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

Sarhaddi, Fatemeh; Azimi, Iman; Axelin, Anna; Niela‐Vilén, Hannakaisa; Liljeberg, Pasi; Rahmani, Amir M.

doi:10.2196/33458

Cited by 24 publications

(15 citation statements)

References 43 publications

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“…In particular, aggregate nighttime HR and RR were higher among individuals in pregnancy related-condition groups compared to healthy individuals -a difference that was observed early in pregnancy through birth, while HRV steadily decreased with gestational age, but showed an inflection at approximately nine weeks leading up to delivery. This phenomenon, that could reflect an individual's physiological readiness for delivery, has been observed in the aggregate among other studies [18,23,26]. However, at the n-of-1 level, this aggregate trend is highly heterogeneous, even among healthy pregnant individuals.…”

Section: Discussionsupporting

confidence: 63%

“…With the ubiquity of the smartphone [15], and the increasing popularity of wearable smart devices [16] such as the smartwatch and the smart ring, the use of such tools for monitoring pregnancy-related signs and symptoms and greatly transforming current knowledge of this important metamorphosis is a potential reality. While the body of research involving personal DHTs in pregnancy is small, a number of small preliminary studies have shown potential utility in using wearables for tracking activity, sleep, and physiologic metrics such as HR and HRV as predictors of pregnancy-related outcomes such as preterm birth and delivery readiness [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps

Goodday,

Yang,

Karlin

et al. 2024

Preprint

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Wearables, apps and other remote smart devices can capture rich, objective physiologic, metabolic, and behavioral information that is particularly relevant to pregnancy. The objectives of this paper were to 1) characterize individual level pregnancy self-reported symptoms and objective features from wearables compared to the aggregate; 2) determine whether pregnancy self-reported symptoms and objective features can differentiate pregnancy-related conditions; and 3) describe associations between self-reported symptoms and objective features. Data are from the Better Understanding the Metamorphosis of Pregnancy study, which followed individuals from preconception to three-months postpartum. Participants (18-40 years) were provided with an Oura smart ring, a Garmin smartwatch, and a Bodyport Cardiac Scale. They also used a study smartphone app with surveys and tasks to measure symptoms. Analyses included descriptive spaghetti plots for both individual-level data and cohort averages for select weekly reported symptoms and objective measures from wearables. This data was further stratified by pregnancy-related clinical conditions such as preeclampsia and preterm birth. Mean Spearman correlations between pairs of self-reported symptoms and objective features were estimated. Self-reported symptoms and objective features during pregnancy were highly heterogeneous between individuals. While some aggregate trends were notable, including an inflection in heart rate variability approximately eight weeks prior to delivery, these average trends were highly variable at the n-of-1 level, even among healthy individuals. Pregnancy conditions were not well differentiated by objective features. With the exception of self-reported swelling and body fluid volume, self-reported symptoms and objective features were weakly correlated (mean Spearman correlations <0.1). High heterogeneity and complexities of associations between subjective experiences and objective features across individuals pose challenges for researchers and highlights the dangers in reliance on aggregate approaches in the use of wearable data in pregnant individuals. Innovation in machine learning and AI approaches at the n-of-1 level could help to accelerate the field.

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Section: Discussionsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps

Goodday,

Yang,

Karlin

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…We used data from late pregnancy (gestational week 36) and 12 weeks postpartum for loneliness prediction. However, it is known that physiological health parameters such as HRV and physical activity change during pregnancy and the postpartum period [43, 44], e.g., physical activity decreases during pregnancy. Generalizing our predictive models to the whole pregnancy requires using data from additional weeks during pregnancy and the postpartum period.…”

Section: Discussionmentioning

confidence: 99%

Predicting maternal social loneliness by passive sensing with wearable devices

Sarhaddi

Azimi

Niela‐Vilén

et al. 2022

Preprint

Self Cite

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Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Objective The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period based on and to identify the important objective physiological parameters in loneliness detection. Methods We conducted a longitudinal study using smartwatches to continuously collect physiological data from 31 women during pregnancy and the postpartum period. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire in gestational week 36 and again at 12 weeks postpartum. Responses to this questionnaire and the background information of the participants were collected via our customized cross-platform mobile application. We leveraged participants’ smartwatch data from the 7 days before and the day of their completion of the UCLA questionnaire for loneliness prediction. We categorized the loneliness scores from the UCLA questionnaire as loneliness (scores >= 12) and non-loneliness (scores < 12). We developed decision tree and gradient boosting models to predict loneliness. We evaluated the models by using a leave-one-participant-out cross validation. Moreover, we discussed the importance of extracted health parameters in our models for loneliness prediction. Results The gradient boosting and decision tree models predicted maternal social loneliness with weighted F1 scores of 0.871 and 0.897, respectively. Our results also show that loneliness is highly associated with activity intensity, activity distribution during the day, resting heart rate (HR), and resting heart rate variability (HRV). Conclusion Our results show the potential benefit and feasibility of using passive sensing with a smartwatch to predict maternal loneliness. Our developed machine learning models achieved a high F1 score for loneliness prediction. We also show that intensity of activity, activity pattern, and resting HR and HRV are good predictors of loneliness. These results indicate the intervention opportunities made available by wearable devices and predictive models to improve maternal well-being by early detection of loneliness.

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“…Final multivariable HRV models were random‐intercept restricted maximum likelihood mixed models that included each of the substance use reports (alcohol, nicotine, and marijuana). Based on the literature, models were further adjusted for subject (level‐2) factors of pregnancy status (pregnant vs. postpartum) and age (Sarhaddi et al, 2022; Shaffer & Ginsberg, 2017). Potential interaction effects between pregnancy status and reports of substance use were examined, but were not significant.…”

Section: Methodsmentioning

confidence: 99%

The effect of the COVID‐19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

Ruyak

Roberts

Chambers

et al. 2023

Alcohol: Clinical and Experimental Research

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Background:The SARS-CoV-2/COVID-19 pandemic has been associated with increased stress levels and higher alcohol use, including in pregnant and postpartum women. In the general population, alcohol use is associated with dysregulation in the autonomic nervous system (ANS), which is indexed by heart rate variability (HRV).The objectives of this study were to: (1) characterize changes in substance use during the SARS-CoV-2/COVID-19 pandemic via a baseline self-report survey followed by mobile ecological momentary assessment (mEMA) of substance use; and (2) examine the associations between momentary substance use and ambulatory HRV measures in pregnant and postpartum women.Methods: Pregnant and postpartum women were identified from the ENRICH-2 prospective cohort study. Participants were administered a baseline structured phone interview that included the Coronavirus Perinatal Experiences (COPE) survey and ascertained the prevalence of substance use. Over a 14-day period, momentary substance use was assessed three times daily, and HRV measurements were captured via wearable electronics. Associations between momentary substance use and HRV measures (root mean square of successive differences [RMSSD] and low frequency/ high frequency [LF/HF] ratio) were examined using a mixed effects model that included within-subject (WS) and between-subject (BS) effects and adjusted for pregnancy status and participant age. Results:The sample included 49 pregnant and 22 postpartum women. From a combination of a baseline and 14-day mEMA surveys, 21.2% reported alcohol use, 16.9% reported marijuana use, and 8.5% reported nicotine use. WS effects for momentary alcohol use were associated with the RMSSD (β = −0.14; p = 0.005) and LF/HF ratio (β = 0.14; p = 0.01) when controlling for pregnancy status and maternal age. No significant associations were observed between HRV measures and instances of marijuana or nicotine use.Conclusions: These findings highlight the negative effect of the SARS-CoV-2/ COVID-19 pandemic on the psychological health of pregnant and postpartum women associated with substance use, and in turn, ANS dysregulation, which potentially puts some women at risk of developing a substance use disorder.

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

Cited by 24 publications

References 43 publications

Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps

Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps

Predicting maternal social loneliness by passive sensing with wearable devices

The effect of the COVID‐19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

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