The use of online pornography, also known as Internet pornography use or cybersex, can be considered a problematic behavior of Internet use with risk of addiction. This problematic use could have adverse effects on sexual development and sexual activity, especially among young people. The aim of this study was to adapt and validate the Cyber Pornography Addiction Test (CYPAT) in a sample of 313 young university students. The participants completed the CYPAT, the Smartphone Addiction Scale (SAS) and the Family Adaptability and Cohesion Scale (FACES-IV). A confirmatory factor analysis was conducted, the internal consistency of the CYPAT was tested and the scale’s concurrent validity was measured by correlating CYPAT scores with the family function variables (cohesion and flexibility) measured on FACES-IV and smartphone addiction measured by the SAS. The results revealed an acceptable fit of the one-factor structure of the scale. The present study thus confirmed the adequate psychometric properties of the brief version of the Cyber Pornography Addiction Scale and its relationship to variables of family maladjustment and problematic smartphone use.

• Addiction
• Internet Pornography
• Validity
• Assessment
• Family Maladjustment

Jimeno MV, Ricarte JJ, Romero D, Mangialavori S, Cacioppo M, et al. (2025) Problematic Cyberpornography Use and Family Maladjustment: Validation of the Cyber Pornography Addiction Test (CYPAT) In Spanish University Students. JSM Sexual Med 9(1): 1147.

Recently, several investigations have highlighted the possible existence of an Internet addiction disorder similar to other addictive behaviors, for example gambling or sex [1]. This type of addiction is defined as behavioral, that is, addiction without substance use. Everitt and Robbins et al., define the shift from Internet misuse to addiction as being when such behavior is no longer under conscious control, becoming automatic and uncontrolled by the prefrontal cortex. Griffiths. et al. adds that addiction can be considered any potentially gratifying activity that might generate social disapproval as a result of associated risks. Internet addiction has several similarities to substance dependence: individuals tend to cut themselves off and neglect work, social life and studies; they undergo psychological changes and mood swings, exhibiting anxiety and impatience [2-4]. Numerous international research have shown that have shown that young people aged between 15 and 24 years show the greatest tendency towards Internet addiction [5], with university students being the most prone in this sense [6]. In Spain, the prevalence of Internet addiction in the adolescent population is 51.5% [7,8].

Internet addiction refers to a variety of specific types, including cybersex addiction [9-11]. Cybersex addiction has several symptoms, such as loss of control, distress, withdrawal, and engaging in sexual activity online despite negative consequences. People may be compulsive in their online sexual behaviors (eg, pornography), exhibiting out- of-control use [12]. The sexual activities included under the general term of cybersex are very diverse [13], with the consumption of pornography being the most frequent among men [14]. This Internet porn addiction typically involves viewing, downloading, and trading online pornography or engaging in adult fantasy role-playing games. This behavior is considered addictive by different authors.

However, to date, there is still no shared consensus within the scientific community on the phenomenon of addiction to cyberpornography. Several authors have found that excessive use of pornography can be conceptualized as an addictive behavior, as it shares similar neurobiological mechanisms with substance use disorder and behavioral addictions. In contrast, other authors have suggested that addictive use of cyberpornography may be defined by non- pathological features including high libido, sensation and information seeking, boredom, and reduction or avoidance of dysphoric or unpleasant states.

Nonetheless scientific investigation into the phenomenon is still ongoing, the term “problematic cyberpornography use” is used in the present study because it covers different theoretical conceptualizations of the term.

Before the creation of the World Wide Web in 1991, the transfer of pornography over computer networks or peer- to-peer file sharing was very limited. Since the beginning of the World Wide Web and the subsequent creation of pornographic websites, the public use of pornography has increased, as it was no longer necessary to physically purchase pornography from adult stores. Accessing pornography has never been easier, especially given the creation of mobile smartphones that seemingly allow access to the Internet anywhere in the world. Almost all of the Internet of Internet pornography is also accessible at no additional cost to the user, and the user can view this pornography without having to identify themselves or leave their homes. The increasing presence of information and communication technologies (ICTs) in individuals’ sex lives has triggered a latent change in sexual behaviors and the emergence of cases of addiction-related pathology [15]. This is especially relevant in the case of younger persons; 89% of the Spanish population over the age of 13 owns a cell phone, and of these devices 87% are smartphones. Sabada et al. reports that cell-phone use currently starts between the ages of 8 and 11. Given these data, Internet sex may be having a significant effect on the adolescent population and this impact could be negatively affecting sexual attitudes, beliefs and behaviors [16,17]. Young people acquire misconceptions about sexuality, impairing their ability to develop a healthy sexual and affective life [17]. It has been observed that the lower the age of exposure to pornography, the greater is the intensity of its effects [18]. It has also been found that the excessive use and abuse of pornography brings negative consequences to people’s health. Between 0.8% and 8% of male and female users typically show signs and symptoms of problematic pornography use [19].

However, despite the problems associated with Internet addiction, and specifically the use of pornography, there is a lack of screening instruments allowing us to understand and assess the negative use of Internet pornography consumption [12]. The tools currently available are too long for functional use and rapid scoring. The length of the Cyber-Pornography Use Inventory, the Compulsive Pornography Compulsion Scale, the Sexual Addiction Screening Test-Revised, and the Internet Sex Screening Test ranges between 23 and 31items. In addition, these instruments have been shown to be useful to measure general hypersexuality, understood as out-of-control sexual behaviors, but their validity to assess perceived Internet pornography addiction has not been analyzed [20].

With the aim of addressing this lack of questionnaires to evaluate Internet pornography addiction, Cacioppo et al. [20], developed the Cyber Pornography Addiction Test (CYPAT), a novel screening tool for cyber pornography, specifically designed to measure the use of Internet pornography. The authors focused on three primary components of addictive behavior: (1) inability to stop a behavior; (2) negative effects of addictive behavior; and (3) generalized obsession with pornography (Delmonico & Miller, 2003).

Present study

The negative impact of the problematic of smartphones and Internet use on individuals and their lives is often underestimated. Indeed, today, problematic cyberpornography use is a growing problem that can affect individuals’ proper functioning. An increasing number of people present health problems related to this type of addiction [21]. Consequently, we consider it essential to have screen tools that allow us to understand the impact of the problematic use of online pornography. In this sense, the immense usefulness of CYPAT in clinical practice and research is clear. In order to facilitate this research, further translation of the CYPAT to other commonly spoken languages, such as Spanish, is therefore warranted.

Hence, the main aim of this study is to adapt and validate the Cyber Pornography Addiction Test (CYPAT) in a sample of young Spanish adults. To this end, we first conducted a confirmatory factor analysis (CFA) to confirm the one-factor structure established in the original study by Cacioppo et al., [20]. In addition, we evaluated the scale’s internal consistency and concurrent validity. To measure the concurrent validity, we first used different variables related to family maladjustment and problematic smartphone use. Several studies have found that family maladjustment is associated with abusive use of cell phones and problematic Internet and cyberpornography use. Family function variables can contribute to the abusive use of cell phones, enhancing the likelihood of young people spending increasing hours in front of a smartphone screen or on the Internet, with no parental control [22]. In addition, growing up in home environments with low levels of family cohesion, difficulties in establishing communication, harmony and secure affective ties increases the likelihood of presenting a greater problematic Internet use [7]. In this regard, Shek and Yu et al. [5], identify variables associated with family dysfunction as risk factors for problematic Internet use, including conflicts in family relationships, lack of care from family members, perceived parental positive attitudes towards addictive behaviors, and low family life satisfaction. It has also been found that antecedents of family dysfunction, such as violence, conflict, and parental separation are indicators leading to excessive Internet use, and consequently, addiction [23]. Young people with problematic internet use present higher levels of insecure attachment and family life dissatisfaction, with their families being typically characterized as punitive and less organized, understanding, cohesive, and adaptive. Finally, the literature reports, on one hand, a direct relationship between a young individual’s environment, mainly the family, and problematic Internet use, and, on the other, a significant association between violence and aggression (mainly in the home environment) and problematic cyberpornography use.

Participants and procedure

A sample of 313 students of {masked for review} (255 women, 58 men), with a mean age of 23.39 (SD = 4.6), voluntarily took part in the study. All the individuals that completed the Spanish version of the CYPAT were university students and provided information on age, sex, gender, education, and professional activities. In addition to the CYPAT, they completed all the other questionnaires described in the measures section. All instruments were administered respecting the norms of privacy. The data were collected anonymously and with the permission of the participants, who gave their informed consent.

This study is part of a larger project where possible risk factors associated with problematic smartphone use were analyzed. This project was approved by the Ethics Committee of {masked for review}

Measures

Demographics questionnaire. A short demographic questionnaire was administered, collecting data on age, sex, ethnic origin, education, place of residence, sexual orientation, marital status and employment status.

Cyber Pornography Addiction Test (CYPAT; [20]). The CYPAT is a self-report scale consisting of 11 items on a 5-point Likert-type scale (from 1 = never to 5 = always) that allows evaluating whether the participant has problems with addiction to pornography (minimum = 11, maximum = 55, rank = 44). An example of an item is: “Sometimes, I feel unable to control the watching of porn sites”. A Cronbach’s alpha coefficient of .96.

Smartphone Addiction Scale (SAS; Kwon, Kim, Cho, & Yang, 2013). The original scale took the form of a self- report questionnaire to measure smartphone addiction. It consisted of 6 factors and 33 items scored on a 6-point Likert-type scale (1: “totally disagree” and 6: “totally agree”) and the internal consistency of the scale (Cronbach’s alpha) was 0.967. The six factors were disruption of daily life, positive anticipation, withdrawal, cyberspace- oriented relationship, overuse, and tolerance. In this research we used the short version of 10 items adapted by Cacioppo et al., and designed for simple screening of smartphone addiction in youth. The internal consistency (Cronbach’s alpha) of this short version was 0.861. An example of items: “Having difficulty concentrating in class, while doing homework or while working due to the use of smartphones”

Unbalanced family functioning: The four unbalanced subscales of the Spanish version of the Family Adaptability and Cohesion Evaluation Scale (FACES IV; [24,25] were used. The four negative subscales (4 items for each subscale) were used to describe problematic family functioning in terms of disconnection, entanglement, rigidity, and chaos. Participants were asked to think about their family of origin and rate how much they agreed with each item using a 5-point Likert scale (5=strongly agree, 1=strongly disagree). An example of an item is: “Once a decision is made in our family, it is very difficult to change that decision” (Rigidity). Cronbach’s alpha coefficients ranged from a minimum of 0.64 (entangled) to a maximum of 0. .80 (stiffness).

In order to validate the CYPAT structure [20], we performed Confirmatory Factor Analysis (CFA) using Amos version 18.0 (Arbuckle, 2009). The maximum likelihood method was used to estimate all model parameters. For identification of the CFA model, item variances were allowed to be estimated freely and the model was standardized by fixing factor variances at 1. As multivariate normality  assumption  was  not  met,  bootstrapping technique was used as it does not rely on assumption of multivariate normality. The number of bootstrap samples for this study was set at 2000. Model fit was assessed by the root-mean-square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker-Lewis index (TLI) and the standardized root mean square residual (SRMR). As regards RMSEA, values lower or equal to 0.08 represent a reasonable fit (Byrne, 2001). According to Bentler (1992), CFI and TLI values greater or equal to 0.09 are indicative of an acceptable fit. For SRMR, Byrne (2001) suggests a cut-off point of < 0.08. Regarding the χ2 test, as the significance of the χ2 is influenced by the sample size, some authors suggest the use of χ2/df ratio as a better measure of the goodness-of-fit of the overall model (Byrne, 2001). Marsh, Balla, and McDonald (1988) recommend an χ2/df ratio < 3 for well-fitting models. Model power was calculated for RMSEA (Preacher & Coffman, 2006).

We also analyze the internal consistency of the CYPAT and assessed the concurrent validity by correlating the CYPAT scores with family function variables measured by FACES-IV, and smartphone addiction measured by the Smartphone Addiction Scale [26].

Descriptive statistics

The descriptive statistics for the CYPAT items are presented in Table 1. The frequency distributions of the items indicated that the entire range of response options were employed for each item, except for items 9 (range 1-3) and 10 (range 1-4). Items were also inspected for severe skewness and kurtosis using the criteria that skewness is > |2| or kurtosis is > |7| for samples larger than 300 [26]. All items were identified as presenting severe skewness and kurtosis, except item 11 which showed no severe kurtosis. (Table 1)

Table 1: Descriptive statistics of items composing the CYPAT scale (n = 313).

Confirmatory factor analysis

As bootstrapping technique was used, Bollen-Stine p-value was calculated to assess model fit, where p-values > .05 indicate a good model fit. The original one- dimensional model reported by Cacioppo et al. [20] was tested. Although Bollen-Stine p was .076, the indices of the model did not show a good fit (CFI = 0.86, TLI = 0.82, RMSEA = 0.13, SRMR = 0.06, χ2(44) = 289.14, p < .001 χ2/df = 6.57). An examination of the modification indices indicated that residuals needed to be correlated in order to improve the model. The correlation of residuals was only included if there was a strong substantive or empirical rationale for allowing these residual variance terms to covary. Residuals were correlated for item 1 (“Sometimes, I feel unable to control the watching of porn sites”) with items 2 (“I neglected my partner or my family because I had to watch porn sites”), 4 (“I told myself to stop using online pornography but I didn’t succeed”), 6 (“I have continued watching porn sites despite some negative consequences”) and 7 (“Sometimes I watch porn sites to forget circumstances or painful situations”); item 2 with items 6 and 9 (“I have lost some important relationships because of watching porn sites”); item 3 (“I ignored my commitments to look at porn sites”) with item 9; and item 7 with item 9. This revised model showed a good fit to the observed data (CFI = 0.96, TLI = 0.94, RMSEA = 0.08, SRMR = 0.04, χ2(36) = 108.64, p < .001, χ2/df = 3.00).

Bollen-Stine p was .640, so the model fit was also adequate. Standardized parameter estimates for the revised model were obtained using bootstrapping technique. The bias corrected percentile method was used. As can be seen in Table 2, factor loadings were between .17 and .50, with p <.05 in all cases. Although loadings for items 9 and 10 were lower than .30, we decided to maintain them because their omission worsened the fit to the observed data (CFI = 0.89, TLI = 0.85, RMSEA = 0.13, SRMR = 0.06, χ2(27) = 175.07, < .001, χ2/df = 6.48). Effect sizes were also calculated using Cohen’s f2 (Cohen, 1992). Cohen’s f2 values between 0.15 and 0.34 indicate a medium effect size and values higher than 0.35 indicate a large effect size. Cohen’s f2 values were between 0.69 and 4.88 in all cases.

The model power value calculated for RMSEA statistic was 0.99 (sample size = 313, df = 36) (Table 2).

Tabla 2: Standardized factor loadings of the CYPAT scale

Internal consistency

Cronbach’s α for the CYPAT scale was .88, meaning the scale showed good internal consistency. For each item, item-total correlations were also good and ranged from.41 to .77 in all samples.

Concurrent validity

The CYPAT scale showed significant correlations with the FACES-IV sub-scales of cohesion (r = -.17, p = .003), flexibility (r = -.12, p = .036), disengaged (r = .15, p = .007), and chaotic (r = .13, p = .024). Nevertheless, no significant correlations were found in the enmeshed and rigid FACES- IV sub-scales (all ps > .136).

Additionally, CYPAT scores also showed a significant positive correlation with smartphone addiction (r = .12, p= .038).

The psychometric findings confirmed the 11-item, one-factor structure of this short version of the scale functioned adequately in our Spanish sample, coinciding with the findings for its original validation in an Italian sample (Cacioppo et al., 2013). Further studies, however, should aim to confirm this similar functioning in different countries, testing the scales by means of statistical analyses such as differential item functioning (DIF) [27]. This test assesses whether the strength of the relationship between each item and the construct of the scale is upheld across groups (nationalities, in this case). In any event, and in line with recent initiatives to strive for more parsimonious instruments (e.g. [28]), our results show that problematic cyberpornography use can be reliably measured using CYPAT. However, the necessity of correlating some residuals in order to improve the model suggests that some items may be too inter-related, and thus do not capture a sufficiently differentiated part of the construct. Consequently, future studies intended to validate the scale should explore the possibility of an even shorter version that might show lower levels of relationships across items. In the same line, and although their elimination did not enhance the model, the fact that items 9 (“I have lost some important relationships because of watching porn sites) and 10 (“I watch porn sites in contexts where I should not (e.g. in other people’s home, at school or at work

...”) loaded lower than .30 suggests they should be more carefully observed in future validations of the scale. The content of these particular items refers to relationships with others, and hence the results of the current study might suggest that the social dimension in our sample of university students does not significantly contribute to the explanation of the phenomenon of problematic cyberpornography use.

Despite the limitations of the functioning of some of the items when analyzed individually, the reliability and validity of any scale should be evaluated in overall terms. In this regard, the selection of items for this version of the CYPAT shows both adequate internal consistency and adequate concurrent validity. Regarding the latter, the participants reporting higher levels of smartphone addiction scored significantly higher on the sex addiction scale. It is reasonable to think this relationship exists given that a smartphone can be used as an electronic device for downloading pornographic content. Indeed, Internet use is one of the strongest indicators of smartphone addiction in university students [29]. Another limitation of the present study was the sample is quite unbalanced between males and females. Future studies on smartphone addiction should examine the frequency of activities within the category of Internet use (e.g. viewing series, listening to music or accessing pornography).

With regard to potential preventive factors for problematic cyberpornography use, our correlational results clearly indicate the potentially significant protective role of family. This is unsurprising since the lack of family cohesion has been associated with Internet gaming disorder [30] and alcohol and drug use [31] in young people. Family cohesion is key to ensuring secure attachment (e.g. [32]), which, in turn, is a protective factor against substance use addictions [33]. In fact, evaluating attachment is increasingly recommended in the prevention of drug addiction [34-38]. These findings lead us to postulate that the same protective mechanisms associated with attachment that appear in other addictive behaviors (e.g. substance abuse) may also play a role in university students with a problematic cyberpornography use.

In sum, this work is the first to describe the adequate psychometric properties of the short version of the Cybernetic Pornography Addiction Test (CYPAT). The results of the study are promising and indicate that this brief and easily administered instrument for screening for pornography addiction has good psychometric properties and can be of use to those working in all contexts where it is necessary to investigate internet pornography behavior, since this short version of the Cybernetic Pornography Addiction Test can be considered the first to clearly define the disorder as an addictive behavior associated with excessive cell-phone use.

María V. Jimeno’s contract is co-financed by the European Development Fund Regional (Feder) in accordance with the Operational Program of the Region of Castilla-La Mancha for Feder 2014-2020, and for the University of Castilla-La Mancha’s own Research Plan.

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