A Randomized Controlled Trial to Evaluate the Effectiveness of a Theory of Planned Behavior-Based Educational Intervention in Reducing Internet Addiction Among Adolescent Girls in Southern Iran

Abstract

Internet addiction among adolescents has emerged as a significant global health issue, contributing to social isolation, academic difficulties, and emotional disorders, with excessive use of social networks further intensifying these challenges. This study evaluated the effectiveness of an educational intervention grounded in the Theory of Planned Behavior (TPB) in reducing Internet addiction and enhancing academic performance among adolescent girls in southern Iran. A randomized controlled trial was conducted with 370 female students aged 15–16 years, selected through two-stage cluster random sampling and divided equally into intervention and control groups. The intervention group participated in a five-week TPB-based program, incorporating lectures, group discussions, and parent–teacher meetings, while the control group received unrelated health education. Data were collected using the Yang Internet Addiction Test (IAT), a TPB-based questionnaire, and academic performance scores (grade point average, GPA) before and after the intervention. A repeated measures ANOVA revealed significant improvements in attitudes, subjective norms, perceived behavioral control, and intentions to reduce Internet use (p < 0.001). Internet addiction scores significantly declined, and GPA improved in the intervention group compared to the control group (p < 0.001, Cohen’s d = 0.950). The findings support TPB-based interventions as effective tools for reducing Internet addiction and improving academic outcomes among adolescents. This study was registered with the Iranian Registry of Clinical Trials (IRCT20131014015015N18).

1. Introduction

The rapid spread of Internet usage in recent years has created significant concern about Internet addiction, especially among adolescents. It is linked to numerous adverse outcomes such as social conflicts, physical health problems, academic struggles, and emotional, as well as behavioral, abnormalities [1,2,3]. In 2023, approximately five billion people worldwide, representing 67.9% of the global population, were Internet users [4]. In Iran, there were 69.83 million Internet users in January 2023, accounting for 68.5% of the population [4].

While the Internet offers substantial benefits, such as providing adolescents with educational opportunities and facilitating social interaction [5], excessive and addictive use poses significant risks. Excessive engagement in online activities, including social networking and gaming, is linked to negative outcomes such as reduced social and academic engagement, lower physical activity, increased aggression, social isolation, family conflicts, and even personality disorders [1,2,6]. Research has shown that Internet addiction is significantly associated with various physical and mental health problems including malnutrition, sedentary behavior, anxiety, depression, and substance misuse, including misuse of alcohol and tobacco [1,2,3,6,7,8].

1.1. Current Status of Internet Addiction

Internet addiction is now widely recognized as a growing global health issue [2,9]. The prevalence of Internet addiction among adolescents ranges from approximately 10% to over 60%, depending on diagnostic criteria and regional context [10,11]. The World Health Organization has emphasized the urgency of targeted interventions to mitigate problematic Internet use among adolescents, highlighting the need for age-appropriate, gender-sensitive, and culturally tailored strategies to promote healthier digital habits [12].

In the Iranian context, approximately 17–45% of adolescents are at risk of Internet addiction [13]. Gender-specific patterns have emerged; male adolescents are more prone to online gaming addiction, while female adolescents are particularly vulnerable to social media overuse, often with more severe psychological outcomes such as depression, anxiety, and social isolation [14].

Female adolescents in southern Iran face additional socio-cultural stressors that may heighten their vulnerability. These include stricter parental monitoring, limited outdoor recreational opportunities, and intense academic pressure—all of which can contribute to excessive Internet reliance as a coping mechanism [15,16]. Research has shown that unsatisfactory family communication and unmet psychological needs significantly predict Internet addiction in this demographic [16].

1.2. Rationale for Current Study

Despite the alarming prevalence rates and severe consequences associated with Internet addiction among adolescents in southern Iran [13], there is a scarcity of theory-based interventions specifically designed to address their unique needs and socio-cultural context [17]. Previous interventions have often been generic in nature, lacking cultural sensitivity and gender-specific considerations that are crucial in this conservative region. Furthermore, the relationship between Internet addiction and academic performance—a priority concern for educational stakeholders and parents—remains underexplored in this population [17].

This study aimed to address these critical gaps by developing and testing a comprehensive intervention adapted for the target population and based on the Theory of Planned Behavior (TPB). By focusing specifically on female adolescents, the research sought to explore the potential of evidence-informed strategies that may be suitable for implementation within educational settings to help reduce Internet addiction and support improved academic outcomes in this vulnerable population.

2. Literature Review

2.1. Educational Interventions for Reducing Internet Addiction

Internet addiction has become a growing public health concern, particularly among adolescents. Educational interventions have been widely recognized as a key strategy for reduction, with increasing evidence supporting the use of theory-driven models over general awareness campaigns [17,18]. Various theoretical approaches have been applied to address Internet addiction, including Cognitive Behavioral Therapy (CBT), the Health Belief Model (HBM), and the BASNEF model.

Studies examining CBT-based interventions have shown promising results in reducing Internet addiction symptoms and improving psychological well-being among college students [19,20]. However, these interventions often require specialized professionals for implementation, potentially limiting their scalability in school settings [21]. Similarly, HBM-based educational interventions have demonstrated improvements in knowledge and perceptions related to Internet addiction but have shown limited impact on actual behavioral change [22].

The BASNEF model has shown promise in addressing Internet addiction among adolescents in the Iranian context. Interventions based on this model have resulted in significant improvements in behavioral constructs and reductions in Internet use time [23]. Recent research has also highlighted the importance of self-regulatory approaches in addressing technology addiction among adolescents, emphasizing the promotion of self-regulative beliefs to counter problematic tendencies [24].

2.2. The Theory of Planned Behavior in Behavioral Change Interventions

Among the theoretical models available, the Theory of Planned Behavior (TPB), developed by Ajzen, has emerged as a promising framework for addressing Internet addiction in adolescents [25,26]. TPB proposes that a person’s behavior is primarily influenced by their intention to perform it, an intention that depends on three components: attitude toward the behavior, subjective norms, and perceived behavioral control (PBC) [27].

The TPB framework is particularly suitable for addressing Internet addiction among adolescents for several reasons. Firstly, it highlights the role of attitude toward the behavior, which can be shaped through educational interventions by raising knowledge about risks and promoting healthier digital habits. Secondly, it emphasizes subjective norms, recognizing the influence of significant others—such as parents, peers, and teachers—on adolescents’ Internet use behaviors. Thirdly, it addresses perceived behavioral control, focusing on adolescents’ perceived ability to regulate their Internet use despite challenges. Finally, it offers a structured and practical framework that can be implemented in school environments without specialized clinical expertise [28].

The TPB model has been successfully applied in various studies of digital behavior. Shahzalal and Adnan (2022) modified the TPB to predict intention to use social media responsibly, identifying attitude, self-control, and prosocial norms as key predictors [25]. Zamanian et al. (2020) evaluated the effect of TPB-based education on computer game dependence among high school male students in Iran, demonstrating decreased addiction and improved knowledge, attitude, and behavioral intention [26].

2.3. Limitations of Existing Studies and Research Gaps

Despite the growing body of research on Internet addiction interventions, several important gaps remain:

1. Most studies focus on university students rather than younger adolescents [17,19,20,25];

2. There has been limited involvement of parents and teachers in interventions [19,20,24];

3. Academic performance is rarely considered as an intervention outcome [19,23,24];

4. Few interventions are tailored specifically for female adolescents [19,20,24,25,26];

5. Some studies conducted follow-ups immediately after the intervention [23].

The Theory of Planned Behavior appears to be a promising framework for addressing these gaps, given its comprehensive focus on attitudinal, normative, and perceived control factors that shape behavioral intentions. Evidence suggests that the Theory of Planned Behavior offers an adaptable and culturally sensitive framework for school-based interventions targeting technology-related behavioral issues among adolescents in Iran [26].

3. Purpose, Research Questions, and Hypotheses of the Study

Given the critical developmental stage of adolescence and the severe consequences of Internet and social media addiction, this study aimed to design and implement a theory-based educational intervention to reduce Internet addiction among adolescent girls in south of Iran. Building on the strengths of the TPB and addressing the identified research gaps, this research sought to investigate whether a TPB-based educational program could reduce Internet addiction and improve academic performance.

Based on the Theory of Planned Behavior framework and previous research findings, the following research questions guided our study:

1. Can a TPB-based educational intervention effectively reduce Internet addiction among adolescent girls in southern Iran?

2. Does this intervention positively impact attitudes toward reducing Internet use, subjective norms, and perceived behavioral control?

3. Will the intervention lead to improved academic performance (GPA) among participants?

4. Are the effects of the intervention maintained at the two-month follow-up assessment?

Furthermore, we made the following hypotheses:

1. The TPB-based educational intervention will significantly improve attitudes toward reducing Internet use, strengthen subjective norms about appropriate Internet usage, and enhance perceived behavioral control over Internet use among adolescent girls. This hypothesis is supported by Zamanian et al.’s findings that TPB-based interventions effectively modified attitudes and perceived control related to digital behavior among Iranian male adolescents [26].

2. The improvements in TPB constructs will lead to significant reductions in Internet addiction scores among participants in the intervention group compared to the control group, consistent with previous studies demonstrating the predictive relationship between TPB constructs and addictive behaviors [26,29].

3. Reduced Internet addiction will be associated with improved academic performance (GPA) among adolescents in the intervention group, as suggested by prior research documenting the negative correlation between Internet addiction and academic achievement [30,31].

4. The positive effects of the intervention on TPB constructs and Internet addiction will be maintained at the two-month follow-up assessment, similar to effects reported in comparable short-term studies [26], while addressing limitations of some previous interventions with shorter follow-up periods [19,23].

4. Method

4.1. Ethical Considerations

This study was registered with the Iranian Registry of Clinical Trials (IRCT20131014015015N18) on 27 September 2018. Ethical approval was obtained from the Ethics Committee of Shiraz University of Medical Sciences (Approval Code: IR.SUMS.REC.1396.176) on 18 February 2018. The study was conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki (1975, revised 2013). Written informed consent was obtained from the parents of all participants prior to data collection.

4.2. Study Design and Population

This randomized controlled trial was conducted in Khuzestan, Iran, in 2018. The study population consisted of 15–16-year-old female students (8th and 9th grade) in schools in Behbahan, Khuzestan province, Iran. This age group represents a critical developmental period for establishing Internet use patterns, and recent evidence suggests an increasing vulnerability to problematic Internet use among female adolescents in this cultural context [14]. The sample size was calculated to be 170 per group, considering a confidence level of 95% (α = 0.05) and a statistical power of 0.95, based on a similar study [32]. To account for a possible 10% dropout rate, 187 participants were initially enrolled in each group. A two-stage cluster random sampling approach was employed. First, four out of six eligible schools were randomly selected using a computer-generated randomization list. Second, these schools were assigned to either intervention or control groups (two schools per group) using simple randomization with a 1:1 allocation ratio from the same randomization list. To ensure allocation concealment, an independent individual not involved in the intervention conducted the randomization process. Participants were masked to their group assignment and only informed that they were participating in an educational study. Following allocation, all female students in grades 8 and 9 within the selected schools were screened, and those who met the inclusion criteria were enrolled in the study. Due to resource constraints, the same person delivered the intervention and enrolled participants. Baseline demographic characteristics were compared between groups to verify randomization adequacy.

The inclusion criteria were female students in the 8th and 9th grades who lived in Behbahan and had access to a smartphone, computer, tablet, and the Internet. Exclusion criteria included unwillingness to continue participation or missing more than one training session. After exclusions, 185 participants remained in each group, and data from these students were analyzed. Participant recruitment and attrition details are presented in the CONSORT flow diagram (Figure 1).

4.3. Study Tools and Measurements

Three questionnaires were employed in this study.

4.3.1. Demographic Information Questionnaire

The demographic information questionnaire was an 8-item questionnaire covering age, the first age of using the Internet, parental occupation and education, and academic performance (in the first and last semesters’ grade point average). In the context of parental education, we categorized education levels as “Academic” (referring to those who had completed higher education degrees such as Associate’s degree, Bachelor’s degree, or higher) and “Non-academic” (referring to those with high school diploma or lower educational attainment). This classification was used to assess the potential influence of parental educational background on adolescents’ Internet usage patterns.

The selection of demographic variables in this study, including age, first age of Internet use, parental occupation and education, and academic performance, was based on existing literature documenting their relevance to adolescent Internet use patterns. Research has shown that earlier exposure to the Internet is associated with a higher likelihood of developing problematic use later in adolescence [33]. Additionally, parental education and occupation are known indicators of socioeconomic status, which influences both access to digital devices and parental mediation strategies [34,35]. Academic performance was included as both a predictor and potential outcome of Internet addiction, given the bidirectional relationship reported in longitudinal studies [30,31].

4.3.2. Yang Internet Addiction Test (IAT)

The Yang Internet Addiction Test (IAT) is a standardized tool used to assess the level of Internet addiction among participants [36]. It consists of 20 items rated on a 5-point Likert scale (0 = not at all to 5 = very much), with total scores ranging from 0 to 100. Higher scores indicate a greater level of Internet addiction. The Yang Internet Addiction Test (IAT) is a validated tool for assessing Internet addiction, with strong psychometric properties. It shows excellent internal consistency (Cronbach’s alpha: 0.90–0.93), high test–retest reliability (Spearman’s correlation: 0.83), and strong construct and convergent validity. Its robustness across diverse cultural contexts underscores its reliability and utility in clinical and research settings [37]. The Persian version demonstrated alpha coefficients of 0.91 in a previous validation study [38]. For the Yang Internet Addiction Test, the Cronbach’s alpha coefficient in our sample was α = 0.89, confirming its reliability for use in this population.

4.3.3. Theory of Planned Behavior (TPB) Questionnaire

This researcher-made questionnaire is based on the constructs of the TPB and is designed specifically to measure attitudes, subjective norms, perceived behavioral control (PBC), and intentions related to Internet addiction. Following the TPB questionnaire design guidelines [39], this tool consists of 13 questions for attitude, 8 for subjective norm, 11 for PBC, and 2 for intention. The questionnaire measured students’ self-reported perceptions across all constructs: attitudes toward reducing Internet use, perceptions of important others’ expectations (subjective norms), perceived ability to control Internet use (PBC), and intentions to reduce Internet addiction. All items were scored on a 5-point Likert scale ranging from “strongly agree” to “strongly disagree”. Both face and content validity were assessed by a panel of 10 experts in health education and health promotion. Items with a Content Validity Ratio (CVR) greater than 0.75 and a Content Validity Index (CVI) greater than 0.79 were retained, following the guidelines of Lawshe and the recommendations of Waltz and Basel [40,41]. Reliability analysis using Cronbach’s alpha was conducted on study data, yielding values of α = 0.87 for the attitude subscale, α = 0.82 for the subjective norms subscale, α = 0.85 for the perceived behavioral control subscale, and α = 0.79 for the intention items.

4.4. Educational Intervention

This study was designed as a 5-week educational intervention based on TPB. Pre-test data and demographic information were collected 1 week before the start of the educational sessions during the orientation day. Post-test data were collected to evaluate the immediate and follow-up effects of the intervention at 1 month and 2 months after the completion of the sessions, respectively. For both the intervention and control groups, these assessments were conducted at identical time intervals following the completion of their respective educational sessions to ensure comparability.

Academic performance was measured using students’ grade point averages (GPA) at two time points: at baseline using first semester grades (collected at the beginning of the study, prior to intervention) and at follow-up using final semester grades. The timing of the study was designed so that the 2-month post-intervention assessment occurred 1 month after the end of the second semester’s final exams, which allowed us to evaluate the intervention’s effects outside of the examination period. This timing provided sufficient opportunity for any changes in Internet usage patterns to potentially impact academic performance, while avoiding the confounding influence of exam-related behaviors. GPA was obtained from official school records with appropriate permissions, using the standard 0–20 Iranian grading scale. Specifically, in the Iranian educational system, semester GPA reflects the weighted average of multiple assessments, including midterm and final exams, quizzes, and class participation.

Participants in the control group received a three-session educational program on adolescent health (each session lasting 45 min), which was unrelated to the study objectives. In contrast, the intervention group participated in a five-session educational program, with each session lasting 45 min per week. The intervention content was developed based on Young’s Internet Addiction Treatment Protocol [42] and adapted to address the gender-specific needs of female adolescents in southern Iran’s cultural context. We utilized a combination of evidence from literature on Internet use patterns among Iranian adolescents and baseline assessment data to tailor the educational content appropriately. Health education specialists were consulted to ensure the cultural relevance and age-appropriateness of the materials, with particular attention to aspects such as appropriate online behavior, family communication patterns, and culturally acceptable alternative activities.

The structure of the sessions was as follows:

• Session 1: Raising knowledge about the time spent online, the nature of Internet addiction, and its effects on different aspects of life, with an emphasis on the attitude construct.
• Session 2: Time management strategies and planning to change Internet usage patterns, focusing on both the attitude and perceived behavioral control constructs. Participants received a booklet based on the book “Net Alert!” [43], which focuses on strategies for overcoming Internet addiction. The booklet was adapted from the original parenting resource to provide adolescents with age-appropriate self-help techniques, Internet usage monitoring tools, and effective time management strategies to develop healthier online habits.
• Session 3: A meeting for parents and teachers, highlighting the subjective norm construct. Parents were educated on effective communication with their children and alternative activities to replace excessive social media use, such as sports.
• Session 4: Discussing emotional and behavioral dynamics in cyberspace, including the risks of chatting with strangers and the dangers of sharing personal photos. The discussion also covered issues related to sexting, using group discussions and lectures.
• Session 5: Reviewing the previous sessions and introducing new activities (such as sports or English and art classes) to replace Internet use. Extracurricular English and art classes were also organized for both schools.

To ensure ethical considerations, the educational content provided to the intervention group was also offered to the control group after the completion of the study.

4.5. Statistical Analysis

The normality of the data was measured using the Shapiro–Wilk test. Chi-square and independent sample t-tests were used to compare demographic variables in the intervention and control groups. A repeated measures ANOVA was used to compare quantitative variables between intervention and control groups across three time points (Time Point 1—baseline; Time Point 2—post-intervention; Time Point 3—2-month follow-up), where group was considered as a between-subject factor and time was considered as a within-subject factor. Effect sizes for between-group comparisons were calculated using Cohen’s d, and for within-group comparisons (across time points), partial eta squared was used to quantify the magnitude of changes.

To specifically examine whether changes were maintained over time (short-term vs. post-intervention), planned contrasts and pairwise comparisons with Bonferroni correction were conducted between consecutive time points (Time Point 1 vs. Time Point 2; Time Point 2 vs. Time Point 3) for each group separately (intervention and control groups). This approach ensured that both short-term effects and post-intervention effects were clearly evaluated. Additionally, effect sizes (Cohen’s d) were calculated to quantify the magnitude of changes between time points and between groups.

Time-by-group interaction effects were analyzed to determine whether the patterns of change differed significantly between the intervention and control groups, allowing us to assess the effectiveness of the intervention relative to the control. The significance level was set at less than 0.05. All statistical analyses were performed using SPSS version 21 (IBM Corp., Armonk, NY, USA).

5. Results

In this study, 370 female students were enrolled in the control and intervention groups. In the demographic variables, no significant difference was observed between the intervention and control groups (

Table 1. Comparison of demographic variables in the intervention and control groups.

Variable		Intervention (N = 185)	Control (N = 185)	p
Age (years)		14.50 ± 0.50	14.54 ± 0.49	0.91 a
The first age of using the Internet (years)		10.30 ± 1.25	10.30 ± 1.30	0.46 a
Father’s Education (%)	Academic	111 (60.0) 74 (40.0)	118 (63.8) 67 (36.2)	0.31 b
	Non-academic
Mother’s Education (%)	Academic	117 (69.2) 68 (36.8)	128 (63.2) 57 (30.8)	0.36 b
	Non-academic
Father’s Job (%)	Employee	52 (28.1)	57 (30.8)	0.83 b
	Nongovernmental job	131 (70.8)	126 (68.1)
	Unemployed	2 (1.1)	2 (1.1)
Mother’s Job (%)	Housewife	162 (87.6) 23 (12.4)	161 (87.0) 24 (13.0)	0.87 b
	Employee

Note: Data are means ± SDs; ^a p-values were calculated using independent t-test; ^b p-values were calculated using Chi-square test; p-values were adjusted using the Bonferroni correction for multiple comparisons.

).

A repeated measures ANOVA was conducted to evaluate changes in the constructs of TPB and behavior (Internet addiction). This analysis indicated no significant differences in the examined variables between the two groups at baseline (p > 0.05). However, after the intervention, significant changes were observed over time in all constructs, including attitude, subjective norms, perceived behavioral control, intention, and behavior (Internet addiction) (p < 0.001, η_p² > 0.2, indicating a large effect size). Furthermore, the interaction between time and group was significant for all constructs (p < 0.001, η_p² > 0.2). Between-group analysis also revealed that the mean scores of all constructs improved significantly in the intervention group compared to the control group (p < 0.001, η_p² > 0.2). For detailed results, refer to

Table 2. Changes in TPB constructs and behavior across three time points.

Construct	Group	Time M (SD)			Within Subjects Effects		p
		Time Point 1 (Baseline)	Time Point 2 (One Month After Intervention)	Time Point 3 (Two Months After Intervention)	p	ηp2	Time	Group	Time × Group
Attitude	Intervention	25.31 ± 6.613	58.22 ± 4.252	54.40 ± 4.580	<0.001	0.985	<0.001	<0.001	<0.001
	Control	25.57 ± 6.609	26.59 ± 7.509	25.63 ± 7.442
Between Subjects Effects		0.706	<0.001	<0.001	<0.001	0.722
Subjective norms	Intervention	21.81 ± 4.747	28.32 ± 5.737	28.58 ± 5.810	<0.001	0.295	<0.001	<0.001	<0.001
	Control	21.11 ± 4.772	23.16 ± 5.921	23.02 ± 6.039
Between Subjects Effects		0.160	<0.001	<0.001	<0.001	0.150
Perceived Behavioral Control	Intervention	23.71 ± 4.205	42.44 ± 3.084	40.44 ± 3.084	<0.001	0.583	<0.001	<0.001	<0.001
	Control	23.32 ± 5.220	22.03 ± 5.903	22.52 ± 6.252
Between Subjects Effects		0.430	<0.001	<0.001	<0.001	0.780
Intention	Intervention	3.55 ± 1.668	7.49 ± 1.239	6.23 ± 2.132	<0.001	0.284	<0.001	<0.001	<0.001
	Control	3.84 ± 2.188	4.06 ± 2.476	4.13 ± 2.498
Between Subjects Effects		0.158	<0.001	<0.001	<0.001	0.243
Internet Addiction (Behavior)	Intervention	66.99 ± 12.981	39.32 ± 5.904	40.00 ± 7.093	<0.001	0.711	<0.001	<0.001	<0.001
	Control	66.69 ± 7.371	67.29 ± 7.023	67.33 ± 2.351
Between Subjects Effects		0.783	<0.001	<0.001	<0.001	0.664

Note: Data are means ± SDs; p-values were calculated using repeated measures ANOVA and adjusted for multiple comparisons using the Bonferroni correction.

.

As shown in

Table 3. Comparison of academic performance scores (GPA) between intervention and control groups.

Group	Before Intervention (First Semester)	After Intervention (Second Semester)	p	Cohen’s d
Intervention	17.12 ± 2.668	18.75 ± 0.912	<0.001	0.835
Control	16.97 ± 2.518	17.15 ± 2.189	0.448	0.055
p	0.589	<0.001
Cohen’s d	0.056	0.950

Note: Data are means ± SDs; p-values were calculated using independent t-test and adjusted for multiple comparisons using the Bonferroni correction.

, no significant difference was observed between the intervention and control groups in academic performance scores at baseline (p = 0.589, Cohen’s d = 0.056). However, after the intervention, the academic performance of the intervention group improved significantly compared to the control group (p < 0.001, Cohen’s d = 0.950). Additionally, within-group comparisons indicated a significant improvement in the intervention group over time (p < 0.001, Cohen’s d = 0.835), while no significant change was observed in the control group (p = 0.448, Cohen’s d = 0.055).

6. Discussion

This study aimed to examine the effectiveness of a TPB-based educational intervention using information from the Young’s Internet Addiction Treatment Protocol in reducing Internet addiction among adolescents. The baseline data showed no significant difference between the intervention and control groups in any of the variables examined. Following the intervention, significant improvements were observed in all TPB constructs (attitudes, subjective norms, perceived behavioral control, and intention) as well as in the primary behavioral outcomes (Internet addiction reduction and academic performance). The improvements in TPB constructs demonstrate the successful implementation of our intervention, while the behavioral changes represent the main impacts of our program. Below, we discuss the mechanisms through which our intervention influenced each construct and outcome.

• Attitude

The intervention group demonstrated a significantly more favorable attitude toward reducing Internet use compared to the control group, with a very large effect size. This change was likely the result of a combination of educational strategies, including raising knowledge about the negative consequences of excessive Internet use (Session 1), teaching time management skills (Session 2), and encouraging self-reflection on personal behavior (Session 1 and 2). Previous research also found attitude-based learning interventions successfully changed Internet-related behavior [23,26]. Additionally, the interactive structure of the program—particularly the group discussions—may have enabled participants to empathize with their peers’ experiences, thereby influencing their emotional attitudes toward excessive use. This interpretation is supported by Schoeps et al. (2020), who found that social interactions and peer attachment foster empathy, which in turn mediates emotional adjustment and behavioral outcomes in adolescents [44]. These changes align with the Theory of Planned Behavior’s conceptualization of attitude, which encompasses both cognitive beliefs and emotional responses toward a behavior [27].

• Subjective Norms

After the intervention, a significant increase in subjective norms was observed in the intervention group with a large effect size. Although this increase may partly be attributed to the involvement of parents and teachers in the third session, consistent with previous findings on the role of social factors in shaping adolescent behavior [45,46,47], it is important to note that the current study did not directly investigate the reasons for this increase. In collectivist cultures such as Iran, the presence of influential figures like parents and teachers can contribute to the development of stronger social perceptions [48,49]. Additionally, delivering the intervention through a community-based approach in schools—which are themselves integral parts of adolescents’ social environments—may have helped reinforce their understanding of the social norms related to the target behavior. In such settings, daily interactions with peers and school staff can strengthen the belief in collective expectations and influence students’ perceptions of subjective norms [50]. Furthermore, part of this change may be explained by the interactive nature of the constructs within the TPB. Given the concurrent improvements in attitude and perceived behavioral control, it is plausible that the interaction between these constructs may have indirectly influenced normative beliefs [51]. However, these interrelations were not statistically examined in the current study and require further investigation in future research.

• Perceived Behavioral Control (PBC)

Following the intervention, a significant improvement in perceived behavioral control was observed in the intervention group, with a large effect size. This notable enhancement may be attributed to several effective components of the intervention program. The time management and Internet use reduction strategies delivered in Sessions 2 and 5 provided participants with practical skills necessary for regulating their online behaviors. Previous studies have shown that teaching individuals specific strategies to cope with behavioral challenges can strengthen their perceived behavioral control and, in turn, positively influence their actions [52,53]. Although our study did not identify which components of the intervention had the greatest impact on perceived behavioral control, the strong observed effect size suggests that segments focusing on practical skill-building were particularly influential. Additionally, this improvement may have been shaped by the interaction between TPB constructs, such that concurrent changes in attitudes and subjective norms also contributed to enhancing participants’ sense of control.

• Intention

The results showed a significant increase in the intention to reduce Internet use among the intervention group, with a large effect size, while no such change was observed in the control group. According to the Theory of Planned Behavior, attitudes, subjective norms, and perceived behavioral control are theorized to influence behavior through the mediating role of intention [54]. The concurrent improvement in all TPB constructs in our study is consistent with this theoretical framework, although we did not statistically test these mediational pathways. Previous research has shown that theory-based educational approaches are effective in influencing behavioral intentions related to various health behaviors. For instance, studies involving adolescents have demonstrated that interventions focusing on awareness, social influences, and behavioral strategies can significantly enhance behavioral intentions [23,26]. The TPB proposes that intention serves as a proximal determinant of behavior, theoretically mediating the relationship between other TPB constructs and behavioral outcomes [54]. While our findings are consistent with these theoretical relationships—showing improvements in both TPB constructs and behavioral outcomes—we did not conduct formal mediation analyses to confirm the specific pathways through which our intervention affected Internet addiction.

• Behavior (Internet Addiction)

Internet addiction scores significantly decreased in the intervention group while remaining unchanged in the control group. This reduction represents the primary behavioral outcome of our study and demonstrates that structured educational interventions can effectively modify problematic Internet use among adolescents. As discussed in the sections related to the TPB constructs, this behavioral change was likely achieved through improvements in attitude, subjective norms, and perceived behavioral control, which in turn enhanced participants’ intention to reduce Internet use. Within the TPB framework, intention is considered the most immediate and proximal predictor of behavior change. These findings are consistent with previous research, highlighting that multi-component interventions grounded in behavioral theories provide an effective approach for reducing harmful digital behaviors among adolescents [11,13].

• Academic Performance (GPA)

An important secondary outcome of this study was the significant improvement in GPA among students in the intervention group. This improvement likely resulted from reduced time spent online and better concentration on studies, though our study design did not allow us to establish the exact causal mechanism. Previous research has demonstrated associations between reduced Internet addiction and improved academic outcomes [11,13,34], supporting our findings.

6.1. Contributions and Implications

6.1.1. Theoretical Contributions

This study makes several important contributions to the existing literature while offering meaningful implications for both research and practice. While our findings confirm the effectiveness of TPB-based interventions shown in previous research [23,26], we specifically targeted adolescent girls in southern Iran, a demographic and regional context that has received limited attention in Internet addiction research [55]. This helps address notable regional and demographic gaps in the literature. Our intervention included a session with parent–teacher participation, acknowledging the importance of social influences, which may be valuable in the collectivist cultural context of Iran, where family and school expectations can influence adolescent behavior. This attention to social context builds upon previous work on Internet addiction interventions [23]. Additionally, by documenting concurrent improvements in academic performance alongside reductions in Internet addiction, our study provides evidence for an important educational benefit of addressing problematic Internet use, a relationship previously underexplored in this population.

6.1.2. Implications for Educational Practice

This study demonstrated that a TPB-based educational intervention effectively reduced Internet addiction among adolescent girls in southern Iran, with concurrent improvements in academic performance. The six-session educational structure we implemented could be readily incorporated within existing school programs with relatively modest resource requirements. School programs addressing Internet addiction should target attitudes, subjective norms, perceived behavioral control, and intention, particularly emphasizing skills that enhance behavioral control such as time management strategies. The improvement in academic performance observed in our study suggests that reducing problematic Internet use may have educational benefits. School administrators concerned about academic outcomes might consider addressing Internet addiction as one strategy for supporting student achievement. This could involve including Internet use management in study skills programs, training teachers to recognize signs of problematic Internet use, and developing school policies promoting healthy digital habits. Schools could also enhance intervention effectiveness through parent education sessions about healthy Internet use, home–school coordination strategies around digital habits, and resources to help parents reinforce skills taught at school. By implementing this approach in an underrepresented region and population, we contribute to a more comprehensive understanding of how theory-based interventions can be applied across diverse contexts. The findings suggest that educational institutions can play an important role in addressing Internet addiction through structured programs that target key psychological constructs while involving relevant social systems.

6.1.3. Implications for Future Research

Our findings suggest several directions for future research. Studies should investigate which specific components of multi-faceted interventions contribute most significantly to outcomes, using dismantling studies or mediation analyses. Future research should also examine the durability of intervention effects through longer follow-up periods to determine whether improvements persist over time. Additionally, researchers should investigate potential moderators of intervention effectiveness, such as baseline severity of Internet addiction, family communication patterns, or access to alternative recreational activities, to develop more personalized intervention approaches. Despite these contributions, several limitations should be acknowledged.

6.2. Limitations

Despite these positive results, there are some limitations that should be considered while generalizing or interpreting the findings of the study. Most specifically, this study was primarily limited by the reliance on subjective tools in assessing Internet addiction and TPB constructs. Because of its relatively short time scale, the intervention effects observed were difficult to follow up, necessitating longitudinal studies to estimate any impacts that may last over the long term. The study population was limited to girls originating from one region of the country and, therefore, it may be challenging to generalize the findings to other demographic groups with different cultural and social features.

It should further be added that future studies should include more objective tools, such as tracking of time spent on the Internet and Internet data usage, to enhance the accuracy of the assessments. Additionally, the use of parent-reported data as a complementary tool for assessing adolescents’ Internet usage patterns—alongside these objective measures—can contribute to a more comprehensive evaluation. Our study did not measure social desirability bias as a covariate, which could be important in future research, particularly in collectivist cultures like Iran where conformity to social norms is valued. Cultural factors such as family hierarchies, gender roles, and educational exportations in Iranian culture likely influenced participants’ receptiveness to the intervention. Long-term research is required to assess the sustainability of intervention effects. Furthermore, increasing the population under study to include participants from more genders, cultures, social classes, and ages can allow for better generalizability of the findings. Finally, integration of TPB with other psychological models may lead to better results in interventions.

7. Conclusions

This study demonstrates that a TPB-based educational intervention can effectively reduce Internet addiction among adolescent girls in southern Iran, with concurrent improvements in academic performance. By implementing this approach in an underrepresented region and population, we contribute to a more comprehensive understanding of how theory-based interventions can be applied across diverse contexts [51,54]. The findings suggest that educational institutions can play an important role in addressing Internet addiction through structured programs that target key psychological constructs while involving relevant social systems. Future research should further clarify the mechanisms of change, identify factors affecting intervention outcomes, and determine the long-term sustainability of benefits. Taken together, these findings reinforce the importance of culturally tailored, theory-based programs in promoting healthier technology use behaviors among adolescents and highlight the role of educational systems as key drivers of behavioral change.