It is well acknowledged that quantitative limits for DFM perform poorly for screening purposes, indicating the need for further refinement [11, 28]. Our study, as far as we know, is the first to extract individual temporal patterns from FM chart data. For this purpose, we have used functional data analysis (FDA) and functional principal components analysis (FPCA). Recognizing that extreme observations were removed before FDA, we found that almost all of the observed variation between women’s smoothed temporal FM curves was accounted for by mere three temporal components; a general FM count level, a linear trend, and a U-shape. These components can be readily interpreted in a biological context.
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 . Analyses of patterns in FM counting charts have mostly been restricted to healthy pregnancies aiming to define limits of normality [21, 22, 33]. These studies have, with few exceptions [20, 34], focused on group averages and deviations from these [14, 21, 22], ignoring that observations from the same woman are naturally ordered in time, and strongly correlated. The conclusions from these studies may therefore be of limited value as key characteristics of FM chart data is unaccounted for. Direct comparisons of our outcomes with previous research may therefore be misleading.
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 day-to-day 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 day-to-day deviations from this temporal mean. For women with a strong increasing, linear trend, such as woman 7 in Figure 2, the crude, overall point-mean will be a poor representation of her temporal pattern, and the accompanying SD will be unrealistically high. However, when considering her temporal mean, the accompanying temporal SD is actually very low. Previous point-wise results will therefore be biased or outright misleading. Indeed, a crude, overall mean of 21 minutes does not capture the linear trend, and the corresponding SD of 9 minutes is a gross overestimate. Moreover, FM charts with comparable mean counting times may hide fundamentally different temporal patterns. The crude, overall mean (SD) for woman 3 in Figure 2 is 24 (16) minutes, similar to woman 7.
Although previous studies have rightly recognized the potential limitations of point-wise measures [20, 33, 35], none have provided meaningful alternatives. Our statistical approach demonstrates how temporal patterns in FM charts hold valuable information for the interpretation of relevant counting measures, and how this can be overlooked when not taking the temporal nature of FM chart data into account. The results indicate that conclusions from previous studies ought to be revisited.
Our results are consistent with previous research in two central areas. First, there is considerable variation in FM between pregnancies, but lower variation within pregnancies [19, 20, 36]. Second, pregnancy characteristics may explain some of the variation in perceived FM between pregnancies [19, 28, 36–38].
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 . However, most of these studies did not account for the high correlation of observations within pregnancies. They were also small, with divergent results .
Another approach has been to compare maternal characteristics of women presenting spontaneously with DFM with reference groups [38, 40], whereas FM counting studies have, with few exceptions [19, 28], mainly reported whether maternal characteristics have been associated with various fixed alarms [19, 36]. They have not explored the association between FM counting patterns and maternal characteristics. Thus there are few studies available to compare with our results.
Overweight and obese women more often report DFM . They are also at increased risk of severe pregnancy complications . However, since many have favorable outcomes [38, 40], it has been suggested that the perceived DFM reflects reduced sensitivity to FM from excess adipose tissue rather than fetal compromise. There is to date no firm knowledge to disentangle these effects .
In line with previous studies [19, 28], we found that maternal obesity was significantly associated with higher counting times compared to the reference group (BMI<25). Yet the effect was very small. Note that the effect of maternal BMI was related to obesity and not overweight. Thus, our study suggests that FM counting is applicable also for overweight and obese women. This is important since these women represent a large and growing risk group for obstetric complications in high income countries . Our result is contrasting a previous study stating that DFM may have greater diagnostic significance in normally weighing women . This former study is influential as it is cited in a recent Cochrane review on management strategies for women perceiving DFM .
Anterior placental site has been reported to decrease a woman’s perception of FM prior to 28 weeks of gestation . We found anterior placental site to be significantly associated with a moderate down-towards-birth pattern (FPC2) and with a U-shaped pattern (FPC3), combining the gradual decrease in counting time with a small increase in late gestation. However, for most women, this effect was small similar to what was found for the general level. As seen in Figure 4, even for the women with the five largest positive and the five largest negative scores, the individual curves show modest changes in terms of minutes. Parity is reported not to influence FM counting once quickening is reached [19, 23, 28]. This corresponds with our findings.
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 [19, 28, 33] or increase [20–22] with advancing gestation, we found increasing gestational age to be associated with shorter counting times (FPC1). However, as mentioned, direct comparisons with previous studies may be misleading, as these tend to not account for the intra-woman correlation in FM chart data. Importantly, with this counting method, it is not normal for women to perceive DFM in late gestation. Note that the statistically significant associations in this study reflect overall relatively small effects.
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 55-97% range previously reported [12, 14, 15, 21, 28, 34]. Our recruitment rate was higher than in previous reports [15, 28], which might have caused a higher drop-out rate. Moreover, mothers were asked to count FM from pregnancy week 24, earlier than in previous studies [12, 14, 15, 28, 34], which might have caused reporting fatigue. Secondly, our sample appears to be skewed towards healthier pregnancies, similar to what have been reported previously [15, 28]. FM counting may be more appealing to mothers with active babies, since they are then reassured about the baby’s well-being within a short time. Therefore, both counting times and day-to-day variability may be underestimated compared to a total population. Thirdly, the FDA approach was well-suited for extracting individual temporal FM counting patterns and for exploring their associations with pregnancy characteristics. However, it was not suitable for capturing the rapid temporal changes introduced by the spikes, i.e. sudden long counting times relative to the body of the woman's observations. Such "alarms" may reflect acute changes to fetal well-being, and merits further investigation. However, a different statistical approach is required for spikes to be captured in long time series. Spikes occur seemingly randomly throughout pregnancy as illustrated in Figure 1 and occur in both healthy and riskier pregnancies . These spikes would tend to be averaged out with our FDA approach. Time has escaped the “fixed” limits for DFM. Before studying pathological FM counting patterns, future analyses should explore extreme observations in the FM chart, as well as other time-dependent out-of-the-ordinary observations, when modeling FM count data.
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 [34, 43]. Before we can expect higher acceptance rates, FM counting must prove useful to both women and care providers. Hence, better information about normal FM and how to interpret FM counting patterns is needed. Although not applicable on an individual level, we have with this comprehensive statistical approach taken an important first step in identifying temporal patterns in FM charts.
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 . Further, maternal characteristics or anterior placental site do not seem to be incompatible with FM counting. Finally, the wide-spread notion that fetal activity decreases in late pregnancy is refuted. With this counting method, a decrease in FM in late pregnancy is not normal. This is a core component of information that should be provided to pregnant women .