The Relationship Between Adolescent Experiential Avoidance and Smartphone Addiction: A Study Based on Latent Transition Analysis and Cross-Lagged Panel Network Analysis

Abstract

With the widespread use of smartphones among adolescents, smartphone addiction has become a growing mental health concern. Adolescents’ limited self-regulation makes them particularly vulnerable to using smartphones to escape real-life stress, heightening addiction risk. However, the heterogeneity of addictive behaviors and the dynamic role of experiential avoidance have been underexplored. This 6-month longitudinal study surveyed 547 Chinese primary and secondary students using the Smartphone Addiction Scale (SAS) and the Acceptance and Action Questionnaire-II (AAQ-II). Latent profile analysis (LPA) and latent transition analysis (LTA) were applied to identify subgroups and examine transitions between these subgroups. Cross-lagged panel network analysis (CLPN) revealed key symptom interactions between experiential avoidance and addiction. The study identified two addiction subgroups: a stable “low-risk group” (84.9 percent) and a “high-risk group,” 51.4 percent of whom transitioned to low risk over time. Logistic regression showed that experiential avoidance significantly predicted high-risk membership (odds ratios [OR] = 1.083–1.102) and deterioration within the low-risk group (OR = 1.036). The CLPN identified “online intimacy” (SPA-3) and “hesitation and overcautious” (EA-7) as driver nodes, with “withdrawal symptoms” (SPA-2) serving as a central node. These findings emphasize the crucial role of experiential avoidance in adolescent smartphone addiction and suggest symptom-level targets for early intervention. The results support acceptance and commitment therapy (ACT) as a promising approach for reducing smartphone addiction among youth.

Keywords

adolescents smartphone addiction experiential avoidance latent profile analysis latent transition analysis cross-lagged panel network

Introduction

In modern society, smartphones have become an indispensable tool in daily life, bringing convenience while also exerting significant negative effects on individuals’ physical and mental health and social functioning. According to data from the China Internet Network Information Center,¹ by December 2023, the Internet penetration rate among minors in China reached 97.2 percent, with 90 percent having dedicated devices for Internet use, primarily using smartphones. Studies have indicated that due to their relatively weaker self-control abilities,² adolescents are more prone to smartphone addiction compared to adults, which can negatively impact their physical and mental development.³ Existing research has shown that adolescent smartphone addiction is associated with emotional experiences such as anxiety, depression, and stress, and even affects impulsive behavior and personality traits.⁴

Regarding the definition of smartphone addiction, there are various perspectives in the academic literature. Some scholars define it as a mental health problem caused by excessive smartphone use,⁵ or as an uncontrollable, intense craving for smartphones.⁶ Other studies refer to it as a condition where adolescents experience impaired physiological, psychological, and social functioning due to uncontrolled smartphone use.⁷ Despite being aware of the negative consequences, addicts struggle to stop using their smartphones, and this loss of control significantly damages their health and social functioning.^8,9 However, existing research often treats smartphone addiction as a unidimensional continuous variable, overlooking its inherent heterogeneity.¹⁰ This simplification may mask the diversity of symptom combinations and developmental differences among key subgroups, such as some adolescents experiencing “natural remission” of addictive behaviors, while others may fall into “chronic deterioration.”¹¹ Therefore, this study proposes Research Question 1: Using latent profile analysis (LPA) and latent transition analysis (LTA), explore the categorization of adolescent smartphone addiction, and investigate the stability and transitions of subgroups over time to reveal the underlying dynamic mechanisms of its development.

In the field of mental health, a large body of research has shown that experiential avoidance is significantly positively correlated with issues such as anxiety, depression, and obsessive-compulsive disorder.^12,13 For example, when individuals feel anxious or depressed, they often engage in avoidance of certain thoughts, emotions, or physical sensations to alleviate distress. The experiential avoidance model (EAM), proposed by Chapman et al.¹⁴ within a behavioral framework, suggests that after encountering triggering events, individuals may experience emotional reactions such as anger, guilt, and sadness. Certain internal traits (e.g., high emotional reactivity, low pain tolerance, poor emotional regulation) and environmental factors interact to prompt individuals to adopt avoidance strategies to reduce discomfort. In addictive behaviors, experiential avoidance is considered one of the key factors in maintaining addiction, as individuals may use substances or engage in behavioral addictions to escape internal discomfort.¹⁵ In contemporary society, smartphones often serve as a tool for avoiding internal experiences, as individuals may use them to distract themselves when feeling anxious, lonely, or bored,¹⁶ temporarily escaping negative emotions. While this behavior may alleviate distress in the short term, it exacerbates smartphone addiction and harms mental health in the long run. The EAM has been widely applied in the study of behavioral addictions, such as smartphone addiction, to explain the formation mechanisms of maladaptive behaviors.¹⁷ However, current research on experiential avoidance and smartphone addiction largely focuses on the relationship between variables and seldom examines how experiential avoidance influences the differentiation and developmental transitions of adolescent smartphone addiction subgroups. Therefore, this study proposes Research Question 2: Introduce experiential avoidance into the latent transition model to examine its predictive role in adolescent smartphone addiction and analyze how it affects the transition paths of different subgroups.

Building on the above research, this study further combines network analysis methods and proposes Research Question 3: Construct a cross-lagged network model of experiential avoidance and smartphone addiction to explore the specific mechanisms between the two. Network analysis, based on dynamic systems models, can visualize the complex relationships between multiple factors.^18,19 This method overcomes the problem in previous research of analyzing total symptom scores and can identify the network structure between symptoms and their core nodes. Interventions targeting core symptoms not only alleviate those specific symptoms but can also lead to improvements in peripheral symptoms, thereby more effectively influencing the entire network.^20,21

In summary, this study conducts a 6-month follow-up survey of adolescents, combining LPA, LTA, and network analysis methods to systematically examine the symptom evolution pathways of adolescent smartphone addiction and the role of experiential avoidance in these processes. This aims to identify key targets for intervention and provide theoretical and practical insights for related intervention practices.

Methods

Research procedure and participants

A convenience sample of primary and secondary school students was recruited from Hebei Province, China. Data were collected at two time points (T1: September 2024; T2: March 2025; 6-month interval). With school approval, trained graduate students administered electronic questionnaires during class time. Written informed consent was obtained from all participants and their guardians.

A total of 620 questionnaires were distributed at T1, yielding 586 valid responses (response rate: 94.5 percent). At T2, 547 participants were successfully followed up (retention rate: 93.3 percent). The final sample comprised 547 students (294 males, 53.7 percent; 253 females, 46.3 percent), aged 9–15 years (M = 11.93, SD = 1.31), including 358 primary school students (57.0 percent) and 189 middle school students (43.0 percent).

Measures

Smartphone Addiction Scale–Short Version

The Chinese version of the Smartphone Addiction Scale–Short Version (SAS-SV),²² developed by Kwon et al.²³ and revised for Chinese adolescents, was used to assess smartphone dependence. This 10-item scale assesses smartphone dependence on a 5-point scale (1 = strongly disagree to 5 = strongly agree), with higher scores indicating greater addiction. Cronbach’s α in this study was 0.92 (T1) and 0.94 (T2).

Acceptance and Action Questionnaire-II

The Chinese version of the Acceptance and Action Questionnaire-II (AAQ-II), originally developed by Bond et al.²⁴ and revised by Cao et al.,²⁵ was used to measure experiential avoidance. This 7-item scale uses a 7-point Likert scale, with higher scores indicating greater experiential avoidance. Cronbach’s α in this study was 0.91 (T1) and 0.93 (T2).

Data analysis

Descriptive statistics and logistic regression analysis for demographic variables were performed using SPSS 24.0. LPA and LTA were conducted with Mplus 8.3. LPA classifies individuals based on response patterns to identify group heterogeneity,²⁶ while LTA tracks latent class transitions over time to reveal dynamic changes.²⁷

Cross-lagged network analysis was conducted in R using the qgraph package for visualizations. A longitudinal cross-lagged panel network (CLPN) model was built using regularized penalized maximum likelihood estimation to estimate autoregressive and cross-lagged effects and perform variable selection. Causal direction was assessed through out-EI and in-EI core indices, and model robustness was evaluated using nonparametric bootstrap resampling (1,000 iterations) and the correlation stability (CS) coefficient, with a CS > 0.25 considered acceptable and > 0.50 indicating high robustness.²⁸

Results

Latent profile analysis of adolescent smartphone addiction

To explore potential subtypes of smartphone addiction among adolescents, a LPA was conducted using scores from the Smartphone Addiction Scale (SAS) at T1 and T2. Models with one to five classes were estimated, and fit indices for each model are presented in Table 1. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), entropy, Lo–Mendell–Rubin Likelihood Ratio Test (LMR-LRT), and Bootstrap Likelihood Ratio Test (BLRT). Lower values of AIC, BIC, and aBIC indicate better model fit.²⁹ A significant p value for LMR-LRT and BLRT indicates that a model with k classes fits significantly better than a model with k − 1 classes.³⁰ Entropy values above 0.80 suggest classification accuracy exceeding 90 percent.³¹

Table 1.

Fit Indices for Latent Profile Analysis Models with Varying Class Numbers at T1 and T2 Time Points

Time	Classes	AIC	BIC	aBIC	Entropy	LMR	BLRT	Smallest group proportion (%)
T1	1	16474.33	16560.42	16560.42	1.00	—	—	—
	2	13641.71	13775.14	13676.74	0.97	<0.001	<0.001	0.24
	3	13125.80	13306.59	13173.26	0.95	0.16	<0.001	0.12
	4	12879.08	13107.21	12938.97	0.96	0.26	<0.001	0.11
	5	12699.07	12974.56	12771.39	0.93	0.50	<0.001	0.07
T2	1	16591.71	16677.80	16614.32	1.00	—	—	—
	2	13690.34	13823.77	13725.37	0.97	<0.001	<0.001	0.22
	3	12874.99	13055.78	12922.45	0.95	<0.05	<0.001	0.13
	4	12610.14	12838.28	12670.04	0.95	0.39	<0.001	0.04
	5	12408.61	12684.09	12480.93	0.96	0.38	<0.001	0.04

BIC, Bayesian Information Criterion.

The results showed that AIC, BIC, and aBIC values decreased as the number of classes increased, with the most substantial decrease occurring between the 1-class and 2-class models. The 2-class model also had the highest entropy value and significant LMR-LRT and BLRT results, suggesting it was superior to other models. Based on these criteria, the 2-class solution was selected as the optimal model for identifying latent subgroups of smartphone addiction (Table 1).

At Time 1 (T1), Class 1 (24.3 percent, n = 133) had the highest scores, indicating severe smartphone addiction, and was labeled the “high-risk group.” Class 2 (75.7 percent, n = 414) had the lowest scores, labeled the “low-risk group” (Fig. 1).

FIG. 1.

T1 LPA classification results of teenagers’ smartphone addiction. SPA1-3, daily interference; SPA4-7, withdrawal symptoms; SPA8, online intimacy; SPA9, overuse; SPA10, tolerance.

At Time 2 (T2), Class 1 (21.9 percent, n = 120) remained the “high-risk group,” while Class 2 (78.1 percent, n = 427) was the “low-risk group” (Fig. 2).

FIG. 2.

T2 LPA classification results of teenagers’ smartphone addiction. LPA, latent profile analysis.

Latent transition analysis

A LTA was conducted to examine changes in subgroup membership between T1 and T2, without covariates. Table 2 presents the transition probabilities.

Table 2.

Latent Transition Probabilities Between Subgroups at T1 and T2

Time-subgroup	T2 low-risk group	T2 high-risk group
T1 low-risk group	0.849	0.151
T1 high-risk group	0.514	0.486

Results showed that 84.9 percent of individuals remained in the low addiction group, while 48.6 percent stayed in the high addiction group. The probability of transitioning from low to high addiction was 15.1 percent, and from high to low addiction was 51.4 percent.

The impact of experiential avoidance on adolescent smartphone addiction subgroups

Based on the confirmation that the two-class model is the best fit, the experiential avoidance score was added as a predictor variable to determine the relationship between smartphone addiction and experiential avoidance. A binary logistic regression analysis was conducted, with the low-risk group serving as the reference group and the predictor variables as independent variables. The results are presented in Table 3. The experiential avoidance scores at both T1 and T2 were found to be significant predictors of high smartphone addiction at the corresponding time points.

Table 3.

Logistic Regression Analysis of Experiential Avoidance on Latent Subgroups

Time	Predictor	High-risk group
Time	Predictor	β	SE	OR	p	95% CI
T1	Experiential avoidance	0.097	0.012	1.102	<0.001	[1.075, 1.129]
T2	Experiential avoidance	0.079	0.012	1.083	<0.001	[1.058, 1.108]

The impact of experiential avoidance on the transition between adolescent smartphone addiction subgroups

To explore the relationship between experiential avoidance and adolescent smartphone addiction subgroup transitions, we developed a latent transition model incorporating gender and experiential avoidance, with individuals who remained in the original subgroup as the reference group. Multivariate logistic regression was used to calculate odds ratios (OR). An OR > 1 indicates a higher probability of transitioning due to experiential avoidance. Results in Table 4 show that gender did not affect transitions, while higher experiential avoidance increased the probability of moving from the low-risk to the high-risk group (OR = 1.036), meaning a 1-point increase in experiential avoidance raised the transition probability by 3.6 percent.

Table 4.

Odds Ratios for Subgroup Transitions from T1 to T2

Factor	Transition	OR	p	95% CI
Gender	Low risk → high risk	1.362	0.27	[0.785, 2.363]
	High risk → low risk	1.491	0.14	[0.872, 2.551]
Experiential avoidance	Low risk → high risk	1.036	<0.05	[1.008, 1.065]
	High risk → low risk	0.991	0.14	[0.958, 1.024]

Cross-lagged network analysis of experiential avoidance and smartphone addiction

To verify the longitudinal relationship between experiential avoidance and smartphone addiction, this study used cross-lagged network analysis to explore the associations between symptom nodes (Fig. 3).

FIG. 3.

Cross-lagged panel network analysis of experiential avoidance and smartphone addiction from T1 to T2. Arrows indicate the unique longitudinal relationships. Green edges represent positive correlations, while red edges represent negative correlations. The color intensity and thickness of the edges indicate stronger relationships.

Within smartphone addiction symptoms, the two strongest cross-lagged edges were SPA-3 (online intimacy) positively predicting SPA-2 (withdrawal symptoms) (weight = 0.59), and SPA-4 (overuse) positively predicting SPA-2 (weight = 0.17). Within experiential avoidance symptoms, the strongest edges were EA-5 (emotional disturbance) positively predicting EA-4 (painful memories) (weight = 0.21), and EA-3 (uncontrollable anxiety) positively predicting EA-4 (weight = 0.14).

For cross-network effects, EA-7 (hesitant and overcautious) positively predicting SPA-2 (weight = 0.34) was the strongest path. Three weak negative paths were also identified: SPA-4 negatively predicting EA-3 (weight = −0.06), SPA-5 (tolerance) negatively predicting EA-3 (weight = −0.08), and SPA-5 negatively predicting EA-7 (weight = −0.08).

Figure 4 displays the centrality indices. The three strongest out-EI nodes were SPA-3, EA-7, and EA-3, indicating these symptoms had greater predictive power for other symptoms. SPA-2 showed the strongest in-EI, indicating it was most strongly predicted by other symptoms. Stability tests confirmed good centrality robustness, with CS-coefficients of 0.75 for both out-EI and in-EI (Appendix 1, Supplementary Figs. S1, S2). Bootstrap difference tests revealed significant differences between edge weights (Appendix 2, Supplemental Figs. S3, S4).

FIG. 4.

Centrality estimates in the T1→T2 cross-lagged network.

Discussion

This study used a longitudinal design, combining LPA, LTA, and cross-lagged network analysis to explore and verify the subgroups and transitions of adolescent smartphone addiction, and analyze the role of experiential avoidance in these processes.

The results showed that based on scores on the SAS, adolescent addiction risk could be divided into high-risk and low-risk groups, with the high-risk group accounting for ∼20–25 percent, and their level of smartphone addiction being significantly higher than that of the low-risk group. This supports the heterogeneity theory of smartphone addiction,⁸ which posits that smartphone addiction is not a single continuum but consists of heterogeneous subgroups, each requiring different intervention strategies.³² The findings also indicate that during both periods, the majority of adolescents were in the low-risk state, and the stability of the high-risk group (48.6 percent) was much lower than that of the low-risk group (84.9 percent). Furthermore, the probability of transitioning from the high-risk group to the low-risk group (51.4 percent) was significantly higher than the reverse transition, suggesting that high addiction states may have a phased characteristic. Some adolescents temporarily enter a high-risk smartphone addiction state due to changes in the environment or emotional regulation needs, such as being influenced by changes in interpersonal relationships or academic pressures, becoming “situational high-risk” individuals. This risk decreases once the situation changes or the stressor is removed, which aligns with the “natural remission” phenomenon observed in substance addiction,³³ highlighting the need to understand adolescent smartphone addiction from a dynamic perspective.

The study further confirmed the negative impact of experiential avoidance on the transition of addiction risk. The higher the level of experiential avoidance, the greater the likelihood of being in the high-risk group. Longitudinally, high experiential avoidance significantly increased the probability of transitioning from the low-risk group to the high-risk group and showed a trend of hindering the recovery of individuals in the high-risk group, potentially forming a vicious cycle of “experiential avoidance-smartphone addiction.” This finding supports the Compensatory Internet Use Theory,³⁴ which suggests that individuals with high experiential avoidance use smartphones as a coping strategy for real-life stress, and this avoidance behavior weakens their ability to solve real-life problems, leading to addiction.

Moreover, gender did not significantly predict adolescent smartphone addiction transitions in this study, contrasting with Ruiz-Ruano et al.³⁵, who found that gender played an important role in the relationship between experiential avoidance and excessive smartphone use in a Spanish adult sample (M_age = 30.97). This discrepancy may stem from sample and cultural differences. First, environmental factors such as academic pressure and peer influence may outweigh gender effects during adolescence, whereas gender-related social role expectations and coping styles become more prominent in adulthood. Second, cross-cultural differences in gender role socialization may influence the expression of experiential avoidance and its relationship with addictive behaviors. A recent meta-analysis suggesting a narrowing trend in gender differences among adolescents³⁶ further supports this interpretation. Future research should examine the complex role of gender across diverse age groups and cultures, particularly through cross-cultural studies investigating how cultural values moderate this relationship.

After verifying that experiential avoidance is a predictive factor for adolescent smartphone addiction, the study further constructed a cross-lagged network. The analysis found that SPA-3 (online intimacy) and EA-7 (hesitant and overcautious) are high Out-EI symptoms in the network, which are key symptoms that activate other nodes to maintain the pathological network.

Online Intimacy refers to an excessive closeness to the smartphone, treating it as an intimate companion, even more so than real friends.²² In the smartphone addiction network, this deep interaction with the smartphone provides immediate rewards to compensate for the emotional deficit in real-life interactions, but excessive dependence weakens real-world social skills and eventually triggers other symptoms in the network.

EA-7 refers to an action inhibition due to overthinking or caution, which involves the avoidance of uncertainty or potential negative experiences. Studies have shown that such avoidance or decision delay behavior is highly correlated with maladaptive Internet use.^37,38 On the one hand, individuals with a high tendency to avoid short-term satisfaction are more likely to adopt avoidance rather than active coping strategies,³⁹ and smartphones, with their satisfying, easily accessible, and attention-demanding characteristics, facilitate such avoidance behaviors.⁴⁰ On the other hand, avoidance behaviors do not effectively solve problems, but instead may exacerbate negative emotions and weaken self-control,⁴¹ making individuals more prone to addiction.

Notably, EA-7 also shows a strong predictive effect on SPA-2 (withdrawal symptoms) in the cross-network, which indicates that withdrawal symptoms are among the most prominent external symptoms when the individual is without a smartphone. SPA-2 is also the highest In-EI symptom in the network, suggesting it can be significantly predicted by other node symptoms, with withdrawal symptoms being the most easily observable.³² The network also contained three weak negative edges, suggesting that excessive use and tolerance behaviors may temporarily reduce negative emotions and avoidance through immediate reinforcement and attention diversion. However, this regulatory effect is limited and may carry long-term risks, warranting further investigation into underlying mechanisms in future studies.

The practical significance of this study lies in several aspects. First, the longitudinal study of adolescent smartphone addiction reveals the key role of experiential avoidance in addiction risk, further validating the applicability of the Compensatory Internet Use Theory and the EAM model in smartphone addiction research. Second, based on cross-lagged network analysis, the study identifies key symptom nodes in adolescent smartphone addiction, providing new targets for clinical intervention. For example, interventions targeting the emotional compensation mechanism of online intimacy may effectively disrupt the self-sustaining process of addiction symptoms.⁴² Future research should combine multimodal data to further validate the psychological basis of these nodes and explore their regulatory role in interventions.

Based on our findings, acceptance and commitment therapy (ACT) is a promising intervention meriting further study. ACT may help adolescents manage life challenges, reduce avoidance, and decrease addictive behaviors through value-driven actions.⁴³ Intervention research broadly shows that psychological approaches, including Cognitive Behavioral Therapy, effectively reduce Internet addiction, with combined interventions yielding stronger effects.⁴⁴ This supports exploring ACT for smartphone addiction, though its specific efficacy requires validation through rigorous randomized controlled trials.

Limitations and future research

This study has several limitations. First, the sample was drawn from a single city, potentially limiting generalizability due to regional cultural and economic factors. Future studies should enhance representativeness by employing systematic sampling across diverse regions and educational backgrounds. Second, the follow-up was limited to two assessments over 6 months, which is insufficient to capture long-term developmental trajectories. Future research should use longer follow-up periods (e.g., 1–2 years) with more frequent assessments (e.g., baseline, 3, 6, 12 months) to better distinguish transient from persistent high-risk individuals and reveal detailed mechanisms. Third, the cultural specificity of the findings requires further examination through cross-cultural comparisons to explore the moderating role of cultural values on symptom networks.

Conclusion

This study, using a longitudinal design, confirms that adolescent smartphone addiction exists in heterogeneous subgroups, with experiential avoidance being a key factor in predicting individual membership in the high-risk group and influencing subgroup transition probabilities. CLPN analysis further revealed that “online intimacy” and “hesitant and overcautious” are core nodes driving the development of the symptom network, while “withdrawal symptoms” serve as a key convergence node. In summary, this study deepens the understanding of the relationship between experiential avoidance and smartphone addiction through multi-level analysis, highlighting key intervention nodes and strategies, and providing valuable insights for effectively addressing adolescent smartphone addiction.

Authors’ Contributions

S.L.: Conceptualization, methodology, supervision, funding acquisition, and writing—review and editing. Xiujun Z.: Conceptualization, methodology, supervision, funding acquisition, and writing—review and editing. X.S.: Conceptualization, formal analysis, writing—original draft, writing—review and editing. Xu Z.: Data curation. Q.Z.: Data curation. Xindi W.: Data curation. Xiaoyan W.: Formal analysis, supervision, writing—review and editing.

Data Availability

Data available on request from the authors.

Ethical Approval

This study was approved by the Ethics Committee of North China University of Science and Technology (approval no. 2024088; issue date: 03/05/2024).

Footnotes

Author Disclosure Statement

The authors report no conflicts of interest.

Funding Information

This research was funded by National Key R&D Program of China (2024YFC2707800). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Supplemental Material

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