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1. A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study

   https://doi.org/10.2196/30991  

   Jimah, Tamara ; Borg, Holly ; Kehoe, Priscilla ; Pimentel, Pamela ; Turner, Arlene ; Labbaf, Sina ; Asgari Mehrabadi, Milad ; Rahmani, Amir M. ; Dutt, Nikil ; Guo, Yuqing ( January 2021 , JMIR Formative Research)

   Background The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user’s engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotionalmore »health using the Two Happy Hearts mobile app–based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results Decreased resting heart rate was significantly correlated with increased heart rate variability (r=–0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.« less
2. Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

   https://doi.org/10.3390/s21072281  

   Sarhaddi, Fatemeh ; Azimi, Iman ; Labbaf, Sina ; Niela-Vilén, Hannakaisa ; Dutt, Nikil ; Axelin, Anna ; Liljeberg, Pasi ; Rahmani, Amir M. ( April 2021 , Sensors)

   Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collectmore »reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.« less
3. A Micro-Level Analysis of Physiological Responses to COVID-19: Continuous Monitoring of Pregnant Women in California

   https://doi.org/10.3389/fpubh.2022.808763  

   Jimah, Tamara ; Kehoe, Priscilla ; Borg, Holly ; Pimentel, Pamela ; Rahmani, Amir ; Dutt, Nikil ; Guo, Yuqing ( April 2022 , Frontiers in Public Health)

   Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological parameters during sleep. Two women in their second and third trimesters, respectively, were selected based on a positive COVID-19 diagnosis. Both women were tested using the polymerase chain reaction method to confirm the presence of the virus during which time we were able to collect these physiological data. In both cases, we observed 3–6 days of peak physiological changes in resting heart rate (HR), heart rate variability (HRV), and respiratory rate (RR), as well as sleep surrounding the onset of COVID-19 symptoms. The pregnant woman in her third trimester showed a significant increase in resting HR ( p = 0.006) and RR ( p = 0.048), and a significant decrease in HRV ( p = 0.027) and deep sleep duration ( p = 0.029). She reported experiencing moderate COVID-19 symptoms and did not require hospitalization. At 38 weeks of gestation, she had a normal deliverymore »and gave birth to a healthy infant. The participant in her second trimester showed similar physiological changes during the 3-day peak period. Importantly, these changes appeared to return to the pre-peak levels. Common symptoms reported by both cases included loss of smell and nasal congestion, with one losing her sense of taste. Results suggest the potential to use the changes in cardiorespiratory responses and sleep for real-time monitoring of health and well-being during pregnancy.« less
4. CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

   https://doi.org/10.3389/frai.2021.543176  

   Kulkarni, Samruddhi S. ; Katebi, Nasim ; Valderrama, Camilo E. ; Rohloff, Peter ; Clifford, Gari D. ( May 2021 , Frontiers in Artificial Intelligence)

   Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34–203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was thenmore »trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.« less
5. Mental Health and Prenatal Bonding in Pregnant Women During the COVID-19 Pandemic: Evidence for Heightened Risk Compared With a Prepandemic Sample

   https://doi.org/10.1177/21677026211049430  

   Morris, Alyssa R. ; Saxbe, Darby E. ( November 2021 , Clinical Psychological Science)

   We compared 572 pregnant women (319 first-time mothers) surveyed in spring 2020, during the first wave of COVID-19 lockdowns in the United States, with 99 pregnant women (all first-time mothers) surveyed before the pandemic (2014–2020). Compared with the prepandemic sample, women assessed during the pandemic showed elevated depression, anxiety, and stress and weaker prenatal bonding to their infants. These findings remained significant when restricting the pandemic sample to first-time mothers only and held after controlling for race/ethnicity, education, and pregnancy stage. Average levels of depression and anxiety within the pandemic group exceeded clinically significant thresholds, and women who estimated that the pandemic had more negatively affected their social relationships reported higher distress. However, pandemic-related changes to social contact outside the household were inconsistently associated with mental health and with some positive outcomes (fewer depressive symptoms, stronger prenatal bonding). Given that prenatal stress may compromise maternal and child well-being, the pandemic may have long-term implications for population health.