An affordable, user-friendly fertility-monitoring tool remains an unmet need. We examine in this study the correlation between pulse rate (PR) and the menstrual phases using wrist-worn PR sensors. 91 healthy, non-pregnant women, between 22-42 years old, were recruited for a prospective-observational clinical trial. Participants measured PR during sleep using wrist-worn bracelets with photoplethysmographic sensors. Ovulation day was estimated with "Clearblue Digital-Ovulation-urine test". Potential behavioral and nutritional confounders were collected daily. 274 ovulatory cycles were recorded from 91 eligible women, with a mean cycle length of 27.3 days (±2.7). We observed a significant increase in PR during the fertile window compared to the menstrual phase (2.1 beat-per-minute, p < 0.01). Moreover, PR during the mid-luteal phase was also significantly elevated compared to the fertile window (1.8 beat-per-minute, p < 0.01), and the menstrual phase (3.8 beat-per-minute, p < 0.01). PR increase in the ovulatory and mid-luteal phase was robust to adjustment for the collected confounders. There is a significant increase of the fertile-window PR (collected during sleep) compared to the menstrual phase. The aforementioned association was robust to the inter- and intra-person variability of menstrual-cycle length, behavioral, and nutritional profiles. Hence, PR monitoring using wearable sensors could be used as one parameter within a multi-parameter fertility awareness-based method.
PMID 28465583 28465583 DOI 10.1038/s41598-017-01433-9 10.1038/s41598-017-01433-9
Cite this article
Shilaih, M., Clerck, V. D., Falco, L., Kübler, F., & Leeners, B. (2017). Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study. Scientific reports, 7(1), 1294. https://doi.org/10.1038/s41598-017-01433-9
Shilaih M, Clerck VD, Falco L, Kübler F, Leeners B. Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study. Sci Rep. 2017;7(1):1294. doi:10.1038/s41598-017-01433-9
Shilaih, Mohaned, et al. "Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study." Scientific reports, vol. 7, no. 1, 2017, pp. 1294.
To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses.
Prospective cohort study.
Western New York. PATIENT(S): Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations. RESULT(S): Algorithms based on serum LH, E2, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E2 metabolite, estrone-3-glucuronide, levels. CONCLUSION(S): The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation.
Fertility AwarenessDiagnosticsMenstrual Cycle
Open Access
Transient exposures may influence fertility and early embryonic development. To assess the time of conception in vivo and conduct concurrent biomonitoring, ovulation must be identified prospectively. We report on the development and validation of a simple, prospective method, the Peak Day method, to determine likely day of ovulation based upon daily observations of cervical fluid. We recruited 98 women to learn the Peak Day method from a brochure, 26 of whom concurrently used the method with blinded daily urine hormone monitoring (estrone glucuronide and luteinizing hormone). All women were instructed to complete an exposure questionnaire immediately upon identifying ovulation. Briefly, the exposure questionnaire captured time-varying and transient exposures such as medication use, water consumption, and amount of sleep. We assessed timely completion of the exposure questionnaire, agreement of women's estimated day of ovulation (EDO) and the EDO by expert review, and agreement between the EDO by expert review and by blinded urine monitoring. Of 147 cycles evaluated, women selected an EDO in 130 (88%) and subsequently completed the periovulatory exposure questionnaire in 122 (94%) cycles. Of the 26 cycles evaluated with blinded hormonal monitoring, the Peak Day "best quality" algorithm, based upon cervical fluid, identified ovulation ± 3 days of the urine monitor in 24 cycles (92%). With simple written instructions, women can identify an estimated day of ovulation and perform periovulatory exposure assessment. The Peak Day method is highly cost-effective and could be applied by researchers to target periconceptional or very early developmental stage exposure assessment.
Accuracy in detecting ovulation and estimating the fertile window in the menstrual cycle is essential for women to avoid or achieve pregnancy. There has been a rapid growth in fertility apps and home ovulation testing kits in recent years. Nevertheless, there lacks information on how well these apps perform in helping users understand their fertility in the menstrual cycle. This pilot study aimed to evaluate and compare the beginning, peak, and length of the fertile window as determined by a new luteinizing hormone (LH) fertility tracking app with the Clearblue Fertility Monitor (CBFM). A total of 30 women were randomized into either a quantitative Premom or a qualitative Easy@Home (EAH) LH testing system. The results of the two testing systems were compared with the results from the CBFM over three menstrual cycles of use. Potential LH levels for estimating the beginning of the fertile window were calculated along with user acceptability and satisfaction. The estimates of peak fertility by the Premom and EAH LH testing were highly correlated with the CBFM peak results (R = 0.99, p < 0.001). The participants had higher satisfaction and ease-of-use ratings with the CBFM compared to the Premom and EAH LH testing systems. LH 95% confidence levels for estimating the beginning of the fertile window were provided for both the Premom and EAH LH testing results. Our pilot study findings suggest that the Premom and EAH LH fertility testing app can accurately detect impending ovulation for women and are easy to use at home. However, successful utilization of these low-cost LH testing tools and apps for fertility self-monitoring and family planning needs further evaluation with a large and more diverse population.
Does sexual intercourse enhance the cycle fecundability in women without known subfertility? Sexual intercourse (regardless of timing during the cycle) was associated with cycle characteristics suggesting higher fecundability, including longer luteal phase, less premenstrual spotting and more than 2 days of cervical fluid with estrogen-stimulated qualities. Human females are spontaneous ovulators, experiencing an LH surge and ovulation cyclically, independent of copulation. Natural conception requires intercourse to occur during the fertile window of a woman's menstrual cycle, i.e. the 6-day interval ending on the day of ovulation. However, most women with normal fecundity do not ovulate on Day 14, thus the timing of the hypothetical fertile window varies within and between women. This variability is influenced by age and parity and other known or unknown elements. While the impact of sexual intercourse around the time of implantation on the probability of achieving a pregnancy has been discussed by some researchers, there are limited data regarding how sexual intercourse may influence ovulation occurrence and menstrual cycle characteristics in humans. This study is a pooled analysis of three cohorts of women, enrolled at Creighton Model FertilityCare 'Creighton Model MultiCenter Fecundability Study' (CMFS: retrospective cohort, 1990-1996), 'Time to Pregnancy in Normal Fertility' (TTP: randomized trial, 2003-2006) and 'Creighton Model Effectiveness, Intentions, and Behaviors Assessment' (CEIBA: prospective cohort, 2009-2013). We evaluated cycle phase lengths, bleeding and cervical mucus patterns and estimated the fertile window in 2564 cycles of 530 women, followed for up to 1 year. Participants were US or Canadian women aged 18-40 and not pregnant, who were heterosexually active, without known subfertility and not taking exogenous hormones. Most of the women were intending to avoid pregnancy at the start of follow-up. Women recorded daily vaginal bleeding, mucus discharge and sexual intercourse using a standardized protocol and recording system for up to 1 year, yielding 2564 cycles available for analysis. The peak day of mucus discharge (generally the last day of cervical fluid with estrogen-stimulated qualities of being clear, stretchy or slippery) was used to identify the estimated day of ovulation, which we considered the last day of the follicular phase in ovulatory cycles. We used linear mixed models to assess continuous cycle parameters including cycle, menses and cycle phase lengths, and generalized linear models using Poisson regression with robust variance to assess dichotomous outcomes such as ovulatory function, short luteal phases and presence or absence of follicular or luteal bleeding. Cycles were stratified by the presence or absence of any sexual intercourse, while adjusting for women's parity, age, recent oral contraceptive use and breast feeding. MAIN Most women were <30 years of age (75.5%; median 27, interquartile range 24-29), non-Hispanic white (88.1%), with high socioeconomic indicators and nulliparous (70.9%). Cycles with no sexual intercourse compared to cycles with at least 1 day of sexual intercourse were shorter (29.1 days (95% CI 27.6, 30.7) versus 30.1 days (95% CI 28.7, 31.4)), had shorter luteal phases (10.8 days (95% CI 10.2, 11.5) versus 11.4 days (95% CI 10.9, 12.0)), had a higher probability of luteal phase deficiency (<10 days; adjusted probability ratio (PR) 1.31 (95% CI 1.00, 1.71)), had a higher probability of 2 days of premenstrual spotting (adjusted PR 2.15 (95% CI 1.09, 4.24)) and a higher probability of having two or fewer days of peak-type (estrogenic) cervical fluid (adjusted PR 1.49 (95% CI 1.03, 2.15)). LIMITATIONS Our study participants were geographically dispersed but relatively homogeneous in regard to race, ethnicity, income and educational levels, and all had male partners, which may limit the generalizability of the findings. We cannot exclude the possibility of undetected subfertility or related gynecologic disorders among some of the women, such as undetected endometriosis or polycystic ovary syndrome, which would impact the generalizability of our findings. Acute illness or stressful events might have reduced the likelihood of any intercourse during a cycle, while also altering cycle characteristics. Some cycles in the no intercourse group may have actually had undocumented intercourse or other sexual activity, but this would bias our results toward the null. The Creighton Model FertilityCare System (CrM) discourages use of barrier methods, so we believe that most instances of intercourse involved exposure to semen; however, condoms may have been used in some cycles. Our dataset lacks any information about the occurrence of female orgasm, precluding our ability to evaluate the independent or combined impact of female orgasm on cycle characteristics. Sexual activity may change reproductive hormonal patterns, and/or levels of reproductive hormones may influence the likelihood of sexual activity. Future work may help with understanding the extent to which exposure to seminal fluid, and/or female orgasm and/or timing of intercourse could impact menstrual cycle function. In theory, large data sets from women using menstrual and fertility tracking apps could be informative if women can be appropriately incentivized to record intercourse completely. It is also of interest to understand how cycle characteristics may differ in women with gynecological problems or subfertility. Funding for the research on the three cohorts analyzed in this study was provided by the Robert Wood Johnson Foundation #029258 (Creighton Model MultiCenter Fecundability Study), the Eunice Kennedy Shriver National Institute of Child Health and Human Development 1K23 HD0147901-01A1 (Time to Pregnancy in Normal Fertility) and the Office of Family Planning, Office of Population Affairs, Health and Human Services 1FPRPA006035 (Creighton Model Effectiveness, Intentions, and Behaviors Assessment). The authors declare that they have no conflict of interest. N/A.