Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study.

Shilaih M ，Clerck V ，Falco L ，Kübler F ，Leeners B ... -  《Scientific Reports》

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study.

Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to 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 that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, 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. We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing 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. We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward 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 90% accuracy (95% CI 0.89 to 0.92). Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. 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 ovulation.

Goodale BM ，Shilaih M ，Falco L ，Dammeier F ，Hamvas G ，Leeners B ... -  《JOURNAL OF MEDICAL INTERNET RESEARCH》

Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle.

Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests that taking one's daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone (LH) test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82%), with the lowest cycle temperature occurring in the fertile window 41% of the time. Most temporal shifts (307/357, 86%) occurred on ovulation day (OV) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, the present study suggests that WST could be a promising, convenient parameter for future multiparameter fertility awareness methods.

Shilaih M ，Goodale BM ，Falco L ，Kübler F ，De Clerck V ，Leeners B ... -  《-》

Prospective 1-year assessment of within-woman variability of follicular and luteal phase lengths in healthy women prescreened to have normal menstrual cycle and luteal phase lengths.

What is the relative length variance of the luteal phase compared to the follicular phase within healthy, non-smoking, normal-weight, proven normally ovulatory, premenopausal women with normal-length menstrual cycles? Prospective 1-year data from 53 premenopausal women with two proven normal-length (21-36 days) and normally ovulatory (≥10 days luteal) menstrual cycles upon enrollment showed that, despite 29% of all cycles having incident ovulatory disturbances, within-woman follicular phase length variances were significantly greater than luteal phase length variances. Many studies report menstrual cycle variability, yet few describe variability in follicular and luteal phase lengths. Luteal lengths are assumed 'fixed' at 13-14 days. Most studies have described follicular and luteal phase variability between-women. This study was a prospective, 1-year, observational cohort study of relative follicular and luteal phase variability both between and within community-dwelling women with two documented normal-length (21-36 days) and normally ovulatory (≥10 days luteal phase) menstrual cycles prior to enrollment. Eighty-one women enrolled in the study and 66 women completed the 1-year study. This study analyzed data from 53 women with complete data for ≥8 cycles (mean 13). Participants were healthy, non-smoking, of normal BMI, ages 21-41 with two documented normal-length (21-36 days) and normally ovulatory (≥10 days luteal phase) menstrual cycles prior to enrollment. Participants recorded first morning temperature, exercise durations, and menstrual cycle/life experiences daily in the Menstrual Cycle Diary. We analyzed 694 cycles utilizing a twice-validated least-squares Quantitative Basal Temperature method to determine follicular and luteal phase lengths. Statistical analysis compared relative follicular and luteal phase variance in ovulatory cycles both between-women and within-woman. Normal-length cycles with short luteal phases or anovulation were considered to have subclinical ovulatory disturbances (SOD). The 1-year overall 53-woman, 676 ovulatory cycle variances for menstrual cycle, follicular, and luteal phase lengths were 10.3, 11.2, and 4.3 days, respectively. Median variances within-woman for cycle, follicular, and luteal lengths were 3.1, 5.2, and 3.0 days, respectively. Menstrual cycles were largely of normal lengths (98%) with an important prevalence of SOD: 55% of women experienced >1 short luteal phase (<10 days) and 17% experienced at least one anovulatory cycle. Within-woman follicular phase length variances were greater than luteal phase length variances (P < 0.001). However, follicular (P = 0.008) and luteal phase length (P = 0.001) variances, without differences in cycle lengths, were greater in women experiencing any anovulatory cycles (n = 8) than in women with entirely normally ovulatory cycles (n = 6). Limitations of this study include the relatively small cohort, that most women were White, initially had a normal BMI, and the original cohort required two normal-length and normally ovulatory menstrual cycles before enrollment. Thus, this cohort's data underestimated population menstrual cycle phase variances and the prevalence of SOD. Our results reinforce previous findings that the follicular phase is more variable than the luteal phase in premenopausal women with normal-length and ovulatory menstrual cycles. However, our study adds to the growing body of evidence that the luteal phase is not predictably 13-14 days long. This medical education project of the University of British Columbia was funded by donations to the Centre for Menstrual Cycle and Ovulation Research. The authors do not have any conflicts of interest to disclose. N/A.

Henry S ，Shirin S ，Goshtasebi A ，Prior JC ... -  《-》

Oura Ring as a Tool for Ovulation Detection: Validation Analysis.

Oura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health. We assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm's performance with the traditional calendar method, which estimates the ovulation date based on an individual's last period start date and average menstrual cycle length. The study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates. The physiology method detected 1113 (96.4%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P<.001) than the calendar method's average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P<.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95% CI 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P<.001). The physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health.

Thigpen N ，Patel S ，Zhang X 《JOURNAL OF MEDICAL INTERNET RESEARCH》