Erdős · Journal of medical Internet research 2025 · cross-sectional survey · n=7187

Pornography-Watching Disorder and Its Risk Factors Among Young Adults: Cross-Sectional Survey.

Level 4 - case-series / case-control

Cross-sectional survey with self-selected convenience sample

PubMed 39778200 · doi:10.2196/49860 · record verified 2026-08-26

What was done

A web-based cross-sectional survey was conducted between September and December 2018 among young adults aged 18 to 35 in Hungary, recruited via social media and a medical school webpage. Out of 9,397 total respondents, 7,187 who had consumed pornography were analyzed. Problematic pornography use was assessed using 10 items adapted from DSM-5 substance use disorder criteria. Multivariable binary logistic regression was used to identify associated factors.

What was found

The prevalence of pornography-watching disorder (PWD) was 4.4% (n=315). Less frequent pornography use showed significantly lower odds of PWD compared to daily use (weekly: OR 0.45, 95% CI 0.33–0.62; monthly: OR 0.18, 95% CI 0.11–0.28; less than monthly: OR 0.05, 95% CI 0.03–0.10; all P<.001). Other variables associated with PWD included male sex (female OR 0.53, 95% CI 0.39–0.72, P<.001), earlier exposure (OR 0.94, 95% CI 0.90–0.98, P=.006), paraphilia (OR 3.95, 95% CI 2.37–6.56, P<.001), dissatisfaction with sexual life (OR 0.94, 95% CI 0.90–0.98, P=.006), difficulty forming personal relationships (OR 0.93, 95% CI 0.88–0.98, P=.005), and adherence to religious norms (OR 1.12, 95% CI 1.06–1.19, P<.001). Protective factors included adequate sexual education (OR 0.67, 95% CI 0.53–0.87, P=.02) and living in the capital (OR 0.52, 95% CI 0.30–0.91, P=.02).

Why it matters

This study provides large-sample prevalence estimates of problematic pornography use using an adapted DSM-5 framework, identifying behavioral, demographic, and sociocultural risk correlates in young adults.

Limits

The cross-sectional design prevents causal inference and cannot determine temporal directionality. Recruitment via online social media and a university webpage introduces volunteer and selection bias. Diagnostic status relied on self-reported survey items rather than structured clinical diagnostic interviews.