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

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.

When pregnancy and pandemic coincide: changes in stress and anxiety over the course of pregnancy

BACKGROUND

Pregnant women experienced high levels of perceived stress and anxiety at the onset of the COVID-19 pandemic. However, the course of stress and anxiety in individual pregnant women during the pandemic is unknown.

METHODS

Participants were 1,087 women ≤20 weeks pregnant in April-May 2020 (T1) at recruitment into the Stony Brook COVID-19 Pregnancy Experiences (SB-COPE) Study, with additional assessments in July-August 2020 (T2) and October 2020 (T3). Growth mixture models conditioned on covariates were used to identify patterns of change over time in pandemic-related stress (originating from feeling unprepared for birth and fearing perinatal infection), pregnancy-specific stress, and anxiety symptoms.

RESULTS

A uniform pattern of change (i.e. one-class solution) in stress perceptions was observed over time. Participants showed the same functional form of decreases in all three types of stress perceptions over the course of their pregnancy and as the pandemic persisted. Initial level of stress did not predict change over time. Anxiety symptoms had a two-class solution in which 25% of participants had high and convex patterns of anxiety, and 75% had low levels with concave patterns.

DISCUSSION

Stress perceptions and anxiety patterns of change over the course of pregnancy during the COVID-19 pandemic were different. Therefore, to evaluate the well-being of pregnant women during a global health crisis, it is important to assess both stress perceptions and emotional stress responses (i.e. anxiety). Screening for anxiety symptoms in early pregnancy would be valuable as symptoms may not spontaneously decrease even when stressful conditions improve.

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.

Impacts of the COVID-19 pandemic on early pregnancy outcomes among women undergoing frozen-thawed embryo transfer: a retrospective cohort study

The effect of COVID-19 pandemic on early pregnancy outcomes among women undergoing frozen-thawed embryo transfer (FET) remains unclear. We aimed to evaluate whether early pregnancy outcomes were altered in patients undergoing FET during the pandemic. In this retrospective cohort study, women conceived through FET in 2016-2021 from two hospitals in China were included. The early pregnancy outcomes were compared using Logistic regression model, including biochemical pregnancy rate (BPR), clinical pregnancy rate (CPR), and early pregnancy loss rate (EPLR). A total of 16,669 (67.2%) and 6,113 (26.8%) FET cycles enrolled before and during the pandemic, respectively. Univariate analyses showed that women undergoing FET during the pandemic had significantly increased BPR (72.9% vs. 69.7%) and CPR (59.5% vs. 55.0%), and significantly decreased EPLR (13.7% vs. 16.7%) compared to pre-pandemic (all P < 0.001). Moreover, after adjustment, the results were in accordance with univariate analysis for CPR [adjusted OR (95%CI) = 1.08 (1.01-1.14)] and EPLR [adjusted OR (95%CI) = 0.82 (0.73-0.91)], while the statistical significance between BPR and the pandemic disappeared. In summary, women conceived by FET did not have a reduced possibility of clinical pregnancy and a higher risk of early pregnancy loss during the pandemic compared with the pre-pandemic.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed/Medline, Scopus, Embase, Web of Science, ERIC and Google Scholar. In our search strategy, 761 articles were returned. The exclusion/inclusion criteria were applied. Finally, 35 articles were selected for extracting data. These included six studies on stress monitoring, six on movement disorders, three on sleep tracking, three on blood pressure, two on heart disease, six on covid pandemic, three on safety and six on validation. The use of smartwatches has been found to be effective in diagnosing the symptoms of various diseases. In particular, smartwatches have shown promise in detecting heart diseases, movement disorders, and even early signs of COVID-19. Nevertheless, it should be emphasised that there is an ongoing discussion concerning the reliability of smartwatch diagnoses within healthcare systems. Despite the potential advantages offered by utilising smartwatches for disease detection, it is imperative to approach their data interpretation with prudence. The discrepancies in detection between smartwatches and their algorithms have important implications for healthcare use. The accuracy and reliability of the algorithms used are crucial, as well as high accuracy in detecting changes in health status by the smartwatches themselves. This calls for the development of medical watches and the creation of AI-hospital assistants. These assistants will be designed to help with patient monitoring, appointment scheduling, and medication management tasks. They can educate patients and answer common questions, freeing healthcare providers to focus on more complex tasks.

Contemporary Understanding of the Central Autonomic Nervous System in Fetal-Neonatal Transition

THE CRITICAL ROLE OF THE CENTRAL AUTONOMIC NERVOUS SYSTEM IN FETAL-NEONATAL TRANSITION: Sarah B. Mulkey, Adre dú Plessis Seminars in Pediatric Neurology Volume 28, December 2018, Pages 29-37 The objective of this article is to understand the complex role of the central autonomic nervous system in normal and complicated fetal-neonatal transition and how autonomic nervous system dysfunction can lead to brain injury. The central autonomic nervous system supports coordinated fetal transitional cardiovascular, respiratory, and endocrine responses to provide safe transition of the fetus at delivery. Fetal and maternal medical and environmental exposures can disrupt normal maturation of the autonomic nervous system in utero, cause dysfunction, and complicate fetal-neonatal transition. Brain injury may both be caused by autonomic nervous system failure and contribute directly to autonomic nervous system dysfunction in the fetus and newborn. The central autonomic nervous system has multiple roles in supporting transition of the fetus. Future studies should aim to improve real-time monitoring of fetal autonomic nervous system function and in supporting typical autonomic nervous system development even under complicated conditions.

ZotCare: a flexible, personalizable, and affordable mhealth service provider

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.

Rational use of drugs to alleviate adverse outcomes caused by COVID-19 quarantine in women with intrahepatic cholestasis of pregnancy

Purpose

This study aimed to investigate the impacts of home quarantine on pregnancy outcomes of women with intrahepatic cholestasis of pregnancy (ICP) during the COVID-19 outbreak and whether the rational use of drugs will change these impacts.

Methods

This multi-center study was conducted to compare the pregnancy outcomes in women with ICP between the home quarantine group and the non-home quarantine group in southwest China. Propensity score matching was performed to confirm the pregnancy outcomes of the medication group and the non-medication group in women with ICP during the epidemic period.

Results

A total of 3,161 women with ICP were enrolled in this study, including 816 in the home quarantine group and 2,345 in the non-home quarantine group. Women with ICP in the home quarantine group had worse pregnancy outcomes, such as a growing risk of gestational diabetes mellitus A1, fetal growth restriction, pre-eclampsia, preterm delivery, and even stillbirth. Drug therapy could alleviate some adverse pregnancy outcomes caused by home quarantine, including pre-eclampsia, preterm delivery, and meconium-stained amniotic fluid.

Conclusion

COVID-19 quarantine would increase the incidence of ICP and lead to adverse pregnancy outcomes in women with ICP. The rational use of drugs reduced some obstetrical complications and improved partial pregnancy outcomes. Our findings suggested that the government and hospitals should enhance their management and life guidance for women with ICP and speed up developing home quarantine guidelines.

Aggressive strategies of the COVID-19 pandemic on the apparel industry of Sri Lanka using structural equation modeling

During the COVID-19 crisis, the apparel industry faced many challenges. Aggressive cost-cutting strategies became a top priority, and in turn, these influenced stressors and adversely affected business sustainability. This study examines the impact of aggressive strategies during the COVID-19 pandemic on business sustainability in the apparel industry of Sri Lanka. Further, it investigates whether the relationship between aggressive cost-cutting strategies and business sustainability was mediated by employee stress, considering aggressive cost reduction strategies and workplace environmental changes. This was a cross-sectional study with data collected from 384 employees in the apparel industry in Sri Lanka. Structural Equation Modelling (SEM) was applied to analyze the direct and indirect effects of aggressive cost reduction strategies and workplace environmental changes on sustainability with mediating effects of stress. Aggressive cost reduction strategies (Beta = 1.317, p = 0.000) and environmental changes (Beta = 0.251, p = 0.000) led to an increase in employee stress but did not affect business sustainability. Thus, employee stress (Beta = -0.028, p = 0.594) was not a mediator in the relationship between aggressive cost-cutting strategies and business sustainability; business sustainability was not a dependent variable. The findings proved that managing workplace stress, particularly improving stressful working environments and aggressive cost reduction strategies, can enhance employee satisfaction. Thus, managing employee stress could be beneficial for policymakers to focus on the area(s) required to retain competent employees. Moreover, aggressive strategies are unsuitable to apply during crisis to enhance business sustainability. The findings provide additional knowledge to the existing literature, enabling employees and employers to predict causes of stress and serve as a significant knowledge base for further studies.

A Descriptive Comparative Pilot Study: Association Between Use of a Self-monitoring Device and Sleep and Stress Outcomes in Pregnancy

Pregnancy is a challenging time for maintaining quality sleep and managing stress. Digital self-monitoring technologies are popular because of assumed increased patient engagement leading to an impact on health outcomes. However, the actual association between wear time of such devices and improved sleep/stress outcomes remains untested. Here, a descriptive comparative pilot study of 20 pregnant women was conducted to examine associations between wear time (behavioral engagement) of self-monitoring devices and sleep/stress pregnancy outcomes. Women used a ring fitted to their finger to monitor sleep/stress data, with access to a self-monitoring program for an average of 9½ weeks. Based on wear time, participants were split into two engagement groups. Using a linear mixed-effects model, the high engagement group showed higher levels of stress and a negative trend in sleep duration and quality. The low engagement group showed positive changes in sleep duration, and quality and experienced below-normal sleep onset latency at the start of the pilot but trended toward normal levels. Engagement according to device wear time was not associated with improved outcomes. Further research should aim to understand how engagement with self-monitoring technologies impacts sleep/stress outcomes in pregnancy.

Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings

BACKGROUND

This study aimed to investigate the role of a time-domain Heart Rate Variability index (the root mean square of successive difference between NN intervals, rMSSD) as a predictor of the onset of postpartum depression. HRV has been related to an increased risk of depression in the general population. However, its role in pregnant women is not clear, and the potential use of smartphone applications to evaluate HRV in this population has not been investigated.

METHODS

In study 1, simultaneous electrocardiogram and smartphone photoplethysmography were collected. The rMSSD was determined from each recording to evaluate the accuracy of a smartphone application in the measurement of HRV. In study 2, 135 pregnant women provided rMSSD values measured through a smartphone application in the prepartum (second or third trimester) and filled in the Edinburgh Postnatal Depression Scale in the postpartum (one month after the childbirth).

RESULTS

Study 1 showed the excellent accuracy of the smartphone application in the measurement of rMSSD. Study 2 indicated that lower prepartum rMSSD predicted higher depressive symptoms in the postpartum (β = -0.217, p = 0.010) after controlling for prepartum depressive symptoms and other potential covariates.

LIMITATIONS

Artefacts (e.g., hand movements) might have corrupted the physiological signal registered.

CONCLUSION

This study showed that a reduced vagal tone, indexed by lower rMSSD, during pregnancy was a predictor of depressive symptoms one month after childbirth. The prepartum period may offer an important timeframe to implement preventive intervention on vagal modulation in order to prevent depressive symptoms in the postpartum.

Differentiated mental health patterns in pregnancy during COVID-19 first two waves in Sweden: a mixed methods study using digital phenotyping

To utilize modern tools to assess depressive and anxiety symptoms, wellbeing and life conditions in pregnant women during the first two waves of the COVID-19 pandemic in Sweden. Pregnant women (n = 1577) were recruited through the mobile application Mom2B. Symptoms of depression, anxiety and wellbeing were assessed during January 2020-February 2021. Movement data was collected using the phone's sensor. Data on Google search volumes for "Corona" and Covid-related deaths were obtained. Qualitative analysis of free text responses regarding maternity care was performed. Two peaks were seen for depressive symptoms, corresponding to the two waves. Higher prevalence of anxiety was only noted during the first wave. A moderating effect of the two waves in the association of depression, anxiety, and well-being with Covid deaths was noted; positive associations during the first wave and attenuated or became negative during the second wave. Throughout, women reported on cancelled healthcare appointments and worry about partners not being allowed in hospital. The association of mental health outcomes with relevant covariates may vary during the different phases in a pandemic, possibly due to adaptation strategies on a personal and societal/healthcare level. Digital phenotyping can help healthcare providers and governmental bodies to in real time monitor high-risk groups during crises, and to adjust the support offered.

The impact of the COVID-19 pandemic on physical activity and sedentary behavior during pregnancy: a prospective study

BACKGROUND

Prior studies evaluating the impact of the COVID-19 pandemic on pregnancy physical activity (PA) have largely been limited to internet-based surveys not validated for use in pregnancy.

METHODS

This study used data from the Pregnancy PA Questionnaire Validation study conducted from 2019-2021. A prospective cohort of 50 pregnant women completed the Pregnancy PA Questionnaire (PPAQ), validated for use in pregnancy, in early, mid, and late pregnancy and wore an ActiGraph GT3X-BT for seven days. COVID-19 impact was defined using a fixed date of onset (March 13, 2020) and a self-reported date. Multivariable linear mixed effects regression models adjusted for age, early pregnancy BMI, gestational age, and parity.

RESULTS

Higher sedentary behavior (14.2 MET-hrs/wk, 95% CI: 2.3, 26.0) and household/caregiving PA (34.4 MET-hrs/wk, 95% CI: 8.5, 60.3 and 25.9 MET-hrs/wk, 95% CI: 0.9, 50.9) and lower locomotion (-8.0 h/wk, 95% CI: -15.7, -0.3) and occupational PA (-34.5 MET-hrs/wk, 95% CI: -61.9, -7.0 and -30.6 MET-hrs/wk, 95% CI: -51.4, -9.8) was observed in middle and late pregnancy, respectively, after COVID-19 vs. before. There was no impact on steps/day or meeting American College of Obstetricians and Gynecologists guidelines.

CONCLUSIONS

Proactive approaches for the promotion of pregnancy PA during pandemic-related restrictions are critically needed.

Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis

OBJECTIVES

The COVID-19 pandemic has significantly affected healthcare systems and daily well-being. However, the reports of the indirect impacts of the pandemic on preterm birth remain conflicting. We performed a meta-analysis to examine whether the pandemic altered the risk of preterm birth.

STUDY DESIGN

This was a systematic review and meta-analysis of the previous literature.

METHODS

We searched MEDLINE and Embase databases until March 2022 using appropriate keywords and extracted 63 eligible studies that compared preterm between the COVID-19 pandemic period and the prepandemic period. A random effects model was used to obtain the pooled odds of each outcome. The study protocol was registered with PROSPERO (No. CRD42022326717).

RESULTS

The search identified 3827 studies, of which 63 reports were included. A total of 3,220,370 pregnancies during the COVID-19 pandemic period and 6,122,615 pregnancies during the prepandemic period were studied. Compared with the prepandemic period, we identified a significant decreased odds of preterm birth (PTB; <37 weeks' gestation; pooled odds ratio [OR; 95% confidence interval (CI)] = 0.96 [0.94, 0.98]; I² = 78.7%; 62 studies) and extremely PTB (<28 weeks' gestation; pooled OR [95% CI] = 0.92 [0.87, 0.97]; I² = 26.4%; 25 studies) during the pandemic, whereas there was only a borderline significant reduction in the odds of very PTB (<32 weeks' gestation; pooled OR [95% CI] = 0.93 [0.86, 1.01]; I² = 90.1%; 33 studies) between the two periods. There was significant publication bias for PTB.

CONCLUSION

Pooled results suggested the COVID-19 pandemic was associated with preterm birth, although there was only a borderline significant reduction for very PTB during the pandemic compared with the prepandemic period. Large studies showed conflicting results, and further research on whether the change is related to pandemic mitigation measures was warranted.

Monitoring one heart to help two: heart rate variability and resting heart rate using wearable technology in active women across the perinatal period

BACKGROUND

Characterizing normal heart rate variability (HRV) and resting heart rate (RHR) in healthy women over the course of a pregnancy allows for further investigation into disease states, as pregnancy is the ideal time period for these explorations due to known decreases in cardiovascular health. To our knowledge, this is the first study to continuously monitor HRV and RHR using wearable technology in healthy pregnant women.

METHODS

A total of 18 healthy women participated in a prospective cohort study of HRV and RHR while wearing a WHOOP® strap prior to conception, throughout pregnancy, and into postpartum. The study lasted from March 2019 to July 2021; data were analyzed using linear mixed models with splines for non-linear trends.

RESULTS

Eighteen women were followed for an average of 405.8 days (SD = 153). Minutes of logged daily activity decreased from 28 minutes pre-pregnancy to 14 minutes by third trimester. A steady decrease in daily HRV and increase in daily RHR were generally seen during pregnancy (HRV Est. = - 0.10, P < 0.0001; RHR Est. = 0.05, P < 0.0001). The effect was moderated by activity minutes for both HRV and RHR. However, at 49 days prior to birth there was a reversal of these indices with a steady increase in daily HRV (Est. = 0.38, P < 0.0001) and decrease in daily RHR (Est. = - 0.23, P < 0.0001), regardless of activity level, that continued into the postpartum period.

CONCLUSIONS

In healthy women, there were significant changes to HRV and RHR throughout pregnancy, including a rapid improvement in cardiovascular health prior to birth that was not otherwise known. Physical activity minutes of any type moderated the known negative consequences of pregnancy on cardiovascular health. By establishing normal changes using daily data, future research can now evaluate disease states as well as physical activity interventions during pregnancy and their impact on cardiovascular fitness.

Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study

OBJECTIVE

We investigated how a commercially available smartwatch that measures peripheral blood oxygen saturation (SpO₂) can detect hypoxemia compared to a medical-grade pulse oximeter.

METHODS

We recruited 24 healthy participants. Each participant wore a smartwatch (Apple Watch Series 6) on the left wrist and a pulse oximeter sensor (Masimo Radical-7) on the left middle finger. The participants breathed via a breathing circuit with a three-way non-rebreathing valve in three phases. First, in the 2-minute initial stabilization phase, the participants inhaled the ambient air. Then in the 5-minute desaturation phase, the participants breathed the oxygen-reduced gas mixture (12% O₂), which temporarily reduced their blood oxygen saturation. In the final stabilization phase, the participants inhaled the ambient air again until SpO₂ returned to normal values. Measurements of SpO₂ were taken from the smartwatch and the pulse oximeter simultaneously in 30-s intervals.

RESULTS

There were 642 individual pairs of SpO₂ measurements. The bias in SpO₂ between the smartwatch and the oximeter was 0.0% for all the data points. The bias for SpO₂ less than 90% was 1.2%. The differences in individual measurements between the smartwatch and oximeter within 6% SpO₂ can be expected for SpO₂ readings 90%-100% and up to 8% for SpO₂ readings less than 90%.

CONCLUSIONS

Apple Watch Series 6 can reliably detect states of reduced blood oxygen saturation with SpO₂ below 90% when compared to a medical-grade pulse oximeter. The technology used in this smartwatch is sufficiently advanced for the indicative measurement of SpO₂ outside the clinic.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04780724.

Prenatal stress assessment using heart rate variability and salivary cortisol: A machine learning-based approach

OBJECTIVE

To develop a machine learning algorithm utilizing heart rate variability (HRV) and salivary cortisol to detect the presence of acute stress among pregnant women that may be applied to future clinical research.

METHODS

ECG signals and salivary cortisol were analyzed from 29 pregnant women as part of a crossover study involving a standardized acute psychological stress exposure and a control non-stress condition. A filter-based features selection method was used to identify the importance of different features [heart rate (HR), time- and frequency-domain HRV parameters and salivary cortisol] for stress assessment and reduce the computational complexity. Five machine learning algorithms were implemented to assess the presence of stress with and without salivary cortisol values.

RESULTS

On graphical visualization, an obvious difference in heart rate (HR), HRV parameters and cortisol were evident among 17 participants between the two visits, which helped the stress assessment model to distinguish between stress and non-stress exposures with greater accuracy. Eight participants did not display a clear difference in HR and HRV parameters but displayed a large increase in cortisol following stress compared to the non-stress conditions. The remaining four participants did not demonstrate an obvious difference in any feature. Six out of nine features emerged from the feature selection method: cortisol, three time-domain HRV parameters, and two frequency-domain parameters. Cortisol was the strongest contributing feature, increasing the assessment accuracy by 10.3% on average across all five classifiers. The highest assessment accuracy achieved was 92.3%, and the highest average assessment accuracy was 76.5%.

CONCLUSION

Salivary cortisol contributed a significant increase in accuracy of the assessment model compared to using a range of HRV parameters alone. Our machine learning model demonstrates acceptable accuracy in detection of acute stress among pregnant women when combining salivary cortisol with HR and HRV parameters.

Impact of COVID-19 pandemic on birth outcomes: A retrospective cohort study in Nanjing, China

Introduction

The coronavirus disease 2019 (COVID-19) pandemic have significantly affected health care systems and daily wellbeing. However, the indirect impacts of the pandemic on birth outcomes are not fully understood. We aimed to examine whether the pandemic altered risk of adverse birth outcomes.

Methods

This retrospective cohort study included all singleton births during 2016–2020 identified in Women's Hospital of Nanjing Medical University. We compared birth outcomes during COVID-19 pandemic (January–December 2020) with before the pandemic (January–December 2016–2019) using Logstic regression adjusted for confounders.

Results

A total of 19,792 and 92,750 births occurred during and before the pandemic, respectively. Maternal characteristics were similar between groups, except maternal age was higher in pandemic cohort. We observed a reduction in preterm birth (PTB, <37 weeks) during the pandemic [5.9 vs. 5.1%, OR (95%CI) = 0.86 (0.80, 0.92)], but the difference disappeared after multivariable adjustment [adjusted OR (95%CI) = 1.02 (0.94, 1.11)]. Moreover, full term infants born during the pandemic had lower birth weights than those born before the pandemic [adjusted β (95% CI) = −17.4 (−23.9, −10.8)]. Consistently, the risks of low birthweight (LBW, <2,500 g) and small for gestational age (SGA, < P₁₀) were increased [LBW: adjusted OR (95%CI) = 1.13 (1.02, 1.24); SGA: adjusted OR (95%CI) = 1.11 (1.02, 1.21)], and the risks of macrosomia (≥4,000 g) and large for gestational age (LGA, ≥P₉₀) were decreased in the pandemic cohort [macrosomia: adjusted OR (95%CI) = 0.82 (0.77, 0.88); LGA: adjusted OR (95%CI) = 0.73 (0.69, 0.77)].

Conclusion

In this study, we observed no change in preterm birth and a decrease in birth weight of full term infants during the pandemic in Nanjing, China.

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

Background

Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.

Objective

The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.

Methods

A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.

Results

HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).

Conclusions

Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.

"Wearables on vogue": a scoping review on wearables on physical activity and sedentary behavior during COVID-19 pandemic

Background

Wearables are intriguing way to promote physical activity and reduce sedentary behavior in populations with and without chronic diseases. However, the contemporary evidence demonstrating the effectiveness of wearables on physical health during the COVID-19 pandemic has yet to be explored.

Aim

The present review aims to provide the readers with a broader knowledge of the impact of wearables on physical health during the pandemic.

Methods

Five electronic databases (Web of Science, Scopus, Ovid Medline, Cumulative Index to Nursing and Allied Health Literature and Embase) were searched. The eligibility criteria of the studies to be included were based on PICOT criteria: population (adults, children and elderly), intervention (wearable, smartphones), comparison (any behavioral intervention), outcome (physical activity or sedentary behavior levels) and time frame (between December 1st, 2019 and November 19th, 2021). The present scoping review was framed as per the guidelines of the Arksey and O'Malley framework.

Results

Of 469 citations initially screened, 17 articles were deemed eligible for inclusion and potential scoping was done. Smartphone-based applications with inbuilt accelerometers were commonly used, while a few studies employed smart bands, smartwatches for physical health monitoring. Most of the studies observed the increased use of wearables in healthy adults followed by elderly, children and pregnant women. Considerable reduction (almost-50%) in physical activity during the pandemic: daily step count (- 2812 steps/min), standing (- 32.7%) and walking (- 52.2%) time was found.

Conclusion

Wearables appears to be impending means of improving physical activity and reducing sedentary behavior remotely during the COVID-19 pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11332-021-00885-x.

Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach

The COVID-19 pandemic seems to be entering its final stage. However, to restore normal life, the applications of smart technologies are still necessary. Therefore, this research is dedicated to exploring the applications of smart technologies that can support mobile healthcare after the COVID-19 pandemic. To this end, this study compares smart technology applications to support mobile healthcare within the COVID-19 pandemic with those before the pandemic, so as to estimate possible developments in this field. In addition, to quantitatively assess and compare smart technology applications that may support mobile healthcare after the COVID-19 pandemic, the calibrated fuzzy geometric mean (CFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is applied. The proposed methodology has been applied to evaluate and compare nine potential smart technology applications for supporting mobile healthcare after the COVID-19 pandemic. According to the experimental results, "vaccine passport and related applications" and "smart watches" were the most suitable smart technology applications for supporting mobile healthcare after the COVID-19 pandemic.

The Clinical Application and Prospect of Smart Prenatal Care and Postpartum Recovery

Scientific and technological advancement has increased the requirement for modern medical systems, leading to smartphone-based intelligent prenatal care and postpartum recovery. This kind of prenatal care and postpartum recovery including a remote monitoring system for fetal heart monitoring, blood glucose, and weight overcomes the restrictions of time and space and provides all-round, convenient, rapid, and accurate services to the medical systems, doctors, and pregnant women. This paper reviews the current research on intelligent medical services for pregnant women, particularly for prenatal care and postpartum recovery.

What may happen if you are pregnant during Covid-19 lockdown? A retrospective study about peripartum outcomes

BACKGROUND

One of the provisions implemented to contain the spread of Covid-19 infections in Italy was the lockdown. Effects of the lockdown on childbirth outcomes and on the well-being of both the mother and the child have not yet been defined. An inadequate diet during pregnancy and a reduced physical activity can predispose women to become overweight or obese and trigger the development of various complications and maternal-fetal adverse outcomes.

METHODS

This is a retrospective study including all consecutive patients who delivered at University Hospital Maggiore della Carità in Novara, Italy, in April-May 2017 (group 1, n=294), a period prior to the pandemic, and during the same months in 2020 (group 2, n=256) during and immediately after lockdown. Clinical data were extracted from The Report "Childbirth Assistance Certificate (CedAP) - Birth Event Analysis".

RESULTS

Demographic characteristics were similar between the two study groups, except for a decreased number of married couples in group 2 (p-value 0.018) and an increased percentage of patients with clinical checkups at Family Planning facilities in 2020 (p-value 0.04). The number of hospitalizations during pregnancy was 26 (8.9%) vs 10 (3.9%) with a significative reduction during 2020 (p-value 0.004). Regarding obstetric outcomes, we observed a significant increase in induction of labour in 2020 (23.9% vs 35.9%; p-value 0. 002), a reduction of amniorrhexis (11.3% vs 5.5% p-value 0.015), a reduction of supine positions with an increase of vertical and all fours positions in 2020 (49.3% vs 61.9% and 9.5% vs 12.4% respectively, p 0.023), and a reduction of left occipito-anterior presented part (63.2% vs 55.4%) in favor of right occipito-anterior (34.7% vs 41.2%, p-value 0.019).

CONCLUSIONS

There were no significant differences either for antepartum or intrapartum complications. Long-term studies are needed to evaluate psychological, behavioral, and epigenetic effects of maternal physical inactivity on obstetric outcomes.

Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

Women's Depressive Symptoms during the COVID-19 Pandemic: The Role of Pregnancy

The Coronavirus Disease 2019 (COVID-19) pandemic has multiple ramifications for pregnant women. Untreated depression during pregnancy may have long-term effects on the mother and offspring. Therefore, delineating the effects of pregnancy on the mental health of reproductive-age women is crucial. This study aims to determine the risk for depressive symptoms in pregnant and non-pregnant women during COVID-19, and to identify its bio-psycho-social contributors. A total of 1114 pregnant and 256 non-pregnant women were recruited via social media in May 2020 to complete an online survey that included depression and anxiety questionnaires, as well as demographic, obstetric and COVID-19-related questionnaires. Pregnant women also completed the Pandemic-Related Pregnancy Stress Scale (PREPS). Pregnant women reported fewer depressive symptoms and were less concerned that they had COVID-19 than non-pregnant women. Among pregnant women, risk factors for depression included lower income, fewer children, unemployment, thinking that one has COVID-19, high-risk pregnancy, earlier gestational age, and increased pregnancy-related stress. Protective factors included increased partner support, healthy behaviors, and positive appraisal of the pregnancy. Thus, being pregnant is associated with reduced risk for depressive symptoms during the pandemic. Increased social support, engaging in health behaviors and positive appraisal may enhance resilience. Future studies of pregnant versus non-pregnant women could clarify the role of pregnancy during stressful events, and clarify aspects of susceptibility and resilience during pregnancy.