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

Menstrual Cycle Matters in Host Attractiveness to Mosquitoes and Topical Repellent Protection

Human hosts exhibit remarkable variability in their attractiveness to mosquitoes, leading to differences in biting rates. It is essential to understand the factors behind this variability if we wish to develop more effective strategies for controlling the transmission of mosquito-borne diseases. While past studies have shed significant light on the forces shaping host attractiveness to mosquitoes, we continue to lack information about variation in attractiveness within individual hosts. For example, little attention has been paid to the potential impact of the menstrual cycle. Our study explored the relationship between the menstrual cycle, host attractiveness to mosquitoes, and the effectiveness of topical mosquito repellents. We found that mosquito landing rate was higher and repellent protection time was shorter during ovulation than during menstruation and the luteal phase. By beginning to clarify the intricate interplay between human physiology and mosquito behavior, our results contribute to the growing body of knowledge regarding the factors that affect within-individual variability in attractiveness to mosquitoes, which has implications for the efficacy of protection and disease prevention strategies.

Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study

STUDY QUESTION

Can algorithms using wrist temperature, available on compatible models of iPhone and Apple Watch, retrospectively estimate the day of ovulation and predict the next menses start day?

SUMMARY ANSWER

Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.

WHAT IS KNOWN ALREADY

Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events.

STUDY DESIGN, SIZE, DURATION

We conducted a prospective cohort study of 262 menstruating females (899 menstrual cycles) aged 14 and older who logged their menses, performed urine LH testing to define day of ovulation, recorded daily basal body temperature (BBT), and collected overnight wrist temperature. Participants contributed between 2 and 13 menstrual cycles.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Algorithm performance was evaluated for three algorithms: one for retrospective ovulation day estimate in ongoing cycles (Algorithm 1), one for retrospective ovulation day estimate in completed cycles (Algorithm 2), and one for prediction of next menses start day (Algorithm 3). Each algorithm's performance was evaluated under multiple scenarios, including for participants with all typical cycle lengths (23-35 days) and those with some atypical cycle lengths (<23, >35 days), in cycles with the temperature change of ≥0.2°C typically associated with ovulation, and with any temperature change included.

MAIN RESULTS AND ROLE OF CHANCE

Two hundred and sixty participants provided 889 cycles. Algorithm 1 provided a retrospective ovulation day estimate in 80.5% of ongoing menstrual cycles of all cycle lengths with ≥0.2°C wrist temperature signal with a mean absolute error (MAE) of 1.59 days (95% CI 1.45, 1.74), with 80.0% of estimates being within ±2 days of ovulation. Retrospective ovulation day in an ongoing cycle (Algorithm 1) was estimated in 81.9% (MAE 1.53 days, 95% CI 1.35, 1.70) of cycles for participants with all typical cycle lengths and 77.7% (MAE 1.71 days, 95% CI 1.42, 2.01) of cycles for participants with atypical cycle lengths. Algorithm 2 provided a retrospective ovulation day estimate in 80.8% of completed menstrual cycles with ≥0.2°C wrist temperature signal with an MAE of 1.22 days (95% CI 1.11, 1.33), with 89.0% of estimates being within ±2 days of ovulation. Wrist temperature provided the next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within ±3 days of menses start) with an MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with ≥0.2°C wrist temperature signal.

LIMITATIONS, REASONS FOR CAUTION

There are several limitations, including reliance on LH testing to identify ovulation, which may mislabel some cycles. Additionally, the potential for false retrospective ovulation estimates when no ovulation occurred reinforces the idea that this estimate should not be used in isolation.

WIDER IMPLICATIONS OF THE FINDINGS

Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.

STUDY FUNDING/COMPETING INTEREST(S)

Apple is the funding source for this manuscript. Y.W., C.Y.Z., J.P., S.Z., and C.L.C. own Apple stock and are employed by Apple. S.M. has research funding from Apple for a separate study, the Apple Women's Health Study, including meeting and travel support to present research findings related to that separate study. A.M.Z.J., D.D.B., B.A.C., and J.P. had no conflicts of interest.

TRIAL REGISTRATION NUMBER

NCT05852951.

Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions

The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, traditional basal body temperature (BBT) measurement methods are susceptible to disruptions in sleep timing and environmental conditions, limiting practical application. This study is aimed to overcome these limitations by introducing a novel feature, heart rate at the circadian rhythm nadir (minHR), for classifying menstrual cycle phases and predicting ovulation. A machine learning model was developed using XGBoost, and data were collected under free-living conditions from 40 healthy women (18-34 years) over a maximum of three menstrual cycles. Three feature combinations- "day," "day + minHR," and "day + BBT"-were evaluated, and model performance was assessed using nested leave-one-group-out cross-validation. The feature "day" represents the number of days elapsed since the onset of menstruation. Participants were stratified into groups depending on high variability and low variability in sleep timing. Results demonstrated that adding minHR significantly improved luteal phase classification and ovulation day detection performance compared to "day" only. Furthermore, in participants with high variability in sleep timing, the minHR-based model outperformed the BBT-based model, significantly improving luteal phase recall and reducing ovulation day detection absolute errors by 2 d (p < 0.05). These findings highlight the robustness and practicality of the minHR-based model for menstrual cycle tracking, particularly in individuals with high variability in sleep timing. The proposed model holds great promise for personalized health management and large-scale epidemiological research.

Oura Ring as a Tool for Ovulation Detection: Validation Analysis

BACKGROUND

Oura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health.

OBJECTIVE

We assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm's performance with the traditional calendar method, which estimates the ovulation date based on an individual's last period start date and average menstrual cycle length.

METHODS

The study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates.

RESULTS

The physiology method detected 1113 (96.4%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P<.001) than the calendar method's average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P<.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95% CI 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P<.001).

CONCLUSIONS

The physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health.

Early menstrual cycle impacts of oestrogen and progesterone on the timing of the fertile window

STUDY QUESTION

What is the effect of oestrogen and progesterone at the beginning of the menstrual cycle in delaying entry into the fertile window?

SUMMARY ANSWER

Both oestrogen and progesterone contribute to a delay in the onset of the fertile window.

WHAT IS KNOWN ALREADY

Oestrogen enhances cervical mucus secretion while progesterone inhibits it.

STUDY DESIGN, SIZE, DURATION

Observational study. Daily observation of 220 menstrual cycles contributed by 88 women with no known menstrual cycle disorder.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Women recorded cervical mucus daily and collected first-morning urine samples for analysis of oestrone-3-glucuronide, pregnanediol-3-alpha-glucuronide (PDG), FHS, and LH. They underwent serial ovarian ultrasound examinations. The main outcome measure was the timing within the cycle of the onset of the fertile window, as identified by the appearance of mucus felt or seen at the vulva.

MAIN RESULTS AND THE ROLE OF CHANCE

Low oestrogen secretion and persistent progesterone secretion during the first week of the menstrual cycle both negatively affect mucus secretion. Doubling oestrogen approximately doubled the odds of entering the fertile window (OR: 1.82 95% CI=1.23; 2.69). Increasing PDG from below 1.5 to 4 µg/mg creatinine was associated with a 2-fold decrease in the odds of entering the fertile window (OR: 0.51 95% CI=0.31; 0.82). Prolonged progesterone secretion during the first week of the menstrual cycle was also statistically significantly associated with higher LH secretion. Finally, the later onset of the fertile window was associated with statistically significant persistently elevated LH secretion during the luteal phase of the previous menstrual cycle.

LIMITATIONS, REASONS FOR CAUTION

This post hoc study was conducted to assess the potential impact of residual progesterone secretion at the beginning of the menstrual cycle. It was conducted on an existing data set because of the scarcity of data available to answer the question. Analysis with other datasets with similar hormone results would be useful to confirm these findings.

WIDER IMPLICATIONS OF THE FINDINGS

This study provides evidence for residual progesterone secretion in the early latency phase of some menstrual cycles, which may delay the onset of the fertile window. This progesterone secretion may be supported by subtly increased LH secretion during the few days before and after the onset of menses, which may relate to follicular waves in the luteal phase. Persistent progesterone secretion should be considered in predicting the onset of the fertile window and in assessing ovulatory dysfunction.

STUDY FUNDING/COMPETING INTEREST(S)

The authors declare no conflicts of interest. No funding was provided for this secondary data analysis.

TRIAL REGISTRATION NUMBER

N/A.

Effects of menstrual cycle on cognitive function, cortisol, and metabolism after a single session of aerobic exercise

AIM

This study aimed to investigate the effects of the two pre-ovulatory and mid-luteal phases of the menstrual cycle on cognitive function, as well as possible mediators of metabolism and salivary cortisol, at rest and after an aerobic exercise session.

STUDY DESIGN

Twelve active young unmarried women aged 22-30 years volunteered to participate in the study. The participants performed a 20-min exercise session on a cycle ergometer at 60-70% of their reserve heart rate twice, during the follicular (pre-ovulation: days 7-10) and luteal (mid-luteal: days 21-24) phases of the menstrual cycle. Saliva samples were collected to measure cortisol. Fat utilization, respiratory exchange ratio (RER), and energy expenditure (during exercise) were measured using a spiroergometer. Cognitive function was assessed using the Stroop test. Cognitive function and cortisol levels were measured before and after each exercise session.

RESULTS

The findings of this study indicated no significant differences in variables during the resting follicular and luteal phases. Cortisol levels and cognitive function were increased after exercise compared with before exercise in both the follicular and luteal phases. Cortisol and fat utilization after exercise were significantly higher in the follicular phase than in the luteal phase. There were no significant differences between the follicular and luteal phasesregarding the effects of exercise on cognitive function, energy expenditure, and RER.

CONCLUSION

In general, the follicular and luteal phases of menstruation may not affect cognitive function in response to a single aerobic exercise session, although they change some metabolic factors and cortisol.

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.

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.

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.

Criteria for implementing artificial intelligence systems in reproductive medicine

This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.

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.

Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.

Timed intercourse for couples trying to conceive

BACKGROUND

Many factors influence fertility, one being the timing of intercourse. The 'fertile window' describes a stage in the cycle when conception can occur and is approximately five days before to several hours after ovulation. 'Timed intercourse' is the practice of prospectively identifying ovulation and, thus, the fertile window to increase the likelihood of conception. Methods of predicting ovulation include urinary hormone measurement (luteinising hormone (LH) and oestrogen), fertility awareness-based methods (FABM) (including tracking basal body temperatures, cervical mucus monitoring, calendar charting/tracking apps), and ultrasonography. However, there are potentially negative aspects associated with ovulation prediction, including stress, time consumption, and cost implications of purchasing ovulation kits and app subscriptions. This review considered the evidence from randomised controlled trials (RCTs) evaluating the use of timed intercourse (using ovulation prediction) on pregnancy outcomes.

OBJECTIVES

To evaluate the benefits and risks of ovulation prediction methods for timing intercourse on conception in couples trying to conceive.

SEARCH METHODS

We searched the Cochrane Gynaecology and Fertility (CGF) Group Specialised Register, CENTRAL, MEDLINE, and Embase in January 2023. We also checked the reference lists of relevant studies and searched trial registries for any additional trials.

SELECTION CRITERIA

We included RCTs that compared methods of timed intercourse using ovulation prediction to other forms of ovulation prediction or intercourse without ovulation prediction in couples trying to conceive.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures recommended by Cochrane to select and analyse studies in this review. The primary review outcomes were live birth and adverse events (such as depression and stress). Secondary outcomes were clinical pregnancy, pregnancy (clinical or positive urinary pregnancy test not yet confirmed by ultrasound), time to pregnancy, and quality of life. We assessed the overall quality of the evidence for the main comparisons using GRADE methods.

MAIN RESULTS

This review update included seven RCTs involving 2464 women or couples. Four of the five studies from the previous review were included in this update, and three new studies were added. We assessed the quality of the evidence as moderate to very low, the main limitations being imprecision, indirectness, and risk of bias. Urinary ovulation tests versus intercourse without ovulation prediction Compared to intercourse without ovulation prediction, urinary ovulation detection probably increases the chance of live birth in couples trying to conceive (risk ratio (RR) 1.36, 95% confidence interval (CI) 1.02 to 1.81, 1 RCT, n = 844, moderate-quality evidence). This suggests that if the chance of a live birth without urine ovulation prediction is 16%, the chance of a live birth with urine ovulation prediction is 16% to 28%. However, we are uncertain whether timed intercourse using urinary ovulation detection resulted in a difference in stress (mean difference (MD) 1.98, 95% CI -0.87 to 4.83, I² = 0%, P = 0.17, 1 RCT, n = 77, very low-quality evidence) or clinical pregnancy (RR 1.09, 95% CI 0.51 to 2.31, I² = 0%, 1 RCT, n = 148, low-quality evidence). Similar to the live birth result, timed intercourse using urinary ovulation detection probably increases the chances of clinical pregnancy or positive urine pregnancy test (RR 1.28, 95% CI 1.09 to 1.50, I² = 0, 4 RCTs, n = 2202, moderate-quality evidence). This suggests that if the chance of a clinical pregnancy or positive urine pregnancy test without ovulation prediction is assumed to be 18%, the chance following timed intercourse with urinary ovulation detection would be 20% to 28%. Evidence was insufficient to determine the effect of urine ovulation tests on time to pregnancy or quality of life. Fertility awareness-based methods (FABM) versus intercourse without ovulation prediction Due to insufficient evidence, we are uncertain whether timed intercourse using FABM resulted in a difference in live birth rate compared to intercourse without ovulation prediction (RR 0.95, 95% CI 0.76 to 1.20, I² = 0%, 2 RCTs, n = 157, low-quality evidence). We are also uncertain whether FABM affects stress (MD -1.10, 95% CI -3.88 to 1.68, 1 RCT, n = 183, very low-quality evidence). Similarly, we are uncertain of the effect of timed intercourse using FABM on anxiety (MD 0.5, 95% CI -0.52 to 1.52, P = 0.33, 1 RCT, n = 183, very low-quality evidence); depression (MD 0.4, 95% CI -0.28 to 1.08, P = 0.25, 1 RCT, n = 183, very low-quality evidence); or erectile dysfunction (MD 1.2, 95% CI -0.38 to 2.78, P = 0.14, 1 RCT, n = 183, very low-quality evidence). Evidence was insufficient to detect a benefit of timed intercourse using FABM on clinical pregnancy (RR 1.13, 95% CI 0.31 to 4.07, 1 RCT, n = 17, very low-quality evidence) or clinical or positive pregnancy test rates (RR 1.08, 95% CI 0.89 to 1.30, 3 RCTs, n = 262, very low-quality evidence). Finally, we are uncertain whether timed intercourse using FABM affects the time to pregnancy (hazard ratio 0.86, 95% CI 0.53 to 1.38, 1 RCT, n = 140, low-quality evidence) or quality of life. No studies assessed the use of timed intercourse with pelvic ultrasonography.

AUTHORS' CONCLUSIONS

The new evidence presented in this review update shows that timed intercourse using urine ovulation tests probably improves live birth and pregnancy rates (clinical or positive urine pregnancy tests but not yet confirmed by ultrasound) in women under 40, trying to conceive for less than 12 months, compared to intercourse without ovulation prediction. However, there are insufficient data to determine the effects of urine ovulation tests on adverse events, clinical pregnancy, time to pregnancy, and quality of life. Similarly, due to limited data, we are uncertain of the effect of FABM on pregnancy outcomes, adverse effects, and quality of life. Further research is therefore required to fully understand the safety and effectiveness of timed intercourse for couples trying to conceive. This research should include studies reporting clinically relevant outcomes such as live birth and adverse effects in fertile and infertile couples and utilise various methods to determine ovulation. Only with a comprehensive understanding of the risks and benefits of timed intercourse can recommendations be made for all couples trying to conceive.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.