Use of ovulation predictor kits as adjuncts when using fertility awareness methods (FAMs): a pilot study.

PURPOSE: Difficult clinical signs such as confusing cervical mucus or erratic basal body temperature can make the use of fertility awareness methods (FAMs) difficult in some cases. The goal of this study was to assess the feasibility of using a cheap urinary luteinizing hormone (LH)-surge identification kit as an adjunct to identify the infertile phase after ovulation when facing these scenarios. METHODS: The study used a block-allocation, crossover, 2-arm methodology (LH kit/FAM vs FAM only). Comparison of the 2 arms was done with regard to the accuracy of identification (yes/no) of the luteal phase in each cycle as confirmed by serum progesterone concentrations. RESULTS: We recruited 23 Canadian women currently using FAM, aged 18 to 48 years, who have had menstrual cycles 25 to 35 days long for the past 3 months and perceive themselves to have difficulty with identifying the infertile phase after ovulation. LH kits identified 100% of the luteal phases, whereas FAM indentified 87% (statistically significant). In those identified cycles, LH kits provided a mean of 10.3 days of infertility, and FAM only provided 10 days of infertility (not statistically significant). CONCLUSIONS: Among this population, LH kits may offer an adjunct for women who may wish to have an additional double-check. However, there are still clinical circumstances when even an LH kit does not provide confirmation. More research in this area is encouraged.

Efficacy of a new postpartum transition protocol for avoiding pregnancy.

INTRODUCTION: The postpartum period is a challenging time for family planning, especially for women who breastfeed. Breastfeeding delays the return of menses (lactational amenorrhea), but ovulation often occurs before first menses. For this reason, a protocol was developed to assist women in identifying their return of fertility postpartum to avoid pregnancy. METHODS: In this prospective, 12-month, longitudinal cohort study, 198 postpartum women aged 20 to 45 years (mean age, 30.2 years) were taught a protocol for avoiding pregnancy with either online or in-person instruction. A hand-held fertility monitor was used to identify the fertile period by testing for urinary changes in estrogen and luteinizing hormone, and the results were tracked on a web site. During lactational amenorrhea, urine testing was done in 20-day intervals. When menses returned, the monitor was reset at the onset of each new menstrual cycle. Participants were instructed to avoid intercourse during the identified fertile period. Kaplan-Meier survival analysis was used to calculate unintentional pregnancy rates through the first 12 months postpartum. RESULTS: There were 8 unintended pregnancies per 100 women at 12 months postpartum. With correct use, there were 2 unintended pregnancies per 100 women at 12 months. CONCLUSION: The online postpartum protocol may effectively assist a select group of women in avoiding pregnancy during the transition to regular menstrual cycles.

Effects of menstrual cycle phase on metabolomic profiles in premenopausal women.

BACKGROUND: Characterization of the normal degree of physiological variation in the metabolomic profiles of healthy humans is a necessary step in the development of metabolomics as both a clinical research and diagnostic tool. This study investigated the effects of the menstrual cycle on (1)H nuclear magnetic resonance (NMR) derived metabolomic profiles of urine and plasma from healthy women. METHODS: In this study, 34 healthy women were recruited and a first void urine and fasting blood sample were collected from each woman at four different time points during one menstrual cycle. Serum hormone levels were used in combination with the menstrual calendar to classify the urine and plasma samples into five different phases i.e. menstrual, follicular, periovulatory, luteal and premenstrual. The urine and plasma samples were analysed using (1)H NMR spectroscopy and subsequent data were analysed using principal component analysis (PCA) and partial least squares discriminant analysis. RESULTS: PCA of the urine spectra showed no separation of samples based on the phases of the menstrual cycle. Multivariate analysis of the plasma spectra showed a separation of the menstrual phase and the luteal phase samples (R(2) = 0.61, Q(2) = 0.41). Subsequent analysis revealed a significant decrease in levels of glutamine, glycine, alanine, lysine, serine and creatinine and a significant increase in levels of acetoacetate and very low density lipoprotein (VLDL CH(2)) during the luteal phase. CONCLUSIONS: These results establish a need to control for metabolic changes that occur in plasma due to the menstrual cycle in the design of future metabolomic studies involving premenopausal women.

Timing clinic visits to phases of the menstrual cycle by using a fertility monitor: the BioCycle Study.

Planning study visits during specific menstrual cycle phases is important if the exposure or outcome is influenced by hormonal variation. However, hormone profiles differ across cycles and across women. The value of using fertility monitors to time clinic visits was evaluated in the BioCycle Study (2005-2007). Women aged 18-44 years (mean, 27.4) with self-reported menstrual cycle lengths of 21-35 days were recruited in Buffalo, New York, for 2 cycles (n = 250). Participants were provided with home fertility monitors that measured urinary estrone-3-glucuronide and luteinizing hormone (LH). The women were instructed to visit the clinic for a blood draw when the monitor indicated an LH surge. The monitor recorded a surge during 76% of the first cycles and 78% of the second cycles. Scheduling visits by using set cycle days or algorithms based on cycle length, such as a midcycle window or a window determined by assuming a fixed luteal phase length, would be simpler. However, even with perfect attendance in a 3-day window, these methods would have performed poorly, capturing the monitor-detected LH surge only 37%-57% of the time. Fertility monitors appear to be useful in timing clinic visits in a compliant population with flexible schedules.

Variability in the phases of the menstrual cycle.

OBJECTIVE: To determine variability in the phases of the menstrual cycle among healthy, regularly cycling women. DESIGN: A prospective descriptive study of a new data set with biological markers to estimate parameters of the menstrual cycles. PARTICIPANTS: One hundred forty one healthy women (mean age 29 years) who monitored 3 to 13 menstrual cycles with an electronic fertility monitor and produced 1,060 usable cycles of data. MEASURES AND OUTCOMES: Variability in the length of the menstrual cycle and of the follicular, fertile, and luteal phases, and menses. The estimated day of ovulation and end of the fertile phase was the peak fertility reading on the monitor (i.e., the urinary luteinizing hormone surge). RESULTS: Mean total length was 28.9 days (SD = 3.4) with 95% of the cycles between 22 and 36 days. Intracycle variability of greater than 7 days was observed in 42.5% of the women. Ninety-five percent of the cycles had all 6 days of fertile phase between days 4 and 23, but only 25% of participants had all days of the fertile phase between days 10 and 17. CONCLUSIONS: Among regularly cycling women, there is considerable normal variability in the phases of the menstrual cycle. The follicular phase contributes most to this variability.