Survey study, 2025
What factors predict whether women will seek help from a doctor for premenstrual symptoms?
About 57% of 592 women with PMS had sought professional help. Women were more likely to seek help if they had work or school problems, thought symptoms were severe, had social difficulties, or had bad past experiences with gynecological care.
What they did
Researchers surveyed 592 women in the UK online who had premenstrual symptoms in multiple cycles. They used computer analysis to identify which symptoms, effects on daily life, and barriers to care predicted whether women had sought medical help specifically for their premenstrual symptoms.
What they found
Of the 592 women, 57.26% had previously seen a healthcare professional for premenstrual symptoms. The strongest predictors of seeking help were: problems with social functioning, thinking symptoms were severe, problems with work or studies, and a history of poor experiences with reproductive or gynecological healthcare.
What it means for you
More than half do seek care, but many don't. Women are more likely to seek help if symptoms affect work, school, or social life, or if they recognize symptoms as severe. Bad past gynecological experiences can increase help-seeking as women seek better care. This could help healthcare providers identify who needs more support.
592
women studied
57.26%
had sought professional help for PMS
4
strongest predictors of help-seeking
About the study
- Original title
- Identifying predictors of formal help-seeking for premenstrual symptoms: A machine learning analysis of symptom, functional impairment and barriers data
- Authors
- Funnell EL, Martin-Key NA, Tomasik J et al.
- Journal
- PLOS mental health
- Year
- 2025
- Study design
- Survey study
- Participants
- 592
- Open access
- Yes
- Read the paper
- doi.org/10.1371/journal.pmen.0000274
Original abstract
Despite the potential severity and burden of premenstrual symptoms, few appear to seek formal care. Given that access to many therapeutic interventions requires formal help-seeking, it is important to understand predictors of this health behaviour. This study employed machine learning to identify symptoms, functional impairment, and barriers to accessing care that predict formal help-seeking for premenstrual symptoms. Data was collected from a UK-based sample using online survey software and explored using descriptive analysis. Group differences in ordinal and categorical data between those who have and have not sought formal help specifically for premenstrual symptoms were examined using Mann-Whitney U tests and Chi-square tests, respectively. Predictive models of help-seeking were built using the decision tree-based machine learning method, Extreme Gradient Boosting (XGBoost). A total of 592 participants with complete data who endorsed premenstrual symptoms in consecutive cycles were included for analysis. Of those, 57.26% (n = 339) had previously seen a healthcare professional specifically for premenstrual symptoms. The model predicting formal help-seeking demonstrated fair performance, with an area under the receiver operating characteristic curve (AUROC) of 0.75 (SD = 0.06), a sensitivity of 0.65 (SD = 0.10), and a specificity of 0.71 (SD = 0.11). The strongest predictors of formal help-seeking were impaired social functioning, thinking that symptoms were severe, impairment in work/studies, and a previous poor care experience for gynaecological/reproductive conditions. These insights may be leveraged to encourage help-seeking behaviour, potentially reducing unnecessary distress or impairment. Improved knowledge of premenstrual symptoms and disorders is vital to facilitate identification of severe symptoms and impaired functioning related to the menstrual cycle. Additionally, improved guidance on when to seek help is required to increase the rate of formal help-seeking, particularly for individuals with high-risk psychological symptoms such as suicidality. More work is needed to determine the specific mechanism by which previous poor care experiences drive further help-seeking for premenstrual symptoms.
Reproduced under CC-BY.
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