Feng Tian
Rong Zhang2,3
Fengbo Liu- 1College of Physical and Education, Zhengzhou University of Light Industry, Zhengzhou, China
- 2School of Education, Cangzhou Normal University, Cangzhou, China
- 3School of Psychology,Beijing Sport University, Beijing, China
Background: Mobile phone addiction (MPA) has become a growing concern among adolescents, particularly within the group of adolescent athletes in high-pressure competitive environments. resilience may serve as a protective factor, but the underlying emotional mechanisms remain unclear. This study aimed to examine the mediating roles of anxiety and depression in the relationship between resilience and MPA among adolescent athletes.
Methods: A total of 401 participants (aged 13–19) were recruited from a sports secondary school in Henan Province, China. Resilience, anxiety, depression, and MPA were measured using validated self-report scales. Structural equation modeling and bootstrap analysis (5,000 resamples) were conducted using Mplus 8.3.
Results: Resilience was not significantly associated with MPA in the direct path. For the indirect path, the pathway from resilience to MPA through depression was significant, whereas the pathway through anxiety was not. And a significant chain mediation was found from resilience to MPA through both anxiety and depression. The total indirect effect was significant, while the direct effect was not.
Conclusion: Resilience is associated with lower levels of MPA in adolescents, primarily through its impact on depressive symptoms, with anxiety playing an indirect role through depression. These findings suggest that interventions promoting resilience and reducing depression may help mitigate problematic phone use in youth.
1 Introduction
As time progresses and technology develops, mobile phones are gradually becoming an integral part of life (1). This increasing reliance often leads to a series of problems when individuals overuse their mobile phones, which can interfere with normal functioning (2). This issue is particularly pronounced among underage groups, who are more susceptible to MPA, which can significantly impact their daily lives (3). In scientific field, MPA, also referred to as problematic mobile phone use, is increasingly recognized as a behavioral addiction marked by excessive, uncontrolled, and compulsive use of mobile devices that interferes with various aspects of daily life (4). It is characterized by symptoms such as preoccupation with phone-related activities, a persistent inability to reduce usage despite awareness of negative consequences, and significant distress or functional impairment when the device is inaccessible (5). The research by Pew Research Center shows that, relative to other age groups, over 95% of American adolescents own a personal phone, and 40% of them feel them spend much time on it (6). As smartphone ownership becomes nearly universal among adolescents and young adults, the boundary between functional use and addictive behavior grows increasingly blurred. The reason is that unlike other behavioral addictions such as internet gaming or gambling, phone use is unique in its omnipresence and integration into daily routines of adolescents used for socializing, academic tasks, entertainment, and emotional escape (7). The consequences of MPA can be serious and may lead to various negative impact. Some of empirical studies have linked it to reduced academic engagement, decreased productivity, impaired interpersonal relationships, and sleep disturbances, particularly due to bedtime screen exposure and nocturnal awakenings triggered by phone notifications (8). From a neuropsychological perspective, it shares core features with other forms of addiction, such as impulsivity, diminished executive control, and heightened sensitivity in some conditions (9). These behavioral and neurocognitive markers highlight the necessity of addressing mobile phone overuse, especially among adolescent group. These not merely as a habit but as a potential public health concern requiring targeted intervention strategies. In order to address this addictive behavior, it is important to identify which emotional factors are more strongly associated with it. Research has shown that athletes are typically spend more time on training, and their levels of MPA are significantly lower compared to non-athlete who rarely engage in physical activity (10). However, MPA is exactly existing with athletes, such as Instagram addiction (11), media loyalty (12) and PUBG addiction (13). Previous research has clearly indicated that there are two major aspects associated with MPA among athletes: one is negative (e.g., anxiety, distraction, disengagement), and the other is positive (e.g., self-regulation, social connectedness) (14). Exploring the complex correlations between MPA and psychological mechanisms is important for developing an influential MPA model for adolescent athlete group.
1.1 The relationship between resilience and MPA
According to the Emotion Regulation Deficit Theory, MPA is clearly associated with deficits in emotion regulation, as individuals who lack this ability are more likely to develop addictive behaviors (15). Individuals with insufficient emotion regulation skills are more likely to engage in maladaptive coping strategies, such as excessive smartphone use, to relieve negative emotions (16). These temporary strategies can develop into behavioral addictions over time.
Resilience, as a protective trait, enhances one’s ability to manage stress and regulate emotions effectively, thereby reducing the likelihood of engaging in emotion-driven addictive behaviors like MPA (17). And resilience has been identified as a key component of mental health and is associated with positive psychological outcomes, particularly during periods of adversity (18). Resilience encompasses both emotional and cognitive components, and its level serves as an effective indicator for assessing an individual’s psychological adaptability when facing changing environments or challenging situations (19). Findings from resilience research suggest that the presence of protective factors may buffer the effects of stress, thereby promoting physical health and preventing the development of psychopathology, even in the face of significant adversity (20). But on the other hand, although resilience originates from the field of developmental psychopathology, there is still no universally established theoretical framework for it (21). From the perspective of physical activity, both meta-analyses and empirical studies have shown that physical activity can serve as a modifiable factor influencing resilience (22). This is partly because physical activity enhances top-down control over bottom-up processes in the brain, thereby promoting self-regulation. By strengthening specific brain regions and large-scale neural circuits, it improves emotional and behavioral regulation, serving as a protective buffer against mental health problems and fostering resilience during this vulnerable developmental period (23). It can be concluded that resilience plays a pivotal role in supporting individuals who are regularly active, thereby reducing MPA, especially among athletes. However, we aim to explore the complex correlations between resilience and MPA on this group, as ample evidence shows that a portion of this population still experiences high levels of MPA (24). Among the many potential factors, an individual’s psychological adaptability in the face of adversity emerges as a crucial indicator worth observing. Moreover, previous research appears to have insufficiently explored the role of resilience in influencing MPA among adolescent athletes (25). Our aim is to extend prior findings. Although existing research suggests that physical activity may help alleviate anxiety, it is clear that it is not the only effective intervention (26). Identifying specific intervention factors can help clarify one aspect of the complex web of related influences. The resilience and MPA status of adolescent athletes remain insufficiently studied. This group typically engages in relatively high levels of physical activity, and based on existing theories, their resilience may also be comparatively stronger. We proposed the following research question:
H1: Resilience is negatively associated with MPA.
1.2 The mediating role of anxiety
Anxiety is regarded as an emotional state in response to the anticipation of potential negative events in the future (27). It is also one of the most common mental disorders (28). Its main symptoms include verbal-subjective manifestations such as worry, overt motor acts such as avoidance, and somato-visceral activity such as muscle tension (29). Common types of anxiety disorders include social anxiety disorder (13% lifetime prevalence), generalized anxiety disorder (6.2% lifetime prevalence), panic disorder (5.2% lifetime prevalence), and agoraphobia (2.6% lifetime prevalence), which frequently co-occurs with panic disorder (30). Potential comorbidities include cardiovascular diseases, gastrointestinal diseases, pulmonary diseases, cancer, chronic pain, and migraine headaches (31). Anxiety disorders are chronic conditions, and research has found that many patients experience symptoms for several years before receiving any therapeutic intervention (32). They typically onset during childhood or adolescence and persist into adulthood (33). The median age of onset is as early as 11 years old. The onset of generalized anxiety disorder is most commonly reported to occur during childhood and adolescence (34). Notably, students at this age are typically in the stage of compulsory education, where anxiety is often frequent, widespread, and intense within the educational environment (35). Moreover, anxiety experienced during this period can lead to a range of problems in life after graduation (36). Researchers have extensively examined the relationship between anxiety disorders and various contextual evaluations. The cognitive consequences of test anxiety can lead to a decline in working memory capacity, thereby impairing the completion of learning tasks (37). Social anxiety can negatively affect individuals in educational, social, and interpersonal domains (38). For children, anxiety disorders may result in chronic school refusal, which can subsequently lead to significant social and academic impairments (39). For adolescents, anxiety disorders can lead to a decline in academic performance and have a detrimental impact on social functioning (28). In adults, anxiety disorders exhibit relatively high comorbidity rates with other psychiatric disorders, including depression, substance abuse/dependence, and attention-deficit hyperactivity disorder (ADHD) (40). Therefore, early intervention and management of anxiety can help alleviate its associated dysfunction and distress.
A review of the literature reveals that the underlying mechanisms influencing adolescent anxiety can be categorized into three main types. The first involves biological factors. In addition to the previously discussed dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, another critical factor is the autonomic nervous system (ANS), which consists of the sympathetic and parasympathetic branches (41). Together, these systems regulate cardiovascular responses: the sympathetic nervous system stimulates the body’s reaction to stress, while the parasympathetic nervous system inhibits it (42). Low vagal (parasympathetic) responsiveness and a low threshold for sympathetic activation are considered potential mechanisms in the development of anxiety and depression (43). The second category involves individual and environmental factors. Studies have found that, within certain subgroups, girls are more likely than boys to develop anxiety and depression disorders (44). Environmental factors include low family socioeconomic status (SES) and parental internalizing problems, both of which have been associated with increased risk of anxiety (45). Parents with anxiety or depression disorders often have limited social resources, which diminishes their ability to help their children cope with stressful social situations, thereby increasing the risk of anxiety and depression in their offspring. In addition, early adverse experiences, such as bereavement, parental divorce, physical abuse, and stressful life events, also contribute to the development of anxiety disorders (46). The third category involves potential risk factors at the intersection of various domains and psychology. For example, the biological consequences of obesity may affect the HPA axis and lead to negative body image perceptions and dissatisfaction due to one’s physical appearance. This in turn, can result in low self-esteem and social anxiety, thereby increasing the likelihood of adolescents developing anxiety (47). This is particularly important among adolescents, as many experience the onset of anxiety symptoms for the first time during high school (48). Early onset is often associated with the development of mental disorders in adulthood (49). From a functional perspective, anxiety often triggers avoidance behaviors and attentional biases toward threat (50). Anxious individuals may turn to mobile devices as a form of safety-seeking or distraction from worry (51). Based on it, we proposed the following research questions:
H2: Anxiety mediates the relationship between resilience and MPA.
1.3 The mediating role of depression
Depression is typically conceptualized as a persistent emotional state characterized by feelings of sadness, hopelessness, and a loss of interest or pleasure in previously enjoyed activities (52). It encompasses not only affective symptoms, but also cognitive impairments such as negative self-evaluation, indecisiveness, and recurrent rumination. Additionally, somatic symptoms, such as fatigue, changes in sleep and appetite, and psychomotor retardation, further compound its impact on functioning (53). Depression is thus a multidimensional construct that exerts broad impairments across emotional, cognitive, and physical domains. Although anxiety and depression are highly comorbid and share overlapping features such as negative affect, their core phenomenological and neurobiological profiles differ substantially (54). Anxiety is predominantly future-oriented, characterized by excessive worry, hypervigilance, and physiological arousal in anticipation of potential threats (55). In contrast, depression is more focused on past or present experiences, marked by low affect, anhedonia, and a pervasive sense of helplessness or resignation. Neurocognitively, anxiety tends to involve hyperactivation of the amygdala and related fear circuits, while depression is more associated with hypoactivation in reward-related brain areas such as the ventral striatum and prefrontal cortex (56). Previous studies have already confirmed a significant association between resilience and depression; however, whether this relationship remains robust within the adolescent athlete population warrants further investigation.
Research has shown that anxiety and depression can significantly affect adolescents’ mental health and overall well-being (57). Given these theoretical, clinical, and neurobiological distinctions, it is conceptually justified to treat anxiety and depression as separate yet potentially interacting mediators in psychological models. This approach allows for a more nuanced examination of how distinct forms of emotional distress may differentially mediate the association between resilience and maladaptive behaviors (58). Depression results in withdrawal, cognitive slowing, and motivational deficits. These distinctions have important implications for behavioral outcomes such as MPA. For example, whereas depressed individuals may use them for passive escape or mood regulation in response to low energy or motivation (59).
A growing body of research underscores the complex interplay between psychological distress and MPA (60). Numerous studies have shown that individuals experiencing heightened levels of anxiety, depression, or emotional dysregulation are significantly more susceptible to problematic phone use (61). For these individuals, mobile phones often serve as an accessible and immediate coping mechanism, offering temporary relief from negative affect through distraction, reassurance-seeking, or virtual social connection. However, such reliance may lead to maladaptive coping patterns, reinforcing avoidance behaviors and ultimately exacerbating psychological symptoms. Among adolescents, a population particularly vulnerable due to ongoing emotional development and identity formation of the relationship appears especially pronounced. Frequent phone use may temporarily alleviate feelings of loneliness or academic stress, but over time, excessive dependence on virtual interactions can erode self-esteem, heighten social anxiety, and impair real-world social functioning (62). Moreover, longitudinal studies suggest a bidirectional relationship: while poor mental health predicts higher levels of MPA, the addiction itself can also lead to a decline in psychological well-being, forming a reinforcing cycle of dysfunction (63). These findings underscore the need for an integrated mental health framework when investigating the antecedents and consequences of MPA, particularly in youth-focused prevention and intervention efforts.
Does resilience play a key role in the relationship between depression and MPA? To what extent does resilience influence the relationship between depression and MPA? We proposed the following research questions:
H3: Depression mediates the relationship between resilience and MPA.
1.4 The chain mediating effect of anxiety and depression
Recent research has increasingly highlighted the psychological challenges faced by adolescents in the digital age, with MPA emerging as a prevalent behavioral concern (62). Resilience has been widely recognized as a protective factor against emotional distress, such as anxiety and depression, both of which are positively linked to the development of MPA (64). While numerous studies have explored these variables in general adolescent populations, little is known about their interactions within the unique context of adolescent athletes. Adolescent athletes are often assumed to benefit from structured training routines and enhanced physical fitness, which may contribute to better emotional regulation (65). However, this group is also subject to unique stressors, such as performance pressure, intense schedules, and limited time autonomy (66). These factors may make them equally—if not more—vulnerable to psychological distress and maladaptive coping behaviors like MPA (24). Given these dual characteristics, it remains unclear whether the protective role of resilience functions similarly among adolescent athletes, and whether anxiety and depression mediate this relationship in the same manner as in the general adolescent population. Therefore, the present study aims to examine the chain relationships among resilience, anxiety, depression, and MPA in adolescent athletes. Specifically, we test a chain mediation model in which anxiety and depression sequentially mediate the relationship between resilience and MPA. And the hypothesis is:
H4: Anxiety and depression sequentially mediate the relationship between resilience and MPA.
Based on the above, we propose the following model (Figure 1):
Figure 1. Hypothesized model.
2 Methods
2.1 Participants
The study was reviewed and approved by the Research Ethics Committee of Zhengzhou University of Light Industry. An online screening questionnaire was used to assess eligibility. Those who met the inclusion criteria were invited to participate in the study. Prior to data collection, informed consent was obtained from all participants, and confidentiality and anonymity were fully ensured. For minors, parental consent was also obtained before the start of the investigation. All procedures were conducted in accordance with ethical guidelines.
A convenience sampling method was used in this study. A total of 500 questionnaires were distributed, and 410 were returned. After excluding 9 invalid responses due to incomplete or inconsistent answers, 401 valid questionnaires were retained for analysis, yielding an effective response rate of 80.2%. The participants were adolescents aged 13 to 19 years (M = 14.56, SD = 0.86), recruited from a sports-focused secondary school in Henan Province, China. The sample consisted predominantly of male students (approximately 77%), and the majority were Han Chinese (over 95%). All participants received regular training in various athletic disciplines as part of their school curriculum. Most participants were under the age of 18.
2.2 Instruments
2.2.1 Connor-Davidson resilience scale
Resilience was assessed using the 10-item Chinese version of the Connor-Davidson Resilience Scale (CD-RISC-10) (67), revised by Chinese researchers for use among adolescent athletes. The scale comprises 10 items rated on a 5-point Likert scale (1 = “not true at all” to 5 = “true nearly all the time”). Higher scores reflect higher levels of psychological resilience. In the current study, the scale demonstrated excellent internal consistency (Cronbach’s α = 0.932).
2.2.2 Generalized anxiety disorder scale
Anxiety symptoms were measured using the 7-item Generalized Anxiety Disorder Scale (GAD-7) modified version, originally developed by Spitzer et al. based on the DSM-IV criteria (68). The scale has been translated into Chinese and psychometrically validated by local researchers for use among Chinese populations (69), including adolescent groups. It is a unidimensional scale rated on a 3-point Likert scale, with higher total scores indicating greater levels of anxiety. A sample item is “Feeling afraid as if something awful might happen.” In the present study, the scale demonstrated good internal consistency (Cronbach’s α = 0.866).
2.2.3 Center for epidemiologic studies depression scale
Depressive symptoms were assessed using the Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D), adapted by Chinese researchers (70). The scale includes 20 items measuring the frequency of depressive symptoms experienced during the past week. Items are rated on a 4-point Likert scale, with higher total scores indicating more severe depressive symptoms. The scale has demonstrated good reliability and validity in Chinese adolescent populations. In the present study, the internal consistency was acceptable (Cronbach’s α = 0.893).
2.2.4 Mobile phone addiction tendency scale
MPA tendency was measured using the Mobile Phone Addiction Tendency Scale (MPATS), developed by Xiong Jie et al. (71). The scale consists of 16 items rated on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”), with higher scores indicating a greater tendency toward MPA. It comprises four dimensions: withdrawal symptoms (e.g., “I feel uneasy when I can’t use my phone”), salience behavior, social soothing, and mood change. The scale has been widely used in Chinese adolescent and young adult populations, and has shown good psychometric properties. In the present study, the internal consistency of the scale was acceptable (Cronbach’s α = 0.931).
2.3 Statistical analysis
As a initial step, we examined the descriptive statistics and bivariate associations among the core variables by computing Pearson correlation coefficients in SPSS 26.0. For mediation analysis, the bootstrap method was adopted based on Hayes’ recommendation. Unlike the Sobel test, the bootstrap method estimates the sampling distribution directly without assuming normality, offering greater statistical power and a lower Type I error rate (72). Therefore, this study employed the bootstrap method with 5,000 resamples to estimate the 95% confidence intervals of the mediation effects. All mediation analyses were conducted using Mplus version 8.3.
According to all indicators were ordinal Likert-type items, we additionally estimated the model using WLSMV with all items treated as categorical, as a sensitivity analysis to verify the stability of the model.
3 Results
3.1 Common method bias check
Since all variables in this study were derived from self-reported questionnaires, common method bias (CMB) may pose a significant threat to discriminant validity. To mitigate potential CMB, several procedural remedies were implemented, including anonymous responses, counterbalanced item sequencing, and standardized administration protocols. We assessed common method bias using Harman’s single-factor test, performing an unrotated exploratory factor analysis on all questionnaire items (73). The analysis revealed 9 components with eigenvalues greater than 1, collectively explaining 61.69% of the total variance. Notably, the first unrotated principal component accounted for 28.81% of the variance, which is below the critical threshold of 50%, indicating that no single factor dominated the variance structure. This result suggests that common method bias is unlikely to pose a serious threat to the validity of the findings.
3.2 Discrimination validity check
We conducted confirmatory factor analysis (CFA) to examine whether the measurement model of the adopted scales holds in our adolescent sample.
We used maximum likelihood estimation with robust standard errors (MLR) to handle non-normality. All items were retained without parceling to avoid information loss. Model modifications were not applied to maintain theoretical purity. The model demonstrated the overall model acceptable fit: χ² (1319) = 2760.36, p <.001, RMSEA = 0.052, CFI = 0.843, TLI = 0.836, SRMR = 0.065. Although the CFI and TLI values were slightly below the conventional threshold of.90, this is not uncommon for models with complex structures, and the other fit indices fell within acceptable ranges. We further test all items factor loadings (Supplementary Table S1), while most standardized of them exceeded the recommended threshold of 0.40, three items fell below this cutoff. Given that the present study employed a widely validated instrument, these items were retained for theoretical consistency, though their weaker performance is noted as a limitation. In conclusion, this model supporting the distinctiveness of the constructs.
3.3 Descriptive statistics and correlation analysis
Table 1 presents the means, standard deviations, and intercorrelations among the key study variables: gender, age, ethnicity, resilience, anxiety, depression, and MPA. Resilience was negatively correlated with anxiety (r = -0.43, p < 0.001), depression (r = -0.53, p < 0.001), and MPA (r = -0.33, p < 0.001), indicating that higher resilience is associated with better psychological adjustment and lower behavioral risk. Anxiety and depression were positively correlated (r = 0.62, p < 0.001), and both showed significant positive associations with MPA (r = 0.37 and r = 0.47, respectively, both p < 0.001). These findings support the hypothesized associations and provide justification for subsequent mediation analysis. Although age was modestly correlated with several variables, these effects were small and are not considered confounding.
Table 1. Descriptive statistics and correlation analysis of variable results.
3.4 Mediator effect analysis
We tested the path hypotheses (H1, H2, H3, H4) using structural equation modeling (SEM) with maximum likelihood (ML) estimation and 5000 bootstrap resamples. This approach allows simultaneous estimation of multiple pathways and accounts for measurement error in latent constructs. Standardized path coefficients and bias-corrected 95% confidence intervals were reported.
Table 2 presents the indirect and total effects estimated via structural equation modeling with 5000 bootstrap resamples. The direct path from resilience to MPA was non-significant (β = -0.135, 95% CI [-0.281, -0.012]), not supporting H1. The indirect path from resilience to MPA via depression (H3) was statistically significant (β = -0.076, 95% CI [-0.132, -0.021]), as was the chained mediation via anxiety and depression (H4; β = -0.095, 95% CI [-0.141, -0.049]). However, the simple mediation pathway via anxiety alone (H3) was not statistically significant (β = -0.051, 95% CI [-0.141, 0.040]). Overall, the total indirect effect was significant (β = -0.222, 95% CI [-0.294, -0.149]), the direct effect was non-significant (β = -0.135, 95% CI [-0.281, 0.012]), indicating that the association between resilience and MPA is largely explained through depression and the chained pathway. These findings support H3 and H4 but not H1 and H2.
Table 2. Results of chain mediating effect of Resilience on MPA.
Although the indirect path via anxiety alone was not statistically significant, the indirect effects via depression and the chained path via anxiety and depression were both significant. These findings support a complete mediation model, suggesting that the mediating factor plays a central role in the relationship between resilience and MPA.
3.5 Sensitive analysis
The WLSMV model (Supplementary Table S2) showed acceptable fit (CFI = 0.942, TLI = 0.939, RMSEA = 0.052). Path coefficients were consistent in direction and significance with the original ML bootstrap results. Specifically, the indirect effects of resilience on MPA through depression, and the chain pathway remained significant. Effect sizes were comparable, with only minor differences in magnitude (Δβ <.05). This indicates that the conclusions about the mediation effects are robust to different methods of handling ordinal data.
4 Discussion
4.1 Summary of key findings
By investigated the relationship between resilience and MPA, focusing on the mediating roles of anxiety and depression. The results partially supported the proposed chain mediation model. Specifically, resilience was negatively associated with both anxiety and depression, while anxiety and depression were positively associated with MPA. Notably, the indirect effects of resilience on MPA through depression alone and through the sequential pathway via anxiety and depression were significant, while the direct path and the anxiety-only mediation were not (Figure 2).
Figure 2. The chain mediation model of Resilience on MPA. *p < 0.05, **p <0.01, ***p < 0.001.
4.2 Interpretation of individual hypotheses
H1 was not supported, the direct path from resilience to MPA was not significant. This suggests that resilience does not have a direct effect on MPA among adolescent athletes. Previous studies have shown similar results. For example, a study among 257 college students demonstrated that physical activity did not directly reduce MPA but exerted its effect indirectly through enhanced psychological resilience and reduced perceived stress (β = -0.03, p > 0.05) (74). A longitudinal study with 964 Chinese university students found that psychological resilience did not directly predict mobile phone addiction (β = -0.159, p < 0.001) (25). This conclusion is further examined within the population of adolescent athletes. This suggests that the association between resilience and MPA may be more complex and primarily mediated by affective factors rather than direct behavioral regulation, or fully mediated by other factors.
Resilience was significantly and negatively associated with both anxiety (β = -0.472, p < 0.01) and depression (β = -0.218, p < 0.001), indicating that individuals with higher levels of resilience tend to experience lower levels of internalizing symptoms. These findings align with a meta-analysis of 19 studies including 224 middle and late adolescents, which found resilience to be moderately negatively correlated with negative mental health indicators (75). Similarly, anxiety did not significantly predict MPA (β = 0.107, p > 0.05). The active lifestyle and team-based social support of adolescent athletes may reduce anxiety’s direct effect on MPA. Compared to general adolescents, athletes potentially tend to engage in more structured physical activities, experience regular coaching support, and develop higher levels of self-discipline and stress regulation (76).
Depression as a robust positive predictor of MPA (β = 0.349, p < 0.001). For example, a cross-sectional study of 500 Chinese college students found depression significantly positively associated with MPA (r = 0.313, p < 0.001) and a strong predictor of Anxiety (β = 0.377, p < 0.001) (77). H3 was supported as depression mediated the relationship between resilience and MPA, consistent with previous findings (78). H4 was supported, showing anxiety’s strong positive association with depression (79).
4.3 Interpretation of mediation effects
The mediation analysis revealed nuanced pathways. While H2 (Resilience → Anxiety → MPA) was not supported, this may be due to potential suppression or overlap effects, which could obscure the true nature of the relationship. Resilience on MPA via depression (H3) was supported, indicating that depression plays an important role as a significant mediator between resilience and MPA. More importantly, H4 was supported: a significant chain mediation path from resilience on MPA through anxiety and depression was observed, highlighting a sequential affective process. This suggests that low resilience may contribute to heightened anxiety, which in turn exacerbates depression, ultimately being associated with higher risk of MPA. These findings emphasize that depression is the emotional mechanism that leads to MPA among adolescent athletes. While anxiety shows consistency but not significance, it might be due to some other factors.
4.4 Theoretical contributions
This study contributes to the broader understanding of MPA by integrating resilience, anxiety, and depression into a single framework. The findings extend the I-PACE model by positioning resilience as a distal personal factor and identifying anxiety and depression as proximal affective driver of MPA. The significance of the chain mediation further supports the affective pathway model, suggesting that behavioral addictions of teenager athletes may result from cascading emotional disturbances.
4.5 Practical implications
The findings provide several practical implications for the prevention of MPA among adolescents, particularly those engaged in regular physical training.The results suggest that resilience is associated with a lower risk of MPA, primarily through its effect on depressive symptoms. Thus, focusing on resilience may be an effective strategy for intervening in MPA behavior. Since depression emerged as the critical mediator, targeted interventions focusing on early identification and treatment of depressive symptoms are important. Identifying effective strategies within schools may help address negative emotion before they develop into addictive behaviors. The results underscores the importance of recognizing the sequential emotional pathway from anxiety to depression to MPA. It suggests that interventions should not treat emotional problems in isolation but adopt a multi-layered approach that addresses co-occurring symptoms holistically. For adolescents in performance-oriented environments, such as sports schools, integrating resilience development into athletic training may be an effective strategy for addressing MPA.
4.6 Limitations
Despite its contributions, this study has limitations. The cross-sectional design precludes causal inference. Longitudinal or experimental designs are needed to confirm the causality of the relationships. Furthermore, the sample sizes of this study were all from physical education middle schools. The original resilience of these sample sizes might be stronger than that of ordinary teenagers (80), and their various psychological qualities were all relatively high (81), resulting in the data not being potentially applicable to teenagers who lack physical exercise. It is also possible to expand the sample size to include ordinary teenagers and incorporate a scale for measuring physical exercise would enhance the generalizability of these findings. Moreover, we employed Harman’s single factor test to assess common method bias. However, this approach has limitations, including its assumption of a single underlying factor and its potential to overlook certain sources of bias, which may limit the robustness of the results. And all data were collected through self-report measures without objective assessments, which could introduce potential sources of bias and instability. The path from resilience to MPA through anxiety may reflect suppression or overlap, which could explain the non-significant association found. Three items exhibited relatively substandard factor loadings, suggesting potential measurement issues in this model. Future research may consider re-examining these items or using alternative measures to enhance construct validity. Future research should explore the role of other emotional or cognitive mediators in the resilience and MPA link.
4.7 Conclusion
(1) Resilience was significantly associated with MPA through complete mediators among adolescents, although the direct path was not significant. (2) The mediating path from resilience to MPA through depression was significant, while the path through anxiety was not. (3) The chain mediation path from resilience to MPA from anxiety and depression was significant. These findings highlight the indirect role of emotional symptoms in linking resilience to MPA among adolescent athletes, suggesting that enhancing resilience could be an effective strategy for intervening the risk of MPA in this specific population.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by Zhengzhou University of Light Industry. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.
Author contributions
FT: Writing – review & editing, Data curation, Formal Analysis, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft. RZ: Supervision, Writing – review & editing. FL: Writing – review & editing, Investigation, Resources.
Funding
The author(s) declare financial support was received for the research, and/or publication of this article. This study was supported in part by grants from the General project of humanities and social science research in universities of Henan Province (2025-ZZJH-281), Doctor Research Fund from Zhengzhou University of Light Industry (2022BSJJSK12), 2024 Henan Province Philosophy and Social Sciences Planning Annual Project (2024CTY038), Zhengzhou University of Light Industry Funding Program for Young Key Teacher Trainees.
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.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
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.2025.1646150/full#supplementary-material
References
1. Harris J, Reeder B, and Pattabiraman K. Mobile phones: Impacts, challenges, and predictions. Hum Behav Emerging Tech. (2019) 1:121–35. doi: 10.1002/hbe2.112
2. Sohn SY, Rees P, Wildridge B, Kalk NJ, and Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. (2019) 19:356. doi: 10.1186/s12888-019-2350-x
3. Boer M, van den Eijnden RJJM, Boniel-Nissim M, Wong SL, Inchley J, Badura P, et al. Adolescents' intense and problematic social media use and their well-being in 29 countries. J Adolesc Health. (2020) 66:S89–99. doi: 10.1016/j.jadohealth.2020.02.014
4. Billieux J, Maurage P, Lopez-Fernandez O, Kuss DJ, and Griffiths MD. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr Addict Rep. (2015) 2:156–62. doi: 10.1007/s40429-015-0054-y
5. Panova T and Carbonell X. Is smartphone addiction really an addiction? J Behav Addict. (2018) 7:252–9. doi: 10.1556/2006.7.2018.49
6. Pew Research Center. How Teens and Parents Approach Screen Time. Pew Research Center (2024). Available online at: https://www.pewresearch.org/internet/2024/03/11/how-teens-and-parents-approach-screen-time (Accessed May 12, 2025).
7. Nesi J and Prinstein MJ. Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. J Abnorm Child Psychol. (2015) 43:1427–38. doi: 10.1007/s10802-015-0020-0
8. Elhai JD, Dvorak RD, Levine JC, and Hall BJ. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord. (2017) 207:251–9. doi: 10.1016/j.jad.2016.08.030
9. Kuss DJ and Griffiths MD. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health. (2015) 12:1286–306. doi: 10.3390/ijerph14030311
10. Anwar R, Ali A, and Ahmed F. Varsity sports to combat smartphone addiction among university students. J Dev Soc Sci. (2024) 5:293–301. doi: 10.47205/jdss.2024(5-I)27
11. Gezgin DM and Mihci C. Smartphone addiction in undergraduate athletes: reasons and effects of using Instagram intensively. Int J Technol Educ Sci. (2020) 4:188–202. doi: 10.46328/ijtes.v4i3.106
12. Argan M, Köse H, Özgen C, and Yalınkaya B. Do sports, take photo and share: phubbing, social media addiction and narcissism of body builders. Eur J Phys Educ Sport Sci. (2019) 5:79–94. doi: 10.46827/ejpe.v0i0.2472
13. Zafar HMB, Ali A, and Ahmed F. Symptoms of PUBG addiction among male adolescents: a comparison between college level student cricketers vs non-athlete college students. Pak Lang Humanit Rev. (2023) 7:150–9. doi: 10.47205/plhr.2023(7-III)15
14. DesClouds P and Durand-Bush N. Smartphones and varsity athletes: A complicated relationship. Front Sports Act Living. (2021) 2:560031. doi: 10.3389/fspor.2020.560031
15. Koole SL. The psychology of emotion regulation: an integrative review. Cognit Emot. (2009) 23:4–41. doi: 10.1080/02699930802619031?needAccess=true
16. Tice DM, Bratslavsky E, and Baumeister RF. Emotional distress regulation takes precedence over impulse control: if you feel bad, do it. J Pers Soc Psychol. (2001) 80:53–67. doi: 10.1037/0022-3514.80.1.53
17. Jeong SH, Kim H, Yum JY, and Hwang Y. What type of content are smartphone users addicted to? SNS vs. games Comput Hum Behav. (2016) 54:10–7. doi: 10.1016/j.chb.2015.07.035
18. Davydov DM, Stewart R, Ritchie K, and Chaudieu I. Resilience and mental health. Clin Psychol Rev. (2010) 30:479–95. doi: 10.1016/j.cpr.2010.03.003
19. Arslan G and Wong P. Embracing life' s challenges: Developing a tool for assessing resilient mindset in second wave positive psychology. J Happiness Health. (2024) 4:1–10. doi: 10.47602/johah.v4i1.53
20. Luthar SS, Cicchetti D, and Becker B. The construct of resilience: A critical evaluation and guidelines for future work. Child Dev. (2000) 71:543–62. doi: 10.1111/1467-8624.00164
21. Hjemdal O, Vogel PA, Solem S, Hagen K, and Stiles TC. The relationship between resilience and levels of anxiety, depression, and obsessive-compulsive symptoms in adolescents. Clin Psychol Psychother. (2011) 18:314–21. doi: 10.1002/cpp.719
22. Silverman MN and Deuster PA. Biological mechanisms underlying the role of physical fitness in health and resilience. Interface Focus. (2014) 4:20140040. doi: 10.1098/rsfs.2014.0040
23. Belcher BR, Zink J, Azad A, Campbell CE, Chakravartti SP, and Herting MM. The roles of physical activity, exercise, and fitness in promoting resilience during adolescence: Effects on mental well-being and brain development. Biol Psychiatry Cognit Neurosci Neuroimaging. (2021) 6:225–37. doi: 10.1016/j.bpsc.2020.08.005
24. Sahin Koybulan S, Altin D, Yararbas G, and Hassoy H. Smartphone addiction and related factors among athletes. Behav Sci. (2024) 14:341. doi: 10.3390/bs14040341
25. Dong F, Bu Z, Jiang S, Liu Y, Lin J, Li J, et al. Cross-lagged panel relationship between physical activity atmosphere, psychological resilience and mobile phone addiction on college students. Sci Rep. (2025) 15:16599. doi: 10.1038/s41598-025-97848-w
26. Stubbs B, Vancampfort D, Rosenbaum S, Firth J, Cosco T, Veronese N, et al. An examination of the anxiolytic effects of exercise for people with anxiety and stress-related disorders: a meta-analysis. Psychiatry Res. (2017) 249:102–8. doi: 10.1016/j.psychres.2016.12.020
27. Barlow DH. Anxiety and its disorders: The nature and treatment of anxiety and panic. 2nd ed. New York: Guilford Press (2004). Available online at: https://books.google.co.th/books?id=Lx9hf-3ZJCQC (Accessed May 20, 2025).
28. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey. Arch Gen Psychiatry. (1994) 51:8–19. doi: 10.1001/archpsyc.1994.03950010008002
29. Lang PJ. Fear reduction and fear behavior: Problems in treating a construct. In: Research in psychotherapy: Proceedings of the 3rd conference. American Psychological Association, Washington, DC (1968). p. 90–102. doi: 10.1037/10546-004
30. Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, and Wittchen HU. Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res. (2012) 21:169–84. doi: 10.1002/mpr.1359
31. Kariuki-Nyuthe C and Stein DJ. Anxiety and related disorders and physical illness. In: Sartorius N, Holt RIG, and Maj M, editors. Comorbidity of mental and physical disorders: A selective review. Karger, Basel (2015). p. 81–7. doi: 10.1159/000365538
32. Bandelow B and Michaelis S. Epidemiology of anxiety disorders in the 21st century. Dialogues Clin Neurosci. (2015) 17:327–35. doi: 10.31887/DCNS.2015.17.3/bbandelow
33. Albano AM, Chorpita BF, and Barlow DH. Childhood anxiety disorders. In: Barlow DH, editor. Clinical handbook of psychological disorders: A step-by-step treatment manual, 3rd ed. Guilford Press, New York (1998). p. 196–243. Available online at: https://books.google.com.sg/books?hl=zh-CN&lr=&id=uoKsJUd-73gC&oi=fnd&pg=PA279&dq (Accessed May 20, 2025).
34. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA: American Psychiatric Publishing (2022). doi: 10.1176/appi.books.9780890425787
35. Robertson L. International handbook of emotions in education. Emot Educ. (2015). doi: 10.1080/02667363.2014.994350
36. Ahmad RJ, Bayan H, Faque T, and Seidi PAM. Prevalence of social anxiety in students of College of Education - University of Garmian. Researchers World. (2017) 8:79. doi: 10.18843/rwjasc/v8i3(1)/12
37. Núñez-Peña MI, Suárez-Pellicioni M, and Bono R. Gender differences in test anxiety and their impact on higher education students' academic achievement. Proc Soc Behav Sci. (2016) 228:154–60. doi: 10.1016/j.sbspro.2016.07.023
38. Taylor CT, Cross K, and Amir N. Attentional control moderates the relationship between social anxiety symptoms and attentional disengagement from threatening information. J Behav Ther Exp Psychiatry. (2016) 50:68–76. doi: 10.1016/j.jbtep.2015.06.001
39. Last CG and Strauss CC. School refusal in anxiety-disordered children and adolescents. J Am Acad Child Adolesc Psychiatry. (1990) 29:31–5. doi: 10.1097/00004583-199001000-00005
40. Biederman J, Newcorn J, and Sprich S. Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. Am J Psychiatry. (1991) 148:564–77. doi: 10.1176/ajp.148.5.564
41. Thayer JF, Åhs F, Fredrikson M, Sollers JJ 3rd, and Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev. (2012) 36:747–56. doi: 10.1016/j.neubiorev.2011.11.009
42. Berntson GG, Cacioppo JT, and Quigley KS. Autonomic determinism: the modes of autonomic control, the doctrine of autonomic space, and the laws of autonomic constraint. Psychol Rev. (1991) 98:459–87. doi: 10.1037/0033-295X.98.4.459
43. Porges SW. The polyvagal theory: Phylogenetic substrates of a social nervous system. Int J Psychophysiol. (2001) 42:123–46. doi: 10.1016/S0167-8760(01)00162-3
44. Hyland P, Shevlin M, Elklit A, and Christoffersen M. Social, familial and psychological risk factors for mood and anxiety disorders in childhood and early adulthood: A birth cohort study using the Danish Registry System. Soc Psychiatry Psychiatr Epidemiol. (2016) 51:331–8. doi: 10.1007/s00127-016-1171-1
45. Telman LGE, van Steensel FJA, Maric M, and Bögels SM. What are the odds of anxiety disorders running in families? A family study of anxiety disorders in mothers, fathers, and siblings of children with anxiety disorders. Eur Child Adolesc Psychiatry. (2018) 27:615–24. doi: 10.1007/s00787-017-1076-x
46. Asselmann E, Wittchen H-U, Lieb R, and Beesdo-Baum K. A 10-year prospective-longitudinal study of daily hassles and incident psychopathology among adolescents and young adults: Interactions with gender, perceived coping efficacy, and negative life events. Soc Psychiatry Psychiatr Epidemiol. (2017) 52:1353–62. doi: 10.1007/s00127-017-1436-3
47. Kostanski M and Gullone E. Adolescent body image dissatisfaction: Relationships with self-esteem, anxiety, and depression controlling for body mass. J Child Psychol Psychiatry Allied Discip. (1998) 39:255–62. doi: 10.1017/S0021963097001807
48. Fergusson DM and Woodward LJ. Mental health, educational, and social role outcomes of adolescents with depression. Arch Gen Psychiatry. (2002) 59:225–31. doi: 10.1001/archpsyc.59.3.225
49. Patton GC, Coffey C, Posterino M, Carlin JB, and Bowes G. Life events and early onset depression: Cause or consequence? Psychol Med. (2003) 33:1203–10. doi: 10.1017/S0033291703008078
50. Mogg K and Bradley BP. A cognitive-motivational analysis of anxiety. Behav Res Ther. (1998) 36:809–48. doi: 10.1016/S0005-7967(98)00063-1
51. Elhai JD, Levine JC, Dvorak RD, and Hall BJ. Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Comput Hum Behav. (2016) 63:509–16. doi: 10.1016/j.chb.2016.05.079
52. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington (VA: American Psychiatric Publishing (2013). Available online at: https://seragpsych.com/wordpress/wp-content/uploads/2019/02/DSM5Update_October2018.pdf (Accessed May 20, 2025).
54. Clark DA and Beck AT. Cognitive therapy of anxiety disorders: science and practice. New York: Guilford Press (2011).
55. Barlow DH. Anxiety and its disorders: the nature and treatment of anxiety and panic. 2nd ed. New York: Guilford Press (2004).
56. Disner SG, Beevers CG, Haigh EA, and Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci. (2011) 12:467–77. doi: 10.1038/nrn3027
57. Racine N, McArthur BA, Cooke JE, Eirich R, Zhu J, and Madigan S. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. (2021) 175:1142–50. doi: 10.1001/jamapediatrics.2021.2482
58. Arslan G. Psychological maltreatment, emotional and behavioral problems in adolescents: The mediating role of resilience and self-esteem. Child Abuse Negl. (2016) 52:200–9. doi: 10.1016/j.chiabu.2015.09.010
59. Elhai JD, Yang H, Fang J, Bai X, and Hall BJ. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: fear of missing out as a mediator. Addict Behav. (2020) 101:105962. doi: 10.1016/j.addbeh.2019.04.020
60. Liu R-D, Hong W, Ding Y, Oei TP, Zhen R, Jiang S, et al. Psychological distress and problematic mobile phone use among adolescents: the mediating role of maladaptive cognitions and the moderating role of effortful control. Front Psychol. (2019) 10:1589. doi: 10.3389/fpsyg.2019.01589
61. Elhai JD, Levine JC, Dvorak RD, and Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav. (2019) 93:210–7. doi: 10.1016/j.chb.2016.12.023
62. Busch PA and McCarthy S. Antecedents and consequences of problematic smartphone use: a systematic literature review of an emerging research area. Comput Hum Behav. (2021) 114:106414. doi: 10.1016/j.chb.2020.106414
63. Augner C, Vlasak T, Aichhorn W, and Barth A. The association between problematic smartphone use and symptoms of anxiety and depression—a meta-analysis. J Public Health. (2023) 45:193–201. doi: 10.1093/pubmed/fdab350
64. Ji X, Feng N, Zhao T, and Cui L. Protective and risk factors in problematic mobile phone use among adolescents: a three-wave longitudinal study. Addict Behav. (2025) 165:108299. doi: 10.1016/j.addbeh.2025.108299
65. Stults-Kolehmainen MA and Sinha R. The effects of stress on physical activity and exercise. Sports Med. (2014) 44:81–121. doi: 10.1007/s40279-013-0090-5
66. Rice SM, Purcell R, De Silva S, Mawren D, McGorry PD, and Parker AG. The mental health of elite athletes: a narrative systematic review. Sports Med. (2016) 46:1333–53. doi: 10.1007/s40279-016-0492-2
67. Gao J, Li Y, and Wu X. Revision and validation of the Connor-Davidson Resilience Scale of coal miners in China. Int J Ind Ergon. (2021) 85:103191. doi: 10.1016/j.ergon.2021.103191
68. Spitzer RL, Kroenke K, Williams JBW, and Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. (2006) 166:1092–7. doi: 10.1001/archinte.166.10.1092
69. He XY, Li CB, Wu WY, Xiao ZP, and Zhang MY. Reliability and validity of the Generalized Anxiety Disorder Scale in general hospitals. Chin Ment Health J. (2010) 24:200–3. doi: 10.3969/j.issn.1002-0829.2010.04.002
70. Zhang J, Wu ZY, Fang G, Li J, Han B-X, and Chen ZY. Establishment of national urban norms for the Center for Epidemiologic Studies Depression Scale (CES-D) in China. Chin Ment Health J. (2010) 24:139–43. doi: CNKI:SUN:ZXWS.0.2010-02-019
71. Xiong J, Zhou ZK, Chen W, You ZQ, and Chen Z-Y. Development of the Mobile Phone Addiction Tendency Scale for college students. Chin Ment Health J. (2012) 26:222–5. doi: CNKI:SUN:ZXWS.0.2012-03-017
72. Hayes AF. Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monogr. (2009) 76:408–20. doi: 10.1080/03637750903310360
73. Podsakoff PM, MacKenzie SB, Lee J-Y, and Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. (2003) 88:879–903. doi: 10.1037/0021-9010.88.5.879
74. Wang X, Liu Z, and Zhang Y. Effects of physical exercise on mobile phone addiction in college students: The chain mediating role of psychological resilience and perceived stress. Int J Environ Res Public Health. (2022) 19:12376. doi: 10.3390/ijerph191912376
75. Sagone E and De Caroli ME. Relationships between psychological well-being and resilience in middle and late adolescents. Proc Soc Behav Sci. (2014) 141:881–7. doi: 10.1016/j.sbspro.2014.05.154
76. Knight CJ, Harwood CG, and Sellars PA. Supporting adolescent athletes' dual careers: The role of an athlete's social support network. Psychol Sport Exerc. (2018) 38:137–47. doi: 10.1016/j.psychsport.2018.06.007
77. Dou X, Lu J, Yu Y, Yi Y, and Zhou L. The impact of depression and anxiety on mobile phone addiction and the mediating effect of self-esteem. Sci Rep. (2024) 14:23004. doi: 10.1038/s41598-024-71947-6
78. Ma A, Yang Y, Guo S, Li X, Zhang S, and Chang H. Adolescent resilience and mobile phone addiction in Henan Province of China: impacts of chain mediating, coping style. PloS One. (2022) 17:e0278182. doi: 10.1371/journal.pone.0278182
79. Gorman JM. Comorbid depression and anxiety spectrum disorders. Depress Anxiety. (1996) 4:160–8. doi: 10.1002/(SICI)1520-6394(1996)4:4<160::AID-DA2>3.0.CO;2
80. Sheng X, Liang K, Li K, Chi X, and Fan H. Association between sports participation and resilience in school-attending students: a cross-sectional study. Front Psychol. (2024) 15:1365310. doi: 10.3389/fpsyg.2024.1365310
Keywords: resilience, MPA, adolescent athletes, emotional mechanisms, anxiety, depression
Citation: Tian F, Zhang R and Liu F (2025) Resilience and mobile phone addiction among adolescent athletes: a chain-mediated model of anxiety and depression. Front. Psychiatry 16:1646150. doi: 10.3389/fpsyt.2025.1646150
Received: 18 June 2025; Accepted: 08 October 2025;
Published: 28 November 2025.
Edited by:
Alessandra Maria Passarotti, University of Illinois Chicago, United StatesReviewed by:
MoonSoo Lee, Korea University, Republic of KoreaMuhammad Reza Aziz Prasetya, State University of Surabaya, Indonesia
Copyright © 2025 Tian, Zhang and Liu. 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: Fengbo Liu, bGZienp1bGlAMTI2LmNvbQ==