Being 'A Google Mom' or securely monitored at home: Perceptions of remote monitoring in maternity care

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

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 collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

Application and Clinical Analysis of Remote Fetal Heart Rate Monitoring Platform in Continuous Fetal Heart Rate Monitoring Images

Fetal heart sound is an important part of fetal monitoring and has attracted extensive research and attention from scholars at home and abroad in recent years. The fetal heart rate, extracted from the fetal heart sound signal, is one of the important indicators that reflect the health of the fetus in the womb. In this study, a maternal-fetal Holter monitor based on f ECG technology was used to collect maternal heart rate, fetal heart rate, and uterine contractions signals, isolate the fetal heart rate, and design an algorithm to extract the fetal heart rate baseline, acceleration, variation, wake-up cycle, and nonlinear parameters. Using statistical methods to analyze the average value and range of various characteristic parameters of fetal heart rate under continuous long-term monitoring, the results show that the baseline has a downward trend from 10 o'clock in the night to 4 o'clock in the morning and is the lowest around 2 o'clock in the morning. The area and acceleration time were significantly higher than those in the suspicious group. However, there was no significant difference in the number of acceleration values between the two groups; the proportion of small mutations in the normal group was lower than that of the suspicious group and the proportion of medium mutations was higher than that of the suspicious group. There is no statistically significant difference in maternal age, gestational age at childbirth, pregnancy comorbidities, and complication rates in the five-level interpretation system of ACOG (2009), RCOG (2007), SOGC (2007), and the United States (2007). The difference of pregnancy and parity in various images was statistically significant, P < 0.05. The second type of fetal heart rate monitoring images appeared in the highest among the diagnostic standards, and the difference in the second type of fetal heart rate monitoring images between the various diagnostic standards was statistically significant, P ≤ 0.001.

Pregnant women's daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

BACKGROUND

Technology enables the continuous monitoring of personal health parameter data during pregnancy regardless of the disruption of normal daily life patterns. Our research group has established a project investigating the usefulness of an Internet of Things-based system and smartwatch technology for monitoring women during pregnancy to explore variations in stress, physical activity and sleep. The aim of this study was to examine daily patterns of well-being in pregnant women before and during the national stay-at-home restrictions related to the COVID-19 pandemic in Finland.

METHODS

A longitudinal cohort study design was used to monitor pregnant women in their everyday settings. Two cohorts of pregnant women were recruited. In the first wave in January-December 2019, pregnant women with histories of preterm births (gestational weeks 22-36) or late miscarriages (gestational weeks 12-21); and in the second wave between October 2019 and March 2020, pregnant women with histories of full-term births (gestational weeks 37-42) and no pregnancy losses were recruited. The final sample size for this study was 38 pregnant women. The participants continuously used the Samsung Gear Sport smartwatch and their heart rate variability, and physical activity and sleep data were collected. Subjective stress, activity and sleep reports were collected using a smartphone application developed for this study. Data between February 12 to April 8, 2020 were included to cover four-week periods before and during the national stay-at-home restrictions. Hierarchical linear mixed models were exploited to analyze the trends in the outcome variables.

RESULTS

The pandemic-related restrictions were associated with changes in heart rate variability: the standard deviation of all normal inter-beat intervals (p = 0.034), low-frequency power (p = 0.040) and the low-frequency/high-frequency ratio (p = 0.013) increased compared with the weeks before the restrictions. Women's subjectively evaluated stress levels also increased significantly. Physical activity decreased when the restrictions were set and as pregnancy proceeded. The total sleep time also decreased as pregnancy proceeded, but pandemic-related restrictions were not associated with sleep. Daily rhythms changed in that the participants overall started to sleep later and woke up later.

CONCLUSIONS

The findings showed that Finnish pregnant women coped well with the pandemic-related restrictions and lockdown environment in terms of stress, physical activity and sleep.

Telehealth: improving maternity services by modern technology

Hypertension is considered one of the most common medical disorders causing complexities in pregnancy. It could be a newly developed pregnancy-induced hypertension (PIH) or a pre-existing hypertension developing into superimposed pre-eclamptic toxaemia. PIH affects approximately 10% of pregnancies and can have a serious impact on both maternal and fetal well-being; hence requires frequent monitoring and timely intervention. National Institute for Health and Care Excellence (NICE) guidelines recommends once or twice weekly monitoring of blood pressure for such patients. The required frequent monitoring comes with certain implications for patients and healthcare services. An average patient with PIH would need to see her healthcare provider once or twice a week until delivery and 6 weeks thereafter. This certainly increases pressure on limited National Health Service (NHS) resources. Home-based monitoring using Telehealth technology can represent a potential solution for achieving good-quality care for the patient without increasing the workload for healthcare providers. We used 'Florence', a text-based technology platform to support home monitoring. We tested its acceptability, feasibility and safety to replace face-to-face appointments for blood pressure monitoring in selected patients with PIH. We implemented our project in three progressive phases using a plan, do, study, act methodology. Florence, telehealth technology was used for blood pressure monitoring instead of face-to-face appointments, and the effect of this innovative technology on the services and the patient experience was studied and necessary modifications were made before progression into the next phase. We recruited 75 patients over 12 months through the progressive phases and replaced around 800 face-to-face appointments by remotely supervised monitoring sessions with Florence successfully, with improved care and patient satisfaction. We also achieved better compliance with the NICE guidelines for blood pressure monitoring in PIH. Our project concluded that Telehealth can be a potential solution for improving care in maternity services, with lesser burden on NHS resources.