Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context

Guidelines for assessing maternal cardiovascular physiology during pregnancy and postpartum

Maternal mortality rates are at an all-time high across the world and are set to increase in subsequent years. Cardiovascular disease is the leading cause of death during pregnancy and postpartum, especially in the United States. Therefore, understanding the physiological changes in the cardiovascular system during normal pregnancy is necessary to understand disease-related pathology. Significant systemic and cardiovascular physiological changes occur during pregnancy that are essential for supporting the maternal-fetal dyad. The physiological impact of pregnancy on the cardiovascular system has been examined in both experimental animal models and in humans. However, there is a continued need in this field of study to provide increased rigor and reproducibility. Therefore, these guidelines aim to provide information regarding best practices and recommendations to accurately and rigorously measure cardiovascular physiology during normal and cardiovascular disease-complicated pregnancies in human and animal models.

A Motivational Technology Perspective on the Use of Smart Wrist-Worn Wearables for Postpartum Exercise and Weight Management

Exercise and weight management is crucial in preventing postpartum depression and long-term obesity that carries the risk of chronic illness among postpartum women. Although communication devices, such as a smart wrist-worn wearable (SWW), can help users be more physically active, the extent to which postpartum women might benefit from this technology is unknown. We examined how SWWs promoted exercise and helped postpartum women return to pre-pregnancy weight. We tested a model based on the premise that a motivational device that prompts users to engage with it can establish healthy daily routines. An online survey of 309 postpartum women who were living in the United States and were current users of SWWs revealed that the device encouraged them to spend time completing workout goals. Technological affordances (i.e. customization, navigability, and interactivity) and subsequent user engagement with the device positively predicted total workout hours among postpartum women. We present practical implications for postpartum care programs and smart wearable developers.

Evaluating the Impact of an App-Delivered Mindfulness Meditation Program to Reduce Stress and Anxiety During Pregnancy: Pilot Longitudinal Study

BACKGROUND

Stress and anxiety during pregnancy are extremely prevalent and are associated with numerous poor outcomes, among the most serious of which are increased rates of preterm birth and low birth weight infants. Research supports that while in-person mindfulness training is effective in reducing pregnancy stress and anxiety, there are barriers limiting accessibility.

OBJECTIVE

The aim of this paper is to determine if mindfulness meditation training with the Headspace app is effective for stress and anxiety reduction during pregnancy.

METHODS

A longitudinal, single-arm trial was implemented with 20 pregnant women who were instructed to practice meditation via the Headspace app twice per day during the month-long trial. Validated scales were used to measure participant's levels of stress and anxiety pre- and postintervention. Physiological measures reflective of stress (heart rate variability and sleep) were collected via the Oura Ring.

RESULTS

Statistically significant reductions were found in self-reported levels of stress (P=.005), anxiety (P=.01), and pregnancy anxiety (P<.0001). Hierarchical linear modeling revealed a statistically significant reduction in the physiological data reflective of stress in 1 of 6 heart rate variability metrics, the low-frequency power band, which decreased by 13% (P=.006). A total of 65% of study participants (n=13) reported their sleep improved during the trial, and 95% (n=19) stated that learning mindfulness helped with other aspects of their lives. Participant retention was 100%, with 65% of participants (n=13) completing about two-thirds of the intervention, and 50% of participants (n=10) completing ≥95%.

CONCLUSIONS

This study found evidence to support the Headspace app as an effective intervention to aid in stress and anxiety reduction during pregnancy.

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Predicting labor onset relative to the estimated date of delivery using smart ring physiological data

The transition from pregnancy into parturition is physiologically directed by maternal, fetal and placental tissues. We hypothesize that these processes may be reflected in maternal physiological metrics. We enrolled pregnant participants in the third-trimester (n = 118) to study continuously worn smart ring devices monitoring heart rate, heart rate variability, skin temperature, sleep and physical activity from negative temperature coefficient, 3-D accelerometer and infrared photoplethysmography sensors. Weekly surveys assessed labor symptoms, pain, fatigue and mood. We estimated the association between each metric, gestational age, and the likelihood of a participant's labor beginning prior to (versus after) the clinical estimated delivery date (EDD) of 40.0 weeks with mixed effects regression. A boosted random forest was trained on the physiological metrics to predict pregnancies that naturally passed the EDD versus undergoing onset of labor prior to the EDD. Here we report that many raw sleep, activity, pain, fatigue and labor symptom metrics are correlated with gestational age. As gestational age advances, pregnant individuals have lower resting heart rate 0.357 beats/minute/week, 0.84 higher heart rate variability (milliseconds) and shorter durations of physical activity and sleep. Further, random forest predictions determine pregnancies that would pass the EDD with accuracy of 0.71 (area under the receiver operating curve). Self-reported symptoms of labor correlate with increased gestational age and not with the timing of labor (relative to EDD) or onset of spontaneous labor. The use of maternal smart ring-derived physiological data in the third-trimester may improve prediction of the natural duration of pregnancy relative to the EDD.

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study

BACKGROUND

Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child.

OBJECTIVE

The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection.

METHODS

We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks post partum. Responses to this questionnaire and background information of the participants were collected through our customized cross-platform mobile app. We leveraged participants' smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores≥12) and nonloneliness (scores<12). We developed decision tree and gradient-boosting models to predict loneliness. We evaluated the models by using leave-one-participant-out cross-validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction.

RESULTS

The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1-scores of 0.897 and 0.872, respectively. Our results also show that loneliness is highly associated with activity intensity and activity distribution during the day. In addition, resting heart rate (HR) and resting HR variability (HRV) were correlated with loneliness.

CONCLUSIONS

Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1-score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being through early detection of loneliness.

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

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

The effect of the COVID-19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

BACKGROUND

The SARS-CoV-2/COVID-19 pandemic has been associated with increased stress levels and higher alcohol use, including in pregnant and postpartum women. In the general population, alcohol use is associated with dysregulation in the autonomic nervous system (ANS), which is indexed by heart rate variability (HRV). The objectives of this study were to: (1) characterize changes in substance use during the SARS-CoV-2/COVID-19 pandemic via a baseline self-report survey followed by mobile ecological momentary assessment (mEMA) of substance use; and (2) examine the associations between momentary substance use and ambulatory HRV measures in pregnant and postpartum women.

METHODS

Pregnant and postpartum women were identified from the ENRICH-2 prospective cohort study. Participants were administered a baseline structured phone interview that included the Coronavirus Perinatal Experiences (COPE) survey and ascertained the prevalence of substance use. Over a 14-day period, momentary substance use was assessed three times daily, and HRV measurements were captured via wearable electronics. Associations between momentary substance use and HRV measures (root mean square of successive differences [RMSSD] and low frequency/high frequency [LF/HF] ratio) were examined using a mixed effects model that included within-subject (WS) and between-subject (BS) effects and adjusted for pregnancy status and participant age.

RESULTS

The sample included 49 pregnant and 22 postpartum women. From a combination of a baseline and 14-day mEMA surveys, 21.2% reported alcohol use, 16.9% reported marijuana use, and 8.5% reported nicotine use. WS effects for momentary alcohol use were associated with the RMSSD (β = -0.14; p = 0.005) and LF/HF ratio (β = 0.14; p = 0.01) when controlling for pregnancy status and maternal age. No significant associations were observed between HRV measures and instances of marijuana or nicotine use.

CONCLUSIONS

These findings highlight the negative effect of the SARS-CoV-2/COVID-19 pandemic on the psychological health of pregnant and postpartum women associated with substance use, and in turn, ANS dysregulation, which potentially puts some women at risk of developing a substance use disorder.

Infective Endocarditis during Pregnancy-Keep It Safe and Simple!

The diagnosis of infective endocarditis (IE) during pregnancy is accompanied by a poor prognosis for both mother and fetus in the absence of prompt management by multidisciplinary teams. We searched the electronic databases of PubMed, MEDLINE and EMBASE for clinical studies addressing the management of infective endocarditis during pregnancy, with the aim of realizing a literature review ranging from risk factors to diagnostic investigations to optimal therapeutic management for mother and fetus alike. The presence of previous cardiovascular pathologies such as rheumatic heart disease, congenital heart disease, prosthetic valves, hemodialysis, intravenous catheters or immunosuppression are the main risk factors predisposing patients to IE during pregnancy. The identification of modern risk factors such as intracardiac devices and intravenous drug administration as well as genetic diagnostic methods such as cell-free deoxyribonucleic acid (DNA) next-generation sequencing require that these cases be addressed in multidisciplinary teams. Guiding treatment to eradicate infection and protect the fetus simultaneously creates challenges for cardiologists and gynecologists alike.

Linking Electronic Health Records With Wearable Technology From the All of Us Research Program

OBJECTIVE

To evaluate the feasibility of using electronic health records (EHRs) and wearable data to describe patterns of longitudinal change in day-level heart rate before, during, and after pregnancy and how these patterns differ by age and body mass index.

DESIGN

Descriptive secondary analysis feasibility study using data from the National Institutes of Health All of Us Research Program.

SETTING

United States.

PARTICIPANTS

Women (N = 89) who had a birth or length of gestation code in the EHR and at least 60 days of Fitbit heart rate data during pregnancy.

METHODS

We estimated pregnancy-related episodes using EHR codes. Time consisted of five 3-month periods: before pregnancy, first trimester, second trimester, third trimester, and after birth. We analyzed data using descriptive statistics and locally estimated scatterplot smoothing.

RESULTS

An average of 330 days (SD = 112) of Fitbit heart rate data (29,392 days) were available from participants. During pregnancy, distinct peaks in heart rate occurred during the first trimester (6% increase) and third trimester (15% increase).

CONCLUSION

Future researchers can examine whether longitudinal timing and patterns of heart rate from wearable devices could be leveraged to detect health problems early in pregnancy.