The Strongest Drivers of Sexual Desire in Relationships

Key points:

• New research employed machine learning to determine the primary drivers of sexual desire in relationships.
• The model was more effective predicting drivers of women's sexual desire than men's.
• Existing sexual satisfaction was a strong predictor of higher sexual desire in couples, but those who had been in relationships longer reported weaker levels of desire.

New research by Vowels et al. (2020) suggests that several factors reliably predict both individual and dyadic sexual desire. This manuscript has been posted as a preprint and is undergoing peer review.

The researchers defined sexual desire as “a motive, drive, or wish to engage in sexual activity either with oneself or with a partner.” They explored which factors were most strongly associated with individual feelings of sexual desire as well as couples’ feelings of sexual desire. The authors noted that sexual desire may be influenced by individual-level variables like attachment style, couple-level variables like relationship length, and societal-level factors like sexual double standards.

The researchers used machine learning to predict sexual desire by analyzing numerous predictor variables in two large samples. Machine learning may be preferable to traditional statistical regression models because of its ability to detect non-linear relationships. Machine learning has also recently been used to predict romantic relationship quality.

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In the current project, the researchers used data from individuals as well as couples to predict sexual desire. They also predicted men’s and women’s sexual desire separately. In the individual sample, more than 800 participants (cis-gender men, women, and nonbinary individuals) completed measures assessing sexual desire, sexual satisfaction, mindfulness, sexual attitudes, and attachment styles. The sample of couples included nearly 400 mixed-gender dyads. This second sample also involved couples in which one member was bisexual. In both the individual and couple samples, most of the participants identified as White and were either married or cohabitating.

The authors found that one of the strongest predictors of sexual desire was sexual satisfaction; individuals and couples who reported stronger feelings of sexual satisfaction also reported increased feelings of sexual desire. Relationship length was also strongly related to sexual desire; those whose relationships were of longer duration reported weaker feelings of sexual desire. Strong expressions of love and feelings of intimacy were also associated with greater sexual desire; however, relationship satisfaction was not a reliable predictor of couples’ feelings of sexual desire. Interestingly, individuals with higher levels of attachment anxiety also reported stronger feelings of sexual desire.

Although factors such as sexual satisfaction, love, and partner’s desire were strong predictors of sexual desire for both men and women, the machine learning model was better at predicting women’s sexual desire than men’s. For this reason, the authors recommend that future research will be necessary in order to determine which factors most strongly influence men’s sexual desire. The authors also note that variables which were not measured in the current studies, such as partner responsiveness or physical attractiveness, should be investigated in future research. Interestingly, most of the variance in sexual desire was related to individual responses rather than couples’ responses, suggesting that treatments intended to improve sexual desire may benefit from targeting individuals as well as couples.

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