Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction
Objectives: The autonomic nervous system (ANS) plays a central role in dynamic adaptation during pregnancy in accordance with the pregnancy demands which otherwise can lead to various pregnancy complications. Despite the importance of understanding the ANS function during pregnancy, the literature l...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1293946/full |
| _version_ | 1797491445348696064 |
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| author | Zahra Sharifi-Heris Zhongqi Yang Amir M. Rahmani Amir M. Rahmani Michelle A. Fortier Michelle A. Fortier Hamid Sharifiheris Miriam Bender |
| author_facet | Zahra Sharifi-Heris Zhongqi Yang Amir M. Rahmani Amir M. Rahmani Michelle A. Fortier Michelle A. Fortier Hamid Sharifiheris Miriam Bender |
| author_sort | Zahra Sharifi-Heris |
| collection | DOAJ |
| description | Objectives: The autonomic nervous system (ANS) plays a central role in dynamic adaptation during pregnancy in accordance with the pregnancy demands which otherwise can lead to various pregnancy complications. Despite the importance of understanding the ANS function during pregnancy, the literature lacks sufficiency in the ANS assessment. In this study, we aimed to identify the heart rate variability (HRV) function during the second and third trimesters of pregnancy and 1 week after childbirth and its relevant predictors in healthy pregnant Latina individuals in Orange County, CA.Materials and methods:N = 16 participants were enrolled into the study from which N = 14 (N = 13 healthy and n = 1 complicated) participants proceeded to the analysis phase. For the analysis, we conducted supervised machine learning modeling including the hierarchical linear model to understand the association between time and HRV and random forest regression to investigate the factors that may affect HRV during pregnancy. A t-test was used for exploratory analysis to compare the complicated case with healthy pregnancies.Results: The results of hierarchical linear model analysis showed a significant positive relationship between time (day) and average HRV (estimated effect = 0.06; p < 0.0001), regardless of being healthy or complicated, indicating that HRV increases during pregnancy significantly. Random forest regression results identified some lifestyle and sociodemographic factors such as activity, sleep, diet, and mental stress as important predictors for HRV changes in addition to time. The findings of the t-test indicated that the average weekly HRV of healthy and non-healthy subjects differed significantly (p < 0.05) during the 17 weeks of the study.Conclusion: It is imperative to focus our attention on potential autonomic changes, particularly the possibility of increased parasympathetic activity as pregnancy advances. This observation may challenge the existing literature that often suggests a decline in parasympathetic activity toward the end of pregnancy. Moreover, our findings indicated the complexity of HRV prediction, involving various factors beyond the mere passage of time. To gain a more comprehensive understanding of this dynamic state, future investigations should delve into the intricate relationship between autonomic activity, considering diverse parasympathetic and sympathetic metrics, and the progression of pregnancy. |
| first_indexed | 2024-03-10T00:47:36Z |
| format | Article |
| id | doaj.art-fa6be6b973e94b9ba9af22486fe2177b |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-03-10T00:47:36Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-fa6be6b973e94b9ba9af22486fe2177b2023-11-23T14:57:28ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-11-011410.3389/fphys.2023.12939461293946Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication predictionZahra Sharifi-Heris0Zhongqi Yang1Amir M. Rahmani2Amir M. Rahmani3Michelle A. Fortier4Michelle A. Fortier5Hamid Sharifiheris6Miriam Bender7Sue and Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, United StatesDepartment of Computer Science, University of California, Irvine, Irvine, CA, United StatesSue and Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, United StatesDepartment of Computer Science, University of California, Irvine, Irvine, CA, United StatesSue and Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, United StatesCenter on Stress and Health, University of California, Irvine, Irvine, CA, United StatesDepartment of Computer Science, Azad University of Ahar, Ahar, IranSue and Bill Gross School of Nursing, University of California, Irvine, Irvine, CA, United StatesObjectives: The autonomic nervous system (ANS) plays a central role in dynamic adaptation during pregnancy in accordance with the pregnancy demands which otherwise can lead to various pregnancy complications. Despite the importance of understanding the ANS function during pregnancy, the literature lacks sufficiency in the ANS assessment. In this study, we aimed to identify the heart rate variability (HRV) function during the second and third trimesters of pregnancy and 1 week after childbirth and its relevant predictors in healthy pregnant Latina individuals in Orange County, CA.Materials and methods:N = 16 participants were enrolled into the study from which N = 14 (N = 13 healthy and n = 1 complicated) participants proceeded to the analysis phase. For the analysis, we conducted supervised machine learning modeling including the hierarchical linear model to understand the association between time and HRV and random forest regression to investigate the factors that may affect HRV during pregnancy. A t-test was used for exploratory analysis to compare the complicated case with healthy pregnancies.Results: The results of hierarchical linear model analysis showed a significant positive relationship between time (day) and average HRV (estimated effect = 0.06; p < 0.0001), regardless of being healthy or complicated, indicating that HRV increases during pregnancy significantly. Random forest regression results identified some lifestyle and sociodemographic factors such as activity, sleep, diet, and mental stress as important predictors for HRV changes in addition to time. The findings of the t-test indicated that the average weekly HRV of healthy and non-healthy subjects differed significantly (p < 0.05) during the 17 weeks of the study.Conclusion: It is imperative to focus our attention on potential autonomic changes, particularly the possibility of increased parasympathetic activity as pregnancy advances. This observation may challenge the existing literature that often suggests a decline in parasympathetic activity toward the end of pregnancy. Moreover, our findings indicated the complexity of HRV prediction, involving various factors beyond the mere passage of time. To gain a more comprehensive understanding of this dynamic state, future investigations should delve into the intricate relationship between autonomic activity, considering diverse parasympathetic and sympathetic metrics, and the progression of pregnancy.https://www.frontiersin.org/articles/10.3389/fphys.2023.1293946/fullautonomic nervous systemhealthy pregnancypregnancy complicationssmart wearable technologyphysiology |
| spellingShingle | Zahra Sharifi-Heris Zhongqi Yang Amir M. Rahmani Amir M. Rahmani Michelle A. Fortier Michelle A. Fortier Hamid Sharifiheris Miriam Bender Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction Frontiers in Physiology autonomic nervous system healthy pregnancy pregnancy complications smart wearable technology physiology |
| title | Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction |
| title_full | Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction |
| title_fullStr | Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction |
| title_full_unstemmed | Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction |
| title_short | Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction |
| title_sort | phenotyping the autonomic nervous system in pregnancy using remote sensors potential for complication prediction |
| topic | autonomic nervous system healthy pregnancy pregnancy complications smart wearable technology physiology |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1293946/full |
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