The dynamic longitudinal relationship between alexithymia and phubbing in adolescents: the mediating role of internalizing problems

The dynamic longitudinal relationship between alexithymia and phubbing in adolescents: the mediating role of internalizing problems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The dynamic longitudinal relationship between alexithymia and phubbing in adolescents: the mediating role of internalizing problems Haibin Huang, Zhiming Zhou, Baojuan Ye, Dai Qi, Yu Deng, Hongxia Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8096209/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study investigated the longitudinal mediation effect of internalizing problems in the association between alexithymia and adolescent phubbing behavior. Methods The research design was employed that combined three-wave longitudinal tracking ( n = 902, with 6-month intervals) and 14-day intensive diary tracking ( n = 373). The dynamic relationships among the three variables were analyzed at both between-person and within-person levels using the cross-lag model and the residual dynamic structural equation modeling. Results (1) At the between-person level, alexithymia and internalizing problems, as well as internalizing problems and phubbing, exhibited bidirectional predictive relationships, whereas alexithymia unidirectionally predicted phubbing. (2) At the within-person level, bidirectional day-to-day predictive associations were observed among alexithymia, internalizing problems, and phubbing. (3) Internalizing problems significantly mediated the longitudinal association between alexithymia and phubbing, and this mediating effect was significant at both the between-person and within-person levels. Conclusion The bidirectional relationship between alexithymia and phubbing differs across long-term stability and short-term fluctuations. Internalizing problems serve as a significant longitudinal mediator linking alexithymia to adolescents’ phubbing behavior. alexithymia internalizing problems phubbing longitudinal study Figures Figure 1 Introduction Phubbing refers to the phenomenon in which an individual, while present in an interpersonal interaction, ignores their face-to-face conversation partners due to excessive use of a mobile device [ 1 ]. With the expansion of internet user populations, adolescent phubbing has become a global social issue [ 2 ]. Adolescence is a critical period for physical and psychological growth and development. Phubbing not only impairs adolescents’ real-world interpersonal communication skills but may also diminish the quality of their friendships and levels of social support, thereby exacerbating emotional problems such as anxiety and depression and posing a potential threat to their mental health. Among Indian adolescents, the prevalence of phubbing has been reported to reach 49.3% [ 3 ], while in Spain, 16.7% of adolescents display behavioral manifestations of phubbing [ 4 ]. According to data from the China Internet Network Information Center (CNNIC), the 56th Statistical Report on China’s Internet Development indicates that as of June 2025, the scale of Chinese internet users had reached 1.123 billion, with an internet penetration rate of 79.7%; among these users, those aged 10–19 accounted for 13.7% [ 5 ]. Previous research has demonstrated that phubbing disrupts face-to-face interpersonal interactions and undermines individuals’ physical and psychological well-being [ 6 ]. In terms of interpersonal relationships, phubbing impairs normal interpersonal communication, thereby negatively impacting relationship quality. Chronic parental phubbing can reduce parent-child intimacy and undermine trust; when adolescents perceive neglect resulting from parental phubbing, it indirectly increases their risk of problematic internet use [ 7 ]. Furthermore, studies on partner phubbing have found that such behavior between romantic partners diminishes relationship quality and life satisfaction, while potentially exacerbating depressive symptoms. In the realm of mental health, the study revealed that adolescent phubbing exerts a negative impact on their own psychological well-being [ 8 ]. Specifically, phubbing behavior increases depressive symptoms by undermining the quality of peer relationships and elevating feelings of psychological need frustration. Notably, adolescent phubbing is also influenced by parental behavior. A cross-lagged panel network analysis demonstrated that parental phubbing is significantly associated with adolescent adaptation issues, including perceived neglect, academic burnout, loneliness, and social isolation [ 9 ]. Furthermore, adolescents tend to mimic their parents’ phubbing behavior, exhibiting similar patterns themselves. In sum, a deeper investigation into the psychological mechanisms underlying adolescent phubbing holds significant theoretical and practical implications for preventing the development of mental-health problems. The Relationship between Alexithymia and Phubbing Alexithymia is a stable personality trait characterized primarily by persistent difficulties in identifying and describing one's own emotions and in understanding the emotions of others [ 10 ]. It is often conceptualized as an impairment in the cognitive processing, experience, and regulation of affect [ 11 ]. Alexithymia has been established as a significant risk factor for various psychological issues, particularly as a key factor influencing interpersonal functioning. Research by Koppelberg et al [ 12 ] indicates a positive correlation between alexithymia and general interpersonal distress, suggesting that difficulties in identifying feelings and difficulties in describing feelings can disrupt emotion regulation, thereby increasing the risk of interpersonal problems. Individuals with high levels of alexithymia often face challenges in establishing and maintaining healthy interpersonal relationships, which may lead to social avoidance behaviors [ 13 , 14 ]. Consequently, phubbing may represent a daily emotion regulation strategy employed by individuals with alexithymia. Furthermore, alexithymia has been found to be a positive predictor of problematic mobile phone use in adolescents [ 15 , 16 ]. Given that problematic mobile phone use and smartphone addiction are among the strongest predictors of phubbing, alexithymia appears to be an antecedent factor for phubbing [ 17 ]. From a theoretical perspective, Izard’s Differential Emotions Theory posits that emotions are fundamental components of the personality system, functioning as central motivational forces [ 18 ]. Emotions play a crucial regulatory role in representing needs and achieving goals: when individual needs are satisfied, they generate positive affective feedback, whereas when needs are frustrated, the resulting negative emotions may motivate adaptive behaviors. Within this framework, alexithymia may impair the normal motivational and regulatory functions of emotion, leading individuals to seek emotional gratification through mobile phone use or to avoid direct social interactions, thereby fostering persistent phubbing behavior. This theoretical perspective aligns with existing empirical evidence and provides a robust foundation for further exploring the mechanisms linking alexithymia and phubbing among adolescents. The Relationship between Alexithymia and Internalizing Problems Drawing on clinical observations, Achenbach classified common psychological and behavioral problems among children and adolescents into internalizing and externalizing categories [ 19 ]. Internalizing problems refer to inwardly directed emotional or behavioral difficulties that are less overtly expressed or observable. Although these problems do not pose a direct threat to others, they pose potential risks to mental health, typically manifesting as symptoms of depression, anxiety, and stress [ 20 , 21 , 22 ]. Individuals with high levels of alexithymia, characterized by impaired emotional awareness and regulation, are more likely to develop internalizing problems [ 23 ]. Furthermore, recent evidence indicates that the “difficulty identifying feelings” dimension of alexithymia constitutes a significant risk factor for internalizing problems, whereas depression and anxiety may, in turn, predict higher levels of difficulty in identifying feelings [ 12 ]. Moreover, Zhang [ 24 ] found a negative correlation between difficulty identifying feelings, a core feature of alexithymia, and internalizing problems, suggesting that children and adolescents with poorer ability to identify their own emotions are more prone to internalizing problems. Consistent with these findings, a meta-analysis further demonstrated that alexithymia is consistently associated with depression and anxiety, showing moderate correlations typically ranging from 0.30 to 0.50. In addition, alexithymia has shown moderate associations with depression across diverse populations, including clinical, community, and school samples [ 25 ]. The relationship between alexithymia and internalizing problems can be explained within the framework of the process model of emotion regulation [ 26 ]. This model posits that identifying emotions is a prerequisite for effective emotion regulation. Difficulties in identifying feelings, a defining characteristic of alexithymia, hinder this initial stage of emotion processing. Consequently, individuals with high alexithymia experience deficits in emotion regulation, which in turn reduce their flexibility in managing emotional responses and make them more vulnerable to internalizing symptoms such as depression, anxiety, and stress. The Relationship between Internalizing Problems and Phubbing Ergün et al. [ 27 ] found that phubbing was positively associated with symptoms of anxiety, depression, and stress. In relation to depression, individuals with higher depressive symptoms tend to rely on their mobile phones to manage negative emotions, a pattern that has been associated with problematic phone use [ 28 , 29 ]. Extending this line of research, Wang et al. [ 30 ] reported that depressive symptoms significantly predicted phubbing. Regarding anxiety and stress, a study of university students showed that individuals with greater social anxiety engaged in phubbing more frequently [ 31 ]. Supporting these findings, Ergün et al. [ 32 ] observed moderate positive associations between phubbing and symptoms of depression, anxiety, and stress. Taken together, these studies indicate a consistent link between phubbing and internalizing symptoms, though the causal pathways underlying these associations remain to be determined. The Compensatory Internet Use Theory [ 33 ] offers a useful framework for understanding phubbing as a behavior that may stem from internalizing problems. The theory proposes that individuals engage in online activities to compensate for psychosocial difficulties and unmet emotional or social needs. Those experiencing higher levels of depression, anxiety, or stress may use their mobile phones as a means of avoiding or disengaging from negative emotional states. When such compensatory use occurs during face-to-face interactions as a habitual strategy for managing anxiety or discomfort, it may manifest as phubbing. In this sense, phubbing can be conceptualized as a coping-oriented behavior adopted by individuals with internalizing problems to manage adverse real-life situations. The Present Study Using data from a three-wave longitudinal survey and a 14-day intensive daily diary study, the present research employed cross-lagged panel models (CLPM) and residual dynamic structural equation modeling (RDSEM) to examine the dynamic associations and underlying mediating mechanisms linking alexithymia, internalizing problems, and adolescent phubbing at both the between-person and within-person levels. This study aims to elucidate the dynamic mechanisms underlying adolescents’ phubbing behavior and to inform the development of evidence-based intervention strategies. Based on the previous literature, the study proposed the following hypotheses: Hypothesis 1 At the between-person level, alexithymia, internalizing problems, and phubbing are expected to exhibit significant bidirectional predictive relationships. Hypothesis 2 At the within-person level, daily variations in alexithymia, internalizing problems, and phubbing would exhibit reciprocal associations. Hypothesis 3 Internalizing problems are expected to mediate the relationship between alexithymia and adolescent phubbing, and this mediation is hypothesized to operate at both the between-person and within-person levels. Method Sample Characteristics and Longitudinal Design Longitudinal Sample This study employed a cluster sampling approach to recruit students from Grades 7, 8, 10, and 11 (excluding graduating classes). A three-wave longitudinal design was implemented over one year, with data collected at 6-month intervals in September 2023 (T1), March 2024 (T2), and September 2024 (T3). At T1, a total of 1,191 valid responses were collected. After data cleaning and validity checks, questionnaires with patterned or inconsistent responses were excluded. Consequently, 902 participants who completed all three survey waves were retained for the final analyses, resulting in a valid retention rate of 75.7% and an attrition rate of 24.3%. Attrition analyses were conducted to compare the retained sample ( n = 902) and the attrition sample ( n = 289) on demographic characteristics (gender, age, residential background) and key study variables (alexithymia, internalizing problems, and phubbing behavior) measured at T1. Results indicated no significant group differences in age ( t = − 1.66, p = 0.10); gender ( χ² = 1.23, p = 0.27); alexithymia ( t = − 1.49, p = 0.14); internalizing problems ( t = − 0.81, p = 0.42); or phubbing behavior ( t = − 1.39, p = 0.14). These findings suggest that sample attrition was non-systematic and that the overall sample structure remained stable across waves. Intensive Longitudinal Subsample Building upon the valid longitudinal sample that completed all three survey waves ( n = 902), a subset of participants was invited to take part in a 14-day intensive daily diary study. A purposive stratified sampling strategy was employed to ensure representativeness across gender, grade level, and alexithymia levels. Among the 412 students who consented to participate, 373 provided complete and valid datasets, resulting in a participation rate of 90.5% and an attrition rate of 9.5%. The final diary subsample comprised 198 male (53.1%) and 175 female (46.9%) participants. After excluding incomplete diary entries, a total of 4,790 valid daily-level observations were retained for analysis. Independent-samples t-tests and chi-square tests conducted on key study variables at T3 ( p > 0.05) revealed no significant differences between the diary subsample and the remaining longitudinal participants, suggesting that the diary subsample was demographically and psychologically comparable to the full sample. Data Collection Procedure The Institutional Review Board of the authors’ university approved all data collection procedures. Written informed consent was obtained from participants and their parents or guardians prior to data collection. Surveys were administered in classroom settings during regular school hours by graduate student research assistants in psychology who had received standardized training in the study protocols. Before distribution, research assistants read aloud standardized instructions describing the study purpose, emphasizing voluntary participation, and assuring confidentiality. Participants were told they could withdraw at any time without penalty. Each assessment session lasted approximately 30 minutes. In the intensive longitudinal study, data were collected over 14 consecutive days. The order of daily items was randomized for each survey, and the assessment took approximately 10 minutes to complete each day. Measures Alexithymia Scale Alexithymia was assessed using the 20-item Toronto Alexithymia Scale (TAS-20) [ 34 ]. The TAS-20 includes three dimensions: Difficulty Identifying Feelings, Difficulty Describing Feelings, and Externally Oriented Thinking. Items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater levels of alexithymia. In the present study, Cronbach’s α coefficients for this scale were 0.82, 0.82, and 0.84 at the three measurement waves, respectively. In the intensive longitudinal study, we selected the three items with the highest factor loadings from the Toronto Alexithymia Scale (TAS-20) and adapted them to assess day-to-day alexithymia. For example, the original item “I find it difficult to describe my feelings” was rephrased as “Over the past 24 hours, I have found it difficult to describe my feelings using appropriate words.” In the present study, the between-person and within-person McDonald’s ω coefficients for this scale were 0.98 and 0.88, respectively. Internalizing Problems Scale Internalizing problems were measured using the 21-item Depression Anxiety Stress Scales (DASS-21) [ 35 ]. The DASS-21 comprises three subscales: Depression, Anxiety, and Stress. All items are rated on a 4-point Likert scale from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time), with higher scores reflecting more severe symptoms. In the present study, Cronbach’s α coefficients for this scale were 0.95, 0.96, and 0.96 at the three measurement waves, respectively. In the intensive longitudinal study, the Depression Anxiety Stress Scales (DASS-21) were adapted into a diary-style version to assess participants’ daily internalizing problems. For example, the original item “I felt down-hearted and blue” was rephrased as “During the past 24 hours, I have felt down-hearted and blue.” In the present study, the between-person and within-person McDonald’s ω coefficients for this scale were 0.97 and 0.86, respectively. Phubbing Behavior Scale Phubbing behavior was assessed using the 15-item Generic Scale of Phubbing (GSP) [ 1 ]. The GSP consists of four dimensions: Nomophobia (fear of being without a mobile phone), Interpersonal Conflict, Self-Isolation, and Problematic Awareness. Items are rated on a 7-point Likert scale ranging from 1 (never) to 7 (always), with higher scores indicating greater levels of phubbing behavior. In the present study, Cronbach’s α coefficients for this scale were 0.92, 0.92, and 0.94 at the three measurement waves, respectively. For the daily diary component, we adapted the GSP into a briefer form suitable for intensive longitudinal assessment by rephrasing items to capture day-to-day variability. For instance, the original item “I would feel annoyed if someone asked me to stop using my phone to talk with them” was revised to “In the past 24 hours, I have felt annoyed when someone asked me to stop using my phone to talk with them.” In the present study, the between-person and within-person McDonald’s ω coefficients for this scale were 0.95 and 0.88, respectively. Data Collection Procedure Data were collected on-site through cluster-based group surveys administered to middle and high school students. All responses were entered into EpiData to ensure accuracy and data integrity. Data analysis was conducted in several stages, following established statistical procedures. First, SPSS 26.0 was used for data cleaning and matching, including the removal of cases showing patterned response tendencies, incomplete participation across the three waves, or inattentive responding. SPSS was also employed to perform demographic difference tests, reliability analyses, descriptive statistics, correlation analyses, and assessments of common method bias. Second, Mplus 8.3 was used to examine the structural validity and longitudinal measurement invariance of all scales, providing a basis for subsequent longitudinal analyses. Finally, model construction and hypothesis testing were conducted using Mplus 8.3 . A cross-lagged panel model (CLPM) was specified, with subscale scores serving as indicators of latent constructs, to assess the longitudinal reciprocal relationships among alexithymia, internalizing problems, and adolescent phubbing behavior at the between-person level. The mediating effect of internalizing problems was also examined. In addition, a residual dynamic structural equation modeling (RDSEM) approach was applied to simultaneously partition between-person variance and model within-person dynamic processes. This approach enabled the examination of within-person dynamic interactions among daily alexithymia, internalizing problems, and phubbing behavior, as well as the intensive longitudinal mediation effect of daily internalizing problems. Missing data were handled using Full Information Maximum Likelihood [ 36 ]. Model fit was assessed using the following benchmarks: RMSEA ≤ 0.08, SRMR ≤ 0.08, and CFI ≥ 0.90 [ 37 ]. Comparisons between nested models relied on the changes in fit indices (ΔCFI and ΔRMSEA). A model was not considered to have a significantly worse fit if the ΔCFI < 0.01 and the ΔRMSEA < 0.015 [ 38 ]. Results Common Method Bias Assessment Results from Harman’s single-factor test indicated that at T1, T2, and T3, nine, eight, and eight factors, respectively, had eigenvalues greater than one. The first factor explained 30.10%, 32.41%, and 33.68% of the total variance, respectively, all below the commonly accepted 40% threshold [ 39 ]. These findings suggest that common method bias was not a major concern in this study. Descriptive Statistics and Correlation Analysis As reported in Table 1 , correlation analyses revealed significant positive associations among alexithymia, internalizing problems, and phubbing across the three measurement occasions (T1, T2, T3). Alexithymia showed moderate to strong positive correlations with internalizing problems(0.33 ≤ r ≤ 0.58, p < 0.001). Similarly, alexithymia was positively correlated with phubbing (0.28 ≤ r ≤ 0.48, p < 0.001), and internalizing problems showed substantial positive associations with phubbing(0.34 ≤ r ≤ 0.59, p < 0.001). These results indicate significant concurrent and lagged associations among alexithymia, internalizing problems, and adolescent phubbing, thereby satisfying the preliminary conditions for proceeding with cross-lagged panel modeling. Table 1 Descriptive Statistics and Correlations for Study Variables ( n = 902) Variables M SD 1 2 3 4 5 6 7 8 9 T1 ALEX 2.69 0.55 1 T2 ALEX 2.75 0.49 0.58 *** 1 T3 ALEX 2.78 0.49 0.50 *** 0.56 *** 1 T1 INT g Problemsg Problems 0.69 0.57 0.55 *** 0.41 *** 0.33 *** 1 T2 INT g Problems 0.67 0.56 0.40 *** 0.50 *** 0.42 *** 0.55 *** 1 T3 INT 0.73 0.58 0.40 *** 0.42 *** 0.58 *** 0.47 *** 0.55 *** 1 T1 PHUB 2.87 1.17 0.48 *** 0.30 *** 0.28 *** 0.59 *** 0.39 *** 0.34 *** 1 T2 PHUB 2.93 1.17 0.37 *** 0.40 *** 0.33 *** 0.42 *** 0.57 *** 0.42 *** 0.58 *** 1 T3 PHUB 2.96 1.08 0.37 *** 0.35 *** 0.42 *** 0.38 *** 0.47 *** 0.55 *** 0.45 *** 0.58 *** 1 Note. * p < 0.05, ** p < 0.01, *** p < 0.001. ALEX = Alexithymia; INT = Internalizing problems; PHUB = Phubbing. All variables are standardized latent factors. Longitudinal Measurement Invariance Testing To examine the stability of the core constructs across time, this study tested longitudinal measurement invariance for the measures of alexithymia, internalizing problems, and phubbing using Mplus 8.3. The analyses followed a sequential model-nesting strategy by progressively evaluating four levels of invariance: configural (M0), weak (metric, M1), strong (scalar, M2), and strict (M3). Because chi-square statistics can be overly sensitive to large samples, we used Chen’s recommended criteria for changes in fit indices: a constrained model was considered invariant when ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 relative to the less constrained model [ 38 ]. As reported in Table 2 , alexithymia met the criteria for strong invariance, whereas both internalizing problems and phubbing satisfied the criteria for strict invariance. Table 2 Measurement Invariance Tests for Alexithymia, Internalizing Problems, and Phubbing Variables χ²/ df CFI TLI RMSEA SRMR model comparison ΔRMSEA ΔCFI ALEX M0 2586.60(1557) 0.896 0.882 0.038 0.055 M1 2657.40(1591) 0.892 0.880 0.039 0.057 M1-M0 0.001 -0.004 M2 2725.00(1619) 0.888 0.878 0.039 0.058 M2-M1 0 -0.004 M3 2983.12(1654) 0.866 0.857 0.042 0.063 M3-M2 0.003 -0.022 INT M0 3833.27(1775) 0.901 0.891 0.051 0.044 M1 3888.59(1810) 0.900 0.892 0.050 0.047 M1-M0 -0.001 -0.001 M2 4027.27(1847) 0.895 0.889 0.051 0.047 M2-M1 0.001 -0.005 M3 4281.56(1889) 0.885 0.881 0.053 0.052 M3-M2 0.002 -0.010 PHUB M0 2135.56(834) 0.918 0.903 0.059 0.054 M1 2179.90(856) 0.917 0.904 0.059 0.057 M1-M0 0 0.001 M2 2271.50(878) 0.913 0.902 0.059 0.059 M2-M1 0 -0.004 M3 2405.35(905) 0.906 0.897 0.061 0.060 M3-M2 0.002 -0.007 Note. M0 = configural invariance; M1 = metric invariance; M2 = scalar invariance; M3 = strict invariance. Structural Equation Model Fit Evaluation To determine the optimal specification, we estimated and compared four nested models: M1 (freely estimated), M2 (equality constraints on autoregressive paths only), M3 (equality constraints on cross-lagged paths only), and M4 (equality constraints on both autoregressive and cross-lagged paths). Model fit statistics are reported in Table 3 . Following Chen, we adopted ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 as thresholds for invariance [ 38 ]. M3 provided an acceptable fit across indices ( χ²/df = 4.12, RMSEA = 0.059, CFI = 0.933, TLI = 0.923, SRMR = 0.049), and the ΔCFI and ΔRMSEA comparisons between M3 and the other models satisfied the prespecified criteria. Balancing parsimony and fit, we therefore selected M3 as the final model for subsequent analyses. Table 3 Structural Equation Modeling Fit Indices Model Model Fit Indices Model Comparison ΔCFI ΔRMSEA χ 2 df CFI TLI RMSEA SRMR M1 1896.95 423 0.926 0.914 0.062 0.048 M2 1899.86 426 0.926 0.914 0.062 0.048 M2vsM1 0 -0.001 M3 1765.88 429 0.933 0.923 0.059 0.049 M3vsM1 0.005 -0.003 M4 1921.37 432 0.926 0.915 0.062 0.050 M4vsM1 -0.002 0 Note. M1 = configural invariance model; M2 = weak (metric) invariance model; M3 = strong (scalar) invariance model; M4 = strict invariance model. According to Chen (2007), model differences can be considered acceptable when the change in comparative fit index (ΔCFI) is less than 0.010 and the change in root mean square error of approximation (ΔRMSEA) is less than 0.015. Between-Person Cross-Lagged Panel Model To assess longitudinal relations among alexithymia, internalizing problems, and phubbing at the between-person level, we estimated a cross-lagged panel model (Fig. 1 ). The model indicated stable predictive associations among the constructs; key results are summarized below. Bidirectional predictions between alexithymia and internalizing problems. As illustrated in Fig. 1 , T1 alexithymia positively predicted T2 internalizing problems ( γ = 0.16, p < 0.001), and T2 alexithymia positively predicted T3 internalizing problems ( γ = 0.13, p < 0.001). Conversely, T1 internalizing problems positively predicted T2 alexithymia ( γ = 0.19, p < 0.001), and T2 internalizing problems positively predicted T3 alexithymia ( γ = 0.17, p < 0.001). Together, these findings indicate a reciprocal cross-lagged relationship between alexithymia and internalizing problems across waves. Bidirectional predictions between internalizing problems and adolescent phubbing. T1 internalizing problems positively predicted T2 phubbing ( γ = 0.10, p < 0.05), and T2 internalizing problems positively predicted T3 phubbing ( γ = 0.10, p < 0.05). In the reverse direction, T1 phubbing positively predicted T2 internalizing problems ( γ = 0.10, p < 0.01), and T2 phubbing positively predicted T3 internalizing problems ( γ = 0.08, p < 0.01). Together, these results reveal a bidirectional cross-lagged relationship between internalizing problems and phubbing across time.Unidirectional prediction between alexithymia and adolescent phubbing. The data indicate a unidirectional association in which alexithymia precedes phubbing: T2 alexithymia positively predicted T3 phubbing ( γ = 0.07, p < 0.05). Phubbing, however, failed to predict alexithymia at any wave. Thus, alexithymia appears to function as an antecedent rather than a consequence of adolescent phubbing. Note All estimates represent standardized coefficients that reached statistical significance ( p < 0.05). Gender and age were controlled for in the analysis. Although the measurement structure of the latent variable for internalizing problems at T2 was consistent with T1, it is not shown in the interest of clarity. As reported in Table 4 , the cross-lagged regression coefficients and their 95% confidence intervals are presented. Table 4 Cross-lagged regression coefficients and 95% confidence intervals. cross-lagged panel model Std.Est S.E. p 95%CI ALEX t1 →INT t2 0.162 0.037 *** [0.088, 0.235] ALEX t1 →PHUB t2 0.065 0.033 --- [-0.001, 0.125] INT t1 →ALEX t2 0.192 0.040 *** [0.115, 0.270] INT t1 →PHUB t2 0.101 0.041 * [0.021, 0.181] PHUB t1 →INT t2 0.095 0.036 ** [0.025, 0.165] PHUB t1 →ALEX t2 -0.015 0.031 --- [-0.075, 0.045] ALEX t2 →INT t3 0.130 0.032 *** [0.067, 0.193] ALEX t2 →PHUB t3 0.065 0.033 * [0.000, 0.130] INT t2 →ALEX t3 0.172 0.038 *** [0.099, 0.246] INT t2 →PHUB t3 0.103 0.042 * [0.020, 0.185] PHUB t2 →INT t3 0.079 0.031 ** [0.019, 0.140] PHUB t2 →ALEX t3 -0.014 0.029 --- [-0.072, 0.043] Note. Bolded estimates indicate statistically significant paths, --- denotes a nonsignificant path. Within-Person Residual Dynamic Structural Equation Modeling To examine daily within-person dynamics, we conducted an intensive longitudinal diary study with 373 participants over 14 consecutive days, extending our prior work. This design produced 4,790 valid daily observations, which we modeled using residual dynamic structural equation modeling (RDSEM). As reported in Table 4 , the intraclass correlation coefficients (ICC) were 0.54 for alexithymia, 0.37 for internalizing problems, and 0.48 for phubbing, indicating that 46% of the variance in alexithymia, 63% in internalizing problems, and 52% in phubbing arose from within-person fluctuations. Within-person correlations further revealed significant positive associations between alexithymia and internalizing problems ( r = 0.39, p < 0.001), alexithymia and phubbing ( r = 0.40, p < 0.001), and internalizing problems and phubbing ( r = 0.32, p < 0.001). Table 5 reports the residual dynamic structural equation modeling (RDSEM) results, which characterize day-level, within-person dynamics among alexithymia, internalizing problems, and phubbing. The analysis revealed reciprocal predictive links between alexithymia and phubbing: prior-day alexithymia positively predicted next-day phubbing ( β = 0.056, 95% CI [0.027, 0.085]), and prior-day phubbing likewise predicted higher next-day alexithymia ( β = 0.037, 95% CI [0.006, 0.069]). Similarly, alexithymia and internalizing problems exhibited bidirectional effects: prior-day alexithymia positively predicted next-day internalizing problems ( β = 0.094, 95% CI [0.060, 0.124]), and prior-day internalizing problems predicted elevated next-day alexithymia ( β = 0.138, 95% CI [0.108, 0.169]). Furthermore, internalizing problems and phubbing exhibited reciprocal day-to-day dynamics: prior-day internalizing problems positively predicted phubbing the following day ( β = 0.029, 95% CI [0.006, 0.055]), and prior-day phubbing in turn positively predicted next-day internalizing problems ( β = 0.031, 95% CI [0.001, 0.068]). Table 5 Within-Person Effect Estimates and Confidence Intervals from RDSEM Analysis Variable Within-Person Standardized Estimate (Mean) Effect(SE) 95% CI Autoregressive Effects ALEX t−1 →ALEX t ( β j ) 0.103(0.020) [0.064, 0.138] INT t−1 →INT t ( β j ) 0.077(0.019) [0.047, 0.123] PHUB t−1 →PHUB t ( β j ) 0.151(0.017) [0.117, 0.182] Autoregressive Effects ALEX t−1 →PHUB t ( c j ) 0.056(0.015) [0.027, 0.085] PHUB t−1 →ALEX t ( f j ) 0.037(0.016) [0.006, 0.069] ALEX t−1 →INT t ( a j ) 0.094(0.017) [0.060, 0.124] INT t−1 →ALEX t ( e j ) 0.138(0.016) [0.108, 0.169] INT t−1 →PHUB t ( b j ) 0.029(0.011) [0.006, 0.055] PHUB t−1 →INT t ( d j ) 0.031(0.017) [0.001, 0.068] Note. Bolded estimates indicate statistically significant paths. β j represents the autoregressive effects of each variable, whereas a j , b j , c j , d j , e j , f j denote the corresponding cross-lagged effects. Longitudinal and Intensive Longitudinal Mediation Effects of Internalizing Problems Using a cross-lagged panel framework with 5,000 bootstrap replications, we evaluated longitudinal mediation and found that alexithymia predicted adolescent phubbing via internalizing problems (indirect effect = 0.017, p < 0.05, 95% CI [0.003, 0.031]). In the reverse direction, phubbing likewise predicted alexithymia through internalizing problems (indirect effect = 0.016, p < 0.05, 95% CI [0.004, 0.029]). Together, these results indicate that internalizing problems functions as a significant longitudinal mediator linking alexithymia and phubbing. In the intensive longitudinal mediation analysis, we constructed a residual dynamic structural equation modeling (RDSEM). Time was included as a Level-1 predictor to control for linear trends. Gender and age were entered as Level-2 covariates. Bayesian Markov chain Monte Carlo (MCMC) estimation was employed, with 20,000 iterations and two chains (Chains = 2) used to estimate model parameters. The statistical significance of parameters was determined based on 95% credible intervals; a path was considered significant if its 95% credible interval did not include zero. The results indicated that internalizing problems significantly mediated the association between alexithymia and phubbing (Indirect 1 = 0.007, 95% CI [0.001, 0.014], p < 0.01; Indirect 2 = 0.006, 95% CI [0.001, 0.013], p < 0.05). Discussion Correlations among Internalizing Problems, Alexithymia, and Phubbing Significant positive correlations emerged among alexithymia, internalizing problems, and phubbing across all three time points, highlighting the close interrelationships between these variables. The strong association between alexithymia and internalizing problems suggests that difficulties in identifying and expressing emotions, core features of alexithymia, may exacerbate internalizing symptoms such as anxiety, depression, and stress [ 40 ]. Simultaneously, individuals with alexithymia may excessively use smartphones to cope with emotion regulation difficulties or to avoid social anxiety by negative emotions [ 41 ]. The strong correlation between internalizing problems and phubbing further indicates that excessive phubbing is closely linked to adolescents’ internalizing symptoms, such as anxiety and depression [ 42 ]. Bidirectional Predictive Relationships among Alexithymia, Internalizing Problems, and Phubbing at Between-Person and Within-Person Levels A reciprocal predictive relationship exists between alexithymia and internalizing problems. At the between-person level, longitudinal cross-lagged panel model analyses indicated that alexithymia at T1 and T2 positively predicted subsequent internalizing problems at T2 and T3, respectively. Concurrently, internalizing problems at T1 and T2 positively predicted subsequent alexithymia at T2 and T3, respectively. At the within-person level, a dynamic structural equation model analysis of intensive longitudinal data revealed that higher-than-usual alexithymia on a given day positively predicted next-day internalizing problems, and vice versa. This study demonstrates a bidirectional predictive relationship between alexithymia and internalizing problems, evident at both the level of stable traits and dynamic day-to-day fluctuations. This finding aligns with previous research. Alexithymia, characterized by deficits in identifying and expressing emotions, may contribute to internalizing problems such as depression, anxiety, and stress by limiting effective processing of negative emotions [ 43 ]. Additionally, individuals with high levels of alexithymia may experience greater psychological distress due to difficulties in articulating negative emotions, thereby exacerbating internalizing problems [ 44 ]. Conversely, internalizing problems may increase emotional suppression and hinder individuals’ ability to recognize and express their emotions, further intensifying alexithymia [ 24 ]. This reciprocal relationship supports theories of emotion regulation, which propose a mutually reinforcing dynamic between impaired emotional processing and mental health difficulties [ 45 ]. A reciprocal predictive relationship was observed between internalizing problems and phubbing. At the between-person level, cross-lagged panel model analyses of longitudinal data revealed that internalizing problems at T1 and T2 positively predicted subsequent phubbing at T2 and T3, respectively. Conversely, phubbing at T1 and T2 also positively predicted later internalizing problems at T2 and T3. At the within-person level, results from a random-intercept dynamic structural equation model indicated that individuals who experienced higher-than-usual internalizing problems on a given day tended to report increased phubbing the following day. Similarly, elevated daily phubbing predicted greater internalizing problems the next day. These findings indicate a bidirectional association between internalizing problems and phubbing at both stable, trait-like and dynamic, state-like levels. This result supports the work of Gao et al. [ 46 ], who suggested that phubbing may exacerbate internalizing problems by fostering perceptions of social exclusion, which can intensify feelings of loneliness and depressive symptoms. Conversely, individuals with heightened internalizing problems may resort to excessive smartphone use as a coping mechanism for social pressure and anxiety, thereby increasing phubbing behavior [ 47 ]. This bidirectional relationship may function through a self-reinforcing cycle: internalizing problems lead individuals to depend on smartphones for coping, which, in turn, increases phubbing. Such behavior diminishes the quality of real-life friendships [ 48 ] and weakens the protective effects of social support against negative emotions, thereby reinforcing internalizing problems and perpetuating a vicious cycle. The Relationship between Alexithymia and Phubbing. At the between-person level, a unidirectional relationship was observed, in which alexithymia at T2 positively predicted phubbing at T3, whereas phubbing did not predict subsequent alexithymia. This result suggests that alexithymia may serve as a predisposing factor: adolescents with high levels of alexithymia may engage in phubbing more frequently as a compensatory avoidance strategy to escape anxiety or stress in face-to-face social interactions [ 49 ]. However, at the between-person level, phubbing did not significantly predict alexithymia. Research at the between-person level typically focuses on trait-like constructs to examine how relatively stable psychological characteristics influence behavioral outcomes. Alexithymia, as a relatively stable personality trait [ 50 ], is primarily shaped and maintained by internal psychological mechanisms, such as deficits in emotional processing, rather than external behavioral habits like smartphone use [ 51 ]. In contrast, at the within-person level, a random-intercept dynamic structural equation model applied to intensive longitudinal data revealed a bidirectional predictive relationship: daily elevations in alexithymia predicted next-day increases in phubbing, and similarly, daily increases in phubbing predicted higher alexithymia the following day. This indicates that daily fluctuations in alexithymia contribute to subsequent avoidant phubbing behavior, which in turn exacerbates alexithymia, forming a self-reinforcing cycle. According to the diathesis-stress model [ 52 ], there is a dynamic bidirectional interaction between an individual’s inherent vulnerability and external stressors eliciting negative emotions, which may accumulate and intensify over time. Daily alexithymia reflects an individual’s emotional processing vulnerability, leading alexithymic adolescents to adopt avoidance-oriented coping strategies such as phubbing. However, such avoidance behavior can impair the quality of in-person communication [ 9 ], thereby aggravating alexithymia on the following day. The findings reveal a differentiated pattern across levels: a unidirectional predictive effect of alexithymia on phubbing at the between-person level, and a bidirectional association at the within-person level. This pattern suggests that the link between alexithymia and phubbing reflects a stable, trait-like tendency across individuals, whereas it operates as a dynamic, state-like process within individuals. Longitudinal Mediating Effects of Internalizing Problems Both longitudinal and intensive longitudinal studies indicated that alexithymia influences phubbing indirectly through internalizing problems, and similarly, phubbing affects alexithymia via internalizing problems. At the between-person level, longitudinal analyses revealed that internalizing problems played a significant mediating role between alexithymia and phubbing. Specifically, alexithymia may exacerbate internalizing problems such as anxiety or depression, which in turn leads to increased phubbing, for instance, individuals with emotional regulation difficulties may rely more heavily on smartphones to escape real-world stressors [ 53 ]. Similarly, phubbing may intensify internalizing problems by triggering feelings of social isolation or interpersonal conflict, thereby indirectly exacerbating alexithymia [ 54 ]. At the within-person level, results from intensive longitudinal modeling showed that fluctuations in alexithymia predict subsequent internalizing problems such as depression, anxiety, and stress [ 40 ]. This may occur because individuals with higher alexithymia possess deficits in emotional identification and processing, leading them to experience more negative affect in response to daily social stressors. These negative emotional states do not dissipate spontaneously but may accumulate over time, eventually contributing to more severe internalizing problems. In this context, smartphone use becomes a preferred coping strategy to avoid social interactions and alleviate distress. In summary, these longitudinal mediation findings support a complex interplay between mental health and technology use, underscoring the central role of internalizing problems as a key mechanism linking emotional traits with behavioral outcomes. Implications Alexithymia may reinforce phubbing by exacerbating internalizing problems, suggesting that phubbing can function as a compensatory or avoidant coping strategy among adolescents with alexithymia-related internalizing symptoms. Consequently, direct restrictions on smartphone use may only address surface symptoms rather than root causes. For effective intervention, we recommend the following approaches: First, enhance adolescents’ ability to identify and express emotions to reduce internalizing problems stemming from emotional recognition, understanding, and expression difficulties. Second, provide timely support for adolescents with significant internalizing problems. Multimodal interventions, such as social activities, physical exercise, drawing, and music, should be employed to prevent worsening internalizing problems and subsequent increases in phubbing. Preventive measures should target adolescents with high trait alexithymia, focusing on training emotional identification and expression skills. Immediate interventions, on the other hand, can disrupt the daily maladaptive cycle between alexithymia and phubbing. Finally, both families and schools should collaborate to offer emotional support and help adolescents establish stable social and emotional regulation mechanisms. Through such comprehensive strategies, it is possible to effectively break the vicious cycle between alexithymia and phubbing, thereby promoting adolescent mental health. Limitations and Future Research Directions Although this study employed a longitudinal design that combined traditional and intensive methods, several limitations should be acknowledged. First, the observation period spanned only one year and included relatively few assessment waves, constraining our ability to capture longer-term trajectories. Second, because participants were in early or middle adolescence, the findings may not generalize to individuals in late adolescence. Future research should therefore examine phubbing behaviors across longer developmental windows that encompass the full adolescent span, probing how alexithymia and internalizing problems predict and shape phubbing over extended timeframes. Moreover, incorporating external and environmental factors alongside other relevant variables would permit construction of more comprehensive mediation models. Given that all participants in the present study were Chinese adolescents, studies that include other ethnic and cultural groups are needed to determine whether similar effects obtain cross-culturally. Finally, adopting random sampling procedures would facilitate recruitment of larger, more representative samples in future investigations. Conclusion This study employed a combined longitudinal and intensive longitudinal design to systematically examine the dynamic relationships among alexithymia, internalizing problems, and phubbing in adolescents at both the between-person and within-person levels. The main findings can be summarized as follows: First, bidirectional predictive associations emerged between alexithymia and internalizing problems, as well as between internalizing problems and phubbing, at both the between- and within-person levels. In contrast, the association between alexithymia and phubbing demonstrated a trait-state dissociation: at the between-person level, only a unidirectional effect from alexithymia to phubbing was identified, whereas at the within-person level, a bidirectional predictive relationship emerged. Second, internalizing problems served as a longitudinal mediator linking alexithymia to phubbing. In summary, this study illuminates a dynamic longitudinal interplay among alexithymia, internalizing problems, and phubbing. It underscores the importance of enhancing adolescents’ emotional identification and expression abilities and providing timely interventions for internalizing problems to prevent and mitigate phubbing and to promote mental health. Declarations Ethics approval and consent to participate All procedures involving human participants were approved by the Research Ethics Committee of Jiangxi Normal University. Written informed consent was obtained from all participants and their legal guardians in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 72164018), the Humanities and Social Sciences Research Planning Foundation of Ministry of Education (Grant No. 22YJA190012), and the 2025 Project of the Hunan Provincial Social Science Achievement Evaluation Committee (Grant No. XSP25YBC572), and the programme of Study on Mental Health Education for College Students in the Context of Digitization of Education ( Grant No. 2024YB0215). Author Contribution Haibin Huang contributed to the conceptualization of the study and was responsible for writing – original draft and writing – review & editing of the manuscript. Zhiming Zhou was responsible for data collection, data processing, and writing – original draft. Qi Dai contributed to writing – review & editing of the manuscript. Yu Deng contributed to writing – review & editing of the manuscript. Hongxia Lin and Min Wang were responsible for English language editing and polishing of the manuscript. Jin Xie contributed to writing – review & editing of the manuscript. Baojuan Ye supervised the overall study, provided project administration, and was responsible for writing – review & editing as well as correspondence with the journal. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgements We sincerely thank all the students, parents, and teachers who participated in this study, as well as all the psychology graduate students who provided invaluable assistance during the data collection process. Language editing assistance was provided with the aid of artificial intelligence tools under the supervision of the authors. Data Availability Due to the nature of this research, participants did not consent to public data sharing; therefore, supporting data are not available. References Chotpitayasunondh V, Douglas KM. How phubbing becomes the norm: The antecedents and consequences of snubbing via smartphone. Comput Hum Behav. 2016;63:9–18. https://doi.org/10.1016/j.chb.2016.05.018 . Guzmán-Brand V, Gelvez-García L. Phubbing en los adolescentes: Un comportamiento que afecta la interacción social. Una revisión sistemática. Rev Estud Psicol. 2022;2(4):7–19. https://doi.org/10.35622/j.rep.2022.04.001 . 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Mother phubbing and adolescent loneliness: A three-way moderation model involving attachment anxiety and need to belong. Child Youth Serv Rev. 2024;164:107878. https://doi.org/10.1016/j.childyouth.2024.107878 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8096209","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556568716,"identity":"9ea4ad4a-8038-48df-ba09-f908777c622e","order_by":0,"name":"Haibin Huang","email":"","orcid":"","institution":"Jiangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Huang","suffix":""},{"id":556568717,"identity":"b9a8ec9d-be6f-4f0b-88c1-99987636205a","order_by":1,"name":"Zhiming Zhou","email":"","orcid":"","institution":"Jiangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Zhou","suffix":""},{"id":556568718,"identity":"367f1570-d787-4126-84c7-d67a7e2d6bf6","order_by":2,"name":"Baojuan Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACHihmZj5w4EOFhJw88VrY2xIfzjhjYWzYQJQWMH3G2JizrSKR4QABDQbHew9++CBzWN5cIi1NmnGeRAJjA/PDRzfwaTlzLllyBs9hw50zko9JF26TyGNnYDM2zsGn5UaOgTQPz2HGDTeAtszcJlHM2MDDJo1Xy/03xr+BWuw33Mgxk+adI5HYcICQlhs8ZiBbEjecAXqft4EILZJn8tIsZ/CkJ284DgrkYxLGhs0E/MJ3/OzhGx97rG03HAZFZU2dnDx788PH+LQoHAASjD3IQsx4lIOAfAOI/EFA1SgYBaNgFIxsAAAQIlJWCmTCOgAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangxi Normal University","correspondingAuthor":true,"prefix":"","firstName":"Baojuan","middleName":"","lastName":"Ye","suffix":""},{"id":556568719,"identity":"9cc9aa2c-2367-4638-a8bc-a35ec5bc864d","order_by":3,"name":"Dai Qi","email":"","orcid":"","institution":"Jiangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Dai","middleName":"","lastName":"Qi","suffix":""},{"id":556568720,"identity":"83161d52-a92b-487e-a1f7-b9463512f350","order_by":4,"name":"Yu Deng","email":"","orcid":"","institution":"Jiangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Deng","suffix":""},{"id":556568721,"identity":"cb4dfde6-3072-4dfc-affd-eb2cd13cc335","order_by":5,"name":"Hongxia Lin","email":"","orcid":"","institution":"Huaihua Junyong Experimental School","correspondingAuthor":false,"prefix":"","firstName":"Hongxia","middleName":"","lastName":"Lin","suffix":""},{"id":556568722,"identity":"4f747a7e-a8a5-442f-afed-bc470b99993b","order_by":6,"name":"Jin Xie","email":"","orcid":"","institution":"Huang huai 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11:04:34","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203593,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8096209/v1/e7beffccf615bdd8baae8d7c.html"},{"id":97691612,"identity":"d9e4ab34-b15d-42df-a3d9-b9053e5e2544","added_by":"auto","created_at":"2025-12-08 11:04:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":231435,"visible":true,"origin":"","legend":"\u003cp\u003eCross-Lagged Panel Model of Alexithymia, Internalizing Problems, and Phubbing\u003c/p\u003e","description":"","filename":"Figure1CrossLaggedPanelModel.pgn.png","url":"https://assets-eu.researchsquare.com/files/rs-8096209/v1/44a349a3495eafb56260bcdb.png"},{"id":107868693,"identity":"718fcfab-018e-42a7-9216-41aad581efa0","added_by":"auto","created_at":"2026-04-27 07:31:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":768790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8096209/v1/d92bfdd0-7868-4d14-9606-14b60e17bdea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The dynamic longitudinal relationship between alexithymia and phubbing in adolescents: the mediating role of internalizing problems","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhubbing refers to the phenomenon in which an individual, while present in an interpersonal interaction, ignores their face-to-face conversation partners due to excessive use of a mobile device [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the expansion of internet user populations, adolescent phubbing has become a global social issue [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Adolescence is a critical period for physical and psychological growth and development. Phubbing not only impairs adolescents\u0026rsquo; real-world interpersonal communication skills but may also diminish the quality of their friendships and levels of social support, thereby exacerbating emotional problems such as anxiety and depression and posing a potential threat to their mental health. Among Indian adolescents, the prevalence of phubbing has been reported to reach 49.3% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while in Spain, 16.7% of adolescents display behavioral manifestations of phubbing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to data from the China Internet Network Information Center (CNNIC), the 56th Statistical Report on China\u0026rsquo;s Internet Development indicates that as of June 2025, the scale of Chinese internet users had reached 1.123\u0026nbsp;billion, with an internet penetration rate of 79.7%; among these users, those aged 10\u0026ndash;19 accounted for 13.7% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous research has demonstrated that phubbing disrupts face-to-face interpersonal interactions and undermines individuals\u0026rsquo; physical and psychological well-being [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In terms of interpersonal relationships, phubbing impairs normal interpersonal communication, thereby negatively impacting relationship quality. Chronic parental phubbing can reduce parent-child intimacy and undermine trust; when adolescents perceive neglect resulting from parental phubbing, it indirectly increases their risk of problematic internet use [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, studies on partner phubbing have found that such behavior between romantic partners diminishes relationship quality and life satisfaction, while potentially exacerbating depressive symptoms. In the realm of mental health, the study revealed that adolescent phubbing exerts a negative impact on their own psychological well-being [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Specifically, phubbing behavior increases depressive symptoms by undermining the quality of peer relationships and elevating feelings of psychological need frustration. Notably, adolescent phubbing is also influenced by parental behavior. A cross-lagged panel network analysis demonstrated that parental phubbing is significantly associated with adolescent adaptation issues, including perceived neglect, academic burnout, loneliness, and social isolation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, adolescents tend to mimic their parents\u0026rsquo; phubbing behavior, exhibiting similar patterns themselves. In sum, a deeper investigation into the psychological mechanisms underlying adolescent phubbing holds significant theoretical and practical implications for preventing the development of mental-health problems.\u003c/p\u003e\n\u003ch3\u003eThe Relationship between Alexithymia and Phubbing\u003c/h3\u003e\n\u003cp\u003eAlexithymia is a stable personality trait characterized primarily by persistent difficulties in identifying and describing one's own emotions and in understanding the emotions of others [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is often conceptualized as an impairment in the cognitive processing, experience, and regulation of affect [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Alexithymia has been established as a significant risk factor for various psychological issues, particularly as a key factor influencing interpersonal functioning. Research by Koppelberg et al [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] indicates a positive correlation between alexithymia and general interpersonal distress, suggesting that difficulties in identifying feelings and difficulties in describing feelings can disrupt emotion regulation, thereby increasing the risk of interpersonal problems. Individuals with high levels of alexithymia often face challenges in establishing and maintaining healthy interpersonal relationships, which may lead to social avoidance behaviors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, phubbing may represent a daily emotion regulation strategy employed by individuals with alexithymia. Furthermore, alexithymia has been found to be a positive predictor of problematic mobile phone use in adolescents [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given that problematic mobile phone use and smartphone addiction are among the strongest predictors of phubbing, alexithymia appears to be an antecedent factor for phubbing [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. From a theoretical perspective, Izard\u0026rsquo;s Differential Emotions Theory posits that emotions are fundamental components of the personality system, functioning as central motivational forces [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emotions play a crucial regulatory role in representing needs and achieving goals: when individual needs are satisfied, they generate positive affective feedback, whereas when needs are frustrated, the resulting negative emotions may motivate adaptive behaviors. Within this framework, alexithymia may impair the normal motivational and regulatory functions of emotion, leading individuals to seek emotional gratification through mobile phone use or to avoid direct social interactions, thereby fostering persistent phubbing behavior. This theoretical perspective aligns with existing empirical evidence and provides a robust foundation for further exploring the mechanisms linking alexithymia and phubbing among adolescents.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe Relationship between Alexithymia and Internalizing Problems\u003c/h2\u003e\u003cp\u003eDrawing on clinical observations, Achenbach classified common psychological and behavioral problems among children and adolescents into internalizing and externalizing categories [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Internalizing problems refer to inwardly directed emotional or behavioral difficulties that are less overtly expressed or observable. Although these problems do not pose a direct threat to others, they pose potential risks to mental health, typically manifesting as symptoms of depression, anxiety, and stress [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Individuals with high levels of alexithymia, characterized by impaired emotional awareness and regulation, are more likely to develop internalizing problems [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, recent evidence indicates that the \u0026ldquo;difficulty identifying feelings\u0026rdquo; dimension of alexithymia constitutes a significant risk factor for internalizing problems, whereas depression and anxiety may, in turn, predict higher levels of difficulty in identifying feelings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, Zhang [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found a negative correlation between difficulty identifying feelings, a core feature of alexithymia, and internalizing problems, suggesting that children and adolescents with poorer ability to identify their own emotions are more prone to internalizing problems. Consistent with these findings, a meta-analysis further demonstrated that alexithymia is consistently associated with depression and anxiety, showing moderate correlations typically ranging from 0.30 to 0.50. In addition, alexithymia has shown moderate associations with depression across diverse populations, including clinical, community, and school samples [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The relationship between alexithymia and internalizing problems can be explained within the framework of the process model of emotion regulation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This model posits that identifying emotions is a prerequisite for effective emotion regulation. Difficulties in identifying feelings, a defining characteristic of alexithymia, hinder this initial stage of emotion processing. Consequently, individuals with high alexithymia experience deficits in emotion regulation, which in turn reduce their flexibility in managing emotional responses and make them more vulnerable to internalizing symptoms such as depression, anxiety, and stress.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe Relationship between Internalizing Problems and Phubbing\u003c/h3\u003e\n\u003cp\u003eErg\u0026uuml;n et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] found that phubbing was positively associated with symptoms of anxiety, depression, and stress. In relation to depression, individuals with higher depressive symptoms tend to rely on their mobile phones to manage negative emotions, a pattern that has been associated with problematic phone use [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Extending this line of research, Wang et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported that depressive symptoms significantly predicted phubbing. Regarding anxiety and stress, a study of university students showed that individuals with greater social anxiety engaged in phubbing more frequently [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Supporting these findings, Erg\u0026uuml;n et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] observed moderate positive associations between phubbing and symptoms of depression, anxiety, and stress. Taken together, these studies indicate a consistent link between phubbing and internalizing symptoms, though the causal pathways underlying these associations remain to be determined. The Compensatory Internet Use Theory [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] offers a useful framework for understanding phubbing as a behavior that may stem from internalizing problems. The theory proposes that individuals engage in online activities to compensate for psychosocial difficulties and unmet emotional or social needs. Those experiencing higher levels of depression, anxiety, or stress may use their mobile phones as a means of avoiding or disengaging from negative emotional states. When such compensatory use occurs during face-to-face interactions as a habitual strategy for managing anxiety or discomfort, it may manifest as phubbing. In this sense, phubbing can be conceptualized as a coping-oriented behavior adopted by individuals with internalizing problems to manage adverse real-life situations.\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eUsing data from a three-wave longitudinal survey and a 14-day intensive daily diary study, the present research employed cross-lagged panel models (CLPM) and residual dynamic structural equation modeling (RDSEM) to examine the dynamic associations and underlying mediating mechanisms linking alexithymia, internalizing problems, and adolescent phubbing at both the between-person and within-person levels. This study aims to elucidate the dynamic mechanisms underlying adolescents\u0026rsquo; phubbing behavior and to inform the development of evidence-based intervention strategies. Based on the previous literature, the study proposed the following hypotheses:\u003c/p\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAt the between-person level, alexithymia, internalizing problems, and phubbing are expected to exhibit significant bidirectional predictive relationships.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAt the within-person level, daily variations in alexithymia, internalizing problems, and phubbing would exhibit reciprocal associations.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003e\u003cem\u003eInternalizing problems are expected to mediate the relationship between alexithymia and adolescent phubbing, and this mediation is hypothesized to operate at both the between-person and within-person levels.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSample Characteristics and Longitudinal Design\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003eLongitudinal Sample\u003c/h2\u003e\u003cp\u003eThis study employed a cluster sampling approach to recruit students from Grades 7, 8, 10, and 11 (excluding graduating classes). A three-wave longitudinal design was implemented over one year, with data collected at 6-month intervals in September 2023 (T1), March 2024 (T2), and September 2024 (T3). At T1, a total of 1,191 valid responses were collected. After data cleaning and validity checks, questionnaires with patterned or inconsistent responses were excluded. Consequently, 902 participants who completed all three survey waves were retained for the final analyses, resulting in a valid retention rate of 75.7% and an attrition rate of 24.3%. Attrition analyses were conducted to compare the retained sample (\u003cem\u003en\u003c/em\u003e = 902) and the attrition sample (\u003cem\u003en\u003c/em\u003e = 289) on demographic characteristics (gender, age, residential background) and key study variables (alexithymia, internalizing problems, and phubbing behavior) measured at T1. Results indicated no significant group differences in age (\u003cem\u003et\u003c/em\u003e = − 1.66, \u003cem\u003ep\u003c/em\u003e = 0.10); gender (\u003cem\u003eχ²\u003c/em\u003e = 1.23, \u003cem\u003ep\u003c/em\u003e = 0.27); alexithymia (\u003cem\u003et\u003c/em\u003e = − 1.49, \u003cem\u003ep\u003c/em\u003e = 0.14); internalizing problems (\u003cem\u003et\u003c/em\u003e = − 0.81, \u003cem\u003ep\u003c/em\u003e = 0.42); or phubbing behavior (\u003cem\u003et\u003c/em\u003e = − 1.39, \u003cem\u003ep\u003c/em\u003e = 0.14). These findings suggest that sample attrition was non-systematic and that the overall sample structure remained stable across waves.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eIntensive Longitudinal Subsample\u003c/h3\u003e\n\u003cp\u003eBuilding upon the valid longitudinal sample that completed all three survey waves (\u003cem\u003en\u003c/em\u003e = 902), a subset of participants was invited to take part in a 14-day intensive daily diary study. A purposive stratified sampling strategy was employed to ensure representativeness across gender, grade level, and alexithymia levels. Among the 412 students who consented to participate, 373 provided complete and valid datasets, resulting in a participation rate of 90.5% and an attrition rate of 9.5%. The final diary subsample comprised 198 male (53.1%) and 175 female (46.9%) participants. After excluding incomplete diary entries, a total of 4,790 valid daily-level observations were retained for analysis. Independent-samples t-tests and chi-square tests conducted on key study variables at T3 (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) revealed no significant differences between the diary subsample and the remaining longitudinal participants, suggesting that the diary subsample was demographically and psychologically comparable to the full sample.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedure\u003c/h3\u003e\n\u003cp\u003eThe Institutional Review Board of the authors’ university approved all data collection procedures. Written informed consent was obtained from participants and their parents or guardians prior to data collection. Surveys were administered in classroom settings during regular school hours by graduate student research assistants in psychology who had received standardized training in the study protocols. Before distribution, research assistants read aloud standardized instructions describing the study purpose, emphasizing voluntary participation, and assuring confidentiality. Participants were told they could withdraw at any time without penalty. Each assessment session lasted approximately 30 minutes. In the intensive longitudinal study, data were collected over 14 consecutive days. The order of daily items was randomized for each survey, and the assessment took approximately 10 minutes to complete each day.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eAlexithymia Scale\u003c/h2\u003e\u003cp\u003eAlexithymia was assessed using the 20-item Toronto Alexithymia Scale (TAS-20) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The TAS-20 includes three dimensions: Difficulty Identifying Feelings, Difficulty Describing Feelings, and Externally Oriented Thinking. Items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater levels of alexithymia. In the present study, Cronbach’s \u003cem\u003eα\u003c/em\u003e coefficients for this scale were 0.82, 0.82, and 0.84 at the three measurement waves, respectively. In the intensive longitudinal study, we selected the three items with the highest factor loadings from the Toronto Alexithymia Scale (TAS-20) and adapted them to assess day-to-day alexithymia. For example, the original item “I find it difficult to describe my feelings” was rephrased as “Over the past 24 hours, I have found it difficult to describe my feelings using appropriate words.” In the present study, the between-person and within-person McDonald’s \u003cem\u003eω\u003c/em\u003e coefficients for this scale were 0.98 and 0.88, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInternalizing Problems Scale\u003c/h2\u003e\u003cp\u003eInternalizing problems were measured using the 21-item Depression Anxiety Stress Scales (DASS-21) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The DASS-21 comprises three subscales: Depression, Anxiety, and Stress. All items are rated on a 4-point Likert scale from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time), with higher scores reflecting more severe symptoms. In the present study, Cronbach’s \u003cem\u003eα\u003c/em\u003e coefficients for this scale were 0.95, 0.96, and 0.96 at the three measurement waves, respectively. In the intensive longitudinal study, the Depression Anxiety Stress Scales (DASS-21) were adapted into a diary-style version to assess participants’ daily internalizing problems. For example, the original item “I felt down-hearted and blue” was rephrased as “During the past 24 hours, I have felt down-hearted and blue.” In the present study, the between-person and within-person McDonald’s \u003cem\u003eω\u003c/em\u003e coefficients for this scale were 0.97 and 0.86, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePhubbing Behavior Scale\u003c/h2\u003e\u003cp\u003ePhubbing behavior was assessed using the 15-item Generic Scale of Phubbing (GSP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The GSP consists of four dimensions: Nomophobia (fear of being without a mobile phone), Interpersonal Conflict, Self-Isolation, and Problematic Awareness. Items are rated on a 7-point Likert scale ranging from 1 (never) to 7 (always), with higher scores indicating greater levels of phubbing behavior. In the present study, Cronbach’s \u003cem\u003eα\u003c/em\u003e coefficients for this scale were 0.92, 0.92, and 0.94 at the three measurement waves, respectively. For the daily diary component, we adapted the GSP into a briefer form suitable for intensive longitudinal assessment by rephrasing items to capture day-to-day variability. For instance, the original item “I would feel annoyed if someone asked me to stop using my phone to talk with them” was revised to “In the past 24 hours, I have felt annoyed when someone asked me to stop using my phone to talk with them.” In the present study, the between-person and within-person McDonald’s \u003cem\u003eω\u003c/em\u003e coefficients for this scale were 0.95 and 0.88, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Procedure\u003c/h2\u003e\u003cp\u003eData were collected on-site through cluster-based group surveys administered to middle and high school students. All responses were entered into \u003cb\u003eEpiData\u003c/b\u003e to ensure accuracy and data integrity. Data analysis was conducted in several stages, following established statistical procedures. First, \u003cb\u003eSPSS 26.0\u003c/b\u003e was used for data cleaning and matching, including the removal of cases showing patterned response tendencies, incomplete participation across the three waves, or inattentive responding. \u003cb\u003eSPSS\u003c/b\u003e was also employed to perform demographic difference tests, reliability analyses, descriptive statistics, correlation analyses, and assessments of common method bias. Second, \u003cb\u003eMplus 8.3\u003c/b\u003e was used to examine the structural validity and longitudinal measurement invariance of all scales, providing a basis for subsequent longitudinal analyses. Finally, model construction and hypothesis testing were conducted using \u003cb\u003eMplus 8.3\u003c/b\u003e. A cross-lagged panel model (CLPM) was specified, with subscale scores serving as indicators of latent constructs, to assess the longitudinal reciprocal relationships among alexithymia, internalizing problems, and adolescent phubbing behavior at the between-person level. The mediating effect of internalizing problems was also examined. In addition, a residual dynamic structural equation modeling (RDSEM) approach was applied to simultaneously partition between-person variance and model within-person dynamic processes. This approach enabled the examination of within-person dynamic interactions among daily alexithymia, internalizing problems, and phubbing behavior, as well as the intensive longitudinal mediation effect of daily internalizing problems. Missing data were handled using Full Information Maximum Likelihood [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Model fit was assessed using the following benchmarks: RMSEA ≤ 0.08, SRMR ≤ 0.08, and CFI ≥ 0.90 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Comparisons between nested models relied on the changes in fit indices (ΔCFI and ΔRMSEA). A model was not considered to have a significantly worse fit if the ΔCFI \u0026lt; 0.01 and the ΔRMSEA \u0026lt; 0.015 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eCommon Method Bias Assessment\u003c/h2\u003e\u003cp\u003eResults from Harman’s single-factor test indicated that at T1, T2, and T3, nine, eight, and eight factors, respectively, had eigenvalues greater than one. The first factor explained 30.10%, 32.41%, and 33.68% of the total variance, respectively, all below the commonly accepted 40% threshold [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings suggest that common method bias was not a major concern in this study.\u003c/p\u003e\u003ch2\u003eDescriptive Statistics and Correlation Analysis\u003c/h2\u003e\u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, correlation analyses revealed significant positive associations among alexithymia, internalizing problems, and phubbing across the three measurement occasions (T1, T2, T3). Alexithymia showed moderate to strong positive correlations with internalizing problems(0.33 ≤ \u003cem\u003er\u003c/em\u003e ≤ 0.58, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Similarly, alexithymia was positively correlated with phubbing (0.28 ≤ \u003cem\u003er\u003c/em\u003e ≤ 0.48, p \u0026lt; 0.001), and internalizing problems showed substantial positive associations with phubbing(0.34 ≤ \u003cem\u003er\u003c/em\u003e ≤ 0.59, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). These results indicate significant concurrent and lagged associations among alexithymia, internalizing problems, and adolescent phubbing, thereby satisfying the preliminary conditions for proceeding with cross-lagged panel modeling.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics and Correlations for Study Variables (\u003cem\u003en\u003c/em\u003e = 902)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 ALEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 INT\u003c/p\u003e\u003cp\u003eg Problemsg Problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 INT\u003c/p\u003e\u003cp\u003eg Problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 INT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 PHUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 PHUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 PHUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. ALEX = Alexithymia; INT = Internalizing problems; PHUB = Phubbing. All variables are standardized latent factors.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eLongitudinal Measurement Invariance Testing\u003c/h2\u003e\u003cp\u003eTo examine the stability of the core constructs across time, this study tested longitudinal measurement invariance for the measures of alexithymia, internalizing problems, and phubbing using Mplus 8.3. The analyses followed a sequential model-nesting strategy by progressively evaluating four levels of invariance: configural (M0), weak (metric, M1), strong (scalar, M2), and strict (M3). Because chi-square statistics can be overly sensitive to large samples, we used Chen’s recommended criteria for changes in fit indices: a constrained model was considered invariant when ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 relative to the less constrained model [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, alexithymia met the criteria for strong invariance, whereas both internalizing problems and phubbing satisfied the criteria for strict invariance.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement Invariance Tests for Alexithymia, Internalizing Problems, and Phubbing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eχ²/\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003emodel comparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eALEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2586.60(1557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2657.40(1591)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM1-M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2725.00(1619)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM2-M1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2983.12(1654)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM3-M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3833.27(1775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3888.59(1810)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM1-M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4027.27(1847)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM2-M1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4281.56(1889)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM3-M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePHUB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2135.56(834)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2179.90(856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM1-M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2271.50(878)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM2-M1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2405.35(905)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM3-M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote. M0 = configural invariance; M1 = metric invariance; M2 = scalar invariance; M3 = strict invariance.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStructural Equation Model Fit Evaluation\u003c/h2\u003e\u003cp\u003eTo determine the optimal specification, we estimated and compared four nested models: M1 (freely estimated), M2 (equality constraints on autoregressive paths only), M3 (equality constraints on cross-lagged paths only), and M4 (equality constraints on both autoregressive and cross-lagged paths). Model fit statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Following Chen, we adopted ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 as thresholds for invariance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. M3 provided an acceptable fit across indices (\u003cem\u003eχ²/df\u003c/em\u003e = 4.12, RMSEA = 0.059, CFI = 0.933, TLI = 0.923, SRMR = 0.049), and the ΔCFI and ΔRMSEA comparisons between M3 and the other models satisfied the prespecified criteria. Balancing parsimony and fit, we therefore selected M3 as the final model for subsequent analyses.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural Equation Modeling Fit Indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eModel Fit Indices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel Comparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1896.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1899.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM2vsM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1765.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM3vsM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1921.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eM4vsM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote. M1 = configural invariance model; M2 = weak (metric) invariance model; M3 = strong (scalar) invariance model; M4 = strict invariance model. According to Chen (2007), model differences can be considered acceptable when the change in comparative fit index (ΔCFI) is less than 0.010 and the change in root mean square error of approximation (ΔRMSEA) is less than 0.015.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eBetween-Person Cross-Lagged Panel Model\u003c/h2\u003e\u003cp\u003eTo assess longitudinal relations among alexithymia, internalizing problems, and phubbing at the between-person level, we estimated a cross-lagged panel model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The model indicated stable predictive associations among the constructs; key results are summarized below. Bidirectional predictions between alexithymia and internalizing problems. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, T1 alexithymia positively predicted T2 internalizing problems (\u003cem\u003eγ\u003c/em\u003e = 0.16, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and T2 alexithymia positively predicted T3 internalizing problems (\u003cem\u003eγ\u003c/em\u003e = 0.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Conversely, T1 internalizing problems positively predicted T2 alexithymia (\u003cem\u003eγ\u003c/em\u003e = 0.19, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and T2 internalizing problems positively predicted T3 alexithymia (\u003cem\u003eγ\u003c/em\u003e = 0.17, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Together, these findings indicate a reciprocal cross-lagged relationship between alexithymia and internalizing problems across waves. Bidirectional predictions between internalizing problems and adolescent phubbing. T1 internalizing problems positively predicted T2 phubbing (\u003cem\u003eγ\u003c/em\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and T2 internalizing problems positively predicted T3 phubbing (\u003cem\u003eγ\u003c/em\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). In the reverse direction, T1 phubbing positively predicted T2 internalizing problems (\u003cem\u003eγ\u003c/em\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), and T2 phubbing positively predicted T3 internalizing problems (\u003cem\u003eγ\u003c/em\u003e = 0.08, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Together, these results reveal a bidirectional cross-lagged relationship between internalizing problems and phubbing across time.Unidirectional prediction between alexithymia and adolescent phubbing. The data indicate a unidirectional association in which alexithymia precedes phubbing: T2 alexithymia positively predicted T3 phubbing (\u003cem\u003eγ\u003c/em\u003e = 0.07, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Phubbing, however, failed to predict alexithymia at any wave. Thus, alexithymia appears to function as an antecedent rather than a consequence of adolescent phubbing.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAll estimates represent standardized coefficients that reached statistical significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Gender and age were controlled for in the analysis. Although the measurement structure of the latent variable for internalizing problems at T2 was consistent with T1, it is not shown in the interest of clarity. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the cross-lagged regression coefficients and their 95% confidence intervals are presented.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCross-lagged regression coefficients and 95% confidence intervals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecross-lagged panel model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStd.Est\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→INT \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.162\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.088, 0.235]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→PHUB \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[-0.001, 0.125]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→ALEX \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.192\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.115, 0.270]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→PHUB \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.021, 0.181]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→INT \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.095\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.025, 0.165]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB \u003csub\u003e\u003cem\u003et1\u003c/em\u003e\u003c/sub\u003e→ALEX \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[-0.075, 0.045]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e→INT \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.130\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.067, 0.193]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX \u003csub\u003et2\u003c/sub\u003e→PHUB \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.000, 0.130]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e→ALEX \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.172\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.099, 0.246]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e→PHUB \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.103\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.020, 0.185]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e→INT \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.079\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.019, 0.140]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB \u003csub\u003e\u003cem\u003et2\u003c/em\u003e\u003c/sub\u003e→ALEX \u003csub\u003e\u003cem\u003et3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e[-0.072, 0.043]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Bolded estimates indicate statistically significant paths, ---\u003cb\u003edenotes a nonsignificant path.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eWithin-Person Residual Dynamic Structural Equation Modeling\u003c/h2\u003e\u003cp\u003eTo examine daily within-person dynamics, we conducted an intensive longitudinal diary study with 373 participants over 14 consecutive days, extending our prior work. This design produced 4,790 valid daily observations, which we modeled using residual dynamic structural equation modeling (RDSEM). As reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the intraclass correlation coefficients (ICC) were 0.54 for alexithymia, 0.37 for internalizing problems, and 0.48 for phubbing, indicating that 46% of the variance in alexithymia, 63% in internalizing problems, and 52% in phubbing arose from within-person fluctuations. Within-person correlations further revealed significant positive associations between alexithymia and internalizing problems (\u003cem\u003er\u003c/em\u003e = 0.39, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), alexithymia and phubbing (\u003cem\u003er\u003c/em\u003e = 0.40, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and internalizing problems and phubbing (\u003cem\u003er\u003c/em\u003e = 0.32, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1765191675.png\" style=\"width: 621px;\"\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the residual dynamic structural equation modeling (RDSEM) results, which characterize day-level, within-person dynamics among alexithymia, internalizing problems, and phubbing. The analysis revealed reciprocal predictive links between alexithymia and phubbing: prior-day alexithymia positively predicted next-day phubbing (\u003cem\u003eβ\u003c/em\u003e = 0.056, 95% CI [0.027, 0.085]), and prior-day phubbing likewise predicted higher next-day alexithymia (\u003cem\u003eβ\u003c/em\u003e = 0.037, 95% CI [0.006, 0.069]). Similarly, alexithymia and internalizing problems exhibited bidirectional effects: prior-day alexithymia positively predicted next-day internalizing problems (\u003cem\u003eβ\u003c/em\u003e = 0.094, 95% CI [0.060, 0.124]), and prior-day internalizing problems predicted elevated next-day alexithymia (\u003cem\u003eβ\u003c/em\u003e = 0.138, 95% CI [0.108, 0.169]). Furthermore, internalizing problems and phubbing exhibited reciprocal day-to-day dynamics: prior-day internalizing problems positively predicted phubbing the following day (\u003cem\u003eβ\u003c/em\u003e = 0.029, 95% CI [0.006, 0.055]), and prior-day phubbing in turn positively predicted next-day internalizing problems (\u003cem\u003eβ\u003c/em\u003e = 0.031, 95% CI [0.001, 0.068]).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWithin-Person Effect Estimates and Confidence Intervals from RDSEM Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eWithin-Person Standardized Estimate (Mean)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect(SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoregressive Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→ALEX\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.103(0.020)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.064, 0.138]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→INT\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.077(0.019)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.047, 0.123]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→PHUB\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.151(0.017)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.117, 0.182]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoregressive Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→PHUB\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.056(0.015)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.027, 0.085]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→ALEX\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.037(0.016)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.006, 0.069]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALEX\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→INT\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.094(0.017)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.060, 0.124]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→ALEX\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ee\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.138(0.016)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.108, 0.169]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→PHUB\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.029(0.011)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.006, 0.055]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHUB\u003csub\u003e\u003cem\u003et−1\u003c/em\u003e\u003c/sub\u003e→INT\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.031(0.017)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e[0.001, 0.068]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. Bolded estimates indicate statistically significant paths. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represents the autoregressive effects of each variable, whereas \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ee\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e denote the corresponding cross-lagged effects.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eLongitudinal and Intensive Longitudinal Mediation Effects of Internalizing Problems\u003c/h2\u003e\u003cp\u003eUsing a cross-lagged panel framework with 5,000 bootstrap replications, we evaluated longitudinal mediation and found that alexithymia predicted adolescent phubbing via internalizing problems (indirect effect = 0.017, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, 95% CI [0.003, 0.031]). In the reverse direction, phubbing likewise predicted alexithymia through internalizing problems (indirect effect = 0.016, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, 95% CI [0.004, 0.029]). Together, these results indicate that internalizing problems functions as a significant longitudinal mediator linking alexithymia and phubbing. In the intensive longitudinal mediation analysis, we constructed a residual dynamic structural equation modeling (RDSEM). Time was included as a Level-1 predictor to control for linear trends. Gender and age were entered as Level-2 covariates. Bayesian Markov chain Monte Carlo (MCMC) estimation was employed, with 20,000 iterations and two chains (Chains = 2) used to estimate model parameters. The statistical significance of parameters was determined based on 95% credible intervals; a path was considered significant if its 95% credible interval did not include zero. The results indicated that internalizing problems significantly mediated the association between alexithymia and phubbing (Indirect 1 = 0.007, 95% CI [0.001, 0.014], \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; Indirect 2 = 0.006, 95% CI [0.001, 0.013], \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations among Internalizing Problems, Alexithymia, and Phubbing\u003c/h2\u003e\u003cp\u003eSignificant positive correlations emerged among alexithymia, internalizing problems, and phubbing across all three time points, highlighting the close interrelationships between these variables. The strong association between alexithymia and internalizing problems suggests that difficulties in identifying and expressing emotions, core features of alexithymia, may exacerbate internalizing symptoms such as anxiety, depression, and stress [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Simultaneously, individuals with alexithymia may excessively use smartphones to cope with emotion regulation difficulties or to avoid social anxiety by negative emotions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The strong correlation between internalizing problems and phubbing further indicates that excessive phubbing is closely linked to adolescents\u0026rsquo; internalizing symptoms, such as anxiety and depression [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eBidirectional Predictive Relationships among Alexithymia, Internalizing Problems, and Phubbing at Between-Person and Within-Person Levels\u003c/h2\u003e\u003cp\u003eA reciprocal predictive relationship exists between alexithymia and internalizing problems. At the between-person level, longitudinal cross-lagged panel model analyses indicated that alexithymia at T1 and T2 positively predicted subsequent internalizing problems at T2 and T3, respectively. Concurrently, internalizing problems at T1 and T2 positively predicted subsequent alexithymia at T2 and T3, respectively. At the within-person level, a dynamic structural equation model analysis of intensive longitudinal data revealed that higher-than-usual alexithymia on a given day positively predicted next-day internalizing problems, and vice versa. This study demonstrates a bidirectional predictive relationship between alexithymia and internalizing problems, evident at both the level of stable traits and dynamic day-to-day fluctuations. This finding aligns with previous research. Alexithymia, characterized by deficits in identifying and expressing emotions, may contribute to internalizing problems such as depression, anxiety, and stress by limiting effective processing of negative emotions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, individuals with high levels of alexithymia may experience greater psychological distress due to difficulties in articulating negative emotions, thereby exacerbating internalizing problems [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Conversely, internalizing problems may increase emotional suppression and hinder individuals\u0026rsquo; ability to recognize and express their emotions, further intensifying alexithymia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This reciprocal relationship supports theories of emotion regulation, which propose a mutually reinforcing dynamic between impaired emotional processing and mental health difficulties [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA reciprocal predictive relationship was observed between internalizing problems and phubbing. At the between-person level, cross-lagged panel model analyses of longitudinal data revealed that internalizing problems at T1 and T2 positively predicted subsequent phubbing at T2 and T3, respectively. Conversely, phubbing at T1 and T2 also positively predicted later internalizing problems at T2 and T3. At the within-person level, results from a random-intercept dynamic structural equation model indicated that individuals who experienced higher-than-usual internalizing problems on a given day tended to report increased phubbing the following day. Similarly, elevated daily phubbing predicted greater internalizing problems the next day. These findings indicate a bidirectional association between internalizing problems and phubbing at both stable, trait-like and dynamic, state-like levels. This result supports the work of Gao et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], who suggested that phubbing may exacerbate internalizing problems by fostering perceptions of social exclusion, which can intensify feelings of loneliness and depressive symptoms. Conversely, individuals with heightened internalizing problems may resort to excessive smartphone use as a coping mechanism for social pressure and anxiety, thereby increasing phubbing behavior [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This bidirectional relationship may function through a self-reinforcing cycle: internalizing problems lead individuals to depend on smartphones for coping, which, in turn, increases phubbing. Such behavior diminishes the quality of real-life friendships [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and weakens the protective effects of social support against negative emotions, thereby reinforcing internalizing problems and perpetuating a vicious cycle.\u003c/p\u003e\u003cp\u003eThe Relationship between Alexithymia and Phubbing. At the between-person level, a unidirectional relationship was observed, in which alexithymia at T2 positively predicted phubbing at T3, whereas phubbing did not predict subsequent alexithymia. This result suggests that alexithymia may serve as a predisposing factor: adolescents with high levels of alexithymia may engage in phubbing more frequently as a compensatory avoidance strategy to escape anxiety or stress in face-to-face social interactions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, at the between-person level, phubbing did not significantly predict alexithymia. Research at the between-person level typically focuses on trait-like constructs to examine how relatively stable psychological characteristics influence behavioral outcomes. Alexithymia, as a relatively stable personality trait [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], is primarily shaped and maintained by internal psychological mechanisms, such as deficits in emotional processing, rather than external behavioral habits like smartphone use [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In contrast, at the within-person level, a random-intercept dynamic structural equation model applied to intensive longitudinal data revealed a bidirectional predictive relationship: daily elevations in alexithymia predicted next-day increases in phubbing, and similarly, daily increases in phubbing predicted higher alexithymia the following day. This indicates that daily fluctuations in alexithymia contribute to subsequent avoidant phubbing behavior, which in turn exacerbates alexithymia, forming a self-reinforcing cycle. According to the diathesis-stress model [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], there is a dynamic bidirectional interaction between an individual\u0026rsquo;s inherent vulnerability and external stressors eliciting negative emotions, which may accumulate and intensify over time. Daily alexithymia reflects an individual\u0026rsquo;s emotional processing vulnerability, leading alexithymic adolescents to adopt avoidance-oriented coping strategies such as phubbing. However, such avoidance behavior can impair the quality of in-person communication [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], thereby aggravating alexithymia on the following day. The findings reveal a differentiated pattern across levels: a unidirectional predictive effect of alexithymia on phubbing at the between-person level, and a bidirectional association at the within-person level. This pattern suggests that the link between alexithymia and phubbing reflects a stable, trait-like tendency across individuals, whereas it operates as a dynamic, state-like process within individuals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eLongitudinal Mediating Effects of Internalizing Problems\u003c/h2\u003e\u003cp\u003eBoth longitudinal and intensive longitudinal studies indicated that alexithymia influences phubbing indirectly through internalizing problems, and similarly, phubbing affects alexithymia via internalizing problems. At the between-person level, longitudinal analyses revealed that internalizing problems played a significant mediating role between alexithymia and phubbing. Specifically, alexithymia may exacerbate internalizing problems such as anxiety or depression, which in turn leads to increased phubbing, for instance, individuals with emotional regulation difficulties may rely more heavily on smartphones to escape real-world stressors [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similarly, phubbing may intensify internalizing problems by triggering feelings of social isolation or interpersonal conflict, thereby indirectly exacerbating alexithymia [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. At the within-person level, results from intensive longitudinal modeling showed that fluctuations in alexithymia predict subsequent internalizing problems such as depression, anxiety, and stress [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may occur because individuals with higher alexithymia possess deficits in emotional identification and processing, leading them to experience more negative affect in response to daily social stressors. These negative emotional states do not dissipate spontaneously but may accumulate over time, eventually contributing to more severe internalizing problems. In this context, smartphone use becomes a preferred coping strategy to avoid social interactions and alleviate distress. In summary, these longitudinal mediation findings support a complex interplay between mental health and technology use, underscoring the central role of internalizing problems as a key mechanism linking emotional traits with behavioral outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eAlexithymia may reinforce phubbing by exacerbating internalizing problems, suggesting that phubbing can function as a compensatory or avoidant coping strategy among adolescents with alexithymia-related internalizing symptoms. Consequently, direct restrictions on smartphone use may only address surface symptoms rather than root causes. For effective intervention, we recommend the following approaches: First, enhance adolescents\u0026rsquo; ability to identify and express emotions to reduce internalizing problems stemming from emotional recognition, understanding, and expression difficulties. Second, provide timely support for adolescents with significant internalizing problems. Multimodal interventions, such as social activities, physical exercise, drawing, and music, should be employed to prevent worsening internalizing problems and subsequent increases in phubbing. Preventive measures should target adolescents with high trait alexithymia, focusing on training emotional identification and expression skills. Immediate interventions, on the other hand, can disrupt the daily maladaptive cycle between alexithymia and phubbing. Finally, both families and schools should collaborate to offer emotional support and help adolescents establish stable social and emotional regulation mechanisms. Through such comprehensive strategies, it is possible to effectively break the vicious cycle between alexithymia and phubbing, thereby promoting adolescent mental health.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Research Directions\u003c/h2\u003e\u003cp\u003eAlthough this study employed a longitudinal design that combined traditional and intensive methods, several limitations should be acknowledged. First, the observation period spanned only one year and included relatively few assessment waves, constraining our ability to capture longer-term trajectories. Second, because participants were in early or middle adolescence, the findings may not generalize to individuals in late adolescence. Future research should therefore examine phubbing behaviors across longer developmental windows that encompass the full adolescent span, probing how alexithymia and internalizing problems predict and shape phubbing over extended timeframes. Moreover, incorporating external and environmental factors alongside other relevant variables would permit construction of more comprehensive mediation models. Given that all participants in the present study were Chinese adolescents, studies that include other ethnic and cultural groups are needed to determine whether similar effects obtain cross-culturally. Finally, adopting random sampling procedures would facilitate recruitment of larger, more representative samples in future investigations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed a combined longitudinal and intensive longitudinal design to systematically examine the dynamic relationships among alexithymia, internalizing problems, and phubbing in adolescents at both the between-person and within-person levels. The main findings can be summarized as follows: First, bidirectional predictive associations emerged between alexithymia and internalizing problems, as well as between internalizing problems and phubbing, at both the between- and within-person levels. In contrast, the association between alexithymia and phubbing demonstrated a trait-state dissociation: at the between-person level, only a unidirectional effect from alexithymia to phubbing was identified, whereas at the within-person level, a bidirectional predictive relationship emerged. Second, internalizing problems served as a longitudinal mediator linking alexithymia to phubbing. In summary, this study illuminates a dynamic longitudinal interplay among alexithymia, internalizing problems, and phubbing. It underscores the importance of enhancing adolescents\u0026rsquo; emotional identification and expression abilities and providing timely interventions for internalizing problems to prevent and mitigate phubbing and to promote mental health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eAll procedures involving human participants were approved by the Research Ethics Committee of Jiangxi Normal University. Written informed consent was obtained from all participants and their legal guardians in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 72164018), the Humanities and Social Sciences Research Planning Foundation of Ministry of Education (Grant No. 22YJA190012), and the 2025 Project of the Hunan Provincial Social Science Achievement Evaluation Committee (Grant No. XSP25YBC572), and the programme of Study on Mental Health Education for College Students in the Context of Digitization of Education ( Grant No. 2024YB0215).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHaibin Huang contributed to the conceptualization of the study and was responsible for writing \u0026ndash; original draft and writing \u0026ndash; review \u0026amp; editing of the manuscript. Zhiming Zhou was responsible for data collection, data processing, and writing \u0026ndash; original draft. Qi Dai contributed to writing \u0026ndash; review \u0026amp; editing of the manuscript. Yu Deng contributed to writing \u0026ndash; review \u0026amp; editing of the manuscript. Hongxia Lin and Min Wang were responsible for English language editing and polishing of the manuscript. Jin Xie contributed to writing \u0026ndash; review \u0026amp; editing of the manuscript. Baojuan Ye supervised the overall study, provided project administration, and was responsible for writing \u0026ndash; review \u0026amp; editing as well as correspondence with the journal. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe sincerely thank all the students, parents, and teachers who participated in this study, as well as all the psychology graduate students who provided invaluable assistance during the data collection process. Language editing assistance was provided with the aid of artificial intelligence tools under the supervision of the authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDue to the nature of this research, participants did not consent to public data sharing; therefore, supporting data are not available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChotpitayasunondh V, Douglas KM. How phubbing becomes the norm: The antecedents and consequences of snubbing via smartphone. 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Child Youth Serv Rev. 2024;164:107878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.childyouth.2024.107878\u003c/span\u003e\u003cspan address=\"10.1016/j.childyouth.2024.107878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alexithymia, internalizing problems, phubbing, longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-8096209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8096209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study investigated the longitudinal mediation effect of internalizing problems in the association between alexithymia and adolescent phubbing behavior.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe research design was employed that combined three-wave longitudinal tracking (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;902, with 6-month intervals) and 14-day intensive diary tracking (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;373). The dynamic relationships among the three variables were analyzed at both between-person and within-person levels using the cross-lag model and the residual dynamic structural equation modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e(1) At the between-person level, alexithymia and internalizing problems, as well as internalizing problems and phubbing, exhibited bidirectional predictive relationships, whereas alexithymia unidirectionally predicted phubbing. (2) At the within-person level, bidirectional day-to-day predictive associations were observed among alexithymia, internalizing problems, and phubbing. (3) Internalizing problems significantly mediated the longitudinal association between alexithymia and phubbing, and this mediating effect was significant at both the between-person and within-person levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe bidirectional relationship between alexithymia and phubbing differs across long-term stability and short-term fluctuations. Internalizing problems serve as a significant longitudinal mediator linking alexithymia to adolescents\u0026rsquo; phubbing behavior.\u003c/p\u003e","manuscriptTitle":"The dynamic longitudinal relationship between alexithymia and phubbing in adolescents: the mediating role of internalizing problems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 11:04:29","doi":"10.21203/rs.3.rs-8096209/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d7ba303-1c7e-4cb5-9abb-70c00bb3af5b","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T23:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 11:04:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8096209","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8096209","identity":"rs-8096209","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}