Network structure of complex interactions of premenstrual syndrome and influencing factors in young adult women

Abstract

BackgroundIn the transition phase from adolescence to adulthood, premenstrual syndrome (PMS) occurs more commonly, with a variety of symptoms. The occurrence of PMS may be the result of a combination of demographic, physiological, psychological and sociological factors. This study aimed to identify the central symptoms of PMS, and explored the complex influencing factors especially the one-to-one inter-relationships factors with specific symptoms. MethodThis is a cross-sectional study conducted in mainland China. 3458 young adult women were assessed. Using the Premenstrual Syndrome Scale (PSS) to assess the PMS, and PSS score was over 6 divided into PMS group, and vice versa. Influencing factors were assessed by a set of self-reported questionnaire. Network analysis was used to examined the interplay of PMS, whilst also considering the influencing factors of PMS. ResultsIn summary, 1479 participants were in PMS group. Anxiety had the highest strength centrality (1.12/1.09), shown higher centrality in the both network. Swelling of the hands or feet also shown higher strength centrality (0.89) in PMS group. PMS is associated with a higher history of dysmenorrhea, and neurotic personality. Neurotic personality - depressed mood/nervousness (0.27/0.23), history of dysmenorrhea - abdominal distension (0.21), had significantly higher weight than other edges in PMS group. ConclusionAnxiety was the most central symptom in the network, and was closely associated with other symptoms like depressed mood, which provided additional evidence for the centrality of emotional features in PMS. Moreover, the influencing factors of PMS combined demographic, physiological, psychological, and sociological factors. According to the central symptoms and factors affecting the specific PMS symptoms in young adult women, targeted intervention is helpful to prevent and alleviate PMS. LimitationCross-sectional design cannot infer the directionality of the associations between variables. All data is self-reported with recall bias and the edge weights across the constructs of influencing factors and PMS were fairly small.