JMIR Publications

ABSTRACT

Background:

Prior research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown women’s nightly basal body temperature increases 0.28-0.56˚C following post-ovulation progesterone production. Women’s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase while skin perfusion decreases significantly following the fertile window’s closing. Past research probed only one or two of these physiological features in a given study, requiring participants come into a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests wearables can detect phase-base shifts in pulse rate and wrist-skin temperature. To date, prior work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown.

Objective:

In this study, we we probed what phase-based differences a wearable bracelet could detect in users’ wrist-skin temperature, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real-time.

Methods:

We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet nightly while sleeping for up to a year or until they became pregnant. In addition to synching the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window.

Results:

We demonstrate that wearable technology can detect significant, concurrent phase-based shifts in wrist-skin temperature, heart rate, and respiratory rate (all ps<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all ps<.05), although these effects only trended towards significance following a Bonferroni correction to maintain a family-wise alpha-level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 89% accuracy.

Conclusions:

Our contributions highlight the impact of artificial intelligence and machine learning’s integration into healthcare. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of the fertile window. Clinical Trial: ClinicalTrials.gov NCT03161873