- This study systematically cataloged the metrics collected by 20 menstrual tracking apps on the Apple platform
- The apps differed widely in the information collected, the language used to describe and characterize menstrual experiences and the degree of specificity for reporting symptoms
- Clinicians and data scientists should collaborate so the data collected with menstrual health apps will be clinically useful and harmonizable across platforms for future aggregated data analysis
Numerous digital apps are available that allow users to track their menstrual periods on a calendar and record related symptoms. Their purposes include preparing for upcoming periods, timing intercourse to avoid or facilitate conception, identifying bleeding irregularities and facilitating conversations with health care professionals.
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Shruthi Mahalingaiah, MD, MS, a reproductive endocrinologist in the Department of Obstetrics & Gynecology at Massachusetts General Hospital, Tatheer Adnan, a visiting scholar at the Harvard School of Public Health, and colleagues recently studied whether the apps are similar enough that the information collected could be harmonized. Their results show that while helpful for individualized tracking, these apps aren't conducive to aggregated data analysis or clinical diagnosis. The team's report appears in Current Opinion in Endocrinology, Diabetes and Obesity.
Menstrual health apps on the Apple iTunes Store were eligible for the study if they had been downloaded more than one million times. 15 of the 20 apps reviewed are also available for the Android operating system.
The apps were downloaded onto an iPhone and the features of each were piloted through a simulated menstrual cycle.
Number and Categories of Observations
All 20 apps permitted users to record at least 10 types of observations, and collectively the apps gathered more than 100 types. The research team categorized them as body metrics, period-related metrics, cervical state, physical symptoms, emotional symptoms, behavioral attributes and activity levels.
All apps collected at least one flow-related metric, physical/pain measure, and behavioral symptom, but only half collected symptoms across all categories. Physical symptoms constituted 48% of metrics.
The Language of Symptoms
There was significant variation in the terminology used to collect metrics related to bleeding days. For example:
- "Bleeding," "spotting," "flow" and "discharge" were used interchangeably
- Terms used for breast sensations included "tender breasts," "breast sensitivity," "sore nipples," "breast pain" and "aching nipples"
- The various terms used to elicit reports of uterine pain/discomfort were "ovulation pain," "pelvic pain," "vaginal pain," "cramps" and "abdominal cramps"
Few apps provided definitions of the metrics collected, and only one of them both provided definitions of metrics and explained how they should be recorded.
Granularity of Symptom Reporting
The amount of detail recorded for symptoms differed by app and by metric. Most apps merely captured the presence or absence of a symptom, although some allowed ranking by severity (or "heaviness," as in the case of menstrual flow), and some permitted qualitative descriptions.
No app collected symptoms in a manner that could be considered "clinical grade." For instance, the apps usually ask users to report pain as present/absent or rank it from mild to severe. Clinicians, on the other hand, would document onset, location, intensity, duration, exacerbating/relieving factors and whether the pain radiates.
The Way Forward
Menstrual health apps aren't advertised as diagnostic tools, but if standardized they would certainly have diagnostic potential. Further, standardization across apps could benefit data analysis in epidemiologic studies, as participants could contribute similar data types while continuing to utilize their preferred tracking app. Clinicians must ensure patients' experiences and needs are being represented, then lend their expertise to direct changes to symptom tracking functionality.
Data scientists could then ensure data are collected in a harmonized manner. This process would be iterative—the phenotypic associations that emerge from digital studies will help clinicians establish which symptoms should be prioritized in future rounds of app design.
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