Tracking Sleep, Temperature, Heart Rate, and Daily Symptoms Across the Menstrual Cycle with the Oura Ring in Healthy Women

Creatine in women's health: bridging the gap from menstruation through pregnancy to menopause

BACKGROUND

Creatine supplementation in women has gained attention for its potential benefits beyond muscle growth, including reproductive health, cognitive health and aging. Women exhibit distinct physiological differences from men, influenced by hormonal fluctuations during pre-menopause, pregnancy, and menopause, and these factors should be considered for their influence on creatine metabolism.

OBJECTIVE

This review aims to provide a historical evaluation of creatine supplementation in women, its potential applications across female-specific life stages, recent research highlights, and targets for future research. The review also considers the impact of hormonal changes on creatine metabolism and effectiveness as a dietary supplementation.

METHODS

This is a narrative overview of historical and recent research evaluating the effects of creatine in women.

RESULTS

Early studies demonstrated the benefits of creatine on exercise performance in women, though they often overlooked menstrual cycle variability. Recent research has begun to account for these hormonal fluctuations, enhancing the understanding of creatine's applications. Creatine supplementation has shown positive effects on muscle strength, exercise performance, and body composition, particularly when combined with resistance training. Additionally, creatine may improve mood and cognitive function, potentially alleviating symptoms of depression. Emerging evidence suggests creatine's benefits during pregnancy and post-menopause, though data on perimenopausal women remains limited.

CONCLUSION

Creatine supplementation presents a promising strategy for enhancing various aspects of women's health across the lifespan. Future research should focus on optimizing dosing strategies, understanding long-term health implications, and exploring creatine's effects during pregnancy and perimenopause.

Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary

The National Center on Sleep Disorders Research of the National Heart, Lung, and Blood Institute at the National Institutes of Health hosted a 2-day virtual workshop titled Big Data Approaches for Novel Mechanistic Insights on Disorders of Sleep and Circadian Rhythms on May 2nd and 3rd, 2024. The goals of this workshop were to establish a comprehensive understanding of the current state of sleep and circadian rhythm disorders research to identify opportunities to advance the field by using approaches based on artificial intelligence and machine learning. The workshop showcased rapidly developing technologies for sensitive and comprehensive remote analysis of sleep and its disorders that can account for physiological, environmental, and social influences, potentially leading to novel insights on long-term health consequences of sleep disorders and disparities of these health problems in specific populations.

Breast tenderness and swelling experiences related to menstrual cycles and ovulation in healthy premenopausal women: Secondary analysis of the 1-year "Prospective Ovulation Cohort"

Breast tenderness and swelling are associated with premenstrual symptoms but are not well described in healthy women. In this 1-year prospective observational study, we examined daily breast tenderness and swelling to determine whether differences existed between normally ovulatory and ovulatory disturbed (short luteal phase and anovulatory) cycles in a cohort of community dwelling, non-smoking, healthy premenopausal women. Enrolment required two consecutive normal-length and normally ovulatory cycles by Quantitative Basal Temperature© analysis. Women (n = 53) ages 20-41 recorded their daily breast experiences in the Menstrual Cycle Diary© across an average of 13.6 cycles. In all 720 cycles, the median breast tenderness was 1.4 (on a 0-4 scale, range 0.0-3.0), in cycles with a mean length of 28.1 days (95% CI 27.5-28.8). Comparison of breast tenderness and breast size (changes from usual) parameters between all normally ovulatory cycles and all ovulatory disturbed cycles in the whole cohort showed significantly higher levels in normally ovulatory (luteal length ≥10 days) in both Breast Tenderness Score [intensity X duration in days; 6.0 (range 1.0-14.0) vs. 3.0 (0.0-11.0) (P=.005)] and breast size [4.0 (2.0-4.0) vs. 4.0 (0.0-4.0) (P=.034]). However, within-woman in the forty-seven women with both normally ovulatory and ovulatory disturbed cycles, breast tenderness (intensity, duration, and Breast Tenderness Score), did not differ between normally ovulatory cycles and cycles with ovulatory disturbances. This study also demonstrated that in all ovulatory cycles, the timing of breast tenderness increased in parallel with breast swelling; the maximum for both was in the late luteal phase.

28 days later: A prospective daily study on psychological well-being across the menstrual cycle and the effects of hormones and oral contraceptives

BACKGROUND

We aimed to study how hormonal status (oral contraceptive [OC] users vs naturally cycling [NC]) affects different dimensions and variability of psychological well-being, and how they relate to sex hormone levels (estradiol, progesterone, and testosterone).

METHODS

Twenty-two NC participants and 18 OC users reported daily affective and physical symptoms and collected daily salivary samples across 28 days. Groups were compared using psychological well-being averages (linear mixed models), day-to-day variability (Levene's test), and network models. Within NC participants, cycle phase effects and time-varying associations between hormones and psychological well-being were assessed using both person-centered mean and change (subtracting mean from daily score) scores.

RESULTS

Lowered variability was found for OC users' agitation, risk-taking, attractiveness, and energy levels. They showed lower overall ratings of happiness, attractiveness, risk-taking, and energy levels (range R2m = .004: .019) but also reported more relaxation, sexual desire, and better sleep quality (range R2m = .005; .01) compared to the NC group. The impact of sex hormones on psychological well-being varied significantly across cycle phases, with the largest effects for progesterone levels.

CONCLUSIONS

Our results confirm that hormonal status is associated with a range of psychological well-being domains beyond mood and sexual desire, including energy levels, feelings of attractiveness, risk taking, and agitation. Lowered variability in OC users versus NC participants fit with 'emotional blunting' as a possible mechanism behind OC's side effects. Our findings that show the menstrual cycle and sex hormones differentially influenced markers of psychological well-being emphasize the need to adequately account for the menstrual cycle.

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.

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.

A call to integrate menstrual cycle influences into just-in-time adaptive interventions for suicide prevention

This paper discusses the scientific rationale and methodological considerations for incorporating the menstrual cycle as a time-varying intra-individual factor in personalized medicine models, such as Just-In-Time Adaptive Interventions (JITAIs). Among patients, accumulating evidence suggests that the normal hormone fluctuations of the menstrual cycle represent a time-varying factor that can trigger or exacerbate psychiatric symptoms, including but not limited to affective dysregulation, suicidality, and irritability. While only a minority of the general female population experiences significant cyclical changes, this hormone-sensitive response appears to be greater among patients with psychiatric disorders, with studies demonstrating that a majority of patients recruited for past-month suicidal ideation demonstrate worsening of their suicidality around menses. However, no interventions target suicidality during this monthly period of elevated risk despite evidence of a clear recurring biological trigger. This unique and recurrent "biotype" of suicidality is well-suited for JITAIs. In addition to providing a rationale for the inclusion of the cycle in JITAI, we provide illustrative options and examples regarding the measurement and implementation of cycle variables in JITAIs. We discuss how JITAIs might be leveraged to use menstrual cycle data to identify states of vulnerability within people and strategically select and deploy interventions based upon their receptivity at various phases in the cycle. Furthermore, we discuss how to integrate passive measures for tracking the menstrual cycle. Although much research is needed before implementation, we maintain that the menstrual cycle represents a critically understudied time-varying feature that may markedly improve the accuracy of JITAI models for predicting suicidality.

Deep learning model using continuous skin temperature data predicts labor onset

BACKGROUND

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset.

METHODS

We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset.

RESULTS

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

CONCLUSION

Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

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.

Menstrual Cycle Variations in Wearable-Detected Finger Temperature and Heart Rate, But Not in Sleep Metrics, in Young and Midlife Individuals

Most studies about the menstrual cycle are laboratory-based, in small samples, with infrequent sampling, and limited to young individuals. Here, we use wearable and diary-based data to investigate menstrual phase and age effects on finger temperature, sleep, heart rate (HR), physical activity, physical symptoms, and mood. A total of 116 healthy females, without menstrual disorders, were enrolled: 67 young (18-35 years, reproductive stage) and 53 midlife (42-55 years, late reproductive to menopause transition). Over one menstrual cycle, participants wore Oura ring Gen2 to detect finger temperature, HR, heart rate variability (root mean square of successive differences between normal heartbeats [RMSSD]), steps, and sleep. They used luteinizing hormone (LH) kits and daily rated sleep, mood, and physical symptoms. A cosinor rhythm analysis was applied to detect menstrual oscillations in temperature. The effect of menstrual cycle phase and group on all other variables was assessed using hierarchical linear models. Finger temperature followed an oscillatory trend indicative of ovulatory cycles in 96 participants. In the midlife group, the temperature rhythm's mesor was higher, but period, amplitude, and number of days between menses and acrophase were similar in both groups. In those with oscillatory temperatures, HR was lowest during menses in both groups. In the young group only, RMSSD was lower in the late-luteal phase than during menses. Overall, RMSSD was lower, and number of daily steps was higher, in the midlife group. No significant menstrual cycle changes were detected in wearable-derived or self-reported measures of sleep efficiency, duration, wake-after-sleep onset, sleep onset latency, or sleep quality. Mood positivity was higher around ovulation, and physical symptoms manifested during menses. Temperature and HR changed across the menstrual cycle; however, sleep measures remained stable in these healthy young and midlife individuals. Further work should investigate over longer periods whether individual- or cluster-specific sleep changes exist, and if a buffering mechanism protects sleep from physiological changes across the menstrual cycle.

Creatine Improves Total Sleep Duration Following Resistance Training Days versus Non-Resistance Training Days among Naturally Menstruating Females

Females historically experience sleep disturbances and overall poor sleep compared to males. Creatine has been proposed to impact sleep; however, the effects are not well known. The purpose of this study was to examine the effects of creatine supplementation on sleep among naturally menstruating females. Twenty-one participants completed a double-blind, randomized controlled trial in which they consumed 5 g creatine + 5 g maltodextrin or placebo, 10 g maltodextrin, daily for 6 weeks. Participants completed resistance training 2x/week using the TONAL® (Tonal Systems Inc., San Francisco, CA, USA) at-home gym. Pre- and post-testing assessed body composition, Pittsburgh Sleep Quality Index (PSQI), dietary intake, and muscular strength. Sleep was assessed nightly using an ŌURA® (Oulu, Finland) ring. Compared to the placebo group, those consuming creatine experienced significant increases in total sleep on training days (p = 0.013). No significant changes in chronic sleep and PSQI (pre-post) were observed. There was a significant increase in TONAL® strength score over time (p < 0.001), with no between-group differences. Participants reduced their total calorie (kcal) (p = 0.039), protein (g/kg) (p = 0.009), carbohydrate (g/kg) (p = 0.023), and fat (g) (p = 0.036) intake over time. Creatine supplementation increases sleep duration on resistance training days in naturally menstruating females.

Associations of luteal phase changes in vagally mediated heart rate variability with premenstrual emotional changes

BACKGROUND

A recent meta-analysis revealed that vagally mediated heart rate variability (vmHRV; a biomarker of emotion regulation capacity) significantly decreases in the luteal phase of the menstrual cycle. As two follow-up studies suggest, these vmHRV decreases are driven primarily by increased luteal progesterone (P4). However, analyses also revealed significant interindividual differences in vmHRV reactivity to the cycle, which is in line with longstanding evidence for interindividual differences in mood sensitivity to the cycle. The present study begins to investigate whether these interindividual differences in vmHRV cyclicity can explain who is at higher risk of showing premenstrual emotional changes. We expected a greater degree of midluteal vmHRV decrease to be predictive of a greater premenstrual increase in negative affect.

METHODS

We conducted an observational study with a naturally cycling community sample (N = 31, M = 26.03 years). Over a span of six weeks, participants completed (a) daily ratings of negative affect and (b) counterbalanced lab visits in their ovulatory, midluteal, and perimenstrual phases. Lab visits were scheduled based on positive ovulation tests and included assessments of baseline vmHRV and salivary ovarian steroid levels.

RESULTS

In line with previous research, multilevel models suggest that most of the sample shows ovulatory-to-midluteal vmHRV decreases which, however, were not associated with premenstrual emotional changes. Interestingly, it was only the subgroup with luteal increases in vmHRV whose negative affect markedly worsened premenstrually and improved postmenstrually.

CONCLUSION

The present study begins to investigate cyclical changes in vmHRV as a potential biomarker of mood sensitivity to the menstrual cycle. The results demonstrate a higher level of complexity in these associations than initially expected, given that only atypical midluteal increases in vmHRV are associated with greater premenstrual negative affect. Potential underlying mechanisms are discussed, among those the possibility that luteal vmHRV increases index compensatory efforts to regulate emotion in those with greater premenstrual negative affect. However, future studies with larger and clinical samples and more granular vmHRV assessments should build on these findings and further explore associations between vmHRV cyclicity and menstrually related mood changes.

Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients

Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient's physiological state during the recovery process may be inferred from sensor data. In this study, we collected smart ring data from 37 inpatients following orthopedic surgery and developed machine learning models to predict if a patient had postsurgical poor pain alleviation. Machine learning models based on the smart ring data were able to predict if a patient had poor pain alleviation during their hospital stay with an accuracy of 70.0%, an F1-score of 0.769, and an area under the receiver operating characteristics curve of 0.762 on an independent test dataset. These values were similar to performance metrics from existing models that rely on static, preoperative patient factors. Our results provide preliminary evidence that wearable device data may help control pain after orthopedic surgery by incorporating real-time, objective estimates of a patient's pain during recovery.

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.

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.

Menstrual cycle patterns and their relationship with measures of well-being and perceived performance metrics in competitive and recreational resistance-trained athletes

BACKGROUND

There is mixed evidence on how the menstrual cycle (MC) affects sports performance, with many studies showing variations in performance during different phases of the MC, while other evidence shows that the MC's effects on performance may be trivial. Therefore, this exploratory longitudinal monitoring study was designed to investigate MC characteristics and symptoms in a resistance-trained (RT) population to look for associations between measures of well-being and perceived performance metrics across the MC.

METHODS

RT females reported their workout habits, perceived performance metrics, and measures of well-being while tracking their MC with detailed methods via daily check-ins in an app.

RESULTS

Most MC characteristics and symptoms in the present RT population aligned with previous research on the general population. However, the frequency of irregular cycles was higher than in previous research on the general population. The amount of individual variation and within-subject cycle-to-cycle variation in MC characteristics and MC symptoms was also high. All measures of well-being were significantly associated with specific days of the MC, demonstrating a change in well-being based on the timing of the MC. Several perceived performance metrics were significantly associated with changes across the MC, while others were not.

CONCLUSIONS

Overall, with the current evidence as it stands, a highly individualized approach should be taken for any training or performance considerations surrounding the MC due to the high levels of individual variation.

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.

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.

Sleeping for One Week on a Temperature-Controlled Mattress Cover Improves Sleep and Cardiovascular Recovery

Body temperature should be tightly regulated for optimal sleep. However, various extrinsic and intrinsic factors can alter body temperature during sleep. In a free-living study, we examined how sleep and cardiovascular health metrics were affected by sleeping for one week with (Pod ON) vs. without (Pod OFF), an active temperature-controlled mattress cover (the Eight Sleep Pod). A total of 54 subjects wore a home sleep test device (HST) for eight nights: four nights each with Pod ON and OFF (>300 total HST nights). Nightly sleeping heart rate (HR) and heart rate variability (HRV) were collected. Compared to Pod OFF, men and women sleeping at cooler temperatures in the first half of the night significantly improved deep (+14 min; +22% mean change; p = 0.003) and REM (+9 min; +25% mean change; p = 0.033) sleep, respectively. Men sleeping at warm temperatures in the second half of the night significantly improved light sleep (+23 min; +19% mean change; p = 0.023). Overall, sleeping HR (-2% mean change) and HRV (+7% mean change) significantly improved with Pod ON (p < 0.01). To our knowledge, this is the first study to show a continuously temperature-regulated bed surface can (1) significantly modify time spent in specific sleep stages in certain parts of the night, and (2) enhance cardiovascular recovery during sleep.

Wearable and ingestible technology to evaluate and prevent exertional heat illness: A narrative review

Exertional heat illness remains a constant threat to the athlete, military service member, and laborer. Recent increases in the number and intensity of environmental heat waves places these populations at an ever increasing risk and can be deadly if not recognized and treated rapidly. For this reason, it is extremely important for medical providers to guide athletes, service members, and laborers in the implementation of awareness, education, and measures to reduce or mitigate the risk of exertional heat illness. Within the past 2 decades, a variety of wearable technology options have become commercially available to track an estimation of core temperature, yet questions continue to emerge as to its use, effectiveness, and practicality in athletics, the military, and the workforce. There is a paucity of data on the accuracy of many of these newer devices in the setting of true heat stroke physiology, and it is important to avoid overreliance on new wearable technology. Further research and improvement of this technology are critical to identify accuracy in the diagnosis and prevention of EHI.

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.

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

Background

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.

Methods

We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a daily estimation of days until labor onset.

Results

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

Conclusion

Continuous skin temperature reflects progression toward labor and hormonal status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

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.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.

The Menstrual Cycle and Sleep

Aspects of sleep change across the menstrual cycle in some women. Poorer sleep quality in the premenstrual phase and menstruation is common in women with premenstrual symptoms or painful menstrual cramps. Although objective sleep continuity remains unchanged across the regular, asymptomatic menstrual cycle, activity in the sleep electroencephalogram varies, with a prominent increase in sleep spindle activity in the postovulatory luteal phase, when progesterone is present, relative to the follicular phase. Menstrual cycle phase, reproductive stage, and menstrual-related disorders should be considered when assessing women's sleep complaints.

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.

Good sleep is a mood buffer for young women during menses

STUDY OBJECTIVES

We sought to elucidate the interaction between sleep and mood considering menstrual cycle phase (menses and non-menses portions of the cycle) in 72 healthy young women (18-33 years) with natural, regular menstrual cycles and without menstrual-associated disorders. This work fills a gap in literature of examining mood in context of sleep and menstrual cycle jointly, rather than individually.

METHODS

Daily subjective measures of sleep and mood, and date of menses were remotely, digitally collected over a 2-month period. Each morning, participants rated their sleep on the previous night, and each evening participants rated the extent of positive and negative mood for that day. Objective sleep was tracked with a wearable (ŌURA ring) during month 2 of the study. Time-lag cross-correlation and mixed linear models were used to analyze the significance and directionality of the sleep-mood relationship, and how the interaction between menstrual cycle status and sleep impacted mood levels.

RESULTS

We found that menstrual status alone did not impact mood. However, subjective sleep quality and menstrual status interacted to impact positive mood (p < .05). After a night of perceived poor sleep quality, participants reported lower positive mood during menses compared to non-menses portions of the cycle, while after a night of perceived good sleep quality participants reported equivalent levels of positive mood across the cycle.

CONCLUSIONS

We suggest that the perception of good sleep quality acts as a mood equalizer, with good sleep providing a protective buffer to positive mood across the menstrual cycle.

Neural substrates underlying rhythmic coupling of female reproductive and thermoregulatory circuits

Coordinated fluctuations in female reproductive physiology and thermoregulatory output have been reported for over a century. These changes occur rhythmically at the hourly (ultradian), daily (circadian), and multi-day (ovulatory) timescales, are critical for reproductive function, and have led to the use of temperature patterns as a proxy for female reproductive state. The mechanisms underlying coupling between reproductive and thermoregulatory systems are not fully established, hindering the expansion of inferences that body temperature can provide about female reproductive status. At present, numerous digital tools rely on temperature to infer the timing of ovulation and additional applications (e.g., monitoring ovulatory irregularities and progression of puberty, pregnancy, and menopause are developed based on the assumption that reproductive-thermoregulatory coupling occurs across timescales and life stages. However, without clear understanding of the mechanisms and degree of coupling among the neural substrates regulating temperature and the reproductive axis, whether such approaches will bear fruit in particular domains is uncertain. In this overview, we present evidence supporting broad coupling among the central circuits governing reproduction, thermoregulation, and broader systemic physiology, focusing on timing at ultradian frequencies. Future work characterizing the dynamics of reproductive-thermoregulatory coupling across the lifespan, and of conditions that may decouple these circuits (e.g., circadian disruption, metabolic disease) and compromise female reproductive health, will aid in the development of strategies for early detection of reproductive irregularities and monitoring the efficacy of fertility treatments.

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.

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.

Concentration QTc analysis of giredestrant: Overcoming QT/heart rate confounding in the presence of drug-induced heart rate changes

Concentration-QTc (C-QTc) analysis has become a common approach for evaluating proarrhythmic risk and delayed cardiac repolarization of oncology drug candidates. Significant heart rate (HR) change has been associated with certain classes of oncology drugs and can result in over- or underestimation of the true QT prolongation risk. Because oncology early clinical trials typically lack a placebo control arm or time-matched, treatment-free baseline electrocardiogram collection, significant HR change brings additional challenges to C-QTc analysis in the oncology setting. In this work, a spline-based correction method (QTcSPL) was explored to mitigate the impact of HR changes in giredestrant C-QTc analysis. Giredestrant is a selective estrogen receptor degrader being developed for the treatment of patients with estrogen receptor-positive (ER+) breast cancer. A dose-related HR decrease has been observed in patients under giredestrant treatment, with significant reductions (>10 bpm) observed at supratherapeutic doses. The QTcSPL method demonstrated superior functionality to reduce the correlation between QTc and HR as compared with the Fridericia correction (QTcF). The effect of giredestrant exposure on QTc was evaluated at the clinical dose of 30 mg and supratherapeutic dose of 100 mg based on a prespecified linear mixed effect model. The upper 90% confidence interval of ΔQTcSPL and ΔQTcF were below the 10 ms at both clinical and supratherapeutic exposures, suggesting giredestrant has a low risk of QT prolongation at clinically relevant concentrations. This work demonstrated the use case of QTcSPL to address HR confounding challenges in the context of oncology drug development for the first time.