Premenstrual symptoms across the lifespan in an international sample: data from a mobile application.
Liisa HantsooShivani RangaswamyKristin VoegtlineRodion SalimgaraevLiudmila ZhaunovaJennifer L PaynePublished in: Archives of women's mental health (2022)
Premenstrual symptoms, including physical and mood symptoms, affect a large proportion of women worldwide. Data on premenstrual symptoms across nations and age groups is limited. In the present study, we leveraged a large international dataset to explore patterns in premenstrual symptom frequency with age. A survey was administered to users of the Flo mobile application (app), aged 18 to 55. The survey queried app users about a range of premenstrual symptoms. Respondents were asked whether they experienced each symptom every menstrual cycle, some cycles, or never. Age was also captured and categorized as 18-27, 28-37, 38-47, 48-55. Data was summarized and Pearson's chi square test for count data assessed differences in symptom frequency by age group. A sample of 238,114 app users from 140 countries responded to the survey. The most common symptoms reported were food cravings (85.28%), mood swings or anxiety (64.18%), and fatigue (57.3%). Absentmindedness, low libido, sleep changes, gastrointestinal symptoms, weight gain, headaches, sweating or hot flashes, fatigue, hair changes, rashes, and swelling were significantly more frequent with increasing age (p's < 0.001). Mood swings and anxiety did not vary by age group. Of the respondents, 28.61% reported that premenstrual symptoms interfered with their everyday life each menstrual cycle. In a large international sample, the majority of women reported premenstrual food cravings, mood changes, and fatigue every menstrual cycle. Mood symptoms did not vary by age group, suggesting that premenstrual mood changes are a persistent issue among women of reproductive age.
Keyphrases
- sleep quality
- depressive symptoms
- bipolar disorder
- physical activity
- weight gain
- polycystic ovary syndrome
- electronic health record
- big data
- type diabetes
- body mass index
- skeletal muscle
- pregnant women
- cross sectional
- metabolic syndrome
- machine learning
- pregnancy outcomes
- data analysis
- birth weight
- artificial intelligence
- cervical cancer screening
- peripheral blood