ArticleBMC women's health2025
A survey assessing the impact of symptoms related to the menstrual cycle and perceptions of workplace productivity: considerations for employer-sponsored menstrual health programs.
Article in BMC women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHormonal-related symptoms experienced during natural or contraceptive-driven menstrual cycles have implications on work-related productivity; however, employer-sponsored menstrual health resources are widely unavailable. Actionable research-based evidence is needed to develop menstrual health programs that proactively help working females mitigate their hormonal-related symptoms and optimize their hormone profiles and work-related performance. This study sought to evaluate the prevalence and severity of hormonal-related symptoms and assess the directional impact of hormonal-related symptoms on work-related productivity across cyclical hormone phases.
methodsA cross-sectional, descriptive questionnaire was used to measure hormonal-related symptoms and work-related productivity in 372 working females of reproductive age in the United States. The validated Menstrual Distress Questionnaire was used to measure the prevalence and severity of hormonal-related symptoms. The Menstrual Cycle-Related Work Productivity Questionnaire was modified and used to assess perceptions of work-related productivity measures across all cyclical hormone phases. Cumulative link mixed models and Bayesian adjacent category models were employed to determine the relationship between hormonal-related symptoms and work-related productivity, independent of age, body mass index (BMI), heavy bleeding experience, cyclical hormone phase, contraceptive use, Exos employment status and other hormonal-related symptoms.
resultsHormonal-related symptoms were present across cyclical hormone phases, and the most severe disturbances were experienced during the bleed-phase. Distributions of perceived work productivity were significantly more negative during the pre-bleed and bleed phases and more positive during the late follicular and early luteal phases. Self-reported hormonal-related symptoms were significantly associated with perceptions of work-related productivity, independent of potential confounders.
conclusionsCyclical hormone fluctuations impact perceived work-related productivity variably by phase. Self-reported hormonal-related symptoms are associated with perceptions of work-related productivity. Our findings identify important considerations for the development of menstrual health programming to optimize the lived experience of female physiology in the workplace.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.