New Findings on the Keys to Sexual Fulfillment

Source: We-Vibe Toys/Unsplash

In new research published in the Journal of Social and Personal Relationships in January of 2022, Laura Vowels and colleagues from the University of Lausanne (Switzerland) used machine learning to identify the strongest predictors of sexual satisfaction.

Machine learning models surpass traditional statistical models because they are able to include large numbers of variables and detect non-linear relationships. The researchers used data from two large samples, including individuals’ and couples’ data. The results revealed that relationship-level factors such as relationship satisfaction and feelings of romantic love are the strongest predictors of sexual satisfaction.

The researchers defined sexual satisfaction as “one’s subjective evaluation of the positive and negative dimensions associated with one’s sexual relationship.” The purpose of the current research was to identify both the strongest and the weakest predictors of sexual satisfaction and whether the strongest predictors varied with partner gender. The researchers speculate that those variables with the strongest associations with sexual satisfaction may be useful in potential treatment interventions.

Participants from two different samples were used in the machine learning analyses. The first sample consisted of individuals recruited via Amazon Mechanical Turk as well as via forums that were geared toward sexual minority groups. These participants included cisgender women, cisgender men, and nonbinary participants. Most participants identified as white and heterosexual; however, a large percentage of participants identified as bisexual, gay, or lesbian.

The second sample comprised mixed-sex couples who were either heterosexual or had one bisexual partner. The final sample contained nearly four hundred couples and 200 individual respondents.

The researchers considered more than 90 different variables as predictors of sexual satisfaction, including demographic variables such as age, gender, and sexual orientation, as well as other individual-level factors such as attachment style, self-esteem, and contraceptive use. They also examined relationship-level factors such as relationship status (e.g., long-term vs. short-term), feelings of love, commitment, and intimacy. Sexual satisfaction was assessed using the General Measure of Sexual Satisfaction Scale. Three sets of machine learning analyses assessed sexual satisfaction models among all participants, just women and just men.

The authors found that the machine learning models explained two to three times more variation in sexual satisfaction than traditional statistical models.

Across all three analyses, relationship satisfaction was the strongest predictor of sexual satisfaction. Besides relationship satisfaction, regular sexual frequency, feelings of love, and dyadic sexual desire were associated with higher sexual satisfaction while a longer relationship was associated with declining sexual satisfaction. Engaging in oral sex was also associated with increased sexual satisfaction.

Although the models could explain slightly more variance in men’s sexual satisfaction than women’s, the top-ten predictors of men’s and women’s sexual satisfaction were similar. Moreover, after the top ten factors, subsequent variables did not explain much additional variance in sexual satisfaction. Factors such as gender, sexual orientation, and religiosity did not strongly impact sexual satisfaction.

The researchers conclude that relationship-level factors have the strongest associations with sexual satisfaction.

Facebook image: Drazen Zigic/Shutterstock