The interplay between attention deficit/hyperactivity disorder and internet addiction: executive dysfunction and insomnia as mediators and the role of physical activity

Background: Attention Deficit/Hyperactivity Disorder (ADHD) and internet addiction (IA) are common among college students and often co-exist. This study investigated the relationship between ADHD symptoms, executive dysfunction, insomnia, and IA in Chinese college students.

Methods: A cross-sectional study was conducted in June 2024 at six universities in Hunan Province, China. Demographic data and symptoms of ADHD, IA, executive dysfunction, insomnia, and physical activity were collected via interviews and self-reported questionnaires. Physical activity level was further quantified and categorized using metabolic equivalents (METs) method. Mediation models were performed to explore the path from ADHD to IA and the role of physical activity in IA symptoms.

Results: Among 1925 students, 12.52% had ADHD symptoms, and 14.03% had IA symptoms. ADHD symptoms were related to IA symptoms (total effects: 0.38, p < 0.001; direct effect: 0.111, p = 0.003), mediated by insomnia (0.161, p < 0.001) and executive dysfunction (0.108, p < 0.001). Compared with no physical activity, moderate-level and high-level physical activities were negatively correlated with IA symptoms, with total relative standardized effects of -0.18 (p = 0.001) and -0.42 (p<0.001), respectively. The relative direct effect of high physical activity levels on IA symptoms was -0.29 (p<0.001), regardless of mediation by insomnia (-0.056 (95%CI, -0.094 to -0.021)) and executive dysfunction (-0.067 (95%CI, -0.105 to -0.033)).

Conclusion: ADHD and IA symptoms are prevalent among Chinese college students. Executive dysfunction and insomnia mediate the relationship between ADHD and IA symptoms. Moderate and high-level physical activities were associated with reduced risk of IA symptoms, mediated by executive dysfunction and insomnia. Physical activity may help mitigate IA symptoms in college students.

1 Introduction

Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by developmentally inappropriate levels of inattention, hyperactivity, emotional impulsivity, cognitive deficits, and associated learning difficulties (1). A comprehensive meta-analysis indicates that approximately 6.26% of Chinese children and adolescents meet criteria for ADHD, consistent with estimated global prevalence (2). ADHD symptoms, although typically emerging in childhood, can persist into adulthood. Many adults currently exhibit clinically significant ADHD symptoms, although they did not fully meet diagnostic criteria for ADHD during childhood (3, 4). Adult ADHD causes problems in academic performance, work, and family relationships, leading to increased individual burden and financial pressure on families and society (5, 6). Additionally, individuals with ADHD often have comorbid psychiatric conditions, such as obsessive-compulsive disorder, sleep disturbances, and anxiety disorders (7–9). Consequently, ADHD has raised considerable public health concerns.

Internet addiction (IA) has emerged as a significant public health issue in the digitalized society, characterized by an overwhelming urge to use the internet and an inability to control usage duration (10). Studies indicate ADHD and internet addiction (IA) are closely correlated, and people with ADHD are more likely to develop IA (11, 12). People with IA tend to have poorer mental health and worse self-esteem (13). Teenagers with ADHD symptoms, in particular, may be more likely to develop IA due to poor social skills and increased depressive tendencies. Additionally, IA can reduce sleep quality by shortening sleep duration (14). According to research by Li and his colleagues, people with ADHD may be more susceptible to developing an addiction to online activities due to their impulsivity and concentration problems (15). Furthermore, a study conducted by Chou and his colleagues revealed that for individuals with ADHD, social challenges may prompt the use of online interactions as a compensatory strategy, thereby increasing the risk for IA (16). Demirtaş and colleagues found that people with ADHD often experience mental health issues like anxiety and depression, which are closely related to IA (17). People showing signs of depression might use online activities as a way to escape from reality, thereby worsening their IA (18). Ko et al. noted that individuals with ADHD in urban areas may face higher level of stress and less social support, making them more vulnerable to developing an IA. This situation is particularly relevant in the context of Chinese universities (19). As the country with the world’s largest online population, China has developed a highly integrated digital environment. Widely used platforms such as Honor of Kings, WeChat, and Douyin are deeply woven into everyday life, which may increase users’ vulnerability to internet addiction (20, 21). Despite substantial evidence demonstrating the frequent co-occurrence of ADHD and IA, the precise directional relationship between these conditions remains unclear.

Executive dysfunction, a common comorbidity of ADHD, affects domains such as working memory, planning, attentional control, and inhibitory control, which are prevalent among individuals with ADHD (22). A long-term study suggests that these issues with executive function, especially problems with impulse control and self-regulation, may put people with ADHD at a higher risk for IA (23). Furthermore, insomnia, commonly seen in ADHD patients, may exacerbate their executive function challenges (24, 25). However, current studies typically focused on these factors separately, with limited consideration of their interplay, thus leaving their complex interactions underexplored.

Physical activity, as a non-pharmacological intervention, has been shown to effectively reduce symptoms of IA and improve related mental health outcomes in randomized controlled trials (26). Additionally, physical activity may also mitigate deficits in executive functioning and alleviate insomnia (27). However, its role as a potential factor in preventing IA risk has not been quantitatively evaluated.

Building on this foundation, we hypothesized that ADHD symptoms, executive dysfunction, and insomnia shared an intrinsic link with IA symptoms and that physical activity was correlated with these conditions. Firstly, we investigated the occurrence of ADHD and IA symptoms among Chinese college students through a cross-sectional study. Then, we evaluated and refined our proposed framework linking ADHD symptoms, executive dysfunction, insomnia, and IA symptoms, by using structural equation modeling (SEM). Finally, we explored the role of physical activity in these relationships. We aimed to explore the mediating effects of executive dysfunction and insomnia between ADHD and IA symptoms and the role of different intensities of physical activity on IA.

2 Materials and methods

2.1 Participants

A cross-sectional study was undertaken among students sampled from six scientific and technology universities in Hunan Province, China. Individuals were included if they (a) were currently enrolled university students aged 16 years or older, (b) demonstrated the cognitive and linguistic capacity to complete the assessment, and (c) were willing to provide documented informed consent. Of the 2,188 students approached, 263 were excluded for not completing all questions. As a result, 1,925 students were included in the study.

2.2 Procedures

All participants received the surveys from their teachers in June 2024. Clear instructions were provided throughout the questionnaire. Teachers received professional instruction from experienced psychiatric specialists to help participants understand each question’s purpose and content, and they offered crucial assistance during survey completion to clarify any misunderstandings or uncertainties. All participants provided informed consent, and participation was entirely voluntary. The study was approved by the Ethics Committee of the Brain Hospital of Hunan Province (2024K008), and all procedures were carried out in accordance with the ethical principles outlined in the Declaration of Helsinki. To protect privacy and encourage honest responses, the survey was conducted anonymously.

2.3 Clinical measures

Demographic information

Basic demographic data collected included the students’ age, sex, and their grades.

2.3.1 Adult ADHD SELF-REPORT SCALE

The ASRS comprises 18 items, each with five possible responses: never, rarely, occasionally, often, and very often. This study used a binary scoring method suggested by Kessler et al., where each question was scored as either 0 or 1 based on symptom severity (28). The scale has two subscales—nine for inattention and nine for hyperactivity—each with possible scores ranging from 0 to 9. A score of 1 is given if the answer indicates clinically significant symptoms; otherwise, it is scored as 0. Seven questions have clinical relevance when answered as sometimes, often, or very often, while the other 11 require answers of frequently or very frequently to be clinically relevant. Based on DSM-5 criteria, this scale yields high specificity in identifying adult ADHD symptoms and demonstrates acceptable internal consistency, with reported Cronbach’s α between 0.63 and 0.72 (29). In this study, the ASRS was used as an ADHD screening tool, with a total score greater than nine indicating clinical level ADHD symptoms, consistent with previous studies (30).

2.3.2 Barkley Deficits in executive functioning scale, short form

The BDEFS-SF is a shortened version of the 89-item BDEFS, consisting of 20 items that measure executive dysfunction through self-report (31). Each item uses a four-point Likert scale (1–4) based on how often symptoms occur: never or seldom, sometimes, often, and always. Scores range from 18 to 72, with higher scores indicating more severe executive function (EF) problems (32). The BDEFS-SF has a Cronbach’s alpha coefficient of 0.94, indicating excellent internal consistency (32).

2.3.3 Athens insomnia scale

The AIS-8 is an eight-item self-assessment tool designed to evaluate sleep issues according to ICD-10 criteria. The first five items address sleep induction, nighttime awakenings, final awakenings, overall sleep duration, and sleep quality, while the final three items assess functioning capacity, well-being, and daytime sleepiness (33). The AIS-8 is a four-point Likert scale and has a total score ranging from 0 to 24. This scale demonstrates high internal consistency with a Cronbach’s α of 0.89, and a cutoff score of 6, suggesting an elevated risk of insomnia.

2.3.4 Chinese internet addiction scale-revision

The CIAS-R is a 19-item self-report questionnaire that evaluates IA symptoms across four categories: tolerance, compulsive use and withdrawal, interpersonal and health-related issues, and time management concerns (34). Each item is rated on a four-point Likert scale, yielding a total score ranging from 19 to 76. A score above 53 is indicative of clinically significant IA symptoms. The CIAS-R has shown strong validity and reliability in China, with a Cronbach’s alpha of 0.96 (34).

2.3.5 Metabolic equivalents

Metabolic equivalent (MET) is a common method to express physical activity levels as multiples of resting metabolic rate (RMR) (35). Our survey included physical activity-related questions, covering frequency, duration, and types of activities. MET values were calculated based on participant responses using the method described by Pearce et al. (36) and converted into persistent marginal MET-hours (mMET-h). We then categorized participants into low, medium, and high physical activity groups using quartiles and compared them against a group that did not engage in regular physical exercise.

2.4 Statistics

The Kolmogorov–Smirnov test was used to assess normality of continuous variables. All rating scales were non-normally distributed. The χ2 test was then utilized for categorical data, while the Mann-Whitney U test was utilized to investigate differences in quantitative variables. A structural equation model (SEM) was employed to evaluate the mediating effects of executive dysfunction and insomnia on the association between ADHD and IA symptoms. Spearman correlations were used as the basis for SEM. To be considered acceptable, the SEM had to meet the following criteria: Tucker–Lewis’s index (TLI) > 0.95, standardized root mean square residual (SRMR) < 0.08, comparative fit index (CFI) > 0.95, and root mean square error of approximation (RMSEA) < 0.08 (37, 38). All analyses were conducted using R. Studio and SPSS 26.0 (IBM, Inc., Chicago), with a two-tailed significance level of 0.05.

3 Results

3.1 Demographic and clinical characteristics

After excluding 263 of the 2,188 college students who participated in the survey, the response rate was 87.98%. Table 1 presented the sociodemographic and clinical characteristics of the participants, including 703 (36.5%) females and 1,222 (63.5%) males. Among them, 14.03% (270 out of 1,925) were identified as having IA symptoms, and 12.52% (241 out of 1925) had ADHD symptoms. The sociodemographic and clinical traits of students with and without IA symptoms was shown in Table 1. Univariate logistic regression was used for all variables, with the χ2 test applied to categorical data and the Mann-Whitney U test for quantitative variables. No statistically significant differences were observed in age, sex, or grade level between the two groups (all p > 0.05).

Table 1

Table 1. Demographics and clinical symptoms between participants with and without internet addiction.

Students with IA symptoms had lower percentages of exercise habits, engaged in lower intensity exercise, spent less time per exercise session, and showed poorer adherence to exercise routines compared to those without IA symptoms (p < 0.05). Additionally, their scores were significantly higher on the BDEFS-SF, AIS, and overall ASRS (all p < 0.001; Table 1). Even after adjusting for factors like age, sex, and grade level, multiple linear regression analysis showed that exercise behaviors, insomnia, executive dysfunction, and ADHD symptoms were strongly linked to IA symptoms (all p < 0.05; Table 2).

Table 2

Table 2. Multiple linear regression analysis for variables correlated with internet addiction.

3.2 Pathways from ADHD symptoms to IA symptoms

There were positive correlations between all symptoms and sub-symptoms, including executive dysfunction, insomnia, and ADHD symptoms (all p < 0.01; Table 3). The absence of multicollinearity among these factors supported the validity of the subsequent mediation analysis. Model fit indices indicated that the proposed model (Figure 1) fit the data well. The path model (Figure 2) showed a total standardized effect of 0.38 (p < 0.001), indicating a positive correlation between IA and ADHD symptoms.

Table 3

Table 3. Spearman rank correlations between internet addiction, insomnia, executive dysfunction, and ADHD.

Figure 1

Figure 1. The hypothesized model for the association between ADHD and internet addiction. Abbreviations: ADHD, Attention Deficit/Hyperactivity Disorder.

Figure 2

Figure 2. The SEM for the association between internet addiction and ADHD mediated by insomnia and executive dysfunction. Abbreviations: ADHD, Attention Deficit/Hyperactivity Disorder. *p < 0.05. **p < 0.01. ***p < 0.001.

IA symptoms were directly associated with executive dysfunction, insomnia, and ADHD symptoms, with significant covariation between executive dysfunction and insomnia (all p < 0.001). Specifically, ADHD symptoms had an impact on IA symptoms, with a standardized direct effect of 0.111(p=0.003) and a standardized indirect effect of 0.269 (p < 0.001). The path coefficient measuring the impact of ADHD symptoms on IA symptoms through insomnia was 0.161 (p < 0.001), while executive dysfunction had a coefficient of 0.108 (p < 0.001), indicating that they mediated the effect of ADHD symptoms on IA symptoms.

3.3 Pathways from physical activity to IA symptoms

Supplementary Table S1 showed the sociodemographic and clinical traits of students with and without exercise habits. Students with exercise habits had lower total scores on the CIAS-R, AIS, and BDEFS-SF (p < 0.001), were typically in lower grade levels, and were more likely to be male than those without exercise habits. Moderate physical activity levels were negatively associated with IA symptoms, according to the simple mediation analysis model (Figure 3; Supplementary Table S2), with a relative direct standardized effect of -0.11 (p = 0.033) and a relative total standardized effect of -0.18 (p = 0.001) compared to those who did not exercise. The path coefficient from moderate physical activity level to IA symptoms mediated by executive function was -0.052 (95% CI, -0.085 to -0.0197). High levels of physical activity were even more strongly inversely correlated with IA symptoms, with a relative direct standardized effect of -0.29 (p < 0.001) and a relative total standardized effect of -0.42 (p < 0.001) when compared to those who did not exercise regularly. The partial relative mediating effect of insomnia on the path from high levels of physical activity to IA symptoms was -0.056 (95% CI, -0.094 to -0.021), which was -0.067 (95% CI, -0.105 to -0.033) for executive dysfunction. However, low amounts of exercise had no significant impact on IA symptoms when compared to no activity.

Figure 3

Figure 3. The association between internet addiction and physical activity level mediated by insomnia and executive dysfunction. *p < 0.05. **p < 0.01. ***p < 0.001.

4 Discussion

In this study, we assessed the prevalence of ADHD and internet addiction (IA) symptoms among Chinese college students, revealing rates of 12.52% for ADHD symptoms and 14.03% for IA symptoms. Findings of structural equation modeling (SEM) demonstrated that executive dysfunction and insomnia jointly mediated the relationship between ADHD and IA symptoms. Specifically, physical activity levels were inversely associated with IA symptoms, with this relationship also being mediated by executive dysfunction and insomnia. These results suggest that executive dysfunction and insomnia serve as key mediators in the pathway from ADHD symptoms to IA, and that physical activity may prevent IA risk by affecting these mediators.

A 2020 meta-analysis reported a global prevalence of 2.58% for persistent adult ADHD and 6.76% for symptomatic adult ADHD (39). Previous studies reported varying prevalence rates of ADHD among Chinese college students. One study of 8,098 students reported a prevalence of 7.26%, whereas another involving 5,693 students found 3.5% (40, 41). The prevalence of ADHD symptoms in our sample was 1.7 to 3.6 times higher than those reported in previous studies. This discrepancy may be attributed to variations in sample size and the scoring method employed in the ASRS.

Research indicates that the prevalence of IA among Chinese university students reaches 7.7% (42). Epidemiological evidence also indicates a high prevalence of IA within the general Chinese population, especially among adolescents (43). This suggests that IA symptoms may occur in individuals without pre-existing conditions, potentially explaining the high detection rate of IA symptoms among college students in our study. Another study involving 8,098 Chinese college students revealed that the prevalence rates of IA were 7.21% in males and 8.17% in females (40). In our study, the detection rate of IA symptoms among college students was found to be relatively high, with a prevalence rate of 14.03%. A South Korean study investigating the relationship between ADHD and IA suggested that IA might be more closely associated with the cognitive and behavioral symptoms of ADHD rather than ADHD diagnosis, and the comorbidity between ADHD and IA might be linked to functional and structural brain abnormalities associated with excessive and pathological internet use (44). These findings collectively indicate that the related dysfunctions in ADHD symptoms are highly likely to lead to disordered internet use.

Effective interventions for individuals with ADHD symptoms could help them in managing their internet use. Our SEM analysis revealed that executive dysfunction serves as a mediator in the relationship between ADHD and IA symptoms. Symptoms of ADHD can lead to difficulties in planning, organization, and time management, which are characteristics of executive dysfunction (45). This dysfunction may impair self-regulation and task management, leading individuals to engage in internet activities as an escape from real-world challenges (46). Therefore, executive dysfunction stemming from ADHD symptoms may increase reliance on the internet, supporting our findings and confirming its mediating role in the ADHD-IA symptoms relationship. Notably, moderate online gaming has been shown to enhance executive function in college students, while excessive use at subclinical levels may similarly impair it (47). Consequently, clinical interventions should adopt a differentiated perspective that acknowledges and accounts for the potential compensatory or functional benefits of internet use.

Another potential mediating factor observed in this study was insomnia. A study focusing on pediatric ADHD patients found that sleep disturbances like insomnia are frequently observed comorbidities in this population (48). Furthermore, Grant et al. reported that insomnia may indirectly contribute to the onset and progression of IA by impairing emotional regulation and impulse control (49). Convergent evidence from the literature and our study confirms that insomnia is a significant mediator in the pathway from ADHD symptoms to IA.

Moreover, this study found that high-level physical activity was negatively associated with IA symptoms, which were mediated by insomnia and executive dysfunction. These findings align with previous research. For instance, A 2015 meta-analysis identified physical activity functions as a comprehensive intervention that directly reduces the risk of IA while also providing indirect protection through the enhancement of mental health (50). Additionally, a systematic review by Alimoradi et al. reported a significant association between IA and sleep disturbances and noted that physical activity improves sleep quality, thereby indirectly reducing IA risk (51). Other studies further suggest that physical activity may alleviate IA symptoms and reduce its incidence (52–54). Notably, our study identified that moderate-to-high levels of physical activity were associated with a reduced risk of IA, thereby offering a supplement to previous findings in the field. However a 2023 multinational study found that among university students in Portugal and Poland, those with a history of SARS-CoV-2 infection, particularly in cases of recurrent infection, exhibited an elevated risk of internet addiction, when regularly engaging in physical activity (55). We speculated that this phenomenon may be associated with regional factors and the impact of COVID-19.

The findings of this study yielded significant clinical and public health insights. First, this study established the prevalence of internet addiction among Chinese college students through a large-scale survey. Second, the application of validated psychometric instruments and SEM allowed for the empirical validation of a multidimensional interaction model that incorporates ADHD symptoms, executive dysfunction, insomnia, and IA. The analysis confirmed the direct effect of ADHD symptoms on IA and identified an indirect pathway mediated by executive dysfunction and insomnia. It also revealed that physical activity intensity moderated the risk of IA. Third, the findings highlighted the need to address both neuropsychological factors, such as executive function and sleep disturbances, and modifiable lifestyle factors, such as physical activity of appropriate intensity, in the design of intervention strategies. This cross-sectional evidence offers preliminary insights that could inform the development of integrated intervention frameworks aimed at reducing IA risk among college students.

The current study has several limitations. First, using self-reported scales may lead to recall bias and reporting bias. Second, the sample was recruited from six universities in Hunan Province, limiting the generalizability of the findings. Third, the cross-sectional design could not determine the causal relationships among these variables. Finally, potentially relevant confounders like mood symptoms or medication were not considered in this study. Future longitudinal studies should incorporate other comorbid psychopathological conditions as confounders and elucidate possible bidirectional and dynamic interplay between ADHD symptoms and IA.

5 Conclusion

This study demonstrates that IA symptoms are highly prevalent among Chinese college students and are associated with ADHD symptoms. Furthermore, our findings highlight the mediating roles of executive dysfunction and insomnia in the relationship between ADHD symptoms and IA symptoms. These results underscore the importance of addressing these modifiable factors in clinical and educational settings. Caregivers and healthcare providers should consider integrating assessments of executive functioning, sleep quality, and physical activity into interventions for students with IA symptoms to optimize outcomes.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by The Ethics Committee of the Brain Hospital of Hunan Province. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

FL: Formal Analysis, Writing – original draft, Writing – review & editing. LZ: Methodology, Project administration, Writing – original draft. HC: Investigation, Methodology, Writing – original draft. ZT: Formal Analysis, Investigation, Writing – original draft. YS: Conceptualization, Data curation, Writing – original draft. JC: Data curation, Funding acquisition, Writing – original draft. YQ: Supervision, Writing – original draft, Writing – review & editing. QL: Funding acquisition, Supervision, Writing – review & editing.

Funding

The author(s) declared financial support was received for this work and/or its publication. The study was funded by a grant from the Hunan Provincial Natural Science Foundation Enterprise Joint Fund (No. 2025JJ90300), the Hunan Provincial Natural Science Foundation (No. 2023JJ30333), and the Scientific Research Project of Guangdong Provincial Administration of Traditional Chinese Medicine(Project No. 20222200).

Acknowledgments

We extend our appreciation to all patients and staff for their involvement and support.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1737793/full#supplementary-material

References

1. American Academy of Pediatrics. Clinical practice guideline: diagnosis and evaluation of the child with attention-deficit/hyperactivity disorder. Am Acad Pediatr. (2000) 105:1158–70. doi: 10.1542/peds.105.5.1158

PubMed Abstract | Crossref Full Text | Google Scholar

2. Wang T, Liu K, Li Z, Xu Y, Liu Y, Shi W, et al. Prevalence of attention deficit/hyperactivity disorder among children and adolescents in China: A systematic review and meta-analysis. BMC Psychiatry. (2017) 17:1–11. doi: 10.1186/s12888-016-1187-9

PubMed Abstract | Crossref Full Text | Google Scholar

3. Fayyad J, Sampson NA, Hwang I, Adamowski T, Aguilar-Gaxiola S, Al-Hamzawi A, et al. The descriptive epidemiology of dsm-iv adult adhd in the world health organization world mental health surveys. ADHD Attention Deficit Hyperactiv Disord. (2017) 9:47–65. doi: 10.1007/s12402-016-0208-3

PubMed Abstract | Crossref Full Text | Google Scholar

4. Green JG, Avenevoli S, Finkelman M, Gruber MJ, Kessler RC, Merikangas KR, et al. Attention deficit hyperactivity disorder: concordance of the adolescent version of the composite international diagnostic interview version 3.0 (Cidi) with the K-sads in the us national comorbidity survey replication adolescent (Ncs-a) supplement. Int J Methods Psychiatr Res. (2010) 19:34–49. doi: 10.1002/mpr.303

PubMed Abstract | Crossref Full Text | Google Scholar

5. Thapar A and Cooper M. Attention deficit hyperactivity disorder. Lancet. (2016) 387:1240–50. doi: 10.1016/s0140-6736(15)00238-x

PubMed Abstract | Crossref Full Text | Google Scholar

6. Health NIo. National institutes of health consensus development conference statement: diagnosis and treatment of attention-deficit/hyperactivity disorder (Adhd). J Am Acad Child Adolesc Psychiatry. (2000) 39:182–93. doi: 10.1097/00004583-200002000-00018

PubMed Abstract | Crossref Full Text | Google Scholar

7. Blanco-Vieira T, Santos M, Ferrão YA, Torres AR, Miguel EC, Bloch MH, et al. The impact of attention deficit hyperactivity disorder in obsessive-compulsive disorder subjects. Depress Anxiety. (2019) 36:533–42. doi: 10.1002/da.22898

PubMed Abstract | Crossref Full Text | Google Scholar

8. Liu X, Liu ZZ, Liu BP, Sun SH, and Jia CX. Associations between sleep problems and adhd symptoms among adolescents: findings from the shandong adolescent behavior and health cohort (Sabhc). Sleep. (2020) 43:zsz294. doi: 10.1093/sleep/zsz294

PubMed Abstract | Crossref Full Text | Google Scholar

9. Quenneville AF, Kalogeropoulou E, Nicastro R, Weibel S, Chanut F, and Perroud N. Anxiety disorders in adult adhd: A frequent comorbidity and a risk factor for externalizing problems. Psychiatry Res. (2022) 310:114423. doi: 10.1016/j.psychres.2022.114423

PubMed Abstract | Crossref Full Text | Google Scholar

10. Guo W, Tao Y, Li X, Lin X, Meng Y, Yang X, et al. Associations of internet addiction severity with psychopathology, serious mental illness, and suicidality: large-sample cross-sectional study. J Med Internet Res. (2020) 22:e17560. doi: 10.2196/17560

PubMed Abstract | Crossref Full Text | Google Scholar

11. Bielefeld M, Drews M, Putzig I, Bottel L, Steinbüchel T, Dieris-Hirche J, et al. Comorbidity of internet use disorder and attention deficit hyperactivity disorder: two adult case-control studies. J Behav Addict. (2017) 6:490–504. doi: 10.1556/2006.6.2017.073

PubMed Abstract | Crossref Full Text | Google Scholar

12. Wang JL, Yin XQ, Wang HZ, King DL, and Rost DH. The longitudinal associations between internet addiction and adhd symptoms among adolescents. J Behav Addict. (2024) 13:191–204. doi: 10.1556/2006.2023.00080

PubMed Abstract | Crossref Full Text | Google Scholar

13. Wartberg L, Kriston L, and Kammerl R. Associations of social support, friends only known through the internet, and health-related quality of life with internet gaming disorder in adolescence. Cyberpsychol Behav Soc Network. (2017) 20:436–41. doi: 10.1089/cyber.2016.0535

PubMed Abstract | Crossref Full Text | Google Scholar

14. Lu JX, Zhai YJ, Chen J, Zhang QH, Chen TZ, Lu CL, et al. Network analysis of internet addiction and sleep disturbance symptoms. Prog Neuropsychopharmacol Biol Psychiatry. (2023) 125:110737. doi: 10.1016/j.pnpbp.2023.110737

PubMed Abstract | Crossref Full Text | Google Scholar

15. Li W, Zhang W, Xiao L, and Nie J. The association of internet addiction symptoms with impulsiveness, loneliness, novelty seeking and behavioral inhibition system among adults with attention-deficit/hyperactivity disorder (Adhd). Psychiatry Res. (2016) 243:357–64. doi: 10.1016/j.psychres.2016.02.020

PubMed Abstract | Crossref Full Text | Google Scholar

16. Chou WJ, Huang MF, Chang YP, Chen YM, Hu HF, and Yen CF. Social skills deficits and their association with internet addiction and activities in adolescents with attention-deficit/hyperactivity disorder. J Behav Addict. (2017) 6:42–50. doi: 10.1556/2006.6.2017.005

PubMed Abstract | Crossref Full Text | Google Scholar

17. Demirtaş OO, Alnak A, and Coşkun M. Lifetime depressive and current social anxiety are associated with problematic internet use in adolescents with adhd: A cross-sectional study. Child Adolesc Ment Health. (2021) 26:220–7. doi: 10.1111/camh.12440

PubMed Abstract | Crossref Full Text | Google Scholar

18. Bai W, Cai H, Wu S, Zhang L, Feng KX, Li YC, et al. Internet addiction and its association with quality of life in patients with major depressive disorder: A network perspective. Transl Psychiatry. (2022) 12:138. doi: 10.1038/s41398-022-01893-2

PubMed Abstract | Crossref Full Text | Google Scholar

19. Ko CH, Yen JY, and Lin PC. Association between urbanization and internet addiction. Curr Opin Psychiatry. (2022) 35:219–25. doi: 10.1097/yco.0000000000000780

PubMed Abstract | Crossref Full Text | Google Scholar

20. Wang Q, Ren H, Long J, Liu Y, and Liu T. Research progress and debates on gaming disorder. Gen Psychiatr. (2019) 32:e100071. doi: 10.1136/gpsych-2019-100071

PubMed Abstract | Crossref Full Text | Google Scholar

21. Fitzgerald R, Sandel TL, and Wu X. Chinese social media: technology, culture and creativity. Discourse Context Media. (2022) 48:100610. doi: 10.1016/j.dcm.2022.100610

Crossref Full Text | Google Scholar

22. Bron TI, Bijlenga D, Boonstra AM, Breuk M, Pardoen WF, Beekman AT, et al. Oros-methylphenidate efficacy on specific executive functioning deficits in adults with adhd: A randomized, placebo-controlled cross-over study. Eur Neuropsychopharmacol. (2014) 24:519–28. doi: 10.1016/j.euroneuro.2014.01.007

PubMed Abstract | Crossref Full Text | Google Scholar

23. Zhou B, Zhang W, Li Y, Xue J, and Zhang-James Y. Motivational but not executive dysfunction in attention deficit/hyperactivity disorder predicts internet addiction: evidence from a longitudinal study. Psychiatry Res. (2020) 285:112814. doi: 10.1016/j.psychres.2020.112814

PubMed Abstract | Crossref Full Text | Google Scholar

24. Weiss MD and Salpekar J. Sleep problems in the child with attention-deficit hyperactivity disorder: defining aetiology and appropriate treatments. CNS Drugs. (2010) 24:811–28. doi: 10.2165/11538990-000000000-00000

PubMed Abstract | Crossref Full Text | Google Scholar

25. Bioulac S, Micoulaud-Franchi JA, and Philip P. Excessive daytime sleepiness in patients with adhd--diagnostic and management strategies. Curr Psychiatry Rep. (2015) 17:608. doi: 10.1007/s11920-015-0608-7

PubMed Abstract | Crossref Full Text | Google Scholar

26. Zhang X, Yang H, Zhang K, Zhang J, Lu X, Guo H, et al. Effects of exercise or tai chi on internet addiction in college students and the potential role of gut microbiota: A randomized controlled trial. J Affect Disord. (2023) 327:404–15. doi: 10.1016/j.jad.2023.02.002

PubMed Abstract | Crossref Full Text | Google Scholar

27. Darnai G, Perlaki G, Zsidó AN, Inhóf O, Orsi G, Horváth R, et al. Internet addiction and functional brain networks: task-related fmri study. Sci Rep. (2019) 9:15777. doi: 10.1038/s41598-019-52296-1

PubMed Abstract | Crossref Full Text | Google Scholar

28. Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, et al. The world health organization adult adhd self-report scale (Asrs): A short screening scale for use in the general population. psychol Med. (2005) 35:245–56. doi: 10.1017/S0033291704002892

PubMed Abstract | Crossref Full Text | Google Scholar

29. Ustun B, Adler LA, Rudin C, Faraone SV, Spencer TJ, Berglund P, et al. The world health organization adult attention-deficit/hyperactivity disorder self-report screening scale for dsm-5. JAMA Psychiatry. (2017) 74:520–6. doi: 10.1001/jamapsychiatry.2017.0298

PubMed Abstract | Crossref Full Text | Google Scholar

30. Karlsson AT, Vederhus J-K, Clausen T, Weimand B, Solli KK, and Tanum L. Levels of impulsivity, hyperactivity, and inattention and the association with mental health and substance use severity in opioid-dependent patients seeking treatment with extended-release naltrexone. J Clin Med. (2021) 10:4558. doi: 10.3390/jcm10194558

PubMed Abstract | Crossref Full Text | Google Scholar

31. Barkley RA. Barkley deficits in executive functioning scale (Bdefs). New York, NY, US: The Guilford Press (2011). p. 174.

Google Scholar

32. Lace JW, McGrath A, and Merz ZC. A factor analytic investigation of the barkley deficits in executive functioning scale, short form. Curr Psychol. (2022) 41:2297–305. doi: 10.1007/s12144-020-00756-7

Crossref Full Text | Google Scholar

33. Soldatos CR, Dikeos DG, and Paparrigopoulos TJ. Athens insomnia scale: validation of an instrument based on icd-10 criteria. J Psychosom Res. (2000) 48:555–60. doi: 10.1016/s0022-3999(00)00095-7

PubMed Abstract | Crossref Full Text | Google Scholar

34. Bai Y and Fan FM. A study on the internet dependence of college students: the revising and applying of a measurement. Psychol Dev Educ. (2005) 21:99–104.

Google Scholar

35. Byrne NM, Hills AP, Hunter GR, Weinsier RL, and Schutz Y. Metabolic equivalent: one size does not fit all. J Appl Physiol. (2005) 99:1112–9. doi: 10.1152/japplphysiol.00023.2004

PubMed Abstract | Crossref Full Text | Google Scholar

36. Pearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R, et al. Association between physical activity and risk of depression: A systematic review and meta-analysis. JAMA Psychiatry. (2022) 79:550–9. doi: 10.1001/jamapsychiatry.2022.0609

PubMed Abstract | Crossref Full Text | Google Scholar

37. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. (1990) 107:238–46. doi: 10.1037/0033-2909.107.2.238

PubMed Abstract | Crossref Full Text | Google Scholar

38. Hu LT and Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Model: A Multidiscip J. (1999) 6:1–55. doi: 10.1080/10705519909540118

Crossref Full Text | Google Scholar

39. Song P, Zha M, Yang Q, Zhang Y, Li X, and Rudan I. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Global Health. (2021) 11:04009. doi: 10.7189/jogh.11.04009

PubMed Abstract | Crossref Full Text | Google Scholar

40. Shen Y, Wang L, Huang C, Guo J, De Leon SA, Lu J, et al. Sex differences in prevalence, risk factors and clinical correlates of internet addiction among chinese college students. J Affect Disord. (2021) 279:680–6. doi: 10.1016/j.jad.2020.10.054

PubMed Abstract | Crossref Full Text | Google Scholar

41. Shen Y, Chan BSM, Liu J, Meng F, Yang T, He Y, et al. Estimated prevalence and associated risk factors of attention deficit hyperactivity disorder (Adhd) among medical college students in a chinese population. J Affect Disord. (2018) 241:291–6. doi: 10.1016/j.jad.2018.08.038

PubMed Abstract | Crossref Full Text | Google Scholar

42. Shen Y, Meng F, Xu H, Li X, Zhang Y, Huang C, et al. Internet addiction among college students in a chinese population: prevalence, correlates, and its relationship with suicide attempts. Depression Anxiety. (2020) 37:812–21. doi: 10.1002/da.23036

PubMed Abstract | Crossref Full Text | Google Scholar

43. Mueller KW, Dreier M, Duven E, Giralt S, Beutel ME, and Woelfling K. Adding clinical validity to the statistical power of large-scale epidemiological surveys on internet addiction in adolescence: A combined approach to investigate psychopathology and development-specific personality traits associated with internet07. J Clin Psychiatry. (2017) 78:2534. doi: 10.4088/JCP.15m10447

PubMed Abstract | Crossref Full Text | Google Scholar

44. Kim D, Lee D, Lee J, Namkoong K, and Jung YC. Association between childhood and adult attention deficit hyperactivity disorder symptoms in korean young adults with internet addiction. J Behav Addict. (2017) 6:345–53. doi: 10.1556/2006.6.2017.044

PubMed Abstract | Crossref Full Text | Google Scholar

45. Johnson AC. Developmental pathways to attention-deficit/hyperactivity disorder and disruptive behavior disorders: investigating the impact of the stress response on executive functioning. Clin Psychol Rev. (2015) 36:1–12. doi: 10.1016/j.cpr.2014.12.001

PubMed Abstract | Crossref Full Text | Google Scholar

46. Mikami AY, Miller M, and Lerner MD. Social functioning in youth with attention-deficit/hyperactivity disorder and autism spectrum disorder: transdiagnostic commonalities and differences. Clin Psychol Rev. (2019) 68:54–70. doi: 10.1016/j.cpr.2018.12.005

PubMed Abstract | Crossref Full Text | Google Scholar

47. Zhao W, Wei T, Zhou R, Wang Y, Wang Y, Ren Z, et al. The influence of online game behaviors on the emotional state and executive function of college students in China. Front Psychiatry. (2021) 12:713364. doi: 10.3389/fpsyt.2021.713364

PubMed Abstract | Crossref Full Text | Google Scholar

48. Becker SP, Cusick CN, Sidol CA, Epstein JN, and Tamm L. The impact of comorbid mental health symptoms and sex on sleep functioning in children with adhd. Eur Child Adolesc Psychiatry. (2018) 27:353–65. doi: 10.1007/s00787-017-1055-2

PubMed Abstract | Crossref Full Text | Google Scholar

49. Grant JE and Chamberlain SR. Sleepiness and impulsivity: findings in non-treatment seeking young adults. J Behav Addict. (2018) 7:737–42. doi: 10.1556/2006.7.2018.71

PubMed Abstract | Crossref Full Text | Google Scholar

50. Rebar AL, Stanton R, Geard D, Short C, Duncan MJ, and Vandelanotte C. A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. Health Psychol Rev. (2015) 9:366–78. doi: 10.1080/17437199.2015.1022901

PubMed Abstract | Crossref Full Text | Google Scholar

51. Alimoradi Z, Lin CY, Broström A, Bülow PH, Bajalan Z, Griffiths MD, et al. Internet addiction and sleep problems: A systematic review and meta-analysis. Sleep Med Rev. (2019) 47:51–61. doi: 10.1016/j.smrv.2019.06.004

PubMed Abstract | Crossref Full Text | Google Scholar

52. Kelley KJ and Gruber EM. Problematic internet use and physical health. J Behav Addict. (2013) 2:108–12. doi: 10.1556/jba.1.2012.016

PubMed Abstract | Crossref Full Text | Google Scholar

53. Peng J, Liu Y, Wang X, Yi Z, Xu L, and Zhang F. Physical and emotional abuse with internet addiction and anxiety as a mediator and physical activity as a moderator. Sci Rep. (2025) 15:2305. doi: 10.1038/s41598-025-85943-x

PubMed Abstract | Crossref Full Text | Google Scholar

54. Liu Y, Duan L, Shen Q, Ma Y, Chen Y, Xu L, et al. The mediating effect of internet addiction and the moderating effect of physical activity on the relationship between alexithymia and depression. Sci Rep. (2024) 14:9781. doi: 10.1038/s41598-024-60326-w

PubMed Abstract | Crossref Full Text | Google Scholar

55. Zalewska A, Gałczyk M, Sobolewski M, and Fernandes H. Internet addiction and physical activity among polish and portuguese students in the final year of the covid-19 pandemic. J Clin Med. (2023) 12:5204. doi: 10.3390/jcm12165204

PubMed Abstract | Crossref Full Text | Google Scholar