Division of Epidemiology, Norwegian Institute of Public Health, PO Box 4404 , Nydalen, 0403, Oslo, Norway
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
Abstract
Background
Fetal movement counting has long been suggested as a screening tool to identify impaired placental function. However, quantitative limits for decreased fetal movement perform poorly for screening purposes, indicating the need for methodological refinement. We aimed to identify the main individual temporal patterns in fetal movement counting charts, and explore their associations with pregnancy characteristics.
Methods
In a populationbased prospective cohort in Norway, 2009–2011, women with singleton pregnancies counted fetal movements daily from pregnancy week 24 until delivery using a modified "counttoten” procedure. To account for intrawoman correlation of observations, we used functional data analysis and corresponding functional principal component analysis to identify the main individual temporal patterns in fetal movement count data. The temporal patterns are described by continuous functional principal component (FPC) curves, with an individual score on each FPC for each woman. These scores were later used as outcome variables in multivariable linear regression analyses, with pregnancy characteristics as explanatory variables.
Results
Fetal movement charts from 1086 pregnancies were included. Three FPC curves explained almost 99% of the variation in the temporal data, with the first FPC, representing the individual overall counting time, accounting for 91% alone. There were several statistically significant associations between the FPCs and various pregnancy characteristics. However, the effects were small and of limited clinical value.
Conclusions
This statistical approach for analyzing fetal movement counting data successfully captured clinically meaningful individual temporal patterns and how these patterns vary between women. Maternal body mass index, gestational age and placental site explained little of the variation in the temporal fetal movement counting patterns. Thus, a perceived decrease in fetal movement should not be attributed to a woman’s basic pregnancy characteristics, but assessed as a potential marker of risk.
Background
Fetal movement (FM) counting by pregnant women has long been suggested as a screening tool to identify impaired placental function. The rationale is that a fetus will respond to reduced uteroplacental blood flow and fetal hypoxia by decreasing gross fetal movements
So far, however, there is no conclusive evidence to support or refute formal FM counting as a means to reduce perinatal morbidity and mortality
The traditional approach to analyzing FM charts has been to focus on pointwise, i.e. daytoday, group averages based on birth outcome
In order to uncover temporal patterns on an individual level, rather than merely look at daytoday group averages, we turn to functional data analysis (FDA), a statistical methodology specifically developed for analyzing curve data, and long time series observations
In a prospective screening scenario, birth outcomes are not yet known. To eventually be able to single out pathological patterns in FM counting, an important initial step is to identify the main temporal patterns in FM count data from a total population, and establish whether the expected temporal pattern of a given woman’s FM count data depends on basic pregnancy characteristics such as maternal body mass index, gestational age and placental position. If so, what is the effect and what are the implications?
The aim of this study was to identify the main temporal patterns in FM count data on an individual level in pregnancies recruited from a total population. We also wanted to explore whether any of these temporal patterns were associated with basic pregnancy characteristics. To the best of our knowledge, our study is the first to extract individual temporal patterns from FM chart data.
Methods
Details of ethical approval
Written informed consent was obtained from all participants. The study was approved by the Regional Committee for Medical Research Ethics, S08694d, 2008/18353, 06.26.2009.
Data collection
From July 2009 to July 2011, all women with singleton pregnancies attending Østfold Hospital Trust for routine ultrasound screening in pregnancy week 17–19 were invited to participate in the study. This routine ultrasound screening captures > 98% of the pregnant population
During the twoyear period 2468 women (41% of eligible pregnancies) agreed to participate in the study and gave written informed consent. Among them 1445 (59%) later submitted their FM charts and constitute the study sample, see flowchart for recruitment (Additional file
Figure S1. Flowchart of recruitment.
Click here for file
Study group, n= 1086
Østfold Hospital, year 2009 ^{ # }, n= 3212
Relative risk (RR)
p ^{ // }
n (%)**
n (%)**
RR (95% CI)
^{#}
^{
//
}
^{**}
The table presents demographic and obstetric characteristics and birth outcome of the total population of pregnant women in Østfold Hospital and for the women included in the analyses.
MATERNAL CHARACTERISTICS
Maternal age, years [mean, SD]
30.7 [4.7]
29.2 [5.2]
Maternal age ≥ 35 years
196 (18.0)
544 (16,9)
1.1 (0.91.2)
0.400
Maternal BMI, kg/m^{2}, [mean, SD]
24.8 [5.1]
Not available


Maternal obesity (BMI ≥ 30 kg/m^{2})
155 (14.3)
Not available


Primiparity
574 (52.9)
1370 (42.7)
1.2 (1.21.3)
<0.001
Daily/occasionally smoking 1.trimester
93 (8.5)
673 (21.0)
0.4 (0.30.5)
<0.001
Anterior placental site
479 (44.1)
Not available


DELIVERY MODE
Vaginal deliveries
905 (83.3)
2545 (78.3)
1.1 (1.01.1)
<0.001
Induced vaginal deliveries
182 (20.1)
304 (11.9)
1.7 (1.42.0)
<0.001
Assisted vaginal delivery
131 (14.5)
282 (11.1)
1.3 (1.11.6)
0.006
Cesarean sections (CS), total
181 (16.7)
706 (21.6)
0.8 (0.70.9)
<0.001
Emergency CS
117 (64.6)
442 (62.6)
1.0 (0.91.7)
0.552
CHARACTERISTICS OF THE NEWBORNS AND BIRTH OUTCOME
Gestational age, weeks [mean,SD]
39.6 [1.6]
39.2 [1.9]
Male gender
562 (51.7)
1726 (52.7)
1.0 (0.91.1)
0.588
Birth weight in grams [mean, SD]
3584 [524]
3492 [623]


Low birth weight (<2500gr)
27 (2.5)
147 (4.5)
0.6 (0.40.9)
0.006
Stillbirth [> 22 weeks, per 1000]
3 [2.7/1000]
8 [2.4/1000]
Preterm (22^{0}36^{6} weeks)
52 (4.8)
213 (6.6)
0.7 (0.51.0)
0.031
Apgar score <7_{5min}
16 (1.5)
55 (1.7)
0.9 (0.51.5)
0.642
Fetal movement counting and recording
The counting protocol (Additional file
Appendix 1. Counting protocol.
Click here for file
Appendix 2. Fetal movement chart.
Click here for file
Missing data in the FM charts
Overall, the 1445 women who submitted their FM charts recorded counting in 77% of days from week 24 until birth, 80% and 59% in the preterm and term period respectively. In many of the FM charts a substantial amount of counting observations was missing. The last 90 days preceding birth only 120 (8%) women had complete FM charts; 518 (36%) had 1–10 percent missing, 448 (31%) had 11–50 percent missing and 359 (25%) had more than 50% missing.
The unit of observation in our analyses is the individual FM chart. Since more than 90% of FM charts had some missing, imputation was necessary before proceeding with further statistical analysis. We chose to include only women with on average more counting days per week than not, i.e. at least 4/7 = 57% of the last 90 days preceding birth. This left 1086 (75%) women for statistical analysis. Comparing demographic and birth outcomes between women who were excluded due to missing observations and those included in the analyses, showed no difference between the groups other than a lower proportion women aged ≥ 35 years among those excluded (Additional file
Table S1. Characteristics of pregnancies excluded from analyses and those included.
Click here for file
Statistical analyses
Descriptive statistics are presented as mean, standard deviation (SD) and range, or frequency and percentage (%). The FM counting observations are heavily skewed, and were log transformed before further statistical analysis. Statistical analysis was performed in SPSS 12.0, R 2.12 and Winbugs 3.0. See Appendix A for details.
Outliers
DFM is often perceived as extreme changes in FM by the mother, and several of the FM charts included such extreme counts reflecting comparably long counting times relative to the body of the woman’s observations. The aim of the current study was to extract the general temporal patterns, and outlying observations were thus removed before further statistical analysis.
Functional data analysis
The FM charts were analyzed using functional data analysis (FDA), a statistical methodology specifically developed for analyzing curve data or long time series
Functional principal component analysis
Principal component analysis (PCA) is a statistical methodology that can be seen as unveiling the internal structure of the data in a way that best describes the variation in the data
Multiple regression models
To explore the effects of normal variants of basic pregnancy characteristics on the temporal FM patterns, the FPC scores were used as outcome variables in univariate and multiple linear regression analyses. Body Mass Index (BMI) was included as a categorical variable according to WHO criteria
Results
FM count data for a random sample of 100 women is shown in Figure
Original fetal movement count data from a random sample of 100 fetal movement charts
Original fetal movement count data from a random sample of 100 fetal movement charts.
Smooth curve fits for nine randomly selected fetal movement charts
Smooth curve fits for nine randomly selected fetal movement charts. Transformation of observed fetal movement count data (dots) to smooth curve fits (solid line), together with removed outlying observations (squares) for 9 randomly selected women. Grey shaded area is 95% CrI for the fit.
Performing functional principal component analysis (FPCA), the three first FPC curves explained 90.7, 6.0 and 2.2% of the total variation between the individual curves, respectively; in sum almost 99% of the total observed variation. These three FPC curves are shown in Figure
The first three functional principal component curves for mean and residuals
The first three functional principal component curves for mean and residuals. The first three functional principal components (FPC) for mean (upper row) and residuals (lower row). The effect of the FPC scores is on a multiplicative scale. The analysis of FM count data were on a logtransformed scale (additive effect) which later has been antilogged (multiplicative effect). Zero score on a component therefore corresponds to multiplying by one and the mean score (stippled line) crosses one.
The first and by far most dominant FPC curve (FPC1) mainly represents the general level of the individual temporal FM curves relative to the overall temporal mean for all women. A high positive score on FPC1 implies longer than average counting times and a large negative score implies shorter than average counting times. Included in FPC1 is also a small increase in counting times the very last days before birth.
The second FPC curve (FPC2) relates to a linear increase or decrease in counting times as the pregnancy proceeds; women with a high score on FPC2 will have a tendency towards increasing counting times as the pregnancy proceeds, while women with a large negative score will have a tendency towards decreasing counting times. A small plateau appears in the FPC2 curve the last days prior to birth.
The third FPC curve (FPC3) has an inverted Ushape, and high scores on FPC3 indicate higher than average counting times in the mid of the counting period, while high negative scores implies shorter counting times in the mid period, compared to what is to be expected for that given general level.
Smooth temporal FM curves for the women with the five highest and five lowest scores for each of the three FPCs are shown in Figure
Individual curves for women with largest positive and negative functional principal component scores
Individual curves for women with largest positive and negative functional principal component scores. Individual curves for the women with the five largest positive (black) and five largest negative (grey) scores for each of the three FPCs for smoothed fit and for residuals. Overall mean superimposed (dotted line).
The FPCs representing women's deviations from their own smooth means can be interpreted in a similar fashion. The three first FPC curves explain 86.6, 7.0 and 4.3% of the total variation in the residuals, respectively; in sum almost 98% of the observed variation. These FPCs are shown in Figure
The first residual FPC curve is by far the most dominant, and represents the overall level for the temporal residuals. The second residual FPC curve relates to increasing or decreasing residual variability in FM counting times approaching birth, while the third residual FPC curve is an inverted Ushape, representing more variability in the beginning and end of the counting period compared to the middle.
Fitted smooth temporal FM curves for women with the five highest and five lowest scores for each of the three FPCs for temporal residuals are shown in Figure
Results from multiple regression analyses
The multiple linear regression results for the association between the variation in the three main FPC for the smoothed temporal mean and various pregnancy characteristics are presented in Table
FPC1 for mean
FPC2 for mean
FPC3 for mean
Univariate linear regression
Multiple linear regression
Univariate linear regression
Multiple linear regression
Univariate linear regression
Multiple linear regression
Effect (95% CI)
pvalue
Effect (95% CI)
pvalue
Effect (95% CI)
pvalue
Effect (95% CI)
pvalue
Effect (95% CI)
pvalue
Effect (95% CI)
pvalue
Level of statistical significance * < 0.1, **< 0.05, ***< 0.001.
^{I} Body Mass Index, kg/m^{2}.
^{II} Maternal overweight (25≤BMI<30) versus maternal normal or underweight (BMI <25.00).
^{III} Maternal obesity (30≤BMI) versus maternal normal or underweight (BMI <25.00).
^{b} Predominantly anterior placental site reported from routine ultrasound examination in pregnancy week 18.
Linear regression with scores on the functional principal components for the smooth curve fits for the mean as the dependent variable and basic pregnancy characteristics as explanatory variables.
Maternal BMI ^{I} categorized
Overweight ^{II}
0.04
0.173
0.11
0.162
−0.18
0.097*
−0.13
0.093
−0.11
0.029**
−0.17
0.028**
(−0.28,1.57)
(−0.04,0.26)
(−0.39,0.03)
(−0.28,0.02)
(−0.21,0.01)
(−0.31,0.02)
Obesity ^{III}
0.09
0.007**
0.25
0.006**
−0.06
0.046**
−0.22
0.014**
0.02
0.711
0.02
0.824
(0.04,2.59)
(0.07,0.42)
(−0.51,0.01)
(−0.40,0.04)
(−0.09,0.14)
(−0.15,0.20)
Primiparity
−0.03
0.353
−0.05
0.383
−0.13
0.119
−0.11
0.082
0.02
0.459
0.06
0.355
(−1.09,0.39)
(−0.18,0.07)
(−0.30,0.03)
(−0.23,0.01)
(−0.05,0.11)
(−0.06,0.18)
Anterior placental site^{b}
0.04
0.183
0.10
0.127
−0.15
<0.001***
−0.32
<0.001***
−0.07
0.026**
−0.15
0.016**
(−0.24,1.25)
(−0.03,0.22)
(−0.60,0.27)
(−0.44, 0.19)
(−0.17,0.11)
(−0.27,0.03)
Gestational age, days
 0.04
0.012**
−0.01
0.006**
0.05
0.111
0.06
0.098
0.06
0.038**
0.08
0.021**
(−0.08,0.01)
(−0.17,0.03)
(−0.00,0.11)
(−0.01, 0.13)
(0.00,0.01)
(0.00,0.15)
Smooth curve fits corresponding to one standard deviation above the overall temporal mean
Smooth curve fits corresponding to one standard deviation above the overall temporal mean. Smooth curve fits for two women (grey solid line) with functional principal components scores (FPC1) corresponding to one standard deviation above the overall temporal mean for all women (black solid line).
Pregnancy characteristics were not significantly associated with the FPCs for residuals, i.e. each woman’s deviations from her own temporal mean (Additional file
Table S2. Linear regression with scores on the functional principal components (FPC) for smooth curve fits for residuals (SD) as the dependent variable.
Click here for file
Discussion
It is well acknowledged that quantitative limits for DFM perform poorly for screening purposes, indicating the need for further refinement
Fetal activity must be seen as a longitudinal process, as its temporal pattern provides important information. However, previous FM counting studies have mainly focused on fixed limits for DFM and their ability to identify risk
A central element of FDA is fitting a smooth curve to the actual observations, effectively separating the underlying signal from the uninformative “noise”, e.g. natural daytoday variation not reflecting any physiological change. As the natural, and random, variation in the counting process is often relatively high, a strong smoothing effect, as we see in our analysis, was expected.
Somewhat surprisingly, we did not find a statistically significant association between higher overall mean FM count and high SD, i.e. a woman’s smooth temporal mean and her daytoday deviations from this temporal mean. For women with a strong increasing, linear trend, such as woman 7 in Figure
Although previous studies have rightly recognized the potential limitations of pointwise measures
Our results are consistent with previous research in two central areas. First, there is considerable variation in FM
By far, the differences in the general level of the fitted temporal FM curves accounted for most of the variation between women. This may simply reflect that activity level between fetuses varies. However, it has also been suggested that women may differ in their ability to perceive FM
Previous studies on the effect of maternal characteristics on women’s ability to perceive FM have not reached clear conclusions. One typical approach has been to compare ultrasound observed FM with those perceived by the mother and explore how these vary with maternal characteristics
Another approach has been to compare maternal characteristics of women presenting spontaneously with DFM with reference groups
Overweight and obese women more often report DFM
In line with previous studies
Anterior placental site has been reported to decrease a woman’s perception of FM prior to 28 weeks of gestation
We aligned our data from birth and 90 days backwards, so that we could capture FM counting patterns approaching delivery. Contrary to previous studies reporting that counting times remain constant
We included pregnancies from a total population in our analyses. FPCA sequentially extracts the various temporal patterns where the variation between women is the largest, second largest and so on. As unfavorable birth outcomes are relatively rare, (possible) temporal patterns related to such pregnancies would not be common in a large group of women, consequently ranging low in relative importance of the FPCA. Extracting a large amount of FPCAs would capture these patterns, but these will, by mathematical construction of the PCA, not affect the main results, i.e. the main temporal patterns.
Three limitations need to be mentioned. Firstly, the compliance with daily counting was towards the lower end of the 5597% range previously reported
There seemed to be reporting fatigue in the FM charts, with compliance rates falling towards term. Previous FM counting studies have consistently reported that continued encouragement from health care providers yields the most complete findings
Our results carry important clinical messages. A perceived change in FM should not be attributed to a woman’s maternal characteristics or placental location, but rather be interpreted as a true change in FM, potentially indicating fetal compromise. This should be clarified in published guidelines
Conclusions
We have successfully extracted the main temporal patterns in FM counting data, both overall and for individual women. Results from previous studies, which do not take intrawoman correlation of counting observations into consideration, might need to be revisited. Overall, pregnancy characteristics explained little of the variation in temporal FM counting patterns, implying that perceived DFM should be interpreted independent from these characteristics.
Appendix A, Statistical procedures
Outlying observations were identified by removing the estimated underlying time series trend in each FM chart, and assessing interquartile range (IQR) for the remaining residuals. IQR above 1.5 was used as the cutoff for being an outlier
We simultaneously performed missing imputation, fitting of smooth curves to each woman’s FM chart, and estimation of general temporal patterns across women by calculation of functional principal components, using a Bayesian approach
To completely specify the Bayesian model, one needs to provide prior distributions for the model parameters. We used independent Gamma (10^{3}, 10^{3}) priors for the variances, and ten eigenfunctions. We ran 1500 simulations, and disregarded the first 500 as burnin. This ensured an R^ of approximately 1 for all parameters, indicating convergence
Credibility intervals (CrI) are the Bayesian parallel to confidence intervals (CI) to assess estimation uncertainty. The methodology applied returns 95% CrIs for the fitted functional objects to the individual FM count data
The Bayesian version of functional principal component analysis applied in this work is described in detail in Crainiceanu and Goldsmith
They also give the general WinBugs code needed to run the analysis. We recommend for the interested reader to obtain more detailed information on the applied FPCA methodology in the sited reference. The statistical analyses were done in R 2.12
For the FDA we used function R2Winbugs
Competing interests
This project is funded in whole by the Research Council of Norway. The authors declare they have no competing interests.
Authors’ contributions
All three authors have contributed to this scientific work and have approved the final version of the manuscript. BAW has been responsible for data collection and for scientific interpretation of the FDA/PCA results, for regression analyses and for writing and revising the manuscript. JR has been responsible for the FDA/FPCA analyses, presentation of the various plots, and for writing the statistics section and the Appendix, and revising the manuscript. JFF had the original idea for the study, has contributed to scientific interpretation of the FDA/FPCA and for revising the manuscript.
Acknowledgements
This study is carried out in close collaboration between the Norwegian Institute of Public Health and Østfold Hospital Trust. We want to thank Tone Larsen, the coordinating midwife at Østfold Hospital Trust for her efforts in recruiting and followingup participants and for data collection and Dr. Christopher Finne Riley for facilitating the study within the hospital. Our colleagues, Jorid Eide and Eli Saastad at the Norwegian Institute of Public Health have provided considerable help and support in making this study possible. Finally we gratefully acknowledge all the mothers for their willingness to contribute to research.
Prepublication history
The prepublication history for this paper can be accessed here: