitted to journal 12 Apr, 2025 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-6405697","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468124481,"identity":"e0bfb72a-3db6-4ed9-9745-da20f403162d","order_by":0,"name":"Aamer Aldbyani","email":"data:image/png;base64,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","orcid":"","institution":"Shandong Xiehe University","correspondingAuthor":true,"prefix":"","firstName":"Aamer","middleName":"","lastName":"Aldbyani","suffix":""},{"id":468124482,"identity":"ffb58836-ce12-468e-a3c0-407fd96ac5b4","order_by":1,"name":"Zhang Chuanxia","email":"","orcid":"","institution":"Shandong Xiehe University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Chuanxia","suffix":""},{"id":468124483,"identity":"b1b64002-6f35-441f-b95d-0d7358b0691f","order_by":2,"name":"Afnan Alhimaidi","email":"","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Afnan","middleName":"","lastName":"Alhimaidi","suffix":""}],"badges":[],"createdAt":"2025-04-08 18:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6405697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6405697/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-025-03485-3","type":"published","date":"2025-10-10T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84306225,"identity":"590e98f1-f006-4e1a-90e8-44723b37f9f9","added_by":"auto","created_at":"2025-06-10 11:24:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6405697/v1/cf138c3c94765ae602a1979c.png"},{"id":84306173,"identity":"257300d9-3dbf-4027-bec1-2c4775e8a30f","added_by":"auto","created_at":"2025-06-10 11:24:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation Effects\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6405697/v1/5b70af21c3d5e136dc4273e2.png"},{"id":84306254,"identity":"e49b43be-320d-4b2b-ad9c-a5e32f65ae81","added_by":"auto","created_at":"2025-06-10 11:24:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration Of SRL-SRS On SAS By DD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6405697/v1/06b5a34bc062a70169b6e222.png"},{"id":93419746,"identity":"7ca840d6-a555-446a-92b7-314542a0aceb","added_by":"auto","created_at":"2025-10-13 16:06:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6405697/v1/48c9f064-9a3c-4566-94f6-00fb0a9813a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Mindfulness on Smartphone Addiction: The Mediating Role of Self-regulation Learning and the Moderating Role of Digital Detox","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmartphones have become integral to university students\u0026rsquo; daily routines, providing a wide range of academic and social functionalities (1). However, excessive smartphone use\u0026mdash;often labeled \u0026ldquo;smartphone addiction\u0026rdquo;\u0026mdash;raises significant concerns, as it disrupts academic performance, social interactions, and overall well-being (2,3). Mindfulness, characterized by a present-moment focus maintained without judgment, has emerged as a promising strategy to reduce compulsive smartphone use (4,5). By fostering self-awareness and emotional regulation, mindfulness-based interventions have been shown to mitigate detrimental patterns of technology engagement (6). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, self-regulation learning\u0026mdash;entailing proactive management of one\u0026rsquo;s cognitive, emotional, and behavioral processes\u0026mdash;appears critical in the relationship between mindfulness and smartphone addiction (7,8). Poor self-regulation has been linked to heightened addictive behavior, whereas mindfulness training can fortify self-regulatory capacities (9,10). Furthermore, digital detox, defined as a deliberate pause from digital device use, may buffer against excessive smartphone reliance by lessening distractions and reinforcing self-regulation efforts (11,12). The present study investigates whether self-regulation learning mediates the link between mindfulness and smartphone addiction and whether digital detox moderates this mediated pathway, aiming to provide a nuanced framework for addressing problematic smartphone use among university students.\u003c/p\u003e\n\u003ch2\u003eMindfulness and Smartphone Addiction\u003c/h2\u003e\n\u003cp\u003eThe use of smartphones has become integral to university students\u0026apos; daily lives, serving multiple purposes such as accessing the internet, engaging with educational resources, communicating with peers, and enjoying various forms of entertainment, including games and social media. Smartphones also facilitate academic learning by providing access to online libraries, research databases, and collaboration tools, thereby enhancing students\u0026rsquo; learning experiences (1). However, the pervasive use of smartphones among students has raised concerns about their negative impacts, particularly when usage becomes excessive or addictive (2).\u003c/p\u003e\n\u003cp\u003eSmartphone addiction, often classified as a non-chemical behavioral addiction, is characterized by compulsive usage patterns that interfere with daily life, social interactions, and academic performance (3). Recent studies indicate that university students are among the most affected demographic groups, as smartphones have become essential for both social engagement and academic activities (13,14). While smartphones offer various benefits, such as promoting social connectivity and providing learning resources, excessive use can lead to adverse psychological, social, and physical outcomes.\u003c/p\u003e\n\u003cp\u003eMindfulness, which is defined as the practice of maintaining present-moment awareness without judgment, serves as a powerful element to decrease addictive behaviors. These practices empower individuals to cultivate a more intentional and balanced relationship with technology. Mindfulness meditation, in particular, has emerged as an effective method for reducing reactivity to digital distractions, promoting mental clarity, and enhancing overall well-being (15). Techniques such as mindful breathing and body scan meditation help individuals anchor themselves in the present moment, fostering relaxation and a deepened sense of embodiment (16).\u003c/p\u003e\n\u003cp\u003eSeveral studies emphasize the effect of mindfulness on smartphone addiction. Research has demonstrated that mindfulness negatively predicts smartphone addiction (5,17) and reduces smartphone addiction (4,18). Experimental evidence also supports the effectiveness of mindfulness interventions on smartphone addiction. For instance, a recent study investigated the effect of a brief online mindfulness-based intervention on mobile phone addiction (6). The findings revealed that the intervention effectively reduced mobile phone addiction. These results align with previous studies demonstrating that high mindfulness plays a crucial role in reducing smartphone addiction (19\u0026ndash;21). Therefore, based on previous research consistently supporting the theory suggesting that higher levels of mindfulness are associated with lower levels of smartphone addiction, we propose the following hypothesis:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1: Mindfulness negatively correlates with smartphone addiction.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSelf-regulation Learning as a mediator\u003c/p\u003e\n\u003cp\u003eRecent research has shown that mindfulness is negatively associated with smartphone addiction among university students. This means that higher levels of mindfulness correspond with lower levels of smartphone addiction. A key factor that might explain this relationship is self-regulation learning.\u003c/p\u003e\n\u003cp\u003eSelf-regulation learning refers to a student\u0026apos;s ability to intentionally manage their thoughts, emotions, and behaviors to achieve educational goals. It involves strategies such as planning, observing one\u0026apos;s actions, monitoring progress, and evaluating outcomes. Studies have demonstrated that mindfulness training significantly improves self-regulation learning skills. For example, mindfulness practices help students enhance their self-control, which is an essential part of self-regulation learning (7,8,22). Furthermore, mindfulness training positively impacts students\u0026apos; ability to regulate their behaviors and maintain a sense of control over their academic activities (10).\u003c/p\u003e\n\u003cp\u003eOn the other hand, poor self-regulation learning has been linked to increased smartphone addiction (9,23,24). Therefore, enhancing mindfulness could strengthen self-regulation learning skills, subsequently reducing problematic smartphone usage among university students, so, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2:\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eSelf-regulation Learning may mediate the relationship between mindfulness\u003c/em\u003e \u003cem\u003eand smartphone addiction.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDigital Detox as a moderator\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital detox refers to the deliberate practice of abstaining temporarily from the use of digital devices such as smartphones, computers, and social media platforms, aiming to improve mental health, increase productivity, and enhance concentration (11,12,25). This intentional disengagement provides individuals with the opportunity to recover from overstimulation and information overload, conditions increasingly prevalent in the contemporary technological environment. Stepping away from digital devices has been associated with reductions in stress and anxiety, thereby offering the mind a restorative break(25). Furthermore, minimizing digital distractions enables individuals to regain cognitive control, thereby enhancing their ability to concentrate on meaningful and goal-oriented tasks.\u003c/p\u003e\n\u003cp\u003eIn educational contexts, digital detox acts as a moderating variable in the relationship between self-regulated learning and smartphone addiction. When individuals engage in digital detoxification, they experience decreased distractions resulting from excessive smartphone usage, allowing them to invest more effectively in self-regulated learning practices and thus achieve educational goals with greater efficiency. Recent research supports the notion that digital detox practices moderate the influence of self-regulated learning on smartphone addiction. Digital detox practices strengthen students\u0026apos; self-control, subsequently reducing excessive reliance on smartphones (26). Similarly, students regularly employing digital detox strategies demonstrated higher levels of self-regulated learning and lower tendencies toward smartphone addiction (27). Therefore, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3:\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eDigital Detox may moderate the relationship between self-regulation Learning and smartphone addiction.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCurrent study\u003c/p\u003e\n\u003cp\u003eAlthough it is plausible to hypothesize that self-regulation learning may mediate the effect of mindfulness on reducing addictive behaviors, this mediation is less evident when it comes to smartphone addiction\u0026mdash;a behavior that is deeply integrated into the daily lives of university students. Therefore, the primary aim of this study is to investigate whether self-regulation learning mediates the relationship between mindfulness and smartphone addiction. Additionally, the study examines whether digital detox moderates the association between self-regulation learning and smartphone addiction. The proposed study model is presented in Figure 1.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eA cross-sectional study was conducted to investigate the relationship between mindfulness and smartphone addiction, while also exploring the potential mediating role of self-regulation and the moderating role of digital detox in these associations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eParticipants\u003c/h2\u003e\n\u003cp\u003eA total of 1,241 college students from Shandong Xiehe University, China, voluntarily participated in this study. Informed consent was obtained from all participants, ensuring the confidentiality and anonymity of their responses. The participants\u0026rsquo; ages ranged from 18 to 22 years (M\u003csub\u003eage\u003c/sub\u003e = 21.12, SD = 2.86). None of the participants had prior experience with mindfulness training or related practices such as digital detox strategies or yoga. This study followed the ethical standards set by the Academic Committee of Shandong Xiehe University, the principles of the Declaration of Helsinki (1964), and comparable guidelines governing research involving human subjects. Detailed demographic characteristics of the participants are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographic characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"87%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 299px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eMale\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e40. 2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e59.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003e3000-10000 RMB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e59.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003e10000-20000 RMB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e25.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003e20000-above RMB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e14.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRMB= The Chinese currency\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMeasurements\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eMindfulness\u003c/strong\u003e.\u0026nbsp;The Chinese version of the Mindful Attention Awareness Scale (MAAS), consisting of 15 items (28), has been validated and demonstrated as a reliable instrument within Chinese contexts (29). Each item is rated on a 6-point Likert scale, ranging from 1 (Almost always) to 6 (Almost never). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.82.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmartphone Addiction\u003c/strong\u003e.\u0026nbsp;The Chinese version of the Smartphone Addiction Scale (SAS), consisting of 10 items (21), has been validated and demonstrated as a reliable instrument within Chinese contexts. Each item is rated on a 6-point Likert scale, ranging from 1 (Strongly disagree) to 6 (Strongly agree). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.86.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Regulation of Learning\u003c/strong\u003e.\u0026nbsp;The Chinese version of the Self-Regulation of Learning Self-Report Scale (SRL-SRS), consisting of 26 items (30), has been validated and demonstrated as a reliable instrument within Chinese contexts (29). Each item is rated on a 4-point Likert scale, ranging from 1 (Never true) to 4 (Always true). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.90.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Detox\u003c/strong\u003e. The Chinese version of the Digital Detoxification Scale (DDS), consists of 10 items (31). Each item is rated on a 5-point Likert scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree). In the present study, the scale demonstrated acceptable internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.78.\u003c/p\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation coefficients were calculated to examine the relationships between the study variables. Mediation analyses were conducted using SPSS with the PROCESS macro (version 3.5). To assess the significance of the mediated effects, 95% confidence intervals were generated through 5,000 bootstrap resamples, following the recommendations of Hayes (32).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation among study variables \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results (Table 2) show that all the study variables are significantly correlated with one another. Mindfulness demonstrated a negative correlation with smartphone addiction. Furthermore, mindfulness was positively correlated with both self-regulation learning and digital detox.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Correlation among the study variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\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: 262px;\"\u003e\n \u003cp\u003e1.\u003cem\u003e\u0026nbsp;MAAS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e2. \u003cem\u003eSAS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.\u003cspan dir=\"RTL\"\u003e41\u003c/span\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e3. \u003cem\u003eSRL-SRS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.\u003cspan dir=\"RTL\"\u003e36\u003c/span\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.\u003cspan dir=\"RTL\"\u003e31\u003c/span\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003e4. \u003cem\u003eDD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.\u003cspan dir=\"RTL\"\u003e40\u003c/span\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.\u003cspan dir=\"RTL\"\u003e60\u003c/span\u003e**\u003cspan dir=\"RTL\"\u003e*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e.\u003cspan dir=\"RTL\"\u003e50\u003c/span\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 262px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNote. *p \u0026lt; 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS= Self-regulation Learning and\u0026nbsp;\u003c/em\u003e\u003cem\u003eDD= Digital Detox\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMediation Effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the influence of mindfulness on smartphone addiction, we employed the bias-corrected percentile bootstrap method (Model 4 in SPSS PROCESS; 5,000 bootstrap samples; 95% confidence interval) to test the mediating effect. Mindfulness was entered as the independent variable, smartphone addiction as the dependent variable, and self-regulation learning as the mediating variable. After controlling for gender, age, and family income (all of which were non-significant), the total effect of mindfulness on smartphone addiction was significant (\u0026beta; = \u0026minus;.41, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), and the direct effect remained significant (\u0026beta; = \u0026minus;.29, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Further details are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Path Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-value (t)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ea (MAAS \u0026rarr; SRL-SRS)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e12.\u003cspan dir=\"RTL\"\u003e86\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eb (SRL-SRS \u0026rarr; SAS)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e10\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ec (Total Effect)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e14\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ec\u0026apos; (Direct Effect)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e10\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eIndirect Effect (a*b)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.\u003cspan dir=\"RTL\"\u003e80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. *p \u0026lt; 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and\u0026nbsp;\u003c/em\u003e\u003cem\u003eDD= Digital Detox\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMindfulness positively predicted self-regulation learning (see Model 1 in Table 4). In turn, self-regulation learning negatively predicted smartphone addiction (see Model 2 in Table 4). Furthermore, the bias-corrected percentile bootstrap method revealed a significant indirect effect of mindfulness on smartphone addiction through self-regulation learning (ab = \u0026minus;0.11, SE = 0.06, 95% CI [\u0026minus;0.162, \u0026minus;0.006]), indicating that self-regulation learning partially mediates this relationship. Refer to Table 4 and Figure 2 for further details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Mediation Effects\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel 1 (SRL-SRS)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel 2 (SAS)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\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: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\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: 72px;\"\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: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eMAAS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e12.\u003cspan dir=\"RTL\"\u003e86\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e10\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cem\u003eSRL-SRS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e10\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 167px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. *p \u0026lt; 0.05., MAAS= Mindfulness, SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and\u0026nbsp;\u003c/em\u003e\u003cem\u003eDD= Digital Detox\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eModeration Effects\u003c/h2\u003e\n\u003cp\u003eThe results (Table 5) show that both self-regulation learning and digital detox significantly predict smartphone addiction, such that higher levels of either correspond to lower levels of smartphone addiction. Moreover, digital detox moderates the relationship between self-regulation learning and smartphone addiction, as evidenced by a negative interaction term (Self-Regulation Learning \u0026times; Digital Detox). This finding indicates that engaging in digital detox practices intensifies the protective effect of self-regulation learning against smartphone addiction. See Figure 3 for more details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Moderation Effects\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"623\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (\u0026Icirc;\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-value (t)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSRL-SRS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSRL-SRS *DD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.\u003cspan dir=\"RTL\"\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003cspan dir=\"RTL\"\u003e4\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.\u003cspan dir=\"RTL\"\u003e00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote. *p \u0026lt; 0.05., SAS= Smartphone Adduction, SRL-SRS = Self-regulation Learning and\u0026nbsp;\u003c/em\u003e\u003cem\u003eDD= Digital Detox\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe first aim of this study was to examine the relationship between mindfulness and smartphone addiction among a sample of 1,241 Chinese college students. The findings indicated a significant negative correlation between mindfulness and smartphone addiction, suggesting that higher levels of mindfulness are associated with reduced tendencies toward problematic smartphone use. These results are consistent with previous research demonstrating a negative relationship between mindfulness and smartphone addiction\u0026nbsp;(4,5,17). Moreover, the findings of the present study revealed a positive correlation between mindfulness and both self-regulated learning and digital detox practices. These findings are partially aligned with prior studies indicating that mindfulness positively correlates with self-regulated learning (33,34) and digital detox (35\u0026ndash;37). The underlying mechanism for these associations may be attributed to the capacity of mindfulness to enhance self-regulation learning skills and digital detox strategies, thereby mitigating the impact of problematic smartphone use. These findings contribute to the broader understanding of how mindfulness interventions may serve as effective approaches for promoting healthier digital habits through improved self-regulation and intentional technology use.\u003c/p\u003e\n\u003cp\u003eThe second aim of our study was to explore the potential mediating role of self-regulated learning in the relationship between mindfulness and smartphone addiction. Our findings supported this hypothesis, demonstrating that self-regulated learning partially mediated the association between mindfulness and smartphone addiction. This indicates that while self-regulated learning plays a significant role in this relationship, it does not fully explain the influence of mindfulness on smartphone addiction. Previous research has established that mindfulness is inversely related to smartphone addiction, with higher levels of mindfulness associated with decreased tendencies toward problematic smartphone use\u0026nbsp;(5,17,18). Moreover, studies have highlighted that mindfulness enhances self-regulation capabilities, which in turn reduces problematic smartphone use (38,39). For instance, mindfulness significantly improved self-control and reduced rumination, thereby mitigating smartphone addiction among college students(5). Similarly, mindfulness and self-control partially mediated the relationship between emotion regulation and mobile phone addiction, indicating a critical pathway through which mindfulness influences technology use (39).\u003c/p\u003e\n\u003cp\u003eTheoretical frameworks from positive psychology and self-regulation theories further support these findings, suggesting that mindfulness enhances self-regulation skills by promoting awareness, attention control, and emotional regulation (40,41). According to the Dual Systems Model, self-regulation serves as a reflective system that moderates impulsive behaviors associated with smartphone addiction, thereby providing a mechanism through which mindfulness exerts its beneficial effects (40). Furthermore, mindfulness-based interventions are known to enhance executive functioning and cognitive flexibility, which are essential components of self-regulation (42). Our findings align with these theoretical insights, demonstrating that enhancing mindfulness can significantly improve self-regulation learning skills, which in turn reduces problematic smartphone usage among university students. This emphasizes the importance of incorporating mindfulness-based interventions in educational settings to foster healthier digital habits and improve overall well-being.\u003c/p\u003e\n\u003cp\u003eThe third aim of this study was to examine whether digital detox moderates the association between self-regulated learning and smartphone addiction. The results confirmed our hypothesis, demonstrating that digital detox practices significantly moderate this relationship. This suggests that engaging in digital detox activities can enhance the positive effects of self-regulated learning on reducing smartphone addiction. These findings are consistent with previous research emphasizing the importance of digital detox practices in improving self-control and reducing smartphone addiction. For instance, higher levels of self-regulated learning were associated with a lower risk of smartphone addiction, especially when accompanied by effective digital detox strategies (43). Furthermore, participating in a one-day digital detox camp improved students\u0026rsquo; self-awareness and interpersonal relationships, ultimately reducing their smartphone addiction (44). Additionally, the use of digital detox applications significantly decreased the negative impacts of smartphone use by promoting conscious disconnection, which supports the positive effects of self-regulated learning on managing smartphone addiction (45). This evidence underscores the importance of integrating digital detox strategies to enhance the effectiveness of self-regulated learning in reducing problematic smartphone use.\u003c/p\u003e\n\u003cp\u003eThis study, like many others relying on self-report measures, is subject to certain limitations that may impact the validity and generalizability of the findings. Self-reported data can introduce biases, such as social desirability bias, wherein participants may respond in ways they perceive as favorable or socially acceptable. This tendency can artificially inflate or distort the results, thereby compromising the accuracy of the findings. Although we sought to minimize such biases by ensuring participant anonymity and emphasizing the importance of honest responses during data collection, these measures alone may not be sufficient. Future research should consider integrating multi-method approaches, including behavioral assessments, peer evaluations, and objective measures (e.g., digital usage tracking), to enhance the robustness and validity of the data.\u003c/p\u003e\n\u003cp\u003eAnother limitation concerns the restricted scope of the study population, as the sample consisted exclusively of students from Shandong Xiehe University. This limited scope constrains the generalizability of the findings to broader populations. To enhance external validity, future research should aim to include participants from a more diverse array of educational institutions, academic disciplines, and cultural backgrounds. Expanding the study to incorporate participants from various geographical regions and ethnicities would provide a more comprehensive understanding of the phenomena under investigation and enhance the applicability of the findings across different contexts.\u003c/p\u003e\n\u003cp\u003eFurthermore, the cross-sectional design employed in this study limits the ability to establish causal relationships among the variables examined. While associations were identified, causality cannot be conclusively determined due to the lack of temporal sequencing inherent in cross-sectional studies. To address this limitation, future research should employ longitudinal designs or experimental methodologies that allow for the observation of changes over time and the establishment of causative links. Additionally, incorporating objective measures, such as digital tracking tools or physiological assessments, would further reduce reliance on self-reported data and enhance the precision of the findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, future studies should adopt a multi-method, longitudinal approach encompassing diverse participant populations to strengthen the reliability, validity, and generalizability of findings. Such improvements will contribute to a more comprehensive understanding of the complex relationships between mindfulness, self-regulated learning, digital detox practices, and smartphone addiction.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides evidence that mindfulness is negatively associated with smartphone addiction among Chinese college students, highlighting that higher mindfulness levels correspond with reduced problematic smartphone use. Furthermore, mindfulness positively correlates with self-regulated learning and digital detox practices, which further contribute to mitigating smartphone addiction. Self-regulated learning partially mediates the relationship between mindfulness and smartphone addiction, emphasizing its role in enhancing mindfulness's effectiveness. Moreover, digital detox practices significantly moderate the association between self-regulated learning and smartphone addiction, enhancing its positive impact. Collectively, these findings underscore the potential of mindfulness-based interventions and digital detox strategies to promote healthier digital habits through improved self-regulation and intentional technology use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eMAAS\u0026nbsp;\u003c/strong\u003e- Mindful Attention Awareness Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAS\u003c/strong\u003e - Smartphone Addiction Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSRL-SRS\u003c/strong\u003e - Self-Regulation of Learning Self-Report Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDDS\u003c/strong\u003e - Digital Detoxification Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMB\u003c/strong\u003e - Renminbi (Chinese currency)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere appreciation to Princess Nourah bint Abdulrahman University (PNURSP2025R553) for their invaluable participation in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support provided by Shandong Xiehe University (SDXIEHE) for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent after being briefed on the study\u0026rsquo;s purpose and procedures. Participation was voluntary, and anonymity and confidentiality were assured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Academic Committee of the College of Humanities, Arts, and Education at Shandong Xiehe University and conducted following the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon a reasonable request, the corresponding author (Dr. Aamer Aldbyani) will provide the data supporting the study\u0026rsquo;s conclusions. For further inquiries regarding data access, please contact the corresponding author at [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKumban W, Cetthakrikul S, Santiworakul A. Smartphone Addiction, Screen Time, and Physical Activity of Different Academic Majors and Study Levels in University Students. International Journal of Environmental Research and Public Health. 2025;22(2):237.\u003c/li\u003e\n \u003cli\u003eAl-Barashdi HS, Bouazza A, Jabur NH. Smartphone addiction among university undergraduates: a literature review. J Sci Res Rep. 2015;4(3):210\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eElamin NO, Almasaad JM, Busaeed RB, Aljafari DA, Khan MA. Smartphone addiction, stress, and depression among university students. Clin Epidemiol Glob Health. 2024; 25:101487.\u003c/li\u003e\n \u003cli\u003eJeong JH, Bae SM. The relationship between perceived stress and smartphone addiction: the mediating effect of rumination and the mediated moderating effect of mindfulness. Psychiatry Investigation. 2024;21(4):340.\u003c/li\u003e\n \u003cli\u003eCheng SS, Zhang CQ, Wu JQ. Mindfulness and smartphone addiction before going to sleep among college students: The mediating roles of self-control and rumination. Clocks \u0026amp; sleep. 2020;2(3):354\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eZhang Z, Wu L, Lu C, Guan T. Effectiveness of a brief online mindfulness-based intervention on different types of mobile phone addiction: mechanisms of influence of trait mindfulness. Frontiers in Psychology. 2025; 16:1400327.\u003c/li\u003e\n \u003cli\u003eOlia F, Saeedmanesh M. The effectiveness of mindfulness training on self-control among university students in Yazd. Mediterranean Journal of Social Sciences. 2016;7.\u003c/li\u003e\n \u003cli\u003eRowland Z, Wenzel M, Kubiak T. Effects of an ultra-brief computer-based mindfulness training on mindfulness and self-control: A randomized controlled trial using a 40-day ecological momentary assessment. Mindfulness. 2019; 10:2312\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eAl-Abyadh MHA, Alatawi MA, Emara EAM, Almasoud SA, Alsetoohy O, Ali ARM. Do Smartphone Addiction and Self-Regulation Failures Affect Students\u0026rsquo; Academic Life Satisfaction? The Role of Students\u0026rsquo; Mind Wandering and Cognitive Failures. Psychology Research and Behavior Management. 2024;1231\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eDarabi F. The effectiveness of mindfulness training on self-regulation and perceived academic control of girl students. Research in Science and Social Justice. 2021;2(2):128\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eMirbabaie M, Stieglitz S, Marx J. Digital detox. Business \u0026amp; Information Systems Engineering. 2022;64(2):239\u0026ndash;46.\u003c/li\u003e\n \u003cli\u003eSyvertsen T. Digital detox: The politics of disconnecting. Emerald Publishing Limited; 2020.\u003c/li\u003e\n \u003cli\u003eAbed SN, Abd RK, Salim ID, Jamal NAR. Prevalence of mobile phone addiction among students in Institute Technical of Kut. Mosul Journal of Nursing. 2017;5(1):33\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eAchangwa C, Ryu HS, Lee JK, Jang JD. Adverse effects of smartphone addiction among university students in South Korea: a systematic review. In: Healthcare. MDPI; 2022. p. 14.\u003c/li\u003e\n \u003cli\u003eBarua R. Harmonizing Body and Mind: Investigating the Combined Impact of Yoga and Mindfulness Meditation on Depression, Anxiety, Stress. In: Global Innovations in Physical Education and Health. IGI Global; 2025. p. 203\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eLinso S. Mindfulness 101: Integrating Mindfulness into the First-Year College Curriculum. 2025;\u003c/li\u003e\n \u003cli\u003eKayiş AR. Mindfulness, impulsivity, and psychological distress: the mediation role of smartphone addiction. British Journal of Guidance \u0026amp; Counselling. 2022;50(5):791\u0026ndash;804.\u003c/li\u003e\n \u003cli\u003eGwak Ji-hye, Seong Go-eun, Park Jin-young, Jeong Yeong-cheol, Jeong Yeong-cheol. The relationship between smartphone addiction, mindfulness and impulsivity. Addiction psychiatry. 2022;26(2):70\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eArpaci I. Relationships between early maladaptive schemas and smartphone addiction: The moderating role of mindfulness. Int J Ment Health Addict. 2021;19(3):778\u0026ndash;92.\u003c/li\u003e\n \u003cli\u003eChen WY, Yan L, Yuan YR, Zhu XW, Zhang YH, Lian SL. Preference for solitude and Mobile phone addiction among Chinese college students: the mediating role of psychological distress and the moderating role of mindfulness. Front Psychol. 2021; 12:750511.\u003c/li\u003e\n \u003cli\u003eKwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013;8(12): e83558.\u003c/li\u003e\n \u003cli\u003eYeh Y chu, Peng YY. When smartphones meet mindful learning: the cluster profiles of passion toward smartphone use, creativity mindsets, and creativity self-efficacy. International Journal of Mobile Learning and Organisation. 2023;17(4):574\u0026ndash;94.\u003c/li\u003e\n \u003cli\u003eZhang MX, Wu AMS. Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addictive Behaviors. 2020; 111:106552.\u003c/li\u003e\n \u003cli\u003eWibowo MRS, Wahidah FRN, Agil HM. Self-regulation and tendency of smartphone addiction among college students. Jurnal Ilmiah Psikologi Terapan. 2024;12(1):33\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eWilcockson TDW, Osborne AM, Ellis DA. Digital detox: The effect of smartphone abstinence on mood, anxiety, and craving. Addictive behaviors. 2019; 99:106013.\u003c/li\u003e\n \u003cli\u003eLi X, Chen W, Wen J. The impact of digital detox on self-regulated learning and smartphone addiction among university students. Journal of Educational Psychology. 2023;115(4):789\u0026ndash;803.\u003c/li\u003e\n \u003cli\u003eZhang Y, Wang P. The role of digital detox in moderating the relationship between self-regulated learning and smartphone addiction. Computers in Human Behavior. 2022;130\u0026ndash;213.\u003c/li\u003e\n \u003cli\u003eBrown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84(4):822.\u003c/li\u003e\n \u003cli\u003eChen S yi, Cui H, Zhou R Lai, Jia Y Yan. Revision of mindful attention awareness scale (MAAS). Chinese Journal of Clinical Psychology. 2012;\u003c/li\u003e\n \u003cli\u003eToering T, Elferink-Gemser MT, Jordet G, Visscher C. Self-regulation of learning and performance level of elite youth soccer players. Journal of Sports Sciences. 2012;30(16):1609\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eImran H, Akhtar S, Mehmood T. Development and Psychometric Properties of Digital Detoxification Scale. Journal of Development and Social Sciences. 2023;4(3):1113\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eHayes AF. PROCESS: A versatile computational tool for observed variable moderation, mediation, and conditional process modeling. 2012.\u003c/li\u003e\n \u003cli\u003eLiebherr M, Brandtner A, Brand M, Tang Y. Digital mindfulness training and cognitive functions: A preregistered systematic review of neuropsychological findings. Ann N Y Acad Sci. 2024;1532(1):37\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003eMacDonald HZ. Associations of five facets of mindfulness with self-regulation in college students. Psychol Rep. 2021;124(3):1202\u0026ndash;19.\u003c/li\u003e\n \u003cli\u003eAini DK, Bukhori B, Bakar ZA. The role of mindfulness and digital detox to adolescent nomophobia. 2021.\u003c/li\u003e\n \u003cli\u003eRemskar M, Western MJ, Ainsworth B. Mindfulness improves psychological health and supports health behavior cognitions: Evidence from a pragmatic RCT of a digital mindfulness‐based intervention. British journal of health psychology. 2024;29(4):1031\u0026ndash;48.\u003c/li\u003e\n \u003cli\u003eAchangwa C, Ryu HS, Lee JK, Jang JD. Adverse effects of smartphone addiction among university students in South Korea: a systematic review. In: Healthcare. MDPI; 2022. p. 14.\u003c/li\u003e\n \u003cli\u003eHan Song, Park Jeong-mi. The mediating effect of mindfulness in the relationship between smartphone addiction, difficulty in emotional regulation, and college life adaptation of students majoring in emergency medical care. Crisisonomy. 2024;20(2):103\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;lden \u0026Ccedil;, Polat K. Mobile Phone Addiction, Emotion Regulation, Mindfulness, and Self-Control Among Adolescents: A Serial Mediation Analysis. Erzincan \u0026Uuml;niversitesi Eğitim Fak\u0026uuml;ltesi Dergisi. 2024;26(4).\u003c/li\u003e\n \u003cli\u003eTan KA, Nik Jaafar NR, Bahar N, Ibrahim N, Baharudin A, Wan Ismail WS, et al. The dual systems model\u0026mdash;impulsivity and narcissism as the reflexive System and self-regulation as the reflective system\u0026mdash;of smartphone addiction. Cyberpsychology, Behavior, and Social Networking. 2024;27(2):156\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eDarmayanti N, Surbakti A. Does Self-Regulated Learning Mediate the Effect of Smartphone Addiction on Academic Procrastination? A SEM Analysis. Journal of Educational, Health \u0026amp; Community Psychology (JEHCP). 2024;13(4).\u003c/li\u003e\n \u003cli\u003eUniyal R, Shahnawaz* MG. Wellbeing and problematic smartphone use: serial mediation of mindfulness and self-compassion. Psychological Reports. 2024;127(4):1705\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eGezgin DM, T\u0026uuml;rk Kurt\u0026ccedil;a T, Mihci C, Lin C, Griffiths MD. The role of self‐regulated learning in modeling the relationships between learning approaches, FoMO, and smartphone addiction among university students. British Journal of Educational Technology. 2025;\u003c/li\u003e\n \u003cli\u003eBalasubramanian N. Impact of smartphone abstinence: A digital detox study among college students in Chennai. Journal of Management and Science. 2024;14(4):25\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eSchmuck D. Does digital detox work? Exploring the role of digital detox applications for problematic smartphone use and well-being of young adults using multigroup analysis. Cyberpsychology, Behavior, and Social Networking. 2020;23(8):526\u0026ndash;32.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mindfulness, Smartphone Addiction, Self-regulation Learning, Digital Detox","lastPublishedDoi":"10.21203/rs.3.rs-6405697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6405697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mindfulness has been extensively studied for its role in reducing addictive behaviors, including smartphone addiction. However, the underlying mechanisms involving self-regulation learning and digital detox in this association remain unclear. This study aimed to investigate the mediating role of self-regulation learning and the moderating role of digital detox in the relationship between mindfulness and smartphone addiction among 1,241 Chinese college students from Shandong Xiehe University. Participants completed the Mindful Attention Awareness Scale (MAAS), the Smartphone Addiction Scale (SAS), the Self-Regulation of Learning Self-Report Scale (SRL-SRS), and the Digital Detoxification Scale (DDS). The results found that mindfulness was negatively associated with smartphone addiction, and self-regulation learning partially played a mediating role in this association. Further analysis revealed that digital detox moderated the relationship between self-regulation learning and smartphone addiction. These results suggest a complex interplay where mindfulness reduces smartphone addiction through improved self-regulation learning, with digital detox further enhancing this effect. This study provides valuable insights into the mechanisms underlying the association between mindfulness and smartphone addiction, emphasizing the importance of promoting self-regulation learning and digital detox strategies.","manuscriptTitle":"The Effect of Mindfulness on Smartphone Addiction: The Mediating Role of Self-regulation Learning and the Moderating Role of Digital Detox","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 11:24:17","doi":"10.21203/rs.3.rs-6405697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-20T05:48:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T03:19:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95974189453905881943258436537701696560","date":"2025-08-01T09:48:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T04:15:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T09:40:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219270529163890015605703891382364380073","date":"2025-06-08T10:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298796507868371548388498434914490171294","date":"2025-06-08T09:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-06T08:54:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T12:12:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-14T06:23:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-12T09:55:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-04-12T09:54:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b881d49-6cfd-46fa-9652-b1d6ec968178","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:01:58+00:00","versionOfRecord":{"articleIdentity":"rs-6405697","link":"https://doi.org/10.1186/s40359-025-03485-3","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2025-10-10 15:57:13","publishedOnDateReadable":"October 10th, 2025"},"versionCreatedAt":"2025-06-10 11:24:17","video":"","vorDoi":"10.1186/s40359-025-03485-3","vorDoiUrl":"https://doi.org/10.1186/s40359-025-03485-3","workflowStages":[]},"version":"v1","identity":"rs-6405697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6405697","identity":"rs-6405697","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}