Quantifying the Affective Dynamics of Pornography Use and Masturbation: An Ecological Momentary Assessment Study

doi:10.21203/rs.3.rs-5094782/v1

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Quantifying the Affective Dynamics of Pornography Use and Masturbation: An Ecological Momentary Assessment Study

https://doi.org/10.21203/rs.3.rs-5094782/v1

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The causal relationships between pornography use, masturbation, moral incongruence, and mental health are poorly understood. While the link between problematic pornography use (PPU) and depression is well documented, the affective dynamics (i.e., emotional shifts over time) associated with pornography use and masturbation have not yet been quantified. Utilizing an Ecological Momentary Assessment (EMA) design, we measured the affective dynamics of mental health variables collected from 22 participants before, during and after pornography use and masturbation, and examined the moderating role of moral incongruence in these relationships. Participants completed an initial survey followed by a four-week EMA, capturing data on sexual activities and mental health variables. Bayesian hierarchical mixed-effects models were employed to analyse affective dynamics. Findings suggest that pornography use and masturbation were linked to changes in affective states that spiked both before and after sexual episodes. The magnitude of these state changes was greater in participants with high moral incongruence, who experienced increases in guilt, shame, loneliness, and difficulty thinking, along with decreased hedonic mood and perception of relationship connectedness, either before or after sexual episodes. Our findings signalled the potential for intermittent spiking effects in craving prior to sexual episodes, as well as potential evidence for ‘brain fog’ following pornography use in both low and high moral incongruence participants. Further, we discovered opponent process dynamics in the mood of high moral incongruence participants, providing a possible causal mechanism that may explain how PPU can lead to depression.

Psychology

pornography

problematic pornography use (PPU)

opponent process

depression

Ecological Momentary Assessment (EMA)

1.1 Problematic Pornography Use

Problematic pornography use (PPU) is characterized by a pattern of pornography consumption that causes distress and functional impairment across personal, social, or occupational areas, and is marked by an inability to control pornography use despite these adverse effects (Bőthe, Nagy, et al., 2024; Ince et al., 2021). PPU has only recently been clinically recognized as an impulse control disorder, having been added as a subcategory of Compulsive Sexual Behavior Disorder (CSBD) in the 11th edition of the International Classification of Diseases (ICD-11) (Kraus et al., 2018). The International Sex Survey (ISS) has estimated that between 3.2 and 16.6% of the populations from 42 countries are predicted to have PPU (Bőthe, Nagy, et al., 2024).

Despite the recent recognition of PPU in the ICD-11, there remains controversy over whether PPU should be classified as an addiction (Bőthe, Potenza, et al., 2024; Brand et al., 2020; Pistre et al., 2023). While compulsive pornography use can have psychosocial consequences, PPU has features overlapping both behavioral addictions and impulse control disorders, and as such may have a distinct aetiology from true addiction (Grubbs, Perry, et al., 2019; Ravish et al., 2024). Despite a lack of professional consensus, there remains a high prevalence of anecdotal evidence for self-perceived “pornography addiction” in popular culture and online discourse.

Prior research has linked PPU to depressed mood, decreased relationship connectedness (Cardoso et al., 2023; Lambert et al., 2012; Maitland & Neilson, 2023; Mestre-Bach & Potenza, 2023; Vaillancourt-Morel et al., 2023), increased anxiety, guilt, shame, loneliness, difficulty thinking, and suicidal thoughts (Cardoso et al., 2023; Fernandez et al., 2021; Guidry et al., 2020; Maitland & Neilson, 2023; McGraw et al., 2024; Mestre-Bach & Potenza, 2023), and with other psychosocial disorders (Chasioti & Binnie, 2021). Despite some evidence that pornography use can have a positive effect on attitudes towards sexuality (Mckee, 2007; Rissel et al., 2017), it has also been associated with lower relationship satisfaction and commitment, especially when pornography use occurs without the partner’s consenting knowledge (Bechara et al., 2003; Floyd & Grubbs, 2022; Grubbs et al., 2021; Kohut et al., 2017; Lambert et al., 2012; Perry, 2016; Vaillancourt-Morel et al., 2023; Willoughby & Dover, 2022; Wright et al., 2017; Zitzman & Butler, 2009). The literature also suggests that PPU can significantly affect the user’s relationships with family, friends, and colleagues (Bridges & Morokoff, 2011; Fagan, 2009; Stack et al., 2004; Stewart & Szymanski, 2012). Hence, there are concerns over the societal and public health consequences of increasing pornographic material consumption (Grubbs & Perry, 2019; Lambert et al., 2012).

One of the key factors predicting self-reported addiction to pornography use is moral incongruence – defined as the feeling of moral disapproval towards one’s own use of pornography (Grubbs et al., 2022; Grubbs, Perry, et al., 2019; Grubbs & Perry, 2019; Lewczuk et al., 2020; Perry, 2018). A review by Floyd and Grubbs of the past 5 years of research has identified that moral incongruence serves as a moderating factor for various pornography-related issues, such as emotional distress, relationship difficulties, and the sensation of addiction (Floyd & Grubbs, 2022). Moral incongruence is influenced by multiple factors, including an individual’s religious beliefs, cultural norms, conservative sexual values, apprehensions about sexual exploitation, and the potential impact on interpersonal relationships (Chen et al., 2023; Hoagland et al., 2023; Ince et al., 2023; Lewczuk et al., 2021; Su et al., 2023). Despite these findings, the causal relationship between moral incongruence, pornography use, and mental health remains unclear.

Understanding the causal mechanisms linking pornography use and affective states is vital to clarify whether PPU is merely an impulse control disorder, or a true addiction that has elements of impulsivity. It will also help to enhance identification of clinically relevant thresholds for PPU.

1.2 Affective chronometry

Many neuropsychological phenomena have a curvilinear decay, such as an exponential or power-law decrease, in response to a stimulus. Examples that unfold over several hours include memory retention (Candia et al., 2019; Kahana & Adler, 2017; Murre & Dros, 2015; Peterson & Peterson, 1959; Subirana et al., 2017; Wixted & Ebbesen, 1991) and emotional responses (Anderson & Adolphs, 2014; Botelho et al., 2018; Dejonckheere et al., 2020; Feidakis, 2011; Heller et al., 2015; Picard, 1997; Solomon & Corbit, 1974). These temporal dynamics can be measured using Ecological Momentary Assessment (EMA), a longitudinal methodology that collects real-time participant data about their thoughts, feelings, or actions, often multiple times a day (Bos et al., 2015; Shiffman & Stone, 1998). This approach, more broadly known as ‘affective chronometry,’ tracks the timing of affective dynamics (Botelho et al., 2018; Dejonckheere et al., 2020; Feidakis, 2011; Heller et al., 2015). For instance, Heller et al. (Heller et al., 2015) used EMA to measure the decay of positive emotions after participants received different monetary rewards. Their findings indicated that participants’ emotional reactions approximated an exponential decay process over time, with the initial amplitude and decay rate correlating with brain activity in fMRI scans. This decay pattern is thought to result from how substances move between body areas, such as hormones moving through neural and vascular compartments (Ibarra et al., 2020).

We propose that curvilinear decay might be seen in individuals after engaging in pornography use or masturbation. By treating sexual activity as a stimulus that generates affective opponent processes in the body (Henry et al., 2023; Solomon & Corbit, 1974), we hypothesized that an EMA would allow us to observe and quantify both the emotional high during a sexual episode (the ‘a-process’), and the negative emotional after-effects (the ‘b-process’) (see Henry et al., 2023 for a more detailed explanation). We also hypothesized that these after-effects would follow an exponential decay curve.

1.3 Aims of the present study

The objectives were:

To model the affective dynamics of pornography users in relation to sexual activity.

To determine whether moral incongruence moderated the affective dynamics produced by pornography use and masturbation.

To evaluate the feasibility of conducting an EMA assessment of pornography users, in preparation for a larger cohort of future participants.

2.1 Study overview

The study recruited participants through two methods: a pilot study at Auckland University of Technology, New Zealand, using flyers, followed by an exploratory study advertised on the r/pornfree subreddit, known for its anti-pornography stance (r/pornfree, 2022). The pilot study participants received $50 NZD gift vouchers, whereas the exploratory study participants volunteered without financial incentives due to funding limitations. Ethics approval for both studies was provided by the Auckland University of Technology Ethics Committee (AUTEC).

The study was delivered in two parts: a pre-EMA survey for collecting demographic and descriptive variables, followed by a four-week EMA to monitor affective dynamics in relation to sexual activity. The pilot study participants also answered a post-EMA survey regarding their EMA experience. Despite different recruitment phases, the core survey questions were consistent between the two studies, allowing us to combine pilot data with the exploratory study data for the final analysis.

2.2 Participants

Inclusion criteria were proficiency in English; regular internet access and a compatible smartphone; use of pornography at least once in the past month; and minimum age of 18. There was no a priori constraint on sample size. We used Wilcoxon’s rank sum test and Fisher's exact test to compare demographic frequencies between the low and high moral incongruence groups; no significant difference in demographic makeup was detected for any metric.

2.3 Pre-EMA measures

The pre-EMA survey was conducted on the online survey and database platform REDCap (Patridge & Bardyn, 2018), containing the following measures.

Demographics and pre-screening

Participants were asked generic demographic questions about age, gender, ethnicity, highest level of schooling, employment, relationship status, and religiosity. For screening purposes and to determine baseline sexual activities, participants were asked to state their frequency of pornography use, masturbation, and sexual intercourse over the past month. They were also asked questions regarding the use of any applications or membership in groups aimed at reducing pornography use or masturbation, such as ‘Reboot’ groups.

Moral incongruence and pornography consumption

Participants were asked to identify their level of moral incongruence related to masturbation with pornography use, masturbation without pornography use, and pornography use without masturbation. These were measured with one item each (e.g., “I believe that pornography use is morally wrong”) on a scale of 0 (not at all) to 6 (very strongly) based on similar measures in prior studies (Grubbs et al., 2015; Grubbs, Kraus, et al., 2019; Lewczuk et al., 2020). Typical pornography use levels and PPU screening was performed with the six-item Problematic Pornography Consumption Scale (PPCS-6) (Bőthe et al., 2020, p. 6).

Baseline mental health variables

Anxiety and depression disorders were screened with the Hospital Anxiety and Depression Scale (HADS) (Snaith, 2003; Spinhoven et al., 1997). A subset of questions from the Multidimensional Fatigue Inventory (MFI) was used to measure typical levels of mental and motivational fatigue (Smets et al., 1995). Trait levels of guilt and shame proneness were assessed with the eight-item Guilt and Shame Experience Scale (GSES) (Maliňáková et al., 2019). Typical levels of loneliness were assessed with the eight-item short-form UCLA Loneliness Scale (ULS-8) (Hays & DiMatteo, 1987). Participants in a relationship were asked a subset of the seven-item relationship assessment scale to measure relationship connectedness and sexual satisfaction within the relationship (Bőthe et al., 2017; Hendrick et al., 1998). Participants’ social desirability rating, which may indicate biased responses, was assessed with the Brief Social Desirability Scale (BSDS) (Haghighat, 2007).

2.4 EMA procedure

EMA data was collected using SEMA3 (Smartphone Ecological Momentary Assessment), an application for longitudinal survey research available on both iOS and Android (O’Brien et al., 2024). Participants engaged in a four-week program, responding to five daily surveys, each lasting 1–2 minutes with a 3-hour response window. Notifications were sent at 9 am, 12 pm, 3 pm, 6 pm, and 9 pm. These surveys were designed to assess participants’ mental health at the moment on a 0–10 scale. Variables measured were mood, anxiety, guilt regarding sexual activity, shame regarding sexual activity, loneliness, difficulty thinking, relationship connectedness, and cravings related to pornography or sexual behavior.

Additionally, participants were asked to complete 2–3-minute surveys immediately following sexual intercourse, masturbation, or pornography use. They were again asked to rate their mental health scores in the current moment, along with questions about the type, intensity, and duration of their sexual episode, including their highest level of happiness during the episode (using an identical scale to the ‘mood’ variable). They were also asked to categorize their episode as one of the following: sexual intercourse with partner(s), pornography use only, masturbation only, pornography use with masturbation only, pornography use with masturbation and orgasm, or masturbation with orgasm only. Finally, they were asked an open-ended question about the triggers that led to their episode, such as: ‘What were the events or triggers that led to you using pornography?’, with responses subject to a qualitative content analysis. Participants were informed that their data would be kept confidential within the research team.

2.5 Statistical analysis

If an affective b-process occurs following pornography use, one would expect the amplitude of this b-process would spike immediately following the episode, before decaying gradually over time. In other words, the person would experience an emotional reaction to the pornography use that would reduce in intensity. The rate at which this intensity changes would also be unlikely to be constant. Therefore, we tested the hypothesis that following a sexual episode, affective dynamics would approximate an exponential decay process of the form

$$\:w\left(t\right)\:=\:a{e}^{-bt}\:+\:c+\epsilon\:$$

where $\:w\left(t\right)$ is the mental health variable, $\:t$ is time with the sexual episode occurring at $\:t=0$, $\:a$ is the initial amplitude or effect size right after the sexual event, $\:b$ is the decay constant that governs how quickly the effect diminishes, $\:c$ is the subject’s baseline level of the mental health variable, and $\:\epsilon\:$ is random noise. Similarly, we tested the hypothesis that prior to a sexual episode, affective dynamics would approximate an exponential growth process of the form:

$$\:w\left(t\right)\:=\:a{e}^{bt}\:+\:c+\epsilon\:$$

which, when plotted, is the mirror image of Eq. 1) around the axis at $\:t=0$. This allowed us to detect any increases in the mental health variable prior to sexual episodes.

As the null hypothesis, we also fitted a model to both pre- and post-episode data of the form:

$$\:w\left(t\right)=d+\epsilon\:$$

where $\:d$ is the average mental health score, indicating a steady state unaffected by the sexual event.

We used the time stamps of EMA responses to measure affective dynamics in an approach similar to that of disease progression modelling (e.g., see Raket, 2020, 2020/2022). This allowed us to model each mental health variable as a function of time and type of sexual episode, such that we had a dataset comprised of mental health data both pre- and post-episode, with each sexual episode aggregated at time $\:t=0$. However, in cases where mental health scores were measured between sexual episodes, data could be considered both ‘post-episode’ and ‘pre-episode’, which could lead to possible cross-contamination. To control for this, we assessed the impact of creating models using both truncated and non-truncated datasets. Truncation in this case refers to exclusion of data that was closer to another sexual episode than the one being examined, thus reducing the confounding influence of the farther episode. For example, take a participant who had two sexual episodes eight hours apart, and completed three EMA surveys during this time, where two of those surveys were performed closer to the first sexual episode. For the truncated analysis, only data from the first two surveys would be analysed relative to the first sexual episode, and the third episode would be excluded.

We employed a Bayesian hierarchical mixed-effects model to account for individual differences, with random intercepts across participants. This approach allowed us to model the individual variability in affective dynamics while estimating overall population-level effects. We used the brms package in R v4.3.0 for Bayesian modeling of our data (Bürkner, 2017; R Core Team, 2022). In lieu of traditional p-values, our analysis employed Bayes Factors (BFs) to gauge the strength of evidence for competing hypotheses, providing evidence for the most appropriate model given the observed data (Kruschke & Liddell, 2018; Michailovs et al., 2024). BFs ranging from 3 to 5 denote "weak" evidence, while 5 to 10 suggests "moderate" evidence, 10 to 100 represents "strong" evidence, and greater than 100 is ”very strong evidence” (Etz & Vandekerckhove, 2016). We reported BF₁₀ when evidence favoured the alternate hypothesis that the model was exponential, and BF₀₁ when evidence favoured the null hypothesis of no effect. Additionally, we provided 95% Credible Intervals (CI’s) to convey the uncertainty in our estimates (Wagenmakers et al., 2018).

We specified the following bounded priors for parameters: uniform priors for $\:a$ (-10 to 10, to cover the entire possible range of initial effect sizes), $\:b$ (-10 to 0 for the decay model, and 0 to 10 for the growth model, representing a half-life ranging from approximately 5 minutes to infinity), and $\:c$ (0 to 10 to cover all possible mental health scores). For the null hypothesis (no effect), we used normally distributed priors for $\:d$ with mean and variance taken from the mental health variable being measured. A selection of the code used to implement these models can be found in the Supplementary Materials.

To test for differences in affective dynamics between participants with low and high moral incongruence, we calculated the difference between each participant's affect scores within 12 hours both leading up to and following sexual episodes and the mean of their affect scores from more than 12 hours before or after sexual episodes1. This difference gave us an approximate measure of the impact of sexual activity on affect scores following the sexual episode, as well as a measure of craving and withdrawal effects leading up to the sexual episode. We then compared these measures between the low and high moral incongruence groups using the BayesFactor package in R to perform Bayesian t-testing (Morey & Rouder, 2012).

[1] This threshold was chosen because in most cases, the exponential models decayed to near baseline within 12 hours.

3.1 Descriptive statistics and demographics

The pre-EMA survey was accessed by 91 participants, with 38 (41.8%) completing the entire survey. Of these, 7 (18.4%) were from the pilot study, from whom 6 contributed data to the EMA; while 31 (81.6%) participants were from the exploratory study, from whom 18 contributed data to the EMA. Of the participants who contributed to the EMA, 20 (90.9%) participants were male, and 16 (72.7%) participants were not in a relationship. Participants’ demographic characteristics are presented in Table 1. Overall compliance rates were 25% for the pilot study, and 29% for the exploratory study. Missing data was not imputed due to its temporal complexity.

Table 1

Demographic data for 22 participants who contributed data to the final EMA. Participants were divided by level of moral incongruence with respect to pornography use with masturbation, using a median split. Additional descriptive statistics can be found in the Supplementary Materials.
Variable	High moral incongruence, N = 10	Low moral incongruence, N = 12
Age (years)	27.30 (11.19)	29.50 (6.68)
Gender
Woman	0 (0%)	2 (17%)
Man	10 (100%)	10 (83%)
Gender diverse	0 (0%)	0 (0%)
Prefer not to say	0 (0%)	0 (0%)
Ethnicity
NZ European	0 (0%)	3 (18%)
Chinese	0 (0%)	1 (6%)
Indian	3 (18%)	0 (0%)
English	2 (12%)	2 (12%)
European/North American Caucasian	7 (41%)	4 (24%)
Middle Eastern	1 (6%)	1 (6%)
Hispanic/Latin American	1 (6%)	3 (18%)
African	0 (0%)	0 (0%)
Other/unspecified	3 (18%)	3 (18%)
Sexual orientation
Heterosexual	10 (100%)	5 (42%)
Bisexual	0 (0%)	5 (42%)
Homosexual	0 (0%)	1 (8.3%)
Asexual	0 (0%)	0 (0%)
Unsure	0 (0%)	1 (8.3%)
Prefer not to say	0 (0%)	0 (0%)
Currently in long term relationship
Yes - I have been with my current partner for at least 1 year	1 (10%)	3 (25%)
Yes - I have been with my current partner for less than a year	2 (20%)	0 (0%)
No - I do not have a partner currently	7 (70%)	9 (75%)
Prefer not to say	0 (0%)	0 (0%)
Importance of religion in life
Not at all important	1 (10%)	8 (67%)
Not too important	2 (20%)	3 (25%)
Somewhat important	4 (40%)	1 (8.3%)
Very important	3 (30%)	0 (0%)
Primary religion
Catholic (including Roman Catholic and Orthodox)	5 (50%)	2 (17%)
Protestant (e.g. Anglican, Orthodox, Baptist, Lutheran, United Church of Canada)	1 (10%)	1 (8.3%)
Christian Orthodox	1 (10%)	1 (8.3%)
Jewish	0 (0%)	0 (0%)
Muslim	0 (0%)	1 (8.3%)
Sikh	0 (0%)	0 (0%)
Hindu	2 (20%)	0 (0%)
Buddhist	0 (0%)	0 (0%)
Atheist (do not believe in God)	1 (10%)	4 (33%)
Agnostic (believe that existence of God is unknowable)	0 (0%)	3 (25%)
Other	0 (0%)	0 (0%)
Prefer not to say	0 (0%)	0 (0%)
Attendance of religious services in past 12 months
Never	2 (20%)	9 (75%)
Seldom	0 (0%)	0 (0%)
A few times a year	4 (40%)	3 (25%)
Once or twice a month	1 (10%)	0 (0%)
Once a week	0 (0%)	0 (0%)
More than once a week	3 (30%)	0 (0%)
Frequency of masturbation with pornography use in past 4 weeks
Never	0 (0%)	2 (17%)
Less than once a week	1 (10%)	0 (0%)
1–2 times a week	3 (30%)	1 (8.3%)
3–4 times a week	1 (10%)	1 (8.3%)
5–6 times a week	1 (10%)	4 (33%)
1–2 times a day	3 (30%)	3 (25%)
More than twice a day	1 (10%)	1 (8.3%)
Frequency of masturbation without pornography use in past 4 weeks
Never	6 (60%)	4 (33%)
Less than once a week	3 (30%)	4 (33%)
1–2 times a week	0 (0%)	2 (17%)
3–4 times a week	1 (10%)	1 (8.3%)
5–6 times a week	0 (0%)	0 (0%)
1–2 times a day	0 (0%)	1 (8.3%)
More than twice a day	0 (0%)	0 (0%)
Frequency of sexual intercourse in past 4 weeks
Never	8 (80%)	7 (58%)
Less than once a week	1 (10%)	2 (17%)
1–2 times a week	1 (10%)	3 (25%)
3–4 times a week	0 (0%)	0 (0%)
5–6 times a week	0 (0%)	0 (0%)
1–2 times a day	0 (0%)	0 (0%)
More than twice a day	0 (0%)	0 (0%)

As our sample size was too small to analyse moral incongruence as a continuous variable, we performed a median split to compare the responses of participants with low moral incongruence (responses regarding moral incongruence due to masturbation with pornography use ranging from 0–3) and high moral incongruence (range 4–6).

3.2 Examining sexual episodes

In total, there were 162 instances of participants using pornography (with or without masturbation), compared to 26 instances of participants masturbating without pornography, and 13 instances of participants having intercourse with a partner. Figure 1 contains boxplots showing the length of all recorded sexual episodes. In terms of length of episode, masturbation coupled with pornography use had the greatest variation, with a range from 1 to 500 minutes. The longest episode of masturbation without pornography use was only 30 minutes long, while the longest episode of sexual intercourse was 90 minutes long. In contrast, there were 15 episodes of pornography use over 100 minutes long, and 5 episodes over 200 minutes long, from more than one participant. This may suggest that some pornography users found content that sustained their interest for considerably longer than masturbation or sexual intercourse alone.

Participants with high moral incongruence had a shorter mean length of pornography use of 25.93 minutes compared to 61.01 minutes for low moral incongruence (p_t−test = 0.011). This significant difference remained even when outliers above 200 minutes were excluded, with mean episode lengths of 22.99 minutes for high moral incongruence versus 37.15 minutes for low moral incongruence (p_t−test = 0.0064). Further, a moderate inverse correlation was found between moral incongruence score and duration of episodes of pornography use with masturbation (r = -0.34, p < 0.001). However, no significant correlation was found between moral incongruence score and duration of masturbation-only episodes (r = -0.21, p = 0.32).

Figure 2 shows mood scores obtained outside of pornography use, and participants’ highest perceived mood scores during pornography use (either accompanied by or without masturbation or orgasm). Both mood and highest perceived mood were measured on the same 0–10 scale. Wilcoxon rank sum tests with FDR-corrected p-values revealed no significant difference between mood scores obtained outside of pornography use when comparing low and high moral incongruence participants (p = 0.44). However, all other comparisons showed significant differences. The most notable difference was observed in the highest mood scores during pornography use between participants with low and high moral incongruence (p = 0.0013). This indicates that participants with low moral incongruence experienced a greater increase in mood during pornography use than participants with high moral incongruence.

3.3 Correlations between mental health variables

Figure 3 shows the Pearson correlations between different mental health variables measured during the EMA, using aggregated within-person EMA data. The strongest correlations were observed between guilt & shame, guilt & loneliness, shame & loneliness, and craving for pornography & craving for sexual intercourse. On the other hand, the strongest negative correlations were identified between relationship connectedness & loneliness, mood & loneliness, and mood & anxiety.

3.4 Affective dynamics of mental health variables

A table of model coefficients for the Bayesian model fitting process, and plots of selected models are presented in the Supplementary Materials. Our sample size was insufficient to produce a meaningful comparison across different types of sexual episodes. Specifically, there was not enough data to differentiate between pornography use combined with masturbation and masturbation alone (Prause, 2017), as only 26 (15%) of the 178 masturbation episodes were not accompanied by pornography use. There were also only 13 episodes of sexual intercourse recorded. Consequently, during the model fitting process, we merged instances of masturbation alone with those involving pornography use, and ignored episodes of sexual intercourse. This allowed us to analyze two groups of sexual episodes:

Group A: Episodes involving pornography use, whether or not accompanied by masturbation or orgasm.
Group B: All Group A episodes, plus episodes of masturbation without pornography use.

For high moral incongruence participants, there was very strong evidence that guilt increased following both Group A and Group B episodes, before decaying to baseline (BF₁₀ > 1000 for all models). For low moral incongruence participants, there was moderate to strong evidence that guilt increased following both Group A (non-truncated data only, BF₁₀ = 6.16) and Group B (non-truncated data only, BF₁₀ = 77.99) episodes. There was strong to very strong evidence that post-episode guilt spikes, which decayed exponentially, were larger in the high moral incongruence group than in the low group across all models (BF₁₀ ranged from 27.32 for non-truncated Group A data to 577.6 for non-truncated Group B data). There was also strong evidence that guilt increased prior to Group B episodes for high moral incongruence participants (truncated data only, BF₁₀ = 11.82); for this model, there was weak evidence for a difference between the magnitude of pre-episode guilt spikes for high versus low moral incongruence participants (BF₁₀ = 4.004). All models for guilt are shown in Fig. 4.

For aggregated within-participant EMA data, shame was strongly correlated with guilt (r_{low moral incongruence} = 0.89, r_{high moral incongruence} = 0.92), meaning that directionally, the models for shame and guilt were similar. For high moral incongruence participants, there was moderate to very strong evidence that shame increased prior to Group A episodes (truncated data, BF₁₀ = 7.56; non-truncated data, BF₁₀ = 233.16), and strong to very strong evidence that shame increased prior to Group B episodes (truncated data, BF₁₀ = 19.88; non-truncated data, BF₁₀ > 1000). There was also very strong evidence that shame increased following both Group A and Group B episodes before decaying to baseline (BF₁₀ > 1000 for all models except for truncated Group B data). For low moral incongruence participants, there was weak to moderate evidence that shame increased following Group B episodes before decaying to baseline (truncated data, BF₁₀ = 3.86; non-truncated data, BF₁₀ = 9.35). There was weak to strong evidence for a difference in the magnitude of post-episode shame spikes between low and high moral incongruence participants for post-episode Group B models (truncated data, BF₁₀ = 4.738; non-truncated data, BF₁₀ = 21.04). For Group A models, there was weak to moderate evidence for no difference in magnitude of pre-episode shame spikes between low and high moral incongruence participants (truncated data, BF₀₁ = 5.605; non-truncated data, BF₀₁ = 4.924).

For high moral incongruence participants, we found strong evidence that loneliness increased following Group A episodes, before decaying to baseline (truncated data, BF₁₀ = 19.91), and strong to very strong evidence that loneliness increased following Group B episodes, before decaying to baseline (truncated data, BF₁₀ = 14.43; non-truncated data, BF₁₀ = 714.62). All models for low incongruence participants showed moderate to very strong evidence in favour of the null hypothesis of no effect. There was weak to moderate evidence for the null hypothesis of no difference in the magnitude of post-episode loneliness spikes between low and high moral incongruence participants for the truncated models (Group A, BF₀₁ = 3.329; Group B, BF₀₁ = 8.137), and inconclusive evidence for the Group B non-truncated model (BF₁₀ = 1.004).

Figure 5 contains piecewise functions derived from the parameters of the exponential models for truncated Group A episodes, showing our proposed model of the affective dynamics of mood in relation to pornography use and masturbation. For high moral incongruence participants, we found strong to very strong evidence that mood decreased following Group A episodes, before decaying to baseline (truncated data, BF₁₀ = 66.57; non-truncated data, BF₁₀ > 1000), and moderate evidence that mood decreased following Group B episodes, before decaying to baseline (truncated data, BF₁₀ = 7.09). We also found strong evidence that for high moral incongruence participants, mood decreased prior to both Group A (truncated data, BF₁₀ = 45.03) and Group B episodes (truncated data, BF₁₀ = 29.03). All models for low incongruence participants showed inconclusive to strong evidence in favour of the null hypothesis of no effect. We found very strong evidence for a difference in the magnitude of post-episode mood dips between low and high moral incongruence participants for truncated post-episode mood models (Group A, BF₁₀ = 1881; Group B, BF₁₀ = 254.1). However, there was also moderate evidence for no difference in post-episode mood dip magnitudes for non-truncated Group B data (BF₀₁ = 6.757), and weak evidence for no difference in pre-episode mood changes between low and high moral incongruence participants for Group B truncated data (BF₀₁ = 4.7619).

Craving for sexual intercourse was strongly correlated with craving for pornography (within-person r = 0.65 for both low and high moral incongruence). For low moral incongruence participants, we found strong evidence that craving for pornography increased following Group A episodes, before decaying to baseline (truncated data, BF₁₀ = 29.96), and very strong evidence that craving for pornography increased following Group B episodes, before decaying to baseline (truncated data, BF₁₀ = 144.92). For high moral incongruence participants, we found weak evidence that craving for sexual intercourse increased following Group A episodes (truncated data, BF₁₀ = 3.81). Further, we found strong to very strong evidence for a significant difference in the magnitude of pre- and post-episode craving spikes for the truncated Group A models (post-episode, BF₁₀ = 65.36; pre-episode, BF₁₀ > 1000). There was also weak to very strong evidence in favour of the null hypothesis of no effect prior to sexual episodes for both levels of moral incongruence, and for both types of craving. However, a visual inspection of the data revealed distinct spikes in pornography craving approximately 15 to 20 hours before pornography use for high moral incongruence participants (Fig. 6), with similar spikes observed for low moral incongruence participants also. These effects were not strong enough to shift the evidence in favour of exponential models.

For high moral incongruence participants, there was very strong evidence that difficulty thinking increased following Group A episodes, before decaying to baseline (non-truncated data, BF₁₀ = 618.7), and strong to very strong evidence that difficulty thinking increased following Group B episodes, before decaying to baseline (truncated data, BF₁₀ = 23.52; non-truncated data, BF₁₀ > 1000). For low moral incongruence participants, there was very strong evidence that difficulty thinking increased following Group B episodes, before decaying to baseline (non-truncated data, BF₁₀ > 1000), while there was very strong evidence that difficulty thinking increased prior to Group B episodes (BF₁₀ > 1000 for non- and truncated data), and moderate evidence that difficulty thinking increased prior to Group A episodes (non-truncated data, BF₁₀ = 8.49). We found moderate evidence for a difference in the magnitude of post-episode spikes in difficulty thinking between low and high moral incongruence participants for the post-episode truncated Group B model (BF₁₀ = 6.108), but also found weak to moderate evidence for no difference in post-episode spike magnitude for non-truncated data (Group A, BF₀₁ = 4.297; Group B, BF₀₁ = 5.886). All models for difficulty thinking are shown in Fig. 7.

For high moral incongruence participants, we found very strong evidence that relationship connectedness decreased following Group A episodes, before decaying to baseline (BF₁₀ > 1000 for non- and truncated data), and very strong evidence that relationship connectedness decreased following Group B episodes, before decaying to baseline (non-truncated data, BF₁₀ > 1000). We also found very strong evidence that for high moral incongruence participants, relationship connectedness decreased prior to Group A episodes (non-truncated data, BF₁₀ = 78.38) and decreased prior to Group B episodes (non-truncated data, BF₁₀ = 81.84). All models for low incongruence participants showed inconclusive to very strong evidence in favour of the null hypothesis of no effect. We found strong evidence for a difference in the magnitude of post-episode dips in relationship connectedness between low and high moral incongruence participants for the post-episode truncated Group B model for relationship connectedness (BF₁₀ = 23.73). However, we also found weak to moderate evidence for no difference in post-episode dips in relationship connectedness between low and high moral incongruence participants for the non-truncated data (Group A, BF₀₁ = 5.848; Group B, BF₀₁ = 3.943).

For low moral incongruence participants, we found weak evidence that anxiety decreased prior to Group B episodes (truncated data, BF₁₀ = 4.29). All other models for both low and high moral incongruence participants showed inconclusive to very strong evidence in favour of the null hypothesis of no effect. We found very strong evidence for a difference in pre-episode spike magnitude in anxiety between low and high moral incongruence participants for the truncated Group B models (BF₁₀ = 2.498e + 06). We also found weak evidence in favour of no difference in pre-episode anxiety changes between low and high moral incongruence participants for the non-truncated Group A models (BF₀₁ = 4.572).

3.5 Content analysis of pornography use triggers

Participant’s responses to the open-ended question about triggers leading up to pornography use or masturbation were categorized according to Table 2. We performed a content analysis of these responses, creating feature categories while reading them. Given that there was insufficient data to conduct a comparison between these groups, data was combined for all sexual episodes and for both high and low moral incongruence.

Table 2

Coding scheme used to categorize open-ended responses about triggers leading up to sexual episodes.
Feature category	Type of feature
Sleep	Sleep aid, pre-sleep routine, waking routine, couldn’t sleep, or disturbed sleep
Craving	Craving for pornography, arousal, or ‘horniness’
Bored	Boredom, nothing to do
Stressed	Stress, overwhelm, burnout
Internet/Social Media	Browsing the Internet or social media
Loneliness/Alone	Feelings of loneliness, or being alone/isolated
Tired	Feelings of tiredness
Thoughts	Sexual thoughts or fantasies
Work	Work, school, or studying
Habit	Habit or routine
Bedroom	In bedroom or in bed
Drugs	Alcohol or marijuana
Distraction	Needed a distraction or procrastinating
Interaction	Real-life interaction with attractive person
Anxiety	Feelings of anxiety
Date	Went on a date with no sexual intercourse
Unattractive	Felt unattractive or wanted to feel more attractive
Dream	Sexual dream
Other	Other or unknown triggers

The six most mentioned triggers (Fig. 8) were Sleep (49), Craving (31), Internet/Social Media (26), Bored (26), Loneliness/Alone (25), and Stressed (21). Despite Craving, Loneliness, and Anxiety being frequently cited as triggers, they did not show significant increases prior to sexual episodes in any of the quantitative models. Possible reasons for this are discussed in Section 5.3.

To our knowledge, this is the first study to quantify the time-varying affective dynamics associated with pornography use and masturbation. Overall, high moral incongruence participants were more likely to experience negative emotional and cognitive effects related to pornography use and masturbation compared to low moral incongruence participants. Prior to pornography use and masturbation, high moral incongruence participants felt increased guilt and shame, along with decreased mood and relationship connectedness. By contrast, low moral incongruence participants experienced reduced anxiety and increased difficulty thinking. Following pornography use and masturbation, high moral incongruence participants experienced temporarily increased guilt, shame, loneliness, difficulty thinking, and craving for sexual intercourse, as well as worsened mood and relationship connectedness. On the other hand, following pornography use and masturbation, low moral incongruence participants only experienced temporarily increased guilt and shame (but to a lesser degree than high moral incongruence participants), difficulty thinking, and craving for pornography. Hence, the evidence provided here suggests that pornography use and masturbation may exacerbate symptoms of guilt, shame, loneliness, depression, and relationship disconnectedness (at least temporarily) rather than relieve them.

4.1 Implications of affective dynamics analysis

Our findings provide preliminary evidence that moral incongruence moderates the affective dynamics governing the relationship between pornography use, masturbation, and mental well-being. In particular, this moderation is evidenced by the heightened feelings of guilt and shame experienced by high moral incongruence participants both before and after sexual episodes, in contrast to those with low moral incongruence.

Recently, Henry et al. (2023) proposed a computational model of opponent processes demonstrating how a frequently repeated addictive behaviour may generate hedonic allostasis, leading to depression. Our findings regarding mood suggest a pattern consistent with allostatic opponent process theory. Specifically, high moral incongruence participants experienced a decrease in mood prior to pornography use, which may or may not have led to them using pornography. During the sexual episode, these participants experienced a peak mood that exceeded baseline (the 'a-process'), then experienced a reversal in mood that dropped below baseline and decayed over time (the 'b-process') (Solomon & Corbit, 1974). Yet mood also decreased prior to pornography use for high moral incongruence participants, creating a triphasic opponent process pattern. Therefore, we believe this is the first paper to demonstrate a possible causal mechanism for a bidirectional relationship between PPU and depression for high moral incongruence subjects.

These findings may also help explain Perry's results (2018), which showed that men who morally reject pornography experience depression even with low use, while those who don’t morally reject pornography only experience depression with high-frequency use. This aligns with Henry et al.'s (2023) allostatic opponent process theory, which suggests that individuals with low moral incongruence only experience a small b-process after pornography use, although this can lead to hedonic depression due to allostasis at high pornography use frequencies. In contrast, those with high moral incongruence experience a larger b-process, leading to depression even at low pornography use frequencies. The b-process in low moral incongruence participants might be too small to detect in our study, partly due to sample size limitations. Further research is needed to validate this theory.

If pre-episode decreases in mood are associated with spikes in craving, then this fits the pattern of an addictive cycle consisting of three phases: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation (Koob & Volkow, 2016; Volkow et al., 2011). Thus, we believe this is also the first paper to empirically demonstrate a possible causal mechanism for how pornography use follows the pattern of addiction for subjects with high moral incongruence. Future research should test whether similar triphasic opponent process patterns are present in other wellbeing variables, as we only measured mood within sexual episodes in this study. The hypothesis that moral incongruence is necessary to produce these patterns should also be tested explicitly.

There is possible evidence for post-episode ‘brain fog’ based on the observed spikes in ‘difficulty thinking’ following pornography use. Both low and high moral incongruence participants experienced spikes in difficulty thinking following pornography use and masturbation. However, low moral incongruence participants also experienced increased difficulty thinking before sexual episodes, unlike their high moral incongruence counterparts. For within-person data from high moral incongruence participants, difficulty thinking was moderately correlated with shame (r = 0.50), guilt (r = 0.49), decreased mood (r = 0.40), and loneliness (r = 0.32), suggesting the post-episode emotional overwhelm of these symptoms may produce cognitive difficulties akin to brain fog. These preliminary findings, though requiring further investigation, could potentially account for some of the unexplained symptoms (such as brain fog) reported by those in online forums such as r/NoFap, a forum dedicated to helping people quit pornography use (Chasioti & Binnie, 2021).

The lack of evidence for a temporal relationship between anxiety and pornography use was surprising, given that several studies have shown a correlation between PPU and increased anxiety (Vieira & Griffiths, 2024). We were also surprised to observe that loneliness did not increase prior to pornography use episodes, as there were 25 instances of ‘Loneliness/Alone’ being mentioned as a trigger in the content analysis. The literature suggests a possible bidirectional relationship between loneliness and PPU, where individuals use pornography to cope with loneliness, which may exacerbate feelings of loneliness, especially for those who have difficulty forming relationships (Mestre-Bach & Potenza, 2023; Vescan et al., 2024). One possible explanation is that participants’ post-hoc analysis of the events leading to pornography use differed from the sensations felt prior to the episode. It may be that craving produces a masking effect that makes it difficult to interpret one’s true emotional state in the moment. Alternatively, an exponential model may not be optimal for explaining the affective dynamics of these variables prior to sexual activity.

The decrease in relationship connectedness before and after sexual episodes for high moral incongruence participants suggests a possible bidirectional link between pornography use and reduced feelings of connectedness to others. This supports prior studies that indicate that higher pornography use negatively impacts perceived relationship quality, although this effect is moderated by the partner's moral and religious beliefs (Ruffing et al., 2022; Szymanski et al., 2015), and by the partner's awareness of pornography use (Vaillancourt-Morel et al., 2023).

Unusually, the evidence for an increase in craving for pornography or sexual intercourse prior to sexual episodes was inconclusive, given craving was mentioned as a trigger 31 times in the open-ended responses. This was a surprising finding, given that craving incubation prior to addictive behaviors such as pornography use is well documented (Allen et al., 2017; Fernandez et al., 2021; Kober et al., 2010; Potenza, 2008; Starcke et al., n.d.; Venniro et al., 2021; Volkow et al., 2011). Furthermore, for some models, we detected increases in craving for pornography after sexual episodes for low moral incongruence participants, and increases in craving for sexual intercourse after sexual episodes for high moral incongruence participants, which was unexpected. While our results show some evidence suggestive of intermittent spikes in craving prior to sexual episodes, further research is required to validate these effects. However, if validated, this may indicate an opportunity for intervention using established techniques such as Cognitive Behavioral Therapy (CBT), mindfulness, and ‘urge surfing’ for managing intermittent cravings by redirecting attention (Briken, 2020; Parsons et al., 2007).

4.2 Length and enjoyment of sexual episodes

The difference in length and enjoyment of pornography use episodes between low and high moral incongruence may suggest different motivations for use. One hypothesis could be that participants with high moral incongruence were more likely to use pornography quickly to alleviate unpleasant feelings (such as low mood), while participants with low moral incongruence might use pornography to alleviate boredom or for extended bouts of pleasure. Maitland and Neilson (2023) showed that individuals who use pornography mainly for enjoyment have social well-being levels similar to those who do not use pornography, while individuals with varied motivations for pornography use and associated negative outcomes often struggle with social well-being, which affects their intimate relationships. Hence, motivation for use may play a role in determining whether pornography use is problematic.

However, our results also possibly show a benefit to moral incongruence in terms of unproductive time saved, since there was a pronounced difference in mean length of pornography use episodes when comparing low to high moral incongruence individuals (61.01 vs 25.93 minutes). These results may also support the ‘supernormal stimulus’ hypothesis, which suggests that pornography hijacks the human’s sexual drive with its endless novelty capable of maintaining sexual arousal for unusually long periods (Barrett, 2010; Hilton Jr, 2013; Koukounas & Over, 2000).

4.3 Implications of qualitative analysis

In order to not overburden participants, we prioritized certain variables in the EMA analysis and excluded others. However, the content analysis identified several of these excluded variables, such as stress, tiredness, boredom, intrusive sexual thoughts, Internet and social media use, and timing factors (e.g., before sleep, just after waking up, or after work). Future studies could ask about these factors either before or during the EMA, to integrate them into the models. In particular, seasonal variables such as participants’ Internet use, work and sleep schedules should be examined.

Several participants reported that unintentionally viewing sexual content on the Internet or social media was a trigger for them, with YouTube, Reddit, Instagram, and Twitter being specific platforms mentioned. In the study by Fernandez et al., electronic media (e.g., “Dating sites, Instagram, Facebook, movies/TV, YouTube, online ads”) were the most common source of external triggers leading to relapse for members of Reboot Nation (Fernandez et al., 2021). This suggests a growing problem for those struggling with PPU, as casual Internet browsing may pose a high risk of triggering pornography use. In our study, 27% of the EMA participants reported using an app or software to block sexual content on their devices. This aligns with a content analysis by Henry et al. (2022), which found that content-blocking apps were the most commonly downloaded to reduce PPU. More research is needed to study the effectiveness of these apps.

Future research should consider adding both proximal and distal qualitative markers (Witkiewitz & Marlatt, 2007), such as post-episode questions about how participants feel immediately after pornography use. This could help clarify the sensations experienced and explain possible links between well-studied variables, such as guilt and shame, and less well-examined variables, such as 'brain fog'. Additionally, studying the content of the pornography used is important, as different types have been linked to varying levels of sexual satisfaction (Nolin et al., 2024) and may affect mental health differently. However, since EMA is more intensive than other survey methods, researchers must be careful not to burden participants with too many questions. For this reason, and due to predicted statistical limitations and low sample size, we selected EMA variables that were less likely to show seasonal patterns. Future research should consider including these variables and controlling for seasonal effects. For example, although we excluded 'boredom' due to its likely association with work hours, the qualitative analysis showed its importance as a trigger for specific participants. Masturbation as a stress-relief mechanism is also well-documented across genders (Csako et al., 2022; Hevesi et al., 2023; Wehrli et al., 2024). Moreover, our findings highlighted the role of sleep and work schedules in triggering cravings, with pornography use often occurring as a sleep aid or after waking up, and for stress relief post-work.

4.4 Limitations

We must be cautious about claiming direct causal effects due to the observational nature of this research. While we can observe that pornography use in some participants precedes negative emotional states, there may be mediating factors involved. For instance, in high moral incongruence participants, pornography use might initially increase guilt and shame, which then lead to increased loneliness, difficulty thinking, and craving for sexual intercourse, along with worsened mood and relationship connectedness. Furthermore, the differences in affective dynamics between high and low moral incongruence groups could be influenced by confounding factors. For example, 100% of the high moral incongruence group identified as heterosexual, whereas the low moral incongruence group was more diverse, with 58.3% of this group identifying as bisexual, homosexual, or unsure of their sexual orientation. This variation might be related to religious beliefs, as high moral incongruence participants were generally more religious. Thus, the observed differences could be due to sexual orientation or other factors, rather than solely moral incongruence (Fisher et al., 2019).

Participants were subject to self-selection and self-report biases, having mainly come from the r/pornfree subreddit, which is generally anti-pornography but is less critical of masturbation (r/pornfree, 2022). Although we aimed to include diverse moral incongruence values, the relationship between moral incongruence and study participation is unclear. For example, potential participants with faith practices might find questions about sexual intercourse inappropriate or objectifying, which could affect their willingness to share experiences, especially if they feel shame. The Hawthorne effect—where individuals alter their behavior because they know they are being observed—may also have influenced participant engagement and responses (Schofield et al., 2019). For instance, participants with high moral incongruence may have been less likely to report a particularly shameful sexual episode in the EMA.

We encountered high participant attrition rates and low compliance rates during the EMA, which is consistent with similar previous studies (Bőthe et al., 2021). It is hard to say how financial incentives affected participation; in fact, the exploratory study with no financial incentive had a higher rate of compliance (29%) compared to the pilot study which had a financial incentive (25%), although these figures include participants who did not complete any EMA surveys. Also, participants were drawn from different populations for these two studies, making them difficult to compare.

Our study was limited by sample size, increasing the uncertainty of parameter estimates in our hierarchical modelling. We were also unable to obtain enough data to analyse the effects of masturbation on its own versus masturbation and pornography use combined (Prause, 2017). There were only 26 instances of masturbation without pornography use, making it impossible to compare this against pornography use with masturbation (152 instances), or even against pornography use by itself (10 instances) or sexual intercourse (13 instances). Future research should account for these low rates and factor them into power calculations.

Efforts to incorporate participant-level random effects led to overfitting and convergence challenges in certain exponential models, likely stemming from insufficient data. In these cases, the Bayes Factor leaned heavily in favour of the null hypothesis of no effect. Furthermore, the decay periods appeared unusually short in most models, as evidenced in Fig. 4 and Fig. 7. In preliminary analyses, we generated models without accounting for individual effects and found that the decay period often spanned several hours to days. This discrepancy may indicate a bias in the models towards certain participants’ data due to the small sample size (McNeish & Stapleton, 2016). To address these issues, future EMA studies should aim for larger sample sizes and include participants as random effects to enhance model convergence and parameter estimation.

4.5 Conclusion

In conclusion, the ‘affective chronometry’ approach – using an EMA to measure one’s temporal dynamics in response to sexual activity – has provided several unique insights into the possible causal relationships between pornography use, masturbation, and mental health. Our findings highlight the complex moderating role of moral incongruence in these relationships and provide a possible causal mechanism for how PPU may be addictive and lead to depressed mood, particularly in people with high moral incongruence towards pornography use. Many potential research directions follow from this study, such as exploring opponent process dynamics in mood and other variables, detecting intermittent craving spikes before pornography use, and examining the phenomenon of 'brain fog' after pornography use.

Conflicts of interest/Competing interests:

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval:

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Auckland University of Technology Ethics Committee (Approval No. 23/80).

Consent to participate:

Informed consent was obtained from all individual participants included in the study.

Funding:

The authors did not receive support from any organization for the submitted work.

Authors’ contributions:

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Nathan Henry. The first draft of the manuscript was written by Nathan Henry and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Code availability:

The code that supports the findings of this study is available in the Open Science Framework (OSF) website at https://osf.io/u7xfq/, DOI 10.17605/OSF.IO/U7XFQ.

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The authors declare no competing interests.

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Quantifying the Affective Dynamics of Pornography Use and Masturbation: An Ecological Momentary Assessment Study

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Abstract

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1 Introduction

1.1 Problematic Pornography Use

1.2 Affective chronometry

1.3 Aims of the present study

2 Methods

2.1 Study overview

2.2 Participants

2.3 Pre-EMA measures

2.4 EMA procedure

2.5 Statistical analysis

3 Results

3.1 Descriptive statistics and demographics

3.2 Examining sexual episodes

3.3 Correlations between mental health variables

3.4 Affective dynamics of mental health variables

3.5 Content analysis of pornography use triggers

4 Discussion

4.1 Implications of affective dynamics analysis

4.2 Length and enjoyment of sexual episodes

4.3 Implications of qualitative analysis

4.4 Limitations

4.5 Conclusion

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