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IJPBS |Volume 3| Issue 2 |APR-JUN |2013|327-333
Research Article
Biological Sciences
STRESS DURING PREGNANCY AND ITS INFLUENCE ON THE OFFSPRING’S HEART RATE:
A PHYSIOLOGICAL STUDY
M.R. Begum1, B.R.H. Ven Den Bergh2, M.S.I. Khan3
1
Assistant Professor of Biostatistics, Department of Agricultural Economics and Social Sciences, Chittagong
Veterinary and Animal Sciences University (CVASU), Khulshi, Chittagong.
2
Professor of developmental psychology, Department of Developmental Psychology, Clinical and Crosscultural
Psychology, University of Tilburg, Netherlands.
3
Assistant Professor of Food Microbiology, Faculty of Nutrition and Food Science, Patuakhali Science and
Technology University, Dumki, Patuakhali.
*Corresponding Author Email: [email protected]
ABSTRACT
Stress, depression and anxiety during pregnancy have negative influence on mothers’ health and their child. The
study involved 190 pregnant women, who gave birth to 191 infants, from Tilburg, Netherlands. Anxiety-related
measurements of mothers were collected every trimester during their pregnancy period, (i.e. 8-14 weeks or 1st
trimester, 15-22 weeks or 2nd trimester, and 31-37 weeks or 3rd trimester) and also after giving birth at two times
(2-4 months and 9-10 months of infants). Their heart rate variability measurements were available for first and
third trimester. The heart rate and heart rate variability of infants were measured during 2-4 months old. The
study was aimed at investigating whether or not prenatal maternal anxiety and heart rate variability of mothers
were related to child heart rate (HR), heart rate variability (HRV). High amount of missing observations in data set
was considered in the analysis. Regression analysis was used to answer the objective. The results demonstrated
that prenatal maternal PRAQ anxiety was associated with decreased child HR and increased child HRV parameters
of HF which were biologically implausible whereas mothers subscale (SCL) anxiety was related to decreased child
HRV of HF which was biologically plausible. Mothers’ HRV parameters in rest condition had both positive and
negative effect on child HR where negative effect was plausible. Mothers’ HRV parameters in stress had negative
impact on child HR which was unaccepted. Mothers HRV parameters in rest condition increased child HRV of HF.
Mothers’ anxiety, depression and heart rate variability parameters during 15-22 weeks and 31-37 weeks of
gestation had an impact on the child heart rate, heart rate variability parameter. Therefore, it is important for
pregnant women to take additional care for improving the condition of new born babies.
KEY WORDS
Stress, heart rate, heart rate variability, high frequency
Page
327
INTRODUCTION
Human beings often faced with some situations
where adaptation becomes critical or next to
impossible that develop stress (Mulder et al., 2002).
An extreme stress is deleterious to health that results
in diseases. Pregnancy, for example, is a stressful
period for women (Keim et al., 2011). Researchers
have suggested that maternal stress, anxiety and
depression or adverse obstetrical conditions are
associated with neonatal mental and physical
disorders (Martini et al., 2010). These kinds of
antenatal psychological problems like antenatal
depression (AD) and/or anxiety range from 8% to 30%
that lead to psycophysiological outcomes in children
(Bowen and Muhajarine 2006; Van Bussel et al.,
2006). The outcomes may be altered temperament of
International Journal of Pharmacy and Biological Sciences (e-ISSN: 2230-7605)
Int J Pharm Biol Sci
M.R. Begum* et al
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children (Hout et al., 2004) or a range of diseases
(Tegethoff et al., 2011).
A maternal history of psychopathology was associated
with an increased mean HR level and lower vagal
modulation in the infant. The relationship between
maternal anxiety symptoms and infant autonomic
indices seemed stronger than that between maternal
depressive symptoms and those indices (Dierckx et
al., 2009).
Heart rate variability (HRV) provides quantitative
markers of autonomic regulation (Akselrod et al.,
1981). HRV in response to stress differs between
individuals because of genetic composition, age,
sleep-awake state and environment. Importantly,
decreased HRV has been shown to be a predictor of
adverse outcomes (Task Force of the European
Society of Cardiology and the North American Society
of Pacing and Electrophysiology, 1996). Depressed
pregnant women had significantly reduced time
domain measures of SDNN (standard deviation of the
Normal to Normal (NN) beat interval) and SDANN
(standard deviation of the average NN intervals) as
well as higher heart rates and lower LF/HF (ratio of
low and high frequency) ratios while asleep (Shea et
al., 2008).
Moreover, the mental health research is a public
health priority due to its impact on both maternal and
child health. In addition, the perinatal period
including both prenatal and neonatal phase for the
infant, pregnancy and postpartum period are very
significant both for the mother as well as for her child
(Satyanarayana et al., 2011). The objective of this
study was to identify the factors related to child’s
heart rate and heart rate variability measure with
multiple regressions while considering the missing
observations.
Page
328
MATERIALS AND METHODS
The data were obtained from 190 pregnant women,
who gave birth to 191 infants, during
their
pregnancy and after parturition in five phases
between 18 May 2009 and 18 September 2011 at
Tilburg in Netherlands.
Prenatal Data
Psychological measures of mother
The Spielberger State Trait Anxiety Inventory (STAI;
Spielberger et al., 1983), consisting of a state and a
trait subscale, each containing 20 items; state anxiety
is conceptualized as a transient emotional condition,
while trait anxiety reflects a dispositional anxiety
proneness; the short version of the Pregnancy
Anxiety Questionnaire (PRAQ) measuring anxieties
related to pregnancy and containing 10 items; the
Edinburgh Pregnancy Depression Scale (EPDS)
containing 10 items measuring depression; the
subscale (SCL) anxiety of the symptom checklist
consisting 10 measuring anxiety; the General Health
Questionnaire (GHQ) measures stress symptoms
experienced during the last weeks; the short version
contains 12 items.
Heart Rate Variability of mothers
During 1st and 3rd trimesters of pregnancy, heart rate
variability (HRV) parameters (SDNN, RMSSD, LF, HF
and LF/HF) of mothers were measured in five distinct
phases where each lasted for 5 minutes requiring 25
minutes for every participant. Data of first, third and
fifth phases were taken in resting condition, whereas
second and fourth phases were taken in stress.
Hence, in every HRV parameters, the first, third and
fifth phases for resting as well as the second and
fourth phase measurements for stress were
summarized by taking their mean. Vrije University
Ambulatory Monitoring System (VU-AMS) (www.vuams.nl.be) was used for the duration of the phases
where Electrocardiogram (ECG) was measured using
three Silver-Silver chloride (Ag/AgCI) electrodes,
placing on the chest and recorded.
Postnatal Infant Data
The data of Child HR and HRV parameters were
collected from children aging between 2 and 4
months. Heart rate variability (HRV) is the changes in
the interval or distance between successive beats
which can be measured by parameters of SDNN,
RMSSD, LF, HF and LF/HF. The meaning of each HRV
parameters has been explained in earlier section.
Each parameter was measured from five blocks of
stimuli. The stimuli were different sounds to
investigate event related potential (ERP). Every five
M.R. Begum* et al
blocks of stimuli measurements were combined for
each HR and HRV parameters by taking the mean.
These HR and HRV were measured for children aging
between 2 and 4 months.
Demographic Variables
Some demographic variables for example, age of
mother, body mass index (BMI) of mother and gender
of child were totally linked with child HR, child HRV
Page
329
Statistical Methodology
Regression analysis is a statistical methodology that
utilizes the relation between two or more
quantitative variables so that a response or outcome
variable can be predicted from other(s) (Kutner, et al.
2004). Multiple regression analysis is one of the most
widely used of all statistical methods. Since the
dataset had missing observations, several techniques
were implemented to deal with missingness.
Complete case (CC) analysis, maximum likelihood
estimation of covariance and mean parameters,
multiple imputation (MI) method were used for
regression analysis. The CC analysis works by deleting
all cases with missing value for at least one of the
variables included in the analysis. Another way of
dealing with missing data for regression model is via
maximum likelihood estimation of covariance and
mean parameters method. Maximum likelihood
method requires large sample size and that the data
are missing at random (MAR). The use of PROC MI,
which provides maximum likelihood (ML) estimates of
the covariance matrix and mean vector using the
expectation-maximization (EM) algorithm (Truxillo,
2005; Paper 111-30). An alternative to these methods
which handle missingness under MAR mechanism is
the Multiple Imputation (MI). It consists in filling M
times (with plausible values) the missing values in
order to generate M data sets which are analyzed
using standard procedures. Afterwards, the results of
the M analyses are combined into a single inference
(Molenberghs and Verbeke, 2005). The software
packages R 2.14.0, SPSS 20, SAS 9.2 were used during
analysis and 5% level of significance was considered.
RESULTS
Multiple regression model was fitted separately for
Child HR and HRV parameter of Child HF to
investigate the important risk factors. Some
demographic variables for example, age of mother,
body mass index (BMI) of mother and gender of child
were totally linked with child HR, child HRV
parameter, hence the analysis included demographics
variables besides anxiety and HRV of mothers
covariates. Backward elimination technique was used
to select model which starts by calculating F statistics
for a model that includes all independent variables.
Then the variables were deleted from the model one
by one until all the variables remaining in the model
produce F statistic significant at the significance level
stay or SLSTAY=0.10.
Child Heart Rate (HR)
Regression Model was fitted to assess relationship
between Child heart rate (HR) and important risk
factors. The distribution of child heart rate was
normal given in Figure 1 (a). The selected model for
Child Heart Rate is:
𝒀𝒊 = 𝜷𝟎 + 𝜷𝟏 ∗ 𝑻𝑹𝑨𝑰𝑻𝑨𝒏𝒙𝒊𝒆𝒕𝒚𝑻𝒓𝒊𝒎𝒆𝟐 + 𝜷𝟐
∗ 𝑷𝑹𝑨𝑸𝑭𝒆𝒂𝒓𝒏𝒆𝒔𝒔𝑻𝒓𝒊𝒎𝒆𝟐 + 𝜷𝟑
∗ 𝑳𝑭𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟏 + 𝜷𝟒
∗ 𝑳𝑭 𝑯𝑭 𝑺𝒕𝒓𝒆𝒔𝒔𝑻𝒓𝒊𝒎𝒆𝟏 + 𝜷𝟓
∗ 𝑺𝑫𝑵𝑵𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜷𝟔
∗ 𝑹𝑴𝑺𝑺𝑫𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜷𝟕
∗ 𝑹𝑴𝑺𝑺𝑫𝑺𝒕𝒓𝒆𝒔𝒔𝑻𝒓𝒊𝒎𝒆𝟑+ 𝜷𝟖
∗ 𝑳𝑭 𝑯𝑭𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜺𝒊
After fitting the model, all assumptions were checked.
There was no multicolinearity present by inspecting
the variance inflation factor (VIF), wherein all VIF < 10
which indicated an absence of colinearity. Graphical
plots expose for normality of residual, constancy of
residual variance and independency of residual in
Figure 2(a, b, c). The Kolmogorov Smirnov test was
employed for testing the normality of error terms by
analyzing the residuals. This test showed that the
error terms were normal with statistic D= 0.092, pvalue 0.15, which gave strong evidence that the
residuals were normally distributed. To check the
constancy of variance, Levene test was performed
which gave a test statistic of t=1.9 with pvalue=0.056, thus giving evidence in favor of
constancy of variance. Only 68 out of 191 cases were
used in the analysis due to missing data. The
reduction in sample size alone might be considered a
problem. To handle these missingness three types of
M.R. Begum* et al
analysis were used. The “PROC REG” in SAS is itself
complete case (CC) analysis which is valid under
missing completely at random (MCAR). Maximum
Likelihood (ML) and Multiple Imputation (MI)
methods are valid under missing at random (MAR).
In Table 1 provides the parameter estimates of child’s
heart rate form three methods. Mothers’ with PRAQ
fearness anxiety trimester 2, mothers with HRV of
LF/HF stress trimester 1 and RMSSD stress trimester 3
had child with lower HR. Mothers HRV of LF rest
trimester 1 was borderline significant. Mothers HRV
of RMSSD rest trimester 3 and LF/HF rest trimester 3
had positive influence on child HR and mothers HRV
of SDNN rest trimester 3 had negative influence on
child HR.
Table 1: Parameter estimates of child Heart Rate (HR)
Complete Case
Maximum Likelihood
(n=68)
(n=139)
146.0288(9.4738)*
159.0822(6.5788)*
Parameters
Intercept
Multiple
Imputation(5)
158.6144(9.5629)*
TRAIT Anxiety Trime 2
8.4942(4.8977)
0.3116(3.42190)
-0.3784(4.0494)
PRAQ fearness Trime 2
-2.4021(0.9975)*
-1.4461(0.80857)
-1.8829(0.7418)*
LF Rest Trime 1
-0.0048(0.0023)*
-0.0009(0.0016)
-0.0004(0.0022)
LF/HF Stress Trime 1
-1.8837(0.8162)*
-2.1264(0.7373)*
-0.9429(0.8776)
SDNN Rest trime 3
-0.2695(0.1427)
-0.3391(0.1168)*
-0.3335(0.1113)*
RMSSD Rest trime 3
0.8848(0.3194)*
0.4465(0.2273)
0.4767(0.2993)
RMSSD Stress Trime 3
-0.5990(0.2843)*
-0.1415(0.1616)
-0.1900(0.2212)
LF/HF Rest Trime 3
1.6033(0.6723)*
1.1877(0.5905)*
0.9667(0.9791)
*P-value<0.05 (t-test)
Parameter
Intercept
Table 2: Parameter estimates of logarithm of child HRV of High Frequency (HF)
Complete Case
Maximum Likelihood
Multiple
(n=68)
(n=147)
Imputation(5)
4.4447(0.5080)*
4.3683(0.3838)*
4.1293(0.4548)*
0.0630(0.0355)
0.0242(0.0254)
0.0124(0.0269)
PRAQ fearness Trime 2
0.1279(0.0762)
0.1472(0.0651)*
0.1206(0.0621)
SCL Anxiety Trime 3
-0.0924(0.0392)*
-0.0612(0.0291)*
-0.0302(0.0368)
LF/HF Rest Trime 1
0.1141(0.0734)
0.0925(0.0601)
0.0957(0.0797)
LF Rest Trime 3
0.00068(0.00027)*
0.00050(0.00018)*
0.00045(0.00022)
LF/HF Rest Trime 3
-0.0984(0.0465)*
-0.1022(0.0395)*
-0.0958(0.0666)
Page
330
EPDS Depression Trime 1
*P-value<0.05 (t-test)
M.R. Begum* et al
Figure 1: a) Histogram of Child Heart Rate and b) Histogram of Child High Frequency
Figure 2: Diagnostic plots for residuals of Child Heart Rate (HR)
Page
331
Figure 3: Diagnostic plots for residuals of logarithm of Child High Frequency (HF)
M.R. Begum* et al
Child High Frequency (HF)
Regression model was fitted to assess the relationship
between child HF and important risk factors. The child
high frequency distribution was not normal shows in
Figure 1 (b). After logarithm transformation the child
HF was almost normal and the final model is:
𝒍𝒏 𝒀𝒊 = 𝜷𝟎 + 𝜷𝟏 ∗ 𝑬𝑷𝑫𝑺𝑫𝒆𝒑𝒓𝒆𝒔𝒔𝒊𝒐𝒏𝑻𝒓𝒊𝒎𝒆𝟏+ 𝜷𝟐 ∗ 𝑷𝑹𝑨𝑸𝑭𝒆𝒂𝒓𝒏𝒆𝒔𝒔𝑻𝒓𝒊𝒎𝒆𝟐 + 𝜷𝟑
∗ 𝑺𝑪𝑳𝑨𝒏𝒙𝒊𝒆𝒕𝒚𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜷𝟒
∗ 𝑳𝑭 𝑯𝑭𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟏 + 𝜷𝟓 ∗ 𝑳𝑭𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜷𝟔 ∗ 𝑳𝑭 𝑯𝑭𝑹𝒆𝒔𝒕𝑻𝒓𝒊𝒎𝒆𝟑 + 𝜺𝒊
After model fitting, all assumptions were validated.
All VIF values were less than 10 which indicate an
absence of multicollinearity. Graphical plots represent
for normality of residual, constancy of residual
variance and independency of residual in Figure 3(a,
b, c). A formal Kolmogorov Smirnov test was used to
support the graphical plots, giving a test statistic of D=
0.069 and p value of 0.15 which provided evidence
that the residuals were normally distributed. A formal
Levene test was used having a test statistic of
t=0.21and p value=0.83 supporting the constancy of
variance of residuals. There were 68 out of 191
observations used in the analysis with 123 missing
observations.
In Table 2 illustrates the model parameter estimates
of logarithm of child HRV of high frequency from
different methods. PRAQ fearness anxiety trimester 2
had positive impact on child HRV of HF. Subscale (SCL)
anxiety of mother decreased child HRV of HF was
reasonable. Again mothers’ HRV of LF rest and LF/HF
rest trimester 3 had positive and negative impact on
the response respectively, wherein we only expected
a positive effect on response.
Page
332
DISCUSSION
In our study, the main objective was to assess the
anxiety and heart rate variability of mothers during
pregnancy and how it influences the infant heart rate,
heart rate variability. Data from a total of 190
pregnant mothers and their 191 child were collected
from Tilburg in Netherlands between 18 May 2009
and 18 September 2011.
The multiple regression models were fitted for the
child heart rate (HR) and child heart rate variability
(HRV) parameters of HF. Multicollinearity was
checked for every final regression model using
variance inflation factor (VIF). The findings from all
regression models revealed that PRAQ fearness
anxiety at 15 to 22 weeks gestation or 2nd trimester
had negative influence on child HR which was
contradictory to the result stated in the article of
Dierckx et al., (2008) where maternal anxiety related
with increased infant HR and decreased vagal
modulation. This indicates reduced parasympathetic
nervous system activity. Mothers HRV of SDNN in rest
condition between 31 and 37 weeks of gestation or
3rd trimester had negative influence on child HR which
was biologically plausible. Commonly, subscale (SCL)
anxiety at 31 to 37 weeks gestation or 3rd trimester
had negative influence on child HRV of HF which was
biologically plausible. A partially related study of
Jacob et al., (2009) reported that maternal life
stressors were associated with lower HRV. Mothers
EPDS depression of 1st trimester or 8 to 14 was not
associated with higher child HRV of HF which was
similar to the statement of Jones et al., (1998) where
infants of mother with prenatal depression measured
from 30 to 35 weeks had significantly lower vagal
modulation whereas Kaplan et al., (2008) stated no
association between prenatal maternal depression
and infant vagal modulation in 2nd trimester. Mothers
HRV of LF and SDNN in rest condition during 31-37
weeks of gestation or 3rd trimester had positive
influence on child HRV of HF which was biologically
plausible. For child HR model, maximum likelihood
method gave parameter estimates with lower
standard error as compared to complete case analysis
and multiple imputation method. Again, for child HRV
parameters of HF model gave lower standard error in
maximum likelihood method as compared to other
two methods.
CONCLUSION
In this study, some associations of maternal anxiety
and maternal HRV with child HR, child HRV were
observed. Although, there was very diverse effect
depending on the variables studied and methods
M.R. Begum* et al
used. It was revealed that the association of mothers
subscale (SCL) anxiety with lower child HRV of HF; and
mothers HRV of SDNN in rest condition with lower
child HR and were biologically accepted whereas the
association of PRAQ fearness anxiety with a lower
child HR and a higher child HRV of HF; mothers HRV of
LF/HF in stress condition in 1st trimester with lower
child HR, and mothers HRV of RMSSD in rest and
stress of 3rd trimester with higher and lower child
HR which were not acceptable. For child HR and child
HRV parameter model maximum likelihood method
gave lower standard error than complete case
analysis and multiple imputation method.
o
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REFERENCES
o
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*Corresponding Author:
Musammet Rasheda Begum*
Page
333
Assistant Professor (Biostatistics)
Department of Agricultural Economics and Social Sciences
Chittagong Veterinary and Animal Sciences University
Khulshi, Chittagong-4202, Bangladesh.
E-mail Address: [email protected]
M.R. Begum* et al

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