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The role of mind wandering and anxiety in the association between internet addiction and hyperactivity-impulsivity: a serial mediation model

BMC Psychology volume 13, Article number: 345 (2025) Cite this article

Abstract

Background

Hyperactivity-Impulsivity have significant negative effects on adolescents’ academic performance, physical and mental health, and social relationships. This study aims to deeply explore the relationship between Hyperactivity-Impulsivity in adolescents and Internet Addiction. Unlike previous studies, this study further explores a potential serial mediation model involving Mind Wandering and Anxiety.

Methods

A total of 2042 adolescents completed assessments using the Internet Addiction Test (IAT), the Mind Wandering Questionnaire (MWQ), the Generalized Anxiety Disorder 2(GAD-2), and the ASRS short scale to evaluate Internet Addiction, Mind Wandering, Anxiety, and Hyperactivity-Impulsivity, respectively.

Results

Internet Addiction, Mind Wandering, and Anxiety significantly influence adolescents’ Hyperactivity-Impulsivity (p <.001). Mediation analysis further indicates that Internet Addiction is associated with Hyperactivity-Impulsivity through the serial mediating effects of Mind Wandering and Anxiety(p <.01). These findings highlight Mind Wandering and Anxiety as key mediators in the link between Internet Addiction and Hyperactivity-Impulsivity in adolescents.

Conclusions

This study sheds light on how Internet Addiction influences Hyperactivity-Impulsivity among adolescents and underscores the importance of preventive measures. We recommend implementing interventions aimed at fostering healthy Internet usage habits and providing robust mental health support to safeguard adolescents’ physical and mental well-being.

Peer Review reports

Introduction

With the rapid development of digital technology, the prevalence of Internet Addiction (IA) among adolescents has become a growing concern [1]. IA is characterized by excessive and uncontrolled internet use, leading to significant impairment in daily life, academic performance, and mental health [2]. A growing body of research suggests that IA is closely linked to cognitive and emotional dysregulation, particularly in relation to Hyperactivity-Impulsivity(HI) [3], a core component of Attention-Deficit/Hyperactivity Disorder (ADHD). However, the mechanisms underlying the IA-HI relationship remain underexplored, warranting further investigation into the cognitive and neural pathways linking excessive internet use to impulsive behaviors in adolescents.

The Interaction of Person-Affect-Cognition-Execution (I-PACE) model, a widely recognized theoretical framework, provides valuable insights into the psychological and neurobiological mechanisms underlying IA [4]. According to the I-PACE model, individuals with IA exhibit impairments in cognitive control, heightened impulsivity, and dysfunctional reward processing, making them more vulnerable to excessive internet use [5]. These deficits are particularly relevant to adolescents with HI, who already experience challenges in executive function and impulse regulation [6]. The model posits that repeated exposure to rewarding online stimuli (e.g., social media, gaming) strengthens maladaptive reinforcement patterns, further exacerbating impulsive tendencies and attentional deficits [6, 7]. Thus, IA may serve as both a consequence and a reinforcing factor for HI, creating a cycle of cognitive and behavioral dysregulation.

Furthermore, executive dysfunction, impulsivity, and reward-processing deficits play a critical role in linking IA to HI. Individuals with IA often display impairments in inhibitory control [8], making it difficult to regulate online engagement and disengage from digital content. These deficits overlap significantly with HI, where impulsivity and poor executive control contribute to difficulties in delaying gratification and maintaining sustained attention. Moreover, IA is associated with altered reward system activity, particularly in the prefrontal cortex and striatum, regions implicated in impulsivity and decision-making [9]. Dysregulated reward sensitivity in adolescents with HI may heighten susceptibility to IA, as they are more prone to seeking immediate gratification through online activities [9, 10].

Despite growing evidence supporting the IA-HI relationship, most studies have examined this association unidirectionally—focusing on how IA contributes to impulsivity and attentional problems. However, HI traits themselves may increase susceptibility to IA [11, 12]. Adolescents with higher levels of impulsivity and inattention may have a greater tendency to engage in excessive internet use as a means of stimulation-seeking or coping with cognitive difficulties [13]. This bidirectional relationship suggests that IA and HI may reinforce each other over time, contributing to a maladaptive cycle of compulsive digital engagement and impaired self-regulation [14,15,16]. Future research should consider longitudinal designs to clarify the causal dynamics between these two constructs.

Given these theoretical perspectives, it is crucial to explore the cognitive and emotional pathways underlying IA and HI. Mind Wandering (MW) and Anxiety have been identified as key mediators that may help explain this relationship [17,18,19]. MW, characterized by frequent shifts of attention away from the present task, is linked to both IA and HI [20,21,22], as excessive digital engagement disrupts attentional control mechanisms [23]. Similarly, Anxiety may exacerbate IA-related compulsive behaviors, as individuals turn to the internet as a coping mechanism for emotional distress [24]. Investigating these mediators can provide a more comprehensive understanding of how IA influences impulsivity and attentional difficulties in adolescents.

Therefore, this study aims to investigate the mediating role of MW and anxiety in the IA-HI relationship based on the I-PACE model (Fig. 1). By combining cognitive and affective mechanisms, this study aims to provide a more nuanced understanding of the interaction between IA and HI, providing valuable insights for research and intervention strategies, especially in situations such as the reduction in social interactions experienced during the COVID-19 pandemic. Ultimately, we seek to deepen our understanding of how digital media influences neurodevelopmental disorders and improve strategies to prevent excessive Internet use.

Fig. 1
figure 1

The model of the relationship between Internet Addiction and Hyperactivity-Impulsivity

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Materials and procedures

Participates and procedures

This study used a stratified cluster sampling method to recruit 2042 adolescents from middle and high schools in Hunan Province, China, with a mean age of 13.69 years (SD = 1.55), including 1086 boys and 956 girls, of which 1256 were enrolled in middle schools and 786 in high schools, covering a wide range of adolescent groups in this age range. Participants completed self-report questionnaires in a classroom setting under the supervision of trained research assistants. Informed consent was obtained from students and their guardians before participation. This study collected self-report questionnaires from participants, and there was a lack of descriptions of participants’ situations by their teachers or parents. This project has received ethical approval from the Ethics Review Committee of the Second Xiangya Hospital of Central South University, ensuring compliance with ethical standards and procedural integrity.

Measurement

Internet addiction

The Internet Addiction Test (IAT), established by Young in 1998, is a tool used to measure excessive internet usage. It comprises 20 questions designed to identify dysfunctional behaviors resulting from overuse, such as poor time management and reduced performance in other life areas. This research utilizes the IAT-7 version, where responses range from 1 to 4, indicating frequency of use [25]. A higher score suggests a stronger predisposition to Internet Addiction. The reliability of this version is confirmed by a Cronbach’s alpha of 0.84.

Mind wandering

The Mind Wandering Questionnaire (MWQ) quantifies the occurrence of off-task thoughts(Ju et al., 2016). It contains five items, rated on a 5-point Likert scale from 1 (rarely) to 5 (frequently), with total scores between 5 and 25. Higher scores denote more frequent mind wandering, indicative of regular attention shifts away from tasks [26]. The MWQ’s validity for different age groups, especially adolescents, is supported by a Cronbach’s alpha of 0.84 in this study.

Anxiety

The Generalized Anxiety Disorder-2 (GAD-2) scale is a brief self-report instrument that measures anxiety symptoms experienced in the past two weeks [27] and is assessed on a 4-point Likert scale (0 = not at all, 1 = a few days, 2 = more than half the days, and 3 = almost every day). The GAD-2 includes two items that assess the frequency of feeling nervous, anxious, or restless and being unable to stop or control worrying, with scores ranging from 0 to 6. Although the GAD-2 is a brief assessment that does not capture the full range of anxiety symptoms as comprehensively as longer scales such as the GAD-7, previous research has shown that the GAD-2 has strong diagnostic accuracy and correlates well with longer anxiety measures without compromising the quality of the data collected [28, 29]. The scale has good reliability and validity, with a Cronbach α of 0.899 in this study.

Attention-Deficit/Hyperactivity symptom

The ASRS Short Version Scale, derived from the Adult ADHD Symptom Inventory and aligned with DSM-IV-TR criteria, provides a swift assessment for potential ADHD in adults [30]. The short version of the scale includes six key items on two dimensions: inattention and hyperactivity-impulsivity. Although originally designed for adults, previous research has demonstrated its applicability to adolescent populations. To ensure its applicability, minor changes were made to the wording of some items to better align with adolescent experiences [31, 32]. Rated over a six-month period on a 5-point scale, the scale’s adaptation in this study, named ASRS-CSVN, demonstrated strong reliability and validity, evidenced by a Cronbach’s alpha of 0.81 and robust model fit indices (CFI = 0.99, RMSEA = 0.05, SRMR = 0.02). This instrument was used to evaluate ADHD behavioral traits in the study.

Statistical analyses

Data cleaning and coding were performed in SPSS 21.0, with further analysis conducted in JASP 0.17. To address common method bias, principal component analysis was employed. Descriptive statistics for demographic information were compiled, including the calculation of means and standard deviations. Partial correlations were computed to examine relationships between variables. Serial mediation analysis was performed using the PROCESS tool, and bootstrapped confidence intervals were used to assess the significance of indirect effects. Specifically, we performed 5,000 bias-corrected bootstrapped resamplings, and a 95% confidence interval that did not contain zero was considered evidence of a significant mediation effect.

Results

Common method bias tests

This study uses the Harman single factor test to evaluate common method bias. By performing unrotated exploratory factor analysis (EFA) on all measurement variables, a single factor is forced to be extracted to test potential bias. The results show that the cumulative variance explained by the extracted single factor is 20%, which is significantly lower than the critical threshold of 40%, indicating that there is no serious common method bias problem in the data. This result supports the validity of the research conclusions.

General demographic information

Table 1 provides the demographic characteristics of the participants in the study. The sample consisted of 2,042 participants, with a higher proportion of male students (53.2%, n = 1086) compared to female students (46.8%, n = 956). Regarding the period of study, the majority of participants were in junior high school (61.5%, n = 1256), with the remainder in senior high school (38.5%, n = 786).

Table 1 Demographic information of participants (n = 2042)
Full size table

Concerning family characteristics, 64.2% of the participants (n = 1310) were not the only child in their families, while 35.8% (n = 732) were only children. In terms of place of residence, 90.2% (n = 1841) of the respondents live in cities, and a smaller proportion of 9.8% (n = 201) live in rural areas. The sample predominantly consisted of individuals from the majority ethnic groups, representing 93.6% (n = 1911), with a smaller representation from ethnic minorities (6.4%, n = 131).

Correlation analysis

Table 2 shows the descriptive statistics and correlation matrix for all study variables. The means and standard deviations for each variable are as follows: IA (M = 14.04, SD = 4.44), MW (M = 12.88, SD = 4.38), anxiety (M = 3.74, SD = 1.62), and HI (M = 5.87, SD = 2.03).Partial correlation analysis controlled for gender, age, education, and place of residence, and the results showed that IA, MW, anxiety, and HI were all positively correlated (r =.29 ~.51, p <.001), supporting the hypothesized relationships. These correlations indicate that higher IA is associated with more severe MW, anxiety, and HI symptoms.

Table 2 Correlation analysis results (n = 2042)
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Analyses of serial mediating effects

This study examined the roles of MW and anxiety in the relationship between IA and HI through a serial mediation model.

The regression analysis results in Fig. 2 and Table 3 reveal the predictive relationship between the variables: First, IA has a significant positive predictive effect on MW (β = 0.524, p <.001); second, when IA and MW jointly predict anxiety, both show significant effects (IA: β = 0.062, p <.001; MW: β = 0.093, p <.001); finally, after controlling other variables, anxiety has the strongest predictive power for HI (β = 0.472, p <.001), while the effect of MW is relatively weak (β = 0.088, p <.001). These results lay a statistical foundation for the subsequent mediation path analysis.

Fig. 2
figure 2

The mediation analysis on the relationship between Internet Addiction

and Hyperactivity-Impulsivity (**P <.01, ***P <.001)

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Table 3 Mediation analysis results (n = 2042)
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The mediation effect decomposition in Table 4 further quantified the mechanism of IA on HI: the total effect of IA on HI was significant (β = 0.203, 95% CI [0.186, 0.221]), and the direct effect was still significant after the introduction of the mediating variable (β = 0.105, 95% CI [0.086, 0.123]), indicating that MW and anxiety only partially mediated the relationship between IA and HI. The indirect effect analysis showed that the three pathways contributed differently: (1) The single-step mediation pathway through MW (IA→MW→HI) contributed 22.7% (indirect effect = 0.046, 95% CI [0.073, 0.126]); (2) The single-step pathway through anxiety (IA→A→HI) contributed 14.3% (indirect effect = 0.029, 95% CI [0.038, 0.095]); (3) The indirect effect of the serial pathway (IA→MW→A→HI) was the strongest (0.099, 95% CI [0.186, 0.258]), accounting for 48.8% of the total effect, highlighting that the chain transmission of MW and anxiety is the core mechanism.

Table 4 Path analysis results(n = 2042)
Full size table

In summary, the regression relationship in Table 3 verifies the theoretical assumptions of the model (such as IA drives MW, and MW and IA jointly affect anxiety), and the effect decomposition in Table 4 clarifies the hierarchy of the mediation path through statistical comparison: although the single-step path exists independently, the absolute advantage of the serial path (IA→MW→A→HI) (accounting for nearly half) shows that the occurrence of health damage is not only directly caused by IA, but also induces anxiety through psychological dissociation, which eventually forms a cumulative effect. This finding suggests that future interventions should give priority to blocking the chain transmission of MW and anxiety, rather than targeting the independent effects of IA or anxiety alone.

Alternative model considerations

Although the current model assumes that IA influences HI, it is also possible that HI contributes to IA, given prior research linking impulsivity and attention deficits to excessive internet use. While this study did not test a reverse or bidirectional model, future research should examine alternative pathways to determine whether HI may also predict IA. Reciprocal relationships may exist, where IA exacerbates HI, which in turn reinforces IA over time.

Discussion

This study investigates the relationship between IA, MW, Anxiety, and HI, focusing on both direct and indirect effects. The findings indicate that IA, MW, and Anxiety are significantly associated with HI. Furthermore, MW and Anxiety mediate the relationship between IA and HI, suggesting that they may play important roles in linking IA to HI. This finding enhances our understanding of the complexity of adolescent mental health challenges and provides a new perspective for developing intervention and prevention strategies.

The association between Internet Addiction and Hyperactivity-Impulsivity

Previous research has primarily emphasized HI as a significant risk factor for IA [33, 34]. However, our findings suggest that IA is also associated with HI, underscoring the importance of recognizing potential bidirectional influences. Adolescents with IA often dedicate extensive time to online activities, limiting opportunities for engagement in cognitive-enhancing pursuits, which may compromise self-regulation over time [35]. Importantly, IA is linked to both MW and HI, yet these constructs operate through distinct mechanisms. MW reflects the spontaneous disengagement of attention toward task-unrelated internal thoughts (e.g., daydreaming or intrusive reflections) [36], whereas HI manifests behaviorally as an inability to sustain attention on external tasks or resist distractions [37].

Moreover, the nature of the Internet may play a role in sustaining attentional difficulties. Fragmented online content and constant exposure to high-reward, low-effort stimuli may reinforce attentional lapses, contributing to increased MW and HI symptoms [38, 39]. Additionally, the influence of IA on cognitive and behavioral outcomes may vary across different cultural and socioeconomic contexts. Factors such as internet accessibility, educational pressures, and parental monitoring styles differ across regions and may shape adolescents’ susceptibility to IA and its effects on attention and impulsivity [40,41,42]. Therefore, considering these contextual differences is crucial for a more comprehensive understanding of the IA-HI association. Neuroimaging studies further support this association, as IA has been linked to structural and functional alterations in brain regions responsible for attention, decision-making, emotion regulation, and cognitive control [43]. These neural changes may differentially impact MW (via default mode network dysregulation) [44] and HI (via impaired executive functioning [45]), further supporting their mechanistic distinction.

However, it is equally important to recognize that HI characteristics, such as impulsivity and inattention, are well-documented risk factors for IA [11, 46]. Adolescents with preexisting HI symptoms may be more prone to IA due to difficulties in delaying gratification, self-regulation deficits, and a heightened preference for instant rewards [47, 48]. The compulsive engagement in online activities may serve as a maladaptive coping mechanism for managing attention deficits or hyperactivity [49]. This reciprocal relationship suggests a reinforcing cycle: adolescents with HI symptoms may be more likely to develop IA, while excessive Internet use may further exacerbate impulsivity and inattention, thereby maintaining or worsening HI-related behaviors [14, 50]. Future longitudinal studies are needed to disentangle the directionality of these associations and better understand their underlying mechanisms.

The mediating role of Mind wandering and anxiety

Consistent with previous research, this study highlights the significant roles of both MW and Anxiety in the relationship between IA and HI [51, 52]. IA may increase the risk of HI by disrupting attentional control and emotional regulation.

MW induced by IA aligns with the executive control failure hypothesis, which suggests that individuals with IA experience deficits in executive control, leading to increased MW and attentional lapses [53]. This aligns with the Default Mode Network (DMN) dysregulation hypothesis, which posits that excessive Internet use reinforces self-referential and task-unrelated thought patterns, impairing attentional control and increasing MW. Additionally, the resource control theory of MW suggests that IA patients struggle to disengage from online content, depleting cognitive resources and exacerbating MW [54]. Persistent MW episodes contribute to attentional disengagement, making it more difficult for adolescents to focus on daily activities, ultimately intensifying HI symptoms [55, 56].

In addition to cognitive mechanisms, IA is also associated with heightened Anxiety, which may further contribute to HI. Frequent Internet use can lead to information overload [57], deplete cognitive control resources, and induce chaotic thinking, fostering emotional distress and impulsive behavior. MW not only disrupts attention and behavior but also contributes to emotional instability, leading to difficulties in regulating emotions. While previous studies have linked MW to attention deficits, our findings emphasize its dual impact on both cognitive and emotional regulation. The inability to concentrate may evoke frustration or Anxiety, reinforcing MW and further exacerbating HI symptoms [58].

Furthermore, our study supports previous findings that IA is positively associated with Anxiety [59, 60]. IA may contribute to Anxiety through both direct and indirect pathways: directly by fostering excessive online engagement that disrupts real-world social interactions, and indirectly by promoting MW, which reinforces negative self-referential thoughts and emotional instability [61]. Adolescents who struggle with real-life social interactions may turn to the Internet for escape, yet excessive reliance on online interactions can exacerbate social anxiety, leading to further isolation and reinforcing IA [62, 63]. This bidirectional relationship creates a reinforcing cycle, wherein heightened Anxiety fuels increased Internet use, further exacerbating MW, attentional difficulties, and HI symptoms. Given these findings, future research should investigate intervention strategies that simultaneously target MW and Anxiety to mitigate their compounding effects on IA-associated HI.

Incorporating Internet addiction (IA) screening into ADHD assessments can help detect comorbidities early, thereby developing more comprehensive treatment plans [64]. However, the lack of standardized diagnostic criteria for IA in DSM-V makes screening and diagnosis challenging [64]. The complex relationship between ADHD and IA is influenced by multiple factors such as age, gender, impulse control, and emotion regulation [18]. Given that this study explored the potential relationship between IA and HI, whether IA should be included in clinical screening still needs further research and verification.

During the COVID-19 pandemic, increased screen time and social isolation exacerbated IA and HI symptoms among adolescents [65, 66]. The prolonged lack of structured activities and home confinement may have reinforced a cycle of IA, MW, and HI [67]. Although this study was conducted post-pandemic, the digital behavior patterns established during the pandemic may continue to impact adolescents’ cognitive and emotional health. Future research should explore the long-term effects of pandemic-induced behaviors and develop targeted interventions, such as digital desensitization and anxiety management programs, to break this cycle and mitigate future health risks.

Implication

To mitigate the adverse effects of IA on adolescents’ cognitive and emotional well-being, a comprehensive, multi-tiered intervention framework is necessary. This framework should integrate individual, familial, educational, and policy-level strategies to disrupt the vicious cycle of “digital dependency-psychological dysregulation.”

Individual-Level Interventions

Combining Cognitive Behavioral Therapy (CBT) and Mindfulness-Based Interventions (MBIs) helps restructure maladaptive thought patterns in adolescents [68], reducing anxiety linked to digital overuse and breaking the cycle between mental wandering and online behaviors. Digital detox programs, like gradual reductions in screen time, can rebuild offline reward systems and reduce attentional fragmentation [69].

Familial Interventions

Balanced parenting strategies, such as encouraging both digital engagement and offline activities, can reduce IA risks. Family activities like movie nights promote discussions, while setting screen-time limits and fostering outdoor activities balance autonomy and discipline [70]. Active family involvement in digital life enhances emotional support and prevents isolation [71].

Educational Interventions

Schools should include cognitive training exercises to improve attentional stability and executive functions [72]. Hybrid online-offline platforms for collaborative projects [73] and partnerships with families to manage screen-time goals [74] can encourage face-to-face interactions and promote self-monitoring.

Policy-Level Interventions

At the policy level, integrating IA screening into routine adolescent mental health assessments is crucial for early identification of high-risk populations, particularly those with ADHD-like symptoms [75]. This enables tiered interventions, such as mindfulness workshops for mild cases and clinical referrals for more severe ones. Policymakers should also advocate for health-conscious digital ecosystems, including age-appropriate platform regulations (e.g., restricting recreational functions after 23:00) and algorithmic adjustments to limit addictive content exposure [76], reducing the addictive potential of digital platforms.

Limitation and future directions

This study examined the dual mediating effects of MW and anxiety in the relationship between IA and HI, offering both theoretical insights and practical implications. However, several limitations must be acknowledged. First, the cross-sectional design prevents causal inferences, making it unclear whether IA exacerbates HI or vice versa; longitudinal studies are needed to clarify directionality. Second, reliance on self-reported data may introduce bias; incorporating behavioral tracking, neurocognitive measures (e.g., EEG, eye-tracking), and parent/teacher reports could improve reliability. Third, although gender, age, and academic level were controlled, socioeconomic status (SES), parental education, and urban-rural differences were not considered, potentially influencing IA, MW, and anxiety. Additionally, as the sample was limited to a single province, regional variations in academic pressure, internet accessibility, and cultural attitudes may affect generalizability, highlighting the need for cross-cultural research. Finally, gender differences should be further explored, as males may engage more in impulsive activities (e.g., gaming), while females may experience social media-driven anxiety. Examining gender as a moderating factor could help develop targeted interventions. Despite these limitations, this study provides important insights into the cognitive-emotional mechanisms linking IA and HI, emphasizing the need for longitudinal, multi-method, and cross-cultural approaches to refine prevention and intervention strategies.

Conclusion

This study identifies significant associations between IA, MW, anxiety, and HI in adolescents, highlighting MW and anxiety as dual mediators in the IA-HI relationship. These findings suggest that interventions targeting cognitive and emotional regulation—such as mindfulness training to reduce MW or cognitive-behavioral strategies to alleviate anxiety—may mitigate IA’s adverse effects. School-based initiatives, including IA screening and digital literacy programs, could promote healthier internet habits, while policymakers might advocate for public health campaigns to raise awareness of IA’s mental health risks. However, the cross-sectional nature of the data necessitates caution; causal claims about IA’s impact require validation through longitudinal research. Similarly, gender-specific mechanisms and pandemic-related confounding factors remain critical areas for further exploration. By addressing these gaps through interdisciplinary collaboration and diverse sampling, future research can inform tailored, evidence-based strategies to safeguard adolescent mental health in an increasingly digital world.

Data availability

The data generated or analyzed for this study are included in this manuscript and the supplementary material, and the data for this article are available on request to the corresponding author.

Abbreviations

ADHD:

Attention Deficit Hyperactivity Disorder

HI:

Hyperactivity-Impulsivity

IA:

Internet Addiction

MW:

Mind Wandering

A:

Anxiety

IAT:

Internet Addiction Test

MWQ:

Mind Wandering Questionnaire

GAD-2:

Generalized Anxiety Disorder-2

I-PACE model:

The Interaction of Person-Affect-Cognition-Execution model

DMN:

default mode network

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Acknowledgements

We would like to thank all participants in the present study.

Funding

This work was supported by the National Natural Science Foundation of China (82071543, 82171509), the Key Research and Development Program of Hunan Province (2023SK2028), the Key Guiding Project of Hunan Health Committee (202103091470), STI2030-Major Projects-2021ZD0200700 and the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0858).

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Author notes

  1. Yan Li and Zheng Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Psychiatry, National Center for Mental Disorders, National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China

    Yan Li, Zheng Zhang, Huijuan Guo, Jizhi Wang, Jiansong Zhou & Xiaoping Wang

  2. School of Educational Sciences, Anhui Normal University, Wuhu, Anhui, China

    Zheng Zhang

  3. Hebei Vocational University of Technology and Engineering, Xingtai, Hebei, China

    Liping Cui

  4. Department of Industrial Design, Lu Xun Academy of Fine Arts, Shenyang, Jilin, China

    Yunfei Wang

  5. Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, No. 139 Middle Renmin Road, 410011, Changsha, Hunan Province, China

    Jiansong Zhou & Xiaoping Wang

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  1. Yan Li
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Contributions

YL and ZZ conceived and designed this study. ZZ performed data analyses, and YL and ZZ drafted the initial manuscript. YFW, LPC, and HJG were responsible for investigation, data curation, and formal analysis. ZZ and JZW were responsible for methodology, investigation, and resources. YL, ZZ, JSZ, and XPW were responsible for data interpretation and manuscript revision. All authors contributed to data collection. All authors have read and agreed to the final manuscript.

Corresponding authors

Correspondence to Jiansong Zhou or Xiaoping Wang.

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Ethics approval and consent to participate

The study protocol was approved by the Human Ethics Committee of the Second Xiangya Hospital of Central South University. All methods were performed in accordance with the study protocol and ethical guidelines and regulations. Electronic informed consent was obtained from all participants, and informed consent for questionnaire collection was obtained from the parents or legal guardians of participants under 16 years of age.

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Li, Y., Zhang, Z., Cui, L. et al. The role of mind wandering and anxiety in the association between internet addiction and hyperactivity-impulsivity: a serial mediation model. BMC Psychol 13, 345 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40359-025-02667-3

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Keywords

BMC Psychology

ISSN: 2050-7283

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