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Psychiatry Investig > Volume 21(12); 2024 > Article
Chen, Wang, Jiang, and Zhang: What Is Excessive? The Screening Frequency of Online Sexual Activities Among Community and Subclinical Males

Abstract

Objective

The purpose of this study was to investigate the different categories of males within two groups, namely a community male sample who engaged in online sexual activities (OSAs) and a subclinical male sample who were out of control in viewing pornography and had sought help. Additionally, the study aimed to determine the threshold for excessive OSAs in both samples.

Methods

A total of 568 community adult males who engaged in OSAs and 567 adult males seeking help for pornography use were recruited online. A latent profile analysis (LPA) was used to identify potential profiles within the samples. Cut-point analyses were conducted to determine optimal cutoff scores for OSAs in each sample.

Results

The LPA revealed two categories with different cutoff scores for OSAs within each sample. Excessive OSA can be a screening tool for detecting out-of-control behavior. More stringent criteria for identifying excessive OSAs can aid in distinguishing problematic pornography use (PPU) from impaired control in viewing sexually-explicit materials (SEMs). Two subcategories of loss of control emerged: high OSAs frequency impaired control and high viewing SEMs impaired control.

Conclusion

The threholds of excessive OSAs varies among community and sub-clinical male samples; this knowledge can assist in screening out individuals with impaired control and selecting individuals with PPU from the subclinical sample.

INTRODUCTION

Online sexual activities (OSAs) encompass engagement in various activities on the internet that cater to sexual gratification. These activities include searching for sex-related information, viewing pornography, seeking potential sexual partners, engaging in sexual chatting, and purchasing sexual services online [1]. In the Chinese social context, official propaganda considers pornography harmful; and the dissemination of pornography is considered a crime in China. However, the internet offers accessibility, affordability, and anonymity, providing individuals with a secure space for sexual experiences [2]. Furthermore, pornography consumption can serve as a substitute for casual sex [3]. As a result, pornography use is still common in China. A significant percentage (approximately 79.58%) of Chinese college students have engaged in at least one type of OSAs within the past year, with the highest proportion being those who have ever viewed pornography online [4]. Similarly, nearly 89% of Chinese adults have engaged in OSAs [5]. These findings aligned with studies conducted among Western population [6,7]. As mentioned, the pornography industry is forbidden in China. The Chinese culture tends to be conservative regarding sexual issues [8]. Thus, the potential adverse effects of extensive OSA within a conservative sexual culture is of concern.
In most cases, engaging in OSAs can have positive effects on individuals such as improving their sex life, providing stress relief, alleviating boredom, and enhancing sexual knowledge [9]. However, OSAs can become excessive and uncontrollable for a small group of individuals, affecting their physical and mental health and possibly even causing sexual violence and crime [10]. The problematic OSAs is associated with negative consequences and functional impairment [11] characterized by repetitively or excessively engaging in OSAs; persistent desires for OSAs or unsuccessful efforts to stop, reduce, or control online sexual behaviors [12]. Studies have shown that the frequency and duration of pornography use determined the quantity of pornography use [13], and the severity of pornography use leads to problematic pornography use (PPU) [14]. Moreover, PPU is more closely related to frequency of use than duration of pornography use [15,16]. Excessive pornography use was deemed an essential prerequisite for the emergence of PPU [17]. Hence, we were inclined to assess the frequency of pornography use. Although OSAs cover a broader range than pornography use, the definition of problematic OSAs suggests that high frequency of engaging in OSAs is a necessary and obvious symptom of problematic OSAs. Nevertheless, the threshold for frequently engaging in OSA that classifies it as problematic use has not been explored.
Therefore, what is considered as excessive (problematic) use of OSAs? The exploration of this question will facilitate the early identification and diagnosis of PPU and problematic use of OSAs. Recent studies have found a strong positive correlation between OSAs and problematic use, frequency of OSAs are strong predictors of problematic use in the community sample [18]. To prevent problematic OSAs, attention ought to be given to individuals’ engagement in cybersex in addition to their frequency of pornography use [19]. Research has found an association between craving pornography and the frequency of engaging in cybersex, as watching pornography has been identified as a gateway to cybersex and vice versa [20]. An established cutoff value for the frequency of OSAs could help individuals to consciously assess whether their engagement is problematic and seeking help is necessary. Without the cutoff, continuous excessive involvement in OSAs can cause physical and psychological harm. Establishing the threshold for excessive engagement in OSAs (frequency) can prompt initial screening for OSAs’ problematic use and cause timely preventive measures to be implemented.
There are more than 20 scales for assessing PPU and problematic OSAs from various perspectives, such as the Problematic Pornography Use Scale (PPUS) [16], Problematic Pornography Consumption Scale (PPCS) [15], Brief Pornography Screen (BPS) [21], and Cyber Pornography Use Inventory-9 [22]. However, most of these scales assess the consumption of online or offline pornography, which is categorized as solitaryarousal activities. These scales do not measure other OSAs such as engaging in sexual chats, seeking or participating in sex webcams, or pursuing sexual partners, which are partneredarousal OSAs [23,24]. Meanwhile, both partnered- and solitaryarousal activities can manifest problematic patterns [25,26]. In addition, the short internet addiction test adapted to OSAs (s-IAT-sex) assesses problematic OSAs. It measures the loss of control in browsing internet sex sites but lacks a threshold for excessive OSAs. Therefore, research must establish a threshold for flagging OSAs with problematic use.
Research has demonstrated that individuals seeking help for pornography-related issues exhibit distinct characteristics that deviate from the norm. Specifically, these individuals demonstrate heightened attention towards sexual stimuli, an increased propensity for sexual arousal and pornography-related cravings, and an overall preoccupation with pornography that significantly impacts their thoughts, emotions, and behaviors [27]. This suggests that the subclinical sample (those who have lost control and sought help regarding pornography use) displays a greater severity of pornography use compared to the community sample. Moreover, according to the two-stage model of addiction, the initial stage is characterized by compulsive reliance and loss of control over a substance or behavior, while the subsequent stage is marked by withdrawal symptoms and an inability to terminate the addiction despite severe negative consequences [28]. To observe the progression of the two stages, we selected two samples, namely community and subclinical samples, to exhibit varying levels of engagement in OSAs.
Based the following considerations 1) the widespread use of OSAs and individuals’ excessive engagement in them, which can result in problematic behavior; 2) the lack of a threshold to measure what extent of excessive OSA is classified as problematic pornography consumption; and 3) the frequency of engaging in OSAs varies between the community and subclinical samples, we aimed to determine the cutoff for the excessive engagement in OSAs by estimating the frequency of engaging in OSAs in community and subclinical samples. Compared to women, men generally have a higher frequency of OSAs [5,20] and are more likely to encounter PPU [7,29]. Thus, adult male participants were recruited for this study. Furthermore, the questionnaire for OSAs classifies sub-types of online sexual behavior according to the definition of OSA [26,30] and is widely employed in Chinese research [5,8,27,29,31-33]. In this study, the frequency of OSAs was measured by the frequency of viewing sexually-explicit materials (SEM), seeking sexual partners, engaging in cybersex, and participating in flirting and sexualrelationship maintenance, as suggested by Shaughnessy et al. [23] and Zheng and Zheng [29]. We used these four sub-types of OSA as profile indicators to identify subgroups within the two samples. PPU can manifest as frequently engaging in various forms of OSA, intense cravings for pornography, compulsive sexual behaviors [12], impaired control [34], and negative outcomes such as poor mental health [35]. Consequently, the quality and characteristics of PPU considered in this study include sexual compulsion [36], loss of control over pornography [21], craving for pornography [37], and overall health status [38]. These variables were investigated to ascertain the disparity between two distinct male groups, determined by the cutoff of the OSA questionnaire.

METHOD

Procedure and participants

The online study of the community sample was conducted through a popular Chinese survey website, namely Wenjuanxing (www.sojump.com). The adult male members of the website received an email with a link to the survey website and a brief introduction to our survey. The introduction informed the recipients that they were eligible for participation if they had engaged in OSAs during the previous 6 months (e.g., reading online pornographic content, browsing pornographic websites, sharing/watching pornographic videos or pictures, and interacting and flirting with others). The subclinical sample was obtained from a Chinese non-profit website (www.ryeboy.org) that aims to help men who feel they have experienced PPU and sought help. When the website visitors registered on the platform, they received a survey notification and completed the questionnaire online after filling out their informed consent. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Department of Applied Psychology, Fuzhou University (protocol code: 20221005).

Measures

Questionnaire of OSAs

The OSAs questionnaire was developed by Shaughnessy et al. [23] and revised by Zheng and Zheng [29]. It measures the frequency of engaging in OSAs and includes 13 items with four factors: 1) viewing SEM, 2) seeking sexual partners, 3) engaging in cybersex, and 4) flirting and sexual-relationship maintenance. Viewing SEM was assessed via five items (e.g., “In the past six months, how often do you visit erotic websites?”). Seeking sexual partners was assessed with two items (e.g., “In the past six months, how many times did you flirt with someone via computer or cell phone network?”). Cybersex was assessed via four items (e.g., “In the past six months, how many times did you masturbate via a video of someone else or watch someone else masturbate?”). Flirting and sexual relationship maintenance was assessed via two items (e.g., “In the past six months, how many people did you maintain an affair or intimate relationship with on the internet?”). Viewing SEM was assessed on a 9-point Likert scale that ranged from 1 (never) to 9 (at least once a day). The other three dimensions were assessed on a 9-point Likert scale ranging from 1 (0 times) to 9 (20 or more times). Higher scores indicated more frequent engagement in OSAs. The Cronbach’s alpha of this scale was 0.82 for the community sample and 0.84 for the subclinical sample.

Indicators of the severity of pornography use

PPCS

The PPCS is a comprehensive tool for assessing PPU, which was developed by BÖthe et al. [15] with inspiration from Griffiths [39]' component model of addiction. This instrument encompasses 18 items on six dimensions, namely salience, mood modification, conflict, tolerance, relapse, and withdrawal. The participants were required to rate their responses on a 7-point Likert scale ranging from 1 (never) to 7 (all the time). The cutoff score of 76 is the threshold for identifying individuals with PPU, whereby a score ≥76 indicates PPU. The Cronbach’s alpha coefficients were 0.95 for the community sample and 0.94 for the subclinical sample.

BPS

The BPS, developed by Kraus et al. [21] is a measure designed to assess the extent of excessive pornography use. It consists of five items rated on a 3-point scale ranging from 0 (never) to 2 (always). A score equal to or higher than four suggests the presence of impaired control in pornography use. The Cronbach’s alpha coefficients were 0.87 for the community sample and 0.74 for the subclinical sample.

Pornography Craving Questionnaire (PCQ)

Developed by Kraus and Rosenberg [37], it was used to assess the degree of individual craving for pornography. It has 12 items scored on a 7-point Likert scale from 1 (completely disagree) to 7 (completely agree), with higher scores indicating a stronger craving for pornography. The Cronbach’s alpha was 0.91 for the community sample and 0.92 for the subclinical sample.

Sexual Compulsivity Scale (SCS)

The 10-item scale, created by Kalichman and Rompa [36], was designed to evaluate sexual compulsivity (e.g., “I think about sex more than I would like to”). The scale ranges from 1 (not at all like me) to 4 (very much like me), with higher scores indicating a stronger inclination towards compulsive pornography. In our current investigation, the Cronbach’s alpha coefficient was 0.91 for the community sample and 0.90 for the subclinical sample, implying a high internal consistency.

Twelve-item General Health Questionnaire (GHQ-12)

The questionnaire was developed by Goldberg and Hillier38 and has been widely used for clinical psychological screening [40]. Comprising 12 items—6 positive and 6 negative—on a 4-point Likert scale, it assesses respondents’ mental health status. For positive items, the scores ranged from 0 (more so/better than usual) to 3 (much worse/less than usual) whereas for negative items, the range was from 0 (not at all) to 3 (much more than usual). The higher the score, the worse the psychological health level. The Cronbach’s alpha of the scale was 0.90 in the community sample and 0.93 in the subclinical sample.

Data analysis

Data preprocessing

Among the community participants, 580 responses were collected; 568 adult male community participants obtained scores that were equal to or greater than 14 on the measure of OSAs (the lowest possible score is 13, which indicates no prior OSAs). Thus, all the participants had engaged in at least one OSA during the previous six months.
The subclinical sample of 567 participants was selected from 1,256 participants; the inclusion criteria were: 1) males over 18 years old, had engaged in any form of OSAs within the previous six months; 2) seeking help for pornography use; and 3) scoring at least four on the BPS, which is used to screen for loss of control in pornography use. The demographic characteristics and pornography-related information of these two samples are shown in Table 1.

LPA

In this study, Mplus 8.3 (Muthén & Muthén, Los Angeles, CA, USA) was used to identify the sub-groups among community males and subclinical males. Given that the measurement invariance tests indicated suboptimal strict invariance between these two groups (Supplementary Table 1), a separate LPA was necessary. The latent profile analysis (LPA) was conducted to determine the number of latent profilesin OSAs. We used several indices: the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-sizeadjusted Bayesian information criterion (aBIC), Lo-Mendell-Rubin likelihood ratio test (LMRT), and entropy. The higher the entropy of the model (0 to 1), the smaller the values of AIC, BIC and aBIC, and the LMRT reached a significance level indicating a good fit of the model and high accuracy.

Cut-point analyses

To establish the cutoff point for the OSAs, cut-point analyses were carried out based on membership in the high frequency groups in the LPA. Treating the membership in the high frequency groups as the gold standard, several metrics— sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy value—were calculated for all OSA cutoff points. Sensitivity was defined as the proportion of true positives within the high-frequency group based on LPA, while specificity was defined as the proportion of true negatives within the low-frequency group. The PPV was defined as the proportion of individuals with positive test results correctly identified as high-frequency users, while the NPV represented the proportion of the participants correctly identified as low-frequency users among those who tested negative [15]. Accuracy was calculated as the ratio of the sum of the true positives and true negatives to the total number of cases. Sensitivity and specificity exhibit an inverse relationship, an increase in sensitivity commonly results in a decrease in specificity. Consequently, with the goal of establishing an optimal cutoff for OSAs while navigating the delicate balance between sensitivity and specificity, accuracy was prioritized, ensuring a comprehensive assessment of the various criteria.
After establishing the optimal cutoff points for both the community and subclinical samples, an independent sample t-test was performed via IBM SPSS 25.0 (IBM Corp., Armonk, NY, USA) to investigate if there was a significant difference between the two groups based on the cutoff of OSAs regarding the various indicators of PPU. This step aimed to confirm whether the cutoff frequency of OSAs is efficacious as a preliminary screening tool for identifying PPU.

RESULTS

Community sample

Potential Profiles of community males

LPA was conducted using the four sub-types of OSAs as profile indicators. As more latent classes were added, the AIC, BIC, and aBIC values were constantly declined, and the entropy value of each model was greater than 0.90. The non-significant p value of the LMRT suggested that the three-class solution should be rejected in favor of the two-class solution (Table 2).
The first class represented high-frequency users (69 individuals, 12.15%), who had significantly higher frequencies across all four sub-types of OSAs compared to the second class of low-frequency users (499 individuals, 87.85%). The comparisons revealed statistically significant differences: viewing SEM: 4.09±1.52 vs. 2.92±1.38, t=-6.54, p<0.001; flirting and relationship maintenance: 4.40±1.49 vs. 1.76±1.26, t=-14.03, p<0.001; seeking sexual partners: 4.59±1.12 vs. 1.22±0.46, t=-24.63, p<0.001; and cybersex: 3.26±1.65 vs. 1.48±0.85, t=-8.78, p<0.001.

Determination of a cutoff score and analysis of the validity

Considering that membership in the high-frequency group was the gold standard, we calculated the sensitivity, specificity, PPV, NPV, and accuracy of the OSAs at possible cutoff points (Table 3). In this cut-point analysis, a score of 45 points emerged as the optimal threshold value, with a sensitivity of 0.71, a specificity of 0.96, and a peak accuracy of 0.93. This indicates that 4.0% of the users with a low frequency of OSA were identified as “high OSAs frequency” and “at-risk users.” Conversely, 29% of those at risk and with a high frequency of engaging in OSAs were incorrectly identified.
The grouping criterion used in this sample had a cutoff score of 45 points for optimal OSAs, dividing the sample into two groups. Those with OSA scores higher than 45 (OSA ≥45, n=69) recorded higher than the cutoffs on both relapse and BPS (Table 4 and Figure 1). Because relapse is a symptom of impaired control [34], and BPS was a measure of out-of-control pornography use, this group was named the high OSAs frequency impaired control (HO-IC) group. Conversely, individuals who obtained a score below 45 (OSA <45, n=499) failed to meet the required criteria on both BPS and PPCS. Consequently, they were designated as the low OSAs frequency non-risk group (LO-NR). Subsequently, independent sample t-tests were conducted to compare PPU-related indicators between the two groups. The results indicate significant differences in PPCS, SCS, BPS, GHQ, and PCQ scores between the two groups (Table 4).

Subclinical sample

Potential profiles of sub-clinical males

The two-profile solution was chosen, similar to the community sample (Table 5). The distribution of profiles was as follows: 10.76% (n=61) for the first group and 89.24% (n=506) for the second group. Profile 1 was designated as the high-frequency OSAs group, characterized by significantly higher scores across all four factors compared to profile 2: viewing SEM (6.05±1.68 vs. 4.32±1.45, t=-7.70, p<0.001), flirting and relationship maintenance (5.80±1.92 vs. 1.58±1.05, t=-16.84, p<0.001), seeking sexual partners (5.46±1.76 vs. 1.30±0.71, t=-18.32, p<0.001), and cybersex (5.02±2.15 vs. 1.61±1.13, t=-12.24, p<0.001). Conversely, the second group was labeled as the high SEM-frequency group because it scored higher in viewing SEM but lower in the other three sub-types.

Determination of a cutoff score and analysis of the validity

Member in the high-frequency group (basing on LPA) was the gold standard. The cutoff underwent a cut-point analysis; the outcome is presented in Table 6. Among these values, 60 emerged as the optimal cutoff, demonstrating a peak accuracy of 0.966. Additionally, the corresponding sensitivity and specificity were 0.820 and 0.984, respectively. Consequently, it was observed that about 18.0% of individuals with a high frequency of pornography use were erroneously identified, while only 1.6% of those with a low frequency of pornography use were classified as high-frequency users. It is noteworthy that accuracy diminishes regardless of whether the cutoff value increases or decreases.
The grouping criterion used was a cutoff score of 60 points for optimal OSAs, based on which we divided the sample into two groups. As seen in Figure 1 and Table 4, both groups scored higher than the cutoff on the sum score of PPCS and BPS, indicating a higher probability of being diagnosed with PPU. Nonetheless, a more thorough examination of the distinct dimensions of PPCS revealed disparities in salience, tolerance, mood modification, and withdrawal symptoms between the two groups. The first group (n=58) appeared high score on all four subtypes of OSAs and scored above the slicing value on BPS and all dimensions of the PPCS (Figure 1, the cutoff of the PPCS total score was 76, and the average value for each dimension was about 4.22), therefore, it was named high OSAs frequency-PPU (HO-PPU). The second group (n=509) only demonstrated high frequency on viewing SEM, scored higher than the cutoff of BPS and four dimensions of PPCS, and was named high viewing SEM frequency-impaired control (HV-IC). Sub-sequently, independent sample t-tests were conducted to compare PPU-related indicators between the two groups. The results indicate significant differences in PPCS, SCS, BPS, GHQ, and PCQ scores between the two groups (Table 4).

DISCUSSION

In this study, community and subclinical adult males were recruited, and their data were analyzed separately. Cut-point analyses showed that an OSAs score of 45 points was the cutoff for excessive OSAs in the community sample, but 60 points was the cutoff in the subclinical sample. The significance of the OSA cutoff varies in regard to screening community and subclinical samples for PPU. In the community group, the OSAs cutoff categorized individuals into two distinct groups: the LO-NR group and the HO-IC group. Similarly, the subclinical sample was segregated into two groups: HO-PPU and the HV-IC individuals. In a broader context, excessive OSAs can be a valuable screening tool for detecting out-of-control in OSAs within the Chinese populace. Furthermore, the implementation of more stringent criteria for excessive OSAs within subclinical samples can aid in distinguishing the PPU from the purely impaired control in viewing SEM. By synthesizing the results obtained from the two samples, it is evident that two distinct subcategories of loss of control (HO-IC and HV-IC) exist.
There are two types of OSAs: solitary- and partnered-arousal activities. Partnered-arousal activities encompass activities such as seeking sexual partners, engaging in cybersex, flirting and maintaining sexual relationships. Solitary-arousal activities, such as viewing SEM, do not involve interpersonal interaction. The HO-IC (community sample) and HO-PPU (subclinical sample) groups scored higher on all four subtypes of OSAs, including both solitary- and partnered-arousal activities. In contrast, the HV-IC group in the subclinical sample engaged more frequently in viewing SEM than other collaborative activities. These results suggest that those who frequently view SEM may be more prone to overuse of pornography and have negative consequences. People browse pornography more due to its ease and convenience compared to other forms of OSAs. The intentional use of SEM is most often coupled with solo-masturbation [41]. Among men who masturbated frequently, 70% used pornography at least once a week [42]. In general, higher pornography consumption was primarily associated with increased masturbation frequency. Another notable finding was that both women and men with frequent pornography use were more likely to report high masturbation and sexual satisfaction than those with low masturbation [43]. In sum, viewing SEM facilitates arousal and solo-masturbation and can fulfill individuals’ sexual desires and enhance their overall sexual contentment. Consequently, this can intensify users’ reliance on viewing SEM, ultimately resulting in a loss of control or PPU. Additionally, our finding supports the notion that browsing pornography is the most prevalent OSA [4,26,30].
The threshold of excessive OSAs vary among the public population and those who exhibit out-of-control pornography use. There are individuals who need to be watchful of the frequency of their engagement in OSAs, because they may experience impaired control and compulsive use (HO-IC group, OSA≥45, n=69). In the subclinical sample, a notable percentage of individuals (HV-IC group, OSA<60, n=509) did not meet the criteria for salience, tolerance, mood modification, and withdrawal of PPU symptoms but had more serious negative consequences (greater sexual compulsivity and poorer health) from a loss of control in viewing SEM. The addiction theory proposed by Griffiths [39] posits that addiction comprises six fundamental components: salience, emotional regulation, conflict, tolerance, repetition, and withdrawal symptoms. Many researchers have highlighted the importance of tolerance and withdrawal symptoms in distinguishing addictive behaviors from those that are out of control [44]. Additionally, a recent study found that withdrawal and mood modification were core features of PPU, and as a coping mechanism, mood modification may be at a greater risk of developing into problematic use [45]. Therefore, the group of users with a lower tendency regarding withdrawal and mood modification was also labeled as having impaired control [27]. Researchers have suggested that compulsive use and unsuccessful efforts to reduce pornography use despite negative consequences is the first step of problematic PPU [46]. At this stage, individuals engage in online pornographic information or other OSAs to gain satisfaction after perceiving an internal need and external cue of relevance [45,47]. The PPU stage is characterized by difficulty to withdraw, repeated failures, and the inability to stop even when experiencing strong negative consequences. While impaired control can predict PPU over a period of six months and play an essential role in screening PPU, it is insufficient to indicate PPU itself [34]. Therefore, the males in the HV-IC group were also considered as having impaired control, although they scored higher than the PPCS cutoff.
When comparing the HO-IC and the HV-IC groups, the former had a higher frequency of all four sub-types of OSAs than the latter. However, regarding the indicators of loss control and PPU, the latter is more serious than the former. Concerning the qualitative indicators for PPU (six dimensions of PPU, SCS, PCQ, and GHQ), there is a difference between the two groups. This distinction lies in the conflict dimension, as the HV-IC scored higher than the cutoff of conflict, while the HO-IC group scored lower than the cutoff. The results indicate that the HV-IC group exhibited heightened conflict characteristics. Conflict refers to the adverse consequences derived from impaired control, including interpersonal conflict and efficiency reduction due to porn use—knowing the activity is causing problems but feeling unable to cut down or cease its use [15]. Thus, their conflict may be the motivation to seek help. The HV-IC group was identified in the subclinical sample. The sample was selected from those who sought help on the website for their pornography use. This finding is in line with extant studies: compared to the quantity of pornography use, negative symptoms such as out-of-control use or worsening intimate relationships, could be better predictors of patients’ willingness to seek treatment [48].
Hence, a significant finding from this study suggests that the loss of control encompasses not only the inability to regulate pornography consumption but also the difficulty in managing other OSAs. Although some studies have revealed that high frequency of OSAs use was not necessarily a problem [17] and at the edge of the symptom network in impaired control and PPU groups [49], the role of OSA frequency in screening for control disorders and PPU is not ruled out. The frequency of engaging in OSAs was important for identifying male adolescents with self-perceived addiction to pornography but actually without behavioral dysregulation [27]. In the community sample, a score of 45 indicated excessive OSAs, while in the subclinical sample, a score of 60 was the cutoff. Excessive OSAs can be a screening tool to identify impaired control in all four dimensions of OSAs in the general public. However, stricter criteria for identifying excessive OSAs (≥60) within subclinical samples are necessary to accurately distinguish between PPU and purely impaired control in viewing SEM. It is advisable to look at the frequency of OSAs as an auxiliary index for the screening and diagnosis of impaired control and problematic use [32].
This study has some limitations. Firstly, all measures were self-report questionnaires with the assumption that participants were aware of and willing to truthfully report their OSAs. The reliability of the results relied on the candor of the participants. Secondly, our data collection relied on network-based self-report measures, which could have introduced certain biases, often overrepresenting younger, urban, and better educated individuals. Moreover, the participants who were interested in completing our questionnaire on pornography use might have been personally troubled by its use, although they did not seek help. Thirdly, because the sample consisted of only adult males, including a small number of non-heterosexual individuals, it was not possible to examine whether the cutoff of the OSAs differed across gender and sexual orientations. To advance future research, the extent of excessive OSAs could be further corroborated by self-reports and clinical diagnoses, thus broadening its application.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2023.0369.
Supplementary Table 1.
Indices of the measurement invariance test for the OSAs cross the community sample and the seeking-help sample
pi-2023-0369-Supplementary-Table-1.pdf

Notes

Availability of Data and Material

The data that supports the findings of this study is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Ying Zhang, Lijun Chen. Data curation: Lijun Chen, Qiqi Wang. Formal analysis: Xiaoliu Jiang, Qiqi Wang. Investigation: Xiaoliu Jiang, Qiqi Wang. Methodology: Lijun Chen, Qiqi Wang. Project administration: Ying Zhang. Resources: Lijun Chen. Supervision: Lijun Chen, Ying Zhang. Visualization: Qiqi Wang. Writing—original draft: Lijun Chen. Writing—review & editing: Lijun Chen, Qiqi Wang.

Funding Statement

None

ACKNOWLEDGEMENTS

None

Figure 1.
Latent profiles based on the subtypes of OSAs in both samples. The grey dot-dashed line represents the cutoff value of the PPCS (the cutoff of PPCS total score is 76, sharding value for each item is about 4.22). OSAs, online sexual activities; SEM, sexually-explicit materials; PPU, problematic pornography use; PPCS, Problematic Pornography Consumption Scale; BPS, Brief Pornography Screen
pi-2023-0369f1.jpg
Table 1.
Demographic characteristics for both samples
Variables Community male sample (N=568) Sub-clinical male sample (N=567) t/χ2 p
Age (yr) 25.29±7.14 27.82±3.56 -7.55 <0.001
Sexual orientation 4.90 0.086
 Homosexual 9 (1.6) 15 (2.6)
 Heterosexual 545 (96.0) 527 (92.9)
 Bisexual 14 (2.5) 25 (4.4)
Education level 4.72 0.193
 Primary school or below 1 (0.2) 6 (1.1)
 Vocational School 45 (7.9) 53 (9.3)
 Secondary School 74 (13.0) 66 (11.6)
 University or above 448 (78.9) 442 (78.0)
Place of residence 0.32 0.956
 Capital city 214 (37.7) 214 (37.7)
 City 198 (34.9) 197 (34.7)
 Town 81 (14.3) 76 (13.4)
 Village 75 (13.2) 80 (14.1)
Working status 85.99 <0.001
 Full-time job 292 (51.4) 419 (73.9)
 Part-time job 15 (2.6) 28 (4.9)
 Odd job 17 (3.0) 19 (3.4)
 Did not work 244 (43.0) 101 (17.8)
Relationship status 3.56 0.064
 Single 302 (53.2) 333 (58.7)
 In a relationship 266 (46.8) 234 (41.3)
Age of first exposure to pornography 15.04±4.39 13.62±3.41 6.06 <0.001
Total OSAs score 29.49±13.03 37.99±16.26 -9.72 <0.001
 Viewing SEM (1-9) 3.06±1.44 4.51±1.56 -16.17 <0.001
 Flirting and relationship maintenance (1-9) 2.08±1.55 2.03±1.75 0.47 0.636
 Seeking sexual partners (1-9) 1.63±1.24 1.74±1.56 -1.35 0.177
 Cybersex (1-9) 1.69±1.14 1.96±1.65 -3.34 <0.001

Data are presented as mean±standard deviation or number (%). OSAs, online sexual activities; SEM, sexually-explicit materials

Table 2.
Fit indices for the latent profile analysis on the OSAs for the community sample
Profiles AIC BIC aBIC Entropy LMRT p (LMRT)
1 7780.74 7815.48 7790.08 - -
2* 6917.28* 6973.73* 6932.46* 0.980* 846.757* <0.001*
3 6687.83 6765.99 6708.85 0.962 232.130 0.200
4 6445.27 6545.14 6472.13 0.978 244.835 0.081
5 6231.16 6352.74 6263.86 0.975 217.260 0.175

* that the two-profile solution was selected as the final model.

OSAs, online sexual activities; AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, sample-size-adjusted Bayesian information criterion; LMRT, the Lo-Mendell-Rubin adjusted likelihood ratio test; p (LMRT), p-value associated with the LMRT

Table 3.
Calculation of cutoff thresholds for OSAs for the community sample
Cutoff score True positive True negative False positive False negative Sensitivity Specificity PPV NPV Accuracy
41 55 459 40 14 0.797 0.920 0.579 0.970 0.905
42 51 465 34 18 0.739 0.932 0.600 0.963 0.908
43 50 473 26 19 0.725 0.948 0.658 0.961 0.921
44 49 477 22 20 0.710 0.956 0.690 0.960 0.926
45* 49* 479* 20* 20* 0.710* 0.960* 0.710* 0.960* 0.930*
46 44 481 18 25 0.638 0.964 0.710 0.951 0.924
47 42 482 17 27 0.609 0.966 0.712 0.947 0.923
48 41 484 15 28 0.594 0.970 0.732 0.945 0.924
49 40 485 14 29 0.580 0.972 0.741 0.944 0.924
50 37 487 12 32 0.536 0.976 0.755 0.938 0.923

* the suggested cutoff threshold.

OSAs, online sexual activities; PPV, positive predictive value; NPV, negative predictive value

Table 4.
Differences in variables related to problematic pornography use among the four groups
Variables Community sample
Sub-clinical sample
ta tb tc da db dc
Group 1 Group 2 Group 3 Group 4
OSA<45 (N=499) OSA≥45 (N=69) OSA<60 (N=509) OSA≥60 (N=58)
PPCS 45.71±22.23 68.41±23.10 77.39±23.08 88.93±25.24 7.91*** 3.57*** 3.03** 1.02 0.50 0.39
 Salience (1-7) 2.10±1.08 3.42±1.36 3.28±1.55 4.28±1.74 7.67*** 4.58*** -0.67 1.18 0.64 -0.09
 Mood modification (1-7) 2.42±1.37 3.69±1.51 3.81±1.62 4.54±1.79 7.08*** 3.23*** 0.60 0.91 0.45 0.08
 Conflict (1-7) 2.94±1.74 3.74±1.78 5.07±1.42 5.23±1.43 3.60*** 0.81 5.93*** 0.46 0.11 0.90
 Tolerance (1-7) 2.31±1.48 3.63±1.62 4.26±1.64 5.06±1.64 6.87*** 3.53*** 2.97** 0.88 0.49 0.38
 Relapse (1-7) 2.97±1.77 4.47±1.68 5.21±1.51 5.68±1.40 6.64*** 2.26* 3.78*** 0.85 0.31 0.49
 Withdrawal (1-7) 2.49±1.41 3.86±1.58 4.17±1.58 4.86±1.68 7.39*** 3.12** 1.54 0.95 0.43 0.20
SCS 20.14±7.07 24.90±6.71 27.90±6.70 32.19±5.83 5.27*** 4.68*** 3.50*** 0.68 0.65 0.45
BPS 3.85±3.03 5.84±2.71 7.64±2.06 8.29±1.95 5.18*** 2.30* 5.32*** 0.67 0.32 0.84
PCQ 34.57±14.45 49.26±15.73 49.93±16.24 60.50±16.76 7.83*** 4.68*** 0.32 1.01 0.65 0.04
GHQ 10.99±6.41 13.42±8.49 18.44±8.29 20.78±8.81 2.28* 2.02* 4.71*** 0.36 0.28 0.60
Age of first exposure to pornography 14.90±4.27 16.00±5.06 13.70±3.37 12.86±3.67 1.93 -1.78 -3.64*** 0.25 -0.25 -0.64
Total OSA score 25.83±8.29 55.94±10.24 33.69±9.45 75.76±14.64 27.42*** 21.38*** -18.17*** 3.52 4.17 -2.33
Solitary-arousal
 Viewing SEM (1-9) 2.79±1.23 5.04±1.37 4.28±1.40 6.47±1.61 14.09*** 11.09*** -4.23*** 1.81 1.54 -0.54
 Flirting and relationship maintenance (1-9) 1.77±1.23 4.33±1.79 1.63±1.11 5.57±2.30 11.52*** 12.94*** -12.22*** 1.95 3.08 -2.23
Partnered-arousal
 Seeking sexual partners (1-9) 1.32±0.69 3.93±1.81 1.38±0.89 4.98±2.32 11.86*** 11.75*** -11.51*** 2.89 3.23 -2.46
 Cybersex (1-9) 1.44±0.77 3.56±1.61 1.57±1.01 5.57±1.87 10.81*** 16.06*** -10.04*** 2.33 3.55 -1.81

Group 1 refers to the low OSAs frequency non-risk (LO-NR) group; Group 2 refers to the high OSAs frequency impaired control (HO-IC) group; Group 3 refers to the high SEM frequency-impaired control (HV-IC) group; and Group 4 refers to high OSAs frequency-PPU (HO-PPU) group. Superscript ‘a’ indicates comparisons between Group 1 and Group 2; Superscript ‘b’ indicates comparisons between Group 3 and Group 4; and Superscript ‘c’ indicates comparisons between Group 2 and Group 3. The cut-off of PPCS total score is 76, sharding value for each item is about 4.22; the cut-off of total BPS score is 4.

* p<0.05;

** p<0.01;

*** p<0.001.

OSAs, online sexual activities; PPCS, Problematic Pornography Consumption Scale; SCS, Sexual Compulsivity Scale; BPS, Brief Pornography Screen; PCQ, Pornography Craving Questionnaire; GHQ, General Health Questionnaire; SEM, sexually-explicit materials

Table 5.
Fit indices for the latent profile analysis on the OSAs for the sub-clinical sample
Profiles AIC BIC aBIC Entropy LMRT p (LMRT)
1 8674.73 8709.46 8684.06 - - -
2* 7591.23* 7647.66* 7606.39* 0.987* 1060.064* <0.001*
3 7280.35 7358.47 7301.33 0.984 311.072 0.092
4 7061.87 7161.69 7088.68 0.981 221.494 0.164
5 6881.32 7002.85 6913.96 0.980 184.719 0.086

* that the two-profile solution was selected as the final model.

OSAs, online sexual activities; AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, sample-size-adjusted Bayesian information criterion; LMRT, the Lo-Menndell-Rubin adjusted likelihood ratio test; p (LMRT), p-value associated with the LMRT

Table 6.
Calculation of cutoff thresholds for OSAs for the sub-clinical sample
Cutoff score True positive True negative False positive False negative Sensitivity Specificity PPV NPV Accuracy
54 55 480 26 6 0.902 0.949 0.679 0.988 0.944
55 53 483 23 8 0.869 0.955 0.697 0.984 0.945
56 51 487 19 10 0.836 0.962 0.729 0.980 0.949
57 51 491 15 10 0.836 0.970 0.773 0.980 0.956
58 50 494 12 11 0.820 0.976 0.806 0.978 0.959
59 50 496 10 11 0.820 0.980 0.833 0.978 0.963
60* 50* 498* 8* 11* 0.820* 0.984* 0.862* 0.978* 0.966*
61 48 499 7 13 0.787 0.986 0.873 0.975 0.965
62 45 501 5 16 0.738 0.990 0.900 0.969 0.963
63 42 501 5 19 0.689 0.990 0.894 0.963 0.958

* the suggested cutoff threshold.

OSAs, online sexual activities; PPV, positive predictive value; NPV, negative predictive value

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