Unpacking sexual behaviors in the digital era: A person-centered approach to online and offline activities

The Internet has become a key medium for sexual exploration and gratification. This study aimed to identify latent subgroups based on engagement in offline sexual behaviors and Online Sexual Activities (OSAs); examine the extent to which classes are predicted by key sociodemographics and OSA motives; and investigate how classes differ on sex addiction, trait emotional intelligence (EI) and satisfaction with life (SWL). 639 Italian active OSA users (61.7 % females) aged 18–77 (M = 31.2, SD = 10.4) completed an online survey assessing: autoerotism, sexual intercourse and OSAs frequency (12 items); OSAs motives (18 items); Bergen-Yale Sex Addiction Scale; Trait Emotional Intelligence Questionnaire–Short Form; Satisfaction with Life Scale. Latent Class Analysis identified three classes: Solitary (32 %), characterized by solitary behaviors; In-person (45 %), marked by in-person sexual activity and limited OSAs; and Highly Engaged (23 %), with frequent offline and online sexual behaviors. R3STEP analysis showed that sex, living and relationship status, religiosity, number of occasional sexual partners, and motives such as Social Pressure, Coping/Escaping, and Fantasy distinguished the classes. The BCH procedure, used to examine distal outcomes, revealed that individuals in the Highly Engaged class exhibited the highest risk of sex addiction, followed by those in the Solitary and In-person classes. Compared to the In-person class, the Highly Engaged group reported lower levels of trait EI and SWL. Findings delve into diverse shapes of sexuality in the digital era, highlighting how and for whom sexual behaviors may be problematic and underscoring the importance of studying OSAs alongside offline sexual behaviors. • LCA identified three sexual behavior classes: Solitary, In-person, Highly Engaged. • Classes differ by sex, living, relationship, religion, casual partners, OSA motives. • Highly engaged shows the highest sex addiction risk and lowest trait EI and SWL. • The study supports integrating online and offline sexual behavior in research.