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Mishra A, Park J, Shapiro I, Fisher-Colbrie T, Baird DD, Suharwardy S, Zhang S, Jukic AMZ, Curry CL. Trends in sensor-based health metrics during and after pregnancy: descriptive data from the apple women's health study. AJOG GLOBAL REPORTS 2024; 4:100388. [PMID: 39296604 PMCID: PMC11408385 DOI: 10.1016/j.xagr.2024.100388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2024] Open
Abstract
Background While it is known that vital signs and behaviors change during pregnancy, there is limited data on timing and scale of changes for sensor-derived health metrics across pregnancy and postpartum. Wearable technology provides an opportunity to understand physiologic and behavioral changes across pregnancy with greater detail, more frequent measurements, and improved accuracy. The aim of this study is to describe changes in physiologic and behavioral sensor-based health metrics during pregnancy and postpartum in the Apple Women's Health Study (AWHS) and their relationship to demographic factors. Methods The Apple Women's Health Study is a digital, longitudinal, observational study that includes U.S. residents with an iPhone and Apple Watch. We evaluated changes from pre-pregnancy through delivery and postpartum for sensor-derived health metrics. Minimum required data samples per day, week and overall were data element specific, and included 12 weeks prior to pregnancy start, and 12 weeks postpartum for pregnancies lasting between 24 and 43 weeks. Findings A total of 757 pregnancies from 733 participants were included. Resting heart rate (RHR) increased across pregnancy, peaking in the third trimester (pre-pregnancy median RHR 65.0 beats per minute [BPM], interquartile range [IQR] 60.0-70.2 B.M. third trimester median RHR 75.5 B.M. IQR 69.0-82.0 B.M., with a decrease prior to delivery and nadir postpartum (postpartum median RHR 62.0 B.M. IQR 57.0-66.0 B.M.. Heart rate variability (HRV) decreased from pre-pregnancy (39.9 milliseconds, IQR 32.6-48.3 milliseconds), reaching a nadir in the third trimester (29.9 milliseconds, IQR 25.2-36.4 milliseconds), before rebounding in the last weeks of pregnancy. Measures of activity, such as exercise minutes, stand minutes, step count and Cardio Fitness were all decreased in each trimester compared to pre-pregnancy, with their nadirs postpartum. Total sleep duration increased slightly in early pregnancy (pre-pregnancy 7.2 hours, IQR 6.7-7.7 hours; 1st trimester 7.4 hours, IQR 6.8-7.9 hours), with the lowest sleep duration postpartum (6.2 hours, IQR 5.4-6.8 hours). Interpretation Resting heart rate increased during pregnancy, with a decrease prior to delivery, while heart rate variability decreased across pregnancy, with an upward trend before delivery. Behavioral metrics, such as exercise and sleep, showed decreasing trends during and after pregnancy. These data provide a foundation for understanding normal pregnancy physiology and can facilitate hypothesis generation related to physiology, behavior, pregnancy outcomes and disease.
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Affiliation(s)
- Anshuman Mishra
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Jihyun Park
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Ian Shapiro
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Tyler Fisher-Colbrie
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Donna D Baird
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC (Baird and Jukic)
| | - Sanaa Suharwardy
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Shunan Zhang
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
| | - Anne Marie Z Jukic
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC (Baird and Jukic)
| | - Christine L Curry
- Apple Inc., Cupertino, CA (Mishra, Park, Shapiro, Fisher-Colbrie, Suharwardy, Zhang, and Curry)
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Keeler Bruce L, González D, Dasgupta S, Smarr BL. Biometrics of complete human pregnancy recorded by wearable devices. NPJ Digit Med 2024; 7:207. [PMID: 39134787 PMCID: PMC11319646 DOI: 10.1038/s41746-024-01183-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
In the United States, normal-risk pregnancies are monitored with the recommended average of 14 prenatal visits. Check-ins every few weeks are the standard of care. This low time resolution and reliance on subjective feedback instead of direct physiological measurement, could be augmented by remote monitoring. To date, continuous physiological measurements have not been characterized across all of pregnancy, so there is little basis of comparison to support the development of the specific monitoring capabilities. Wearables have been shown to enable the detection and prediction of acute illness, often faster than subjective symptom reporting. Wearables have also been used for years to monitor chronic conditions, such as continuous glucose monitors. Here we perform a retrospective analysis on multimodal wearable device data (Oura Ring) generated across pregnancy within 120 individuals. These data reveal clear trajectories of pregnancy from cycling to conception through postpartum recovery. We assessed individuals in whom pregnancy did not progress past the first trimester, and found associated deviations, corroborating that continuous monitoring adds new information that could support decision-making even in the early stages of pregnancy. By contrast, we did not find significant deviations between full-term pregnancies of people younger than 35 and of people with "advanced maternal age", suggesting that analysis of continuous data within individuals can augment risk assessment beyond standard population comparisons. Our findings demonstrate that low-cost, high-resolution monitoring at all stages of pregnancy in real-world settings is feasible and that many studies into specific demographics, risks, etc., could be carried out using this newer technology.
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Affiliation(s)
- Lauryn Keeler Bruce
- UC San Diego Health Department of Biomedical Informatics, University of California San Diego, San Diego, CA, USA
- Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA, USA
| | - Dalila González
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - Subhasis Dasgupta
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA, USA
| | - Benjamin L Smarr
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA, USA.
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA.
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Bester M, Nichting TJ, Joshi R, Aissati L, Oei GS, Mischi M, van Laar JOEH, Vullings R. Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study. J Clin Med 2024; 13:2442. [PMID: 38673715 PMCID: PMC11051424 DOI: 10.3390/jcm13082442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity-e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave-may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration.
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Affiliation(s)
- Maretha Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Thomas J. Nichting
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Rohan Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Lamyae Aissati
- Faculty of Health, Medicine and Life Science, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Guid S. Oei
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Judith O. E. H. van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, 5504 DB Veldhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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Basavaraj C, Grant AD, Aras SG, Erickson EN. Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.25.24303344. [PMID: 38464102 PMCID: PMC10925356 DOI: 10.1101/2024.02.25.24303344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
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.
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Affiliation(s)
- Chinmai Basavaraj
- Department of Computer Science, The University of Arizona, Tucson, AZ, USA
| | | | - Shravan G Aras
- Center for Biomedical Informatics and Biostatistics, The University of Arizona Health Sciences, Tucson, AZ, USA
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Agarwal AK, Gonzales R, Scott K, Merchant R. Investigating the Feasibility of Using a Wearable Device to Measure Physiologic Health Data in Emergency Nurses and Residents: Observational Cohort Study. JMIR Form Res 2024; 8:e51569. [PMID: 38386373 PMCID: PMC10921319 DOI: 10.2196/51569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/20/2023] [Accepted: 01/07/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Emergency departments play a pivotal role in the US health care system, with high use rates and inherent stress placed on patients, patient care, and clinicians. The impact of the emergency department environment on the health and well-being of emergency residents and nurses can be seen in worsening rates of burnout and cardiovascular health. Research on clinician health has historically been completed outside of clinical areas and not personalized to the individual. The expansion of digital technology, specifically wearable devices, may enhance the ability to understand how health care environments impact clinicians. OBJECTIVE The primary objective of this pilot study was to assess the feasibility and acceptability of using wearable devices to measure and record physiologic data from emergency nurses and resident physicians. Understanding strategies that are accepted and used by clinicians is critical prior to launching larger investigations aimed at improving outcomes. METHODS This was a longitudinal pilot study conducted at an academic, urban, level 1 trauma center. A total of 20 participants, including emergency medicine resident physicians and nurses, were equipped with a wearable device (WHOOP band) and access to a mobile health platform for 6 weeks. Baseline surveys assessed burnout, mental health, and expectations of the device and experience. Participants provided open-ended feedback on the device and platform, contributing to the assessment of acceptance, adoption, and use of the wearable device. Secondary measures explored early signs and variations in heart rate variability, sleep, recovery, burnout, and mental health assessments. RESULTS Of the 20 participants, 10 consistently used the wearable device. Feedback highlighted varying experiences with the device, with a preference for more common wearables like the Apple Watch or Fitbit. Resident physicians demonstrated higher engagement with the device and platform as compared with nurses. Baseline mental health assessments indicated mild anxiety and depressive symptoms among participants. The Professional Fulfillment Index revealed low professional fulfillment, moderate workplace exhaustion, and interpersonal disengagement. CONCLUSIONS This pilot study underscores the potential of wearable devices in monitoring emergency clinicians' physiologic data but reveals challenges related to device preferences and engagement. The key takeaway is the necessity to optimize device and platform design for clinician use. Larger, randomized trials are recommended to further explore and refine strategies for leveraging wearable technology to support the well-being of the emergency workforce.
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Affiliation(s)
- Anish K Agarwal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Health Care Transformation and Innovation, Penn Medicine, Philadelphia, PA, United States
| | - Rachel Gonzales
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Health Care Transformation and Innovation, Penn Medicine, Philadelphia, PA, United States
| | - Kevin Scott
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Raina Merchant
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Health Care Transformation and Innovation, Penn Medicine, Philadelphia, PA, United States
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Jasinski SR, Rowan S, Presby DM, Claydon EA, Capodilupo ER. Wearable-derived maternal heart rate variability as a novel digital biomarker of preterm birth. PLoS One 2024; 19:e0295899. [PMID: 38295026 PMCID: PMC10829979 DOI: 10.1371/journal.pone.0295899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/01/2023] [Indexed: 02/02/2024] Open
Abstract
Despite considerable health consequences from preterm births, their incidence remains unchanged over recent decades, due partially to limited screening methods and limited use of extant methods. Wearable technology offers a novel, noninvasive, and acceptable way to track vital signs, such as maternal heart rate variability (mHRV). Previous research observed that mHRV declines throughout the first 33 weeks of gestation in term, singleton pregnancies, after which it improves. The aim of this study was to explore whether mHRV inflection is a feature of gestational age or an indication of time to delivery. This retrospective case-control study considered term and preterm deliveries. Remote data collection via non-invasive wearable technology enabled diverse participation with subjects representing 42 US states and 16 countries. Participants (N = 241) were retroactively identified from the WHOOP (Whoop, Inc.) userbase and wore WHOOP straps during singleton pregnancies between March 2021 and October 2022. Mixed effect spline models by gestational age and time until birth were fit for within-person mHRV, grouped into preterm and term births. For term pregnancies, gestational age (Akaike information criterion (AIC) = 26627.6, R2m = 0.0109, R2c = 0.8571) and weeks until birth (AIC = 26616.3, R2m = 0.0112, R2c = 0.8576) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio = 279.5). For preterm pregnancies, gestational age (AIC = 1861.9, R2m = 0.0016, R2c = 0.8582) and time until birth (AIC = 1848.0, R2m = 0.0100, R2c = 0.8676) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio = 859.4). This study suggests that wearable technology, such as the WHOOP strap, may provide a digital biomarker for preterm delivery by screening for changes in nighttime mHRV throughout pregnancy that could in turn alert to the need for further evaluation and intervention.
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Affiliation(s)
- Summer R. Jasinski
- Department of Data Science and Research, WHOOP Inc, Boston, MA, United States of America
| | - Shon Rowan
- Department of Obstetrics and Gynecology, West Virginia University School of Medicine, Morgantown, WV, United States of America
| | - David M. Presby
- Department of Data Science and Research, WHOOP Inc, Boston, MA, United States of America
| | - Elizabeth A. Claydon
- Department of Social and Behavioral Sciences, West Virginia University School of Public Health, Morgantown, WV, United States of America
| | - Emily R. Capodilupo
- Department of Data Science and Research, WHOOP Inc, Boston, MA, United States of America
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Bester M, Almario Escorcia MJ, Fonseca P, Mollura M, van Gilst MM, Barbieri R, Mischi M, van Laar JOEH, Vullings R, Joshi R. The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography. Sci Rep 2023; 13:21100. [PMID: 38036597 PMCID: PMC10689737 DOI: 10.1038/s41598-023-47980-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023] Open
Abstract
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.
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Affiliation(s)
- M Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands.
| | - M J Almario Escorcia
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - P Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
| | - M Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
| | - R Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - J O E H van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, De Run 4600, 5504 DB, Veldhoven, The Netherlands
| | - R Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - R Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
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Erickson EN, Gotlieb N, Pereira LM, Myatt L, Mosquera-Lopez C, Jacobs PG. Predicting labor onset relative to the estimated date of delivery using smart ring physiological data. NPJ Digit Med 2023; 6:153. [PMID: 37598232 PMCID: PMC10439919 DOI: 10.1038/s41746-023-00902-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/10/2023] [Indexed: 08/21/2023] Open
Abstract
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.
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Affiliation(s)
- Elise N Erickson
- College of Nursing / College of Pharmacy, The University of Arizona, Tucson, AZ, USA.
- Midwifery Division, School of Nursing, Oregon Health & Science University, Portland, OR, USA.
| | | | - Leonardo M Pereira
- Department of Obstetrics & Gynecology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Leslie Myatt
- Department of Obstetrics & Gynecology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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Changes in Maternal Heart Rate and Autonomic Regulation following the Antenatal Administration of Corticosteroids: A Secondary Analysis. J Clin Med 2023; 12:jcm12020588. [PMID: 36675517 PMCID: PMC9866172 DOI: 10.3390/jcm12020588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
While the effect of antenatally administered corticosteroids on fetal heart rate (HR) and heart rate variability (HRV) is well established, little information is available on how these drugs affect maternal physiology. In this secondary analysis of a prospective, observational cohort study, we quantify how corticosteroids affect maternal HR and HRV, which serve as a proxy measure for autonomic regulation. Abdominal ECG measurements were recorded before and in the five days following the administration of betamethasone—a corticosteroid commonly used for fetal maturation—in 46 women with singleton pregnancies. Maternal HR and HRV were determined from these recordings and compared between these days. HRV was assessed with time- and frequency-domain features, as well as non-linear and complexity features. In the 24 h after betamethasone administration, maternal HR was significantly increased (p < 0.01) by approximately 10 beats per minute, while HRV features linked to parasympathetic activity and HR complexity were significantly decreased (p < 0.01 and p < 0.001, respectively). Within four days after the initial administration of betamethasone, HR decreases and HRV features increase again, indicating a diminishing effect of betamethasone a few days after administration. We conclude that betamethasone administration results in changes in maternal HR and HRV, despite the heterogeneity of the studied population. Therefore, its recent administration should be considered when evaluating these cardiovascular metrics.
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