A Network Analysis of the Fear Avoidance Model of Genital Pain.

Using a novel data-driven network approach, this study aimed to examine the interconnection between the key elements of the Fear-Avoidance Model of female genital pain - sexual arousal, fear-avoidant cognitions, and motivational coping - and its associated factors to predict the intensity and frequency of genital pain across women over time. Network modeling allowed for a comprehensive evaluation of the Fear-Avoidance model while capturing the dynamic features of genital pain. We estimated a cross-sectional and a temporal, contemporaneous, and between-persons network model on convenience-based data of 543 female students (mean age = 23.7 years,  = 3.6) collected at three time points. Results showed that lubrication, pain catastrophizing, pain avoidance, fear-avoidance beliefs, sexual satisfaction, anxiety, and frequency of coital and non-coital sex predicted pain, with lubrication being the most consistent predictor across estimations. The network of women with recurrent genital pain showed a similar pattern as the network of the total sample, except that pain avoidance and fear-avoidance beliefs rather than pain catastrophizing predicted pain directly, and frequency of coital and non-coital sexual activities played a more prominent role. These results suggest that the main problem of genital pain centers around women not being sufficiently aroused during intercourse and inadequate ways of pain coping, which are critical targets of cognitive-behavioral therapy treatment and should be developed further.