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The impact of academic anxiety on smartphone addiction among college students: the mediating role of self-regulatory fatigue and the moderating role of mindfulness

BMC Psychology volume 13, Article number: 354 (2025) Cite this article

Abstract

Background

Academic anxiety is recognized as a risk factor of smartphone addiction among college students. However, the underlying mechanisms and moderating factors remain insufficiently explored.

Method

This study examines the impact of academic anxiety on smartphone addiction, focusing on the mediating role of self-regulatory fatigue and the moderating role of mindfulness. Using convenience sampling strategy, a cross-sectional survey was implemented. Data were collected from a survey of 685 college students by using the Academic Anxiety Scale, Smartphone Addiction Scale, Self-Regulatory Fatigue Scale, and Mindfulness Scale. The hypothesized moderated mediation model was analyzed using Hayes’ (2017) PROCESS macro (Model 59).

Results

Moderated mediation analysis finds that academic anxiety significantly positively predicts smartphone addiction (β = 0.30, t = 7.24, 95%CI=[0.22, 0.38], p<0.001) in college students. Academic anxiety also indirectly predicts smartphone addiction through self-regulatory fatigue (β = 0.09, t = 2.43, 95%CI=[0.02, 0.16], p<0.05), with the mediation effect accounting for 11.76%. The mediation pathways are moderated by mindfulness. Compared with students with low mindfulness, the influence of academic anxiety on self-regulatory fatigue is stronger in students with high mindfulness(β = 0.10, t = 3.85, 95%CI=[0.05, 0.16], p<0.001). However, compared with students with low mindfulness, the influence of self-regulatory fatigue on smartphone addiction is weaker in students with high mindfulness(β=-0.08, t= -2.53, 95%CI=[-0.15, -0.02], p<0.05). That is, among individuals with a high level of mindfulness, mindfulness enhances the positive relationship between academic anxiety and self-regulatory fatigue while weakening the positive association between self-regulatory fatigue and smartphone addiction.

Conclusion

These findings elucidate the internal mechanisms linking academic anxiety to smartphone addiction and underscoring the dual role of mindfulness. The results offer valuable insights for developing strategies to prevent and manage smartphone addiction among college students.

Peer Review reports

Introduction

In China, as of January 2025, the number of smartphone users has reached 1.108 billion, with individuals under 30 years old constituting 29.8% of this population [1].The widespread adoption of multifunctional smartphones has significantly enriched people’s daily life, rendering them indispensable for social interaction, entertainment, work, and financial management. However, the pervasive use of smartphones may also lead to numerous adverse effects, notably the increasing prevalence of smartphone addiction [2]. Smartphone addiction refers to the overuse of smartphone along with withdrawal symptoms as well as functional impairment [3]. It poses substantial risks to individuals’ physical and mental health, including impaired sleep quality [4, 5], attention deficits [6], and increased vulnerability to depression [7, 8]. College students are particularly susceptible to smartphone addiction, with an estimated incidence rate of approximately 46% [7]. Previous research demonstrated that smartphone addiction adversely affects students’ academic engagement and achievement [9,10,11]. So, in the context of widespread adoption of smartphones, exploring the influencing factors of smartphone addiction among college students is crucial for improving life quality and mental health of college students.

The Interaction of Person-Affect-Cognition-Execution (I-PACE) model posits that the interaction between personality traits, emotional and cognitive responses, and deficits in self-regulation contributes to the development of Internet use disorders [12]. This model suggests that anxiety, as a distal factor linked to environmental stress, can lead to addictive behaviors through maladaptive cognitive processes. Results of previous studies indicate that anxiety(e.g., general or social anxiety) is a critical factor of smartphone addiction [13,14,15,16,17]. However, there has been limited exploration of the impact of academic anxiety—a specific form of anxiety—on smartphone addiction. In reality, academic anxiety is one of the most frequently experienced negative emotional states among college students [18] and represents a significant risk factor for their mental health [19]. Additionally, recent studies have highlighted the protective role of mindfulness in mitigating self-regulatory fatigue [20] and addictive behaviors [21,22,23]. Based on the I-PACE model, the present study aims to explore whether academic anxiety, a kind of specific negative emotion in education environment is relevant to smartphone addiction among college students, and explore the role of self-regulatory fatigue and mindfulness.

The relationship between academic anxiety and smartphone addiction

Because of traditional cultural values and rapid economic expansion, academic achievement is emphasized in Mainland China. So, mainland Chinese students commonly experience high levels of academic anxiety [24]. Academic anxiety refers to the frequent negative emotions that students experience in academic settings [25], reflecting their emotional responses to academic achievement [26]. According to Agnew’s general strain theory, negative emotion, which is caused by environmental stress, can lead individuals to engage in addictive behaviors as a means of alleviating these emotions [27]. The compensatory function of internet al.lows individuals to manage academic anxiety in a pathological manner, fostering dependency on compensatory tools such as smartphones. Thus, smartphone addiction may serve as a coping mechanism to mitigate the intensity of anxiety in response to perceived threats. Previous research has explored relationship between academic anxiety and smartphone addiction [11, 28]. For example, Zhang and Zeng found that academic anxiety served as a complete mediator in the relationship between smartphone addiction and academic achievement [11]. Besides, Zeng and his colleagues found that academic anxiety played a mediating role in the effect of social isolation on smartphone addiction [29]. Therefore, we hypothesize that academic anxiety may be positively associated with smartphone addiction among college students (H1).

The mediating role of self-regulatory fatigue

Self-regulatory fatigue may mediate the relationship between academic anxiety and smartphone addiction. On the one hand, according to the strength model of self-control [30, 31], short-term self-control activities temporarily deplete self-regulation resources, which leads to the state of ego depletion, and prolonged self-control activities may result in sustained resources depletion, manifesting as self-regulatory fatigue. College students under academic anxiety need to regulate their emotions, which may lead to ego depletion, and students under long-term academic anxiety may have a high level of self-regulation fatigue. Previous research found that academic pressure was positively associated with self-regulatory fatigue [32]. On the other hand, when college students experience long-term depletion of self-regulation resources, they may be addicted to smartphones. Reinforcement sensitivity theory posits that individuals have two motivational systems: the behavior approach system (BAS) and the behavior inhibition system (BIS) [33]. BAS can promotes behaviors that lead to satisfaction and is sensitive to reward stimuli, while BIS inhibits anxiety-inducing behaviors and is sensitive to punishment signals [34]. Self-regulatory fatigue may reduce BIS capacity, thereby increasing BAS sensitivity and leading to impulsive behaviors such as smartphone addiction [35,36,37]. Previous research found that self-regulatory fatigue was positively associated with smartphone addiction [38]. Therefore, we hypothesize that self-regulatory fatigue mediates the relationship between academic anxiety and smartphone addiction (H2).

The moderating role of mindfulness

Mindfulness refers to a conscious mental state characterized by focusing on present-moment experiences without judgement or reaction [39, 40]. Recently, the Monitor and Acceptance theory(MAT) elaborates on two critical elements of mindfulness: attention monitoring and acceptance. Attention monitoring is defined as ongoing awareness of present moment sensory and perceptual experiences. Acceptance is defined as a mental attitude of non-judgment, openness and receptivity, and equanimity toward internal and external experiences [41]. While positive effects of mindfulness have been widely recognized (enhancing mental health, well-being as well as decreasing pressure) [42,43,44], the restriction and side effects of mindfulness are poorly understood [41].

Mindfulness may moderate the relationship between academic anxiety, self-regulatory fatigue, and smartphone addiction. Firstly, it may moderate the relationship between academic anxiety and smartphone addiction. Drawing on Cohen and Wills’ stress-buffering hypothesis, Creswell and Lindsay suggest that mindfulness can buffer stress and mitigate its adverse consequences [45, 46]. Results of previous research indicate that individuals with higher levels of trait mindfulness tend to engage in more frequent reappraisal, which leads to lower levels of craving [47]. So, individuals with high mindfulness are more accepting of their current state [48], and therefore less likely to use smartphones excessively to alter their circumstances, resulting in lower levels of smartphone addiction. Conversely, individuals with low mindfulness are less accepting of their academic anxiety, making them more prone to smartphone addiction as a coping strategy. Therefore, we hypothesize that mindfulness moderates the relationship between academic anxiety and smartphone addiction (H3). Additionally, mindfulness may protect against self-regulatory fatigue under academic anxiety. Mindfulness emphasizes the non-judgmental acceptance of the present moment. So, mindfulness may reduce maladaptive emotional and behavioral reactions, thereby supporting the maintenance and enhancement of self-control [49]. Studies have shown that mindfulness can counteract ego depletion caused by emotional suppression [49]. Previous results of study indicate that mindfulness is relevant to higher HRV, emotional regulation and inner calmness [50], thus reducing ego depletion. Therefore, we hypothesize that mindfulness moderates the relationship between academic anxiety and self-regulatory fatigue (H4). Finally, when individuals experience self-regulatory fatigue, those with higher mindfulness may exhibit lower levels of smartphone addiction. Mindfulness is closely related to response inhibition, with individuals exhibiting higher mindfulness maintaining greater alertness and inhibitory capacity [51]. Intervention studies have shown that mindfulness training enhances control functions, including interference inhibition and sustained attention [52,53,54]. We hypothesize that mindfulness moderates the relationship between self-regulatory fatigue and smartphone addiction (H5).

The present study

Taken together, previous studies have found that academic anxiety is positively relevant to smartphone addiction among college students [11, 28–29]. However, internal mechanisms as well as protective traits between academic anxiety and smartphone addiction remain poorly understood. Based on the I-PACE model, this study designs a moderated mediation model (Fig. 1) to explore the mechanisms through which academic anxiety, self-regulatory fatigue, and mindfulness interact to influence smartphone addiction among college students.The following hypotheses were formulated:

H1 Academic anxiety may be positively associated with smartphone addiction among college students.

H2 Self-regulatory fatigue mediates the relationship between academic anxiety and smartphone addiction.

H3 Mindfulness moderates the relationship between academic anxiety and smartphone addiction.

H4 Mindfulness moderates the relationship between academic anxiety and self-regulatory fatigue.

H5 Mindfulness moderates the relationship between self-regulatory fatigue and smartphone addiction.

Fig. 1
figure 1

Moderated mediation model

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Methods

Participants

This study employed a web-based survey methodology, distributing questionnaires through nationwide university student mutual aid groups. This study used convenience sampling strategy. Out of the 713 responses collected, 685 valid responses were retained after excluding invalid responses characterized by consecutively answering the same option or completing survey with an excessively brief completion time. This resulted in an effective response rate of 96.07%. The final sample comprised 332 males (48.5%) and 353 females (51.5%), with a mean age of 20.45 ± 2.06 years.

Previous study found a significant positive correlation between anxiety levels and smartphone addiction behaviors in university students (r = 0.292, p < 0.001) [55]. Owing to the absence of precise population parameter estimates during the study’s preliminary phase, and considering methodological and sample characteristics alignment (e.g., age demographics, measurement tools) with the cited literature, the reported correlation coefficient was employed as the effect size for a G*Power analysis. The analysis revealed a statistical power (1–β) of 0.99, substantially surpassing the predefined threshold of 0.80. These findings confirm that the current sample size of 685 participants satisfies statistical power requirements, enabling robust detection of hypothesized effects between variables.

Measures

Academic anxiety scale

Academic anxiety was assessed using the Academic Anxiety subscale of the Academic Emotions Questionnaire (AEQ), originally developed by Dong and Yu [56]. This subscale consists of 7 items, such as “Sometimes I feel like my grades are worse than others, and I feel like I’ve let my family and teachers down.” Respondents rated their agreement on a 5-point Likert scale ranging from 1 (not at all true) to 5 (very true). All the items were averaged as the total scores of academic anxiety scale (range from 1 to 5), with higher scores indicating elevated levels of academic anxiety. The Cronbach’s coefficient in this study was 0.91.

Smartphone addiction scale

Based on previous smartphone addiction study [57], smartphone addiction was measured using a 17-item subset from the Mobile Phone Problem Use Scale (MPPUS), which is originally developed by Bianchi and Phillips [58]. An example item is, “I become anxious if I haven’t checked my messages or if my phone is turned off for a while.” Participants rated the frequency of these experiences on a 5-point Likert scale from 1 (never) to 5 (always). All the items were averaged as the total scores of smartphone addiction scale (range from 1 to 5), with higher scores reflecting a greater degree of smartphone addiction. The Cronbach’s coefficient in this study was 0.92.

Self-regulatory fatigue scale

Self-regulatory fatigue was measured using the Chinese version of the Self-Regulatory Fatigue Scale (SRFS), revised by Wang and his colleagues [59]. The SRFS includes 16 items, such as “I try to avoid or think about things that bother me.” Participants rated their agreement on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). All the items were averaged as the total scores of self-regulatory fatigue scale (range from 1 to 5), with higher scores indicated greater levels of self-regulatory fatigue. The Cronbach’s coefficient in this study was 0.86.

Mindfulness scale

Mindfulness was assessed using the Chinese version of the Child and Adolescent Mindfulness Measure (CAMM), revised by Liu and her colleagues [60]. The CAMM consists of 10 items, such as “Some of my feelings are bad, and I think I shouldn’t have those feelings.” Responses were scored on a 5-point Likert scale from 1 (never) to 5 (always). After reverse scoring the relevant items, all the items were averaged as the total scores of mindfulness scale (range from 1 to 5), with higher scores indicated greater mindfulness. In the current study, the Cronbach’s α for this scale was 0.86.

Statistical analysis

This study utilized SPSS 25.0 and the PROCESS macro to analyze the effects of academic anxiety on smartphone addiction, with specific focus on the mediating role of self-regulatory fatigue and the moderating role of mindfulness. Prior to formal analysis, preliminary assumption testing was performed to validate model assumptions.

Results

Common method Bias test

To assess the potential impact of common method bias, we employed Harman’s single-factor test [61]. The analysis revealed eight factors with eigenvalues greater than 1, with the first factor accounting for 31.33% of the total variance, which is below the critical threshold of 40%. This result suggests that common method bias is unlikely to be a significant issue in this study.

Descriptive statistics

As summarized in Table 1, academic anxiety demonstrated statistically significant positive correlations with both self-regulatory fatigue (r = 0.60, p < 0.001) and smartphone addiction (r = 0.61, p < 0.001), alongside a negative correlation with mindfulness (r=-0.67, p < 0.001). Furthermore, self-regulatory fatigue displayed positive associations with smartphone addiction (r = 0.47, p < 0.001) and negative associations with mindfulness (r=-0.53, p < 0.001). Notably, mindfulness exhibited the negative relationship with smartphone addiction (r=-0.67, p < 0.001).

Table 1 Bivariate correlations, means, and standard deviations (N = 685)
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Relationship between academic anxiety and smartphone addiction: testing the moderated mediation effect

This study assessed multicollinearity among independent variables using the variance inflation factor (VIF). Results indicated that all VIF values fell below the critical threshold of 10 (academic anxiety = 2.27, self-regulatory fatigue = 1.66, mindfulness = 2.30), confirming the absence of significant multicollinearity issues. The hypothesized moderated mediation model was subsequently analyzed using Hayes’ (2017) PROCESS macro (Model 59). To ensure robust parameter estimation, bootstrap resampling with 5,000 iterations was implemented [62]. The results (see Table 2) showed that, after accounting for the mediator (self-regulatory fatigue), the moderator (mindfulness), the interaction term between mindfulness and academic anxiety, and the interaction term between mindfulness and self-regulatory fatigue, the direct effect of academic anxiety on smartphone addiction remained significant (β = 0.30, SE = 0.04, 95%CI=[0.22,0.38], p < 0.001), supporting Hypothesis 1. Additionally, academic anxiety positively predicted self-regulatory fatigue (β = 0.09, SE = 0.04, 95%CI=[0.02, 0.16], p < 0.05), and self-regulatory fatigue positively predicted smartphone addiction (β = 0.09, SE = 0.04, 95% CI = [0.02, 0.16], p < 0.05), indicating that academic anxiety influences smartphone addiction both directly and indirectly via self-regulatory fatigue, with the mediation effect accounting for 11.76% of the total effect, confirming Hypothesis 2.

Table 2 Moderated mediation analysis of the impact of academic anxiety on smartphone addiction
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Further, the interaction between academic anxiety and mindfulness did not significantly predict smartphone addiction (β=-0.02, SE = 0.03, 95% CI = [-0.08,0.04], p = 0.59), indicating that mindfulness does not moderate the direct effect of academic anxiety on smartphone addiction. However, the interaction between academic anxiety and mindfulness significantly predicted self-regulatory fatigue (β = 0.10, SE = 0.03, 95% CI = [0.05,0.16], p < 0.001), suggesting that mindfulness moderates the relationship between academic anxiety and self-regulatory fatigue. Simple slope analysis (Fig. 2) revealed that for individuals with low mindfulness (M − 1SD), academic anxiety significantly predicted self-regulatory fatigue (simple slope = 0.29, t = 5.05, p < 0.001), and this effect was even stronger among those with high mindfulness (M + 1SD, simple slope = 0.49, t = 11.91, p < 0.001), which contradicts Hypothesis 4. Additionally, the interaction between self-regulatory fatigue and mindfulness significantly predicted smartphone addiction (β = -0.08, SE = 0.03, 95% CI = [-0.15,-0.02], p < 0.05), indicating that mindfulness also moderates the effect of self-regulatory fatigue on smartphone addiction. Simple slope analysis (Fig. 3) showed that for individuals with low mindfulness (M − 1SD), self-regulatory fatigue significantly predicted smartphone addiction (simple slope = 0.17, t = 3.28, p < 0.01), whereas for those with high mindfulness (M + 1SD), this effect was not significant (simple slope = 0.004, t = 0.10, p = 0.92), supporting Hypothesis 5.

Fig. 2
figure 2

The moderating role of mindfulness in the influence of academic anxiety on self-regulating fatigue

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Fig. 3
figure 3

The moderating role of mindfulness in the influence of self-regulating fatigue on smartphone addiction

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Discussion

The main contribution of our study is to explore how self-regulatory fatigue and mindfulness influence the relationship between academic anxiety and smartphone addiction among Chinese university students. According to the conceptual model, our study employed a moderated mediation analysis to reveal three findings. First, academic anxiety was significantly related to higher levels of smartphone addiction. Second, self-regulatory fatigue functions as a mediator in the relationship between academic anxiety and smartphone addiction. Third, the mediation model was moderated by mindfulness, with mindfulness increasing self-regulatory fatigue when academic anxiety was higher and decreasing smartphone addiction when university students experienced self-regulatory fatigue. These findings offer practical implications for the development of interventions aimed at mitigating smartphone addiction among university students.

The effect of academic anxiety on smartphone addiction

The present study found that academic anxiety is a significant positive predictor of smartphone addiction, thus confirming Hypothesis 1. This finding aligns with prior studies [11, 28–29]. Additionally, the current findings provided empirical support for the General Strain Theory. Under the pressure of academic demands, college students may turn to digital outlets, such as smartphones, as a coping mechanism to alleviate their academic anxiety, thereby achieving psychological compensation [63]. However, excessive reliance on smartphones as a compensatory tool can foster dependence, ultimately leading to smartphone addiction.

The mediating role of self-regulatory fatigue

While previous studies have found a positive relationship between academic anxiety and smartphone addiction [11, 29], the underlying mechanisms remain unexplored. This study addresses this gap by examining the mediating role of self-regulatory fatigue. The results indicate that academic anxiety contributes to smartphone addiction via self-regulatory fatigue, thus confirming Hypothesis 2. Firstly, according to the Strength Model of Self-Control, anxiety, as a negative emotion, can deplete self-regulatory resources and leads to a state of self-regulatory depletion [30]. Previous studies found that anxiety was related with self depletion [64, 65]. This study finds that academic anxiety increases levels of self-regulatory fatigue among college students. Secondly, in a state of self-regulatory fatigue, the immediate psychological rewards provided by personalized smartphone services can exacerbate the risk of smartphone addiction. Prior research has linked self-regulatory depletion to both smartphone and internet addiction [66, 67]. Our findings suggest that persistent self-regulatory fatigue functions as a potential mechanism underlying the relationship between academic anxiety and smartphone addiction.

The moderating role of mindfulness

This study also examined the moderating role of mindfulness. The results reveal that mindfulness moderates both the pathway from academic anxiety to self-regulatory fatigue and the pathway from self-regulatory fatigue to smartphone addiction. In the first pathway, individuals with high levels of mindfulness exhibited a stronger positive relationship between academic anxiety and self-regulatory fatigue, which was contrary to Hypothesis 4. Mindfulness involves the monitoring of bodily responses and the awareness of emotional intensity [68]. Although mindfulness is often seen as a positive personality trait in the clinical intervention, some research indicate that the monitoring function of mindfulness may have adverse effects. Research found that attention monitoring could increase stress [69]. Some researches found that training of attention monitoring could trigger trauma and increase blood pressure and cortisol levels [69, 70]. In particular, self-focused and excessive attention monitoring may increase cortisol stress responses [69], exacerbate symptoms of depression and anxiety [71], reduce pain tolerance [72], and even worsen episodes of certain mental disorders [73]. So, due to the attention monitoring function of mindfulness, high mindfulness traits may exacerbate negative experiences, such as academic anxiety. According to the Strength Model of Self-Control [30], regulating such heightened emotional states demands greater self-control resources, leading to increased self-regulatory fatigue among individuals with high levels of mindfulness.Therefore, in the context of academic anxiety, the heightened awareness facilitated by mindfulness may intensify emotional responses, thereby exacerbating self-regulatory fatigue.

In the second pathway, the study found that individuals with high mindfulness exhibited a weaker positive relationship between self-regulatory fatigue and smartphone addiction, consistent with Hypothesis 5. This finding supports the protective role of mindfulness against addictive behaviors, as observed in previous research [47]. Mindfulness encourages non-judgmental acceptance of one’s current experiences, allowing individuals to manage their thoughts and feelings without becoming overwhelmed by them [74]. Consequently, college students with high mindfulness may be less likely to engage in critical self-evaluation during states of self-regulatory fatigue and more willing to accept their situation, thereby reducing their risk of smartphone addiction. Therefore, enhancing mindfulness among college students represents a promising strategy for preventing smartphone addiction.

Unlike Hypothesis 3, mindfulness doesn’t moderate the effect of academic anxiety on smartphone addiction. This may be due to the reason that relationship between academic anxiety and smartphone addiction may be automatic. According to Agnew’s General Strain Theory, academic anxiety may lead students to engage in smartphone addictive behaviors as a means of alleviating anxiety [27]. So, smartphone addiction of academic anxiety students may be an automatic progress. Another explanation may consider the fact that most of university students doesn’t accept mindfulness training. So, their mindfulness trait may tend to remain in a natural state. Future studies may explore whether mindfulness training can buffer academic anxiety’s impact on smartphone addiction.

Limitations

Firstly, mindfulness consists of two core components: monitoring and acceptance [41]. Future research should explore the independent effects of these components on mitigating smartphone addiction. Secondly, previous studies found that prevention training(e.g., mindfulness-based stress reduction therapy, with meditation as its core practice [75])and daily practice(e.g., Yoga and Tai Chi [76, 77]) could increase the level of mindfulness. In the future, intervention study is needed to explore whether mindfulness training can reduce the level of smartphone addiction among students with high academic anxiety. Thirdly, this is a cross-sectional study. Any causal relationship based on the associations observed in our study should be inferred cautiously. Further researches are needed to increase its enhance. Laboratory experiment are needed to explore whether manipulating academic anxiety could lead to the increase of smartphone addiction inclination. For example, whether academic anxiety priming (e.g., imagining failing an exam that may lead to delayed graduation; recalling experiences of exam failures) can lead to higher willingness of using smartphone among university students? Besides, future research could employ a cross-lagged panel design to examine the prediction direction of the relationships between academic anxiety and smartphone addiction. Fourthly, this study was focused on students from Chinese universities, lacking support from sample data from other regions, which restricts the generalizability of the conclusions. In light of this, future research could explore relationships between smartphone addiction, self-regulatory fatigue, mindfulness, and academic anxiety by including samples from diverse geographical regions. Finally, all data were collected through self-report questionnaires. So, recall bias and the subject-expectancy effect may be difficult to avoid. Peer nomination and behavioral tasks could be added to make the conclusion more realistic and reliable in the future.

Conclusion

In summary, this study found that: (1) academic anxiety among college students is significantly positively correlated with self-regulatory fatigue and smartphone addiction, and self-regulatory fatigue is significantly positively correlated with smartphone addiction; (2) self-regulatory fatigue partially mediates the relationship between academic anxiety and smartphone addiction; (3) mindfulness moderates the mediating role of self-regulatory fatigue in the pathways between academic anxiety and smartphone addiction. Specifically, compared to individuals with low mindfulness, those with high mindfulness experience a stronger effect of academic anxiety on self-regulatory fatigue but a weaker effect of self-regulatory fatigue on smartphone addiction.

Data availability

The datasets used and/or analyses during the current study are available from the corresponding author on reasonable request.

References

  1. China Internet Network Information Center. The 55th Statistical Report on China’s Internet Development.https://www.cnnic.cn/n4/2025/0117/c88-11229.html, 2025-1-17.

  2. Chen L, Yan Z, Tang W, et al. Mobile phone addiction levels and negative emotions among Chinese young adults: the mediating role of interpersonal problems. Comput Hum Behav. 2016;55:856–66.

    Article  Google Scholar 

  3. Lin YH, Chang LR, Lee YH, et al. Development and validation of the smartphone addiction inventory (SPAI). PLoS ONE. 2014;9(6):e98312.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wu R, Niu Q, Wang Y, et al. The impact of problematic smartphone use on sleep quality among Chinese young adults: investigating anxiety and depression as mediators in a three-wave longitudinal study. Psychol Res Behav Manage. 2024;17:1775–86.

    Article  Google Scholar 

  5. Zhang MX, Wu AM. Effects of smartphone addiction on sleep quality among Chinese university students: the mediating role of self-regulation and bedtime procrastination. Addict Behav. 2020;111:106552.

    Article  PubMed  Google Scholar 

  6. Zheng F, Gao P, He M, et al. Association between mobile phone use and inattention in 7102 Chinese adolescents: A population-based cross-sectional study. BMC Public Health. 2014;14(1):1–7.

    Article  Google Scholar 

  7. Jun S. The reciprocal longitudinal relationships between mobile phone addiction and depressive symptoms among Korean adolescents. Comput Hum Behav. 2016;58:179–86.

    Article  Google Scholar 

  8. Yang X, Zhou Z, Liu Q, et al. Mobile phone addiction and adolescents’ anxiety and depression: the moderating role of mindfulness. J Child Fam Stud. 2019;28(3):822–30.

    Article  Google Scholar 

  9. Kates AW, Wu H, Coryn CL. The effects of mobile phone use on academic performance: A meta-analysis. Comput Educ. 2018;127:107–12.

    Article  Google Scholar 

  10. Zhang Y, Qin X, Ren P. Adolescents’ academic engagement mediates the association between internet addiction and academic achievement: the moderating effect of classroom achievement norm. Comput Hum Behav. 2018;89:299–307.

    Article  Google Scholar 

  11. Zhang J, Zeng Y. Effect of college students’ smartphone addiction on academic achievement: the mediating role of academic anxiety and moderating role of sense of academic control. Psychol Res Behav Manage. 2024;17:933–44.

    Article  Google Scholar 

  12. Davis RA. A cognitive-behavioral model of pathological internet use. Comput Hum Behav. 2001;17(2):187–95.

    Article  Google Scholar 

  13. Elhai JD, Gallinari EF, Rozgonjuk D, et al. Depression, anxiety and fear of missing out as correlates of social, non-social and problematic smartphone use. Addict Behav. 2020;105:106335.

    Article  PubMed  Google Scholar 

  14. Enez Darcin A, Kose S, Noyan CO, et al. Smartphone addiction and its relationship with social anxiety and loneliness. Behav Inform Technol. 2016;35(7):520–25.

    Article  Google Scholar 

  15. Gao T, Li J, Zhang H, et al. The influence of alexithymia on mobile phone addiction: the role of depression, anxiety and stress. J Affect Disord. 2018;225:761–66.

    Article  PubMed  Google Scholar 

  16. Matar Boumosleh J, Jaalouk D. Depression, anxiety, and smartphone addiction in university students-A cross sectional study. PLoS ONE. 2017;12(8):e0182239.

    Article  PubMed  PubMed Central  Google Scholar 

  17. You Z, Zhang Y, Zhang L, et al. How does self-esteem affect mobile phone addiction? The mediating role of social anxiety and interpersonal sensitivity. Psychiatry Res. 2019;271:526–31.

    Article  PubMed  Google Scholar 

  18. Collie RJ, Ginns P, Martin AJ, et al. Academic buoyancy mediates academic anxiety’s effects on learning strategies: an investigation of English-and Chinese-speaking Australian students. Educational Psychol. 2017;37(8):947–64.

    Article  Google Scholar 

  19. Pekrun R, Elliot AJ, Maier MA. Achievement goals and achievement emotions: testing a model of their joint relations with academic performance. J Educ Psychol. 2009;101(1):115–35.

    Article  Google Scholar 

  20. Zhang X, Wang Y, Lu H, et al. The effects of mindfulness-based training intervention on employees’ ego depletion and work engagement: A field experiment based on ESM. Manage Rev. 2022;34(8):192–204. In Chinese.

    Google Scholar 

  21. Chang H, Meng X, Li Y, et al. The effect of mindfulness on social media addiction among Chinese college students: A serial mediation model. Front Psychiatry. 2023;14:1087909.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jiang W, Liu S, Liu M et al. The relationship between mindfulness and academic burnout in senior high school students during COVID-19 pandemic: the chain mediating role of social anxiety and smartphone addiction tendency. Curr Psychol. 2024;1–10.

  23. Li D, Xu Y, Cao S. How does trait mindfulness weaken the effects of risk factors for adolescent smartphone addiction? A moderated mediation model. Behav Sci. 2023;13(7):540.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hesketh T, Ding QJ. Anxiety and depression in adolescents in urban and rural China. Psychol Rep. 2005;96:435–44.

    Article  PubMed  Google Scholar 

  25. Martin AJ. Examining a multidimensional model of student motivation and engagement using a construct validation approach. Br J Educ Psychol. 2007;77(2):413–40.

    Article  PubMed  Google Scholar 

  26. Pekrun R, Goetz T, Titz W, et al. Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychol. 2002;37(2):91–105.

    Article  Google Scholar 

  27. Agnew R. Foundation for a general strain theory of crime and delinquency. Criminology. 1992;30(1):47–88.

    Article  Google Scholar 

  28. Yang Z, Asbury K, Griffiths MD. An exploration of problematic smartphone use among Chinese university students: associations with academic anxiety, academic procrastination, self-regulation and subjective wellbeing. Int J Mental Health Addict. 2019;17:596–614.

    Article  Google Scholar 

  29. Zeng Y, Zhang J, Wei J, et al. The impact of undergraduates’ social isolation on smartphone addiction: the roles of academic anxiety and social media use. Int J Environ Res Public Health. 2022;19(23):15903.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Baumeister RF, Vohs KD, Tice DM. The strength model of self-control. Curr Dir Psychol Sci. 2007;16(6):351–55.

    Article  Google Scholar 

  31. Nes LS, Ehlers SL, Whipple MO, et al. Self-regulatory fatigue in chronic multi-symptom illnesses: scale development, fatigue, and self-control. J Pain Res. 2013;6:181–88.

    PubMed  PubMed Central  Google Scholar 

  32. Yuhuan Z, Pengyue Z, Dong C, et al. The association between academic stress, social support, and self-regulatory fatigue among nursing students: a cross-sectional study based on a structural equation modelling approach. BMC Med Educ. 2022;22(1):789.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gray JA. Perspectives on anxiety and impulsivity: A commentary. J Res Pers. 1987;21(4):493–509.

    Article  Google Scholar 

  34. Smillie LD, Pickering AD, Jackson CJ. The new reinforcement sensitivity theory: implications for personality measurement. Personality Social Psychol Rev. 2006;10(4):320–35.

    Article  Google Scholar 

  35. Dong G, Lu Q, Zhou H, et al. Impulse Inhibition in people with internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neurosci Lett. 2010;485(2):138–42.

    Article  PubMed  Google Scholar 

  36. Khang H, Kim JK, Kim Y. Self-traits and motivations as antecedents of digital media flow and addiction: the internet, mobile phones, and video games. Comput Hum Behav. 2013;29(6):2416–24.

    Article  Google Scholar 

  37. Schmeichel BJ, Harmon-Jones C, Harmon-Jones E. Exercising self-control increases approach motivation. J Personal Soc Psychol. 2010;99(1):162–73.

    Article  Google Scholar 

  38. Wang C, Lei L, Qiao X. The mediating effects of pursuing pleasure between self-regulatory fatigue and smartphone addiction among undergraduate students: the moderating effects of connectedness to nature. Psychol Dev Educ. 2021;37(4):601–8. In Chinese.

    Google Scholar 

  39. Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Personal Soc Psychol. 2003;84(4):822–48.

    Article  Google Scholar 

  40. Kabat-Zinn J. Mindfulness–based interventions in context: past, present, and future. Clin Psychol Sci Pract. 2003;10:144–56.

    Article  Google Scholar 

  41. Lindsay EK, Creswell JD. Mechanisms of mindfulness training: monitor and acceptance theory (MAT). Clin Psychol Rev. 2017;51:48–59.

    Article  PubMed  Google Scholar 

  42. Keng SL, Smoski MJ, Robins CJ. Effects of mindfulness on psychological health: A review of empirical studies. Clin Psychol Rev. 2011;31(6):1041–56.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Nyklíček I, Kuijpers KF. Effects of mindfulness-based stress reduction intervention on psychological well-being and quality of life: is increased mindfulness indeed the mechanism? Ann Behav Med. 2008;35(3):331–40.

    Article  PubMed  Google Scholar 

  44. You C, Liu Y. The effect of mindfulness on online self-presentation, pressure, and addiction on social media. Front Psychol. 2022;13:1034495.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57.

    Article  PubMed  Google Scholar 

  46. Creswell JD, Lindsay EK. How does mindfulness training affect health? A mindfulness stress buffering account. Curr Dir Psychol Sci. 2014;23(6):401–7.

    Article  Google Scholar 

  47. Garland EL, Roberts-Lewis A, Kelley K, et al. Cognitive and affective mechanisms linking trait mindfulness to craving among individuals in addiction recovery. Subst Use Misuse. 2014;49(5):525–35.

    Article  PubMed  Google Scholar 

  48. Bishop SR, Lau M, Shapiro S, et al. Mindfulness: A proposed operational definition. Clin Psychol Sci Pract. 2004;11(3):230–41.

    Article  Google Scholar 

  49. Friese M, Messner C, Schaffner Y. Mindfulness meditation counteracts self-control depletion. Conscious Cogn. 2012;21(2):1016–22.

    Article  PubMed  Google Scholar 

  50. Burg JM, Wolf OT, Michalak J. Mindfulness as self-regulated attention. Swiss J Psychol. 2012;71(3):135–39.

    Article  Google Scholar 

  51. Waters AJ, Reitzel LR, Cinciripini P, et al. Associations between mindfulness and implicit cognition and self-reported affect. Substance Abuse. 2009;30(4):328–37.

    Article  PubMed  Google Scholar 

  52. Ivanovski B, Malhi GS. The psychological and neurophysiological concomitants of mindfulness forms of meditation. Acta Neuropsychiatrica. 2007;19(2):76–91.

    Article  PubMed  Google Scholar 

  53. Wang Y, Xin T, Liu X, et al. Mindfulness can reduce automatic responding: evidences from Stroop task and prospective memory task. Acta Psychol Sinica. 2012;44(9):1180–88. In Chinese.

    Article  Google Scholar 

  54. Wenk-Sormaz H. Meditation can reduce habitual responding. Altern Ther Health Med. 2005;11(2):42–58.

    PubMed  Google Scholar 

  55. Ju H, Lv J. Current status and influencing factors of anxiety and mobile phone addiction among college students. Chin J Health Stat. 2022;39(4):555–57. In Chinese.

    Google Scholar 

  56. Dong Y, Yu G. The development and application of an academic emotions questionnaire. Acta Psychol Sinica. 2007;39(5):852–60. In Chinese.

    Google Scholar 

  57. Leung L. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. J Child Media. 2008;2(2):93–113.

    Article  Google Scholar 

  58. Bianchi A, Phillips JG. Psychological predictors of problem mobile phone use. Cyberpsychology Behav. 2005;8(1):39–51.

    Article  Google Scholar 

  59. Wang L, Zhang J, Wang J, et al. Validity and reliability of the Chinese version of the Self-Regulatory fatigue scale in young adults. Chin Mental Health J. 2015;29(4):290–94. In Chinese.

    Google Scholar 

  60. Liu X, Chi X, Zhang J, et al. Validation of child and adolescent mindfulness measure (CAMM) in Chinese adolescents. Psychol Explor. 2019;39(3):250–56. In Chinese.

    Google Scholar 

  61. Zhou H, Long L. Statistical remedies for common method biases. Adv Psychol Sci. 2004;12(6):942–50. In Chinese.

    Google Scholar 

  62. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford; 2017.

  63. Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Hum Behav. 2014;31(1):351–54.

    Article  Google Scholar 

  64. Prem R, Kubicek B, Diestel S, et al. Regulatory job stressors and their within-person relationships with ego depletion: the roles of state anxiety, self-control effort, and job autonomy. J Vocat Behav. 2016;92:22–32.

    Article  Google Scholar 

  65. Fan J, Cheng Y, Tang M, et al. The mediating role of ego depletion in the relationship between state anxiety and academic procrastination among university students. Sci Rep. 2024;14(1):15568.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Ding Q, Zhang Y, Zhou Z. Fear of missing out and social networking sites addiction: A moderated mediation model. J Psychol Sci. 2020;27(5):1140–46. In Chinese.

    Google Scholar 

  67. Wang S, Zhang Y, Zhang Y, et al. The effect of intolerance of uncertainty on smartphone addiction: A moderated mediation model of self-regulatory fatigue and feeling of the passage of time. Curr Psychol. 2024;43:17118–30.

    Article  Google Scholar 

  68. Chin B, Lindsay EK, Greco CM, et al. Psychological mechanisms driving stress resilience in mindfulness training: A randomized controlled trial. Health Psychol. 2019;38(8):759–68.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Engert V, Kok BE, Papassotiriou I, et al. Specific reduction in cortisol stress reactivity after social but not attention-based mental training. Sci Adv. 2017;3(10):e1700495.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Dobkin PL, Irving JA, Amar S. For whom May participation in a mindfulness-based stress reduction program be contraindicated? Mindfulness. 2012;3:44–50.

    Article  Google Scholar 

  71. Sahdra BK, Ciarrochi J, Parker PD, et al. Are people mindful in different ways? Disentangling the quantity and quality of mindfulness in latent profiles and exploring their links to mental health and life effectiveness. Eur J Pers. 2017;31(4):347–65.

    Article  Google Scholar 

  72. Evans DR, Eisenlohr-Moul TA, Button DF, et al. Self‐regulatory deficits associated with unpracticed mindfulness strategies for coping with acute pain. J Appl Soc Psychol. 2014;44(1):23–30.

    Article  PubMed  Google Scholar 

  73. Eisenlohr-Moul TA, Walsh EC, Charnigo RJ, et al. The what and the how of dispositional mindfulness: using interactions among subscales of the Five-Facet mindfulness questionnaire to understand its relation to substance use. Assessment. 2012;19(3):276–86.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Lindsay EK, Young S, Brown KW et al. Mindfulness training reduces loneliness and increases social contact in a randomized controlled trial. Proceedings of the National Academy of Sciences. 2019;116(9):3488-93.

  75. Khoury B, Sharma M, Rush SE, et al. Mindfulness-based stress reduction for healthy individuals: A meta-analysis. J Psychosom Res. 2015;78(6):519–28.

    Article  PubMed  Google Scholar 

  76. Salmon P, Lush E, Jablonski M, et al. Yoga and mindfulness: clinical aspects of an ancient mind/body practice. Cogn Behav Pract. 2009;16(1):59–72.

    Article  Google Scholar 

  77. Wall RB. Tai Chi and mindfulness-based stress reduction in a Boston public middle school. J Pediatr Health Care. 2005;19(4):230–37.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank the participants for their involvement in this study.

Funding

This study was funded by the 2020 13th 5-years plan of national sciences of education sciences, Young Teachers Research Program of the Ministry of Education (EHA200421).

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Author notes

  1. Linghao Kong and Mingzhe Zhao are co-first authors who contribute equally to this work.

Authors and Affiliations

  1. School of Education, Huazhong University Of Science And Technology, No. 1037 Luoyu Road, Hongshan District, Wuhan, Hubei Province, China

    Linghao Kong & Mingzhe Zhao

  2. Research Center of Lushan Culture, Jiujiang University, Jiujiang, China

    Weijun Huang

  3. Psychological Therapy Department, The Fifth People’s Hospital Of Jiujiang, Jiujiang, China

    Weijuan Zhang

  4. School of Education Science, Gannan Normal University, Ganzhou, China

    Junlin Liu

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  1. Linghao Kong
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Contributions

Linghao Kong: conceptualization, Methodology, Formal analysis and investigation, original draft preparation, Funding acquisition, review and editing; Mingzhe Zhao: conceptualization, Formal analysis and investigation, original draft preparation, review and editing, Supervision; Weijun Huang: Edited and revised, Funding acquisition, Supervision; Weijuan Zhang: conceptualization, Funding acquisition, Supervision; Junlin Liu: conceptualization, Methodology, Formal analysis and investigation.

Corresponding author

Correspondence to Mingzhe Zhao.

Ethics declarations

Ethics approval and consent to participate

This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of The Fifth People’s Hospital Of Jiujiang (No.jjwy202423). Informed consent was obtained from all participants involved in this study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Kong, L., Zhao, M., Huang, W. et al. The impact of academic anxiety on smartphone addiction among college students: the mediating role of self-regulatory fatigue and the moderating role of mindfulness. BMC Psychol 13, 354 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40359-025-02696-y

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Keywords

BMC Psychology

ISSN: 2050-7283

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