Trends in Heart Rate and Heart Rate Variability during Pregnancy and Three Months Postpartum: Continuous Monitoring in a Free-Living Context (Preprint)

2021 ◽  
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilen ◽  
Pasi Liljeberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Time Domain ◽  
Second Trimester ◽  
Third Trimester ◽  
Non Invasive ◽  
The Third ◽  
Invasive Method ◽  
Frequency Power

BACKGROUND Heart rate variability (HRV) is a non-invasive method reflecting autonomic nervous system (ANS) regulations. Altered HRV is associated with adverse mental or physical health complications. ANS also has a central role in physiological adaption during pregnancy causing normal changes in HRV. OBJECTIVE Assessing trends in heart rate (HR) and HRV parameters as a non-invasive method for remote maternal health monitoring during pregnancy and three months postpartum. METHODS Fifty-eight pregnant women were monitored using an Internet-of-Things (IoT)-based remote monitoring system during pregnancy and 3-months postpartum. Pregnant women were asked to continuously wear Gear sport smartwatch to monitor their HR and HRV. In addition, a cross-platform mobile application was utilized for collecting pregnancy-related information. The trends of HR and HRV parameters were extracted using reliable data. We also analyzed the trends of normalized HRV parameters based on HR to remove the effect of HR changes on HRV trends. Finally, we exploited 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<.01). Time-domain HRV parameters, average normal interbeat intervals (AVNN), standard deviation of normal interbeat intervals (SDNN), root mean square of the successive difference of normal interbeat intervals (RMSSD), normalized SDNN (nSDNN), and normalized RMSSD (nRMSSD) decreased significantly during the second trimester (P<.001) then increased significantly during the third trimester (P<.01). Some of the frequency domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF (nHF) decreased significantly during the second trimester (P<.01), and HF increased significantly during the third trimester (P<.01). In the postpartum period, nRMSSD decreased (P<.05), and the LF to HF ratio (LF/HF) increased significantly (P<.01). CONCLUSIONS Our study showed that HR increased and HRV parameters decreased as the pregnancy proceeded, and the values returned to normal after the delivery. Moreover, our results show that HR started to decrease while time-domain HRV parameters and HF started to increase during the third trimester. Our results also demonstrate the possibility of continuous HRV monitoring in everyday life settings.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Comparison of fetal heart rate variability by symbolic dynamics at the third trimester of pregnancy and low-risk parturition

Heliyon ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e03485 ◽  
Author(s):  
Cristian Iván Montalvo-Jaramillo ◽  
Adriana Cristina Pliego-Carrillo ◽  
Miguel Ángel Peña-Castillo ◽  
Juan Carlos Echeverría ◽  
Enrique Becerril-Villanueva ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Symbolic Dynamics ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Low Risk ◽  
Third Trimester ◽  
The Third ◽  
Fetal Heart Rate Variability

Gestation specific reference intervals for thyroid function tests in pregnancy

2016 ◽  
Vol 54 (8) ◽  
Author(s):  
Süleyman Akarsu ◽  
Filiz Akbiyik ◽  
Eda Karaismailoglu ◽  
Zeliha Gunnur Dikmen
Keyword(s):  
Pregnant Women ◽  
Thyroid Function ◽  
First Trimester ◽  
Reference Intervals ◽  
Specific Reference ◽  
Thyroid Function Tests ◽  
Second Trimester ◽  
Third Trimester ◽  
The Third ◽  
Function Tests

AbstractThyroid function tests are frequently assessed during pregnancy to evaluate thyroid dysfunction or to monitor pre-existing thyroid disease. However, using non-pregnant reference intervals can lead to misclassification. International guidelines recommended that institutions should calculate their own pregnancy-specific reference intervals for free thyroxine (FT4), free triiodothyronine (FT3) and thyroid-stimulating hormone (TSH). The objective of this study is to establish gestation-specific reference intervals (GRIs) for thyroid function tests in pregnant Turkish women and to compare these with the age-matched non-pregnant women.Serum samples were collected from 220 non-pregnant women (age: 18–48), and 2460 pregnant women (age: 18–45) with 945 (39%) in the first trimester, 1120 (45%) in the second trimester, and 395 (16%) in the third trimester. TSH, FT4 and FT3 were measured using the Abbott Architect i2000SR analyzer.GRIs of TSH, FT4 and FT3 for first trimester pregnancies were 0.49–2.33 mIU/L, 10.30–18.11 pmol/L and 3.80–5.81 pmol/L, respectively. GRIs for second trimester pregnancies were 0.51–3.44 mIU/L, 10.30–18.15 pmol/L and 3.69–5.90 pmol/L. GRIs for third trimester pregnancies were 0.58–4.31 mIU/L, 10.30–17.89 pmol/L and 3.67–5.81 pmol/L. GRIs for TSH, FT4 and FT3 were different from non-pregnant normal reference intervals.TSH levels showed an increasing trend from the first trimester to the third trimester, whereas both FT4 and FT3 levels were uniform throughout gestation. GRIs may help in the diagnosis and appropriate management of thyroid dysfunction during pregnancy which will prevent both maternal and fetal complications.

Differences in heart rate variability between Asian and Caucasian children living in the same Canadian community

2006 ◽  
Vol 31 (3) ◽  
pp. 277-282 ◽  
Author(s):  
Katharine E Reed ◽  
Darren E.R Warburton ◽  
Crystal L Whitney ◽  
Heather A McKay
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Aerobic Fitness ◽  
Time Domain ◽  
High Frequency ◽  
Analysis Of Covariance ◽  
Fitness Testing ◽  
High Frequency Power ◽  
Asian Children ◽  
Frequency Power

Heart rate variability (HRV) is an umbrella term for a variety of measures that assess autonomic influence on the heart. Reduced beat-to-beat variability is found in individuals with a variety of cardiac abnormalities. A reduced HRV positively correlates with obesity, poor aerobic fitness, and increasing age. Racial (black-white) differences are apparent in adults and adolescents. We aimed to evaluate (i) Asian-Caucasian differences in HRV and (ii) differences in HRV between girls and boys. Sixty-two children (30 male (15 Caucasian, 15 Asian) and 32 female (15 Caucasian, 17 Asians)) with a mean age of 10.3 ± 0.6 y underwent 5 min resting HRV recording, fitness testing (Leger's 20 m shuttle), and self-assessed maturity. Outcome HRV measures were a ratio of low to high frequency power (LF:HF), standard deviation of R-R intervals (SDRR) and root mean square of successive R-R intervals (RMSSD). Data were compared between groups using analysis of covariance (ANCOVA). There were no race or sex differences for time domain variables, mean R-R, body mass index, or blood pressure. Compared with Caucasian children, Asian children displayed a higher adjusted (fitness, R-R interval) LF:HF ratio (72.9 ± 59.4 vs. 120.6 ± 85.3, p < 0.05). Girls demonstrated a higher adjusted LF:HF power than boys (117.2 ± 85.1 vs. 76.6 ± 62.4, p = < 0.05). In conclusion, Asian and Caucasian children display different frequency domain components of heart rate variability.Key words: autonomic nervous system, sympathetic, vagal, race, aerobic fitness, sex.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Glycaemic profile in the second and third trimesters of normal pregnancy compared to non-pregnant adult females

2019 ◽  
Vol 13 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Aruna Nigam ◽  
Neha Varun ◽  
Sumedha Sharma ◽  
YP Munjal ◽  
Anupam Prakash
Keyword(s):  
Blood Glucose ◽  
Pregnant Women ◽  
Normal Pregnancy ◽  
Postprandial Blood Glucose ◽  
Second Trimester ◽  
Third Trimester ◽  
Glycaemic Variability ◽  
The Third ◽  
Data Points ◽  
Mean Blood Glucose

Aim To assess the glycaemic profile and glycaemic variation in the second and third trimesters of normal pregnancies. Methodology Healthy pregnant women aged 19–35 years between 24 and 36 weeks of gestation were recruited for ambulatory glucose profile monitoring. A total of 18 women in the second trimester, 15 women in the third trimester and 9 healthy non-pregnant women were recruited providing, respectively, 205 days (19,680 data points), 147 days (14,112 data points) and 100 days (9,600 data points) for analysis. Results Mean blood glucose level was 20.2% lower in the second trimester and 10.6% lower in the third trimester than non-pregnant women (p < 0.001). In pregnancy, it took 15 to 20 minutes more to reach peak postprandial blood glucose levels compared to non-pregnant women (p = 0.003). Glycaemic variability was more in the third trimester (p < 0.001). Conclusion There is tight blood sugar control along with lower mean blood glucose in healthy pregnant women compared to non-pregnant women. Despite this tight glycaemic control, glycaemic variability is higher during pregnancy.

Heart Rate Variability in Diabetes Patients

2006 ◽  
Vol 34 (3) ◽  
pp. 291-296 ◽  
Author(s):  
H Kudat ◽  
V Akkaya ◽  
AB Sozen ◽  
S Salman ◽  
S Demirel ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Frequency Domain ◽  
Autonomic Neuropathy ◽  
Time Domain ◽  
Cardiovascular Autonomic Neuropathy ◽  
Chronic Complications ◽  
Frequency Power ◽  
Diabetes Patients

Diabetes mellitus can cause cardiovascular autonomic neuropathy and is associated with increased cardiovascular deaths. We investigated cardiovascular autonomic neuropathy in diabetics and healthy controls by analysis of heart rate variability. Thirty-one diabetics and 30 age- and sex-matched controls were included. In the time domain we measured the mean R-R interval (NN), the standard deviation of the R-R interval index (SDNN), the standard deviation of the 5-min R - R interval mean (SDANN), the root mean square of successive R - R interval differences (RMSSD) and the percentage of beats with a consecutive R - R interval difference > 50 ms (pNN50). In the frequency domain we measured high-frequency power (HF), low-frequency power (LF) and the LF/HF ratio. Diabetes patients had lower values for time-domain and frequency-domain parameters than controls. Most heart rate variability parameters were lower in diabetes patients with chronic complications than in those without chronic complications.

Reproducibility of Heart Rate Variability Measures in Patients with Chronic Heart Failure

1996 ◽  
Vol 91 (4) ◽  
pp. 391-398 ◽  
Author(s):  
Piotr Ponikowski ◽  
Massimo Piepoli ◽  
Aham A. Amadi ◽  
Tuan Peng Chua ◽  
Derek Harrington ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Time Domain ◽  
Low Frequency ◽  
Variation Coefficient ◽  
Very Low Frequency ◽  
Low Frequency Power ◽  
Frequency Power

1. In patients with chronic heart failure, heart rate variability is reduced with relative preservation of very-low-frequency power (< 0.04 Hz). Heart rate variability has been measured without acceptable information on its stability and the optimal recording periods for enhancing this reproducibility. 2. To this aim and to establish the optimal length of recording for the evaluation of the very-low-frequency power, we analysed 40, 20, 10 and 5 min ECG recordings obtained on two separate occasions in 16 patients with chronic heart failure. The repeatability coefficient and the variation coefficient were calculated for the heart rate variability parameters, in the time-domain (mean RR, SDRR and pNN50), and in the frequency-domain: very low frequency (< 0.04 Hz), low frequency (0.04–0.15 Hz), high frequency (0.15–0.40 Hz), total power (0–0.5 Hz). 3. Mean RR remained virtually identical over time (variation coefficient 8%). The reproducibility of time-domain (variation coefficient 25–139%) and of spectral measures (variation coefficient 45–111%) was very low. The stability of the heart rate variability parameters was only apparently improved after square root and after log transformation. 4. Very-low-frequency values derived from 5 and 10 min intervals were significantly lower than those calculated from 40 and 20 min intervals (P < 0.005). Discrete very-low-frequency peaks were detected in 11 out of 16 patients on the first 40, 20 and 10 min recording, but only in seven out of 16 when 5 min segments were analysed. 5. The reproducibility of both time or frequency-domain measures of heart rate variability in patients with chronic heart failure may vary significantly. Square root or log-transformed parameters may be considered rather than absolute units in studies assessing the influence of management on heart rate variability profile. Recordings of at least 20 min in stable, controlled conditions are to be recommended to optimize signal acquisition in patients with chronic heart failure, if very-low-frequency power in particular is to be studied.

scholarly journals Continuous 7-Month Internet of Things–Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study (Preprint)

2019 ◽  
Author(s):  
Johanna Saarikko ◽  
Hannakaisa Niela-Vilen ◽  
Eeva Ekholm ◽  
Lotta Hamari ◽  
Iman Azimi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Postpartum Period ◽  
Continuous Monitoring ◽  
Second Trimester ◽  
Third Trimester ◽  
Activity Data ◽  
The Third ◽  
Nulliparous Women ◽  
Health Parameters

BACKGROUND Monitoring during pregnancy is vital to ensure the mother’s and infant’s health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments. OBJECTIVE This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)–based monitoring system. METHODS This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period. RESULTS Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (<i>P</i>&lt;.001). The decrease continued by a mean of 1339 (95% CI 474-2205) steps to the postpartum period (<i>P</i>=.004). Sleep during pregnancy also decreased from the second trimester to the third trimester by a mean of 20 minutes (95% CI –0.7 to 42 minutes; <i>P</i>=.06) and sleep time shortened an additional 1 hour (95% CI 39 minutes to 1.5 hours) after delivery (<i>P</i>&lt;.001). The mean resting heart rate increased toward the third trimester and returned to the early pregnancy level during the postpartum period. CONCLUSIONS The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up.

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