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Eva K. Pressman
Janet A. DiPietro
Kathleen A. Costigan
Alyson K. Shupe
Division of Maternal– Fetal Medicine
Department of Maternal & Child
Health
Johns Hopkins University
Baltimore, MD 21205
Timothy R. B. Johnson
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Fetal Neurobehavioral
Development: Associations
with Socioeconomic Class and
Fetal Sex
Department of Obstetrics &
Gynecology
University of Michigan
Ann Arbor, MI 48109
Received 10 March 1997; accepted 22 September 1997
ABSTRACT: This longitudinal study investigated neurobehavioral development in the human
fetus from 24 to 36 weeks gestation. Subject (N ⫽ 103) were stratified by socioeconomic class.
Fetal data were collected for 50 min at three intervals, and included measures of heart rate,
movement, and biobehavioral patterns. Repeated measures analysis of variance by fetal sex
and maternal socioeconomic status was used to detect maturation effects and group differences.
With advancing gestation, fetuses exhibited reduced heart rate, increased heart rate variability
and coupling between movement and heart rate, increased movement vigor, and more biobehavioral concordance. Male fetuses displayed higher heart rate variability throughout gestation
and somewhat earlier emergence of biobehavioral organization than females. Fetuses of women
of lower socioeconomic status had reduced heart rate variability, moved less often and with
less vigor, showed less coupling between movement and heart rate, and had fewer episodes of
synchronous quiescence/activity. Results are discussed in terms of development of the central
nervous system. 䉷 1998 John Wiley & Sons, Inc. Dev Psychobiol 33: 79– 91, 1998
Keywords: fetus; fetal heart rate; fetal movement; socioeconomic status; sex differences
“If we pursue our quest beyond the newborn period,
we find ourselves suddenly in an entirely new situation, where our organism is not seen, nor scarcely felt
nor heard” (Sontag & Richards, 1938, p. 1)
Over the last decade, significant progress has been
made in describing the development of the fetus. The
challenge, to devise methods of validly detecting and
quantifying fetal behavior, remains unchanged in the
Correspondence to: J. A. DiPietro
Contract grant sponsor: NICHD
Contract grant number: R01HD27592
䉷 1998 John Wiley & Sons, Inc.
60 years since the original Fels study of fetal behavior.
Descriptive studies of fetal neurobehavior have begun
to elucidate the ontogeny of maturation prior to birth
for functions such as fetal heart rate (FHR) and variability (Dalton, Phil, Dawes, & Patrick, 1983; Dawes,
Houghton, Redman, & Visser, 1982; Martin, 1978;
Yoshizato et al., 1994), behavioral state (Groome,
Bentz, & Singh, 1995; Nijhuis & van de Pas, 1992;
Pillai & James, 1990; van Vliet, Martin, Nijhuis, &
Prechtl, 1985), qualitative motor patterns (de Vries,
Visser, & Prechtl, 1982; Roodenburg, Wladimiroff,
CCC 0012-1630/98/010079-13
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van Es, & Prechtl, 1991), activity level (Patrick,
Campbell, Carmichael, Natale, & Richardson, 1982;
Roberts, Griffin, Mooney, Cooper, & Campbell, 1980;
Sival, 1993), and the association between FHR
changes and fetal movement (DiPietro, Hodgson, Costigan, Hilton, & Johnson, 1996a; Timor-Tritsch, Dierker, Zador, Hertz, & Rosen, 1978; Vintzileos, Campbell, & Nochimson, 1986). However, small sample
sizes and homogeneity of sample characteristics often
preclude investigation of maternal and fetal characteristics which may influence developmental course. Recently, we have reported results of a comprehensive
study of fetal neurobehavior (DiPietro, Hodgson, Costigan, Hilton, & Johnson, 1996b) in which 31 fetuses
were longitudinally recorded in the latter half of gestation. The data reported in this manuscript detail the
results from two additional, larger samples of fetuses.
Our current goals are to provide replication and confirmation of the description of fetal neurobehavioral
development provided by the initial sample, as well as
to investigate maternal and fetal characteristics which
may affect fetal maturational patterns.
Identification of these factors is based on observation of influences on postnatal functioning. Sex differences in neonatal mortality and morbidity, particularly in preterm infants, have been widely described
(Brothwood, Wolke, Gamsu, Benson, & Cooper,
1986; Gualtieri & Hicks, 1985; McGregor, Leff, Orleans, & Baron, 1992) with boys at elevated risk. Male
fetuses are also more vulnerable to teratogenic risks,
including hypoxia, than females (Spinillo et al., 1994;
Weinberg, Zimmerberg, & Sonderegger, 1992). Paradoxically, boys weigh more at birth (Cogswell & Yip,
1995) but appear to be less mature than girls of comparable gestational age in several systems, including
skeletal and respiratory development (Khoury, Marks,
McCarthy, & Zaro, 1985; Tanner, 1978). In addition,
studies of nonhuman primates suggest sex differentials
in the rate of cortical development (Bachevalier &
Haggar, 1991).
Based on the consistent findings of greater male
vulnerability, it would be reasonable to expect accelerated antenatal neurological development in female
fetuses. Studies which have reported analyses by sex
for specific aspects of fetal functioning, including fetal
heart rate (Dawes et al., 1982; Petrie & Segalowitz,
1980), motility patterns (Rayburn, 1990; Robertson,
1986), and state (Pillai, James, & Parker, 1992), have
failed to detect sex differences. However, most articles
on fetal development do not specify whether sex effects were tested.
The effect of socioeconomic class on perinatal morbidity and mortality is also well known. Offspring of
less well-educated, economically disadvantaged
women are more often preterm and/or low birth-
weight, and are less likely to survive (Jonas, Roder &
Chan, 1992; Kliegman, 1995; Peacock, Bland & Anderson, 1995). The mechanism by which social class
exerts its effect on pregnancy outcome is not well understood. Factors studied include patterns of medical
care (Poland, Ager, Olson, & Sokol, 1990), maternal
health and behaviors, including substance use (Amini,
Catalano, Hirsch, & Mann, 1994; Cogswell & Yip,
1995), race and ethnicity (Bird, 1995; Kramer, 1987),
and psychosocial stress (Lobel, Dunkel-Schetter, &
Scrimshaw, 1992). Two reports have detected differences in functioning of at-risk fetuses (referred for antenatal testing) between groups that varied by socioeconomic status. In these, fetuses of poorer, less
well-educated women had faster baseline heart rates
(Johnson et al., 1992) and were less likely to meet
criteria established for fetal well-being based on heart
rate accelerations on a standard clinical assessment
(Paine, Strobino, Witter, & Johnson, 1991).
Infant morbidity and mortality are points along a
continuum of reproductive casualty (Pasamanick &
Knobloch, 1966). We propose that the origins of the
disparity in perinatal outcome across fetal sex and socioeconomic class commence earlier in gestation, and
are manifest as differences in fetal neurobehavioral development. There is accumulating evidence that fetal
neurobehavior reflects neural function (Hepper, 1995)
and differs in fetuses who are neurologically compromised (Horimoto et al., 1993) or are exposed to atypical antenatal conditions (Mulder, 1993). Thus, we
predict that male fetuses and fetuses of impoverished
women will manifest less-mature fetal autonomic and
neurobehavioral development at each gestational age.
This would include reductions in heart rate variability,
biobehavioral organization, and the degree to which
there is integration of heart rate and motor functioning.
METHODS
Subjects
Subjects were healthy, pregnant women and their singleton fetuses. The intent of recruitment was to select
low-risk fetuses from two socioeconomic groups. The
first group was composed of 52 middle- to upper-middle-class women (Group 1); the second group included
51 women recruited from a hospital-based prenatal
clinic which provides care to low-income women
(Group 2). Inclusion in either sample required an uncomplicated pregnancy and good pregnancy dating,
based on one or all of the following: pregnancy test
within 2 weeks of missed menstrual period and/or 1st
trimester obstetric or ultrasound examination. Women
who reported smoking cigarettes an/or other substance
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use or had evidence of either in their medical charts
were excluded. Ascertainment of substance use was
somewhat more complete for Group 2, because the
prenatal clinic provides routine toxicology screens at
entry into care and at delivery. Medical records of subjects in Group 1 were examined at delivery, and although toxicology testing was routinely implemented
for some, it was not as universal as in the clinic sample. Demographic and medical information was collected by interview and medical chart review. Some
conditions associated with elevated antepartum or intrapartum risk were detected as the fetuses approached
term, such as mildly elevated blood pressure and reduced amniotic fluid level, but none were considered
serious enough to pose a significant threat to pregnancy outcome. As such, the sample includes a range
of conditions which are commonly encountered late in
pregnancy but typically lack clinical significance.
Three weighted scales of prepregnancy, pregnancy,
and intrapartum risk factors were scored for each subject (Hobel, Hyvarinen, Okada, & Oh, 1973).
A total of 54 women were recruited in Group 1.
Subjects were excluded for preterm delivery (1) and
gestational diabetes (1), leaving 52 women in this sample. A total of 63 women began the study protocol in
Group 2. Of these, eliminations from further study inclusion occurred for the following reasons: inconsistencies in pregnancy dating (1), incarceration (1), positive drug toxicology testing (2), premature (⬍ 36
weeks) delivery (2), gestational diabetes (2), and missing more than one study visit (4). The remaining 51
women comprised the final sample for Group 2.
Demographic information for each group is presented in Table 1. Group 1 was recruited through advertisements in campus and hospital-based
publications. Although recruitment was not specifically targeted at middle-class women, women who
volunteered were well educated and employed in
semiprofessional or professional occupations. All had
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private insurance and received prenatal care through
private physicians. Group 2 was recruited specifically
through advertisements posted in the prenatal clinic
and with assistance from nursing staff there. All met
eligibility requirements for clinic enrollment, based on
household composition and income (at or less than
185% of the poverty level). Ninety percent of women
also received Medicaid; the remainder were uninsured.
The majority (59%) received income from Aid to
Families with Dependent Children (AFDC).
Forty-five percent of women in Group 2 were employed, all in positions classified as “skilled trades” or
lower. There was no overlap in the household income
distribution between groups. Income data were collected on different scales for each group in order to
capture socioeconomic variability in Group 2. Income
for Group 1 was reported in $10,000 per annum increments beginning at $10,000; income for Group 2
was reported in $200 monthly increments beginning
at $0 (Monthly income grouping was based on pilot
testing of reporting preferences.) In Group 1, 86% reported family incomes above $40,000 per year, no
subject reported an income of less than $20,000, and
only 1 subject, a student, reported an income between
$20,000 – 30,000. Conversely, in Group 2, 82% reported receiving less than $800 per month ($9,600 per
year), the majority of these (62%) received less than
half this amount.
There was some overlap in levels of maternal education between groups; in Group 2, 22% had received
some post-high-school education (as compared to 87%
in Group 1). However, post-secondary training was
predominantly at the vocational or community college
level in Group 2, while the majority of college-educated subjects in Group 1 received 4-year degrees.
There was no overlap between groups at the extremes:
No member of Group 1 had less than a high school
education, and no member of Group 2 had graduate
level training.
Table 1. Maternal and Infant Characteristics
Group 1
(n ⫽ 52)
Maternal age
Maternal education (yr)
First prenatal visit (GA)
Prepregnancy risk score
Pregnancy risk score
Intrapartum risk score
Gestational age at delivery
Infant birth weight (g)
5-min Apgar
Group 2
(n ⫽ 51)
M
SD
Range
M
SD
Range
29.9
16.3
7.8
3.0
1.7
8.2
39.6
3502
8.9
3.5
2.6
2.0
5.3
2.7
8.5
1.1
470
.5
21–39
12–20
4–13
0–32
0–10
0–25
37–41
2612–4394
7–10
21.8
11.8
9.3
4.8
3.4
10.6
39.1
3237
8.9
2.9
1.7
2.6
6.7
4.6
8.7
1.4
386
.4
18– 30
7– 16
5– 15
0– 26
0– 20
0– 36
37– 42
2495– 4192
8– 10
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The inclusion criterion for maternal age in Group
1 was 20 years or older, and our original intent was to
maintain this criterion for Group 2 as well. However
an eligibility confound with parity became rapidly evident: Few primiparous women in the clinic population
were older than 20. This necessitated extending the
maternal age for inclusion in Group 2 to 18 years or
older. Rates of primiparity in the final samples were
64% for group 1 and 45% for Group 2, and this was
not a significant difference, ␹2(1, N ⫽ 103) ⫽ 2.80,
p ⬍ .10. Most women in Group 1 were married
(94%), most women in Group 2 were not (92%). The
ethnic composition of each group was as follows:
Group 1, 77% Caucasian, 10% African – American,
13% Other ethnicity; Group 2, 4% Caucasian, 94%
African – American, 2% Other ethnicity.
All women delivered normal infants who were discharged from the regular newborn nursery according
to routine schedules. Caesarian section rates were 21%
and 31% for Groups 1 and 2, respectively. All 5-min
Apgar scores were 7 or greater. Infant characteristics
are presented in Table 1. The percentage of male infants in Group 1 was 60% (n ⫽ 31) and 35% (n ⫽
18) in Group 2, a significant difference in distribution,
␹2(1, N ⫽ 103) ⫽ 6.12, p ⬍ .01.
toward or away from the transducer. The resultant signal is output in the form of spikes on a polygraphic
tracing in arbitrary voltage units, and corresponds almost exclusively to limb and body movement of the
fetus (Besinger & Johnson, 1989; Maeda, 1990;
Maeda, Tatsumura, & Nakajima, 1991; Ohta, 1985).
Analog output for fetal heart rate and movement signals from this monitor were sampled at 5 Hz and digitized online, concurrently with fetal monitoring.
Materials
Fetal Data Collection and Quantification
Fetal heart rate (FHR) and fetal movement (FM) data
were collected from a fetal actocardiograph (Toitu,
MT320) using a single wide array Doppler transducer
positioned on the maternal abdomen with an elastic
belt. FHR is determined by the processing of Dopplergenerated waveforms using autocorrelation techniques, in which small segments of sequential waveforms are matched to detect each serial heart beat.
FM is detected by processing Doppler signals in a different manner. Higher frequency Doppler signals
(150 – 220 Hz) are generated by motion of the fetal
heart. Thus, standard FHR monitoring requires a
Doppler signal sensitive enough to detect movement
changes that are as small as 1 – 2 mm. Lower frequency signals, which would be produced by maternal
and fetal body activity, are typically filtered out as
noise and discarded. Instead of discarding these signals, the actograph bandpasses both the highest frequency (i.e., FHR) and the lowest frequency signals
(i.e., maternal movement and respiration). Actograph
signals are generated by a change in the returned
Doppler waveform; if there is no movement, the returned signal will retain the same frequency as the
emitted signal. If the fetus is moving, the echo will be
returned at a different frequency which is proportional
to the velocity with which the fetal body part moves
Fetal Heart Rate. Distinguishing artifactual from actual data is a difficult but critical component in quantifying FHR early in the 3rd trimester because motor
activity can result in poor quality signal if the fetal
heart moves beyond the Doppler field. The digital data
underwent a series of error rejection procedures based
on moving averages of acceptable values. These algorithms were developed after comparing the polygraphic output of the monitor to the computerized output of several hundred records and ultimately
validated against visual inspection of 7,500 min of collected polygraphic data. Minutes in which two-thirds
of the data (i.e., 40 s) or more were rejected were not
included in data quantification. Details of the error rejection program are available upon request. The mean
rates of error rejection observed were 7.6%, 5.0%, and
5.2%, at 24, 30, and 36 weeks, respectively. The lower
range for rejected FHR was less than 1% at each gestational age; the upper range was 30.7% for 1 subject
at 30 weeks. However, the next-highest individual rejection rate was 21% (at 24 weeks).
PROCEDURE
Subjects were tested at 24, 30, and 36 weeks gestational age.1 To control for potential diurnal and prandial effects, subjects were tested at the same time each
visit, either at 1:00 or 3:00 pm. Women were instructed to eat 11⁄2 hr prior to testing, but not again
before testing. Subjects received a brief ultrasound
exam at each visit to determine fetal position and to
provide photographs for parents. Maternal heart rate,
pulse oxygen saturation (SpO2), and blood pressure
were measured at the beginning of each recording.
Women were monitored in a left lateral recumbent position while resting quietly.
1Based on experience, we anticipated that adding a visit closer
to term would be associated with a high attrition rate, and this was
confirmed (49.5% delivered ⱕ39 wks). Because subjects with missing data violate assumptions of repeated measures analyses, we decided to schedule the final visit for the week prior to term.
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The following fetal heart rate measures were then
calculated: (a) mean fetal heart rate, the mean of the
fifty 1-min epochs; and (b) mean fetal heart rate variability, computed as the standard deviation for each 1min epoch, again averaged over the 50-min recording.
Fetal Movement. The actograph signal is output in
arbitrary units (a.u.s.) which range from 0 to 100. Signals of less than 25 a.u.s. may be produced by fetal
breathing or hiccups, which generate incidental fetal
movement but are not considered motor activity, or by
smaller movements which may not always be reliably
detected (Maeda et al., 1991). This limit is also employed when the actograph is used for clinical detection of movement during antepartum testing, providing a conservative threshold for movement detection.
A movement bout was defined as commencing each
time the actograph signal attained or exceeded
25 a.u.s. and terminating when the signal fell below
25 a.u.s. for at least 10 consecutive s. The duration
of each movement bout was calculated from the first
time the signal reached or exceeded 25 a.u.s. through
the last 25 a.u.s. signal. Thus, each bout might represent a single excursion of a limb or a more complex
gross body movement.
The following fetal movement variables were computed: (a) number of movement bouts; (b) amplitude,
the mean of the amplitudes of all spikes occurring
within each movement bout; and (c) activity level (total number of movement bouts multiplied by the mean
movement duration). This measure represents the total
amount of time (min) the fetus was moving during the
recording.
FM – FHR Coupling. As the fetus matures, acceleratory changes of FHR become more closely associated
with episodes of fetal movement. Every fetal movement was categorized as being either coupled or uncoupled based on whether or not it was accompanied
by an excursion in FHR ⱖ 5 bpm for ⱖ 5 s above the
FHR baseline, within 5 s before or 15 s following the
movement onset. FHR baseline was fit through a moving algorithm applied to the data after artifact reduction. Because the baseline is a smoothed version of the
actual data, fetuses with high levels of background
FHR variability must exceed their baseline rate to meet
this criteria. For each coupled movement, the latency
between the onset of the FHR change relative to the
onset of the FM was calculated. The coupling index
was computed as: (total coupled FM ⫼ all FM) * 100.
Fetal Biobehavioral Pattern. Four biobehavioral patterns (BBP) were coded from the polygraphic record,
based on concordance of FM and FHR patterns. Our
83
scoring method is based on methods developed for
ascertaining fetal state (Nijhuis, Prechtl, Martin, &
Bots, 1982). FHR patterns were coded in 3-min windows in accord with existing protocols (van Vliet et
al., 1985) which classify FHR into four patterns of
variability. We developed four categories for scoring
actograph-generated movement patterns, also based on
3-min windows, which ranged from no movement
(FM 1) to continuous movement (FM 4). Biobehavioral patterns were attributed as follows: FHR A with
FM 1 ⫽ BBP A; FHR B with FM 1, 2, or 3 ⫽
BBP B; FHR C with FM 1 ⫽ BBP C; and FHR D
with FM 3 or 4 ⫽ BBP D. Identification of biobehavioral patterns through actocardiograph data has been
previously documented. (Gallagher, Costigan, &
Johnson, 1992). While the patterns we identify may
correspond to previously identified states of quiet
sleep (1F), active sleep (2F), quiet awake (3F), and
active awake (4F), respectively, they are not isomorphic due to the lack of eye movement data. The percentage of time a fetus displayed each pattern was calculated. Two variables were used for analysis: the
cumulative percentage of time in which any BBP was
evident (biobehavioral concordance), and the percent
in which either BBP A (extreme quiescence) or BBP
D (extreme activity) was displayed. The latter variable
was created because the hallmark of the developing
biobehavioral organization in the fetus is the integration of periods of synchronous activity in more than
one domain.
Reliability Training and Testing. In each sample, interrater reliability was achieved by dual independent
coding of each polygraphic tracing for the first 10 subjects until training criteria were achieved. During coding, reliability was maintained by sampling one record
from each of the remaining subjects, stratified by gestational age. Ongoing interrater agreement for FHR
pattern data was 98% exact matching of score,
Kappa ⫽ .90, and 94.5% exact, Kappa ⫽ .87 for FM
patterns, for both samples combined. Two coders
scored each tracing, and disputes were resolved
through consensus.
Data Analysis. The primary analysis strategy for the
fetal measures employed repeated measures multivariate analysis of variance (MANOVA) by socioeconomic group (high vs. low) and fetal sex (male vs.
female). These analyses estimated the developmental
trends over gestation from 24 to 36 weeks as well as
the effects of group inclusion. The following interaction terms were computed: Sex ⫻ Time, SES ⫻
Time, Sex ⫻ SES, and Sex ⫻ SEX ⫻ Time. Because
analysis of variance incorporates unique sums of
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squares to correct for the effect of each independent
variable (i.e., sex and class), this procedure provides
statistical control for the unequal distribution of fetal
sex across socioeconomic group (SES). That is, all
reported F values for either sex or SES test only the
variance that can be uniquely attributed to either sex
or SES. Three subjects in Group 2 were excluded from
the repeated measures analyses due to missing data at
the 30-week point.
RESULTS
Demographic data presented in Table 1 confirm that
women in Group 2 were significantly younger,
t(101) ⫽ 12.79, p ⬍ .0001, and less well-educated,
t(101) ⫽ 10.41, p ⬍ .0001, than those in Group 1.
There were no significant differences either in prepregnancy or intrapartum risk scales or in 5-min Apgar
scores, but Group 2 had higher scores during pregnancy, t(101) ⫽ ⫺ 2.27, p ⬍ .05. Infants born to
women in the lower SES group were significantly
lighter at birth, t(101) ⫽ 3.13, p ⬍ .01, although there
was no difference in length of gestation, t(101) ⫽
1.66. Boys weighed significantly more than girls,
M birthweight ⫽ 3509.2 g boys, 3245.8 g girls,
t(101) ⫽ 3.10, p ⬍ .01, again, without a difference in
gestational age, t(101) ⫽ .44.
Developmental Effects. Combined and group means
for each fetal measure are presented in Table 2. F values for the repeated measure (i.e., gestational) main
effects are presented in the second column. Group ⫻
Time interaction values are presented in Table 3. Fetal
heart rate declined significantly between 24 and 36
weeks, while variability in heart rate increased. Fetal
movement, measured by either the number of movements or the total amount of time spent moving, did
not change over time. However, a significant Time ⫻
SES effect, discussed in a following section, moderates this conclusion. In contrast, the amplitude, or
vigor, of movements did increase with gestation,
F(2, 190) ⫽ 4.27, p ⬍ .01.
Fetuses who display more movement bouts must
have more instances of coupled movements to achieve
the same level of FM – FHR coupling as those that
move less often. As expected, the degree of coupling
was negatively correlated with the number of movements at each age, rs ⫽ ⫺ .17, ⫺ .41, and ⫺ .50, respectively. For this reason, the analysis of FM – FHR
coupling included the number of movements at each
gestational age as a covariate. With advancing gestation, the amount of coupling between fetal movements
and small changes in heart rate increased, while the
latency between the two events decreased.
There were developmental changes in biobehavioral patterns. That is, the percent of time in which any
biobehavioral pattern was evident increased and, conversely, the amount of time in which no biobehavioral
pattern could be detected decreased. In addition, periods of synchronous activity/quiescence became more
frequent over gestation. At 24 weeks, 40 (38.8%) subjects displayed no periods of biobehavioral patterns,
this value decreased to 2 at 30 weeks, and 0 at 36
weeks. Biobehavioral Pattern B was most common,
accounting for 84.6%, 83.5%, and 74.5% of the observation time at 24, 30, and 36 weeks, respectively.
This was followed by BBP D (1.5%, 5.2%, and 8.8%,
respectively), and BBP A (1%, 2.3%, and 5.4%). BBP
C was rarely observed (one and four instances at 30
and 36 weeks, respectively).
Effect of Fetal Sex
Over time, male fetuses had significantly greater variability in heart rate. In addition, development of biobehavioral patterning differed between the sexes. Table
2 indicates a significant sex difference, but the Sex ⫻
Time interaction (Table 3) was also significant. Examination of the means reveals that the sex difference
in the variable is driven by the increased level of biobehavioral concordance for male fetuses at 24 weeks,
but female fetuses attained similar levels by 30 weeks.
The sex difference in mean biobehavioral concordance
at 24 weeks was maintained in post-hoc analyses
which excluded subjects who did not display any concordance, t(61) ⫽ 2.07, p ⬍ .05. There was no effect
for sex in the measure of quiescence/activity, although
at 36 weeks, males displayed fewer episodes of quiescence only, F(2, 99) ⫽ 4.34, p ⬍ .05.
Effect of Maternal Socioeconomic Status
Repeated measures MANOVA was used to determine
whether there were group differences on maternal measures of pulse rate, oxygen saturation, and
blood pressure [calculated as mean arterial
pressure:((2 * diastolic) ⫹ systolic)/3]. While there
were significant effects for gestation for each measure
(mean arterial pressure and pulse rate increased; oxygen saturation decreased), only oxygen saturation
yielded a significant group difference. Lower SES
women had consistently higher oxygen saturation
across gestational age, F(1, 98) ⫽ 10.98, p ⬍ .001.
Lower SES was associated with less variability in
fetal heart rate. In addition, although there were no
significant differences in fetal heart rate, there was a
significant Time ⫻ SES effect: Fetuses in the lower
SES group showed less decrease in fetal heart rate over
gestation, F(2, 192) ⫽ 4.05, p ⬍ .05. Post-hoc anal-
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Table 2. Means for Fetal Measures by Fetal Sex and Maternal Socioeconomic Class
Fetal
Measure
Heart rate
24 weeks
Overall
(n ⫽ 100)
F(time)
(2, 192)
145.6
(6.0)
30 weeks
141.2
(6.7)
36 weeks
139.5
44.52***
(7.4)
Heart rate variability
24 weeks
3.8
(.9)
30 weeks
4.7
(1.3)
36 weeks
5.3
64.42***
(1.6)
Movement bouts
24 weeks
54.7
(18.6)
30 weeks
53.9
(19.4)
36 weeks
48.8
2.42@
(19.9)
Activity level
24 weeks
8.6
(6.0)
30 weeks
8.9
(7.7)
36 weeks
8.7
.08
(7.9)
Movement Amplitude
24 weeks
37.3
(3.9)
30 weeks
37.6
(5.1)
36 weeks
38.8
4.27**
(4.9)
FM– FHR Coupling (%)
24 weeks
21.6
(11.1)
30 weeks
30.4
(13.3)
36 weeks
38.4
45.54***
(15.8)
Coupling latency (s)
24 weeks
4.3
(2.6)
30 weeks
3.4
(1.8)
36 weeks
2.8
10.00***
(2.0)
Biobehavioral Organization (%)
24 weeks
54.0
(47.0)
30 weeks
90.6
(19.3)
36 weeks
89.3
41.92***
(13.1)
High SES
(n ⫽ 52)
Low SES
(n ⫽ 48)
146.0
(5.5)
139.9
(6.1)
138.4
(7.6)
145.1
(6.5)
142.7
(7.0)
140.7
(7.0)
3.9
(.9)
5.1
(1.4)
5.7
(1.7)
3.6
(.8)
4.3
(.9)
4.8
(1.3)
61.1
(15.6)
54.2
(20.0)
49.2
(20.8)
47.7
(19.3)
53.7
(18.9)
48.5
(19.2)
10.0
(5.9)
9.0
(7.7)
8.5
(7.8)
7.0
(5.8)
8.7
(7.8)
9.0
(8.1)
38.5
(3.8)
38.3
(3.9)
38.8
(4.7)
36.0
(3.7)
36.9
(6.2)
38.8
(5.0)
22.7
(11.8)
32.5
(14.9)
38.8
(16.5)
20.2
(10.2)
28.1
(11.1)
37.9
(15.2)
4.5
(2.2)
3.3
(2.0)
2.7
(1.8)
4.0
(2.9)
3.6
(1.4)
3.0
(2.1)
62.1
(45.9)
94.0
(12.1)
89.6
(13.8)
45.2
(47.2)
86.8
(25.6)
87.9
(13.3)
F(SES)
(1, 96)
1.24
6.36**
4.12*
.64
4.12*
3.76*
.05
2.40
Male
(n ⫽ 47)
Female
(n ⫽ 53)
145.7
(5.1)
140.6
(6.1)
138.8
(6.8)
145.4
(6.7)
141.8
(7.1)
140.1
(7.8)
3.9
(.8)
5.1
(1.4)
5.8
(1.6)
3.7
(.9)
4.4
(1.1)
4.9
(1.5)
55.6
(18.8)
50.3
(20.5)
49.0
(21.2)
53.8
(18.6)
57.2
(18.0)
48.7
(18.9)
9.5
(6.6)
8.8
(8.5)
8.9
(8.1)
7.7
(5.4)
9.0
(7.1)
8.6
(7.8)
37.4
(3.9)
37.8
(4.1)
38.5
(5.3)
37.1
(4.0)
39.5
(6.0)
39.1
(4.4)
22.3
(11.0)
35.3
(14.2)
38.6
(15.4)
20.9
(11.2)
26.0
(10.9)
38.2
(16.3)
4.5
(2.2)
3.1
(1.5)
2.8
(2.0)
4.1
(2.8)
3.8
(1.9)
2.8
(2.0)
67.2
(44.4)
92.6
(18.2)
89.6
(13.1)
42.3
(46.5)
88.7
(21.5)
88.0
(14.0)
F(sex)
(1, 96)
.10
4.95*
1.27
.10
.21
1.12
.08
4.95*
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Table 2. Means for Fetal Measures by Fetal Sex and Maternal Socioeconomic Class Continued
Fetal
Measure
Overall
(n ⫽ 100)
Quiescence/Activity (%)
24 weeks
1.5
(3.5)
30 weeks
5.6
(8.7)
36 weeks
11.7
(16.4)
F(time)
(2, 192)
High SES
(n ⫽ 52)
Low SES
(n ⫽ 48)
1.8
(4.0)
6.8
(9.3)
14.4
(19.4)
1.1
(2.9)
4.9
(8.5)
8.9
(11.9)
18.01***
F(SES)
(1, 96)
Male
(n ⫽ 47)
Female
(n ⫽ 53)
1.1
(2.9)
7.4
(9.9)
12.3
(18.1)
1.8
(3.9)
4.6
(7.7)
11.2
(14.9)
4.30*
F(sex)
(1, 96)
.65
Note. Standard deviations (SD) in parentheses.
@
p ⬍ .10. *p ⬍ .05. **p ⬍ .01. ***p ⬍ .001.
yses revealed that heart rate in the lower SES group
was significantly higher at 30 weeks t(101) ⫽
⫺ 2.10, p ⬍ .05, and approached significance at 36
weeks t(101) ⫽ ⫺ 1.65, p ⬍ .10.
Fetuses of the lower SES group moved significantly
less often and with less vigor. However, there was a
significant Time ⫻ SES interaction for movement
bouts. Examination of the data indicates that this effect
was the result of exceptionally few movements in the
lower SES group at 24 weeks, and the change in movements between 24 and 30 weeks for that group was an
increase, as opposed to a decrease as in the upper SES
group. Repeated measures analysis of only the subjects in the upper SES group found a significant linear decrease in movement bouts over gestation,
F(2, 196) ⫽ 3.59, p ⬍ .05. Lower SES was also associated with reduced incidence of FM – FHR coupling.
Fetuses in the lower SES group were less likely to
exhibit synchronous periods of quiescence/activity, although there was no difference in overall level of biobehavioral concordance. Examination of the frequency
of highly active versus highly inactive periods indicates that this result is driven by consistently lower
rates of high activity/variability in the lower SES
group. Both instances of failure to display any clear
biobehavioral patterning at 30 weeks involved female
fetuses from the lower SES group.
Potential Interactive Effects. No interactive effects
for group status were observed. None of the F values
for either Sex ⫻ SES or Sex ⫻ Class ⫻ SES neared
significance (Table 3).
Post-Hoc Analyses. Because there was variation in
maternal characteristics in each group, within group
analyses were conducted to determine whether the factors which varied by social class affected fetal development within social class in the same manner. Maternal age and education were used as covariates in
these repeated measures analyses. In the upper SES
group, higher maternal education was significantly associated with increased heart rate variability,
F(1, 50) ⫽ 5.77, p ⬍ .05, and FM – FHR coupling,
F(1, 50) ⫽ 3.94, p ⬍ .05. In the lower SES group,
there was a similar relation between maternal education and coupling, but it did not attain significance,
F(1, 45) ⫽ 3.25, p ⬍ .10, and higher maternal education was associated with better biobehavioral concordance, F(1, 46) ⫽ 5.36, p ⬍ .05. The only signif-
Table 3. F values for Fetal Sex, SES, and Developmental Interactions
Fetal Measures
Heart rate
Heart rate variability
Movement bouts
Activity level
Movement amplitude
FM– FHR coupling
Coupling latency
Biobehavioral organization
*p ⬍ .05. **p ⬍ .01.
Sex ⫻ Time
(2, 192)
Class ⫻ Time
(2, 192)
Sex ⫻ Class
(1, 96)
Sex ⫻ Class ⫻ Time
(2, 192)
.21
.11
1.43
.30
.07
2.77
1.66
4.36**
4.05*
.19
4.59**
1.81
2.13
.71
.85
.60
.04
.04
.24
.01
.41
1.49
.50
.26
.48
.83
.64
.09
.48
.32
1.71
1.16
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icant association for maternal age was with increased
periods of state quiescence/activity, F(1, 46) ⫽ 5.46,
p ⬍ .05, in the lower SES group.
The two groups differed on the Hobel pregnancy
risk scale, which weights the presence of a variety of
minor and major medical risk factors. There were no
major medical risk factors in this group, and the variable was not normally distributed. There were unequal
variances between groups: Thirty-two and 18 subjects
in the high and low SES groups, respectively, did not
exhibit any risk factors. However, composite scale
scores are of limited utility in developing biologically
plausible mechanisms. Item analysis revealed that four
conditions accounted for almost all the points scored
on this scale: mild anemia (9 – 10.9 gm/dl), cystitis,
vaginal spotting in the first trimester, and maternal prepregnancy weight ⬍ 100 lbs or ⬎ 200 lbs. Of these,
only the incidence of mild anemia, n ⫽ 20 low SES,
5 high SES; ␹2(1, N ⫽ 103) ⫽ 10.7, p ⬍ .001, was
sufficient to analyze separately. Comparisons of
mildly anemic versus not anemic subjects in the low
SES group (repeated measures MANOVA) did not detect differences in the development of any fetal neurobehavioral measure.
DISCUSSION
These results confirm and extend existing knowledge
of neurobehavioral ontogeny in the human fetus. Fetal
maturation is associated with decreasing heart rate and
increasing variability in heart rate more frequent and
more temporally coupled relations between discrete
episodes of fetal movement and heart rate, and development of biobehavioral patterning. Each of these is
characteristic of the development of parasympathetic
tone and modulation of sympathetic activation. The
same developmental patterns were detected in our earlier work, which included an additional point at each
end of the period tested in this study (i.e., at 20 and
38/39 weeks). Moreover, the mean values for each fetal measure at the comparable gestational ages were
highly similar to those reported here, providing us with
reassurance in the application and validity of our measures.
As in our previous work, the vigor (i.e., amplitude)
of fetal movement increased with time, but the current
study failed to support a decrease in fetal activity level.
Mean values for activity in the middle-class group
alone (10 to 8.5 min) are comparable to those from 24
to 36 weeks in the earlier study (12 to 9 min; DiPietro
et al., 1996b), so we conclude that lack of replication
is a function of the circumscribed age range. At least
one other ultrasound-based study (Roodenburg et al.,
87
1991) supports the finding of a decline in activity level
during this gestational period.
Both FM – FHR coupling and biobehavioral patterning involve the developing integration between
two aspects of function: heart rate and motor behavior.
During gestation, fetal movement becomes more
closely associated with predominantly acceleratory excursions in fetal heart rate, although episodic decreases
in heart rate have also been observed (Sorokin et al.,
1982). The prevailing view is that fetal movements
alone do not stimulate increased cardiac output, but
that both cardiac and somatic systems are activated
coincidentally. This orientation has emerged from observational studies of the temporally synchronous nature of activation in both systems (Timor-Tritsch et
al., 1978) as well as experimental manipulations of
fetal sheep preparations in which heart rate accelerations are less frequent, but of robust magnitude, during
temporary fetal paralysis (Bocking, Harding, & Wickham, 1985). The current results, that both the degree
of FM – FHR coupling increases and the temporal association between them becomes more tightly linked
during gestation replicates our earlier report (DiPietro
et al., 1996a).
Male and female fetuses developed similarly on
most fetal measures. In contrast to a previous report
(DiPietro et al., 1996b), no difference in any measure
of fetal activity level was detected. Our pique at this
contradiction led to a pursuit of a variety of post-hoc
strategies to uncover sex differences which may have
been obscured by socioeconomic effects, but none
were detected. Postnatally, activity level is the most
robust behavioral sex difference that has been studied
(Eaton & Enns, 1986), although there are many studies
which have failed to document this effect because the
within sex variance is typically large. Individual variation in fetal activity is also large (i.e., from 0 to 40%
of observation time in these samples, which is in accord with other reports) and it is likely that this creates
instability in replicating between group effects in
small samples. Are there sex differences in fetal activity level or not? At the current time, we are not confident in either position.2
Male fetuses had higher variability in heart rate.
Because heart rate variability is, in part, mediated by
parasympathetic development, this measure has been
used for several decades as an indicator of neurologic
2
In an effort to better address this question, we referred to our
previous data. In that sample, while there was a significant sex effect
over gestation, t-test comparisons at specific points in gestation revealed only two times in which males fetuses were significantly
more active: 20 and 38/39 weeks gestation. Both of these points
were beyond the gestational period of the current study. Thus, gestational age may interact with fetal sex in an unknown manner.
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Pressman et al.
integrity in infancy research (Porges, 1983). As such,
this finding is contrary to the direction of our hypothesis. At least one study has found that preterm boys
more often have low heart rate variability than girls
(DiPietro, Caughy, Cusson, & Fox, 1994). If both are
true, that male fetuses have higher heart rate variability
but male neonates have lower, it could be indicative
of greater difficulty in vagal moderation for boys during the transition to the extrauterine environment. The
sex difference in biobehavioral patterns observed early
in gestation is probably a function of the difference in
heart rate variability, because the most common biobehavioral pattern (BBP B) requires a sufficient level
of variability. The recent discovery of a sex difference
in concentrations of an antiinflammatory agent (Interleukin-1 receptor antagonist) in amniotic fluid which
may be responsible for lower female perinatal risk
(Romero et al., 1994) may spur interest in antenatal
sex differences in general. We hope that further replication and more consistent reporting policies of sex
differences, or the lack of them in fetal research, will
provide clearer understanding of sex differences prior
to birth.
Unlike fetal sex, socioeconomic status affected fetuses in the manner predicted and exerted an effect on
all fetal domains. Fetuses of socioeconomically disadvantaged women had lower heart rate variability,
and heart rate declined less precipitously. Thus FHR
of this group was consistently higher and less variable.
Fetuses in the lower SES group moved less often and
with less vigor, displayed less FM – FHR coupling,
and less often exhibited synchronous periods of quiescence/activity. There were no interactions with fetal
sex, and only one significant interaction with time (i.e.,
number of movements), indicating that the effect of
socioeconomic class is on the level, not the developmental course, of these measures.
There were large demographic differences on variables such as maternal age, which reflect real-world
differences in these groups of childbearing women.
Young maternal age alone is not a significant risk factor for poor outcome; in fact the opposite is true (Cnattingius, Forman, Berendes, & Isotalo, 1992). While
women in the lower SES group had higher pregnancy
complication scores, which can be interpreted as
poorer nonpregnancy specific health (e.g., increased
incidence of cystitis and obesity), during testing
women in this group actually displayed better signs of
maternal adaptation to pregnancy (i.e., higher oxygen
saturation) than the upper SES group. We were unable
to detect an effect of mild anemia, the most common
pregnancy condition.
There was an obvious racial difference in group
composition. While several investigators have analyzed the effect of race on fetal development, it is very
difficult to provide adequate controls for socioeconomic status. While we cannot discount the possibility
that racial differences in fetal functioning exist, we
think that factors inherent in socioeconomic class (i.e.,
differences in maternal nutrition, health status, teratogenic exposure, etc.) currently provide more conceptually compelling mechanisms by which fetal function may be affected. Group differences in diet, for
example, may exert both temporal effects as a function
of the composition of the previous meal, or more
chronic effects based on maternal nutriture. Diet may
directly affect specific fetal domains, such as motor
behavior, or exert a “top-down” effect on motor behavior by altering fetal state. Although there are few
studies investigating the role of maternal nutriture on
fetal neurobehavior, moderate zinc deficiency in rhesus monkeys has been associated with variations in
fetal activity level during gestation (Golub et al.,
1992). In our subgroup analyses, higher maternal education was positively associated with more optimal
fetal functioning on several measures within each
group. Maternal education is only a proxy for those
unmeasured factors which have physiologic impact on
the fetus. There were differences in each group on the
measures associated with maternal education, but the
overall direction of higher education/better development was consistent. There is also evidence to suggest
a mediating role of psychosocial stress, particularly for
low-income women (Lobel et al., 1992). Because of
differences in the types of relevant psychosocial stressors measured in each group, these data could not be
analyzed using the current strategy, and will be the
subject of a future report.
The role of fetal behavioral state in this research
requires comment. As originally defined (e.g., Nijhuis
et al., 1982), ascertainment of fetal behavioral state
requires the use of continuous ultrasound to detect fetal eye movements (FEM). We do not visualize the
fetus continuously for a variety of methodologic reasons, including subject compliance, observer reliability, and FHR signal interference generated by competing sources of ultrasound. Some of these issues
have been discussed previously (DiPietro et al., 1996b;
DiPietro & Johnson, 1997). In addition, mature fetal
states are not consistently observed until 36 weeks gestation (e.g., Martin, 1981). It is for these reasons that
the term “biobehavioral patterns” rather than “states”
was used in this research. It has been reported that
actocardiograph data alone have good sensitivity and
specificity in detecting both sleep states and active
waking, but not for active waking or unclassifiable pe-
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riods (Arabin, Riedewald, Zacharias, & Saling, 1988).
However, our experience leads to different conclusions. We find that the actograph is most useful in
detecting the development of periods of quiescence
and activity but that active sleep cannot be completely
attributed without eye movement data. The use of only
two (FHR, FM) variables instead of three (FHR, FM,
FEM) makes it likely that this method will overestimate the amount of time in concordant states, and for
State 2F in particular. We consistently report less incidence of periods of BBP A than others report states
of IF (quiet sleep). We attribute this to stricter definition of low variability in scoring FHR Pattern A than
used by other investigators and are in the process of
evaluating whether to retain or broaden our scoring.
Consistent with ultrasound-based reports of infrequent
observation of periods of fetal quiet waking (3F), BBP
C was uncommon at any gestational age. Finally, our
attribution rate for unclassifiable states is 10% of the
observation time at 36 weeks, which is in the midrange
of unclassifiable states reported by ultrasound-based
studies. Addition of a third variable would not alter
this figure, because discordancy of two variables is
already present.
Part of the difficulty in longitudinal study of fetal
state development is that variability in heart rate has
different implications at different ages. For example,
earlier in gestation low variability is a sign of neurologic immaturity, while later in gestation low variability coincident with a lack of somatic activity is a
sign of neurologic maturity. However, because there
is some association between somatic activity and heart
rate variability even early in gestation, there is utility
in scoring biobehavioral patterning at this time and it
affords a measure of development throughout gestation. However, our experience is that the features of
each FM and FHR component pattern become qualitatively different over gestation and we do not consider early BBP A or B to be indicative of mature fetal
states. BBP D appears infrequently in early gestation
and does not contribute significantly to the biobehavioral organization variable before 30 weeks. So, while
it is clear that fetal eye movement data are necessary
for ultimate determination of fetal state, the identification of biobehavioral patterns provides an opportunity to quantify aspects of fetal state development (that
is, the integration of more than one domain of funtion)
which may otherwise be methodologically untenable.
In conclusion, while the findings concerning the
role of fetal sex in neurobehavioral development are
somewhat equivocal, those for socioeconomic class
are not. These results illustrate the nongeneralizability
of studies of fetal development which are restricted to
89
a single social-class group. More importantly, they imply that morbidity and anthropometric differences in
newborns as a function of social class are underscored
by antenatal differences in central nervous system
maturation. These results are particularly striking because study recruitment criteria selected for only the
least at-risk pregnancies in a high-risk group. Thus,
the effects of social class are probably underestimated,
and may be more pronounced in the general population. The measures used in this study provide converging evidence for diffuse differences in antenatal
neural development in the lower SES group. While
depressed variability in heart rate and increased heart
rate level both implicate deficits in parasympathetic
tone, the observed depression of fetal activity, particularly at the initial assessment, suggests that sympathetic activation may also be affected. Near the 29th
week of gestation, central mediation of FHR regulation proceeds from a caudal to a more rostral locus
(Yoshizato et al., 1994). Integration of motor and heart
rate domains of function, as reflected in measures of
FM – FHR coupling as well as biobehavioral patterns,
has been proposed to reflect cortical mediation. For
example, episodic FM – FHR coupling is considered
to reflect dual activation of motoric and cardiovascular
processes modulated by diffusion across proximal cortical loci (Timor-Tritsch et al., 1978) and coincident
autonomic activity (DiPietro et al., 1996b). Similarly,
the development of more extended periods of quiescence and activity in both FHR and FM domains is
the hallmark of emerging biobehavioral maturation.
As gestation progresses, variability increases while episodes of movement become less diffuse (Pillai et al.,
1992). Although the site of central control for processes which regulate the development fetal state integration has not been established, it is clear that the
emergence of cyclicity is expressed via generalized
brain activation (Groome & Watson, 1992). While the
etiology of the socioeconomic effect on the fetus observed in this study is not known, given the role of
neurologic development in the expression of fetal neurobehavior, and the similar reductions in fetal functioning observed in compromised fetuses, detection of
associations between poverty and fetal maturation may
have profound implications for child outcome.
NOTES
The investigators wish to thank the diligent and generous
participation of our study families, without which this research would not have been possible, and the Division of
Maternal-Fetal Medicine for its support.
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