Do menstrual tracking apps measure all the symptoms that matter?

Abstract Menstrual tracking applications (or “apps”) are widely used for everyday tracking of physical and mood symptoms across the menstrual cycle and in recent years data from commercial apps have been utilized by researchers to look at psychological phenomena across the menstrual cycle. In the era of big data, tracking apps may serve as a powerful research tool to better understand mental health dynamics across the menstrual cycle. However, the empirical validity of the tracking applications has not been evaluated for its concordance with valid and commonly utilized psychological measures or clinically relevant menstrual cycle symptoms. The present study extracted the symptoms provided by 46 frequently downloaded menstrual tracking apps to evaluate alignment between app content and known physiological and mood symptoms associated with the menstrual cycle. A total of 2622 decisions were made regarding concordance of the symptoms provided by apps with DSM-5 diagnostic criteria for Premenstrual Dysphoric Disorder, a menstrual cycle symptom measure used in research to capture common symptoms across the menstrual cycle, and the Positive and Negative Affect Schedule (PANAS). We found that, on average, menstrual tracking apps only captured 50% of common menstrual cycle symptoms, 42% of PMDD symptoms, and 11% of PANAS items. These findings highlight the discrepancy between commercially available menstrual tracking apps and the research literature on measurement of menstrual cycle symptoms and mood and emotion. We discuss the implications of these discrepancies and make recommendations for clinicians, researchers, and developers.

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