Identification and prediction of the fertile window with a new web-based medical device using a vaginal biosensor for measuring the circadian and circamensual core body temperature

Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting

The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.

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.

State-of-the-art and future perspectives in infertility diagnosis: Conventional versus nanotechnology-based assays

According to the latest World Health Organization statistics, around 50 to 80 million people worldwide suffer from infertility, amongst which male factors are responsible for around 20 to 30 % of all infertility cases while 50 % were attributed to the female ones. As it is becoming a recurrent health problem worldwide, clinicians require more accurate methods for the improvement of both diagnosis and treatment schemes. By emphasizing the potential use of innovative methods for the rapid identification of the infertility causes, this review presents the news from this dynamic domain and highlights the benefits brought by emerging research fields. A systematic description of the standard techniques used in clinical protocols for diagnosing infertility in both genders is firstly provided, followed by the presentation of more accurate and comprehensive nanotechnology-related analysis methods such as nanoscopic-resolution imaging, biosensing approaches and assays that employ nanomaterials in their design. Consequently, the implementation of nanotechnology related tools in clinical practice, as recently demonstrated in the selection of spermatozoa, the detection of key proteins in the fertilization process or the testing of DNA integrity or the evaluation of oocyte quality, might confer excellent advantages both for improving the assessment of infertility, and for the success of the fertilization process.

Mucosa-interfacing electronics

The surface mucosa that lines many of our organs houses myriad biometric signals and, therefore, has great potential as a sensor-tissue interface for high-fidelity and long-term biosensing. However, progress is still nascent for mucosa-interfacing electronics owing to challenges with establishing robust sensor-tissue interfaces; device localization, retention and removal; and power and data transfer. This is in sharp contrast to the rapidly advancing field of skin-interfacing electronics, which are replacing traditional hospital visits with minimally invasive, real-time, continuous and untethered biosensing. This Review aims to bridge the gap between skin-interfacing electronics and mucosa-interfacing electronics systems through a comparison of the properties and functions of the skin and internal mucosal surfaces. The major physiological signals accessible through mucosa-lined organs are surveyed and design considerations for the next generation of mucosa-interfacing electronics are outlined based on state-of-the-art developments in bio-integrated electronics. With this Review, we aim to inspire hardware solutions that can serve as a foundation for developing personalized biosensing from the mucosa, a relatively uncharted field with great scientific and clinical potential.

AACC Guidance Document on the Use of Point-of-Care Testing in Fertility and Reproduction

Background

The AACC Academy revised the reproductive testing section of the Laboratory Medicine Practice Guidelines: Evidence-Based Practice for Point-of-Care Testing (POCT) published in 2007.

Methods

A panel of Academy members with expertise in POCT and laboratory medicine was formed to develop guidance for the use of POCT in reproductive health, specifically ovulation, pregnancy, premature rupture of membranes (PROM), and high-risk deliveries. The committee was supplemented with clinicians having Emergency Medicine and Obstetrics/Gynecology training.

Results

Key recommendations include the following. First, urine luteinizing hormone (LH) tests are accurate and reliable predictors of ovulation. Studies have shown that the use of ovulation predicting kits may improve the likelihood of conception among healthy fertile women seeking pregnancy. Urinary LH point-of-care testing demonstrates a comparable performance among other ovulation monitoring methods for timing intrauterine insemination and confirming sufficient ovulation induction before oocyte retrieval during in vitro fertilization. Second, pregnancy POCT should be considered in clinical situations where rapid diagnosis of pregnancy is needed for treatment decisions, and laboratory analysis cannot meet the required turnaround time. Third, PROM testing using commercial kits alone is not recommended without clinical signs of rupture of membranes, such as leakage of amniotic fluid from the cervical opening. Finally, fetal scalp lactate is used more than fetal scalp pH for fetal acidosis due to higher success rate and low volume of sample required.

Conclusions

This revision of the AACC Academy POCT guidelines provides recommendations for best practice use of POCT in fertility and reproduction.

Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms

BACKGROUND

Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.

METHODS

This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning.

RESULTS

We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all P < 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction.

CONCLUSIONS

By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation.

TRIAL REGISTRATION

ChiCTR2000036556. Registered 24 August 2020.

D, R. C. M, T. G. K, A. P. 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

Objective: Determine the accuracy of a novel technique for confirmation of the day of ovulation and prediction of ovulation in subsequent cycles for the purpose of conception using a skin-worn sensor in a population with ovulatory dysfunction.

Methods: A total of 80 participants recorded consecutive overnight temperatures using a skin-worn sensor at the same time as a commercially available vaginal sensor for a total of 205 reproductive cycles. The vaginal sensor and its associated algorithm were used to determine the day of ovulation, and the ovulation results obtained using the skin-worn sensor and its associated algorithm were assessed for comparative accuracy alongside a number of other statistical techniques, with a further assessment of the same skin-derived data by means of the “three over six” rule. A number of parameters were used to divide the data into separate comparative groups, and further secondary statistical analyses were performed.

Results: The skin-worn sensor and its associated algorithm (together labeled “SWS”) were 66% accurate for determining the day of ovulation (±1 day) or the absence of ovulation and 90% accurate for determining the fertile window (ovulation day ±3 days) in the total study population in comparison to the results obtained from the vaginal sensor and its associated algorithm (together labeled “VS”).

Conclusion: SWS is a useful tool for confirming the fertile window and absence of ovulation (anovulation) in a population with ovulatory dysfunction, both known and determined by means of the timing of ovulation. The body site where the skin-worn sensor was worn (arm or wrist) did not appear to affect the accuracy. Prior diagnosis of known causes of ovulatory dysfunction appeared to affect the accuracy to a lesser extent than those cycles grouped into late ovulation and “early and normal ovulation” groups. SWS is a potentially useful tool for predicting ovulation in subsequent cycles, with greater accuracy obtained for the “normal ovulation” group.

Modelling menstrual cycle length in athletes using state-space models

The ability to predict an individual's menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00-47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that [Formula: see text] [Formula: see text] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is [Formula: see text] [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] days. To assess the performance and prediction accuracy of the model, each woman's last observation was used as test data. The root mean square error (RMSE), concordance correlation coefficient and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.

The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study

Background

As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation.

Objective

This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles.

Methods

This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases.

Results

Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P<.001) and had a higher true-positive rate (54.9% vs 20.2%) for detecting ovulation; however, it also had a higher false-positive rate (8.8% vs 3.6%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2% for wrist skin temperature and 84.8% for BBT. Both temperatures had low negative predictive values (8.8% for wrist skin temperature and 10.9% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves.

Conclusions

For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures.

Using Circadian Rhythm Patterns of Continuous Core Body Temperature to Improve Fertility and Pregnancy Planning

Objective

Review relationships among circadian clocks, core body temperature (CBT), and fertility in women.

Methods

Scoping literature review.

Results

Circadian clocks are a ubiquitous adaptation to the most predictable environmental events - the daily cycles of light and dark. Core body temperature (CBT) also follows a circadian rhythm. Additionally, CBT is tightly controlled by a combination of neuronal circuits that begin in the hypothalamus and involve many other portions of the brain as well as a wide range of peripheral mechanisms. In women with normal reproductive function, the diurnal temperature pattern for CBT is strongly influenced by the menstrual cycle of reproductive hormones, primarily estradiol and progesterone, which modulate the activity of hypothalamic neural circuits involved in body temperature control, resulting in an infradian CBT rhythm.

Conclusions

Analysis of CBT via continuous recording reveals patterns in the interactions of circadian and infradian CBT rhythms capable of accurately predicting the fertility window and hormonal patterns suggesting oligo-ovulation and subfertility. New wearable technologies can facilitate employment of hormone-associated changes in CBT for pregnancy planning and offer clinical insight to infertility and menopause.

Continuous Body Temperature Monitoring to Improve the Diagnosis of Female Infertility

Introduction Ovulatory dysfunction is a major cause of female infertility. We evaluated the use of continuous body temperature monitoring with a vaginal biosensor to improve standard diagnostic procedures for determining ovulatory dysfunction. Material and Methods This prospective interventional study was performed in a reproductive medicine department of a university hospital. The menstrual cycles of 51 women with infertility were monitored and analysed using three different strategies: sonographic and hormonal assessment (standard approach), continuous core body temperature measurement and analysis using the algorithm of OvulaRing, and lowest daily body temperature measurement monitored with a vaginal biosensor and analysed based on the body temperature curves used in natural family planning. Results Statistically significant differences were found in the temperature curves of women with luteal phase deficiency and polycystic ovary syndrome compared to women with normal menstrual cycles. The analysis of individual cyclofertilograms can be used to detect cycle phases and estimate the date of ovulation. Conclusions Continuous body temperature monitoring with a vaginal biosensor can improve the standard diagnostic procedures used to determine ovulatory dysfunction, especially if dysfunction is due to luteal phase deficiency and polycystic ovary syndrome. Analysis of the lowest daily body temperature combined with the basal body temperature measurements used in fertility awareness methods may be equieffective to continuous body temperature measurements with OvulaRing. The results of this study show that a revised diagnostic approach using fewer hormonal assessments combined with continuous body temperature monitoring can reduce the number of appointments in an infertility clinic as well as the costs.

Temperature regulation in women: Effects of the menstrual cycle

Core body temperature changes across the ovulatory menstrual cycle, such that it is 0.3°C to 0.7°C higher in the post-ovulatory luteal phase when progesterone is high compared with the pre-ovulatory follicular phase. This temperature difference, which is most evident during sleep or immediately upon waking before any activity, is used by women as a retrospective indicator of an ovulatory cycle. Here, we review both historical and current literature aimed at characterizing changes in core body temperature across the menstrual cycle, considering the assessment of the circadian rhythm of core body temperature and thermoregulatory responses to challenges, including heat and cold exposure, exercise, and fever. We discuss potential mechanisms for the thermogenic effect of progesterone and the temperature-lowering effect of estrogen, and discuss effects on body temperature of exogenous formulations of these hormones as contained in oral contraceptives. We review new wearable temperature sensors aimed at tracking daily temperature changes of women across multiple menstrual cycles and highlight the need for future research on the validity and reliability of these devices. Despite the change in core body temperature across the menstrual cycle being so well identified, there remain gaps in our current understanding, particularly about the underlying mechanisms and microcircuitry involved in the temperature changes.

Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles

The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index (BMI). Menstrual cycle parameters, including menstruation, basal body temperature (BBT) and luteinising hormone (LH) tests as well as age and BMI were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95% CI: 10-30) and mean luteal phase length was 12.4 days (95% CI: 7-17). Mean cycle length decreased by 0.18 days (95% CI: 0.17-0.18, R 2 = 0.99) and mean follicular phase length decreased by 0.19 days (95% CI: 0.19-0.20, R 2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14% higher in women with a BMI of over 35 relative to women with a BMI of 18.5-25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.

A Tube-Integrated Painted Biosensor for Glucose and Lactate

Developing a simple and direct approach for sensitive, specific, and rapid detection of metabolic compounds is of great importance for a variety of biological, medical, and food applications. Tubes are a highly portable and accessible container shape which are widely used for scientific research in cell biology and chemical synthesis, and which are also of great use in domestic health care applications. Here, we show for the first time the development of a tube-based painted amperometric biosensor for the detection of glucose and lactate. The sensor was prepared by printing carbon graphite and silver/silver chloride inks on the interior wall of the tube and then immobilizing glucose oxidase or lactate oxidase on the sensor. The sensor showed a sensitive, rapid, and reliable detection of glucose and lactate. We anticipate that these results could open new avenues for the development of painted biosensors, and toward advanced biosensor applications.