Li Cheng
Han Xie- 1Hubei Preschool Education Research Center, Hubei University of Education, Wu Han, China
- 2Teacher Education Research Center, Hubei University of Education, Wu Han, China
Objectives: In the digital era, the relationship between smartphone addiction and creativity among Chinese undergraduates has drawn increasing attention. This study aimed to explore how depression mediates the relationship between smartphone addiction and creativity, and how positive rumination moderates this mediating effect, with the goal of clarifying the underlying psychological mechanisms and providing insights for promoting creativity and mental well - being among this population.
Methods: A cross - sectional study was carried out. Undergraduate students from three Chinese provinces were sampled through a questionnaire distributed via the Wenjuanxing online platform. The questionnaire measured smartphone addiction, depression, creativity, and positive rumination. A total of 401 valid responses were obtained. Moderated mediation analysis was employed to examine the relationships among these variables.
Results: The analysis showed that smartphone addiction significantly predicted depression, but had no significant direct effect on creativity. Depression negatively predicted creativity. It was confirmed that depression mediated the relationship between smartphone addiction and creativity. Moreover, positive rumination moderated the relationship between depression and creativity, and a protective effect was observed when the level of positive rumination was higher. The moderated mediation model proposed in this study was validated.
Conclusions: The study successfully validated the moderated mediation model, indicating that positive rumination weakens the negative impact of depression on creativity in the context of smartphone addiction. The findings suggest that positive rumination can potentially help alleviate the adverse effects of excessive smartphone use on the creative thinking of Chinese undergraduates.
1 Introduction
In the information age, creativity has become one of the most valuable assets for contemporary undergraduates (1). Creativity is critical to academic achievement, driving innovation, solving problems, and adapting to rapidly changing social demands (2–5). In an era of knowledge explosion and technological innovation, where college students are expected to develop creativity to become future leaders and change agents, smartphones offer a prime opportunity (6). Undergraduates can use smartphones as learning, communication, and creative expression tools, leveraging apps and features to explore ideas, connect widely, and potentially create their own digital content, thereby unlocking their potential and contributing to digital evolution (7, 8). However, the proliferation and excessive use of smartphones may negatively affect this critical ability. Research indicates that smartphone overuse can reduce the volume of several brain regions associated with cognition, impulse control, emotions and behaviors, and brain reward systems, which may adversely affect an individual’s memory and learning capabilities (9). Therefore, the relationship between smartphone addiction and creativity needs to be explored among Chinese undergraduates, and the underlying mechanisms must be identified. This study aims to provide a theoretical foundation for developing strategies to mitigate the potential negative effects of smartphone addiction on creativity and foster a more balanced and healthy use of technology among Chinese undergraduates.
Smartphones provide numerous forms of gratification, such as sociability, entertainment, information retrieval, time management, and social identity maintenance (10–13). Smartphones have become an integral part of daily life, and research has shown that certain people become so attached to their devices that they experience separation anxiety when without them (14, 15). Previous studies have linked smartphone addiction and mental health issues, particularly depression (16, 17). Depression, which is characterized by persistent feelings of sadness and lack of interest or pleasure in activities, has been identified as a critical factor impeding creative expression (18). This study contributes to the literature by providing a more nuanced understanding of the mechanisms linking technology use, mental health, and cognitive function. By dissecting the relationships between smartphone addiction, depression, and creativity, we offer insights into the psychological consequences of technology use and potential avenues for intervention.
Smartphones, primarily relying on internet-based apps, are rendered versatile and ubiquitously carried by their portability and app-installation capacity. Therefore, the portability of smartphones, the variety and appeal of apps, instant gratification, social interaction, multitasking, and personalized recommendations all work together to make it easier for users to develop mobile phone addiction. Lin et al. (19) regarded smartphone addiction as a form of technological addiction. Smartphone addiction symptoms may differ from those of substance addiction. Smartphone addiction has similarities to DSM-5 substance-related disorders in terms of compulsive behavior, functional impairment, withdrawal, and tolerance (20). Meta-analysis results derived from 24 countries show that smartphone addiction is increasing across the world (21). Smartphone addiction has received extensive attention, especially among undergraduates (22–25).
According to the Cognitive Resource Theory (26), individuals have finite cognitive resources that must be allocated among different cognitive tasks. In the context of smartphone addiction, individuals addicted to smartphones tend to engage in activities such as excessive social media use and compulsive gaming, which consume a substantial portion of their cognitive resources (27, 28). Consequently, when confronted with creative tasks that demand focused attention, information integration, and the generation of novel ideas, the paucity of available cognitive resources, resulting from the preoccupation with smartphones, impedes the creative process and the manifestation of creativity (29, 30). For instance, during creative writing endeavors or design undertakings, interruptions and distractions caused by smartphone addiction can disrupt the continuity and profundity of cognitive processing, thereby thwarting the full exploitation of creative capabilities.
Recent psychological research has provided empirical support for the significant association between smartphone use and creativity. Olson et al. (31) surveyed 48,000 participants and found that a negative correlation was seen in a small sample, larger samples showed only weak correlations. Guan et al. (32) studied 998 college students and found a positive relationship between mobile phone use and creative ideation, mediated by critical thinking and creative self-efficacy; however, this study did not focus on addiction behavior. Li et al. (33) provided neuroimaging evidence that individuals with smartphone addiction have reduced brain activity and weaker functional connectivity during creative idea generation, suggesting that smartphone addiction adversely affects creativity. Other researches had demonstrated that smartphone addiction negatively impacts brain regions associated with cognitive control, a process central to creative idea generation (34, 35). Overall, the latest evidence supports the negative effects of smartphone addiction on creativity.
Depression is a psychological disorder manifesting as a major cause of negative emotion (36). Meta-analytic evidence demonstrates that depression, as a behavioral outcome, is consistently and positively associated with smartphone addiction across diverse geographical regions and subject populations (37). And a common consequence of depression is a significant decline in productivity (38). Moreover, negative or neutral mood states are less effective than positive affect in fostering creative performance (39). Cognitive neuroscience has found that the medial prefrontal cortex, which is essential for associative processing and creativity (40), displays abnormal activity in individuals with depression (41). This abnormality can lead to overinhibition of the medial temporal lobe, thereby constraining the activation of crucial associative networks for creativity (42). Furthermore, cognitive flexibility, which is markedly higher in highly creative individuals, tends to be reduced in individuals with depression compared with controls (43). The negative correlation between problem-solving ability and psychopathology further complicates the assumed link between depression and reduced creative performance (44, 45). In summary, depression may be negatively correlated with creativity among college students, and it plays a mediating role between smartphone addiction and creativity.
Although creativity may be linked to smartphone addiction through depression, not all people with depression experience decreased creativity (46). This heterogeneity in outcomes could be explained by theoretical frameworks emphasizing the dual nature of cognitive processes in depression,such as perseverative cognition theory. Perseverative cognition theory holds that the results of perseverative cognition are uncertain and depend on the value of the thinking content and one’s mood during reflection (47, 48). Perseverative cognition is characterized by repetitive or chronic activation in response to one or more psychological stressors, representing a common stress response pattern (47). Rumination, in particular, constitutes a form of perseverative cognition (49). In previous studies, rumination has been defined as repetitive thinking about negative events and regarded as a negative cognitive process (50, 51). However, some researchers, such as Martin and Tesser (52) believe it can be positive. Frone (53) explicitly proposed the concepts of negative and positive work rumination. Moreover, Yang et al. (54) developed the Positive and Negative Rumination Scale, and found that scores on the positive rumination subscales were positively and significantly associated with life satisfaction and optimism. Inspired by the above-mentioned studies, exploring the buffering effects of positive rumination has great value.
In the process of researching rumination, some researchers have already proposed the concept of positive rumination and developed the corresponding scale (55), defining it as the tendency of individuals to generate a positive emotional state due to repeatedly pondering over their own positive qualities, positive emotional experiences, and a good living environment. Rumination can have beneficial effects in multiple respects. From a cognitive processing perspective, it is an in-depth cognitive exploration of past experiences and unachieved goals. Through this repetitive thinking, individuals may dissect the details of their actions and decisions, similar to how scientists meticulously analyze experimental processes and results during research impasses, which makes understanding the self-regulatory functions of positive rumination important (53). This could potentially lead to discovering overlooked aspects and novel insights, thereby opening new avenues for problem-solving and progress. With respect to self-growth and learning, rumination is a crucial mechanism of self-reflection (56). When individuals ruminate on past mistakes or unfinished tasks, they can extract valuable lessons and identify areas for improvement. For instance, students can ruminate on their learning strategies and exam performance to pinpoint knowledge gaps and subsequently modify their study methods, ultimately contributing to their personal and academic development.
From the perspective of emotion processing, emotion regulation (ER) theory (57) illustrates how individuals manage emotional intensity, duration, and expression through cognitive/behavioral strategies to adapt to environmental demands. Existing research has identified positive rumination as a critical emotion regulation mechanism: it enhances, sustains, and amplifies positive emotions by actively reflecting on positive experiences (58–61). This process strengthens neural representations of positive affect via cognitive restructuring, forming a cycle of adaptive emotion and cognition. As a strategy that amplifies positive affect through cognitive restructuring, positive rumination may counteract depression’s tendency to deplete cognitive resources, thereby preserving the mental flexibility essential for creative idea generation. This theoretical link motivates our hypothesis: positive rumination moderates the relationship between depression and creativity.
This study investigated the mediating role of depression and the moderating role of rumination between smartphone addiction and creativity among undergraduate students. Based on the literature review, three hypotheses were formulated for this study:
H1: Smartphone addiction is negatively related to creativity.
H2: Depression mediates the relationship between smartphone addiction and creativity. Additionally, smartphone addiction is positively related to depression, which, in turn, is negatively related to creativity.
H3: Positive rumination moderates the relationship between depression and creativity.
Based on the above theoretical considerations and hypothetical deductions, this study proposes the conceptual model shown in Figure 1.
Figure 1. The conceptual model.
2 Method
2.1 Participants and procedures
A convenience sampling method was employed in the current study. To reduce potential selection bias and enhance sample diversity, several targeted strategies were implemented during the recruitment process. First, three universities in Hubei Province were purposively selected to represent varying educational tiers: (a) “Double First-Class” institutions, (b) provincial key universities, and (c) local colleges, thereby ensuring diversity in both academic resources and student backgrounds. Second, within each selected class, students were stratified based on gender and academic performance (categorized into top, middle, and bottom tertiles according to GPA). Class supervisors then randomly invited participants from each stratum, ensuring proportional representation across subgroups. These measures were taken to minimize the limitations inherent in convenience sampling and to enhance the reliability and generalizability of the study findings.These measures were taken to reduce the selection bias caused by the convenience sampling method and ensure the reliability and applicability of the study results. The data were collected via the Wenjuanxing platform between August 5 and September 5, 2024. Participants took approximately 15 minutes to complete the questionnaire. A total of 451 responses were initially obtained.After excluding participants with regular response patterns and abnormal response times, 401 (64 male, 337 female) valid questionnaires were retained for analysis, yielding an effective response rate was 87.75%. Table 1 shows the demographic characteristics of the participants.
Table 1. Demographic characteristics of the participants (n = 401).
2.2 Measures
2.2.1 Demographic characteristics
This study’s demographic characteristics included gender, grades, only-child status, and hometown. Gender was set as dummy variables (male = 1, female = 0). Grade level (including freshman, sophomore, junior, and senior) was captured with a single-choice question. Only child status (yes or no) and hometown (urban or rural area) were incorporated to comprehensively understand the participants’ background characteristics relevant to the research. Previous findings indicate that the demographic variables mentioned above may be linked to the current main variables (62, 63). Thus, demographic variables were controlled as covariables in the data analysis process.
2.2.2 Smartphone addiction
Based on Leung’s (64) Mobile Phone Addiction Index, the questionnaire contains 17 items, ranging from “completely disagree” scored as 1 point to “completely agree” scored as 5 points. The higher the score, the more evident the addiction tendency. Cronbach’s α in this study was 0.91.
2.2.3 Depression
Depression was assessed using the Depression Anxiety and Stress Scale-21 developed by Lovibond et al. (65). The depression subscale has seven items scored on a 4-point Likert scale from 0 (“not applicable”) to 3 (“always applicable”). Higher scores indicate more severe depression. Cronbach’s α in this study was 0.87.
2.2.4 Positive rumination
The rumination was assessed by the Ruminative Response Scale developed by Nolen-Hoeksema et al. (66). This scale includes three dimensions–symptom-related rumination, reflective pondering, and brooding–with a total of 22 items. It adopts a 4-point Likert scoring method, ranging from 1 (“never”) to 4 (“always”). A higher total score indicates more severe ruminative thinking. In this study, we focused on the role of positive rumination; through reflective pondering, one may focus on analyzing and exploring issues from multiple angles and levels in a rational and critical way of thinking, and distinguishing various viewpoints and possibilities during the thinking process. Therefore, we analyzed the reflective pondering dimension of this scale. Cronbach’s α in this study was 0.88.
2.2.5 Creativity
Creativity was assessed using the Individual Innovation Scale developed by Scott and Bruce (67). The scale has six items scored on a 5-point Likert scale ranging from 1 (“not applicable”) to 5 (“always applicable”). One of the example items is “I am able to come up with creative ideas.” For all 6 items, Cronbach’s α in this study was 0.94.
2.3 Data analyses
Common method bias testing was employed to assess the quality and reliability of the research data and eliminate artificial covariance between variables that may arise from using the same method or source to collect data, thereby ensuring the accuracy and objectivity of the research findings. Subsequently, Pearson’s correlation analysis was employed to explore the correlation between the study variables. A mediation and moderated mediation model was then implemented using the PROCESS macro (Model 14) for SPSS version 3.4. We first examined the mediating role of depression in the association between smartphone addiction with creativity and then examined the moderating effect of rumination on the mediating role of depression in the link between smartphone addiction and creativity. Mediation and moderated mediation models were interpreted using standardized path estimates (β) and squared-multiple correlations (R2). All analyses were conducted using SPSS version 24.0.
3 Results
3.1 Common method bias
The data collection method of this study was to issue questionnaires completed by Chinese Undergraduates according to their actual conditions. Considering this study used self-report measures, there were concerns about common method bias (68). Harman’s single-factor method was used for the calculations and tests. The test results showed that the maximum factor variance interpretation rate was 34.06%, which did not reach the critical standard of 40%, indicating no serious common method bias.
3.2 Descriptive statistics and correlation analysis
As shown in Table 2, smartphone addiction was positively correlated with depression (r = .304, p <.01) and rumination (r = .183, p <.01). Depression was negatively correlated with creativity (r = -.190, p <.01) and positively correlated with rumination (r = .617, p <.01).
Table 2. Correlations among the variables.
3.3 Testing the mediating role of depression
As shown in Table 3, the predictive effect of smartphone addiction on depression was significant (β = 0.239, SE = .038, p <.001). The direct effect of smartphone addiction on the dependent variable creativity was not significant (β = .053, SE = .037, p > 0.05), and the negative predictive effect of depression on creativity was significant (β = -.194, SE = .047, p <.001).
Table 3. Mediation and moderated mediation models (n = 401).
3.4 Testing the moderated mediation model
According to Model 14 (see Table 3), the interaction of depression and rumination was positively related to creativity, and the effect size was large (β = .159, SE = .058, p = .007). This result indicates that rumination moderates the relationship between depression and creativity. A simple slope analysis graph is shown in Figure 2.
Figure 2. Moderated effect of rumination.
4 Discussion
In the context of the exponential growth of digitalization in China (69), the present study was designed to comprehensively investigate the relationship between smartphone addiction and creativity in an undergraduate population. It further explored the mediating effect of depression, which was hypothesized to play a crucial role in bridging the association between smartphone addiction and creativity. Additionally, the moderating role of rumination was examined as it was postulated to influence the nature and strength of the relationship. The findings contribute to a deeper understanding of the underlying psychological and cognitive mechanisms governing the relationship between smartphone addiction and creativity. Such insights could pave the way for developing more effective preventive and therapeutic measures to address smartphone addiction and its associated consequences among college students, thereby promoting psychological well-being and academic success.
4.1 Smartphone addiction and creativity
There was no significant correlation between smartphone addiction and creativity, and the direct effect was not significant, which is inconsistent with Hypothesis 1. The reason for this result may be that the impact of smartphones on creativity hinges on usage patterns. Responsible use of information and communication technologies (ICTs) can boost creativity, yet addictive and dependent usage turns smartphones into tools that suppress creative expression. Instead of leveraging smartphones and the internet as aids for academic tasks while exercising creativity, many young people rely on them to complete assignments passively, bypassing creative thinking (70). This result may be more pronounced among individuals with pathological smartphone addiction.
4.2 The mediating role of depression
The results showed that depression positively correlated with smartphone addiction and negatively correlated with creativity. From theoretical and mechanistic perspectives, college students exhibit unique cognitive and psychological traits. Smartphone addiction in this group is characterized by excessive reliance on the information and entertainment provided by mobile devices, representing a misallocation of cognitive resources. This diverts attention and time from creativity-nurturing activities. For example, addicted students often interrupt academic and practical pursuits in favor of mobile stimuli such as social media and short videos. Such resource misallocation and reality evasion among college students can trigger depressive emotions (71, 72). Based on cognitive-behavioral theory, the negative outcomes of smartphone addiction, such as academic delay, social decline, and loss of self-efficacy, lead to negative self-perceptions and depressive feelings (73–76). Depression, which is a negative psychological state, significantly inhibits undergraduate creativity. Neuropsychologically, it correlates with prefrontal cortex dysfunction, which affects the cognitive functions essential for creativity (77). Depressed students face attention regulation imbalances, reduced motivation (as per motivation theory), and impaired cognitive flexibility, all of which impede their creative performance (78–80). In summary, depression mediates the relationship between mobile phone addiction and creativity among college students. These findings enrich our understanding of the underlying mechanisms, guiding research in this area toward greater refinement and systematization.
4.3 The moderating role of positive rumination
This study also found that positive rumination moderated the relationship between depression and creativity. Specifically, positive rumination can attenuate the negative impact of depression on creativity. This result enriches our understanding of the complex relationship between depression and creativity, and highlights the significance of individual cognitive coping styles.
From a cognitive appraisal theory perspective (81), positive rumination involves adaptive cognitive processing of internal and external stimuli. When individuals engage in positive rumination, they are more likely to constructively appraise their depressive experiences. Instead of passively succumbing to negative affect, they actively seek to reframe and make sense of it. This cognitive reappraisal process is hypothesized to activate neural pathways associated with positive affect and self-regulation, which, in turn, may counteract the neural circuitry underlying the negative impact of depression on creativity. In the context of depressive emotions, positive ruminators may perceive the negative feelings brought about by depression as a signal for self-growth and change (82). By reflecting on their own thinking patterns, behavioral habits, and life experiences, they attempt to tap into potential creative resources. For example, they may gain more opportunities for introspection from the loneliness induced by depression, thereby stimulating an in-depth exploration of the inner world and providing unique materials and perspectives for creative thinking. This positive cognitive restructuring process helps break the shackles of depressive emotions on creativity, enabling individuals to transcend the interference of negative emotions and unleash their creative potential to some extent. This suggests that when intervening in the issue of decreased creativity caused by mobile phone addiction, we should not only focus on alleviating depressive emotions but also pay attention to cultivating individuals’ positive rumination ability and guiding them to adopt positive cognitive strategies to deal with stress and setbacks in life.
4.4 Limitations and future directions
Although this study on the relationships between smartphone addiction, depression, rumination, and creativity represents progress, it also has some limitations.
While this study identifies significant associations between variables, the cross-sectional design precludes conclusions about causal directions. Longitudinal research is needed to explore temporal dynamics, such as whether depression precedes smartphone addiction or vice versa, and how positive rumination may influence these trajectories over time. The questionnaire survey, although useful for large samples, suffers from self-report bias. Participants might have inaccurately reported mobile phone addiction and depression due to social desirability concerns. Future research could integrate behavioral experiments and physiological measures, such as electroencephalogram monitoring. By directly observing brain activity during phone use, we can better understand the neural mechanisms and their impact on creativity, thereby minimizing self-report errors. While efforts were made to diversify the sample, the use of convenience sampling may constrain the generalizability of the results. Subsequent studies will broaden the sampling framework to address this limitation. We examined the mediating and moderating roles of depression and rumination, other variables may need to be considered. Social support networks and personality traits (e.g., openness and neuroticism) may also be involved.
Future research can adopt the triangulation method, integrating multiple sources of data to enhance the quality of the study. Behavioral observation data can be incorporated by objectively recording usage behaviors through mobile phone usage monitoring software, which can be cross - verified with self - report data. Physiological measurement data can also be introduced, using fMRI and EEG technologies to monitor brain activities and provide evidence of neural mechanisms. Meanwhile, it is recommended to conduct longitudinal studies to track the dynamic changes of variables and clarify the causal sequence, and carry out experimental studies to test causal relationships through group - based interventions. The coordinated use of multiple methods will deepen the exploration of relationships among smartphone addiction, positive rumination, and creativity, and strengthen theoretical and practical foundations.
When applying the research findings, it is necessary to consider the characteristics of Chinese culture. In a collectivist cultural context, individual creativity often serves collective goals. The support from family and social networks is of great significance in resisting smartphone addiction and maintaining mental health and creativity. Therefore, when applying the research results in fields such as education and psychological intervention, elements of collective cooperation and family support should be integrated to enhance the adaptability and effectiveness of interventions. Cross - cultural comparative research can be carried out. By comparing China with Western individualistic cultures or other cultural regions, we can explore how cultural factors moderate the relationships among smartphone addiction, positive rumination, and creativity, and build a universal theoretical model. Meanwhile, exploring effective intervention measures in different cultural backgrounds can promote theoretical development and practical applications on a global scale.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Full Name: Hubei University of Education Institutional Review Board Affiliation: Hubei University of Education, City Wu Han, China. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because This study is a risk-free, anonymous survey that has been carefully reviewed and approved by the ethics committee to ensure it poses no risk or harm to participants. When distributing the questionnaires, participants are first provided with an informed consent form. The form clearly states that if participants do not wish to take part in the survey, they are under no obligation to complete the questionnaire. Conversely, if they agree to participate, they may proceed to fill out the questionnaire. Therefore, by choosing to complete the questionnaire, participants are indicating their consent to participate in this study.
Author contributions
LC: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. HX: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. The study was supported by the Hubei Province education science planning 2022 key topics (grant number: 2022GA078). Additional financial support to undertake this research was provided by the Hubei Teacher Education Research Center (grant number: jsjy202101).
Acknowledgments
We thank the Hubei Teacher Education Research Center and the Hubei Provincial Education Science Planning Leading Group for supporting the study. We are also grateful to all the students who willingly participated in the study.
Conflict of interest
The authors declare that the research 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) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1570547/full#supplementary-material
References
1. Miranda J, Navarrete C, Noguez J, Molina-Espinosa JM, Ramírez-Montoya MS, and Navarro-Tuch SA. The core components of education 4.0 in higher education: Three case studies in engineering education. Comput Electric Engineer. (2021) 93:1–27. doi: 10.1016/j.compeleceng.2021.03.001
2. Gajda A, Karwowski M, and Beghetto RA. Creativity and academic achievement: A meta-analysis. J Educ Psychol. (2017) 109:269. doi: 10.1037/edu0000157
3. Paulus PB and Nijstad BA eds. Group creativity: Innovation through collaboration. Oxford: Oxford university press (2003). doi: 10.5860/choice.41-5582
4. Weisberg RW. Creativity: Understanding innovation in problem solving, science, invention, and the arts. Hoboken, NJ: John Wiley & Sons (2016).
5. Richard V, Lebeau JC, Becker F, Inglis ER, and Tenenbaum G. Do more creative people adapt better? An investigation into the association between creativity and adaptation. Psychol Sport Exer. (2018) 38:80–9. doi: 10.1016/j.psychsport.2018.06.003
6. Leis A, Tohei A, and Cooke SD. Smartphone assisted language learning and autonomy. Int J Computer-Assisted Lang Learn Teach (IJCALLT). (2015) 5:75–88. doi: 10.4018/ijcallt.2015070105
7. Martin F and Ertzberger J. Here and now mobile learning: An experimental study on the use of mobile technology. Comput Educ. (2013) 68:76–85. doi: 10.1016/j.compedu.2013.06.003
8. Mulyani MA, Razzaq A, Ridho SLZ, and Anshari M. Smartphone and mobile learning to support experiential learning. In: 2019 International Conference on Electrical Engineering and Computer Science (ICECOS). Piscataway, NJ: IEEE (2019). p. 6–9. doi: 10.1109/ICECOS47846.2019.8986098
9. Liebherr M, Schubert P, Antons S, Montag C, and Brand M. Smartphones and attention, curse or blessing?—A review on the effects of smartphone usage on attention, inhibition, and working memory. Comput Hum Behav Rep. (2020) 1:100005. doi: 10.1016/j.chbr.2020.100005
10. Bian M and Leung L. Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Soc Sci Comput Rev. (2015) 33:61–79. doi: 10.1177/0894439314528610
11. Kuss DJ, Kanjo E, Crook-Rumsey M, Kibowski F, Wang GY, and Sumich A. Problematic mobile phone use and addiction across generations: The roles of psychopathological symptoms and smartphone use. J Technol Behav Sci. (2018) 3:141–9. doi: 10.1007/s41347-018-0058-8
12. Kwon M, DJ K, Cho H, and Yang S. The smartphone addiction scale: Development and validation of a short version for adolescents. PloS One. (2013) 8:e83558. doi: 10.1371/journal.pone.0083558
13. Skierkowski D and Wood RM. To text or not to text? The importance of text messaging among college-aged youth. Comput Hum Behav. (2012) 28:744–56. doi: 10.1016/j.chb.2011.09.009
14. Cheever NA, Rosen LD, Carrier LM, and Chavez A. Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Comput Hum Behav. (2014) 37:290–7. doi: 10.1016/j.chb.2014.03.010
15. King DL, Haagsma MC, Delfabbro PH, Gradisar M, and Griffiths MD. Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clin Psychol Rev. (2013) 33:331–42. doi: 10.1016/j.cpr.2012.12.001
16. Chen C, Zhang KZ, Gong X, Zhao SJ, Lee MK, and Liang L. Examining the effects of motives and gender differences on smartphone addiction. Comput Hum Behav. (2017) 75:891–902. doi: 10.1016/j.chb.2017.06.010
17. Lin YH, Wong BY, Lin SH, Chiu YC, Pan YC, and Lee YH. Development of a mobile application (App) to delineate “digital chronotype” and the effects of delayed chronotype by bedtime smartphone use. J Psychiatr Res. (2019) 110:9–15. doi: 10.1016/j.jpsychires.2019.01.010
18. Kaufman JC and Sternberg RJ eds. The Cambridge Handbook of Creativity. New York, NY: Cambridge University Press (2010).
19. Lin Y-H, Chang L-R, Lee Y-H, Tseng H-W, Kuo TBJ, and Chen S-H. Development and validation of the smartphone addiction inventory (SPAI). PloS One. (2014) 9:e98312. doi: 10.1371/journal.pone.0098312
20. Panova T and Carbonell X. Is smartphone addiction really an addiction? J Behav Addict. (2018) 7:252–9. doi: 10.1556/jba-7.2.2018.100
21. Olson JA, Sandra DA, Colucci ÉS, Al Bikaii A, Chmoulevitch D, Nahas J, et al. Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Comput Hum Behav. (2022) 129:107138. doi: 10.1016/j.chb.2022.107138
22. Salehan M and Negahban A. Social networking on smartphones: When mobile phones become addictive. Comput Hum Behav. (2013) 29:2632–9. doi: 10.1016/j.chb.2013.05.028
23. Roberts JA, Pullig C, and Manolis C. I need my smartphone: A hierarchical model of personality and cell-phone addiction. Pers Individ Differ. (2015) 79:13–9. doi: 10.1016/j.paid.2015.02.002
24. Aljomaa SS, Qudah MFA, Albursan IS, Bakhiet SF, and Abduljabbar AS. Smartphone addiction among university students in the light of some variables. Comput Hum Behav. (2016) 61:155–64. doi: 10.1016/j.chb.2016.03.030
25. Ratan ZA, Parrish AM, Zaman SB, Alotaibi MS, and Hosseinzadeh H. Smartphone addiction and associated health outcomes in adult populations: A systematic review. Int J Environ Res Public Health. (2021) 18:12257. doi: 10.3390/ijerph182212257
26. Vecchio RP. Theoretical and empirical examination of cognitive resource theory. J Appl Psychol. (1990) 75:315–21. doi: 10.1037/0021-9010.75.3.315
27. De Dreu CKW, Baas M, and Nijstad BA. Hedonic tone and activation level in the mood-creativity link: Toward a dual pathway to creativity model. J Pers Soc Psychol. (2008) 94:739–56. doi: 10.1037/0022-3514.94.5.739
28. Hong W, Liu RD, Ding Y, Sheng X, and Zhen R. Mobile phone addiction and cognitive failures in daily life: The mediating roles of sleep duration and quality and the moderating role of trait self-regulation. Addictive Behav. (2020) 107:106383. doi: 10.1016/j.addbeh.2020.106383
29. Wilmer HH, Sherman LE, and Chein JM. Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Front Psychol. (2017) 8:605. doi: 10.3389/fpsyg.2017.00605
30. Barr N, Pennycook G, Stolz JA, and Fugelsang JA. The brain in your pocket: Evidence that Smartphones are used to supplant thinking. Comput Hum Behav. (2015) 48:473–80. doi: 10.1016/j.chb.2015.02.029
31. Olson JA, Sandra DA, Langer EJ, Raz A, and Veissière SP. Creativity and smartphone use: Three correlational studies. Int J Human–Computer Interact. (2023) 39:2920–5. doi: 10.1080/10447318.2022.2157767
32. Guan J, Yang Y, Ma W, Li G, and Liu C. The relationship between mobile phone use and creative ideation among college students: The roles of critical thinking and creative self-efficacy. Psychol Aesthet Creativ Arts. (2024). doi: 10.1037/aca0000695
33. Li X, Li Y, Wang X, and Hu W. Reduced brain activity and functional connectivity during creative idea generation in individuals with smartphone addiction. Soc Cogn Affect Neurosci. (2023) 18:nsac052. doi: 10.1093/scan/nsac052
34. Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, and Carter CS. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci. (2012) . 12:241–68. doi: 10.3758/s13415-011-0083-5
35. Benedek M and Fink A. Toward a neurocognitive framework of creative cognition: the role of memory, attention, and cognitive control. Curr Opin Behav Sci. (2019) 27:116–22. doi: 10.1016/j.cobeha.2018.11.002
36. Cox DW, Kealy D, Kahn JH, Wojcik KD, Joyce AS, Ogrodniczuk JS, et al. The attenuating effect of depression symptoms on negative-affect expression: Individual and group effects in group psychotherapy for personality disorders. J Couns Psychol. (2019) 66:351. doi: 10.1037/cou0000354
37. Shahjehan A, Shah SI, Qureshi JA, and Wajid A. A meta-analysis of smartphone addiction and behavioral outcomes Int'l J Management Studies. (2021) 28:103–25. doi: 10.32890/ijms2021.28.2.5
38. Fredrickson BL. Positivity: Groundbreaking research reveals how to embrace the hidden strength of positive emotions, overcome negativity, and thrive. New York, NY: Crown Publishers/Random House (2009).
39. Isen AM and Reeve J. The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motiv Emot. (2005) 29:295–323. doi: 10.1007/s11031-005-5859-2
40. Dietrich A. The cognitive neuroscience of creativity. Psychonom Bull Rev. (2004) 11:1011–26. doi: 10.3758/BF03196731
41. Bar KJ. The anxious brain: How neural circuits interact to produce anxiety symptoms. Neurosci Biobehav Rev. (2009) 33:477–91.
42. Beaty RE and Kenett YN. Associative thinking at the core of creativity. Trends Cogn Sci. (2023) 27:671–83. doi: 10.1016/j.tics.2023.04.002
43. Ruiz FJ and Odriozola-González P. The role of psychological inflexibility in Beck’s cognitive model of depression in a sample of undergraduates. Anales Psicol. (2016) 32:441–7. doi: 10.6018/analesps.32.2.214551
44. Aldao A, Nolen-Hoeksema S, and Schweizer S. Emotion-regulation strategies across psychopathology: A meta-analytic review. Clin Psychol Rev. (2010) 30:217–37. doi: 10.1016/j.cpr.2009.11.004
45. Silvia PJ and Kimbrel NA. A dimensional analysis of creativity and mental illness: Do anxiety and depression symptoms predict creative cognition, creative accomplishments, and creative self-concepts? Psychol Aesthet Creativ Arts. (2010) 4:2–10. doi: 10.1037/a0016494
46. Ottaviani C, Thayer JF, Verkuil B, Lonigro A, Medea B, Couyoumdjian A, et al. Physiological concomitants of perseverative cognition: A systematic review and meta-analysis. psychol Bull. (2016) 142:231–59. doi: 10.1037/bul0000036
47. Watkins ER. Constructive and unconstructive repetitive thought. psychol Bull. (2008) 134:163–206. doi: 10.1037/0033-2909.134.2.163
48. Brosschot JF, Gerin W, and Thayer JF. The perseverative cognition hypothesis: A review of worry, prolonged stress-related physiological activation, and health. J Psychosom Res. (2006) 60:113–24. doi: 10.1016/j.jpsychores.2005.06.074
49. Nolen-Hoeksema S, Wisco BE, and Lyubomirsky S. Rethinking rumination. Perspect psychol Sci. (2008) 3:400–24. doi: 10.1111/j.1745-6924.2008.00088.x
50. Conway M, Csank PA, Holm SL, and Blake CK. On assessing individual differences in rumination on sadness. J Pers Assess. (2000) 75:404–25. doi: 10.1207/S15327752JPA7503_04
51. Blanke ES, Neubauer AB, Houben M, Erbas Y, and Brose A Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect using dynamic structural equation modeling. Emotion. (2022) 22:1773–88. doi: 10.1037/emo0000946
52. Martin LL and Tesser A. Some ruminative thoughts. Adv Soc Cogn. (1996) 9:1–47. Available at: https://psycnet.apa.org/record/1996-97335-001.
53. Frone MR. Relations of negative and positive work experiences to employee alcohol use: Testing the intervening role of negative and positive work rumination. J Occup Health Psychol. (2015) 20:148–60. doi: 10.1037/a0038375
54. Yang H, Wang Z, Song J, Lu J, Huang X, Zou Z, et al. The positive and negative rumination scale: Development and preliminary validation. Curr Psychol. (2020) 39:483–99. doi: 10.1007/s12144-018-9950-3
55. Feldman GC, Joormann J, and Johnson SL. Responses to positive affect: A self-report measure of rumination and dampening. Cogn Ther Res. (2008) 32:507–25. doi: 10.1007/s10608-007-9169-8
56. Larsen RJ and Prizmic Z. Affect regulation. In: Vohs KD and Baumeister RF, editors. Handbook of Self-Regulation: Research, Theory, and Applications. Guilford Press, New York, NY (2004). p. 40–61.
57. Gross JJ. The emerging field of emotion regulation: An integrative review. Rev Gen Psychol. (1998) .2:271–99. doi: 10.1037/1089-2680.2.3.271
58. Gómez-Baya D and Mendoza R. Trait emotional intelligence as a predictor of adaptive responses to positive and negative affect during adolescence. Front Psychol. (2018) 9:2525. doi: 10.3389/fpsyg.2018.02525
59. Palmer CA and Gentzler AL. Adults’ self-reported attachment influences their savouring ability. J Positive Psychol. (2018) 13:290–300. doi: 10.1080/17439760.2017.1279206
60. Vanderlind WM, Everaert J, and Joormann J. Positive emotion in daily life: Emotion regulation and depression. Emotion. (2022) 22:1614–24. doi: 10.1037/emo0000944
61. Ma TW, Bryant FB, and Hou WK. Associations of trait positive emotion regulation with everyday emotions: An experience sampling approach. Int J Psychol. (2020) 55:871–81. doi: 10.1002/ijop.12650
62. Ilha Villanova AL and Pina e Cunha M. Everyday creativity: A systematic literature review. J Creat Behav. (2021) 55:673–95. doi: 10.1002/jocb.481
63. Mullet DR, Willerson A, Lamb KN, and Kettler T. Examining teacher perceptions of creativity: A systematic review of the literature. Think Skills Creativ. (2016) 21:9–30. doi: 10.1016/j.tsc.2016.05.001
64. Leung YC. Development and validation of the mobile phone addiction inventory (MPAI). J Behav Addict. (2015) 4:123–35.
65. Lovibond PF and Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. (1995) 33:335–43. doi: 10.1016/0005-7967(94)00075-U
66. Nolen-Hoeksema S, Morrow J, and Jannay L. A prospective study of depression and posttraumatic stress symptoms after a natural disaster: The 1989 Loma Prieta earthquake. J Pers Soc Psychol. (1991) 61:115–21. doi: 10.1037/0022-3514.61.1.115
67. Scott G and Bruce HA. Innovation and creativity in organizations: A state-of-the-art analysis. Hum Resour Manage Rev. (1994) 4:257–77. doi: 10.5465/256701
68. Hao L and Lirong Y. Assessing common method bias in management research: A comparison of Harman’s single-factor test and the common latent factor approach. Manage Res Methods. (2004) 12:234–45.
69. Osadcha KP, Osadchyi VV, and Spirin O. Current state and development trends of e-learning in China. Inf Technol Learn Tools. (2021) 5:208–27. doi: 10.33407/itlt.v85i5.4399
70. Morales Rodríguez FM, Lozano JMG, Linares Mingorance P, and Pérez-Mármol JM. Influence of smartphone use on emotional, cognitive and educational dimensions in university students. Sustainability. (2020) . 12:6646. doi: 10.3390/su12166646
71. Yu J, Sun Y, and Gao W. The impact of information overload on creative thinking: A theoretical and empirical study. J Business Res. (2019) 102:567–80.
72. Gioia F, Rega V, and Boursier V. Problematic internet use and emotional dysregulation among young people: A literature review. Clin Neuropsych. (. (2021) 18:41. doi: 10.36131/cnfioritieditore20210104
73. Ozgul O, Orsal O, Unsal A, and Ozalp SS. Evaluation of internet addiction and depression among university students. Procedia-Social Behav Sci. (2013) 82:445–54. doi: 10.1016/j.sbspro.2013.06.291
74. Ha JH, Kim SY, Bae SC, Bae S, Kim H, Sim M, et al. Depression and internet addiction in adolescents. Psychopathology. (2007) 40:424–30. doi: 10.1159/000107426
75. Craparo G, Messina R, Severino S, Fasciano S, Cannella V, Gori A, et al. The relationships between self-efficacy, internet addiction and shame. Indian J psychol Med. (2014) 36:304–7. doi: 10.4103/0253-7176.135386
76. Yang X, Ma H, Zhang L, Xue J, and Hu P Perceived social support, depressive symptoms, self-compassion, and mobile phone addiction: A moderated mediation analysis. Behav Sci. (2023) 13:769. doi: 10.3390/bs13090769
77. De Souza LC, Volle E, Bertoux M, Czernecki V, Funkiewiez A, Allali G, et al. Poor creativity in frontotemporal dementia: a window into the neural bases of the creative mind. Neuropsychologia. (2010) 48:3733–42. doi: 10.1016/j.neuropsychologia.2010.09.010
78. Rohde K, Adolph D, Dietrich DE, and Michalak J. Mindful attention regulation and non-judgmental orientation in depression: A multi-method approach. Biol Psychol. (2014) 101:36–43. doi: 10.1016/j.biopsycho.2014.06.009
79. Soltani E, Shareh H, Bahrainian SA, and Farmani A. The mediating role of cognitive flexibility in correlation of coping styles and resilience with depression. Pajoohandeh J. (2013) 18:88–96.
80. Franzen J and Brinkmann K. Anhedonic symptoms of depression are linked to reduced motivation to obtain a reward. Motiv Emot. (2016) 40:300–8. doi: 10.1007/s11031-015-9529-3
81. Ellsworth PC. Some implications of cognitive appraisal theories of emotion. Cogn Emotion. (1991) 1:143–61.
Keywords: undergraduates, smartphone addiction, creativity, depression, rumination
Citation: Cheng L and Xie H (2025) Smartphone addiction and creativity in Chinese undergraduates: a moderated mediation model analysis. Front. Psychiatry 16:1570547. doi: 10.3389/fpsyt.2025.1570547
Received: 03 February 2025; Accepted: 23 May 2025;
Published: 13 June 2025.
Edited by:
Fengshi Jing, City University of Macau, Macao SAR, ChinaReviewed by:
Nipin Kalal, All India Institute of Medical Sciences Jodhpur, IndiaHongyang Liu, Palacký University, Olomouc, Czechia
Copyright © 2025 Cheng and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Han Xie, eGllaGFuMTk5M0AxNjMuY29t