Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent Cycles Using a Skin-Worn Sensor in a Population With Ovulatory Dysfunction: A Side-by-Side Comparison With Existing Basal Body Temperature Algorithm and Vaginal Core Body Temperature Algorithm

The effectiveness of cervical mucus electrical impedance compared to basal body temperature to determine fertility window

BACKGROUND

Serial serum hormone measurements and transvaginal ultrasound are reliable measures to predict ovulation. These measures are inconvenient and expensive therefore, basal body temperature charting (BBT) and urine ovulation predictor kits (OPK) for luteinizing hormone are often used to determine the 6-day fertile window. However, BBT does not clearly change until 1-2 days after ovulation. Additionally, while OPK can indicate positivity prior to ovulation, false readings are common. A novel alternative approach involves measuring electrolyte trends in cervical mucus using electrical impedance spectroscopy. Cervical mucus electrolyte measurements are associated with hormone level changes during the menstrual cycle. The purpose of this study was to compare the effectiveness of cervical mucus electrical impedance and basal body temperature. We sought to determine if cervical mucus electrolyte measurements provided improved detection of the ovulation day and therefore, improve fertility timing for women.

METHODS

14 healthy women between 18 and 44 years of age with normal menstrual cycles were enrolled in the Observational Study. Participants measured BBT and cervical mucus electrical impedance daily for 3 menstrual cycles using Kegg (Lady Technologies Inc. San Francisco, California, USA). Ovulation date for each cycle was confirmed by measuring hormone levels in urine and serum, and by vaginal ultrasound.

RESULTS

Electrical impedance was significantly different between the follicular phase versus ovulatory date (p = 0.007) and between the luteal phase versus the ovulatory date (p = 0.007). A significant difference in the rate of change of cervical impedance measurements in the pre-ovulatory follicular phase was found compared to BBT (p = 0.0225). The sensitivity (+ 7.14%), specificity (+ 20.35%), and accuracy (+ 17.59) to determine the 1-day fertility window was significantly higher using cervical mucus impedance compared to BBT.

CONCLUSIONS

BBT is considered unreliable for evaluating ovulatory function. Cervical mucus electrical impedance offers a novel measure of electrolyte changes associated with hormone levels. We report that pre-ovulatory electrical impedance patterns demonstrated higher sensitivity, specificity, and accuracy for determining the fertility window when compared to BBT. These findings suggest that changes in electrical impedance may provide an accurate method for predicting ovulation and for measuring ovulatory function.

Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology: Systematic Review

BACKGROUND

Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate.

OBJECTIVE

The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity.

METHODS

A review of PubMed or MEDLINE was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle.

RESULTS

Fertility cycle-tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate.

CONCLUSIONS

More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.

Can Wrist-Worn Medical Devices Correctly Identify Ovulation?

(1) Background: Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window. (2) Methods: We conducted a prospective observational study including women aged 18-45 years without current hormonal therapy who used a wrist-worn medical device and urinary ovulation tests for a minimum of three cycles. We analyzed the accuracy of both the retrospective and prospective algorithms using a generalized linear mixed-effects model. The findings were compared to real-world data from bracelet users who also reported urinary ovulation tests. (3) Results: A total of 61 study participants contributing 205 cycles and 6081 real-life cycles from 3268 bracelet users were included in the analysis. The mean error in identifying ovulation with the wrist-worn medical device retrospective algorithm in the clinical study was 0.31 days (95% CI -0.13 to 0.75). The retrospective algorithm identified 75.4% of fertile days, and the prospective algorithm identified 73.8% of fertile days correctly within the pre-specified equivalence limits (±2 days). The quality of the retrospective algorithm in the clinical study could be confirmed by real-world data. (4) Conclusion: Our data indicate that wearable sensors may be used to accurately detect the periovulatory period.

Contemporary evaluation of women and girls with abnormal uterine bleeding: FIGO Systems 1 and 2

Abnormal uterine bleeding (AUB) is common, often debilitating, and may affect over 50% of reproductive-aged women and girls. Whereas AUB is a collection of symptoms that include intermenstrual bleeding and abnormalities in period duration, cycle length, and regularity, it is heavy menstrual bleeding (HMB) that is most contributory to iron deficiency and related anemia. It is apparent that AUB, in general, and HMB, in particular, remain underrecognized and underreported. FIGO created two systems for assessing and classifying AUB. FIGO System 1 defines the bleeding pattern using four primary descriptors: frequency, duration, regularity, and flow volume. FIGO System 2 provides a structured classification system of possible causes of AUB, using the acronym PALM-COEIN. "PALM" refers to structural causes of AUB (Polyp, Adenomyosis, Leiomyoma, Malignancy), and "COEI" refers to nonstructural causes (Coagulopathy, Ovulatory dysfunction, Endometrial, and Iatrogenic). The "N" is reserved for those entities that are currently not otherwise classified. Using FIGO System 1 as a gateway to FIGO System 2 streamlines the investigation of reproductive-aged women and girls with AUB. Understanding the pathogenesis of the FIGO System 2 "PALM-COEIN" causes helps interpret investigations and the onward management of AUB. Numerous evidence gaps exist concerning AUB; however, if researchers and trialists universally adopt FIGO Systems 1 and 2 for the assessment and diagnosis of AUB, clear translatable research findings can be applied globally.

Optimal diagnostic fever thresholds using non-contact infrared thermometers under COVID-19

Fever screening is an effective method to detect infectors associated with different variants of coronavirus disease 2019 (COVID-19) based on the fact that most infectors with COVID-19 have fever symptoms. Non-contact infrared thermometers (NCITs) are widely used in fever screening. Nevertheless, authoritative data is lacking in defining "fever" at different body surface sites when using NCITs. The purpose of this study was to determine the optimal diagnostic threshold for fever screening using NICTs at different body surface sites, to improve the accuracy of fever screening and provide theoretical reference for healthcare policy. Participants (n = 1860) who were outpatients or emergency patients at Chengdu Women's and Children's Central Hospital were recruited for this prospective investigation from March 1 to June 30, 2021. NCITs and mercury axillary thermometers were used to measure neck, temple, forehead and wrist temperatures of all participants. Receiver operating characteristic curves were used to reflect the accuracy of NCITs. Linear correlation analysis was used to show the effect of age on body temperature. Multilinear regression analysis was used to explore the association between non-febrile participant's covariates and neck temperature. The mean age of participants was 3.45 ± 2.85 years for children and 28.56 ± 7.25 years for adults. In addition 1,304 (70.1%) participants were children (≤12), and 683 (36.7%) were male. The neck temperature exhibited the highest accuracy among the four sites. Further the optimal fever diagnostic thresholds of NCITs at the four body surface measurement sites were neck (36.75 °C, sensitivity: 0.993, specificity: 0.858); temple (36.55 °C, sensitivity: 0.974, specificity: 0.874); forehead (36.45 °C, sensitivity: 0.961, specificity: 0.813); and wrist (36.15 °C, sensitivity: 0.951, specificity: 0.434). Based on the findings of our study, we recommend 36.15, 36.45, 36.55, and 36.75 °C as the diagnostic thresholds of fever at the wrist, forehead, temple and neck, respectively. Among the four surface sites, neck temperature exhibited the highest accuracy.