Abstract
Background and aims
The Moral Incongruence Model of Pornography Use proposes that pornography-use-related problems may be present due to problematic pornography use (PPU) and/or moral disapproval (MD) of pornography use. Despite some supporting empirical evidence, no study has tested the presence of different pornography-use profiles based on individuals' behavioral dysregulation (i.e., PPU) and moral values concerning pornography use. The generalizability of previous findings to diverse populations has also been limited given the scarcity of studies conducted outside of Western countries.
Methods
Using data from the International Sex Survey (42 countries, N = 66,994; Mage = 32.16 years, SD = 12.27), we conducted latent profile analysis to identify pornography-use profiles based on individuals' frequency of use, MD, and PPU. The profiles were compared along a wide range of pornography-use-related, sexuality-related, and psychological correlates.
Results
Six pornography-use profiles were identified, including two increased risk groups (i.e., Increased risk of PPU without MD and Increased risk of PPU with some MD). Several factors differentiated between the increased risk vs. no/low risk profiles (e.g., relatedness satisfaction) as well as between the two increased risk profiles (e.g., religiosity). Apart from behavioral dysregulation, moral values concerning pornography use played an important role in distinguishing pornography-use profiles and demonstrated the importance of inquiring about MD when working with individuals with pornography-use-related problems.
Conclusion
Findings also support recent calls for better-integrated sex therapy and sexual medicine perspectives into pornography-use-related problems research and care.
Large-scale and nationally representative survey studies from North America, Europe, and Australia suggest that more than two-thirds (i.e., 70–95%) of adults report lifetime pornography use, with women using pornography around once a month and men using it once a week or more frequently (Bőthe, Tóth-Király, Griffiths, et al., 2021; Grubbs, Kraus, & Perry, 2019; Herbenick et al., 2020; Lewczuk, Glica, Nowakowska, Gola, & Grubbs, 2020; Rissel et al., 2017). Among these individuals, 1–11% report pornography-use-related problems, which could result in enduring impairments across multiple areas of functioning, including potential mental and physical health as well as interpersonal and social issues (e.g., job loss, divorce; Grubbs, Kraus, et al., 2019; Lewczuk et al., 2020; Rissel et al., 2017; Sniewski & Farvid, 2020; Wéry et al., 2016). Even though out-of-control pornography use is one of the most common manifestations of Compulsive Sexual Behavior Disorder (CSBD; i.e., persistent patterns of poorly controlled sexual behaviors along with significant distress and functional impairment), recently included in the 11th edition of the International Classification of Diseases (ICD-11), not all individuals with pornography-use-related problems meet the diagnostic criteria for CSBD (Kraus et al., 2018; Reed et al., 2022; Reid et al., 2012; World Health Organization, 2022).
The Moral Incongruence Model of Pornography Use, an empirically supported integrative framework, offers a potential explanation for this phenomenon (Grubbs & Perry, 2019; Grubbs, Perry, Wilt, & Reid, 2019). This model posits that while a range of individuals may report problems with pornography use, only some experience dysregulated use. Indeed, some individuals may experience problems due to dysregulated, compulsive, or excessive use (i.e., problematic pornography use [PPU]), in line with the CSBD diagnostic guidelines. Others may experience distress due to their pornography use conflicting with their moral or sexual values, such as the moral disapproval (MD) of pornography use (i.e., pornography problems due to moral incongruence [PPMI]). Notably, PPMI is described in the CSBD diagnostic guidelines as an additional clinical feature: If an individual's distress is completely due to moral judgments and disapproval of sexual behaviors or impulses, the diagnosis of CSBD should not be made. Finally, some individuals may experience problems with their pornography use due to both dysregulation and MD (i.e., PPU with PPMI). They should be diagnosed with CSBD and their PPMI should be addressed in treatment (Kraus & Sweeney, 2019).
The ICD-11 diagnostic guidelines for CSBD also highlight the importance of careful consideration of boundaries with normality, given the wide variation in the nature and frequency of individuals' sexual thoughts, impulses, and behaviors (World Health Organization, 2022). The frequency of pornography use is a potential indicator of poorly controlled or dysregulated behavior, as a meta-analysis suggested that the quantity of pornography use had a positive, moderate association with PPU (L. Chen et al., 2022; World Health Organization, 2022). However, individuals with frequent pornography use who exhibit neither impaired control over their use nor significant distress or functional impairment should not be diagnosed with CSBD (World Health Organization, 2022). The significance of this differentiation is further supported by the notion that a significant portion of people recreationally engage in high-frequency pornography use – for example, due to higher levels of sexual desire – without any problem (Bőthe, Tóth-Király, Potenza, Orosz, & Demetrovics, 2020; Carvalho, Štulhofer, Vieira, & Jurin, 2015; Štulhofer, Bergeron, & Jurin, 2016; Štulhofer, Jurin, & Briken, 2016).
Although the ICD-11 stresses the importance of accurate differential diagnosis of individuals with PPU vs. PPMI vs. non-problematic, high-frequency pornography use (World Health Organization, 2022), no study has provided empirical evidence for the presence of different pornography-use groups simultaneously considering the frequency of use, MD, and PPU. Preliminary findings across three studies using person-centered statistical approaches suggest five distinct pornography-use profiles of community and treatment-seeking adults from Canada, China, and Hungary, based on pornography-use frequency and PPU, or PPU and MD (Bőthe, Tóth-Király, et al., 2020; L. Chen, Jiang, Luo, Kraus, & Bőthe, 2021; Vaillancourt-Morel et al., 2017). The first profile included those who used pornography with a low frequency and did not report pornography-use-related problems (ranging between 68 and 76% of individuals across studies). The second profile included those who used pornography with a low frequency but felt highly distressed about it (i.e., potential PPMI, 13% of individuals). The third profile included those who used pornography frequently without pornography-use-related problems (ranging between 19 and 29% of individuals across studies). The fourth profile included those who used pornography frequently and reported PPU as well (ranging between 3 and 13% of individuals across studies). Finally, the fifth profile included those with high-frequency pornography use and PPU with MD (29% of individuals). However, this last estimate is based on a treatment-seeking male sample, resulting in the potential overestimation of the profile's size. These distinct groups did not only differ in their pornography-use characteristics, but also in their sociodemographic, sexual, and psychological characteristics (e.g., impulsivity and depression were higher in the PPU group than in the high-frequency, non-problematic-use group) (Bőthe, Tóth-Király, et al., 2020; L. Chen et al., 2021; Vaillancourt-Morel et al., 2017). These preliminary findings underscore the need for accurate diagnosis of individuals with pornography-use-related problems and careful differentiation on the basis of considerations regarding PPU, MD, and pornography-use frequency. In addition, characterizing these distinct groups along a wide range of sociodemographic, sexuality-related, and psychological characteristics may provide possible targets for prevention and intervention strategies uniquely tailored to the specific needs of individuals with different pornography-use habits.
Although the aforementioned findings support the presence of different pornography use profiles among individuals, they include several limitations (for overviews, see Grubbs, Hoagland, et al., 2020; Grubbs & Kraus, 2021). First, no study has simultaneously considered individuals' pornography-use frequency, MD, and PPU to create and compare pornography-use groups, despite each characteristic being important in differentiating between problematic and non-problematic patterns of use. Another main limitation pertains to the homogeneity of samples. Although culture, gender, and sexual orientation are discussed in the ICD-11 as important features in diagnosing CSBD, previous studies were mostly conducted among heterosexual or gay men and in Western countries, significantly limiting the generalizability of findings and knowledge of PPU and/or PPMI (L. Chen et al., 2022; Grubbs, Hoagland, et al., 2020; Jennings, Gleason, & Kraus, 2022; Kowalewska, Gola, Kraus, & Lew-Starowicz, 2020). This is problematic as both sexual behaviors and moral values are outcomes of a complex set of social, cultural, and historical processes (Ahorsu et al., 2023; L. Chen et al., 2022; Parker, 2009; Vaillancourt-Morel & Bergeron, 2019; World Health Organization, 2022). These culture-related variations concerning pornography-use-related problems have been emphasized in a recent meta-analysis. Findings suggested that the associations between the quantity of pornography use and PPU were stronger in more conservative countries (e.g., China), illustrating the importance of examining pornography-use-related problems in a multicultural context (L. Chen et al., 2022).
The first aim of the present study was to identify pornography-use profiles based on individuals' pornography-use frequency, MD,1 and PPU in a culturally-, gender-, and sexually- diverse sample of individuals from 42 countries. Based on the notions of the Moral Incongruence Model of Pornography Use, clinical reports, and previous empirical work (Bőthe, Tóth-Király, et al., 2020; L. Chen et al., 2021; Grubbs, Perry, et al., 2019; Grubbs & Perry, 2019; Kraus & Sweeney, 2019; Vaillancourt-Morel et al., 2017), five distinct profiles of pornography use were hypothesized: (P1) low-frequency, non-problematic use, (P2) high-frequency, non-problematic use, (P3) low-frequency PPMI, (P4) high-frequency PPU, (P5) high-frequency PPU with PPMI, see Table 1 for the hypothesized profile configurations.
Configurations of hypothesized pornography-use profiles
| High-frequency pornography use | Moral disapproval of pornography | Problematic pornography use | Use profile |
| X | X | X | Low-frequency, non-problematic use (P1) |
| ✓ | X | X | High-frequency, non-problematic use (P2) |
| X | ✓ | ✓ | Low-frequency, PPMI (P3) |
| ✓ | X | ✓ | High-frequency, PPU (P4) |
| ✓ | ✓ | ✓ | High-frequency, PPU with PPMI (P5) |
Note. PPMI = pornography problems due to moral incongruence; PPU = problematic pornography use.
The second aim of the study was to provide a comprehensive portrait of the identified use profiles by comparing them across sociodemographic, pornography-use-related, sexuality-related, and psychological characteristics that have previously differentiated individuals with different pornography use profiles or that are clinically relevant for PPU and PPMI. Sociodemographic characteristics included participants' gender, sexual orientation, age, relationship status, religious affiliation, and country of residence (Bőthe, Tóth-Király, et al., 2020; L. Chen et al., 2021, 2022; Vaillancourt-Morel et al., 2017). Concerning pornography-use-related characteristics, age at first pornography use, duration of pornography use per session, pornography-use motivations, and past and present treatment-seeking for pornography use were compared across the profiles (Bőthe, Tóth-Király, Bella, et al., 2021; Bőthe, Tóth-Király, et al., 2020; L. Chen et al., 2021; Grubbs, Wright, Braden, Wilt, & Kraus, 2019). Sexuality-related characteristics included the frequency of masturbation and partnered sexual activities as well as sexual well-being indicators (i.e., sexual desire, sexual satisfaction, sexual function, and sexual distress) (Bőthe, Tóth-Király, Demetrovics, & Orosz, 2021; Bőthe, Tóth-Király, et al., 2020; Bőthe, Vaillancourt-Morel, Dion, Štulhofer, & Bergeron, 2021; Vaillancourt-Morel et al., 2017; Štulhofer, Bergeron, & Jurin, 2016; Štulhofer, Jurin, & Briken, 2016). As for psychological characteristics, religiosity, impulsivity, compulsivity, basic psychological needs, depressive and anxiety symptoms, adult attention deficit hyperactivity disorder (ADHD), alcohol use disorder, and substance use were also considered (Bőthe, Koós, Tóth-Király, Orosz, & Demetrovics, 2019; Bőthe, Tóth-Király, et al., 2019, 2020; Grant Weinandy, Lee, Hoagland, Grubbs, & Bőthe, 2023; Grubbs, Perry, et al., 2019; Kraus, Potenza, Martino, & Grant, 2015). Hypothesized differences between the profiles are presented in Table S1. All research questions and hypotheses were preregistered.
Method
Procedure
This study used data from the International Sex Survey (ISS) (Bőthe, Koós, Nagy, Kraus, et al., 2021). The ISS is an international, multi-language, cross-sectional, self-report survey among a community sample of adults using a preregistered study protocol (link to the general study protocol preregistration). Recruitment was conducted in 42 countries2 between October 2021 and May 2022 using different advertisement strategies (e.g., social media posts, contacting sexuality-related organizations). Individuals were eligible to participate in the study if they reached the minimal age for participation in their country (e.g., participants needed to be aged 20 years or above in Taiwan, or 18 years or above in Canada). Eligible participants completed an anonymous survey on the Qualtrics Research Suite (Qualtrics, 2022), which took approximately 25–45 min. Participants did not receive compensation for their participation, but they could select one of the non-profit, sexuality-related international organizations to receive a 0.50 USD donation (the donation was limited to a maximum of 1000 USD). The list of collaborating countries, a detailed description of the translation and data collection procedures, and more details about the eligibility criteria are described in the study protocol (Bőthe, Koós, Nagy, Kraus, et al., 2021). For complete transparency of data use, all published papers and conference presentations are listed on the project's related Open Science Framework (OSF) pages (link to publications; link to conference presentations), and these links are included in all published papers. The study was approved by all collaborating countries' national/institutional ethics review boards (link to ethics approvals).
Participants
After thorough data cleaning (see data cleaning procedure at https://doi.org/10.17605/OSF.IO/DK78R), 82,243 participants (Mage = 32.39 years, SD = 12.52) were included in the final ISS dataset (link to participants' detailed sociodemographic characteristics by country). In the present study, we included data from all participants who used pornography in the past year as we wanted to examine those individuals who use pornography (N = 66,994; Mage = 32.16 years, SD = 12.27). Participants' sociodemographic information is detailed in Table 2.
Participants' sociodemographic characteristics
| Variables | N = 66,994 | % |
| Country of residence | ||
| Algeria | 22 | <0.01 |
| Australia | 544 | 0.8 |
| Austria | 631 | 0.9 |
| Bangladesh | 184 | 0.3 |
| Belgium | 528 | 0.8 |
| Bolivia | 336 | 0.5 |
| Brazil | 3,114 | 4.7 |
| Canada | 2,262 | 3.4 |
| Chile | 1,002 | 1.5 |
| China | 1,602 | 2.4 |
| Colombia | 1,329 | 2.0 |
| Croatia | 2,032 | 3.0 |
| Czech Republic | 1,183 | 1.8 |
| Ecuador | 233 | 0.4 |
| France | 1,403 | 2.1 |
| Germany | 2,493 | 3.7 |
| Gibraltar | 50 | 0.1 |
| Hungary | 10,038 | 15.0 |
| India | 166 | 0.3 |
| Iraq | 68 | 0.1 |
| Ireland | 1,354 | 2.0 |
| Israel | 942 | 1.4 |
| Italy | 1,766 | 2.6 |
| Japan | 516 | 0.8 |
| Lithuania | 1,558 | 2.3 |
| Malaysia | 987 | 1.5 |
| Mexico | 1,683 | 2.5 |
| New Zealand | 2,446 | 3.7 |
| North Macedonia | 935 | 1.4 |
| Panama | 288 | 0.4 |
| Peru | 2,229 | 3.3 |
| Poland | 7,919 | 11.8 |
| Portugal | 1,701 | 2.5 |
| Slovakia | 975 | 1.5 |
| South Africa | 1,436 | 2.14 |
| South Korea | 1,181 | 1.8 |
| Spain | 1,766 | 2.6 |
| Switzerland | 928 | 1.4 |
| Taiwan | 2,223 | 3.3 |
| Turkey | 741 | 1.1 |
| United Kingdom | 1,131 | 1.7 |
| United States of America | 2,064 | 3.1 |
| Other | 1,005 | 1.5 |
| Sex assigned at birth | ||
| Male | 31,231 | 46.6 |
| Female | 35,753 | 53.4 |
| Gender (original answer options in the survey) | ||
| Masculine/Man | 30,514 | 45.6 |
| Feminine/Woman | 34,008 | 50.8 |
| Indigenous or other cultural gender minority identity (e.g., two-spirit) | 136 | 0.2 |
| Non-binary, gender fluid, or something else (e.g., genderqueer) | 2,051 | 3.1 |
| Other | 254 | 0.4 |
| Gender (categories used in the analyses) | ||
| Man | 30,514 | 45.6 |
| Woman | 34,008 | 50.8 |
| Gender-diverse individuals | 2,441 | 3.6 |
| Trans status | ||
| No, I am not a trans person | 64,440 | 96.2 |
| Yes, I am a trans man | 328 | 0.5 |
| Yes, I am a trans woman | 244 | 0.4 |
| Yes, I am a non-binary trans person | 763 | 1.1 |
| I am questioning my gender identity | 1,020 | 1.5 |
| I don't know what it means | 183 | 0.3 |
| Sexual orientation (original answer options in the survey) | ||
| Heterosexual/Straight | 44,107 | 65.8 |
| Gay or lesbian | 4,273 | 6.4 |
| Heteroflexible | 5,482 | 8.2 |
| Homoflexible | 493 | 0.7 |
| Bisexual | 6,869 | 10.3 |
| Queer | 862 | 1.3 |
| Pansexual | 1,769 | 2.6 |
| Asexual | 731 | 1.1 |
| I do not know yet or I am currently questioning my sexual orientation | 1,624 | 2.4 |
| None of the above | 603 | 0.9 |
| I don't want to answer | 161 | 0.2 |
| Sexual orientation (categories used in the analyses) | ||
| Heterosexual | 44,107 | 65.8 |
| Gay or lesbian | 4,273 | 6.4 |
| Bisexual | 6,869 | 10.3 |
| Queer and pansexual | 2,631 | 3.9 |
| Homo- and heteroflexible identities | 5,975 | 8.9 |
| Asexual | 731 | 1.1 |
| Questioning | 1,624 | 2.4 |
| Other | 603 | 0.9 |
| Highest level of education | ||
| Primary (e.g., elementary school) | 786 | 1.2 |
| Secondary (e.g., high school) | 16,818 | 25.1 |
| Tertiary (e.g., college or university) | 49,376 | 73.7 |
| Current status in education | ||
| Not in education | 40,573 | 60.6 |
| In primary education (e.g., elementary school) | 50 | 0.1 |
| In secondary education (e.g., high school) | 1,331 | 2.0 |
| In tertiary education (e.g., college or university) | 25,011 | 37.3 |
| Work status | ||
| Not working | 16,569 | 24.7 |
| Working full-time | 35,650 | 53.2 |
| Working part-time | 8,949 | 13.4 |
| Doing odd jobs | 5,809 | 8.7 |
| Socioeconomic status | ||
| Considers life circumstances among the worst | 156 | 0.2 |
| Considers life circumstances much worse than average | 582 | 0.9 |
| Considers life circumstances worse than average | 3,547 | 5.3 |
| Considers life circumstances average | 21,437 | 32.0 |
| Considers life circumstances better than average | 26,037 | 38.9 |
| Considers life circumstances much better than average | 12,173 | 18.2 |
| Considers life circumstances among the best | 3,056 | 4.6 |
| Residence | ||
| Metropolis (population is over 1 million people) | 22,164 | 33.1 |
| City (population is between 100,000–999,999 people) | 24,105 | 36.0 |
| Town (population is between 1,000–99,999 people) | 17,071 | 25.5 |
| Village (population is below 1,000 people) | 3,645 | 5.4 |
| Relationship status | ||
| Single | 23,119 | 34.5 |
| In a relationship | 22,487 | 33.6 |
| Married or common-law partners | 19,195 | 28.7 |
| Widow or widower | 273 | 0.4 |
| Divorced | 1,899 | 2.8 |
| Relationship status (categories used in the analyses) | ||
| Single | 25,291 | 37.8 |
| In a relationship | 41,682 | 62.2 |
| Religious affiliation | ||
| Buddhist | 1,145 | 1.7 |
| Christian | 18,399 | 27.5 |
| Confucianist | 14 | <0.1 |
| Hindu | 209 | 0.3 |
| Jain | 8 | <0.1 |
| Jewish | 925 | 1.4 |
| Muslim | 899 | 1.3 |
| Sikh | 28 | <0.1 |
| Spiritist | 366 | 0.6 |
| Taoist | 514 | 0.8 |
| Spiritual but not committed to one religion | 9,493 | 14.2 |
| I am not religious | 33,229 | 49.6 |
| Other | 1,717 | 2.6 |
Note. Percentages might not add up to 100% due to missing data.
Measures
The list of all variables along with a detailed description of all items and scales can be found in the ISS study protocol (Bőthe, Koós, Nagy, Kraus, et al., 2021). Each scale's psychometric properties (e.g., factor structure; measurement invariance across subgroups) have been examined in the first phase of the ISS publications, and validation studies have been published or are in the process of being published (link to published papers). Each used scale was fully or partially invariant across languages, countries, and genders in the aforementioned validation papers; see details in the cited papers below or contact the first author for further information. The following variables were used as profile indicators: past-year pornography-use frequency, Problematic Pornography Consumption Scale (Bőthe et al., 2018; Bőthe, Nagy, et al., 2024), and MD of pornography use (see details on the computation of these variables in the Supplemental Materials).
Profile correlates included sociodemographic characteristics (i.e., gender identity, sexual orientation, age, relationship status, religious affiliation, and country of residence) and pornography-use-related descriptive characteristics (i.e., age at first pornography use, duration of pornography use per session in minutes, and past and current treatment-seeking for pornography use) and pornography-use motivations (Pornography Use Motivations Scale) (Bőthe, Tóth-Király, Bella, et al., 2021; Koós et al., 2024). Sexuality-related characteristics included past-year masturbation frequency, past-year sexual frequency (total and romantic partner), sexual satisfaction (Global Measure of Sexual Satisfaction) (Lawrance & Byers, 1998), sexual desire (Sexual Desire Inventory-2) (Spector, Carey, & Steinberg, 1996), sexual function (Arizona Sexual Experience Scale) (McGahuey et al., 2000), and sexual distress (Sexual Distress Scale) (Derogatis et al., 2011; Lin et al., 2024; Pâquet et al., 2018). Psychological characteristics included religiosity (Grubbs, Kraus, et al., 2019), impulsivity (Short UPPS-P Impulsive Behavior Scale) (Billieux et al., 2012; Fournier et al., 2024), compulsivity (Compulsive Personality Assessment Scale) (Fineberg, Sharma, Sivakumaran, Sahakian, & Chamberlain, 2007), basic psychological needs (Basic Psychological Needs Satisfaction and Frustration Scale) (B. Chen et al., 2015), depressive and anxiety symptoms (Brief Symptom Inventory) (Asner-Self, Schreiber, & Marotta, 2006; Quintana et al., 2024), ADHD (Adult ADHD Self-Report Scale) (Kessler et al., 2005; Lewczuk et al., 2024), alcohol use disorder (Alcohol Use Disorders Identification Test) (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001; Horváth et al., 2023) and substance use frequency (Alcohol, Smoking, and Substance Involvement Screening Test) (Humeniuk et al., 2010; Lee et al., 2023) (see details on the computation of these variables in the Supplemental Materials).3
Statistical analyses
Following the preregistered analytic plans, descriptive statistics and correlations between the study variables were computed in SPSS 29 (IBM Corp, 2021), while the remaining analyses were conducted using Mplus 8.8 (Muthén & Muthén, 2022). Preliminary measurement models were estimated to verify the psychometric properties of our multi-item measures and obtain standardized factor scores (with M = 0 and SD = 1) for the main analyses (see details in the Supplemental Materials). When compared to manifest scale scores (i.e., the sum or average of the items forming a scale), factor scores provide a way to preserve the nature of the underlying measurement model and partially control for unreliability (Morin, 2023; Skrondal & Laake, 2001). These analyses are presented in the online Supplemental Materials and support the adequacy and reliability of all factors.
We used latent profile analysis (LPA) to identify pornography-use profiles based on participants' pornography-use frequency, MD, and PPU (i.e., profile indicators). Alternative LPA solutions, including one to eight pornography-use profiles, were estimated using Mplus' robust maximum likelihood estimator. In the selection of the optimal number of profiles, we considered the meaningfulness, theoretical adequacy, and statistical adequacy of the profile solutions (Morin, 2016; Morin, McLarnon, & Litalien, 2020). A variety of statistical indicators were used to test the adequacy of the profile solutions, including the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC (CAIC), the Sample-Size-Adjusted BIC (SSABIC), the adjusted Lo-Mendell Rubin (aLMR) likelihood ratio test, and the Bootstrap Likelihood Ratio Test (BLRT). A lower value on the AIC, BIC, CAIC, and SSABIC suggests a better-fitting solution. A non-significant p-value for aLMR and BLRT suggests the superiority of a model with one less profile based on the principle of parsimony. Results from statistical simulation studies have demonstrated the utility of the CAIC, BIC, SSABIC, and BLRT, while showing that the AIC and aLMR are not reliable indicators of the number of profiles (e.g., Diallo, Morin, & Lu, 2016, 2017; Peugh & Fan, 2013). For this reason, we only reported these indicators (i.e., AIC and aLMR) to ensure complete disclosure of information but did not use them to guide model selection. Furthermore, as the BIC, CAIC, and SSABIC often keep improving with the addition of more profiles, the graphical examination of “elbow plots” could facilitate the decision-making where the point after which the slope flattens suggests that the optimal number of profiles has been reached. We also reported the entropy (i.e., classification accuracy). After the identification of the final profile solution by considering their meaningfulness, theoretical adequacy, and statistical adequacy, we compared them along the sociodemographic, pornography-use-related, sexuality-related, and psychological characteristics listed in Table S1 (i.e., profile correlates). We used Mplus' BCH and DCAT auxiliary functions to compare the profiles across the aforementioned continuous and non-continuous variables, respectively (Morin et al., 2020).
Ethics
The authors assert that all procedures contributing to this work comply with the relevant national and institutional committees' ethical standards on human experimentation and the Helsinki Declaration. The study was approved by all collaborating countries' national/institutional ethics review boards or the local ethics committees considered the study exempt and did not further assess the study as it had already been approved by the ethics committees of the principal investigators' institutions: https://osf.io/n3k2c/?view_only=838146f6027c4e6bb68371d9d14220b5.
Results
Identification of pornography-use profiles
The results from the solutions, including different numbers of profiles, are reported in Table S8 and graphically displayed in Figure S1 of the Supplemental Materials. Generally, entropy values remained moderate across solutions (varying between 0.70 and 0.79). Inspection of the information criteria showed that all four reached their lowest values at the seven-profile solution, while the aLMR and the BLRT appear to support the eight-profile solution. A complementary examination of the elbow plots supported this conclusion as all information criteria kept decreasing with the inclusion of a new profile, although this decrease became negligible after the six-profile solution. On this basis, solutions including five to seven profiles were more carefully contrasted. This inspection revealed that all solutions were statistically proper and that increasing the number of profiles from five to six resulted in the addition of a theoretically meaningful, well-defined, and distinct profile. In contrast, adding a seventh (or eighth) profile did not bring additional information, but simply resulted in the division of one existing profile into two smaller ones characterized by similar shapes. For these reasons, the six-profile solution was retained for interpretation and further analyses. See Fig. 1 for a graphic depiction of this final solution and Table 3 for the exact within-profile means and variances.
Graphic depiction of the identified pornography use profiles
Note. Scores were estimated from factor scores with a mean of 0 and a standard deviation of 1 or were standardized prior to the analyses. Thus, the reported values for all variables are standardized scores. PPU: problematic pornography use. MD: moral disapproval of pornography use. SD: standard deviation.
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2024.00054
Exact within-profile means, variances, and 95% confidence intervals [95% CI] from the final six-profile solution
| 1. No risk of PPU without MD profile (n = 6,610; 9.87%) | 2. No risk of PPU with some MD profile (n = 6,624; 9.89%) | 3. Low risk of PPU without MD profile (n = 17,594; 6.26%) | 4. Low risk of PPU with some MD profile (n = 15,413; 23.01%) | 5. Increased risk of PPU without MD profile (n = 13,285; 19.83%) | 6. Increased risk of PPU with some MD profile (n = 7,468; 11.15%) | |
| Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | |
| Pornography use frequency | −1.329 [−1.342, −1.317] | −1.381 [−1.391, −1.370] | −0.030 [−0.062, 0.002] | −0.193 [−0.227, −0.160] | 1.042 [1.009, 1.074] | 1.018 [0.978, 1.057] |
| PPU | −0.782 [−0.812, −0.751] | −0.773 [−0.808, −0.738] | −0.175 [−0.204, −0.147] | 0.044 [0.018, 0.071] | 0.766 [0.737, 0.795] | 1.087 [1.046, 1.128] |
| MD | −0.653 [−0.665, −0.641] | 0.980 [0.950, 1.010] | −0.717 [−0.734, −0.700] | 0.793 [0.735, 0.771] | −0.674 [−0.686, −0.661] | 0.960 [0.926, 0.994] |
| Variance [95% CI] | Variance [95% CI] | Variance [95% CI] | Variance [95% CI] | Variance [95% CI] | Variance [95% CI] | |
| Pornography use frequency | 0.059 [0.055, 0.062] | 0.053 [0.051, 0.056] | 0.363 [0.345, 0.382] | 0.405 [0.388, 0.421] | 0.313 [0.300, 0.325] | 0.322 [0.309, 0.336] |
| PPU | 0.275 [0.252, 0.297] | 0.312 [0.285, 0.340] | 0.338 [0.325, 0.351] | 0.460 [0.447, 0.473] | 0.408 [0.386, 0.431] | 0.473 [0.446, 0.500] |
| MD | 0.088 [0.086, 0.090] | 0.788 [0.766, 0.811] | 0.073 [0.068, 0.077] | 0.753 [0.735, 0.771] | 0.084 [0.081, 0.087] | 0.853 [0.828, 0.878] |
Note. CI: Confidence interval; PPU: problematic pornography use; MD: moral disapproval of pornography use. Profile indicators were estimated from factor scores with a mean of 0 and a standard deviation of 1. Thus, the reported values for all variables are standardized scores.
Profile 1 (No risk of PPU without MD profile) included 9.87% of the participants who displayed lower-than-average scores on all three profile indicators. In this profile, individuals used pornography approximately once a year, reported no dysregulation concerning their use, and did not disapprove of pornography use from a moral perspective. Profile 2 (No risk of PPU with some MD profile) comprised 9.89% of participants displaying lower-than-average pornography use frequency (i.e., once a year) and PPU (i.e., no dysregulation), and higher-than-average yet still low levels of MD. Profile 3 (Low risk of PPU without MD profile) was the largest, including 26.26% of individuals displaying average pornography use frequency (i.e., approximately once a month) and PPU (i.e., low levels of dysregulation), and lower-than-average MD (i.e., did not disapprove of pornography use). Profile 4 (Low risk of PPU with some MD profile) included 23.01% of individuals reporting similar pornography use habits as members of the Low risk of PPU without MD profile. They used pornography around once a month and had low levels of dysregulation. They had higher than average yet still low levels of MD (i.e., somewhat disagreed with pornography use being morally wrong). Profile 5 (Increased risk of PPU without MD profile) comprised 19.83% of participants with higher-than-average pornography use frequency (i.e., approximately two to three times a week) and PPU (i.e., somewhat elevated levels of dysregulation), and lower than average MD. Finally, Profile 6 (Increased risk of PPU with some MD profile) included 11.15% of participants with higher-than-average scores on all three profile indicators. Individuals in this profile reported similar pornography use habits as their peers in the Increased risk of PPU without MD profile (i.e., watching pornography approximately two to three times a week and having somewhat elevated levels of dysregulation), and they also reported higher-than-average yet still low levels of MD. Importantly, even though individuals in the two Increased risk profiles reported elevated dysregulation compared to other individuals in the sample, these profiles' mean score did not meet the pre-established cut-off score on the PPU measure (i.e., having ≥76 points).
Profile membership breakdown across sociodemographic characteristics
We characterized the six profiles across a wide range of sociodemographic variables, with the results reported in Table 4. In brief, most men (94%) belonged to the Low risk and Increased risk profiles, while the majority of women (88%) belonged to the No risk and Low risk profiles. The ratio of gender-diverse individuals4 in the profiles was more balanced as 15% belonged to the No risk, 51% to the Low Risk, and 34% to the Increased risk profiles, similar to the composition of profiles by relationship status, religious affiliation, and country of residence. A higher proportion of gay and lesbian individuals (50%) belonged to the two Increased risk profiles compared to individuals with all other sexual orientations (20–31%). Participants in the no MD profiles were slightly older than their peers in the with some MD profiles.
Profile membership breakdown across sociodemographic characteristics
| Sociodemographic characteristics | 1. No risk of PPU without MD profile (n = 6,610; 9.87%) | 2. No risk of PPU with some MD profile (n = 6,624; 9.89%) | 3. Low risk of PPU without MD profile (n = 17,594; 6.26%) | 4. Low risk of PPU with some MD profile (n = 15,413; 23.01%) | 5. Increased risk of PPU without MD profile (n = 13,285; 19.83%) | 6. Increased risk of PPU with some MD profile (n = 7,468; 11.15%) |
| Gender | ||||||
| Men | 3.0% | 3.3% | 22.3% | 20.1% | 32.5% | 18.9% |
| Women | 16.2% | 16.0% | 29.7% | 25.6% | 8.2% | 4.2% |
| Diverse | 7.8% | 7.2% | 28.2% | 22.8% | 23.1% | 10.9% |
| Sexual orientation | ||||||
| Heterosexual | 10.5% | 10.6% | 25.6% | 23.6% | 18.2% | 11.5% |
| Gay or lesbian | 4.4% | 5.0% | 24.5% | 15.8% | 37.1% | 13.3% |
| Bisexual | 8.8% | 9.3% | 28.2% | 23.7% | 20.2% | 9.9% |
| Queer/pansexual | 10.1% | 8.5% | 32.3% | 21.7% | 19.8% | 7.5% |
| Homo- and heteroflexible | 10.1% | 8.5% | 28.7% | 21.5% | 20.6% | 10.6% |
| Asexual | 15.6% | 15.5% | 25.1% | 24.3% | 13.0% | 6.5% |
| Questioning | 9.3% | 11.0% | 23.5% | 27.7% | 16.5% | 12.1% |
| Other | 10.1% | 10.1% | 25.6% | 25.2% | 19.2% | 9.8% |
| Relationship status | ||||||
| Single | 7.9% | 8.9% | 24.0% | 23.3% | 22.1% | 13.7% |
| In a relationship | 11.1% | 10.5% | 27.6% | 22.8% | 18.4% | 9.6% |
| Past treatment-seeking for pornography use | ||||||
| Yes | 0.7% | 3.6% | 7.0% | 19.6% | 26.9% | 42.2% |
| No, because I haven't had any problems with my porn viewing. | 12.1% | 11.4% | 30.6% | 22.3% | 17.8% | 5.9% |
| No, because I haven't felt that it was a serious problem. | 1.7% | 4.2% | 11.8% | 26.5% | 30.6% | 25.1% |
| No, because I haven't known where I should seek help. | 1.1% | 3.1% | 4.1% | 22.6% | 21.5% | 47.6% |
| No, because I would have felt uncomfortable or embarrassed. | 0.5% | 2.4% | 4.1% | 25.5% | 17.9% | 49.5% |
| No, because I couldn't afford it. | 1.6% | 3.5% | 6.1% | 21.1% | 24.3% | 43.4% |
| No, because of other reason. | 7.3% | 12.1% | 17.5% | 24.6% | 18.2% | 20.3% |
| Current treatment-seeking for pornography use | ||||||
| Yes | 1.3% | 2.9% | 6.3% | 18.9% | 25.9% | 44.7% |
| No, because I don't have any problems with my porn viewing. | 11.8% | 11.3% | 30.1% | 22.6% | 18.0% | 6.1% |
| No, because I don't feel that it is a serious problem. | 1.5% | 3.8% | 10.9% | 25.6% | 31.1% | 27.0% |
| No, because I don't know where I should seek help. | 0.7% | 1.1% | 3.3% | 22.0% | 24.3% | 48.7% |
| No, because I would feel uncomfortable or embarrassed. | 0.4% | 2.1% | 3.3% | 23.3% | 18.2% | 52.6% |
| No, because I couldn't afford it. | 2.0% | 2.3% | 4.9% | 21.2% | 23.2% | 46.5% |
| No, because of other reason. | 6.0% | 8.3% | 14.9% | 21.6% | 20.0% | 29.1% |
| Religious affiliation | ||||||
| Christian | 9.7% | 13.2% | 22.3% | 27.0% | 14.8% | 13.1% |
| Buddhist | 5.7% | 6.8% | 20.4% | 24.9% | 24.4% | 17.7% |
| Hindu | 6.4% | 6.9% | 16.6% | 23.0% | 21.8% | 25.3% |
| Muslim | 4.9% | 10.1% | 12.9% | 28.7% | 14.6% | 28.9% |
| Spiritual but not committed to one religion | 11.0% | 10.7% | 26.6% | 24.0% | 17.8% | 9.9% |
| I am not religious | 10.2% | 8.0% | 29.3% | 20.1% | 22.9% | 9.5% |
| Other | 8.0% | 9.3% | 24.5% | 22.6% | 23.9% | 11.5% |
| Jewish | 7.8% | 13.9% | 22.3% | 28.3% | 16.0% | 11.8% |
| Taoist | 4.7% | 4.3% | 19.2% | 26.0% | 26.2% | 19.6% |
| Confucianist | 13.0% | 2.6% | 14.1% | 43.6% | 15.3% | 11.5% |
| Sikh | 7.3% | 7.4% | 27.8% | 22.1% | 26.2% | 9.2% |
| Spiritist | 9.3% | 8.2% | 25.7% | 20.8% | 24.3% | 11.7% |
| Jain | 0.0% | 15.5% | 15.1% | 10.2% | 52.3% | 7.0% |
| Country of residence | ||||||
| Algeria | 10.9% | 6.4% | 4.3% | 22.9% | 19.2% | 36.3% |
| Australia | 7.6% | 7.3% | 28.8% | 19.9% | 25.7% | 10.7% |
| Austria | 11.8% | 9.1% | 30.6% | 21.2% | 19.6% | 7.7% |
| Bangladesh | 2.7% | 8.6% | 8.8% | 44.8% | 11.4% | 23.7% |
| Belgium | 12.3% | 5.4% | 28.6% | 14.9% | 28.3% | 10.5% |
| Bolivia | 5.2% | 11.4% | 17.8% | 28.4% | 18.6% | 18.5% |
| Brazil | 6.8% | 8.3% | 23.0% | 19.1% | 26.7% | 16.1% |
| Canada | 9.2% | 7.0% | 31.2% | 16.5% | 27.2% | 8.8% |
| Chile | 8.8% | 9.4% | 24.9% | 21.8% | 21.7% | 13.4% |
| China | 7.1% | 7.5% | 15.9% | 32.2% | 21.3% | 16.0% |
| Colombia | 15.2% | 16.1% | 21.9% | 27.3% | 9.4% | 10.1% |
| Croatia | 12.9% | 8.0% | 35.9% | 20.9% | 15.8% | 6.5% |
| Czech Republic | 15.4% | 10.2% | 32.3% | 19.7% | 15.4% | 7.0% |
| Ecuador | 9.7% | 10.6% | 21.2% | 25.4% | 15.9% | 17.3% |
| France | 11.5% | 9.2% | 27.3% | 18.9% | 22.6% | 10.4% |
| Germany | 13.4% | 10.4% | 30.2% | 19.7% | 19.5% | 6.9% |
| Gibraltar | 16.2% | 9.3% | 32.3% | 16.9% | 15.6% | 9.7% |
| Hungary | 6.7% | 8.3% | 24.3% | 24.6% | 22.8% | 13.4% |
| India | 4.1% | 9.2% | 17.5% | 17.9% | 29.2% | 22.0% |
| Iraq | 7.5% | 7.5% | 10.0% | 23.7% | 17.0% | 34.4% |
| Ireland | 8.6% | 12.2% | 24.6% | 25.7% | 18.4% | 10.4% |
| Israel | 7.3% | 15.0% | 19.2% | 32.6% | 14.6% | 11.2% |
| Italy | 12.6% | 6.9% | 37.4% | 16.2% | 21.2% | 5.6% |
| Japan | 4.4% | 3.3% | 25.2% | 16.7% | 35.5% | 15.0% |
| Lithuania | 9.7% | 14.1% | 22.6% | 28.2% | 15.7% | 9.7% |
| Malaysia | 3.9% | 8.8% | 15.5% | 26.3% | 22.1% | 23.4% |
| Mexico | 11.7% | 18.3% | 18.7% | 29.1% | 11.5% | 10.8% |
| New Zealand | 9.5% | 9.5% | 28.1% | 20.2% | 23.3% | 9.5% |
| North Macedonia | 9.5% | 6.8% | 31.5% | 21.6% | 21.9% | 8.7% |
| Panama | 8.9% | 9.7% | 24.0% | 21.4% | 21.2% | 14.8% |
| Peru | 9.7% | 12.4% | 24.2% | 24.3% | 18.6% | 10.8% |
| Poland | 15.8% | 12.3% | 33.6% | 22.3% | 11.1% | 4.8% |
| Portugal | 19.3% | 12.3% | 30.5% | 20.4% | 12.4% | 5.0% |
| Slovakia | 5.8% | 11.6% | 20.3% | 29.0% | 19.1% | 14.2% |
| South Africa | 7.2% | 13.2% | 19.3% | 26.9% | 18.2% | 15.2% |
| South Korea | 3.4% | 8.5% | 17.1% | 31.2% | 20.1% | 19.6% |
| Spain | 7.8% | 14.8% | 20.2% | 31.5% | 14.3% | 11.4% |
| Switzerland | 11.6% | 9.9% | 29.5% | 20.8% | 20.3% | 7.8% |
| Taiwan | 5.1% | 3.0% | 19.6% | 20.5% | 30.3% | 21.5% |
| Turkey | 9.1% | 4.8% | 31.6% | 14.6% | 29.3% | 10.7% |
| United Kingdom | 10.4% | 9.5% | 29.0% | 21.2% | 21.1% | 8.8% |
| United States of America | 7.6% | 6.5% | 28.9% | 17.4% | 28.3% | 11.3% |
| Other | 6.6% | 10.1% | 21.2% | 26.5% | 17.4% | 18.2% |
| Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | |
| Age | 0.082 [0.053, 0.111] | −0.105 [−0.132, −0.078] | 0.043 [0.023, 0.063] | −0.152 [−0.172, −0.132] | 0.180 [0.156, 0.204] | −0.086 [−0.115, −0.057] |
Note. CI: Confidence interval. Age was standardized prior to the analyses with a mean of 0 and a standard deviation of 1.
Correlates of profile membership
The results from the analyses of associations between profile membership and correlates and their effect sizes are reported in Table 5. Several correlate comparisons were statistically significant and weak to moderate in effect size, thus supporting the construct validity of the profiles. With respect to the pornography use-related characteristics, results revealed that the levels of correlates were the highest (i.e., longest duration of pornography use per session, higher scores across all pornography use motivation factors) in the Increased risk of PPU with some MD profile, followed by the Increased risk of PPU without MD, Low risk of PPU with some MD, Low risk of PPU without MD, No risk of PPU without MD, and No risk of PPU with some MD profiles. In addition, most participants who sought treatment for their pornography use in the past or were currently in treatment for their pornography use belonged to the two Increased risk profiles (69–71%).
Correlates' means and pairwise comparisons between the six profiles
| 1. No risk of PPU without MD profile (n = 6,610; 9.87%) | 2. No risk of PPU with some MD profile (n = 6,624; 9.89%) | 3. Low risk of PPU without MD profile (n = 17,594; 6.26%) | 4. Low risk of PPU with some MD profile (n = 15,413; 23.01%) | 5. Increased risk of PPU without MD profile (n = 13,285; 19.83%) | 6. Increased risk of PPU with some MD profile (n = 7,468; 11.15%) | Differences between profiles | Cramer's V | |
| Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | Mean [95% CI] | |||
| Pornography use-related characteristics | ||||||||
| Age of first pornography use | 0.402 [0.367, 0.437] | 0.380 [0.345, 0.415] | 0.058 [0.038, 0.078] | 0.045 [0.025, 0.065] | −0.327 [−0.345, −0.309] | −0.334 [−0.358, −0.310] | 6 = 5 < 4 = 3 < 2 = 1 | 0.05 |
| Duration of pornography use per session (in minutes) | −0.254 [−0.274, −0.234] | −0.320 [−0.340, −0.300] | −0.134 [−0.150, −0.118] | −0.079 [−0.099, −0.059] | 0.309 [0.282, 0.336] | 0.427 [0.386, 0.468] | 2 < 1 < 3 < 4 < 5 < 6 | 0.09 |
| Sexual pleasure PUM | −0.853 [−0.875, −0.831] | −0.985 [−1.007, −0.963] | −0.120 [−0.134, −0.106] | −0.113 [−0.129, −0.097] | 0.790 [0.774, 0.806] | 0.825 [0.803, 0.847] | 2 < 1 < 3 = 4 < 5 < 6 | 0.32 |
| Sexual curiosity PUM | −0.343 [−0.368, −0.318] | −0.476 [−0.500, −0.452] | −0.095 [−0.111, −0.079] | −0.049 [−0.067, −0.031] | 0.493 [0.473, 0.513] | 0.457 [0.432, 0.482] | 2 < 1 < 3 < 4 < 5 < 6 | 0.15 |
| Fantasy PUM | −0.684 [−0.706, −0.662] | −0.789 [−0.809, −0.769] | −0.224 [−0.238, −0.210] | −0.082 [−0.098, −0.066] | 0.839 [0.823, 0.855] | 0.946 [0.922, 0.970] | 2 < 1 < 3 < 4 < 5 < 6 | 0.30 |
| Boredom avoidance PUM | −0.607 [−0.627, −0.587] | −0.646 [−0.664, −0.628] | −0.264 [−0.278, −0.250] | −0.070 [−0.086, −0.054] | 0.800 [0.782, 0.818] | 0.983 [0.959, 1.007] | 2 < 1 < 3 < 4 < 5 < 6 | 0.29 |
| Lack of sexual satisfaction PUM | −0.624 [−0.646, −0.602] | −0.654 [−0.676, −0.632] | −0.194 [−0.208, −0.180] | −0.012 [−0.028, 0.004] | 0.722 [0.704, 0.740] | 0.900 [0.876, 0.924] | 2 = 1 < 3 < 4 < 5 < 6 | 0.26 |
| Emotional suppression/distraction PUM | −0.712 [−0.730, −0.694] | −0.751 [−0.769, −0.733] | −0.319 [−0.333, −0.305] | −0.041 [−0.057, −0.025] | 0.887 [0.871, 0.903] | 1.126 [1.102, 1.150] | 2 < 1 < 3 < 4 < 5 < 6 | 0.35 |
| Stress reduction PUM | −0.797 [−0.815, −0.779] | −0.877 [−0.895, −0.859] | −0.289 [−0.303, −0.275] | −0.091 [−0.107, −0.075] | 0.963 [0.947, 0.979] | 1.097 [1.073, 1.121] | 2 < 1 < 3 < 4 < 5 < 6 | 0.38 |
| Self-exploration PUM | −0.470 [−0.494, −0.446] | −0.638 [−0.662, −0.614] | −0.099 [−0.115, −0.083] | −0.086 [−0.104, −0.068] | 0.586 [0.566, 0.606] | 0.513 [0.488, 0.538] | 2 < 1 < 3 = 4 < 5 < 6 | 0.18 |
| Sexuality-related characteristics | ||||||||
| Past-year masturbation frequency | −0.867 [−0.894, −0.840] | −0.958 [−0.985, −0.931] | −0.147 [−0.163, −0.131] | −0.259 [−0.277, −0.241] | 0.905 [0.887, 0.923] | 0.885 [0.861, 0.909] | 2 < 1 < 3 < 4 < 6 = 5 | 0.29 |
| Past-year sexual frequency (total) | 0.156 [0.129, 0.183] | 0.061 [0.032, 0.090] | 0.193 [0.173, 0.213] | −0.033 [−0.055, −0.011] | −0.133 [−0.155, −0.111] | −0.343 [−0.374, 0.312] | 6 < 5 < 4 < 2 < 1 < 3 | 0.06 |
| Past-year sexual frequency (romantic partner) | 0.033 [0.000, 0.066] | 0.058 [0.025, 0.091] | 0.137 [0.115, 0.159] | 0.006 [−0.019, 0.031] | −0.158 [−0.189, −0.127] | −0.237 [−0.284, −0.190] | 6 < 5 < 4 = 1 < 3; 6 < 5 < 2 = 1 < 3; 4 < 2 | 0.03 |
| Sexual satisfaction | 0.016 [−0.008, 0.040] | 0.006 [−0.018, 0.030] | 0.033 [0.017, 0.049] | −0.090 [−0.106, 0.074] | −0.261 [−0.279, −0.243] | −0.367 [−0.389, −0.345] | 6 < 5 < 4 < 2 = 1 = 3 | 0.06 |
| Partner-related desire | −0.292 [−0.319, −0.265] | −0.348 [−0.375, −0.321] | −0.055 [−0.073, −0.037] | −0.158 [−0.176, −0.140] | 0.338 [0.318, 0.358] | 0.242 [0.217, 0.267] | 2 < 1 < 4 < 3 < 6 < 5 | 0.10 |
| Solidarity desire | −0.678 [−0.703, −0.653] | −0.797 [−0.822, −0.772] | −0.148 [−0.164, −0.132] | −0.223 [−0.241, −0.205] | 0.768 [0.750, 0.786] | 0.735 [0.711, 0.759] | 2 < 1 < 4 < 3 < 6 < 5 | 0.25 |
| Attractive person-related desire | −0.458 [−0.483, −0.433] | −0.530 [−0.555, −0.505] | −0.175 [−0.193, −0.157] | −0.136 [−0.154, −0.118] | 0.608 [0.588, 0.628] | 0.591 [0.566, 0.616] | 2 < 1 < 3 < 4 < 6 = 5 | 0.18 |
| Sexual function problems | 0.409 [0.382, 0.436] | 0.460 [0.433, 0.487] | 0.065 [0.047, 0.083] | 0.166 [0.148, 0.184] | −0.445 [−0.463, −0.427] | −0.367 [−0.392, −0.342.] | 5 < 6 < 3 < 4 < 1 < 2 | 0.14 |
| Sexual distress | −0.216 [−0.240, −0.192] | −0.152 [−0.176, −0.128] | −0.215 [−0.231, −0.199] | 0.046 [0.028, 0.064] | 0.279 [0.259, 0.299] | 0.599 [0.574, 0.624] | 1 = 3 < 2 < 4 < 5 < 6 | 0.11 |
| Psychological characteristics | ||||||||
| Religiosity | −0.034 [−0.058, −0.010] | 0.308 [−0.281, 0.335] | −0.134 [−0.150, −0.118] | 0.252 [0.234, 0.270] | −0.112 [−0.130, −0.094] | 0.402 [0.375, 0.429] | 3 = 5 < 1 < 4 < 2 < 6 | 0.09 |
| Impulsivity: Lack of premeditation | −0.011 [−0.036, 0.014] | −0.061 [−0.086, −0.036] | −0.033 [−0.051, −0.015] | 0.046 [0.028, 0.064] | 0.006 [−0.014, 0.026] | 0.164 [0.137, 0.191] | 2 = 3 < 5 < 4 < 6; 2 < 1 = 5 < 4 < 6; 1 = 3 | 0.03 |
| Impulsivity: Position urgency | −0.105 [−0.130–0.080] | −0.063 [−0.088, −0.038] | −0.138 [−0.156, −0.120] | 0.038 [0.020, 0.056] | 0.065 [0.045, 0.085] | 0.333 [0.306, 0.360] | 3 = 1 < 2 < 4 < 5 < 6 | 0.06 |
| Impulsivity: Sensation-seeking | −0.122 [−0.147, −0.097] | −0.137 [−0.162, −0.112] | −0.038 [−0.056, −0.020] | −0.033 [−0.051, −0.015] | 0.147 [0.127, 0.167] | 0.141 [0.116, 0.166] | 2 = 1 < 3 = 4 < 6 = 5 | 0.04 |
| Impulsivity: Negative urgency | −0.067 [−092, −0.042] | 0.020 [−0.005, 0.045] | −0.153 [−0.171, −0.135] | 0.069 [0.051, 0.087] | −0.010 [−0.030, 0.010] | 0.344 [0.319, 0.369] | 3 < 1 < 5 = 2 < 4 < 6 | 0.06 |
| Impulsivity: Lack of perseverance | −0.093 [−0.118, −0.068] | −0.074 [−0.099, −0.049] | −0.083 [−0.101, −0.065] | 0.051 [0.033, 0.069] | 0.065 [0.045, 0.085] | 0.234 [0.207, 0.261] | 1 = 3 = 2 < 4 = 5 < 6 | 0.04 |
| Compulsivity: Perfectionism | −0.127 [−0.151, −0.103] | 0.077 [0.053, 0.101] | −0.193 [−0.209, −0.177] | 0.083 [0.065, 0.101] | 0.019 [0.001, 0.037] | 0.324 [0.300, 0.348] | 3 < 1 < 5 < 2 = 4 < 6 | 0.07 |
| Compulsivity: Anankasticism | −0.128 [−0.152, −0.104] | 0.024 [0.000, 0.048] | −0.196 [−0.212, −0.180] | 0.074 [0.058, 0.090] | 0.032 [0.014, 0.050] | 0.365 [0.341, 0.389] | 3 < 1 < 2 = 5 < 4 < 6 | 0.08 |
| Basic Psychological Needs: Autonomy satisfaction | 0.067 [0.042, 0.092] | 0.029 [0.004, 0.054] | 0.107 [0.089, 0.125] | −0.054 [−0.072, −0.036] | −0.031 [−0.051, −0.011] | −0.235 [−0.260, −0.210] | 6 < 4 = 5 < 2 = 1 < 3 | 0.04 |
| Basic Psychological Needs: Relatedness satisfaction | 0.153 [0.128, 0.178] | 0.101 [0.076, 0.126] | 0.146 [0.128, 0.164] | −0.038 [−0.056, −0.020] | −0.164 [−0.184, −0.144] | −0.369 [−0.394, −0.344] | 6 < 5 < 4 < 2 < 3 = 1 | 0.07 |
| Basic Psychological Needs: Competence satisfaction | 0.025 [0.000, 0.050] | −0.035 [−0.060, −0.010] | 0.091 [0.073, 0.109] | −0.077 [−0.095, −0.059] | 0.028 [0.008, 0.048] | −0.170 [−0.195, −0.145] | 6 < 4 < 2 < 1 = 5 < 3 | 0.04 |
| Basic Psychological Needs: Autonomy frustration | −0.159 [−0.184, −0.134] | −0.022 [−0.047, 0.003] | −0.197 [−0.215, −0.179] | 0.065 [0.047, 0.083] | 0.071 [0.053, 0.089] | 0.394 [0.370, 0.418] | 3 < 1 < 2 < 4 = 5 < 6 | 0.08 |
| Basic Psychological Needs: Relatedness frustration | −0.176 [−0.200, −0.152] | −0.034 [−0.058, −0.010] | −0.206 [−0.222, −0.190] | 0.091 [0.073, 0.109] | 0.100 [0.082, 0.118] | 0.451 [0.426, 0.476] | 2 < 1 = 3 < 4 = 5 < 6 | 0.09 |
| Basic Psychological Needs: Competence frustration | −0.111 [−0.136, −0.086] | −0.004 [−0.029, 0.021] | −0.173 [−0.191, −0.155] | 0.088 [0.070, 0.106] | 0.022 [0.002, 0.042] | 0.351 [0.326, 0.376] | 3 < 1 < 2 = 5 < 4 < 6 | 0.07 |
| Depressive symptoms | −0.094 [−0.119, −0.069] | 0.015 [−0.010, 0.040] | −0.149 [−0.167, −0.131] | 0.090 [0.072, 0.108] | 0.079 [0.059, 0.099] | 0.410 [0.385, 0.435] | 3 < 1 < 2 < 5 = 4 < 6 | 0.07 |
| Anxiety symptoms | −0.028 [−0.053, −0.003] | 0.093 [0.068, 0.118] | −0.122 [−0.140, −0.104] | 0.111 [0.093, 0.129] | −0.014 [−0.034, 0.006] | 0.327 [0.302, 0.352] | 3 < 1 = 5 < 2 = 4 < 6 | 0.06 |
| Adult ADHD symptoms | −0.146 [−0.171, −0.121] | −0.030 [−0.055, −0.005] | −0.178 [−0.196, −0.160] | 0.068 [0.050, 0.086] | 0.088 [0.068, 0.108] | 0.405 [0.380, 0.430] | 3 = 1 < 2 < 4 = 5 < 6 | 0.07 |
| Alcohol consumption | −0.050 [−0.072, −0.028] | −0.136 [−0.160, −0.112] | 0.059 [0.043, 0.075] | −0.013 [−0.031, 0.005] | 0.160 [0.140, 0.180] | 0.045 [0.018, 0.072] | 2 < 1 < 4 < 6 = 3 < 5 | 0.04 |
| Alcohol problems | −0.065 [−0.087, −0.043] | −0.077 [−0.101, −0.053] | 0.003 [−0.013, 0.019] | 0.073 [0.055, 0.091] | 0.154 [0.136, 0.172] | 0.213 [0.188, 0.238] | 2 = 1 < 3 < 4 < 5 < 6 | 0.04 |
| Substance use | −0.025 [−0.047, −0.003] | −0.136 [−0.160, −0.112] | 0.083 [0.067, 0.099] | −0.044 [−0.060, −0.028] | 0.150 [0.130, 0.170] | −0.004 [−0.029, 0.021] | 2 < 4 < 6 < 3 < 5; 2 < 4 = 1 < 3 < 5; 1 = 6 | 0.04 |
Note. CI: Confidence interval; PUM: pornography use motivation. Correlates were either estimated from factor scores with a mean of 0 and a standard deviation of 1 or were standardized prior to the analyses. Thus, the reported values for all variables are standardized scores.
Individuals in the two Increased risk profiles reported the highest frequency of masturbation and the lowest frequency of past year sexual activities, followed by the Low risk and No risk profiles. Individuals in the Increased risk of PPU with some MD profile had the lowest levels of sexual satisfaction and highest levels of sexual distress, followed by the Increased risk of PPU without MD, Low risk, and No risk profiles. In contrast, members of the Increased risk of PPU without MD profile reported the highest levels of sexual desire, followed by members of the Increased risk of PPU with some MD, Low risk, and No risk profiles.
Concerning psychological characteristics, similar general trends can be observed with the two Increased risk profiles showing the least desirable correlates. Participants in the Increased risk profiles had the highest levels of specific aspects of impulsivity (e.g., sensation seeking, lack of perseverance), basic psychological needs frustration, and alcohol use problems, and the lowest levels of basic psychological needs satisfaction. At the same time, the two Increased risk profiles also showed some significant differences, with participants in the Increased risk of PPU with some MD profile having the highest levels of religiosity, followed by the other No/Low risk of PPU with some MD and without MD profiles. Similarly, participants in the Increased risk of PPU with some MD profile had the highest levels of compulsivity, followed by the Low risk of PPU with some MD, No risk of PPU with some MD, Increased risk of PPU without MD, and No risk and Low risk of PPU without MD profiles. Alcohol use and substance use were the highest in the Increased risk of PPU without MD profile.
Discussion
Despite recent advancements in the field of pornography-use-related problems, previous findings' generalizability to diverse populations was strongly limited due to theoretical and methodological shortcomings (e.g., relative lack of studies outside of Western countries and among individuals with diverse gender identities and sexual orientations) (Grubbs, Hoagland, et al., 2020; Grubbs & Kraus, 2021). Therefore, the aims of the present study were to identify MD and dysregulation-based pornography use profiles among a diverse sample of adults and characterize them along pornography-use-related, sexuality-related, and psychological correlates to provide a comprehensive portrait of pornography users worldwide. We identified six profiles of use, based on individuals' pornography use frequency, PPU, and MD. Given the importance of the Increased risk of PPU profiles from prevention, intervention, and public health perspectives (Briken et al., 2024; Grubbs, Floyd, & Kraus, 2023; Kraus & Sweeney, 2019; Nelson & Rothman, 2020), we focused on the discussion of factors that differentiated between the increased risk vs. no/low risk profiles as well as between the two increased risk profiles (i.e., Increased risk of PPU without MD vs. Increased risk of PPU with some MD).
Profiles of pornography use
As hypothesized, two low-frequency, non-problematic use profiles emerged in the present sample. The No risk of PPU without MD profile corresponded to hypothesized P1 and the No risk of PPU with some MD profile somewhat corresponded to hypothesized P3 (see Table 1). Individuals in these profiles had infrequent and non-problematic pornography use habits. However, members of the No risk of PPU with some MD profile reported somewhat higher levels of MD, in line with prior findings whereby a group of low-frequency pornography users were distressed about their pornography use (Vaillancourt-Morel et al., 2017). Even though a high-frequency, non-problematic use profile was hypothesized (P2) based on the findings of a large-scale study from Hungary with three independent samples (Bőthe, Tóth-Király, et al., 2020), this profile did not emerge in the present, more culturally diverse sample. Instead, two average-frequency, non-problematic use profiles were identified, with one of them including individuals who did not disapprove of pornography use (Low risk of PPU without MD profile) and the other one including those who had some levels of MD (Low risk of PPU with some MD profile). These two average-use profiles represented half of the total sample. Thus, the high-frequency, non-problematic use profile might be more culture-specific than previously proposed (Bőthe, Tóth-Király, et al., 2020), warranting further investigation. Moreover, the frequency of pornography use may not play as an essential role in differentiating between pornography-use profiles as other characteristics of consumption (e.g., MD, binge use, content escalation), corroborating that mere frequency of pornography use and sexual behaviors may not be central symptoms of PPU and CSBD (Bőthe, Lonza, Štulhofer, & Demetrovics, 2020; Bőthe, Tóth-Király, et al., 2020; Ince et al., 2024; Jiang et al., 2022; Werner, Štulhofer, Waldorp, & Jurin, 2018; Wordecha et al., 2018).
Similarly to the no/low risk profiles, two increased risk groups were identified (Increased risk of PPU without MD and Increased risk of PPU with some MD profiles), with MD being the differentiating characteristic between them. These profiles showed similarities with the hypothesized problematic use profiles (P4 and P5) (Bőthe, Tóth-Király, et al., 2020; L. Chen et al., 2021; Vaillancourt-Morel et al., 2017). Yet, it is important to emphasize that despite having elevated dysregulation compared to other individuals in the sample, the mean PPU score of these profiles did not meet the pre-established cut-off score on the PPU measure (Bőthe et al., 2018; Bőthe, Nagy, et al., 2024). Thus, they should not be considered as individuals with PPU or CSBD, but as individuals who may be at elevated risk of developing such problems.
Comparison of increased risk and no/low risk pornography-use profiles
In line with our hypothesis (Bőthe, Tóth-Király, et al., 2020; Vaillancourt-Morel et al., 2017), the majority of women belonged to the No/Low risk profiles, while a higher percentage of men were included in the Increased risk profiles. The proportion of gender-diverse individuals in the Increased risk profiles was lower than men's but higher than women's, showing similarities with the occurrence of PPU across genders (Bőthe, Nagy, et al., 2024). Single individuals were also overrepresented in the Increased risk profiles, corroborating previous findings (Bőthe, Tóth-Király, et al., 2020). Moreover, a higher proportion of gay and lesbian individuals belonged to the Increased risk profiles compared to individuals of all other sexual orientations. These findings highlight the importance of including individuals with diverse genders and sexual orientations when studying pornography use-related problems and considering these characteristics when working with such problems, as they may experience unique stress factors (e.g., minority stress) (Borgogna, Mcdermott, Aita, & Kridel, 2019, 2022; Jennings et al., 2022, 2024). No clear patterns of differences were observed concerning other sociodemographic characteristics (e.g., age, country of residence). As these results may represent the absence of true differences or derive from the sample's characteristics (e.g., use of a self-selected, non-representative sample), further studies with more balanced samples are needed to corroborate these findings.
Individuals in the Increased risk profiles started to watch pornography at a younger age and used it for a longer period at each watching session than others in the No/Low risk groups (Bőthe, Tóth-Király, et al., 2020). They also reported higher levels of each pornography use motivations, but emotional distraction/suppression and stress reduction motivations differentiated the most between the No/Low risk and Increased risk profiles. These findings support previous empirical evidence suggesting that individuals with PPU and/or PPMI may turn to pornography to cope with negative emotions or stress, potentially due to the easy accessibility and instant availability of online pornography (Bőthe, Tóth-Király, Bella, et al., 2021; Bőthe, Vaillancourt-Morel, et al., 2024; Grubbs, Wright, et al., 2019; Lew-Starowicz, Lewczuk, Nowakowska, Kraus, & Gola, 2020). These results are also of diagnostic importance as emotion dysregulation has been shown to be associated with higher levels of PPU and CSBD and it is still debated whether using sexual activities as an emotion regulation strategy should be a diagnostic criterion for CSBD (Briken et al., 2024; Gola et al., 2020; Grubbs, Reid, et al., 2023; Lew-Starowicz et al., 2020; World Health Organization, 2022). In addition, members of the Increased risk profiles reported higher masturbation frequency and lower frequency of sexual activities with a partner compared to the No/Low risk groups. They also reported greater sexual desire and distress, as well as lower sexual satisfaction, supporting the notion that problems with pornography use may negatively relate to sexual health and well-being (Bőthe, Tóth-Király, et al., 2020; Bőthe, Tóth-Király, Griffiths, et al., 2021; Dwulit & Rzymski, 2019; Grubbs & Gola, 2019; Hoagland & Grubbs, 2021; Vaillancourt-Morel et al., 2017). However, it is important to note that the present study used a cross-sectional study design, and thus, it is also a plausible explanation that individuals turn to pornography when their sexual or romantic lives and relationships are not satisfying.
Finally, partly supporting our hypotheses, specific aspects of impulsivity (e.g., sensation seeking) and basic psychological needs (e.g., relatedness frustration) differentiated better between members of the Increased risk profiles and No/Low risk profiles than other psychological characteristics (e.g., ADHD symptoms). In line with the propositions of Self-Determination Theory and previous findings on pornography-use profiles, individuals who felt isolated, perceived lacking social support, or could not develop meaningful relationships with others might have found pornography an easy way to feel some connection potentially due to its increasingly immersive nature (e.g., virtual reality pornography) (Butler, Pereyra, Draper, Leonhardt, & Skinner, 2018; Bőthe, Tóth-Király, et al., 2020; Elsey, van Andel, Kater, Reints, & Spiering, 2019; Vansteenkiste & Ryan, 2013). However, given the lack of data on the type of pornography used in the present sample (e.g., “classic” online pornography or virtual reality pornography) to further investigate this hypothesis, future studies are warranted. Previous studies among treatment-seeking populations documented more substance use-related issues among individuals with PPU/CSBD (Ballester-Arnal, Castro-Calvo, Giménez-García, Gil-Juliá, & Gil-Llario, 2020; Kraus et al., 2015; Wéry et al., 2016). Our findings corroborated these results, suggesting that alcohol use problems may be more common among members of the Increased risk profiles.
To conclude, individuals in the Increased risk profiles reported worse sexual health and well-being as well as more issues with impulse control and social relations than others in the No/Low risk groups. These findings provide empirical support for recent calls to consider the sexual and relational aspects of pornography-use-related problems and CSBD as well as integrate sex therapy and sexual medicine perspectives into their treatment (Briken et al., 2024; Briken & Turner, 2022; Bőthe, Potenza, & Demetrovics, 2024; Lew-Starowicz & Coleman, 2022).
Differentiating between increased risk pornography-use profiles with and without MD
In line with the Moral Incongruence Model of Pornography Use and our hypotheses, MD emerged as an essential differentiating factor between pornography-use profiles (Grubbs & Perry, 2019; Grubbs, Perry, et al., 2019). Indeed, all profiles had a variant in which individuals had some MD. As MD bears importance from a differential diagnostic perspective (Kraus & Sweeney, 2019; World Health Organization, 2022), we deemed it crucial to highlight differences between the Increased risk of PPU without MD and Increased risk of PPU with some MD profiles.
In general, individuals in the Increased risk of PPU with some MD profile reported significantly more issues with almost all pornography-related, sexuality-related, and psychological characteristics than their peers in the Increased risk of PPU without MD group. However, when considering those characteristics that differentiated best between these two profiles, individuals in the Increased risk of PPU with some MD profile reported higher levels of sexual distress, religiosity, negative urgency, compulsivity, and depressive and anxiety symptoms than participants in the Increased risk of PPU without MD group, while members of the Increased risk of PPU without MD group had a higher alcohol and substance use frequency (Briken et al., 2022). MD may add an additional layer of distress when an individual experiences problems with their pornography use, in particular when it is combined with compulsive tendencies. Alternatively, the elevated levels of the aforementioned clinical characteristics in the Increased risk of PPU with some MD profile may suggest an underlying vulnerability in this group, warranting further examination. Finally, another potential hypothesis is that individuals in the Increased risk of PPU without MD profile may be less aware and critical about pornography use patterns and related negative consequences, resulting in having better self-reported psychological indicators than their peers in the Increased risk of PPU with some MD profile (Rogers, Pinedo, Villatoro, & Zemore, 2019). These findings further show the importance of assessing anxiety, depression, and negative urgency (e.g., impulsive actions taking place when experience intense negative affect or emotional states) when diagnosing pornography-use-related problems, as mood-related issues may not only be common among individuals experiencing pornography-use-related problems but may also help in the differential diagnostic process as well (Bőthe, Vaillancourt-Morel, et al., 2024; Grant Weinandy et al., 2023; Kraus et al., 2015).
Finally, no at-risk or high-risk of PPU profiles were identified in the present study (i.e., no profile's mean score on the PPU measure reached the cut-off score for PPU). The lack of an at-risk/high-risk PPU profile may derive from the characteristics of the sample (i.e., a large sample of individuals from the general population) as individuals who are at risk of experiencing PPU (i.e., approximately 3% of the current sample, see Bőthe, Nagy, et al., 2024) were likely to be included in the Increased risk of PPU profiles rather than emerging as a distinct profile. These points should be kept in mind when considering potentially negative health correlates of types and patterns of pornography use. Further studies among clinical populations are warranted to examine the roles of pornography use frequency, MD, and PPU among individuals who seek treatment for their pornography use.
Limitations and future directions
Apart from the general limitations of the ISS (link to general limitations), some specific limitations need to be considered concerning the present study. The sample was not representative of each country's population and some groups were overrepresented (e.g., individuals with higher levels of education, those who were more open to discussing sexuality, or who were more sexually active). Even though the sample was more diverse in terms of participants' cultural background, gender identity, and sexual orientation than in previous studies (Grubbs, Hoagland, et al., 2020; Klein, Savaş, & Conley, 2021), findings should be interpreted with caution and replicated in future studies among nationally representative samples. Individuals in the present study reported relatively low levels of MD in general (M = 2.49 [SD = 1.68] on a scale ranging between 1 and 7). Thus, future studies are needed to corroborate the presence of the identified profiles among individuals with high/higher levels of MD. Moreover, recent empirical evidence suggests that the interaction between an individual's MD and pornography use frequency may be the most optimal operationalization of MI (Grubbs, Floyd, Griffin, Jennings, & Kraus, 2022; Grubbs, Kraus, Perry, Lewczuk, & Gola, 2020); however, it was not feasible to conduct LPA with this interaction term. Future studies should further test the role of moral values concerning pornography-use-related problems across diverse samples, accounting for the interaction of pornography use frequency and MD. Individuals may report moral disapproval of pornography use for several reasons (e.g., religiosity, concerns about the potential effects of pornography on children and adolescents, feminist values, or concerns about abuse and exploitation), and moral beliefs about pornography use as well as use patterns (e.g., PPU) may change over time (Grubbs, Kraus, et al., 2020; Hoagland, Rotruck, Moore, & Grubbs, 2023; Štulhofer, Rousseau, & Shekarchi, 2020). Thus, future studies are warranted to examine whether considering the reasons underlying moral opposition to pornography may yield more nuanced findings in terms of different pornography-use profiles and whether memberships in such profiles are stable over time.
Conclusions
Addressing the limitations of previous studies (Grubbs, Hoagland, et al., 2020; Grubbs & Kraus, 2021), the present study examined MD and dysregulation-based pornography-use profiles and their correlates among a large, diverse population (e.g., cultural background). Findings suggest that six different pornography-use profiles may emerge when considering different aspects of watching habits, including two Increased risk profiles. Several correlates differentiated well between increased risk and no/low risk profiles, with sexuality- and social-relational factors playing important roles (e.g., sexual distress, relatedness satisfaction). These findings support recent calls to integrate sex therapy and sexual medicine perspectives into pornography-use-related care (Briken et al., 2024; Briken & Turner, 2022; Bőthe, Potenza, & Demetrovics, 2024; Lew-Starowicz & Coleman, 2022). Moral values concerning pornography use played a crucial role in the identification of pornography-use profiles and demonstrated the importance of inquiring about one's MD of pornography use when working with clients with pornography-use-related problems (Grubbs & Perry, 2019; Grubbs, Perry, et al., 2019; Kraus & Sweeney, 2019; World Health Organization, 2022).
Funding
B.B. was supported by the FRQSC – Research Support for New Academics (NP) Program during the finalization of this study. L.N. was supported by the ÚNKP-22-3 and ÚNKP-23-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. S.W.K. was supported by the Kindbridge Research Institute. Z.D. was supported by the Hungarian National Research, Development, and Innovation Office (Grant numbers: KKP126835). C.-Y.L. was supported by the WUN Research Development Fund (RDF) 2021 and the Higher Education Sprout Project, the Ministry of Education at the Headquarters of University Advancement at the National Cheng Kung University (NCKU); C.L. received support from the WUN Research Development Fund (RDF) 2021; F.P.P. was supported by Fondecyt Grant N.1241841; G.C. was partially supported by PRIN project 2022 (number 20224SX547); G.O. was supported by the ANR grant of the Chaire Professeur Junior of Artois University and by the Strategic Dialogue and Management Scholarship (Phase 1 and 2); G.C.Q.G. was supported by the SNI #073–2022 (SENACYT, Rep. of Panama); H.F. was supported by Grant-in-Aid for Transformative Research Areas (A) (Japan Society for The Promotion of Science, JP21H05173), Grant-in-Aid for Scientific Research (B) (Japan Society for The Promotion of Science, 21H02849), Grant-in-Aid for Scientific Research (C) (Japan Society for The Promotion of Science, 23K07013), The Telecommunications Advancement Foundation, and The Smoking Research Foundation.; J. Bi received support from the WUN Research Development Fund (RDF) 2021; K. Le was supported by Sonatina grant awarded by National Science Centre, Poland, grant number: 2020/36/C/HS6/00005.; K. Lu was supported by Charles University institutional support programme Cooperatio-Health Sciences; K.R. was supported by a funding from the Hauts-de-France region (France) called “Dialogue Stratégique de Gestion 2 (DSG2)”; L.C. was supported by the National Social Science Foundation of China (Grant No. 19BSH117); P.M.-T. was supported by Universidad Cientifica del Sur; R.I.C. was supported by Auckland University of technology, 2021 Faculty Research Development Fund; R.G. was supported by Charles University institutional support programme Cooperatio-Health Sciences; S.B. was supported by a Tier 1 Canada Research Chair; S.U.R.T. was supported by Brain Korea 21 (BK21) program of National Research Foundation of Korea.
Author contributions
Conceptualization: B.B., L.N., M.K., Z.D., M.N.P., S.W.K., J.B.G. Data curation: all authors. Formal analysis: I.T.K., B.B. Funding acquisition: B.B., L.N., S.W.K., and Z.D. Investigation: all authors. Methodology: B.B., I.T.K., L.N., M.K., Z.D., M.N.P., S.W.K., J.B.G. Project administration: B.B., L.N., M.K., Z.D., and S.W.K. Resources: all authors. Software: B.B. and I.T.K. Supervision: B.B., Z.D., M.N.P., S.W.K., and J.B.G. Validation: B.B., I.T.K., N.P., L.N., M.K., Z.D., M.N.P., S.W.K., and J.B.G. Writing – original draft: B.B., I.T.K. Writing – review & editing: all authors
Conflict of interest
The authors declare no conflict of interest with the content of this manuscript. S.W.K discloses that he has received funding from the International Center for Responsible Gaming, M.G.M Resorts International, Center for the Application of Substance Abuse Technologies, Taylor Francis, Springer Nature, The Nevada Problem Gambling Project, Sports Betting Alliance, and Kindbridge Research Institute. M.N.P. discloses that he has consulted for and advised Game Day Data, Addiction Policy Forum, AXA, Idorsia, Baria-Tek, and Opiant Therapeutics; been involved in a patent application involving Novartis and Yale; received research support from the Mohegan Sun Casino and the Connecticut Council on Problem Gambling; consulted for or advised legal and gambling entities on issues related to impulse control and addictive behaviors; provided clinical care related to impulse-control and addictive behaviors; performed grant reviews; edited journals/journal sections; given academic lectures in grand rounds, CME events and other clinical/scientific venues; and generated books or chapters for publishers of mental health texts. The University of Gibraltar receives funding from the Gibraltar Gambling Care Foundation, an independent, not-for-profit charity. ELTE Eötvös Loránd University receives funding from Szerencsejáték Ltd. (the gambling operator of the Hungarian government) to maintain a telephone helpline service for problematic gambling. R.G. is the shareholder of Adiquit Ltd. which is currently developing apps for addiction recovery. V.S. discloses that she received funding from the Lithuanian Health Promotion Fund for providing educational materials and lectures on Problematic Internet use. BB, MNP, and JB are associate editors, while ZD is the editor-in-Chief of the Journal of Behavioral Addictions.
Acknowledgements
The authors would like to thank Anastasia Lucic and Natasha Zippan for their help with project administration and data collection, and Abu Bakkar Siddique, Anne-Marie Menard, Clara Marincowitz, Club Sexu, Critica, Digital Ethics Center (Skaitmeninės etikos centras), Día a Día, Ed Carty, El Siglo, Jakia Akter, Jayma Jannat Juma, Kamrun Nahar Momo, Kevin Zavaleta, Laraine Murray, L’Avenir de l’Artois, La Estrella de Panamá, La Voix du Nord, Le Parisien, Lithuanian National Radio and Television (Lietuvos nacionalinis radijas ir televizija), Mahfuzul Islam, Marjia Khan Trisha, Md. Rabiul Islam, Md. Shahariar Emon, Miriam Goodridge, Most. Mariam Jamila, Nahida Bintee Mostofa, Nargees Akter, Niamh Connolly, Rafael Goyoneche, Raiyaan Tabassum Imita, Raquel Savage, Ricardo Mendoza, Saima Fariha, SOS Orienta and Colegio de Psicólogos del Perú, Stephanie Kewley, Sumaiya Hassan, Susanne Bründl, Tamim Ikram, Telex.hu, Trisha Mallick, Tushar Ahmed Emon, Wéo, and Yasmin Benoit for their help with recruitment and data collection.
Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2024.00054.
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Appendix Members of the International Sex Survey Consortium:
Rafael Ballester-Arnal, Dominik Batthyány, Sophie Bergeron, Joël Billieux, Peer Briken, Julius Burkauskas, Georgina Cárdenas-López, Joana Carvalho, Jesús Castro-Calvo, Lijun Chen, Giacomo Ciocca, Ornella Corazza, Rita I. Csako, Andrea Czakó, David P. Fernandez, Elaine F. Fernandez, Hironobu Fujiwara, Johannes Fuss, Roman Gabrhelík, Ateret Gewirtz-Meydan, Biljana Gjoneska, Mateusz Gola, Hashim T. Hashim, Md. Saiful Islam, Mustafa Ismail, Martha C. Jiménez-Martínez, Tanja Jurin, Ondrej Kalina, Verena Klein, András Költő, Chih-Ting Lee, Sang-Kyu Lee, Karol Lewczuk, Chung-Ying Lin, Christine Lochner, Silvia López-Alvarado, Kateřina Lukavská, Percy Mayta-Tristán, Dan J. Miller, Oľga Orosová, Gábor Orosz, Sungkyunkwan University's research team, Fernando P. Ponce, Gonzalo R. Quintana, Gabriel C. Quintero Garzola, Jano Ramos-Diaz, Kévin Rigaud, Ann Rousseau, Marco De Tubino Scanavino, Marion K. Schulmeyer, Pratap Sharan, Mami Shibata, Sheikh Shoib, Vera Sigre-Leirós, Luke Sniewski, Ognen Spasovski, Vesta Steibliene, Dan J. Stein, Aleksandar Štulhofer, Berk C. Ünsal, Marie-Pier Vaillancourt-Morel, Marie Claire Van Hout
Recent findings suggest that PPMI may be most accurately operationalized as the interaction between one's MD of pornography use and pornography-use frequency (Grubbs et al., 2022; Grubbs, Kraus, et al., 2020). Our interest in these two variables in addition to PPU would mean creating interaction terms between three variables. Unfortunately, when involving three or more variables, interaction effects are typically difficult to interpret. Person-centered approaches, in contrast, naturally facilitate this process by being able to accommodate, at the same time, multiple variables as profile indicators. Their other advantage is that they could reveal profiles with different combinations of MD and pornography-use frequency (e.g., low-frequency, problematic use with high levels of MD, or high-frequency problematic use with high levels of MD).
Egypt, Iran, Pakistan, and Romania were included in the study protocol paper as collaborating countries (Bőthe, Koós, et al., 2021); however, it was not possible to get ethical approval for the study in a timely manner in these countries. Chile was not included in the study protocol paper as a collaborating country (Bőthe, Koós, et al., 2021) as it joined the study after publishing the study protocol. Therefore, instead of the planned 45 countries (Bőthe, Koós, et al., 2021), only 42 individual countries are considered in the present study, see details at https://osf.io/n3k2c/.
As not all of the scales were fully invariant across languages, countries, or other characteristics of participants (e.g., Short UPPS-P Impulsive Behavior Scale; Fournier et al., 2024) when validating them using the ISS dataset, we opted to use factor scores instead of “raw” total scores to account for potential measurement biases (Morin, 2023; Skrondal & Laake, 2001). See details in the Supplemental Materials.
Gender-diverse individuals are individuals who do not identify with the binary categories of “men” and “women” regardless of their trans status (e.g., nonbinary individuals).
