Examining the relationship between problematic pornography use and mental health status among adolescents of Bangladesh

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This cross-sectional study aimed to examine the relationship between problematic pornography use and mental health issues among adolescents in Bangladesh. Methods: This study employed a cross-sectional design to evaluate the relationship between problematic pornography uses with mental health status of adolescents in Bangladesh. The survey, which involved demographic questions and scales like the University of California, Los Angeles (UCLA) Loneliness Scale, the Multidimensional Scale of Perceived Social Support (MSPSS), the General Anxiety Disorder-7, and the Brief Pornography Screen, included a sample of 601 teenagers. Data were analyzed using hierarchical linear regression models after the scales underwent step-by-step validation. Results: It was discovered that boys scored higher (6.71) than girls (3.16), with the mean score for problematic pornography use (PPU) being 5.13. The Brief Pornography Scale revealed substantial relationships with anxiety (r=0.358), loneliness (r=0.382), and perceived social support (r=0.276). The structural modeling equation and hierarchical linear regression analysis showed that problematic use of pornography was associated with being male (r=0.26 to 0.45, p=0.001), higher monthly family cost (r=0.12 to 0.33, p=0.05), accessing pornography online (r=-0.14 to -0.27, p=0.05), consuming pornography through TV/DVD/downloaded videos (r=-0.44 to -0.55). However, the RSES scale did not reveal any correlation between problematic pornography consumption and self-esteem. Conclusion: This study found significant relationship with problematic pornography use and mental health status of Bangladeshi adolescent. It emphasizes the significance of raising awareness about the danger of problematic pornography use. The results of this study highlight the necessity for focused interventions and educational initiatives addressing the possible hazards connected to this population's pornographic use. Biological sciences/Psychology/Human behaviour Health sciences/Health care/Health services Pornography mental health anxiety loneliness social support adolescent Bangladesh Figures Figure 1 Figure 2 Introduction Studies have shown that the prevalence of sexually risky behaviors, such as texting, the number of sexual partners, the age of the first sexual encounter, and others, is on the rise among young people[ 1 ] [ 2 ]. The consumption of pornographic media is also becoming increasingly prevalent among this demographic [ 3 ] [ 4 ]. This rise might be attributable to the major hormonal, physical, psychological, and emotional shifts that take place throughout the transition from childhood to early adulthood [ 5 ] [ 6 ] [ 7 ] [ 8 ]. The use of pornographic content's major medium has shifted from periodicals to online platforms such as the internet [ 9 ]. As a result of the anonymity, accessibility, and cost offered by the internet, pornography is now more readily available than it has ever been before [ 10 ]. The widespread use of mobile devices among young adults [ 11 ] has also contributed to an even higher increase in the availability of pornographic content [ 12 ]. These behaviors have also been linked to concerns with mental health [ 13 ] [ 14 ]. Reports of pornographic use by females range from 19.0–78.4%, while reports of pornographic use by males range from 40.0–79.0% [ 3 ] [ 12 ]. The literature has highlighted growing concerns about the psychological impact of pornography on young individuals, largely focusing on their access to and consumption of explicit material. Extensive research suggests that the prevalent use of modern technology contributes to the development of addictive patterns, including problematic pornography use [ 15 ], which can lead to decreased levels of self-esteem, life satisfaction, and increased social isolation. Numerous studies have consistently demonstrated a relationship between increased pornography use and various mental health issues in both adults and adolescents, such as depression, anxiety, and stress [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 20 ]. Conducted in educational institutions, longitudinal research revealed that high levels of pornography use at the age of 16 predicted the presence of psychosomatic symptoms by age of 18 [ 21 ]. Additionally, being female, separation of parents, and enrolling in skill-based education programs were identified as additional predictors of psychosomatic symptoms. Factors such as frequency of use, viewer’s age, and gender may influence the relationship between pornography and mental health [ 22 ], although other factors could also play a role. Of notable importance, empirical research has unveiled a noteworthy association between the consumption of pornographic content and the adoption of high-risk behaviors, including excessive alcohol consumption and drug abuse [ 23 ] [ 24 ] [ 25 ] . Our study aimed to investigate the association between problematic pornography use and mental health status among adolescents in Bangladesh. With a lack of research in this context, we explored factors like low self-esteem, anxiety, loneliness, and perceived social support. Methodology Study Design and Participants: Utilizing a cross-sectional research design, data for this study were gathered from601 adolescents. Target population for this research consisted of any adolescent residing in Bangladesh. Adolescents without any consent form were not included in the study. A convenience type of non-probability sampling technique was utilized to select participants, ensuring accessibility and practicality in data collection. The data collection procedure commenced in February 2023 and concluded in September 2023. A self-administered questionnaire was employed for data collection. Data was collected through a 7 person team of volunteers across Bangladesh. Before data collection began, the questionnaire underwent a pre-testing phase to assess its validity and reliability (Crohnbach’s alpha: 0.73). The questionnaire comprised sections that captured socio-demographic information, self-esteem, anxiety, loneliness, perceived social support, substance use, and age of exposure to pornography. To ensure ease of data collection, a digital format was adopted. The targeted participants were provided with a Google form, and each unique Internet Protocol (IP) address was allowed to submit a single response. The data collection process followed a sequential structure within the Google form. Only upon indicating their consent by selecting the option "Yes," participants were able to proceed to subsequent sections of the form. Throughout the data collection process, stringent measures were implemented to ensure the utmost anonymity and confidentiality of the study participants. Their identities were carefully protected, safeguarding their privacy and maintaining the highest level of data security. Data encryption and coding were employed to protect the data's integrity and facilitate secure storage and analysis. The study was carried out in accordance with the Institutional Research Ethics and the Declaration of Helsinki and its latest amendment in October 2013 or its comparable ethical standards. This study's protocol was approved by institutional review board (IRB)/ethical review committee (ERC) of North South University (#2022IOR-NSU/lRB/1203). Measures An informed consent form and a structured questionnaire were both used to gather data for this investigation. Comprising various sections, the questionnaire encompassed an array of domains, including sociodemographic characteristics, substance use, the UCLA Loneliness Scale, the General Anxiety Disorder-7 (GAD-7) scale, the Brief Pornography Screen (BPS) scale, and the Multidimensional Scale of Perceived Social Support (MSPSS) scale. Participants were asked to provide responses to each section, enabling a comprehensive exploration of the relevant constructs within the study. Statistical Analysis The data is initially stored in Microsoft Excel and then transferred to IBM SPSS Statistics 23.0, Smart PLS 4, and R 4.2.2 for additional analysis. Using crosstabs in SPSS, sample characteristics of adolescents are analyzed. Through the utilization of SPSS, various aspects were examined in the current study, including the frequency distribution, reliability, component matrix, and associations among the Brief Pornography Screen, UCLA Loneliness Scale, Rosenberg's Self-Esteem Scale, Multidimensional Scale of Perceived Social Support, and General Anxiety Disorder-7. In the current study, structural equation modeling (SEM) is performed in Smart PLS 4.0. In order to identify factors linked to problematic pornography use, hierarchical linear regression is performed in R 4.2.2. Results Descriptive statistics Table 1 show that among 601 adolescents, the majorities (334) of them were male, and 267 were females, with a mean age of 17.07 years. Most of the adolescents (78.9%) were living in urban areas, while only small percentages (4.5%) were living in rural areas. The majority of the adolescents (35.8%) came from families with a monthly expenditure greater than 100,000 Bangladeshi Taka. The majority of the adolescents (87.4%) are Muslims. Most of the adolescents (82.2%) do not smoke, and the majority (96.5%) does not consume alcohol. About one-third of the adolescents (32.1%) reported having a sleeping problem. The majority of adolescents (48.1%) first consumed pornography at the age of 13–15 years, and the most common medium of use was internet (60.7%). The most common source of first concept of pornography was friends (42.6%). The majority of adolescents (66.7%) reported having a positive anxiety level. The mean score for problematic pornography use (PPU) was 5.13, with male adolescents reporting a higher mean score (6.71) than female adolescents (3.16). Table 1 Sample characteristics of adolescents Male(n = 334) Female(n = 267) Total(n = 601) Age (Mean ± SD) 17.20 ± 0.88 16.91 ± 1.10 17.07 ± 0.99 Residence Urban 71.0% (n = 237) 88.8% (n = 237) 78.9% (n = 474) Semi-urban 14.7% (n = 49) 7.5% (n = 20) 11.5% (n = 69) Rural 6.6% (n = 22) 1.9% (n = 5) 4.5% (n = 27) Other 7.8% (n = 26) 1.9% (n = 5) 5.2% (n = 31) Monthly family expenditure < 10,000 6.0% (n = 20) 10.9% (n = 29) 8.2% (n = 49) 10,001–30,000 4.2% (n = 14) 18.4% (n = 49) 10.5% (n = 63) 30,001–50,000 7.2% (n = 24) 17.6% (n = 47) 11.8% (n = 71) 51,001-100,000 44.3% (n = 148) 20.6% (n = 55) 33.8% (n = 203) > 100,000 38.3% (n = 128) 32.6% (n = 87) 35.8% (n = 215) Religion Islam 86.5% (n = 289) 88.4% (n = 236) 87.4% (n = 525) Hinduism 7.5% (n = 25) 7.5% (n = 20) 7.5% (n = 45) Christianity 5.7% (n = 19) 1.1% (n = 3) 3.7% (n = 22) Other 0.3% (n = 1) 3.0% (n = 8) 1.5% (n = 9) Smoking status No 79.0% (n = 264) 86.1% (n = 230) 82.2% (n = 494) Yes 21.0% (n = 70) 13.9% (n = 37) 17.8% (n = 107) Alcohol consumption No 97.0% (n = 324) 95.9% (n = 256) 96.5% (n = 580) Yes 3.0% (n = 10) 4.1% (n = 11) 3.5% (n = 21) Sleeping problem No 75.7% (n = 253) 58.1% (n = 155) 67.9% (n = 408) Yes 24.3% (n = 81) 41.9% (n = 112) 32.1% (n = 193) Age at first pornography consumption 10–12 5.7% (n = 19) 18.4% (n = 49) 11.3% (n = 68) 13–15 56.3% (n = 188) 37.8% (n = 101) 48.1% (n = 289) 16–18 37.4% (n = 125) 40.4% (n = 108) 38.8% (n = 233) 18+ 0.6% (n = 2) 3.4% (n = 9) 1.8% (n = 11) Medium of pornography consumption Books/Magazines 8.1% (n = 27) 2.6% (n = 7) 5.7% (n = 34) Internet 65.9% (n = 220) 54.3% (n = 145) 60.7% (n = 365) TV/DVD/Downloaded videos 6.9% (n = 23) 39.3% (n = 105) 21.3% (n = 128) Other 19.2% (n = 64) 3.7% (n = 10) 12.3% (n = 74) Getting first concept of pornography from Friends 44.9% (n = 150) 39.7% (n = 106) 42.6% (n = 256) Elder brothers or sisters 13.2% (n = 44) 22.1% (n = 59) 17.1% (n = 103) Other 41.9% (n = 140) 38.2% (n = 102) 40.3% (n = 242) Anxiety Positive 76.0% (n = 254) 55.1% (n = 147) 66.7% (n = 401) Negative 24.0% (n = 80) 44.9% (n = 120) 33.3% (n = 200) PPU (Mean ± SD) 6.71 ± 2.79 3.16 ± 2.79 5.13 ± 3.297 Perceived Social Support Score (PSSS) Family (Mean ± SD) 21.14 ± 5.09 17.66 ± 7.40 19.60 ± 6.45 Friends (Mean ± SD) 19.68 ± 5.72 18.20 ± 6.15 19.04 ± 5.96 Significant other (Mean ± SD) 19.85 ± 4.57 17.85 ± 6.14 18.96 ± 5.42 PSSS total (Mean ± SD) 60.67 ± 13.37 53.75 ± 16.54 57.60 ± 15.19 Self-Esteem Normal 88.3% (n = 295) 85.8% (n = 229) 87.2% (n = 524) Low self-esteem 11.7% (n = 39) 14.2% (n = 38) 12.8% (n = 77) UCLA Loneliness score (Mean ± SD) 6.95 ± 2.05 5.89 ± 2.08 6.48 ± 2.13 PPU, problematic pornography use (BPS total score); SD, standard deviation. Validity and reliability analysis: Table 2 presents the reliability coefficients (Cronbach's alpha) and correlations between the Brief Pornography Screen (BPS) and other measures. The BPS has high reliability (α = 0.843) and is positively correlated with perceived social support (r = 0.276), loneliness (r = 0.382), and anxiety (r = 0.358), but weakly correlated with self-esteem (r = 0.061). The Multidimensional Scale of Perceived Social Support (MSPSS) shows high reliability (α = 0.904) and positive correlations with both BPS and self-esteem (r = 0.276 and r = 0.154, respectively). However, it does not significantly correlate with loneliness or anxiety. The Rosenberg's Self-Esteem Scale (RSES) has high reliability (α = 0.824), modestly correlates with BPS (r = 0.061), and negatively correlates with loneliness (r = -0.011), but not with social support or anxiety. The UCLA Loneliness Scale exhibits strong reliability (α = 0.823), positively correlating with depression and anxiety, but lacks significant correlations with self-esteem or social support. The General Anxiety Disorder-7 (GAD-7) scale demonstrates strong reliability (α = 0.820) and significant positive correlations with BPS, loneliness, and adverse correlation with self-esteem, but no significant correlation with social support. Table 2 Reliability and associations between the Brief Pornography Screen (BPS), UCLA Loneliness Scale, Rosenberg's Self-Esteem Scale (RSES), Multidimensional Scale of Perceived Social Support (MSPSS), and GAD-7 (General Anxiety Disorder-7) Cronbach’s alpha Range BPS MSPSS RSES UCLA Loneliness Scale GAD-7 BPS 0.843 0–10 -- 0.276 ** 0.061 0.382 ** 0.358 ** MSPSS 0.904 12–84 0.276 ** -- 0.154 ** 0.040 0.031 RSES 0.824 0–30 0.061 0.154 ** -- -0.011 -0.128 ** UCLA Loneliness Scale 0.823 3–9 0.382 ** 0.040 -0.011 -- 0.482 ** GAD-7 0.820 0–21 0.358 ** 0.031 -0.128 ** 0.482 ** -- ** p-value < 0.01. Table 3 displays the results of an adolescent pornography use, presenting the frequency distribution and component matrix of the five-item BPS. The component matrix shows the correlation coefficients between each BPS statement and the latent variable, with the statement "You find it difficult to resist strong urges to use pornography" having a correlation coefficient of 0 .796. Table 3 Frequency distribution and component matrix of five item Brief Pornography Screen (BPS) among adolescents. Never n (%) Occasionally n (%) Very often n (%) Component matrix You find yourself using pornography more than you want to. 215(35.8) 178(29.6) 208(34.6) 0.849 You have attempted to “cut back” or stop using pornography, but were unsuccessful. 122(20.3) 137 (22.8) 342(56.9) 0.564 You find it difficult to resist strong urges to use pornography. 269(44.8) 122(20.3) 210(34.9) 0.796 You find yourself using pornography to cope with strong emotions (e.g., sadness, anger, loneliness, etc.). 272(45.3) 158(26.3) 171(28.5) 0.845 You continue to use pornography even though you feel guilty about it. 196(32.6) 181(30.1) 224(37.3) 0.848 Structural Equational modelling: The findings of the discriminant validity analysis for the concept measures that were utilized in the research are presented in Table 4 . The diagonal values in the table reflect the square root of the average variance extracted (AVE) for each construct, and the table itself illustrates the correlation coefficients between the various constructs. The numbers that lie outside of the diagonal represent the correlations that exist between the constructs. The correlation coefficient for the BPS construct is 0.732, which is greater than the correlation coefficients for other constructs, which range from 0.186 to 0.458. The square root of the AVE for the BPS construct is 0.732. The square root of the AVE for the GAD construct is 0.734, which is greater than the correlation coefficients with other constructs, which range from 0.212 to 0.578. In a similar vein, the GAD construct was found to have a higher AVE. The findings of this analysis reveal that the construct measures that were utilized in the study had appropriate discriminant validity. This indicates that the constructs being measured by the various instruments are unique from one another rather than overlapping with one another (Fig. 1 ). Table 4 Discriminant validity BPS GAD MSPSS RSES UCLA BPS GAD 0.421 MSPSS 0.314 0.212 RSES 0.186 0.257 0.221 UCLA 0.458 0.578 0.123 0.142 The results of the structural model in SEM is shown in Table 5 , which examines the relationships between the latent variables (BPS, GAD, MSPSS, RSES, and UCLA) and their corresponding observed indicators. The results suggest that the path from GAD to BPS is statistically significant (t = 5.84), indicating that higher levels of generalized anxiety disorder (GAD) are associated with higher levels of problematic pornography use (BPS) (Fig. 2 ). Similarly, the path from MSPSS to BPS is also statistically significant (t = 4.902), suggesting that higher levels of perceived social support (MSPSS) are associated with lower levels of problematic pornography use. However, the paths from GAD to RSES, MSPSS to RSES, and RSES to BPS are not statistically significant (t < 1.96), indicating that there is no evidence of a significant relationship between these variables (Fig. 2 ). On the other hand, the paths from UCLA to BPS and UCLA to GAD are statistically significant (t = 5.555 and t = 16.618, respectively), suggesting that higher levels of loneliness (UCLA) are associated with higher levels of problematic pornography use (BPS) and higher levels of generalized anxiety disorder (GAD).The structural model suggests that GAD, MSPSS, and UCLA are important predictors of problematic pornography use, but RSES is not. Table 5 Structural model results Sample mean Standard Error T statistics Decisions GAD -> BPS 0.257 0.044 5.84 Supported GAD -> RSES 0.016 0.221 0.742 Not Supported MSPSS -> BPS 0.221 0.042 4.902 Supported MSPSS -> RSES 0.224 0.192 1.673 Not Supported RSES -> BPS 0.094 0.115 1.356 Not Supported UCLA -> BPS 0.232 0.041 5.555 Supported UCLA -> GAD 0.508 0.031 16.618 Supported Factors associated with problematic pornography use The Table 6 shows the results of a hierarchical linear regression analysis that aimed to identify factors associated with problematic pornography use. The table presents the results for three models. Model 1 includes only the constant (intercept) term and the independent variables, while Model 2 adds demographic variables to the model. Model 3 includes all independent variables. Table 6 shows the beta (β) coefficients, which indicate the strength and direction of the association between each independent variable and the dependent variable (problematic pornography use), When other variables in the model are in control, the independent variables include: age of first exposure to pornography (in three categories), sex, residence, monthly family cost, religion, smoking status, alcohol consumption, sleeping problem, and different sources of pornography consumption knowledge. The results show that problematic pornography use was associated with being male (β = 0.26, p < 0.001), having a higher monthly family cost (β = 0.12 to 0.33, p < 0.05), being a Muslim (β = -0.08 to -0.18, p < 0.05), using pornography on the internet (β = -0.14 to -0.27, p < 0.05) and consuming pornography through TV/DVD/downloaded videos (β = -0.44 to -0.55, p < 0.001). Age of first exposure to pornography, residence, smoking status, alcohol consumption, sleeping problem, and different sources of pornography consumption knowledge were not significant predictors of problematic pornography use. Table 6 Hierarchical linear regression: Factors associated with problematic pornography use (N = 601) Independent Predictors Model 1 Model 2 Model 3 B β (p-value) B β (p-value) B β (p-value) Constant 4.34 6.08 1.67 First PC Age 13–15 -0.27 -0.04 (< 0.001) ** 0.13 0.02 (< 0.001) ** 0.07 0.01 (0.224) First PC Age 16–18 0.24 0.04 (0.484) 0.41 0.06 (0.726) 0.22 0.03 (0.567) First PC Age 18+ -1.10 -0.05 (0.202) -0.46 -0.02 (0.563) -0.27 -0.01 (0.729) Sex Male 2.99 0.45 (< 0.001) ** 1.99 0.30 (< 0.001) ** 1.73 0.26 (< 0.001) ** Residence Rural -0.99 -0.06 (0.158) -0.65 -0.04 (0.319) -0.70 -0.04 (0.268) Residence Semi-Urban -0.14 -0.01 (0.808) -0.28 -0.03 (0.606) -0.19 -0.02 (0.711) Residence Urban -0.85 -0.11 (0.105) -0.55 -0.07 (0.260) -0.37 -0.05 (0.427) Monthly Family Cost 10000–30000 0.66 0.06 (0.200) 0.34 0.03 (0.467) 0.48 0.05 (0.295) Monthly Family Cost 30000–50000 0.54 0.05 (0.278) 0.69 0.07 (0.126) 0.59 0.06 (0.180) Monthly Family Cost 51000–100000 1.86 0.27 ( 100000 2.29 0.33 (< 0.001) ** 1.25 0.18 (0.003) ** 0.88 0.13 (0.037) * Religion Hinduism -1.26 -0.10 (0.078) -0.97 -0.08 (0.143) -0.80 -0.06 (0.215) Religion Islam -1.77 -0.18 (0.003) ** -1.17 -0.12 (0.04) * -0.90 -0.09 (0.111) Religion Other -1.18 -0.04 (0.297) -1.67 -0.06 (0.112) -1.17 -0.04 (0.251) Smoking status Never -0.44 -0.05 (0.216) -0.23 -0.03 (0.512) Smoking status Regularly -0.98 -0.03 (0.312) -0.78 -0.03 (0.410) Smoking status sometimes -0.20 -0.02 (0.683) -0.09 -0.01 (0.849) Alcohol consumption Never 1.37 0.08 (0.083) 1.45 0.08 (0.062) Alcohol consumption Regularly -3.63 -0.04 (0.190) -2.74 -0.03 (0.313) Alcohol consumption Sometimes 1.22 0.04 (0.270) 1.41 0.05 (0.190) Sleeping problem Yes -0.26 -0.03 (0.250) -0.29 -0.04 (0.183) Pornography Media Internet -1.85 -0.27 (< 0.001) ** -0.97 -0.14 (0.035) * Pornography Media Other -0.42 -0.04 (0.454) -0.20 -0.02 (0.714) Pornography Media TV/DVD/Downloaded videos -4.39 -0.55 (< 0.001) ** -3.53 -0.44 (< 0.001) ** Pornography consumption knowledge from Friends -0.12 -0.02 (0.707) -0.17 -0.03 (0.567) Pornography consumption knowledge from Others -0.66 -0.10 (0.035) * -0.80 -0.12 (0.008) * UCLA score 0.17 0.11 (0.002) * GAD score 0.08 0.12 (< 0.001) ** MSPSS score 0.02 0.11 (< 0.001) ** RSES score 0.01 0.01 (0.716) \(\:{R}^{2}\) 0.3843 0.5053 0.5386 Adjusted \(\:{R}^{2}\) 0.3696 0.4829 0.5143 Model Characteristics F = 26.12 (p-value < 0.01) ** F = 22.55 (p-value < 0.01) ** F = 22.18 (p-value < 0.01) ** B Unstandardized coefficient, β Standardized coefficient, * p-value < 0.05, ** p-value < 0.01. Discussion Our research examines the prevalence of problematic pornography use among adolescents and its associations with mental health status. Significant sex differences were observed in pornography consumption and its association with mental health. A study conducted with undergraduate Bangladeshi students found that 72% reported engaging with pornography at least once in their lifetimes [ 26 ]. This rate was lower compared to studies from India (80%), Sweden (98%), and Australia (87%), but higher than a previous studies in Bangladesh (42%) [ 27 ] [ 28 ] [ 29 ] [ 30 ]. Using the Brief Pornography Screen (BPS), our study assessed attitudes and behaviors related to problematic pornography use. The findings revealed a substantial prevalence of problematic pornography use among school-going students, with over one-third experiencing difficulties in controlling their consumption, resisting urges, and suffering from adverse mental health outcomes which is similar to previous studies [ 29 ]. Several factors contribute to this high volume of problematic use, including adolescent curiosity, societal discouragement, and peer influence, lack of comprehensive sex education, easy internet accessibility, unrealistic portrayals, and emotional/psychological factors. Among the adolescents included in this study, it is noteworthy that the age at which they were first exposed to pornography was comparatively early, indicating a relatively low age of initial exposure, with the majority reporting exposure between the ages of 13 and 15. This finding aligns with previous research indicating that adolescents are exposed to pornography at increasingly younger ages due to easy accessibility through the internet and other digital platforms [ 17 ]. Early exposure to pornography raises concerns regarding its potential influence on adolescent sexual development and attitudes toward sex. The present study employed a hierarchical linear regression analysis to investigate the factors linked to problematic pornography use while controlling for demographic variables. Male gender, higher monthly family expenditure, adherence to the Muslim faith, and the utilization of television/DVD/downloaded videos were found to be the substantial predictors of problematic pornography use. Previous studies also found being Muslim and male to be significant predictors of this outcome while being male was one of the most common ones [ 17 ]. One plausible explanation for this could be that the ventromedial prefrontal cortex (vmPFC) of males and females is activated sexually differently. According to studies of sexual neuroimaging, females have lesser responses to visually erotic stimuli than males [ 31 ]. In addition, it is probable that society's and cultures strong influences, best exemplified by a sexual double standard, overlook male sexual expressions and undermine female sexuality [ 31 ]. Secondly, the association between higher monthly family costs and problematic pornography use may be attributed to the notion that adolescents from more affluent backgrounds may have greater access to technology, including devices and internet connectivity, facilitating increased exposure to and consumption of pornography. Furthermore, the observation that being a Muslim was a significant predictor implies that religious beliefs and cultural norms play a role in shaping individuals' attitudes towards pornography, potentially leading to a higher likelihood of perceiving pornography use as problematic within this particular religious’ context which is similar to other studies in this field [ 32 ]. Lastly, the significance of consuming pornography through TV/DVD/downloaded videos suggests that the medium through which pornography is accessed and consumed influences the development of problematic usage patterns. This may be attributed to the potential for continuous and repetitive exposure to explicit content when using these specific sources, which can contribute to habituation, escalation, and the reinforcement of these problematic behaviors. These findings underscore the significance of gender-related dynamics, socio-economic circumstances, and specific modalities of pornography consumption in elucidating adolescents' proclivity for engaging in problematic pornography use. Consistent with prior research [ 33 ], a significant number of students attending school in our study reported experiencing symptoms of severe and extremely severe anxiety, tension, and loneliness. The findings from this study indicate a potential association between problematic pornography use and elevated levels of loneliness and anxiety among adolescents. These results are consistent with previous research, which also supports the notion that pornography use can contribute to feelings of loneliness and heightened anxiety in this population [ 34 ] [ 35 ]. It is plausible that adolescents who experience loneliness might turn to pornography as a coping mechanism to alleviate their feelings of isolation or escape from real-world social interactions. The temporary gratification and perceived connection offered by pornography may serve as a substitute for genuine social connections, thereby exacerbating feelings of loneliness over time. Conversely, adolescents with strong perceived social support networks may develop healthier coping mechanisms and alternative sources of companionship, reducing their reliance on problematic pornography use. Moreover, the findings from our study unveiled a significant correlation between higher levels of problematic pornography use and elevated levels of Generalized Anxiety Disorder (GAD). This observation suggests a potential association between anxiety and the consumption of pornography. These results contribute to the growing body of literature that posits a plausible link between pornography use and compromised psychosocial well-being among adolescents and college students who exhibit addictive behaviors related to internet pornography [ 17 ] [ 36 ] [ 25 ]. One possible explanation for the association between problematic pornography use and psychosocial problems in adolescents revolves around the content and nature of pornography itself. Pornographic materials often portray unrealistic and idealized sexual scenarios, which can distort adolescents' perceptions of sexuality, body image, and relationships. Exposure to explicit content can lead to feelings of inadequacy and increased anxiety about sexual performance and attractiveness. Excessive pornography consumption may desensitize individuals to sexual stimuli, affecting intimate relationships. Moderating factors like pre-existing body image concerns and the ability to distinguish fantasy from reality can influence the relationship between problematic pornography use and self-esteem. The complex interplay between individual factors and specific content may contribute to the observed weak correlation in the study. Our study found a significant correlation between problematic pornography use and decreased levels of perceived social support, as measured by the Brief Pornography Scale and the Multidimensional Scale of Perceived Social Support (MSPSS). This highlights the importance of social support as a potential protective factor for adolescents exposed to internet pornography. Adolescents with higher social support, especially from parents and friends, showed less negative impact from internet pornography exposure, consistent with a Korean study from 2019 [ 37 ]. To promote overall well-being, interventions should provide accurate knowledge, foster critical thinking, and create supportive environments for adolescents navigating the challenges of pornography use. Future research should explore the long-term consequences of problematic pornography use and evaluate the effectiveness of interventions to mitigate its harmful effects. a bit concise but keep original elements Limitations Firstly, this study involved participants from one country and caution is necessary in relation to generalizability and various limitations exist that require consideration for both the interpretation of the results. Secondly, as with all surveys utilizing self-reporting, there is potential for recall bias. Thirdly, the cross sectional design of this study limits the ability to explore causality, Fourthly, We only looked at the mental health consequences and social support issues relating to pornography but there are still a wide array of issues that need to be investigated in future studies. Conclusion In conclusion, this research sheds light on the prevalence of problematic pornography use among adolescents and its association with mental health status. The findings reveal a substantial presence of problematic pornography use among school-going students, with male gender, higher family expenditure, adherence to the Muslim faith, and utilization of specific media sources being significant predictors of such behavior. Early exposure to pornography at a relatively young age raises concerns about its potential impact on adolescent sexual development and attitudes toward sex. The study establishes a potential link between problematic pornography use and elevated levels of loneliness and anxiety, emphasizing the need for interventions that promote accurate knowledge, critical thinking, and supportive environments to address these issues. Moreover, the observed correlation between problematic pornography use and decreased levels of perceived social support underscores the importance of strengthening social support networks to mitigate the negative effects of internet pornography exposure. Future research should delve into the long-term consequences of problematic pornography use and evaluate the effectiveness of targeted interventions to safeguard adolescent well-being. Declarations Data availability statement: The dataset analyzed in the current study is available from the corresponding author upon request. Funding: N/A Conflict of Interest: N/A References Yang X et al (2019) Risky Sexual Behaviors and Associated Factors Among College Students in Lusaka, Zambia, Arch. Sex. Behav. , vol. 48, no. 7, pp. 2117–2123, Oct. 10.1007/s10508-019-1442-5 Ingram LA, Macauda M, Lauckner C, Robillard A (2019) Sexual Behaviors, Mobile Technology Use, and Sexting Among College Students in the American South, Am. J. 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M.A., Timing of puberty and sexuality in men and women. no april (2005) https://doi.org/10.1007/s10508-005-1797-7 Fabio D’Orlando (last) (ed) (2011) The demand for pornography, no. pp. 51–75, Nov. 2009. https://doi.org/10.1007/s10902-009-9175-0 The Smartphone Difference [Online] Washington, DC: Pew Research Center. Price J, Patterson R, Regnerus M, Walley J (Jan. 2016) How Much More XXX is Generation X Consuming? Evidence of Changing Attitudes and Behaviors Related to Pornography Since 1973. J Sex Res 53(1):12–20. 10.1080/00224499.2014.1003773 Vanden Abeele M, Campbell SW, Eggermont S, Roe K (Jan. 2014) Sexting, Mobile Porn Use, and Peer Group Dynamics: Boys’ and Girls’ Self-Perceived Popularity, Need for Popularity, and Perceived Peer Pressure. Media Psychol 17(1):6–33. 10.1080/15213269.2013.801725 Agardh A, Cantor-Graae E, Östergren P-O (2012) Youth, Sexual Risk-Taking Behavior, and Mental Health: a Study of University Students in Uganda, Int. J. Behav. 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Front Psychol 11:613244. 10.3389/fpsyg.2020.613244 Grubbs JB, Stauner N, Exline JJ, Pargament KI, Lindberg MJ (2015) Perceived addiction to Internet pornography and psychological distress: Examining relationships concurrently and over time. Psychol Addict Behav 29(4):1056–1067. 10.1037/adb0000114 Maddock ME, Steele K, Esplin CR, Hatch SG, Braithwaite SR (2019) What Is the Relationship Among Religiosity, Self-Perceived Problematic Pornography Use, and Depression Over Time? Sex. Addict. Compulsivity , vol. 26, no. 3–4, pp. 211–238, Oct. 10.1080/10720162.2019.1645061 Pornography use and depressive symptoms,Examining the role of moral incongruence. Society and Mental Health.pdf Adolescents’ health behaviours and its associations with psychological variables.pdf Andrie EK, Sakou II, Tzavela EC, Richardson C, Tsitsika AK (Oct. 2021) Adolescents’ Online Pornography Exposure and Its Relationship to Sociodemographic and Psychopathological Correlates: A Cross-Sectional Study in Six European Countries. Children 8(10):925. 10.3390/children8100925 Shor E (2019) Age, Aggression, and Pleasure in Popular Online Pornographic Videos, Violence Women , vol. 25, no. 8, pp. 1018–1036, Jun. 10.1177/1077801218804101 Hald GM (2006) Gender Differences in Pornography Consumption among Young Heterosexual Danish Adults, Arch. Sex. Behav. , vol. 35, no. 5, pp. 577–585, Oct. 10.1007/s10508-006-9064-0 Harper C, Hodgins DC (Jun. 2016) Examining Correlates of Problematic Internet Pornography Use Among University Students. J Behav Addict 5(2):179–191. 10.1556/2006.5.2016.022 Al Mamun MA, Yasir Arafat SM, Ambiatunnahar M, Griffiths MD (2019) Attitudes and Risk Factors of Pornography Consumption Among Bangladeshi University Students: An Exploratory Study, Int. J. Ment. Health Addict. , vol. 17, no. 2, pp. 323–335, Apr. 10.1007/s11469-018-0021-7 Das AM More than 80 percent of high school students exposed to porn, says study, indian express , india, july 30. [Online]. Available: http://www.newindianexpress.com/states/kerala/2013/jul/30/More-than-80-percent-of-high-school-students-exposed-to-porn-says-study-501873.html Donevan M, Mattebo M (2017) The relationship between frequent pornography consumption, behaviours, and sexual preoccupancy among male adolescents in Sweden, Sex. Reprod. Healthc. , vol. 12, pp. 82–87, Jun. 10.1016/j.srhc.2017.03.002 Young Australians’ use of pornography and associations with sexual risk behaviours.pdf Khan RH (2018) Does the addiction in online pornography affect the behavioral pattern of undergrad private university students in Bangladesh? Int J Health Sci, 12, 3 Thorbjörnsson LMCB Facts and Prejudices: Psychological Differences Between Women and Men, pp. 61–95 Husain O, THE ELEPHANT IN THE ROOM : PORNOGRAPHY ADDICTION AND THE AMERICAN MUSLIM COMMUNITY Beiter R et al (2015) The prevalence and correlates of depression, anxiety, and stress in a sample of college students, J. Affect. Disord. , vol. 173, pp. 90–96, Mar. 10.1016/j.jad.2014.10.054 Efrati Y, Amichai-Hamburger Y (2019) The Use of Online Pornography as Compensation for Loneliness and Lack of Social Ties Among Israeli Adolescents, Psychol. Rep. , vol. 122, no. 5, pp. 1865–1882, Oct. 10.1177/0033294118797580 Butler MH, Pereyra SA, Draper TW, Leonhardt ND, Skinner KB (Feb. 2018) Pornography Use and Loneliness: A Bidirectional Recursive Model and Pilot Investigation. J Sex Marital Ther 44(2):127–137. 10.1080/0092623X.2017.1321601 Kohut T, Štulhofer A (2018) Is pornography use a risk for adolescent well-being? An examination of temporal relationships in two independent panel samples, PLOS ONE , vol. 13, no. 8, p. e0202048, Aug. 10.1371/journal.pone.0202048 Shin J, Lee CH (May 2019) Exposure to internet pornography and sexually aggressive behaviour: protective roles of social support among Korean adolescents. J Sex Aggress 25(2):90–104. 10.1080/13552600.2018.1528795 Additional Declarations The authors declare no competing interests. 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The consumption of pornographic media is also becoming increasingly prevalent among this demographic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This rise might be attributable to the major hormonal, physical, psychological, and emotional shifts that take place throughout the transition from childhood to early adulthood [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The use of pornographic content's major medium has shifted from periodicals to online platforms such as the internet [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a result of the anonymity, accessibility, and cost offered by the internet, pornography is now more readily available than it has ever been before [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The widespread use of mobile devices among young adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] has also contributed to an even higher increase in the availability of pornographic content [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These behaviors have also been linked to concerns with mental health [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Reports of pornographic use by females range from 19.0\u0026ndash;78.4%, while reports of pornographic use by males range from 40.0\u0026ndash;79.0% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature has highlighted growing concerns about the psychological impact of pornography on young individuals, largely focusing on their access to and consumption of explicit material. Extensive research suggests that the prevalent use of modern technology contributes to the development of addictive patterns, including problematic pornography use [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which can lead to decreased levels of self-esteem, life satisfaction, and increased social isolation. Numerous studies have consistently demonstrated a relationship between increased pornography use and various mental health issues in both adults and adolescents, such as depression, anxiety, and stress [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConducted in educational institutions, longitudinal research revealed that high levels of pornography use at the age of 16 predicted the presence of psychosomatic symptoms by age of 18 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, being female, separation of parents, and enrolling in skill-based education programs were identified as additional predictors of psychosomatic symptoms. Factors such as frequency of use, viewer\u0026rsquo;s age, and gender may influence the relationship between pornography and mental health [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], although other factors could also play a role. Of notable importance, empirical research has unveiled a noteworthy association between the consumption of pornographic content and the adoption of high-risk behaviors, including excessive alcohol consumption and drug abuse [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eOur study aimed to investigate the association between problematic pornography use and mental health status among adolescents in Bangladesh. With a lack of research in this context, we explored factors like low self-esteem, anxiety, loneliness, and perceived social support.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants:\u003c/h2\u003e \u003cp\u003eUtilizing a cross-sectional research design, data for this study were gathered from601 adolescents. Target population for this research consisted of any adolescent residing in Bangladesh. Adolescents without any consent form were not included in the study. A convenience type of non-probability sampling technique was utilized to select participants, ensuring accessibility and practicality in data collection.\u003c/p\u003e \u003cp\u003eThe data collection procedure commenced in February 2023 and concluded in September 2023. A self-administered questionnaire was employed for data collection. Data was collected through a 7 person team of volunteers across Bangladesh. Before data collection began, the questionnaire underwent a pre-testing phase to assess its validity and reliability (Crohnbach\u0026rsquo;s alpha: 0.73). The questionnaire comprised sections that captured socio-demographic information, self-esteem, anxiety, loneliness, perceived social support, substance use, and age of exposure to pornography.\u003c/p\u003e \u003cp\u003eTo ensure ease of data collection, a digital format was adopted. The targeted participants were provided with a Google form, and each unique Internet Protocol (IP) address was allowed to submit a single response. The data collection process followed a sequential structure within the Google form. Only upon indicating their consent by selecting the option \"Yes,\" participants were able to proceed to subsequent sections of the form. Throughout the data collection process, stringent measures were implemented to ensure the utmost anonymity and confidentiality of the study participants. Their identities were carefully protected, safeguarding their privacy and maintaining the highest level of data security. Data encryption and coding were employed to protect the data's integrity and facilitate secure storage and analysis. The study was carried out in accordance with the Institutional Research Ethics and the Declaration of Helsinki and its latest amendment in October 2013 or its comparable ethical standards. This study's protocol was approved by institutional review board (IRB)/ethical review committee (ERC) of North South University (#2022IOR-NSU/lRB/1203).\u003c/p\u003e \u003c/div\u003e"},{"header":"Measures","content":"\u003cp\u003eAn informed consent form and a structured questionnaire were both used to gather data for this investigation. Comprising various sections, the questionnaire encompassed an array of domains, including sociodemographic characteristics, substance use, the UCLA Loneliness Scale, the General Anxiety Disorder-7 (GAD-7) scale, the Brief Pornography Screen (BPS) scale, and the Multidimensional Scale of Perceived Social Support (MSPSS) scale. Participants were asked to provide responses to each section, enabling a comprehensive exploration of the relevant constructs within the study.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe data is initially stored in Microsoft Excel and then transferred to IBM SPSS Statistics 23.0, Smart PLS 4, and R 4.2.2 for additional analysis. Using crosstabs in SPSS, sample characteristics of adolescents are analyzed. Through the utilization of SPSS, various aspects were examined in the current study, including the frequency distribution, reliability, component matrix, and associations among the Brief Pornography Screen, UCLA Loneliness Scale, Rosenberg's Self-Esteem Scale, Multidimensional Scale of Perceived Social Support, and General Anxiety Disorder-7. In the current study, structural equation modeling (SEM) is performed in Smart PLS 4.0. In order to identify factors linked to problematic pornography use, hierarchical linear regression is performed in R 4.2.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that among 601 adolescents, the majorities (334) of them were male, and 267 were females, with a mean age of 17.07 years. Most of the adolescents (78.9%) were living in urban areas, while only small percentages (4.5%) were living in rural areas. The majority of the adolescents (35.8%) came from families with a monthly expenditure greater than 100,000 Bangladeshi Taka. The majority of the adolescents (87.4%) are Muslims. Most of the adolescents (82.2%) do not smoke, and the majority (96.5%) does not consume alcohol. About one-third of the adolescents (32.1%) reported having a sleeping problem. The majority of adolescents (48.1%) first consumed pornography at the age of 13\u0026ndash;15 years, and the most common medium of use was internet (60.7%). The most common source of first concept of pornography was friends (42.6%). The majority of adolescents (66.7%) reported having a positive anxiety level. The mean score for problematic pornography use (PPU) was 5.13, with male adolescents reporting a higher mean score (6.71) than female adolescents (3.16).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eSample characteristics of adolescents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;267)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;601)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.0% (n\u0026thinsp;=\u0026thinsp;237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.8% (n\u0026thinsp;=\u0026thinsp;237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.9% (n\u0026thinsp;=\u0026thinsp;474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7% (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5% (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5% (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6% (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5% (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8% (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9% (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2% (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMonthly family expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0% (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9% (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2% (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,001\u0026ndash;30,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2% (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4% (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5% (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30,001\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2% (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6% (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8% (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51,001-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.3% (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6% (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8% (n\u0026thinsp;=\u0026thinsp;203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.3% (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.6% (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.8% (n\u0026thinsp;=\u0026thinsp;215)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIslam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.5% (n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.4% (n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.4% (n\u0026thinsp;=\u0026thinsp;525)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHinduism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5% (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5% (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5% (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristianity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7% (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1% (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3% (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0% (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5% (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.0% (n\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.1% (n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.2% (n\u0026thinsp;=\u0026thinsp;494)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0% (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9% (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8% (n\u0026thinsp;=\u0026thinsp;107)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.0% (n\u0026thinsp;=\u0026thinsp;324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.9% (n\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.5% (n\u0026thinsp;=\u0026thinsp;580)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0% (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1% (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5% (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSleeping problem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.7% (n\u0026thinsp;=\u0026thinsp;253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1% (n\u0026thinsp;=\u0026thinsp;155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9% (n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3% (n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9% (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.1% (n\u0026thinsp;=\u0026thinsp;193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAge at first pornography consumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7% (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4% (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.3% (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.3% (n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8% (n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1% (n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.4% (n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.4% (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.8% (n\u0026thinsp;=\u0026thinsp;233)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6% (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4% (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8% (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMedium of pornography consumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBooks/Magazines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1% (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6% (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7% (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9% (n\u0026thinsp;=\u0026thinsp;220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.3% (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.7% (n\u0026thinsp;=\u0026thinsp;365)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV/DVD/Downloaded videos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9% (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.3% (n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3% (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2% (n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7% (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3% (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGetting first concept of pornography from\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.9% (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.7% (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.6% (n\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElder brothers or sisters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2% (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1% (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1% (n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.9% (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.2% (n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.3% (n\u0026thinsp;=\u0026thinsp;242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.0% (n\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.1% (n\u0026thinsp;=\u0026thinsp;147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7% (n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0% (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.9% (n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.3% (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPU (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePerceived Social Support Score (PSSS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriends (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.68\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant other (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSSS total (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.67\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.75\u0026thinsp;\u0026plusmn;\u0026thinsp;16.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.60\u0026thinsp;\u0026plusmn;\u0026thinsp;15.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSelf-Esteem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.3% (n\u0026thinsp;=\u0026thinsp;295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.8% (n\u0026thinsp;=\u0026thinsp;229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.2% (n\u0026thinsp;=\u0026thinsp;524)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow self-esteem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7% (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2% (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8% (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA Loneliness score (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePPU, problematic pornography use (BPS total score); SD, standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidity and reliability analysis:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the reliability coefficients (Cronbach's alpha) and correlations between the Brief Pornography Screen (BPS) and other measures. The BPS has high reliability (α\u0026thinsp;=\u0026thinsp;0.843) and is positively correlated with perceived social support (r\u0026thinsp;=\u0026thinsp;0.276), loneliness (r\u0026thinsp;=\u0026thinsp;0.382), and anxiety (r\u0026thinsp;=\u0026thinsp;0.358), but weakly correlated with self-esteem (r\u0026thinsp;=\u0026thinsp;0.061). The Multidimensional Scale of Perceived Social Support (MSPSS) shows high reliability (α\u0026thinsp;=\u0026thinsp;0.904) and positive correlations with both BPS and self-esteem (r\u0026thinsp;=\u0026thinsp;0.276 and r\u0026thinsp;=\u0026thinsp;0.154, respectively). However, it does not significantly correlate with loneliness or anxiety. The Rosenberg's Self-Esteem Scale (RSES) has high reliability (α\u0026thinsp;=\u0026thinsp;0.824), modestly correlates with BPS (r\u0026thinsp;=\u0026thinsp;0.061), and negatively correlates with loneliness (r = -0.011), but not with social support or anxiety. The UCLA Loneliness Scale exhibits strong reliability (α\u0026thinsp;=\u0026thinsp;0.823), positively correlating with depression and anxiety, but lacks significant correlations with self-esteem or social support. The General Anxiety Disorder-7 (GAD-7) scale demonstrates strong reliability (α\u0026thinsp;=\u0026thinsp;0.820) and significant positive correlations with BPS, loneliness, and adverse correlation with self-esteem, but no significant correlation with social support.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eReliability and associations between the Brief Pornography Screen (BPS), UCLA Loneliness Scale, Rosenberg's Self-Esteem Scale (RSES), Multidimensional Scale of Perceived Social Support (MSPSS), and GAD-7 (General Anxiety Disorder-7)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRSES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUCLA Loneliness Scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;10\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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA Loneliness Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.482\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.482\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the results of an adolescent pornography use, presenting the frequency distribution and component matrix of the five-item BPS. The component matrix shows the correlation coefficients between each BPS statement and the latent variable, with the statement \"You find it difficult to resist strong urges to use pornography\" having a correlation coefficient of 0 .796.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eFrequency distribution and component matrix of five item Brief Pornography Screen (BPS) among adolescents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOccasionally n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery often n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComponent matrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou find yourself using pornography more than you want to.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou have attempted to \u0026ldquo;cut back\u0026rdquo; or stop using pornography, but were unsuccessful.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e342(56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou find it difficult to resist strong urges to use pornography.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou find yourself using pornography to cope with strong emotions (e.g., sadness, anger, loneliness, etc.).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272(45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou continue to use pornography even though you feel guilty about it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181(30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Structural Equational modelling:","content":"\u003cp\u003eThe findings of the discriminant validity analysis for the concept measures that were utilized in the research are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The diagonal values in the table reflect the square root of the average variance extracted (AVE) for each construct, and the table itself illustrates the correlation coefficients between the various constructs. The numbers that lie outside of the diagonal represent the correlations that exist between the constructs. The correlation coefficient for the BPS construct is 0.732, which is greater than the correlation coefficients for other constructs, which range from 0.186 to 0.458. The square root of the AVE for the BPS construct is 0.732. The square root of the AVE for the GAD construct is 0.734, which is greater than the correlation coefficients with other constructs, which range from 0.212 to 0.578. In a similar vein, the GAD construct was found to have a higher AVE. The findings of this analysis reveal that the construct measures that were utilized in the study had appropriate discriminant validity. This indicates that the constructs being measured by the various instruments are unique from one another rather than overlapping with one another (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eDiscriminant validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRSES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUCLA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the structural model in SEM is shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which examines the relationships between the latent variables (BPS, GAD, MSPSS, RSES, and UCLA) and their corresponding observed indicators. The results suggest that the path from GAD to BPS is statistically significant (t\u0026thinsp;=\u0026thinsp;5.84), indicating that higher levels of generalized anxiety disorder (GAD) are associated with higher levels of problematic pornography use (BPS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the path from MSPSS to BPS is also statistically significant (t\u0026thinsp;=\u0026thinsp;4.902), suggesting that higher levels of perceived social support (MSPSS) are associated with lower levels of problematic pornography use. However, the paths from GAD to RSES, MSPSS to RSES, and RSES to BPS are not statistically significant (t\u0026thinsp;\u0026lt;\u0026thinsp;1.96), indicating that there is no evidence of a significant relationship between these variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the other hand, the paths from UCLA to BPS and UCLA to GAD are statistically significant (t\u0026thinsp;=\u0026thinsp;5.555 and t\u0026thinsp;=\u0026thinsp;16.618, respectively), suggesting that higher levels of loneliness (UCLA) are associated with higher levels of problematic pornography use (BPS) and higher levels of generalized anxiety disorder (GAD).The structural model suggests that GAD, MSPSS, and UCLA are important predictors of problematic pornography use, but RSES is not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural model results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecisions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD -\u0026gt; BPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD -\u0026gt; RSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS -\u0026gt; BPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS -\u0026gt; RSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSES -\u0026gt; BPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA -\u0026gt; BPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA -\u0026gt; GAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Factors associated with problematic pornography use","content":"\u003cp\u003eThe Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results of a hierarchical linear regression analysis that aimed to identify factors associated with problematic pornography use. The table presents the results for three models. Model 1 includes only the constant (intercept) term and the independent variables, while Model 2 adds demographic variables to the model. Model 3 includes all independent variables. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the beta (β) coefficients, which indicate the strength and direction of the association between each independent variable and the dependent variable (problematic pornography use), When other variables in the model are in control, the independent variables include: age of first exposure to pornography (in three categories), sex, residence, monthly family cost, religion, smoking status, alcohol consumption, sleeping problem, and different sources of pornography consumption knowledge. The results show that problematic pornography use was associated with being male (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), having a higher monthly family cost (β\u0026thinsp;=\u0026thinsp;0.12 to 0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), being a Muslim (β = -0.08 to -0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), using pornography on the internet (β = -0.14 to -0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and consuming pornography through TV/DVD/downloaded videos (β = -0.44 to -0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age of first exposure to pornography, residence, smoking status, alcohol consumption, sleeping problem, and different sources of pornography consumption knowledge were not significant predictors of problematic pornography use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical linear regression: Factors associated with problematic pornography use (N\u0026thinsp;=\u0026thinsp;601)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Predictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ (p-value)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst PC Age 13\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01 (0.224)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst PC Age 16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06 (0.726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03 (0.567)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst PC Age 18+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (0.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (0.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01 (0.729)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (0.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04 (0.268)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence Semi-Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (0.808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (0.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (0.711)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11 (0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07 (0.260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05 (0.427)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Family Cost 10000\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03 (0.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.295)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Family Cost 30000\u0026ndash;50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06 (0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Family Cost 51000\u0026ndash;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15 (0.013) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12 (0.043) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Family Cost\u0026thinsp;\u0026gt;\u0026thinsp;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18 (0.003) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13 (0.037) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion Hinduism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10 (0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08 (0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (0.215)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion Islam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18 (0.003) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12 (0.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09 (0.111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (0.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06 (0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04 (0.251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status Never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05 (0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (0.512)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status Regularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (0.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (0.410)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status sometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (0.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01 (0.849)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption Never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 (0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08 (0.062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption Regularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (0.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (0.313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption Sometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (0.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.190)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleeping problem Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04 (0.183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePornography Media Internet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14 (0.035) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePornography Media Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (0.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (0.714)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePornography Media TV/DVD/Downloaded videos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.55 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.44 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePornography consumption knowledge from Friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (0.707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03 (0.567)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePornography consumption knowledge from Others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (0.035) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.12 (0.008) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCLA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11 (0.002) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11 (\u0026lt;\u0026thinsp;0.001) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSES score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01 (0.716)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.3843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.5386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.3696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.4829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.5143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;26.12 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;22.55 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;22.18 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eB Unstandardized coefficient, β Standardized coefficient, * p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur research examines the prevalence of problematic pornography use among adolescents and its associations with mental health status. Significant sex differences were observed in pornography consumption and its association with mental health. A study conducted with undergraduate Bangladeshi students found that 72% reported engaging with pornography at least once in their lifetimes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This rate was lower compared to studies from India (80%), Sweden (98%), and Australia (87%), but higher than a previous studies in Bangladesh (42%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Using the Brief Pornography Screen (BPS), our study assessed attitudes and behaviors related to problematic pornography use. The findings revealed a substantial prevalence of problematic pornography use among school-going students, with over one-third experiencing difficulties in controlling their consumption, resisting urges, and suffering from adverse mental health outcomes which is similar to previous studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Several factors contribute to this high volume of problematic use, including adolescent curiosity, societal discouragement, and peer influence, lack of comprehensive sex education, easy internet accessibility, unrealistic portrayals, and emotional/psychological factors.\u003c/p\u003e \u003cp\u003eAmong the adolescents included in this study, it is noteworthy that the age at which they were first exposed to pornography was comparatively early, indicating a relatively low age of initial exposure, with the majority reporting exposure between the ages of 13 and 15. This finding aligns with previous research indicating that adolescents are exposed to pornography at increasingly younger ages due to easy accessibility through the internet and other digital platforms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Early exposure to pornography raises concerns regarding its potential influence on adolescent sexual development and attitudes toward sex.\u003c/p\u003e \u003cp\u003eThe present study employed a hierarchical linear regression analysis to investigate the factors linked to problematic pornography use while controlling for demographic variables. Male gender, higher monthly family expenditure, adherence to the Muslim faith, and the utilization of television/DVD/downloaded videos were found to be the substantial predictors of problematic pornography use. Previous studies also found being Muslim and male to be significant predictors of this outcome while being male was one of the most common ones [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One plausible explanation for this could be that the ventromedial prefrontal cortex (vmPFC) of males and females is activated sexually differently. According to studies of sexual neuroimaging, females have lesser responses to visually erotic stimuli than males [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, it is probable that society's and cultures strong influences, best exemplified by a sexual double standard, overlook male sexual expressions and undermine female sexuality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Secondly, the association between higher monthly family costs and problematic pornography use may be attributed to the notion that adolescents from more affluent backgrounds may have greater access to technology, including devices and internet connectivity, facilitating increased exposure to and consumption of pornography. Furthermore, the observation that being a Muslim was a significant predictor implies that religious beliefs and cultural norms play a role in shaping individuals' attitudes towards pornography, potentially leading to a higher likelihood of perceiving pornography use as problematic within this particular religious\u0026rsquo; context which is similar to other studies in this field [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Lastly, the significance of consuming pornography through TV/DVD/downloaded videos suggests that the medium through which pornography is accessed and consumed influences the development of problematic usage patterns. This may be attributed to the potential for continuous and repetitive exposure to explicit content when using these specific sources, which can contribute to habituation, escalation, and the reinforcement of these problematic behaviors. These findings underscore the significance of gender-related dynamics, socio-economic circumstances, and specific modalities of pornography consumption in elucidating adolescents' proclivity for engaging in problematic pornography use.\u003c/p\u003e \u003cp\u003eConsistent with prior research [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a significant number of students attending school in our study reported experiencing symptoms of severe and extremely severe anxiety, tension, and loneliness. The findings from this study indicate a potential association between problematic pornography use and elevated levels of loneliness and anxiety among adolescents. These results are consistent with previous research, which also supports the notion that pornography use can contribute to feelings of loneliness and heightened anxiety in this population [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is plausible that adolescents who experience loneliness might turn to pornography as a coping mechanism to alleviate their feelings of isolation or escape from real-world social interactions. The temporary gratification and perceived connection offered by pornography may serve as a substitute for genuine social connections, thereby exacerbating feelings of loneliness over time.\u003c/p\u003e \u003cp\u003eConversely, adolescents with strong perceived social support networks may develop healthier coping mechanisms and alternative sources of companionship, reducing their reliance on problematic pornography use. Moreover, the findings from our study unveiled a significant correlation between higher levels of problematic pornography use and elevated levels of Generalized Anxiety Disorder (GAD). This observation suggests a potential association between anxiety and the consumption of pornography. These results contribute to the growing body of literature that posits a plausible link between pornography use and compromised psychosocial well-being among adolescents and college students who exhibit addictive behaviors related to internet pornography [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. One possible explanation for the association between problematic pornography use and psychosocial problems in adolescents revolves around the content and nature of pornography itself. Pornographic materials often portray unrealistic and idealized sexual scenarios, which can distort adolescents' perceptions of sexuality, body image, and relationships. Exposure to explicit content can lead to feelings of inadequacy and increased anxiety about sexual performance and attractiveness. Excessive pornography consumption may desensitize individuals to sexual stimuli, affecting intimate relationships. Moderating factors like pre-existing body image concerns and the ability to distinguish fantasy from reality can influence the relationship between problematic pornography use and self-esteem. The complex interplay between individual factors and specific content may contribute to the observed weak correlation in the study.\u003c/p\u003e \u003cp\u003eOur study found a significant correlation between problematic pornography use and decreased levels of perceived social support, as measured by the Brief Pornography Scale and the Multidimensional Scale of Perceived Social Support (MSPSS). This highlights the importance of social support as a potential protective factor for adolescents exposed to internet pornography. Adolescents with higher social support, especially from parents and friends, showed less negative impact from internet pornography exposure, consistent with a Korean study from 2019 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo promote overall well-being, interventions should provide accurate knowledge, foster critical thinking, and create supportive environments for adolescents navigating the challenges of pornography use. Future research should explore the long-term consequences of problematic pornography use and evaluate the effectiveness of interventions to mitigate its harmful effects. a bit concise but keep original elements\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eFirstly, this study involved participants from one country and caution is necessary in relation to generalizability and various limitations exist that require consideration for both the interpretation of the results. Secondly, as with all surveys utilizing self-reporting, there is potential for recall bias. Thirdly, the cross sectional design of this study limits the ability to explore causality, Fourthly, We only looked at the mental health consequences and social support issues relating to pornography but there are still a wide array of issues that need to be investigated in future studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this research sheds light on the prevalence of problematic pornography use among adolescents and its association with mental health status. The findings reveal a substantial presence of problematic pornography use among school-going students, with male gender, higher family expenditure, adherence to the Muslim faith, and utilization of specific media sources being significant predictors of such behavior. Early exposure to pornography at a relatively young age raises concerns about its potential impact on adolescent sexual development and attitudes toward sex. The study establishes a potential link between problematic pornography use and elevated levels of loneliness and anxiety, emphasizing the need for interventions that promote accurate knowledge, critical thinking, and supportive environments to address these issues. Moreover, the observed correlation between problematic pornography use and decreased levels of perceived social support underscores the importance of strengthening social support networks to mitigate the negative effects of internet pornography exposure. Future research should delve into the long-term consequences of problematic pornography use and evaluate the effectiveness of targeted interventions to safeguard adolescent well-being.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed \u0026nbsp;in the current study is available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang X et al (2019) Risky Sexual Behaviors and Associated Factors Among College Students in Lusaka, Zambia, \u003cem\u003eArch. Sex. 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An examination of temporal relationships in two independent panel samples, \u003cem\u003ePLOS ONE\u003c/em\u003e, vol. 13, no. 8, p. e0202048, Aug. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0202048\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0202048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin J, Lee CH (May 2019) Exposure to internet pornography and sexually aggressive behaviour: protective roles of social support among Korean adolescents. J Sex Aggress 25(2):90\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13552600.2018.1528795\u003c/span\u003e\u003cspan address=\"10.1080/13552600.2018.1528795\" 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":"Pornography, mental health, anxiety, loneliness, social support, adolescent, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-3870491/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3870491/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003ePornographic use has been associated with poor mental health consequences. This cross-sectional study aimed to examine the relationship between problematic pornography use and mental health issues among adolescents in Bangladesh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study employed a cross-sectional design to evaluate the relationship between problematic pornography uses with mental health status of adolescents in Bangladesh. The survey, which involved demographic questions and scales like the University of California, Los Angeles (UCLA) Loneliness Scale, the Multidimensional Scale of Perceived Social Support (MSPSS), the General Anxiety Disorder-7, and the Brief Pornography Screen, included a sample of 601 teenagers. Data were analyzed using hierarchical linear regression models after the scales underwent step-by-step validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e It was discovered that boys scored higher (6.71) than girls (3.16), with the mean score for problematic pornography use (PPU) being 5.13. The Brief Pornography Scale revealed substantial relationships with anxiety (r=0.358), loneliness (r=0.382), and perceived social support (r=0.276). The structural modeling equation and hierarchical linear regression analysis showed that problematic use of pornography was associated with being male (r=0.26 to 0.45, p=0.001), higher monthly family cost (r=0.12 to 0.33, p=0.05), accessing pornography online (r=-0.14 to -0.27, p=0.05), consuming pornography through TV/DVD/downloaded videos (r=-0.44 to -0.55). However, the RSES scale did not reveal any correlation between problematic pornography consumption and self-esteem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study found significant relationship with problematic pornography use and mental health status of Bangladeshi adolescent. It emphasizes the significance of raising awareness about the danger of problematic pornography use. The results of this study highlight the necessity for focused interventions and educational initiatives addressing the possible hazards connected to this population's pornographic use.\u003c/p\u003e","manuscriptTitle":"Examining the relationship between problematic pornography use and mental health status among adolescents of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-01-06 20:39:23","doi":"10.21203/rs.3.rs-3870491/v2","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}},{"code":1,"date":"2024-01-30 11:59:03","doi":"10.21203/rs.3.rs-3870491/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":"ac8855d9-ba96-4722-abb8-580ce9a818ef","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28456677,"name":"Biological sciences/Psychology/Human behaviour"},{"id":28456678,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2024-08-19T04:40:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 20:39:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3870491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3870491","identity":"rs-3870491","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}