Interpretation of the BBT chart: using the “Gap” technique compared to the Coverline technique

A multi-site study of the relationship between photoperiod and ovulation rate using Natural Cycles data

Many species exhibit seasonal patterns of breeding. Although humans can shield themselves from many season-related stressors, they appear to exhibit seasonal patterns of investment in reproductive function nonetheless, with levels of sex steroid hormones being highest during the spring and summer months. The current research builds on this work, examining the relationship between day length and ovarian function in two large samples of women using data from the Natural Cycles birth control application in each Sweden and the United States. We hypothesized that longer days would predict higher ovulation rates and sexual motivation. Results revealed that increasing day length duration predicts increased ovulation rate and sexual behavior, even while controlling for other relevant factors. Results suggest that day length may contribute to observed variance in women's ovarian function and sexual desire.

Confirmation of human ovulation in assisted reproduction using an adhesive axillary thermometer (femSense®)

Objective

Timing for sexual intercourse is important in achieving pregnancy in natural menstrual cycles. Different methods of detecting the fertile window have been invented, among them luteinization hormone (LH) to predict ovulation and biphasic body basal temperature (BBT) to confirm ovulation retrospectively. The gold standard to detect ovulation in gynecology practice remains transvaginal ultrasonography in combination with serum progesterone. In this study we evaluated a wearable temperature sensing patch (femSense®) using continuous body temperature measurement to confirm ovulation and determine the end of the fertile window.

Methods

96 participants received the femSense® system consisting of an adhesive axillary thermometer patch and a smartphone application, where patients were asked to document information about their previous 3 cycles. Based on the participants data, the app predicted the cycle length and the estimated day of ovulation. From these predictions, the most probable fertile window and the day for applying the patch were derived. Participants applied and activated the femSense® patch on the calculated date, from which the patch continuously recorded their body temperature throughout a period of up to 7 days to confirm ovulation. Patients documented their daily urinary LH test positivity, and a transvaginal ultrasound was performed on day cycle day 7, 10, 12 and 14/15 to investigate the growth of one dominant follicle. If a follicle reached 15 mm in diameter, an ultrasound examination was carried out every day consecutively until ovulation. On the day ovulation was detected, serum progesterone was measured to confirm the results of the ultrasound. The performance of femSense® was evaluated by comparing the day of ovulation confirmation with the results of ovulation prediction (LH test) and detection (transvaginal ultrasound).

Results

The femSense® system confirmed ovulation occurrence in 60 cases (81.1%) compared to 48 predicted cases (64.9%) with the LH test (p = 0.041). Subgroup analysis revealed a positive trend for the femSense® system of specific ovulation confirmation within the fertile window of 24 h after ovulation in 42 of 74 cases (56.8%). Cycle length, therapy method or infertility reason of the patient did not influence accuracy of the femSense® system.

Conclusions

The femSense® system poses a promising alternative to the traditional BBT method and is a valuable surrogate marker to transvaginal ultrasound for confirmation of ovulation.

Organic Field-Effect Transistor-Based Ultrafast, Flexible, Physiological-Temperature Sensors with Hexagonal Barium Titanate Nanocrystals in Amorphous Matrix as Sensing Material

Organic field-effect transistors (OFETs) with hexagonal barium titanate nanocrystals (h-BTNCs) in amorphous matrix as one of the bilayer dielectric systems have been fabricated on a highly flexible 10 μm thick poly(ethylene terephthalate) substrate. The device current and mobility remain constant up to a bending radius of 4 mm, which makes the substrate suitable for wearable e-skin applications. h-BTNC films are found to be highly temperature-sensitive, and the OFETs designed based on this material showed ultraprecision measurement (∼4.3 mK), low power (∼1 μW at 1.2 V operating voltage), and ultrafast response (∼24 ms) in sensing temperature over a range of 20-45 °C continuously. The sensors are highly stable around body temperature and work at various extreme conditions, such as under water and in solutions of different pH values and various salt concentrations. These properties make this sensor unique and highly suitable for various healthcare and other applications, wherein a small variation of temperature around this temperature range is required to be measured at an ultrahigh speed.

Detection of ovulation, a review of currently available methods

The ability to identify the precise time of ovulation is important for women who want to plan conception or practice contraception. Here, we review the current literature on various methods for detecting ovulation including a review of point‐of‐care device technology. We incorporate an examination of methods to detect ovulation that have been developed and practiced for decades and analyze the indications and limitations of each—transvaginal ultrasonography, urinary luteinizing hormone detection, serum progesterone and urinary pregnanediol 3‐glucuronide detection, urinary follicular stimulating hormone detection, basal body temperature monitoring, and cervical mucus and salivary ferning analysis. Some point‐of‐care ovulation detection devices have been developed and commercialized based on these methods, however previous research was limited by small sample size and an inconsistent standard reference to true ovulation.

Working conditions associated with ovarian cycle in a medical center nurses: a Taiwan study

AIM

Decreased basal body temperature measurements predict ovulation with an accuracy of 74%. The anovulatory cycle exhibits an ovarian monophasic pattern. This study evaluated the relationship between ovarian cycle pattern and sociodemographic characteristics, menstrual history and work characteristics of nurses in a Taiwan medical center.

METHODS

Of 200 nurses recruited, 151 were analyzed. Each subject completed questionnaires and provided life recordings and daily basal body temperature measurements during the 14-week study.

RESULTS

The analytical results demonstrated that work place (P = 0.014) and work shift (P = 0.048) are significantly related to ovarian cycle pattern. Nurses who worked in emergent care units and wards had a higher prevalence of irregular ovarian cycle pattern. Approximately 53% of nurses who worked rotating shifts exhibited irregular ovarian cycle pattern.

CONCLUSIONS

Nurses who either had rotating shift work or worked in emergent care units and wards should be concerned with their own ovarian cycle pattern for their health.

Bayesian hierarchical functional data analysis via contaminated informative priors

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.