Low sexual desire in women : an empirical investigation of predictors and psychological treatment

Abstract

The present thesis aimed at 1) investigating the change over time in levels of sexual desire as well as pre-dictors of low sexual desire in women, and 2) evaluating brief psychological treatment interventions for women with low sexual desire. To achieve the first aim, cross-lagged structural equation models and net-work analysis models were fitted to a population-based data set with responses from 2,173 Finnish wom-en (Mage = 25.5 years, SD = 5.0). The aforementioned data set included two waves of data collected seven years apart. To achieve the second aim, two psychological intervention conditions were evaluated in a clinical sample of 70 women (Mage = 39.2 years, SD = 9.8) from Stockholm, Sweden, and Turku, Finland. Participants in the clinical study reported low and distressing sexual desire before treatment took place.

In the population-based sample, reported levels of sexual desire were highly variable over time, with most of the variance being accounted for by something else than previous levels of sexual desire. Relationship status played a significant role with regards to sexual function and changes in levels of sexual desire. Body dissatisfaction and intercourse-related pain showed network patterns suggesting that they might predict decreases in sexual desire. Participants in both intervention conditions showed significant im-provements in sexual desire as well as secondary outcomes such as sexual distress and sexual satisfaction. Approximately half of the participants who provided responses after the intervention reached a clinically significant change in sexual desire, suggesting inter-individual variability in treatment response.

Interventions for low sexual desire in women seem promising, as our results point towards sexual desire being a changeable trait. Our results suggest that relationship-related factors should be taken into account in psychological interventions for low sexual desire. Tailored interventions based on individual time-series networks are also encouraged.