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Expanding access to the internet has resulted in more and earlier consumption of online pornography. At the same time, a higher prevalence of erectile dysfunction (ED) among young men is seen. Increased pornography consumption has been suggested as a possible explanation for this rise.
The aim of this study was to better understand associations between problematic pornography consumption (PPC) and ED.
A 118-item survey was published online, and data collection took place between April 2019 and May 2020. Of the 5770 men who responded, the responses from 3419 men between 18 years old and 35 years old were analyzed. The survey used validated questionnaires such as the Cyber Pornography Addiction Test (CYPAT), International Index of Erectile Function (IIEF-5), and Alcohol Use Disorders Identification Test-Concise (AUDIT-C). The estimated amount of porn watching was calculated. Univariable and multivariable analyses were performed. For the multivariable analysis, a logistic regression model using a directed acyclic graph was used.
According to their IIEF-5 scores, 21.48% (444/2067) of our sexually active participants (ie, those who attempted penetrative sex in the previous 4 weeks) had some degree of ED. Higher CYPAT scores indicating problematic online pornography consumption resulted in a higher probability of ED, while controlling for covariates. Masturbation frequency seemed not to be a significant factor when assessing ED.
This prevalence of ED in young men is alarmingly high, and the results of this study suggest a significant association with PPC.
Research Registry researchregistry5111; https://tinyurl.com/m45mcaa2
As we cannot ignore the presence of the internet (and therefore also the presence of explicit sexual materials) in the lives of young people, the effects of pornography consumption on mental and sexual well
Several reports have underlined positive effects of pornography consumption (more sexual comfort and self-acceptance, lower levels of shame and anxiety towards personal sexual orientation, increased interest in sex and sexual experimentation, relational happiness, more acceptance towards different sexual activities) [
What cannot be denied is that expanding access to the internet has resulted in more and earlier consumption of online pornography. Between 50% and 70% of adult men use pornography on a regular basis, and for adolescent lifetime use, the numbers are even higher than 80% [
Getting an erection is, of course, a process that requires the neurological, hormonal, and circulatory systems to work together. Inability to get or maintain an erection can be due to penile (vascular or neurological) problems or to more centralized issues (eg, depression, lack of desire, anxiety), but these conventional components cannot entirely explain the higher prevalence of ED in young men [
Certain scholars attribute these self-perceived difficulties to moral incongruence towards pornography consumption, and some link it to addiction, while others are more critical and question whether addiction models are applicable based on findings from neuroscientific studies [
A better understanding of the associations between PPC and ED might add new insights in the prevention and treatment of ED, especially in young men. These associations are complex and can only be fully understood in a multidisciplinary setting, as they require knowledge from different research fields (eg, psychology, sexology, sociology, urology) [
We organized 4 brainstorming meetings between a urologist and 2 medical practitioners with an interest in sexual health. First, a framework was designed identifying variables (including pornography consumption) that could possibly contribute to ED in young men. After a literature review by the team members, several indicators linked to pornography consumption (eg, frequency of use, age at start of use, problematic use) were listed in another brainstorming meeting. A literature review was performed identifying validated scales to measure our outcome and exposure variables, and based on scientific evidence available at that time, certain questionnaires were chosen. Then, a 118-item questionnaire was developed including questions on demography, medical history, alcohol and drug usage, sexual preferences, ED, masturbation, pornography consumption, and partner satisfaction using validated scales such as the Cyber Pornography Addiction Test (CYPAT), International Index of Erectile Function (IIEF-5), and Alcohol Use Disorders Identification Test-Concise (AUDIT-C) [
We used the definition of pornography as defined by Kraus et al [
CYPAT was included to measure the exposure, PPC. It is composed of 11 items scored on a 5-point Likert scale. Higher sum scores (minimum=11, maximum=55) indicate more problematic behavior [
Outcome (ED) was measured using the IIEF-5 questionnaire, composed of 5 questions scored on a Likert scale focusing on erectile function and intercourse satisfaction. The possible sum scores for the IIEF-5 range from 5 to 25, and ED was classified into 2 categories based on the sum scores: ED (5-21) versus no ED (22-25) [
After a thorough review by the authors, an online, English, web-based survey was created using the Qualtrics platform and tested several times by the team members. One of the drawbacks of a web-based survey is that due to the possible nonrepresentative nature of the internet-based study sample, it can be difficult to draw population-based conclusions [
The link to the questionnaire [
To evaluate the association between PPC and ED in those who had penetrative sex in the previous 4 weeks, the initial research group teamed up with a psychologist, a sociologist, and an epidemiologist to participate in multidisciplinary brainstorming sessions to conceptualize the exposure-outcome relationship between PPC and ED. A directed acyclic graph (DAG) was used to visualize the associations between the covariates and CYPAT and ED and to classify the covariates as confounders (common causes of CYPAT and ED), mediators (covariates on the causal pathway from CYPAT to ED), and colliders (CYPAT and ED independently cause a third variable). DAGs have become an established framework for the analysis of causal inference in epidemiology and are used to show how associations translate into causal relations [
Descriptive statistics summarizing and describing the characteristics of the data (eg, demographics, sexual interests, masturbation frequency, PPC) were performed both for those men who had penetrative sex during the preceding 4 weeks and those who had not. The pornography consumption time (PCT) in minutes per week was calculated post hoc based on frequency of masturbation, the number of times pornography was used for masturbation, and the average length of 1 pornography session.
Nonparametric tests (chi-square, Kruskal Wallis, and Mann-Whitney-U) were used for the univariate analyses. A significance level of .05 was used to determine statistical significance.
The minimal sufficient adjustment sets for estimating the direct effect of PPC on ED were identified using the DAGitty v3.0 web application and included in a multivariable regression analysis [
This multivariable logistic regression model was constructed to estimate the effect of the selected covariates. Odds ratios (OR) and corresponding 95% CIs were calculated to evaluate the strength of the associations. All statistical analyses were performed using R software v4.02, RStudio v 1.3.959, and Jamovi v1.8.
A total of 5770 men responded to our questionnaire spread mainly but not exclusively via newspaper or radio (2423/5770, 41.99%), social media (1789/5770, 31.00%), and student mailing (577/5770, 10.00%); 2351 participants (2351/5770, 40.75%) were excluded because they were over 35 years of age. Median time to complete the questionnaire was 20 minutes. Eventually, the results of 3419 participants were analyzed. As IIEF-5 asks questions specifically about problems during penetrative sexual intercourse, the participants were divided in 2 categories: those who had penetrative sex in the previous 4 weeks and those who did not. See
Flowchart for participant selection. ED: erectile dysfunction; IIEF-5: International Index of Erectile Function, Short version.
Demographics.
Characteristics | Penetration attempt in the past 4 weeks? | Overall (n=3419) | |||
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No (n=933) | Yes (n=2067) |
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Age (years), median (minimum-maximum) | 22.0 (2.00-35.0) | 25.0 (0.340-35.0) | 24.0 (0.340-35.0) | ||
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Less than high school diploma | 34.0 (3.6) | 57.0 (2.8) | 112 (3.3) | |
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High school diploma or equivalent degree | 387 (41.5) | 545 (26.4) | 1093 (32.0) | |
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Bachelor’s degree | 320 (34.3) | 808 (39.1) | 1278 (37.4) | |
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Master’s degree | 183 (19.6) | 606 (29.3) | 865 (25.3) | |
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Doctorate | 9 (1.0) | 51 (2.5) | 71 (2.1) | |
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Single | 814 (87.2) | 368 (17.8) | 1340 (39.2) | |
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In a “new” relationship (<6 months) | 28 (3.0) | 201 (9.7) | 277 (8.1) | |
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In a longstanding relationship (>6 months) | 74 (7.9) | 1107 (53.6) | 1327 (38.8) | |
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Engaged or married | 15 (1.6) | 387 (18.7) | 435 (12.7) | |
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Divorced or widowed | 2 (0.2) | 4 (0.2) | 9 (0.3) | |
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Missing | 0 (0) | 0 (0) | 31 (0.9) | |
“ |
|||||
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No, I never smoked | 644 (69.0) | 1162 (56.2) | 1981 (57.9) | |
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No, but I did smoke in the past | 94 (10.1) | 356 (17.2) | 502 (14.7) | |
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Yes, but only occasionally | 108 (11.6) | 321 (15.5) | 477 (14.0) | |
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Yes | 87 (9.3) | 228 (11.0) | 349 (10.2) | |
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Missing | 0 (0) | 0 (0) | 110 (3.2) | |
AUDIT-Ca score, median (minimum-maximum) | 5.00 (1.00-12.0) | 5.00 (1.00-12.0) | 5.00 (1.00-12.0) | ||
Missing AUDIT-C values, n (%) | 133 (14.3) | 117 (5.7) | 442 (12.9) | ||
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No | 573 (61.4) | 1661 (80.4) | 2393 (70.0) | |
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Yes | 360 (38.6) | 406 (19.6) | 822 (24.0) | |
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Missing | 0 (0) | 0 (0) | 204 (6.0) | |
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Heterosexual only | 631 (67.6) | 1477 (71.5) | 2235 (65.4) | |
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Heterosexual mostly | 122 (13.1) | 302 (14.6) | 446 (13.0) | |
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Heterosexual, somewhat more bisexual | 36 (3.9) | 83 (4.0) | 126 (3.7) | |
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As equally heterosexual as homosexual | 16 (1.7) | 25 (1.2) | 46 (1.3) | |
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Homosexual, somewhat more bisexual | 18 (1.9) | 14 (0.7) | 32 (0.9) | |
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Homosexual mostly | 33 (3.5) | 47 (2.3) | 80 (2.3) | |
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Homosexual only | 64 (6.9) | 112 (5.4) | 184 (5.4) | |
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Asexual | 8 (0.9) | 2 (0.1) | 10 (0.3) | |
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Missing | 0 (0) | 0 (0) | 247 (7.2) | |
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Women only | 653 (70.0) | 1536 (74.3) | 2322 (67.9) | |
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Women mostly | 112 (12.0) | 266 (12.9) | 396 (11.6) | |
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Women somewhat more than men | 25 (2.7) | 51 (2.5) | 80 (2.3) | |
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Men and women equally | 12 (1.3) | 20 (1.0) | 37 (1.1) | |
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Men somewhat more than women | 21 (2.3) | 18 (0.9) | 40 (1.2) | |
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Men mostly | 38 (4.1) | 46 (2.2) | 85 (2.5) | |
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Men only | 70 (7.5) | 123 (6.0) | 202 (5.9) | |
|
Missing | 0 (0) | 0 (0) | 247 (7.2) | |
“ |
|||||
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Women only | 614 (65.8) | 1439 (69.6) | 2182 (63.8) | |
|
Women mostly | 118 (12.6) | 303 (14.7) | 437 (12.8) | |
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Women somewhat more than men | 33 (3.5) | 67 (3.2) | 107 (3.1) | |
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Men and women equally | 24 (2.6) | 45 (2.2) | 70 (2.0) | |
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Men somewhat more than women | 23 (2.5) | 26 (1.3) | 54 (1.6) | |
|
Men mostly | 40 (4.3) | 56 (2.7) | 101 (3.0) | |
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Men only | 74 (7.9) | 119 (5.8) | 200 (5.8) | |
|
Missing | 0 (0) | 0 (0) | 247 (7.2) | |
CYPATb score, median (mininum-maximum) | 18.0 (11.0-52.0) | 16.0 (11.0-55.0) | 17.0 (11.0-55.0) | ||
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|||||
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11-13 | 151 (16.2) | 464 (22.4) | 617 (18.0) | |
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13-16 | 134 (14.4) | 360 (17.4) | 494 (14.4) | |
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16-21 | 175 (18.8) | 390 (18.9) | 565 (16.5) | |
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21-55 | 208 (22.3) | 381 (18.4) | 589 (17.2) | |
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Missing | 265 (28.4) | 472 (22.8) | 1154 (33.8) | |
IIEFc score, mean (SD) | N/Ad | 22.8 (2.78) | 22.7 (2.97) | ||
IIEF score, median (minimum-maximum) | N/A | 24.0 (6.00-25.0) | 24.0 (5.00-25.0) | ||
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||||
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No EDe | N/A | 1474 (71.3) | 1523 (44.5) | |
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Mild ED | N/A | 327 (15.8) | 349 (10.2) | |
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Mild-moderate ED | N/A | 63 (3.0) | 74 (2.2) | |
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Moderate ED | N/A | 11 (0.5) | 14 (0.4) | |
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Severe ED | N/A | 3 (0.1) | 6 (0.2) | |
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Missing | N/A | 189 (9.1) | 1453 (42.5) | |
“How nervous are you to have any kind of sexual contact?”, median (minimum-maximum) | 6.20 (0-10.0) | 1.10 (0-10.0) | 2.00 (0-10.0) | ||
Missing values for “How nervous are you to have any kind of sexual contact?”, n (%) | 330 (35.4) | 295 (14.3) | 1043 (30.5) | ||
“Do you sometimes feel enormous pressure to perform in bed or to keep an erection while having sex?”, median (minimum-maximum) | 6.00 (0-10.0) | 2.00 (0-10.0) | 3.00 (0-10.0) | ||
Missing values for “Do you sometimes feel enormous pressure to perform in bed or to keep an erection while having sex?”, n (%) | 443 (47.5) | 285 (13.8) | 1146 (33.5) | ||
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<10 | 38.0 (4.1) | 131 (6.3) | 169 (4.9) | |
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10-12 | 323 (34.6) | 773 (37.4) | 1098 (32.1) | |
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13-14 | 314 (33.7) | 748 (36.2) | 1062 (31.1) | |
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15-17 | 57 (6.1) | 137 (6.6) | 194 (5.7) | |
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≥18 | 12 (1.3) | 9 (0.4) | 21 (0.6) | |
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Missing | 189 (20.3) | 269 (13.0) | 875 (25.6) | |
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<10 | 7 (0.8) | 20 (1.0) | 27 (0.8) | |
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10-12 | 126 (13.5) | 303 (14.7) | 429 (12.5) | |
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13-14 | 350 (37.5) | 851 (41.2) | 1202 (35.2) | |
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15-17 | 218 (23.4) | 536 (25.9) | 755 (22.1) | |
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≥18 | 32 (3.4) | 74 (3.6) | 106 (3.1) | |
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Missing | 200 (21.4) | 283 (13.7) | 900 (26.3) | |
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Regularly more than once a day | 104 (11.1) | 138 (6.7) | 242 (7.1) | |
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(Almost) every day | 282 (30.2) | 516 (25.0) | 798 (23.3) | |
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A few times a week, not every day | 280 (30.0) | 756 (36.6) | 1038 (30.4) | |
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A few times a month | 26 (2.8) | 143 (6.9) | 169 (4.9) | |
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Once a week | 43 (4.6) | 173 (8.4) | 216 (6.3) | |
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Once a month | 2 (0.2) | 32 (1.5) | 34 (1.0) | |
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Less than once a month | 4 (0.4) | 27 (1.3) | 31 (0.9) | |
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Never | 3 (0.3) | 13 (0.6) | 16 (0.5) | |
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Missing | 189 (20.3) | 269 (13.0) | 875 (25.6) | |
PCTf, median (minimum-maximum) | 47.3 (0-1580) | 35.0 (0-1560) | 39.4 (0-1580) | ||
Missing PCT values, n (%) | 220 (23.6) | 361 (17.5) | 998 (29.2) | ||
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0-12 | 106 (11.4) | 438 (21.2) | 545 (15.9) | |
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12-35 | 148 (15.9) | 433 (20.9) | 582 (17.0) | |
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35-73 | 196 (21.0) | 408 (19.7) | 604 (17.7) | |
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73-1575 | 263 (28.2) | 427 (20.7) | 690 (20.2) | |
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Missing | 220 (23.6) | 361 (17.5) | 998 (29.2) |
aAUDIT-C: Alcohol Use Disorders Identification Test-Concise.
bCYPAT: Cyber Pornography Addiction Test.
cIIEF: International Index of Erectile Function.
dN/A: not applicable.
eED: erectile dysfunction.
fPCT: pornography consumption time.
With 84.91% (2160/2544) of the participants starting masturbating between the ages of 10 years and 14 years, masturbation was a common practice in our study population. Most of our participants masturbated multiple times per week, with more than 70% (1836/2544, 72.17%) masturbating between a few times a week to daily and 9.51% (242/2544) masturbating even regularly, at more than once a day. Those who were not sexually active in the past 4 weeks seemed to masturbate more often (
Of our study participants, 98.98% (2518/2544) had consumed pornography during masturbation. Pornography was consumed during a median 8.4 of 10 masturbation sessions. In fact, 17.70% (441/2492) of our study population never masturbated without pornography consumption, and 91.40% (2222/2431) of viewers skipped to the best parts of the videos they watched.
The median age at which the participants started masturbating to porn was 13-14 years in our population. The average porn session lasted between 5 minutes and 30 minutes in 81.50% (1973/2421) of our population. For 11.03% (267/2421), when they masturbated to pornography, it lasted, on average, more than 30 minutes. The median calculated PCT was 39.4 minutes per week. There was a statistically significant difference (
Reasons for watching sexually explicit material, where they watched, and with whom they watched.
Responses | n (%) | |
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|
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Because I’m horny | 3145 (84.33) |
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Stress relieve | 1994 (53.47) |
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To produce arousal for masturbating | 1932 (51.81) |
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Lack of real sexual contact | 1597 (42.82) |
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Out of boredom | 1548 (41.51) |
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Out of habit | 1253 (33.60) |
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To be able to experience fantasies or things not done/forbidden in real life | 1229 (32.96) |
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Sexual development/learning how to... | 819 (21.96) |
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Because masturbating without porn isn’t arousing enough | 757 (20.30) |
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To spice things up with sexual partner(s) (watching together) | 740 (19.84) |
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Other | 108 (2.90) |
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|
|
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Alone | 3662 (97.94) |
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With sexual patner(s) | 1601 (42.81) |
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With friend(s) | 1022 (27.33) |
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With stranger, online date, webcam | 358 (9.57) |
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At home/apartment/bedroom | 3726 (99.60) |
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Public places/toilets | 1137 (30.39) |
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Work | 1034 (27.64) |
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Friend's house/apartment | 859 (22.96) |
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Romantic partner’s home | 857 (22.91) |
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Others (eg, stranger’s house) | 591 (15.80) |
Correlations between pornography consumption time (PCT; minutes/week) and age at which masturbation to pornography started.
As there is no defined cutoff score for CYPAT, we divided the scores of our participants into quartiles, according to the distribution of the CYPAT scores [
Correlation between Cyber Pornography Addiction Test (CYPAT) scores and pornography consumption time (PCT; minutes).
An earlier starting age was correlated with higher CYPAT scores (
Of our participants, 21.61% (525/2429) indicated a need to watch an increasing amount of or increasingly extreme pornography to achieve the same level of arousal, and 10.39% (252/2425) needed to do this to get the same rigidity of their penis.
Of the sexually active participants, 72.74% (1238/1702) said they never had erectile, arousal, or climaxing difficulties in the previous 4 weeks when masturbating with porn, compared with 64.8% (456/704) of the sexually inactive men. Only 43.03% (756/1757) of the sexually active men had never had erectile, arousal, or climaxing difficulties when masturbating without porn, compared with 39.43% (289/733) of the sexually inactive men.
Of the sexually active men who were classified as having ED, 61.4% (213/347) admitted to never having erectile, arousal, or climaxing difficulties when masturbating with porn, versus 32.5% (115/354) when masturbating without porn.
Correlation between Cyber Pornography Addiction Test (CYPAT) score and age at which masturbation to pornography started.
The following results are based on the participants who attempted sexual intercourse during the past 4 weeks (2067/3419).
According to their IIEF-5 scores, 21.48% (444/2067) of our sexually active participants (ie, those who attempted penetrative sex in the previous 4 weeks) had some degree of ED (mild: 77/444, 17.4%; mild-moderate: 15/444, 3.4%; moderate: 3/444, 0.6%; severe: 1/444, 0.2%). Most ED was mild (IIEF-5 score: 17-21). However, this mild ED bothered 61.2% (272/444) of affected individuals.
Regarding the correlation between PPC and ED, as shown in
Of the participants classified as having ED, 27.7% (123/444) needed to watch more or more extreme pornography to achieve the same level of arousal, compared with 18.9% (84/444) in those participants who did not experience this need.
Of the participants who had started masturbating to porn at a very early age (<10 years), 58% (11/19) had some form of ED (
Regarding the correlation between PCT and ED, we could not find a statistically significant correlation between ED and PCT when divided in quartiles (
Correlation between Cyber Pornography Addiction Test (CYPAT) scores and erectile dysfunction (ED). 0=No, 1=Yes.
Correlation between pornography consumption time (PCT; minutes) and erectile dysfunction (ED). 0=No, 1=Yes.
There was a statistically significant difference in the percentage of ED between people that frequently watched porn for more than 30 consecutive minutes (84/341, 24.6%) and those who did not (261/1330, 19.62%;
Regarding the correlation between masturbation frequency and ED, there was no statistically significant difference in masturbation frequency between the ED and no ED groups (
Regarding other possible confounders and ED, we observed a difference in self-reported presence of morning and spontaneous erections between those affected by ED (spontaneous: 344/404, 85.1%; morning: 375/404, 92.8%) and those not affected by ED (spontaneous: 1321/1474, 89.62%; morning: 1408/1474, 95.52%;
There was a small but statistically significant (
In our study population, we could not find a correlation between AUDIT-C score and ED (median score was 5 in both groups). However, smoking was correlated with worse erectile function (active: 517/1966, 26.3%; occasionally: 497/1966, 25.3%; never: 411/1966, 20.9%; past: 332/1966, 16.9%;
In the ED group, 17.4% (77/443) and 4.5% (20/443) said that most or a lot of their sexual contact happened under the influence of alcohol or drugs, respectively, versus 6.50% (99/1523) and 1.97% (30/1523) of those without ED (
There was no significant correlation between the use of antidepressants and ED in our study population (
Relationship satisfaction also seemed to be correlated with ED (
Correlations between masturbation frequency and erectile dysfunction (ED). 0=No, 1=Yes.
A multivariable logistic regression model was built using the presence or absence of ED as a dichotomous outcome variable, taking exposure and other selected variables into account. CYPAT, performance pressure, and libido were considered as continuous variables (not normally distributed). Sexual identity, masturbation frequency, relationship status, real sex versus pornography preference, use of antidepressants, and partner satisfaction were considered as categorical variables.
There were 8 factors that were statistically significant (
Logistic regression model coefficients and odds ratios for erectile dysfunction.
Predictor | Estimatea | SE |
|
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Odds ratio | Lower 95% CI | Upper 95% CI | ||||||||
Intercept | –160.857 | 0.4993 | –322.144 | .001 | 0.200 | 0.0752 | 0.533 | ||||||||
CYPATb score | 0.05586 | 0.0122 | 456.757 | <.001 | 1.057 | 1.0324 | 1.083 | ||||||||
Performance pressure | 0.26506 | 0.0271 | 977.692 | <.001 | 1.304 | 1.2361 | 1.375 | ||||||||
Libido (“How would you rate your libido?” [scale 1-10]) | –0.23398 | 0.0579 | –403.809 | <.001 | 0.791 | 0.7064 | 0.887 | ||||||||
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Never | 134.592 | 0.8864 | 151.842 | .13 | 3.842 | 0.6761 | 21.829 | |||||||
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Less than once a month | –0.88466 | 0.9204 | –0.96113 | 0.34 | 0.413 | 0.0680 | 2.508 | |||||||
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Once a month | –0.14011 | 0.6079 | –0.23046 | 0.82 | 0.869 | 0.2640 | 2.862 | |||||||
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A few times a month | –0.41440 | 0.3257 | –127.235 | 0.20 | 0.661 | 0.3490 | 1.251 | |||||||
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Once a week | 0.13267 | 0.2592 | 0.51177 | 0.61 | 1.142 | 0.6870 | 1.898 | |||||||
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(Almost) every day | –0.22186 | 0.1846 | –120.164 | 0.23 | 0.801 | 0.5578 | 1.150 | |||||||
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Regularly more than once a day | –0.06230 | 0.2866 | –0.21741 | 0.83 | 0.940 | 0.5358 | 1.648 | |||||||
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Yes | 0.71061 | 0.4453 | 159.573 | 0.11 | 2.035 | 0.8503 | 4.872 | |||||||
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Extremely satisfied | –0.55291 | 0.2038 | –271.344 | 0.007 | 0.575 | 0.3859 | 0.858 | |||||||
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Extremely unsatisfied | 0.00388 | 0.5325 | 0.00729 | 0.99 | 1.004 | 0.3536 | 2.850 | |||||||
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Moderately unsatisfied | –0.24571 | 0.2816 | –0.87252 | 0.38 | 0.782 | 0.4504 | 1.358 | |||||||
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Neither satisfied nor unsatisfied | –0.30223 | 0.2834 | –106.627 | 0.29 | 0.739 | 0.4241 | 1.288 | |||||||
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Men and women equally | –0.85470 | 0.5628 | –151.873 | 0.13 | 0.425 | 0.1412 | 1.282 | |||||||
|
Men mostly | 0.58608 | 0.4662 | 125.715 | 0.21 | 1.797 | 0.7206 | 4.481 | |||||||
|
Men only | 0.97824 | 0.2858 | 342.253 | <.001 | 2.660 | 1.5190 | 4.657 | |||||||
|
Men somewhat more than women | 0.29131 | 0.6834 | 0.42628 | 0.67 | 1.338 | 0.3506 | 5.107 | |||||||
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Women mostly | –0.18640 | 0.2140 | –0.87090 | 0.38 | 0.830 | 0.5456 | 1.262 | |||||||
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Women somewhat more than men | 0.57143 | 0.4021 | 142.123 | 0.16 | 1.771 | 0.8052 | 3.894 | |||||||
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Divorced/widowed | 159.908 | 13.664 | 117.029 | 0.24 | 4.949 | 0.3399 | 72.036 | |||||||
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Engaged/married | –0.13043 | 0.2211 | –0.58989 | 0.56 | 0.878 | 0.5690 | 1.354 | |||||||
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In a “new” relationship (<6 months) | 0.81798 | 0.2448 | 334.148 | <.001 | 2.266 | 1.4024 | 3.661 | |||||||
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Single | 0.75201 | 0.2346 | 320.566 | .001 | 2.121* | 1.3394 | 3.360 | |||||||
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Real sex gives me a higher level of arousal than pornography | –0.18049 | 0.1902 | –0.94916 | 0.34 | 0.835 | 0.5751 | 1.212 | |||||||
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Real sex gives me a lower level of arousal than pornography | 0.84936 | 0.3043 | 279.130 | 0.005 | 2.338 | 1.2878 | 4.245 |
aEstimates represent the log odds of “ED” vs “No ED.”
bCYPAT: Cyber Pornography Addiction Test.
Since 2006, with the rise of so-called “porn tube sites,” pornography has become widely available and easily accessed on the internet. With just a click, the consumer can indulge his fantasies in ways that would never be possible in real life. Although our study was not intended to examine pornography consumption habits in the general population, the high pornography consumption rates in our study were similar to those in several population studies [
However, since many men start using pornography at a very early age and masturbate more with the help of pornography than without, it is important to study its possible consequences on erectile function.
Frequency of pornography use did not seem to have an important impact on the occurrence of ED. Only when consuming pornography for more than 30 minutes in a row was the frequency of ED slightly higher, but most participants (89%) do not consume pornography for more than 30 minutes.
For the multivariate analysis, a DAG was used to guide the multivariable data analysis to avoid inappropriate adjustment for variables on a causal path between exposure and outcome. Our DAG might be biased since the associations between the covariates are not well known. We hypothesize that the DAG is in proximity of the truth since it was based on the best available evidence and multidisciplinary subject matter expertise when evidence was not available. Age, a well-known covariate for ED, was not included in the DAG because the effect of age was not considered important in our target population (≤35 years of age). Alternative logistic regression models including other variables such as “duration of one pornography session,” “masturbation ratio with and without pornography,” and “whether pornography is needed to climax” were examined but had lower performance and resulted in a poor model fit compared with the model based on the DAG. Among the model’s covariates, there was little multicollinearity, and no extreme influential observations were detected, thus meeting the assumptions for logistic regression.
More PPC, as measured by CYPAT in our study, resulted in a higher probability of ED, while controlling for covariates. While an OR of 1.06 seems low, it is important to remember that for each increase in CYPAT score, the chance of ED increased by 6%. As the CYPAT consists of 11 questions and for each question, a score of 1-5 is given, it means that when 3 questions are scored 1 point higher, the odds for ED increase by 18%, which is high. While a longitudinal study will be necessary to draw conclusions, it is striking how many young men who are not having sexual intercourse (yet) have high CYPAT scores.
There was a wide variety of PCT for different CYPAT categories, meaning that the time of pornography consumption is not necessarily predicting CYPAT scores and vice versa.
Two other instruments are recommended to assess PPC [
Masturbation frequency is often seen as a confounding factor when examining pornography consumption and relational happiness [
ED was reported more frequently by men identifying themselves as homosexual and by men who had sexual fantasies about other men, but not necessarily identifying themselves as homo- or bisexual. Janssen and Bancroft [
Single men and men in a new relationship reported more ED than men in a longstanding relationship. Performance pressure, anxiety, and insecurity are important factors to assess when a young man consults for ED. Finding pornography more arousing than real sex also contributes to this situational ED (56% ED in our study sample versus 17% for those who found real sex more arousing). This was also seen in a study by Berger et al [
One of the strengths of our study is that we assessed ED with a validated and broadly used scale with well-defined cut-off values to evaluate ED in a clinical urological context. However, many participants who did not have intercourse in the preceding 4 weeks were excluded from the analysis, as the IIEF questions were related to sexual intercourse during this period. In that sense, a questionnaire evaluating (situational) ED in young men based on questions not relating to sexual intercourse would be of great value. The ED part of the Male Sexual Health Questionnaire seems promising; however, no cut-off values are available as of yet. Also, the newly developed Masturbation Erection Index could be of great value [
On the other hand, it is clear that the ED seen in our study is situational, as many participants experiencing some ED during partnered sex did not experience ED nor climaxing difficulties while masturbating with pornography. In a clinical setting, while questioning a young patient presenting with ED, it can be interesting to question erectile function while masturbating with and without pornography consumption separately.
It should be said that our results are based on a survey sample. As is seen with the association between frequency of pornography use and problematic pornography use, it is possible that the association between PPC and ED could even be stronger in a clinical sample of treatment-seeking individuals [
Therefore, we should not wait to assess PPC in young males consulting for ED. Earlier studies showed only 3%-4.4% of men will consider themselves addicted to pornography [
However, most men experiencing ED possibly due to PPC cannot be considered as “addicted.” The prevalence of ED already significantly increases with moderate CYPAT scores. Is it possible this situational ED is an early warning signal for the impact pornography has on their sexual functioning?
As pornography consumption is common nowadays (and is even growing, with 11% increased global traffic during the COVID-19 pandemic [
While the existence of “porn addiction” is disputed, there seems a strong correlation between CYPAT and ED. We found an OR of 1.06 for every point increase in CYPAT scores and ED. Indeed, to this day, “PIED,” “porn addiction,” and “sex addiction” do not exist as diagnosable entities in the DSM-5. On the other hand, very recently, the World Health Organization included a new related diagnosis to their ICD-11, CSBD, under which compulsive use of pornography could be classified. It was categorized as an impulse control disorder and not as a behavioral addiction because there is insufficient evidence to do so [
Of course, further neuroscientific and psychophysiological studies will be necessary to explain why PPC can have an effect on ED. To achieve tumescence, a man needs sexual arousal, and this may come from visual stimulation. With sufficient arousal, nitric oxide is released in the penile cavernous tissue and the GTP-cGMP-5’GMP cascade is started. This is the physiological target for PDE-5 inhibitors that are commonly used to treat ED. In the absence of arousal, there will be no erection. A hypothesis is that pornography may give such an extreme visual stimulus that it overactivates the reward system in our brains [
As our study shows a higher ED rate in those who started consuming pornography at an earlier age, we need to learn more about adolescents’ pornography use in a larger social and cultural development context [
We consider the large number of observations, the use of validated scales, the multidisciplinary approach, and the multivariable analyses based on a DAG as the major strengths of this study. Although the study was conducted with great care, possible biases might have been introduced. The study sample might not be fully representative of the population intended to be analyzed. Men with sexual health problems might have been more prone to participate in the study, resulting in a higher prevalence of ED in the study sample. Measurement bias due to using CYPAT as a (initially unintended) measure for PPC may also be present. It is also possible that recall bias was present caused by differences in the accuracy of the recollections retrieved by study participants regarding past behavior, especially when estimating the PCT per week. Also, one of the big problems in this field of research is that there is basically no control group, since nearly all young men seem to watch pornography during masturbation.
The association we found between PPC and ED does not necessarily mean that PPC causes ED. It is perfectly possible that ED leads to higher levels of pornography consumption. However, our multivariable analysis was based on a DAG model that included ED as outcome parameter, not the other way around. This suggests a possible causal association, although more research is needed to investigate causality in depth. Also, DAGs are usually used to encode a priori assumptions about relationships between variables to express causal assumptions and to guide the data collection and analysis [
The prevalence of ED in young men is alarmingly high, and the results of this study suggest a significant association with PPC. Higher CYPAT scores result in a higher probability of ED, when controlling for covariates. Masturbation frequency is not a significant factor when assessing ED. Multivariable analysis identified sexual orientation, experiencing performance pressure, and relationship status as important factors when evaluating the effect of PPC on ED in young men.
Alcohol Use Disorders Identification Test-Concise
compulsive sexual behavior disorder
Cyber Pornography Addiction Test
directed acyclic graph
Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
erectile dysfunction
International Classification of Diseases, 11th Revision
International Index of Erectile Function, Short version
odds ratio
pornography consumption time
porn-induced erectile dysfunction
problematic pornography consumption
Problematic Pornography Consumption Scale
TJ, KFP, and GDW conceptualized the study. TJ curated the data, performed the investigation, and administered the project. TJ, BG, and GDW designed the methodology and performed the formal analysis. GDW provided the resources, and BG provided the software. GVH, IG, and GDW supervised the study, and TJ and BG created the visualizations. TJ, BG, GVH, IG, and GDW wrote the original manuscript draft, and all authors reviewed and edited the manuscript.
None declared.