Menstrual Cycle Influence on COVID-19 Detection Using Machine Learning and a Wearable Medical Device [A120

Abstract

INTRODUCTION: Prior research has demonstrated how immune functioning and physiological signals fluctuate across the menstrual cycle, with eumenorrheic women more likely to become ill during the luteal phase. Examining such changes during the current pandemic, we explored how the relationship between menstrual cycle phase and physiological signals impacts a wearable medical device’s ability to detect COVID-19. METHODS: The largest institutional review board‒approved wearable device study monitoring SARS-CoV-2’s effects on biophysiology to date, COVID-RED aims to develop a machine learning algorithm predicting an infection up to 3 days prior to symptom onset. Wearing the device nightly, participants (N=17,824) sync it with a mobile application and log SARS-CoV-2 diagnostic tests, symptoms, and menses in the app’s Daily Diary. The algorithm ingests physiological and self-reported features to provide each user with a real-time update about their likelihood of infection. RESULTS: Daily infection likelihood and predictions of ovulation using proprietary algorithms were generated during a 9-month period for 1,574 eumenorrheic women (n=3,281 menstrual cycles) not currently on hormonal birth control. The negative/positive ratio of predicted COVID-19 cases during the 5-day period preceding ovulation was 2.94 compared to 4.83 in the 5 days post-ovulation (chi-square (1, N=33,920)=343.34, P<.0001). Participants reported 22 SARS-CoV-2 positive test results, with five times as many confirmed infections occurring in the postovulatory period (n=10) compared to the preceding 10-day window (n=2). CONCLUSION: Demonstrating that machine-learning algorithms ingesting wearable data should consider menstrual cycle impact, our findings suggest that women may be more susceptible to SARS-CoV-2 during their luteal phase, with further studies needed to disentangle underlying mechanisms.