Survey study, 2025
Can researchers develop a reliable tool to screen for PMS in the workplace?
Yes, researchers created a new screening tool with four parts that reliably identifies PMS and predicts who will miss work because of it.
What they did
Cross-sectional study of 3,239 working women recruited online in October 2021. They answered 47 questions about PMS symptoms. Researchers used statistical analysis to identify which questions grouped together.
What they found
331 women (10%) reported PMS and 393 (12%) had taken sick leave because of PMS. The tool has four parts: autonomic dysfunction (Cronbach's alpha 0.93), psychiatric symptoms (0.94), lack of work efficiency (0.93), and abdominal symptoms (0.95). The tool predicted work absenteeism with 78% sensitivity, 57% specificity, and area under the curve of 0.735.
What it means for you
About 10% of working women have PMS, and 12% took sick leave because of it. The new tool reliably identifies PMS and predicts work absence. Limitation: online recruitment and cross-sectional design means we cannot prove cause and effect.
331
women with PMS (10%)
393
took sick leave due to PMS (12%)
78%
sensitivity for predicting work absence
About the study
- Original title
- Development of a premenstrual syndrome scale for working women and its validation against work productivity
- Authors
- Takenoshita C, Shimizu K, Iida M et al.
- Journal
- Research Square
- Year
- 2025
- Study design
- Survey study
- Participants
- 3,239
- What was tested
- Development and validation of screening tool
- Read the paper
- doi.org/10.21203/rs.3.rs-6644419/v1
Original abstract
Abstract Objective We aimed to develop a new screening tool for premenstrual syndrome (PMS) to be used in the workplace. Methods In October 2021, we recruited 3,239 working women with menstruation via an internet research company and asked 47 questions about PMS-related symptoms. Results Of the participants, 331 women had experienced PMS (10%), and 393 women had taken sick leave because of PMS-associated symptoms (12%). Explanatory factor analyses with maximum likelihood and Promax rotation identified four domains with 27 items, including "Autonomic dysfunction symptoms'' (Cronbach’s α = 0.93), "Psychiatric symptoms" (Cronbach’s α = 0.94), "Lack of work efficiency" (Cronbach’s α = 0.93), and “Abdominal symptoms” (Cronbach’s α = 0.95). Using a split-half sample for the confirmatory factor analysis, moderately fit model indices for the four-factor solution were confirmed. We also confirmed the developed scale’s criterion validity using existing PMS screening criteria and its concurrent validity through high correlation coefficients with Copenhagen Burnout Inventory scores. The receiver operating characteristic curve yielded a good predictive ability for work absenteeism, including a sensitivity of 78%, a specificity of 57%, and an area under the curve of 0.735. Conclusion A highly reliable and valid new scale for PMS was developed with efficacy for screening for work absenteeism.
Reproduced under CC-BY.
Topics
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