Chengdu, a major city in western China, consists of 12 districts, 3 counties, and 5 county-level cities. Covering a total area of 14,335 square kilometers (with urbanized areas accounting for approximately 6.6%), it has a resident population of about 16.6 million, 74.4% of whom live in urban areas. As the capital of Sichuan Province, Chengdu is home to 623 primary schools, 317 junior high schools, and 156 K-9 schools. The baseline data for this study were collected from December 23, 2019, to January 13, 2020, as part of the “Positive Youth Development Program,” supported by the Tian Jiabing Foundation under the Jockey Club’s Youth Enhancement Scheme. To ensure sample representativeness, Chengdu was first stratified into different regions based on its economic profile. One district was then randomly selected from each stratum, and within each selected district, a school was chosen at random from the available list of schools. Ultimately, five schools were selected for participation: one primary school, one junior high school, and three K-9 schools. Of these, one school was located in the city center, two in the southern suburbs, and two in the northern suburbs. To capture a comprehensive range of data, we invited all first- to ninth-grade students willing to participate, with no specific inclusion or exclusion criteria. A total of 8,968 children and adolescents aged 6 to 16 years were recruited from the selected schools through stratified sampling, with 8,968 questionnaires distributed and 8,816 valid responses collected, resulting in a response rate of 96.90%. Among the participants, there were 4,546 boys (51.56%) and 4,270 girls (48.44%). Findings revealed that internet addiction behaviors were generally more prevalent among male students than female students. This study aims to understand the current state of positive youth development and associated psychosocial and behavioral issues, as well as to assess the effectiveness of health education and promotion programs in schools in fostering youth development and addressing psychological and behavioral challenges. Further details regarding the study design and methods are available in other publications (17).

This study used a cross-sectional survey design, employing a self-administered questionnaire completed by students in their respective classrooms. Prior to the survey, the research team provided a detailed explanation of the study’s purpose to both students and their legal guardians, emphasizing data confidentiality and the voluntary nature of participation. Legal guardians were required to sign a written informed consent form. The study received approval from the Medical Ethics Committee of ** University (approval number: K2020025). The questionnaire was administered in a classroom setting, with two trained research assistants present to supervise and ensure that all students had ample time to complete the questionnaire independently. For younger students, the homeroom teacher read each question aloud and provided clarification when students had questions. After completion, research assistants collected the questionnaires and conducted a quick review to ensure completeness; students were asked to refill any missing responses when necessary. For more detailed information on the study design and methods, please refer to previous publications (17).

In this study, Epidata 3.1 software was used for double data entry and review to ensure data accuracy and consistency. Data analysis was conducted using SPSS 25.0 software. Descriptive statistics for categorical data were presented as frequencies and proportions, and differences between groups were compared using the chi-square test. For continuous data, were presented as (X̅ ± S) deviation, and independent sample t-tests were employed to compare means between two groups if they met the assumptions of normal distribution and homogeneity of variance. When normality was satisfied but variances were unequal, a corrected t-test was used.

To analyze the association between deviant behavior in the IA risk group and the normal group, Spearman’s rank correlation coefficient was used to assess the strength of the association between two variables, with results presented as r and p values. In addition, this study conducted a hierarchical regression analysis on IA scores, using age, gender, origin (urban/rural), and grade as control variables, and the five dimensions of family environment and delinquency scores as independent variables. The significance level for hypothesis testing was set at α =0.05.

The significance level for hypothesis testing was set at α = 0.05. To address the issue of alpha inflation caused by multiple testing, Bonferroni correction was applied to reduce the risk of Type I errors. All p values were evaluated after applying the multiple testing correction, with the significance level still set at α = 0.05.

Among 8,816 primary and secondary school students, 1,244 (14.11%) were identified as being at risk of IA. Analysis shows that the IA risk among male students is generally significantly higher than that of female students (p < 0.05), and the detection rate of IA is higher among rural students compared to urban students (p < 0.05). Further stratified analysis revealed that the detection rate of IA was significantly higher among middle school students compared to primary school students. Specifically, there was no significant fluctuation in the IA detection rate among students from grades 1 to 5 (p > 0.05). However, beginning from grade 6, the IA detection rate showed a significant increasing trend year by year (χ² = 202.081, p < 0.05) (Table 1).

The data indicate that the overall detection rate of deviant behaviors is 66.73%. Within the IA risk group, the detection rate of deviant behaviors is 85.21%, compared to 63.68% in the normal group. The difference between these groups is statistically significant (p < 0.05) (Table 2).

Among the 12 types of deviant behaviors, deceit (r = 0.201), running away from home (r = 0.215), and damaging others’ property (r = 0.209) show a weak correlation with the risk of IA, with these differences being statistically significant (p < 0.05) (Table 3).

The data indicate that the average family function score for the IA risk group is 80.78 ± 25.42, which is significantly higher than the average score for the normal group (60.91 ± 22.84). The difference between the two groups is statistically significant (Z = −25.898, p < 0.05). The scores for the five dimensions: Family Interactions, Communication, Conflict and Harmony, Parental Care, and Parental Control also show statistically significant differences between the two groups (p < 0.05) (Table 4, higher scores indicate poorer family functioning).

The study conducted a hierarchical regression analysis on IA scores, using age, gender, origin (urban/rural), and grade as control variables, while the five dimensions of family environment and delinquency scores were included as independent variables. The results indicated that among demographic variables, being male, grade level, and rural background positively predicted internet addiction. After controlling for demographic variables and other factors, all five dimensions of family environment and delinquency scores positively predicted internet addiction. Specifically, the change in R² was 8.7% for mutual concern among family members and 9.7% for student delinquency behavior. Together, family environment and delinquency behavior explained 22.1% of the variance (Table 5).

The results of this study indicate that the detection rate of individuals at risk for IA is 14.11%. This finding is consistent with the results obtained by Sun MX and Li H (24, 25) using different questionnaires on similar populations, highlighting the significant concern regarding IA among primary and secondary school students in China. Currently, research on IA, both domestically and internationally, primarily focuses on adolescents aged 12 and above, as well as university students. There is relatively less research and intervention targeting IA in primary school students, despite adolescents being a high-risk group for IA (26–28). It is important not to overlook the dependency on the internet among younger primary and secondary school students, and even those in lower grades.

The study’s findings show that the incidence rate of IA is higher in secondary school students compared to primary school students. Although the detection rate of IA risk does not fluctuate significantly among primary school students in grades 1 to 5, the lack of clear normative data prevents us from accurately assessing the addiction risk levels in primary school students. Some studies have suggested (12, 29) that deviant behavior levels follow an inverted U-shaped trajectory with age, where the incidence of deviant behaviors increases from childhood to early adolescence and then decreases, regardless of different upbringing methods. This study found that the incidence rate of IA increases year by year after grade 6. Whether the trend of IA behavior aligns with the occurrence of deviant behaviors if the survey scope is extended to high school and university students requires further research for confirmation.

This study indicates that the detection rate of IA risk is significantly higher among male students compared to female students (15.71% vs. 12.41%). Previous research has indicated (30) that boys are more likely to engage in massively multiplayer online role-playing games (MMORPGs) and violent games, whereas girls’ online activities tend to focus more on casual games, social interactions, messaging, and online shopping. However, some domestic scholars have found (24) that there is no significant gender difference in the IA risk among primary and secondary school students. With the rapid advancement of computer network technology, the integration of intelligent network systems into daily life has become increasingly close. Particularly during the COVID-19 pandemic, when people were largely confined to their homes and schools shifted their educational activities online, this reliance on the internet was further intensified. During adolescence, girls, with their heightened self-esteem and growing self-awareness, often prefer interacting and communicating with their peers, hoping to find a sense of belonging within peer groups as a means to maintain their self-esteem. The virtual online world holds a strong appeal for adolescent girls who are in a rebellious phase and are particularly curious. Moreover, the convenience of smart services might make it easier for girls with weaker self-control to fall into IA, becoming immersed in the virtual world. However, these observations are based on behavioral tendencies and still require further research to confirm the underlying mechanisms. Thus, whether there are significant gender differences in IA among primary and secondary school students requires more in-depth research evidence for validation.

The results of this study indicate a significant difference in the detection rate of IA risk between urban and rural students (12.9% in urban areas, 16.3% in rural areas), consistent with the findings of Wang et al. (31). However, earlier studies on urban–rural differences in IA have yielded inconsistent results (32, 33). It is generally assumed that the higher network penetration and stability, along with the abundance of network resources and devices in urban areas, would result in a higher addiction risk. Recent studies, however, have shown (9, 31, 34) that the internet penetration rate among rural minors has reached 96.5% (compared to 97.5% for urban minors). The widespread use of smartphones and the absence of parents contribute to rural left-behind children developing compensatory behaviors and a “let it be” attitude, leading to their immersion in the internet. The lack of family education and unreasonable command-style communication further exacerbates this issue, causing rural left-behind children to exhibit rebellious behavior in response to the lack of familial affection.

Deviant behaviors can negatively impact students’ future development, family relationships, academic performance, and even societal stability, potentially leading to significant economic costs. This study found that the detection rate of deviant behaviors was 63.68% in the normal IA group, compared to 85.21% in the high-risk group. Although the exact mechanisms linking IA and deviant behaviors remain unclear, previous research has suggested that factors such as sensation-seeking, peer influence, and IA are positively correlated in pairs, with peer effects and deviant behaviors being considered significant contributors to IA (24, 29). Koot (35) propose that adolescents often view the internet as a “safe haven” in social interactions, especially when facing rejection or isolation in real life. IA may increase adolescents’ susceptibility to negative peer influence, thereby fostering deviant behaviors. Furthermore, emotional and behavioral issues may serve as bridges between IA and deviant behaviors. Ken and Guan (36) highlighted that internalizing problems (e.g., anxiety, depression), when interacting with family dynamics, could heighten psychological stress in adolescents, making them more inclined to seek escape in online environments, which can lead to IA and subsequent externalizing behaviors (e.g., aggression). Our findings showed a weak correlation between IA risk and certain deviant behaviors—such as deceit, running away from home, and property damage (r = 0.201, 0.215, and 0.209, respectively)—further suggesting that IA may be a predictive factor for certain deviant behaviors. This weak correlation may be attributed to differences in IA classification criteria and the complex psychological and environmental factors underlying deviant behaviors. Future research should further explore the role of family functioning, internalizing, and externalizing issues as mechanisms connecting IA and deviant behaviors, to deepen our understanding of the complex interactions between these behaviors.

The findings of this study indicate that individuals at high risk for IA score significantly lower in family functioning than those at normal risk, suggesting that poor family functioning may be a contributing factor to IA. Existing research highlights the importance of emotional support, communication quality, and moderate parental control in influencing adolescent behavior. Ken and Guan (36) noted that a lack of emotional support and family conflicts can intensify adolescents’ internalizing issues, such as anxiety and depression, which in turn may lead them to rely on the internet as a coping mechanism. Additionally, Jo et al. (11) found that the frequency and duration of internet use are closely linked to IA, as frequent internet use can serve as a means for adolescents to escape real-life challenges. Our study further reveals that mutual care among family members, open communication, harmonious conflict resolution, parental warmth, and appropriate parental control are significant factors influencing IA risk, with communication quality having a particularly notable impact. Although frequent communication can enhance parent–child relationships, if the content of these interactions is filled with conflict or negativity, it may inadvertently increase IA risk. This aligns with findings by Ken and Guan, who observed that negative family interactions could drive adolescents toward excessive internet use as an escape from reality (36). Furthermore, high levels of family conflict and an inhospitable family environment may prompt adolescents to use the internet as a way to vent emotional stress, thus exacerbating IA risk. Excessively strict parental control can also foster adolescent resistance, further increasing the likelihood of IA. Koot (35) found that in adolescents at risk for IA, moderate parental monitoring and emotional support serve as protective factors against excessive internet use.

In summary, this study highlights the critical role of family functioning in providing emotional support and behavioral management for adolescents at high risk for IA. Future interventions should focus on enhancing family emotional support and improving communication quality to reduce IA risk, rather than solely relying on restrictions on screen time. By detailing specific aspects of family functioning, this study provides a theoretical foundation for developing more targeted family-based interventions.

The findings of this study hold significant practical implications, particularly for the prevention and intervention of IA and related problematic behaviors among adolescents.

In conclusion, this study suggests specific intervention measures, including enhancing the quality of family communication, reducing family conflict, implementing balanced parental guidance, and providing targeted support for high-risk groups. Additionally, promoting positive youth development through school and community activities, combined with managing internet use through structured time limits, can help reduce IA behaviors. These measures can not only assist adolescents in better managing their internet habits but also mitigate the psychological and behavioral issues arising from excessive internet use.