Wearable technology for health monitoring during pregnancy: an observational cross-sectional survey study

Consumer insights from a feasibility study on remote and extended use of a novel non-invasive wearable fetal electrocardiogram monitor

The COVID-19 pandemic accelerated the adoption of telehealth and remote monitoring in obstetric care. This study assessed pregnant patients' perceptions before and after using a novel non-invasive fetal electrocardiogram (NI-FECG) device. The trial is prospectively registered on the Australia New Zealand Clinical Trials Registry (ANZCTRN12621001260819; submitted June 9th, 2021; approved September 17th, 2021). Seventy participants from 36 weeks' gestation completed pre- and post-use surveys. Interest in continuous and home fetal monitoring was high (79% and 90%, respectively). Post-use, 89% reported satisfaction; over 90% comfortable wearing and removing the sensor. Extended use was acceptable to 76%, and only 3% reported high skin irritation. Sentiment analysis highlighted themes of reassurance, convenience, and reduced anxiety. Some suggested smaller, wireless design. Analysis by natural language processing and clustering provided deeper insights. Findings support strong interest in at-home fetal monitoring; further refinement and education are needed to enhance acceptability. Future research should assess long-term effects on anxiety and clinical outcomes.

Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review

Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.

Biometrics of complete human pregnancy recorded by wearable devices

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.

Feasibility and acceptability of measuring prenatal stress in daily life using smartphone-based ecological momentary assessment and wearable physiological monitors

High levels of stress during pregnancy can have lasting effects on maternal and offspring health, which disproportionately impacts families facing financial strain, systemic racism, and other forms of social oppression. Developing ways to monitor daily life stress during pregnancy is important for reducing stress-related health disparities. We evaluated the feasibility and acceptability of using mobile health (mHealth) technology (i.e., wearable biosensors, smartphone-based ecological momentary assessment) to measure prenatal stress in daily life. Fifty pregnant women (67% receiving public assistance; 70% Black, 6% Multiracial, 24% White) completed 10 days of ambulatory assessment, in which they answered smartphone-based surveys six times a day and wore a chest-band device (movisens EcgMove4) to monitor their heart rate, heart rate variability, and activity level. Feasibility and acceptability were evaluated using behavioral meta-data and participant feedback. Findings supported the feasibility and acceptability of mHealth methods: Participants answered approximately 75% of the surveys per day and wore the device for approximately 10 hours per day. Perceived burden was low. Notably, participants with higher reported stressors and financial strain reported lower burden associated with the protocol than participants with fewer life stressors, highlighting the feasibility of mHealth technology for monitoring prenatal stress among pregnant populations living with higher levels of contextual stressors. Findings support the use of mHealth technology to measure prenatal stress in real-world, daily life settings, which shows promise for informing scalable, technology-assisted interventions that may help to reduce health disparities by enabling more accessible and comprehensive care during pregnancy.

Feasibility of continuous smart health monitoring in pregnant population: A mixed-method approach

The utilization of smart monitoring technology offers potential for enhancing health outcomes, yet its feasibility and acceptance among Hispanic pregnant individuals remain uncertain. This is particularly crucial to investigate within the context of apparently healthy individuals identified as low risk, who still face a 10% likelihood of complications. Given their frequent underrepresentation in healthcare services and relative lack of attention, improving the feasibility of remote monitoring in this population could yield significant benefits. To address this gap, our study aimed to adapt and evaluate the practicality of a smart monitoring platform among healthy Hispanic pregnant women during the second and third trimesters of pregnancy, as well as one week following childbirth, a period when complications often arise. This longitudinal study followed n = 16 participants for an average of 17 weeks. Participants were instructed to wear the Oura ring for objective data collection, including activity, sleep, and heart rate, and to complete survey questions through REDcap to assess mental health and lifestyle factors. The study framework utilized the RE-AIM approach, with acceptability and adherence as key components of the feasibility evaluation. Our findings revealed that completion rates for biweekly and monthly surveys remained consistently high until after childbirth (approximately 80%), while daily question completion remained above 80% until 38th week of gestation, declining thereafter. The wearing rate of the Oura ring remained consistently above 80% until the 35th gestational week, decreasing to around 31% postpartum. Participants cited barriers to wearing the ring during the postpartum period, including difficulties managing the newborn, forgetfulness, and concerns about scratching the baby's skin. The enrollment rate was 71.42%, with an attrition rate of 6.25%. Thematic analysis of one-on-one interviews identified three main themes: personal desire for health improvement, social acceptability and support, and conditions influencing device/platform efficiency. In conclusion, while adherence varied based on gestational week and survey frequency, the study demonstrated strong acceptability of the smart monitoring platform among the study population, indicated by the high enrollment rate. Qualitative insights underscored the significance of personal motivation, social support, and device/platform efficiency in enhancing patient engagement with digital health monitoring during pregnancy, offering valuable considerations for future healthcare interventions in this domain.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Continuously monitoring the human machine? - A cross-sectional study to assess the acceptance of wearables in Germany

Background: Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Methods: Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Results: Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. Conclusion: The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.

Mitigating Health-Related Uncertainties During Pregnancy: The Role of Smart Health Monitoring Technologies

Pregnancy is a time filled with uncertainties, which can be challenging and lead to fear or anxiety for expectant parents. Health monitoring technologies that allow monitoring of the vital signs of both the mother and fetus offer a way to address health-related uncertainties. But are smart health monitoring technologies (SHMTs) actually an effective means to reduce uncertainties during pregnancy, or do they have the opposite effect? Using conceptual reasoning and phenomenological approaches grounded in existing literature, this Viewpoint explores the effects of SHMTs on health-related uncertainties during pregnancy. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.Using conceptual reasoning and phenomenological approaches grounded in existing literature, the effects of SHMTs on health-related uncertainties during pregnancy are explored. The argument posits that while SHMTs can alleviate some health-related uncertainties, they may also create new ones. This is particularly the case when the abundance of vital data overwhelms pregnant persons, leads to false-positive diagnoses, or raises concerns about the accuracy and analysis of data. Consequently, it is concluded that the use of SHMTs is not a cure-all for overcoming health-related uncertainties during pregnancy. Since the use of such monitoring technologies can introduce new uncertainties, it is important to carefully consider where and for what purpose they are used, use them sparingly, and promote a pragmatic approach to uncertainties.