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

Consumer wearables and personal devices for tracking the fertile window

The market for technology that tracks ovulation to promote conception is rapidly expanding in the United States, targeting the growing audience of technologically proficient, reproductive-age female consumers. In this narrative review, 23 different, nonprescription wearables and devices designed to help women track their fertile window were identified as currently, commercially available in the United States. The majority of these utilize measurements of basal body temperature or combinations of various urinary hormones. This clinical opinion characterizes the scant available research validating the accuracy of these technologies. It further examines research oversight, discusses the utility of these wearables and devices to consumers, and considers these technologies through an equity lens. The discussion concludes with a call for innovation, describing promising new technologies that not only harness unique physiologic parameters to predict ovulation, but also focus on cost-effectiveness with the hope of increasing access to these currently costly devices and wearables.

Understanding wrist skin temperature changes to hormone variations across the menstrual cycle

Consumer devices are increasingly used to monitor peripheral body temperature (PBT) for menstrual cycle tracking, but the link between PBT and hormone variations remains underexplored. This study examines the relationship between these variables with a focus on nightly wrist skin temperature (WST). Fifty participants provided physiological and self-reported data, including WST, daily step counts, glucose levels, hormone levels (E3G, LH), and diary entries. Results show a negative correlation between WST and hormone levels when E3G and LH are below average, and this trend was robust to demographics and self-reported stress. Increased variance between mid-cycle hormonal peaks and WST fluctuations may stem from differences between basal body temperature (BBT) and WST. This research suggests that algorithms reliant on body temperature for tracking hormonal changes or other aspects of the menstrual cycle may need to account for increased variance in WST trends if they are meant to be deployed on wearable devices.

Current Ovulation and Luteal Phase Tracking Methods and Technologies for Fertility and Family Planning: A Review

Ovulation is critical for both conception and overall health, but many people who may ovulate are not tracking ovulation or any other part of their menstrual cycle. Failure to track ovulation, especially in those trying to conceive, can lead to fertility challenges due to absent ovulation, mistiming intercourse, or an undetected luteal phase defect. Ovulatory disorders and mistiming intercourse are both primary causes of infertility, and tracking ovulation is shown to decrease the average time to conception. While there are many tracking methods and apps available, the majority are predictive apps or ovulation predictor kits and do not test or track both successful ovulation and the health of the luteal phase, leading to missing information that could contribute to diagnosis or successful conception. Here, we review why ovulation tracking and a healthy luteal phase are important for those trying to conceive. We present currently available ovulation tracking methods that detect both ovulation and the luteal phase, including cervical mucus, urinary hormone testing, and basal body temperature, and discuss the use, advantages, and disadvantages of each. Finally, we consider the role of digital applications and tracking technologies in ovulation tracking.

Dimensional Affective Sensitivity to Hormones across the Menstrual Cycle (DASH-MC): A transdiagnostic framework for ovarian steroid influences on psychopathology

Fluctuations in progesterone (P4) and estradiol (E2) across the menstrual cycle can exert direct effects on biological systems implicated in neuropsychiatric disorders and represent a key biological source of variability in affective, cognitive, and behavioral disorders. Although these cyclical symptoms may be most readily identified when they occur exclusively in relation to the menstrual cycle, as in DSM-5 premenstrual dysphoric disorder, symptom changes of similar magnitude occur in a larger proportion of people with ongoing psychiatric disorders. Studies investigating cyclical regulation of brain and behavior often produce inconsistent results, which may be attributed to a lack of focus on specific hormonal events and individual differences in related sensitivities. We propose a transdiagnostic Dimensional Affective Sensitivity to Hormones across the Menstrual Cycle (DASH-MC) framework, postulating that atypical neural responses to several key hormonal events provoke specific temporal patterns of affective and behavioral change across the menstrual cycle. We review prospective and experimental evidence providing initial support for these dimensions, which include (1) luteal-onset negative affect caused by a sensitivity to E2 or P4 surges (mediated by neuroactive metabolites such as allopregnanolone), typified by irritability and hyperarousal; (2) perimenstrual-onset negative affect caused by a sensitivity to low or falling E2, typified by low mood and cognitive dysfunction; and (3) preovulatory-onset positive affect dysregulation caused by a sensitivity to E2 surges, typified by harmful substance use and other risky reward-seeking. This multidimensional, transdiagnostic framework for hormone sensitivity can inform more precise research on ovarian steroid regulation of psychopathology, including further mechanistic research, diagnostic refinement, and precision psychiatry treatment development. Additionally, given the high rates of hormone sensitivity across affective disorders, the DASH-MC may guide broader insights into the complex neurobiological vulnerabilities driving female-biased affective risk, as well as potential triggers and mechanisms of affective state change in psychiatric disorders.

Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity and Effectiveness Study

BACKGROUND

Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion.

OBJECTIVE

This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts.

METHODS

In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests, and a temperature shift was identified using a specific criterion called the "three-over-six rule." This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity.

RESULTS

The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements, suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBT provides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT throughout the menstrual cycle.

CONCLUSIONS

This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics.

COVID-19 Vaccine Side Effects and Long-Term Neutralizing Antibody Response : A Prospective Cohort Study

BACKGROUND

Concern about side effects is a common reason for SARS-CoV-2 vaccine hesitancy.

OBJECTIVE

To determine whether short-term side effects of SARS-CoV-2 messenger RNA (mRNA) vaccination are associated with subsequent neutralizing antibody (nAB) response.

DESIGN

Prospective cohort study.

SETTING

San Francisco Bay Area.

PARTICIPANTS

Adults who had not been vaccinated against or exposed to SARS-CoV-2, who then received 2 doses of either BNT162b2 or mRNA-1273.

MEASUREMENTS

Serum nAB titer at 1 month and 6 months after the second vaccine dose. Daily symptom surveys and objective biometric measurements at each dose.

RESULTS

363 participants were included in symptom-related analyses (65.6% female; mean age, 52.4 years [SD, 11.9]), and 147 were included in biometric-related analyses (66.0% female; mean age, 58.8 years [SD, 5.3]). Chills, tiredness, feeling unwell, and headache after the second dose were each associated with 1.4 to 1.6 fold higher nAB at 1 and 6 months after vaccination. Symptom count and vaccination-induced change in skin temperature and heart rate were all positively associated with nAB across both follow-up time points. Each 1 °C increase in skin temperature after dose 2 was associated with 1.8 fold higher nAB 1 month later and 3.1 fold higher nAB 6 months later.

LIMITATIONS

The study was conducted in 2021 in people receiving the primary vaccine series, making generalizability to people with prior SARS-CoV-2 vaccination or exposure unclear. Whether the observed associations would also apply for neutralizing activity against non-ancestral SARS-CoV-2 strains is also unknown.

CONCLUSION

Convergent self-report and objective biometric findings indicate that short-term systemic side effects of SARS-CoV-2 mRNA vaccination are associated with greater long-lasting nAB responses. This may be relevant in addressing negative attitudes toward vaccine side effects, which are a barrier to vaccine uptake.

PRIMARY FUNDING SOURCE

National Institute on Aging.

Menstrual cycle phases and dosage of synthetic hormonal contraceptives influence diurnal rhythm characteristics of distal skin temperature

This study aimed to explore how natural menstrual cycle phases and dosage of oral hormonal contraceptives (OC) influence the diurnal rhythm of distal skin temperature (DST) under real-life conditions. Participants were 41 healthy females (23.9 ± 2.48 y), comprising 27 females taking monophasic hormonal oral contraceptives (OC users) and 14 females with menstrual cycles (non-OC users). Wrist DST was continuously recorded and averaged over two consecutive 24-hour days during (pseudo)follicular and (pseudo)luteal menstrual phases. Diurnal rhythm characteristics, i.e. acrophase and amplitude, describing timing and strength of the DST rhythm, respectively, were calculated using cosinor analysis. Results show that non-OC users experienced earlier diurnal DST maximum (acrophase, p = 0.019) and larger amplitude (p = 0.016) during the luteal phase than during the follicular phase. This was observed in most (71.4%) but not all individuals. The OC users showed no differences in acrophase or amplitude between pseudoluteal and pseudofollicular phases. OC users taking a higher dosage of progestin displayed a larger amplitude for DST rhythm during the pseudoluteal phase (p = 0.009), while estrogen dosage had no effect. In conclusion, monophasic OC cause changes in diurnal DST rhythm, similar to those observed in the luteal phase of females with menstrual cycles, suggesting that synthetic progestins act in a similar manner on skin thermoregulation as progesterone does.

Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study

BACKGROUND

Despite its importance to women's reproductive health and its impact on women's daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures.

OBJECTIVE

The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle.

METHODS

This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle.

RESULTS

The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458).

CONCLUSIONS

We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices.

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.

Changes in Exhaled Carbon Dioxide during the Menstrual Cycle and Menopause

Introduction

The menstrual cycle (MC) reflects multifaceted hormonal changes influencing women's metabolism, making it a key aspect of women's health. Changes in hormonal levels throughout the MC have been demonstrated to influence various physiological parameters, including exhaled carbon dioxide (CO2). Lumen is a small handheld device that measures metabolic fuel usage via exhaled CO2. This study leverages exhaled CO2 patterns measured by the Lumen device to elucidate metabolic variations during the MC, which may hold significance for fertility management. Additionally, CO2 changes are explored in menopausal women with and without hormonal replacement therapy (HRT).

Methods

This retrospective cohort study analyzed exhaled CO2 data from 3,981 Lumen users, including eumenorrheal women and menopausal women with and without HRT. Linear mixed models assessed both CO2 changes of eumenorrheal women during the MC phases and compared between menopausal women with or without HRT.

Results

Eumenorrheic women displayed cyclical CO2 patterns during the MC, characterized by elevated levels during the menstrual, estrogenic and ovulation phases and decreased levels during post-ovulation and pre-menstrual phases. Notably, despite variations in cycle length affecting the timing of maximum and minimum CO2 levels within a cycle, the overall pattern remained consistent. Furthermore, CO2 levels in menopausal women without HRT differed significantly from those with HRT, which showed lower levels.

Conclusion

This study reveals distinct CO2 patterns across MC phases, providing insights into hormonal influences on metabolic activity. Menopausal women exhibit altered CO2 profiles in relation to the use or absence of HRT. CO2 monitoring emerges as a potential tool for tracking the MC and understanding metabolic changes during menopause.

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.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.

The more symptoms the better? Covid-19 vaccine side effects and long-term neutralizing antibody response

Protection against SARS-CoV-2 wanes over time, and booster uptake has been low, in part because of concern about side effects. We examined the relationships between local and systemic symptoms, biometric changes, and neutralizing antibodies (nAB) after mRNA vaccination. Data were collected from adults (n = 364) who received two doses of either BNT162b2 or mRNA-1273. Serum nAB concentration was measured at 1 and 6 months post-vaccination. Daily symptom surveys were completed for six days starting on the day of each dose. Concurrently, objective biometric measurements, including skin temperature, heart rate, heart rate variability, and respiratory rate, were collected. We found that certain symptoms (chills, tiredness, feeling unwell, and headache) after the second dose were associated with increases in nAB at 1 and 6 months post-vaccination, to roughly 140-160% the level of individuals without each symptom. Each additional symptom predicted a 1.1-fold nAB increase. Greater increases in skin temperature and heart rate after the second dose predicted higher nAB levels at both time points, but skin temperature change was more predictive of durable (6 month) nAB response than of short-term (1 month) nAB response. In the context of low ongoing vaccine uptake, our convergent symptom and biometric findings suggest that public health messaging could seek to reframe systemic symptoms after vaccination as desirable.

Wearable sensors for prediction of intraamniotic infection in women with preterm premature rupture of membranes: a prospective proof of principle study

PURPOSE

To evaluate the use of wearable sensors for prediction of intraamniotic infection in pregnant women with PPROM.

MATERIALS AND METHODS

In a prospective proof of principle study, we included 50 patients diagnosed with PPROM at the University Hospital Zurich between November 2017 and May 2020. Patients were instructed to wear a bracelet during the night, which measures physiological parameters including wrist skin temperature, heart rate, heart rate variability, and breathing rate. A two-way repeated measures ANOVA was performed to evaluate the difference over time of both the wearable device measured parameters and standard clinical monitoring values, such as body temperature, pulse, leucocytes, and C-reactive protein, between women with and without intraamniotic infection.

RESULTS

Altogether, 23 patients (46%) were diagnosed with intraamniotic infection. Regarding the physiological parameters measured with the bracelet, we observed a significant difference in breathing rate (19 vs 16 per min, P < .01) and heart rate (72 vs 67 beats per min, P = .03) in women with intraamniotic infection compared to those without during the 3 days prior to birth. In parallel to these changes standard clinical monitoring values were significantly different in the intraamniotic infection group compared to women without infection in the 3 days preceding birth.

CONCLUSION

Our results suggest that wearable sensors are a promising, noninvasive, patient friendly approach to support the early detection of intraamniotic infection in women with PPROM. However, confirmation of our findings in larger studies is required before implementing this technique in standard clinical management.

Factors Used by Mobile Applications to Predict Female Fertility Status and Their Reported Effectiveness: A Scoping Review

Family planning, whether for pregnancy prevention or conception, is of pivotal importance to women of reproductive age. As hormonally driven methods, such as oral contraceptive pills, are widely used but have numerous side effects, women often seek alternative non-hormonal, non-invasive options, including fertility-tracking mobile applications (apps). However, the effectiveness of these apps as a method of contraception and conception planning has not been extensively vetted. The goal of this scoping review is to identify the various factors used by apps marketed as a method of contraception and/or family planning to predict a woman's fertility status, as well as their documented effectiveness. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, a literature search was performed in CINAHL, MEDLINE, and Alt HealthWatch databases for articles published between October 1, 2017, and October 4, 2022. Quality assessment of eligible full-text articles was conducted using the Joanna Briggs Institute critical appraisal tools. A total of 629 articles were screened. Overall, 596 articles were excluded and the remaining 33 articles underwent full-text review. Seven articles were included in the final analysis, yielding data on the following five apps: Natural Cycles, Ava Fertility, Clearblue Connected, Ovia Fertility, and Dynamic Optimal Timing (DOT). Data supporting the effectiveness of these apps is limited. All apps provided predictions on fertility status throughout a woman's menstrual cycle using proprietary algorithms, biometric data, and self-reported menstrual cycle data. Further research, particularly independent research following a randomized controlled design, on the efficacy of these apps is needed to produce more robust results.

Survey Analysis of Quantitative and Qualitative Menstrual Cycle Tracking Technologies

Background and Objectives: Digital health and personalized medicine are advancing at an unprecedented pace. Users can document their menstrual cycle data in a variety of ways, including smartphone applications (apps), temperature tracking devices, and at-home urine hormone tests. Understanding the needs and goals of women using menstrual cycle tracking technologies is the first step to making these technologies more evidence based. The purpose of this study was to examine the current use of these technologies and explore how they are being used within the context of common hormonal and reproductive disorders, like polycystic ovary syndrome (PCOS), endometriosis, and infertility. Materials and Methods: This was a cross-sectional study evaluating menstrual cycle tracking technology use. Participants were recruited in January-March 2023 using social media groups and a Marquette Method instructor email listserv. Data were collected using an electronic survey with Qualtrics. Data collected included participant demographics, menstrual cycle characteristics, reproductive health history, and menstrual cycle tracking behavior. Results: Three-hundred and sixty-eight participants were included in the analysis. Women had various motivations for tracking their menstrual cycles. Most participants (72.8%) selected "to avoid getting pregnant" as the primary motivation. Three hundred and fifty-six participants (96.7%) reported using a fertility awareness-based method to track and interpret their menstrual cycle data. The Marquette Method, which utilizes urine hormone tracking, was the most frequently used method (n = 274, 68.2%). The most frequently used cycle technology was a urine hormone test or monitor (n = 299, 81.3%), followed by a smartphone app (n = 253, 68.8%), and a temperature tracking device (n = 116, 31.5%). Women with PCOS (63.6%), endometriosis (61.8%), and infertility (75%) in our study reported that the use of tracking technologies aided in the diagnosis. Most participants (87.2%) reported a high degree of satisfaction with their use and that they contributed to their reproductive health knowledge (73.9%). Conclusions: Women in our study reported avoiding pregnancy as their primary motivation for using menstrual cycle tracking technologies, with the most frequently used being a urine hormone test or monitor. Our study results emphasize the need to validate these technologies to support their use for family planning. Given that most women in this study reported using a fertility awareness-based method, the results cannot be generalized to all users of menstrual cycle tracking technologies.

Changes in Heart Rate, Heart Rate Variability, Breathing Rate, and Skin Temperature throughout Pregnancy and the Impact of Emotions-A Longitudinal Evaluation Using a Sensor Bracelet

(1) Background: Basic vital signs change during normal pregnancy as they reflect the adaptation of maternal physiology. Electronic wearables like fitness bracelets have the potential to provide vital signs continuously in the home environment of pregnant women. (2) Methods: We performed a prospective observational study from November 2019 to November 2020 including healthy pregnant women, who recorded their wrist skin temperature, heart rate, heart rate variability, and breathing rate using an electronic wearable. In addition, eight emotions were assessed weekly using five-point Likert scales. Descriptive statistics and a multivariate model were applied to correlate the physiological parameters with maternal emotions. (3) Results: We analyzed data from 23 women using the electronic wearable during pregnancy. We calculated standard curves for each physiological parameter, which partially differed from the literature. We showed a significant association of several emotions like feeling stressed, tired, or happy with the course of physiological parameters. (4) Conclusions: Our data indicate that electronic wearables are helpful for closely observing vital signs in pregnancy and to establish modern curves for the physiological course of these parameters. In addition to physiological adaptation mechanisms and pregnancy disorders, emotions have the potential to influence the course of physiological parameters in pregnancy.

Continuous body temperature as a window into adolescent development

Continuous body temperature is a rich source of information on hormonal status, biological rhythms, and metabolism, all of which undergo stereotyped change across adolescence. Due to the direct actions of these dynamic systems on body temperature regulation, continuous temperature may be uniquely suited to monitoring adolescent development and the impacts of exogenous reproductive hormones or peptides (e.g., hormonal contraception, puberty blockers, gender affirming hormone treatment). This mini-review outlines how traditional methods for monitoring the timing and tempo of puberty may be augmented by markers derived from continuous body temperature. These features may provide greater temporal precision, scalability, and reduce reliance on self-report, particularly in females. Continuous body temperature data can now be gathered with ease across a variety of wearable form factors, providing the opportunity to develop tools that aid in individual, parental, clinical, and researcher awareness and education.

Influence of Menstrual Cycle on Internal and External Load in Professional Women Basketball Players

The menstrual cycle can be seen as a potential determinant of performance. This study aims to analyze the influence of the menstrual cycle in women on sports performance, more specifically on the internal and external load of professional women basketball players. The sample consisted of 16 women players and 14 training sessions were recorded. A descriptive analysis of the mean and standard deviation of the variables according to the different phases of the menstrual cycle was performed, as well as an ANCOVA, partial Eta2 effect size criteria, and Bonferroni’s Post Hoc test to identify differences among phases. The results establish that ovulation is the phase in which higher values of external load are recorded and, therefore, the late follicular phase is the time of the cycle where a greater intensity in explosive distance, accelerations and decelerations are recorded. Considering women’s hormonal cycles, understanding their function and the individual characteristics of each athlete is essential since it allows for the development of specific training, the prevention of injuries and therefore positively affects the performance of women players. To this end, individual training profiles should be created in specific contexts, not following general rules. In addition, psychological factors and the specific position of the athletes should be monitored.

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.

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.

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.

The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review

Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.

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.

Infrared Thermography in Wound Care, Surgery, and Sports Medicine: A Review

For many years, the role of thermometry was limited to systemic (core body temperature) measurements (e.g., pulmonary catheter) or its approximation using skin/mucosa (e.g., axillary, oral, or rectal) temperature measurements. With recent advances in material science and technology, thermal measurements went beyond core body temperature measurements and found their way in many medical specialties. The article consists of two primary parts. In the first part we overviewed current clinical thermal measurement technologies across two dimensions: (a) direct vs. indirect and (b) single-point vs. multiple-point temperature measurements. In the second part, we focus primarily on clinical applications in wound care, surgery, and sports medicine. The primary focus here is the thermographic imaging modality. However, other thermal modalities are included where relevant for these clinical applications. The literature review identified two primary use scenarios for thermographic imaging: inflammation-based and perfusion-based. These scenarios rely on local (topical) temperature measurements, which are different from systemic (core body temperature) measurements. Quantifying these types of diseases benefits from thermographic imaging of an area in contrast to single-point measurements. The wide adoption of the technology would be accelerated by larger studies supporting the clinical utility of thermography.

Reliability of Non-Contact Infrared Thermometers for Fever Screening Under COVID-19

Purpose

Fever is one of the most typical clinical symptoms of coronavirus disease 2019 (COVID-19), and non-contact infrared thermometers (NCITs) are commonly used to screen for fever. However, there is a lack of authoritative data to define a “fever” when an NCIT is used and previous studies have shown that NCIT readings fluctuate widely depending on ambient temperatures and the body surface site screened. The aim of this study was to establish cut-off points for normal temperatures of different body sites (neck, forehead, temples, and wrist) and investigate the accuracy of NCITs at various ambient temperatures to improve the standardization and accuracy of fever screening.

Patients and Methods

A prospective investigation was conducted among 904 participants in the outpatient and emergency departments of Chengdu Women’s and Children’s Central Hospital. Body temperature was measured using NCITs and mercury axillary thermometers. A receiver operating characteristic curve was used to determine the accuracy of body temperature detection at the four body surface sites. Data on participant characteristics were also collected.

Results

Among the four surface sites, the neck temperature detection group had the highest accuracy. When the neck temperature was 37.35°C as the optimum fever diagnostic threshold, the sensitivity was 0.866. The optimum fever diagnostic thresholds for forehead, temporal, and wrist temperature were 36.65°C, 36.65°C, and 36.75°C, respectively. Moreover, triple neck temperature detection had the highest sensitivity, up to 0.998, whereas the sensitivity of triple wrist temperature detections was 0.949. Notably, the accuracy of NCITs significantly reduced when the temperature was lower than 18°C.

Conclusion

Neck temperature had the highest accuracy among the four NCIT temperature measurement sites, with an optimum fever diagnostic threshold of 37.35°C. Considering the findings reported in our study, we recommend triple neck temperature detection with NCITs as the fever screening standard for COVID-19.

Moving beyond inclusion: Methodological considerations for the menstrual cycle and menopause in research evaluating effects of dietary nitrate on vascular function

Recent reports have acknowledged the underrepresentation of women in the field of dietary nitrate (NO₃^-) research. Undoubtedly, greater participation from women is warranted to clarify potential sex differences in the responses to dietary NO₃^- interventions. However, careful consideration for the effects of sex hormones - principally 17β-estradiol - on endogenous nitric oxide (NO) synthesis and dietary NO₃^- reductase capacity is necessary for improved interpretation and reproducibility of such investigations. From available literature, we present a narrative review describing how hormonal variations across the menstrual cycle, as well as with menopause, may impact NO biosynthesis catalyzed by NO synthase enzymes and NO₃^- reduction via the enterosalivary pathway. In doing so, we address methodological considerations related to the menstrual cycle and hormonal contraceptive use relevant for the inclusion of premenopausal women along with factors to consider when testing postmenopausal women. Adherence to such methodological practices may explicate the utility of dietary NO₃^- supplementation as a means to improve vascular function among women across the lifespan.