Global Menstrual Cycle Symptomatology

BackgroundAlthough a shared experience throughout the world, menstrual symptomatology is underreported and often misunderstood, by both individuals who menstruate and their providers. This study aimed to investigate symptomatology throughout the menstrual cycle, including the frequency of symptoms reported for each phase of the cycle and the relationship between symptoms. MethodsThis study included self-reported cycle information and symptoms during ovulatory menstrual cycles in mobile application users. Symptom frequency was described overall, by phase, and by day. Relationships between symptoms were examined through correlation coefficients and term frequency-inverse document frequency analysis. Results437,577 unique users and 896,051 unique cycles were included in the analysis. Somatic symptoms were most frequently reported, logged in 88.3% of cycles. Of the total symptoms logged, somatic, gastrointestinal, and negative mood symptoms were most prevalent in the late luteal and bleeding days. In contrast, positive mood and discharge were most reported in the fertile window. Mood symptoms were highly correlated with each other (energetic mood and happy mood r=0.62, anxious mood and obsessive thoughts r=0.60). Fatigue was highly correlated with somatic and gastrointestinal symptoms such as bloating (r=0.62), headache (r=0.60), backache (r=0.58), and nausea (r=0.55). ConclusionsMenstrual cycle-related symptoms are very common and vary throughout the menstrual cycle. While negative symptoms, including pain, bloating, and negative mood, are most commonly reported, women also report positive symptoms, such as energetic and happy mood. Self-reported data from cycle tracking and health apps can improve our understanding of the prevalence and variability of menstrual-related symptoms. Plain English SummaryOur study includes data collected from users of a menstrual tracking application. We found that menstrual symtpoms are common, throughout the menstrual cycle, and include both positive and negative mood and physical symptoms. This large data set may help to normalize menstrual symptoms and provide a basis for future research and understanding.