







































 Humanities and Social Science Research; Vol. 8, No. 1; 2025 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v8n1p117 

 117 Published by IDEAS SPREAD 

 

The Relationship Between Parental Monitoring and Adolescent 

Smartphone Addiction: The Longitudinal Mediating Role of Parent-

Child Attachment 

Xu Long1 & Wang Qin1 

1 School of Education, Chongqing Normal University, China 

Correspondence: Xu Long, School of Education, Chongqing Normal University, Chongqing, 401331, China. 

 

Received: January 29, 2025; Accepted: February 27, 2025; Published: March 1, 2025 

 

Abstract 

This study investigated the long-term effects of parental monitoring on mobile phone addiction among high school 

students and examined the longitudinal mediating role of parent-child attachment. A three-wave longitudinal study 

spanning 1.5 years was conducted using the Parental Monitoring Scale, Parent-Child Attachment Scale, and 

Mobile Phone Addiction Scale. Valid data were collected from 405 students (grades 10–11) at a Chongqing 

secondary school across three assessments. The results demonstrated that: 1) Parental monitoring significantly 

negatively predicted adolescents’ mobile phone addiction over time; 2) The mediating effect of parent-child 

attachment remained consistent across different time points, indicating stability in its intermediary role throughout 

the study period. These findings suggest that parental monitoring not only directly reduces mobile phone addiction 

but also exerts an indirect influence by enhancing parent-child attachment. The study highlights the enduring 

protective role of parental involvement and relational bonds in mitigating technology-related behavioral issues 

during adolescence. 

Keywords: parental monitoring, mobile phone addiction, parent-child attachment, high school students 

1. Introduction 

The widespread adoption of smartphones has exacerbated concerns about mobile phone addiction among 

adolescents. According to the China Internet Network Information Center(CNNIC, n.d.), China currently hosts 

188 million underage internet users, with 99.7% relying primarily on mobile devices for online access. Compared 

to adults, adolescents exhibit weaker self-control, rendering them more susceptible to excessive smartphone 

use(Hefner et al., 2019). Research indicates that mobile phone addiction is associated with sleep 

disturbances(Tettamanti et al., 2020), diminished well-being (Horwood & Anglim, 2019), interpersonal 

conflicts(Elhai et al., 2019), and even severe psychological issues such as risk-taking behaviors(Vannucci et al., 

2020). Consequently, identifying the causes of adolescent mobile phone addiction and developing intervention 

strategies hold significant practical implications. 

The negative effects of excessive smartphone use on adolescents are well-documented. Existing studies have 

explored predictors of mobile phone addiction from various perspectives, including individual traits, physiological 

factors, psychological factors, and environmental factors. At the individual level, gender and age have been 

identified as significant predictors of mobile phone addiction. A meta-analysis on nomophobia (fear of being 

without a mobile phone) found that the prevalence of mobile phone addiction among adults is approximately 21%, 

with higher rates observed among females and younger individuals(Farchakh et al., 2021). Adolescents and 

students are particularly vulnerable, with around 20% showing tendencies toward mobile phone addiction(Lin et 

al., 2021). Personal traits such as low self-esteem, loneliness, and anxiety are also closely linked to mobile phone 

addiction, as these individuals are more likely to engage in prolonged smartphone use, further exacerbating their 

dependency(Kara et al., 2021). 

At the physiological and psychological levels, smartphone addiction is closely associated with both physical and 

mental health. Excessive smartphone use can lead to attention deficits, reduced work performance, and increased 

levels of anxiety and depression(Kim et al., 2018). Prolonged use of smartphones can also result in physical 

discomfort, such as neck and wrist pain(Alabdulwahab et al., 2017). Furthermore, a meta-analysis on adolescent 

smartphone addiction and sleep found that addicted adolescents are at a higher risk of developing sleep disorders, 

which can lead to further complications such as anxiety, depression, memory decline, and obesity (Cappuccio et 

al., 2008). 



hssr.ideasspread.org   Humanities and Social Science Research Vol. 8, No. 1; 2025 

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Environmental factors, particularly those related to family and school, also play a crucial role in adolescent 

smartphone addiction. Family environment, especially parental media education, significantly influences 

adolescent behavior (Deng et al., 2020). A study of 471 middle school students found that parents who are 

themselves heavy smartphone users ("phubbers") contribute to their children's smartphone addiction(Zhang et al., 

2021). While positive parental intervention can effectively reduce the risk of addiction, excessive monitoring and 

content restrictions may paradoxically accelerate it(Chen et al., 2019)). Psychological control by parents has also 

been shown to increase the likelihood of smartphone addiction among adolescents(Jiang et al., 2022). Moreover, 

technological interference in parent-child interactions can negatively affect children's behavior(McDaniel & 

Radesky, 2018). Research indicates that the more parents interfere with technology during interactions, the more 

likely adolescents are to develop smartphone addiction(Liu et al., 2018). 

Parental monitoring has emerged as a critical variable in the family context. Grounded in Clark’s(2011) theoretical 

framework, parental monitoring encompasses positive intervention (e.g., communicative guidance) and restrictive 

monitoring (e.g., enforcing usage rules). While positive intervention reduces addiction risks through effective 

parent-child communication(van den Eijnden et al., 2010), the role of restrictive monitoring remains contentious. 

On one hand, it may prevent addiction by limiting screen time((Ding et al., 2019); on the other hand, it could 

trigger psychological reactance by suppressing autonomy needs(Hefner et al., 2019). These contradictory findings 

highlight the necessity to clarify the pathways and boundary conditions of parental monitoring. 

Parent-child attachment may serve as a pivotal mechanism underlying these discrepancies. Attachment theory 

posits that secure parent-child attachments buffer negative emotions and reduce addictive behaviors(Seo et al., 

2016), whereas insecure attachment predisposes individuals to compensatory phone dependency for emotional 

fulfillment(Dwyer, 2005). As a key dimension of parenting, parental monitoring may indirectly influence mobile 

phone addiction by shaping attachment quality. Empirical evidence suggests that authoritative parenting enhances 

parent-child attachment, whereas excessive monitoring weakens relational bonds. Thus, parental monitoring may 

mitigate mobile phone addiction by fostering secure attachment. 

In summary, this study investigates parental monitoring as a predictor of adolescent mobile phone addiction, with 

parent-child attachment as a mediator, while examining the longitudinal stability of this mediating effect. The 

findings will contribute to a deeper understanding of the complex interplay between parental monitoring, parent-

child attachment, and adolescent smartphone addiction, offering valuable insights for developing effective 

intervention strategies. 

2. Method 

2.1 Participants 

This study employed a cluster convenience sampling method to recruit students from grades 7 to 11 (excluding 

grade 9) at a secondary school in Chongqing Municipality, China. After obtaining informed consent from school 

administrators, homeroom teachers, students, and their parents, questionnaires were distributed offline to 

participants. 

Data were collected across three waves: April 2023 (T1), October 2023 (T2), and May 2024 (T3). At T1, 

participants were in the second semester of grade 10. Initial recruitment included 566 students, but attrition 

occurred at T2 and T3 due to missing student IDs/names, absences, or transfers. After matching data across all 

three waves, 405 participants (attrition rate: 28.4%) were retained for analysis. 

The final sample comprised 176 males (43.50%) and 229 females (56.50%). Regarding family structure, 90.9% (n 

= 368) reported living in dual-parent households, while 14.60% (n = 59) came from single-parent households. 

Notably, 151 participants (37.30%) were identified as left-behind children (raised by relatives due to parental 

migration), and 254 (62.70%) were non-left-behind children 

2.2 Measures 

(1) Parental Monitoring Questionnaire 

Parental monitoring was assessed using an 8-item scale developed by Shek(2005), which measures parents’ 

awareness and concern about their children’s activities. Responses were recorded on a 5-point Likert scale (1 

=strongly disagreeto 5 =strongly agree), with higher scores indicating greater parental monitoring. The scale 

demonstrated good structural validity, and its Cronbach’s α coefficients across the three waves were 0.874, 0.905, 

and 0.888, respectively. 

(2) Parent-Child Attachment Scale (Short Version of IPPA) 

Parent-child attachment was evaluated using a 13-item short version of the Inventory of Parent and Peer 



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Attachment (IPPA), adapted by Li et al.(2021). The scale employs a 5-point Likert response format (1 =never 

trueto 5 =always true) and includes three dimensions:trust(e.g., “My parents respect my 

feelings”),communication(e.g., “I share my thoughts with my parents”),andalienation(reverse-coded). The scale 

showed strong reliability and validity, with Cronbach’s α coefficients of 0.921, 0.905, and 0.900 across waves. 

(3) Smartphone Addiction Scale (SAS-SV) 

Smartphone addiction was measured using the 10-item short version of the Smartphone Addiction Scale(Kwon et 

al., 2013). Participants rated items (e.g., “I feel impatient without my smartphone”) on a 6-point Likert scale (1 

=strongly disagreeto 6 =strongly agree), with higher total scores indicating more severe addiction. The scale 

exhibited excellent reliability, with Cronbach’s α values of 0.929, 0.961, and 0.829 at T1, T2, and T3, respectively. 

2.3 Common Method Bias 

Given the self-reported nature of the data, Harman’s single-factor test was conducted to assess common method 

bias across the three waves. Results showed that the first factor accounted for 29.48%, 38.10%, and 33.64% of the 

variance at T1, T2, and T3, respectively—all below the 40% threshold, indicating no severe common method bias. 

2.4 Data Analysis 

Data were analyzed using SPSS 27.0 for correlation analyses and the PROCESS macro(温忠麟 et al., 2005) to 

test cross-sectional mediation effects. Longitudinal mediation models were constructed with Mplus 8.3 to examine 

temporal pathways. 

3. Results 

3.1 Correlation Analysis 

As shown in Table 1, parental monitoring (T1–T3) was significantly and positively correlated with parent-child 

attachment at all time points: T1 (r= 0.442, p< 0.01), T2 (r= 0.357,p< 0.01), and T3 (r= 0.220, p< 0.01). These 

stable correlations suggest that higher parental monitoring corresponds to stronger parent-child attachment. 

Parental monitoring was negatively associated with smartphone addiction across waves: T1 (r= -0.155, p< 0.01), 

T2 (r= -0.159, p< 0.01), and T3 (r= -0.052, p< 0.05). While the T3 correlation was weaker, the overall trend 

supports the protective role of parental monitoring against addiction. 

Parent-child attachment also exhibited consistent negative correlations with smartphone addiction: T1 (r= -0.241, 

p< 0.01), T2 (r= -0.240, p< 0.01), and T3 (r= -0.165, p< 0.01). These results underscore the critical role of secure 

attachment in mitigating excessive smartphone use. 

 

Table 1. Correlation Test 

 

3.2 Longitudinal Mediation Model of Parent-Child Attachment Between Parental Monitoring and Mobile Phone 

Addiction 

To further examine the longitudinal mediating role of parent-child attachment in the relationship between parental 

monitoring and mobile phone addiction across three time points, a mediation model was constructed (see Figure 

1; only significant paths displayed). The model demonstrated acceptable fit indices: CFI = 0.936, TLI = 0.876, 

SRMR = 0.067, and χ²/df = 5.14, meeting standard criteria for model adequacy. Autoregressive effects for all three 

  1 2 3 4 5 6 7 8 9 

1 T1 Parental Monitoring --         

2 T2 Parental Monitoring 0.531** --        

3 T3 Parental Monitoring 0.407** 0.548** --       

4 T1 Parent-Child Attachment 0.442** 0.385** 0.287** --      

5 t2 Parent-Child Attachment 0.357** 0.472** 0.382** 0.635** --     

6 T3 Parent-Child Attachment 0.220** 0.341** 0.459** 0.456** 0.569** --    

7 T1 Smartphone Addiction -0.155** -0.223** -0.164** -0.241** -0.228** -0.158** --   

8 T2 Smartphone Addiction -0.159** -0.209** -0.208** -0.240** -0.292** -0.264** 0.562** --  

9 T3 Smartphone Addiction -0.052 -0.203** -0.124* -0.165** -0.270** -0.220** 0.424** 0.523** -- 

Note. *p<0.05, **p<0.01 



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variables were statistically significant with high coefficients, indicating strong temporal stability across 

measurements. 

After controlling for autoregressive effects and concurrent correlations, parental monitoring at Time 1 (T1) 

significantly and positively predicted parent-child attachment at Time 2 (T2), β = 0.056, p = 0.013. In turn, parent-

child attachment at T2 significantly and negatively predicted mobile phone addiction at Time 3 (T3), β = -0.18, p 

= 0.020. Bootstrap analysis with 5,000 resamples was conducted to test the longitudinal mediating effect of parent-

child attachment. The results revealed a significant indirect effect (ab = -0.018), with a 95% bias-corrected 

bootstrap confidence interval of [-0.037, -0.004] (p = 0.037), confirming the significant mediating role of parent-

child attachment in the longitudinal relationship between parental monitoring and subsequent mobile phone 

addiction. 

 

Figure 1. Longitudinal Mediation Test 

 

Notes: Standardized path coefficients are reported.Fit indices align with conventional thresholds (CFI/TLI ≥ 0.90, 

SRMR ≤ 0.08; Schreiber et al., 2006). Negative mediation effect suggests higher parental monitoring strengthens 

parent-child attachment, which in turn reduces later mobile phone addiction risk. 

 

4. Discussion 

The study revealed that parent-child attachment significantly mediated the relationship between parental 

monitoring and smartphone addiction during the first wave of measurement (T1). Over time, fluctuations in the 

strength of parent-child attachment—whether increasing or decreasing—were found to directly influence 

children’s smartphone usage behaviors. This underscores that parent-child attachment is not merely a stable 

emotional bond but also serves as a longitudinal predictor of children’s future psychological states and behavioral 

tendencies. Specifically, the stability and intensity of parent-child attachment exhibited a negative correlation with 

smartphone addiction risk, highlighting the critical need for parents to cultivate positive parent-child relationships 

as a preventive strategy. 

Furthermore, the manifestations of smartphone addiction were observed to evolve over time, suggesting the 

necessity for dynamically adjusted intervention strategies. Although no significant association between parent-

child attachment and smartphone addiction was detected at the third measurement wave (T3), the potential role of 

parental monitoring in mitigating addiction risk remained evident. Future research should prioritize investigating 

the temporal trajectories of smartphone addiction, particularly how developmental stages modulate the impact of 

attachment relationships. Adolescents in different age groups may interpret and respond to parental monitoring 

and support divergently, leading to age-specific variations in addiction susceptibility. Longitudinal monitoring and 

analysis of these dynamics will enhance our understanding of risk factors and inform tailored family- and school-

based interventions. 

The findings emphasize the importance of incorporating longitudinal perspectives into intervention design. 

Strategies targeting parental monitoring and parent-child attachment should be sustained and developmentally 

adaptive. For instance, as children mature, parents should gradually transition from direct supervision to fostering 

autonomy and decision-making skills. This shift from control to empowerment not only strengthens children’s 

self-efficacy but also preserves relational harmony, thereby reducing reliance on smartphones. Additionally, 

interventions should integrate age-appropriate educational programs and family activities to strengthen parent-

child bonds, complemented by efforts to enhance adolescents’ emotional regulation and social skills. Equipping 

youth with these competencies may promote healthier choices in navigating digital environments. 



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In conclusion, this longitudinal study elucidates the dynamic interplay among parental monitoring, parent-child 

attachment, and smartphone addiction. Parental monitoring was shown to predict the quality of parent-child 

attachment over time, with attachment serving as a significant mediator in the pathway to addiction. These findings 

underscore the pivotal role of family dynamics in preventing maladaptive behaviors and provide actionable 

insights for evidence-based interventions. Future investigations should explore the applicability of these 

relationships across diverse cultural contexts and developmental stages to refine intervention frameworks and 

maximize their efficacy. 

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