EFFECTS OF PERINATAL FACTORS ON BODY MASS INDEX 
AND PHYSICAL FITNESS OF SCHOOL-AGE CHILDREN
Miha LUCOVNIK1*, Gregor STARC2, Petra GOLJA3, Ivan VERDENIK4, Irena STUCIN GANTAR1
Received: Jun 26, 2017
Accepted: Mar 7, 2018
Original scientific article
Objective: To examine the effects of various maternal and neonatal perinatal factors on the child’s body mass 
index (BMI) and physical fitness at school-age.
Methods: Data from two registries, the SLOfit database (a national surveillance system of children’s motor and 
physical development) and Slovenian National Perinatal Information System (NPIS) were analysed. Perinatal 
data for 2,929 children born in 2008 were linked to results of SLOfit testing of these children in 2016. Linear 
regression analysis was used to assess the potential relationship between several perinatal factors (very 
preterm birth, birth mass, maternal age, hypertensive disorders of pregnancy, gestational diabetes, parity, 
plurality, maternal pre-pregnancy BMI, mode of delivery, presentation, Apgar score at 5 minutes, and admission 
to a neonatal intensive care unit (NICU)) and child’s BMI or child’s physical fitness index (PFI) at the age of eight 
years. We also included child’s school grade and maternal educational level in the analysis. A p value <0.05 was 
considered statistically significant.
Results: Children born to mothers with lower pre-pregnancy BMI and higher education had lower BMI and higher 
PFI (p<0.001) at school-age. Physical fitness was also inversely associated with nulliparity (p<0.001) and NICU 
admission (p=0.020).
Conclusions: Among all perinatal factors studied, higher maternal education and lower pre-pregnancy BMI seem 
to be the most significant determinants of child’s BMI and physical fitness at school-age.
Namen: Ugotoviti morebitne dolgoročne učinke perinatalnih dejavnikov na indeks telesne mase (ITM) in 
gibalno učinkovitost otrok v starosti osem let.
Metode: Analizirali smo podatke baze SLOfit (Nacionalnega sistema spremljanja telesnega in gibalnega razvoja 
otrok in mladine) in Nacionalnega perinatalnega informacijskega sistema Slovenije (NPIS). Perinatalne podatke 
za 2929 otrok, rojenih leta 2008, smo povezali z rezultati njihovih meritev SLOfit v letu 2016. Z linearno 
regresijo smo ugotavljali morebitne povezave med otrokovim ITM in indeksom gibalne učinkovitosti (IGU) 
ter naslednjimi perinatalnimi dejavniki: zelo prezgodnji porod, porodna masa, pariteta, število plodov, ITM 
matere pred zanositvijo, način poroda, vstava, ocena po Apgarjevi in potreba po neonatalni intenzivni terapiji. 
V analizo smo vključili tudi razred osnovne šole, ki ga je otrok obiskoval, in materino stopnjo izobrazbe. Za 
statistično značilno smo upoštevali vrednost p<0,05.
Rezultati: Otroci, rojeni materam z višjo izobrazbo in nižjim ITM pred zanositvijo, so imeli v starosti osem 
let nižji ITM in višji IGU (p<0,001). Otroci, ki so potrebovali neonatalno intenzivno terapijo (ne glede na 
gestacijsko starost) (p=0,020), in prvorojenci (p<0,001) so imeli slabšo gibalno učinkovitost.
Zaključki: Med vsemi preučenimi perinatalnimi dejavniki sta stopnja izobrazbe matere in njen ITM pred 
zanositvijo v največji meri vplivala na ITM in gibalno učinkovitost otroka v starosti osem let.
ABSTRACT
Keywords: 
body mass index, 
pre-pregnancy obesity, 
physical fitness, 
preterm birth, 
maternal education
IZVLEČEK
Ključne besede: 
indeks telesne mase, 
debelost, nosečnost, 
porod, gibalna 
učinkovitost, 
izobrazba matere
*Corresponding author: Tel. + 386 1 318 681; E-mail: mihalucovnik@yahoo.com
10.2478/sjph-2018-0011 Zdr Varst. 2018;57(2):81-87
81
VPLIV PERINATALNIH DEJAVNIKOV NA INDEKS TELESNE MASE 
IN GIBALNO UČINKOVITOST OSNOVNOŠOLCEV
Lucovnik M, Starc G, Golja P, Verdenik I, Stucin Gantar I. Effects of perinatal factors on body mass index and physical fitness of school-age children. 
Zdr Varst. 2018;57(2):81-87. doi: 10.2478 /sjph-2018-0011.
© Nacionalni inštitut za javno zdravje, Slovenija. 
1University Medical Centre Ljubljana, Department of Perinatology, Division of Obstetrics and Gynecology, 
Slajmerjeva 4, 1000 Ljubljana, Slovenia
2University of Ljubljana, Faculty of Sport, Gortanova 22, 1000 Ljubljana, Slovenia
3University of Ljubljana, Biotechnical Faculty, Jamnikarjeva 101, 1000 Ljubljana, Slovenia
4University Medical Centre Ljubljana, Clinic for Gynecology and Obstetrics, Division of Perinatology, 
Slajmerjeva 3, 1000 Ljubljana, Slovenia
1 INTRODUCTION
Data on pregnancy complications, labour, delivery, and 
perinatal outcomes are being extensively collected in all 
developed countries. However, due to logistic difficulties 
of a long-term follow-up, only the short-term outcomes, 
such as Apgar scores, neonatal respiratory morbidity, 
admission to neonatal intensive care units (NICU), 
etc., are mostly analysed (1). Of all perinatal factors, 
prematurity has been the most studied in terms of its 
long-term consequences. Preterm birth has been shown 
in observational studies to be associated with decreased 
physical fitness and impaired cognitive functions in later 
life (2–9). Studies on impacts of other perinatal factors, 
such as hypertensive disorders of pregnancy, gestational 
diabetes, intrauterine growth restriction, plurality, parity, 
mode of delivery, etc., on long-term health are, however, 
lacking.
Studies on children born preterm suggest that childhood 
obesity and poor physical fitness could be among potential 
adverse long-term effects of complications in the perinatal 
period (2–14). Childhood obesity and poor physical fitness 
at school-age are associated with many preventable 
diseases and present a serious current and future public 
health problem, especially because they track well into 
adulthood and are associated with numerous adverse 
health outcomes (15–18). With global prevalence of 
childhood obesity continually growing, it is important 
to identify potential perinatal risk factors that could 
be effective targets of future public health initiatives 
tackling obesity (19, 20). Moreover, poor physical fitness 
in childhood has been associated with unfavourable 
psychological, social and cognitive development (21–32). 
It is, therefore, important to establish the impact of 
perinatal factors, such as preterm birth and others, on 
long-term physical development of children.
All births in Slovenia are being registered in the Slovenian 
National Perinatal Information System (NPIS) since 1987. 
The country also has a monitoring system of children’s 
motor and physical development, the SLOfit system, 
implemented in all schools in 1987, which includes almost 
the entire school-going population from ages 6 to 18. The 
aim of the present study was to link the data from both 
registers in order to examine the potential relationship 
between several biological and environmental perinatal 
factors and child’s body mass index (BMI) and physical 
fitness at school-age.
2 METHODS
We analysed the 2016 SLOfit data for children born at our 
institution in 2008, at their age of eight, and linked them 
to their NPIS perinatal data. 
The SLOfit system is a national surveillance system 
of children’s motor and physical development. Every 
April, qualified physical education teachers perform the 
measurements in all primary and secondary schools, 
following the official measurement protocol. The SLOfit 
test battery includes eight motor tests (arm-plate tapping, 
standing long jump, polygon course backwards, sit-ups, 
standing reach touch, bent arm hang, 60-meter run and 
600-meter run), as well as measurements of child’s height, 
weight and triceps skinfold thickness. Measurements are 
always organized in school gyms between 08:00 and 14:00. 
Subjects are weighed barefoot in their shorts and T-shirts 
to the nearest 0.1 kg with portable scales of various brands; 
height is measured with stadiometers of various brands to 
the nearest 0.1 cm. In order to include and evaluate the 
children’s measurements in the SLOfit system, and to use 
the data for scientific purposes, children need the written 
consent of their parents; throughout the existence of 
this system, the response rates in primary schools have 
remained above 94%.
The Slovenian National Perinatal Information System 
(NPIS) registers all deliveries in Slovenia at ≥22 weeks of 
pregnancy or when the birth mass is ≥ 500 g. Registration is 
mandatory by law in the country’s 14 maternity units and 
more than 140 variables are entered into a computerized 
database by the attending midwife and physician. To 
assure quality of data collection, controls are built in 
the computerized system, data is audited periodically, 
and comparisons are made with international databases, 
such as the Vermont Oxford network, in which Slovenia 
participates.
SLOfit data of the eight motor tests were used to calculate 
the physical fitness index (PFI). The z-score of each test 
was computed and the PFI was computed as the mean 
of all eight z-scores as: mean of eight z-scores x ten + 
50. Child’s height and weight measurements were used 
to calculate the body mass index (BMI). By definition BMI 
(both in children and in mothers) was calculated as an 
individual’s weight in kilograms, divided by the height in 
metres squared. BMI was categorized according to the 
Institute of Medicine criteria as underweight (<18.5 kg/
m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/
m2) or obese (≥30 kg/m2) (33).
Student’s T test was used to compare maternal and 
neonatal characteristics in the study group (the group 
for which SLOfit and NPIS data could be matched) and 
in the group in which SLOfit and NPIS data could not be 
matched. Linear regression analysis was used to assess 
the potential relationship between several perinatal 
factors (very preterm birth (<32 0/7 weeks), small for 
gestational age (SGA; birth mass of <10th percentile for 
gestation), large for gestational age (LGA; birth mass of 
>90th percentile for gestation), maternal age at birth, 
hypertensive disorders of pregnancy (diagnosed in 2008 
10.2478/sjph-2018-0011 Zdr Varst. 2018;57(2):81-87
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10.2478/sjph-2018-0011 Zdr Varst. 2018;57(2):81-87
83
in Slovenia as blood pressure of ≥140/90 mmHg after 20 
weeks’ gestation with or without proteinuria), gestational 
diabetes mellitus (diagnosed in Slovenia in 2008 using a 
two-step approach: screening with 50g oral glucose load 
and, when indicated, performing a 100g oral glucose 
tolerance test based on Carpenter and Coustan criteria), 
parity (nullipara vs. multipara), plurality (singleton vs. 
twin pregnancies), maternal pre-pregnancy BMI, mode 
of delivery (caesarean vs. vaginal delivery), presentation 
(cephalic vs. breech), Apgar score at 5 minutes <6, and 
admission to a neonatal intensive care unit (NICU)) and 
child’s BMI and PFI at the age of eight years. We also 
included child’s school grade and maternal educational 
level (defined as primary or vocational school; high 
school or college, and university degree or higher) in the 
regression analysis.
A p value of <0.05 was considered statistically significant. 
The software used for statistical analysis was IBM SPSS 
Statistics for Windows Version 21.0 (Armonk, NY: IBM 
Corp.). 
3 RESULTS
A total of 6,894 neonates were born at our institution in 
2008. Ninety stillbirths and early neonatal deaths were 
excluded leaving 6,804 children for analysis. We were 
able to match 2,929 (43%) of these children from SLOfit 
database to NPIS data due to unavailability of complete 
identifiers in the NPIS register. In other words, NPIS 
includes mothers’ data for identification of children and 
the initial names of children are later often changed. The 
SLOfit database only includes children’s data from official 
school records.
Table 1 presents the comparison between maternal and 
neonatal characteristics in the study group (the group for 
which SLOfit and NPIS data could be matched) and in the 
group in which we were unable to match the SLOfit and 
NPIS data. 
Table 1. The comparison between maternal and neonatal characteristics in the study group and in the group in which SLOfit and NPIS data 
could not be matched.
Data are shown as mean ± SD, or as N (%); BMI body mass index; NICU neonatal intensive care unit; * represents statistical significance 
(p<0.05) (Student’s T test)
Low maternal educational level 
(primary or vocational school degree)*
High maternal educational level 
(university degree or higher)
Very preterm birth (<32 0/7 weeks)*
SGA
LGA
Maternal age (years)*
Hypertensive disorders of pregnancy 
Gestational diabetes
Nulliparity*
Twin pregnancy*
Maternal pre-pregnancy BMI*
Cesarean section
Breech presentation
Apgar score at 5 min <6
NICU admission*
0.03
0.40
<0.001
0.08
0.56
<0.001
0.93
0.90
<0.001
0.02
<0.001
0.07
0.43
0.72
<0.001
135 (3.5%)
1,665 (43.0%)
101 (2.6%)
134 (3.5%)
133 (3.4%)
30.0±4.8
125 (3.2%)
175 (4.5%)
2,298 (59.3%)
210 (5.4%)
23.0±4.2
666 (17.2%)
212 (5.5%)
21 (0.5%)
371 (9.6%)
133 (4.6%)
1,225 (42.0%)
39 (1.3%)
79 (2.7%)
108 (3.7%)
30.4±4.6 
93 (3.2%)
130 (4.5%)
1,223 (41.9%)
122 (4.2%)
23.4±4.2
454 (15.5%)
147 (5%)
14 (0.5%)
205 (7.0%)
PNon-matched 
group
Matched study 
group
10.2478/sjph-2018-0011 Zdr Varst. 2018;57(2):81-87
84
Tables 2 and 3 present the results of the regression 
analysis. Children born to mothers with lower pre-
pregnancy BMI and higher education had lower BMI and 
higher PFI (p<0.001). Physical fitness was lso inversely 
associated with nulliparity (p<0.001) and NICU admission 
(p=0.020), but not with other factors, including very 
preterm birth (p=0.719). 
Table 2.
Table 3.
The relationship between perinatal factors and child’s body mass index (BMI) at the age of eight.
The relationship between perinatal factors and child’s physical fitness index (PFI) at the age of eight.
B regression model coefficient; SE model’s coefficient standard error; BMI body mass index; NICU neonatal intensive care unit; * represents 
statistical significance (p<0.05)
B regression model coefficient; SE model’s coefficient standard error; BMI body mass index; NICU neonatal intensive care unit; * represents 
statistical significance (p<0.05)
High maternal educational level 
(university degree or higher)*
Very preterm birth (<32 0/7 weeks)
SGA
LGA
Maternal age 
Hypertensive disorders of pregnancy 
Gestational diabetes
Nulliparity
Twin pregnancy
Maternal pre-pregnancy BMI*
Cesarean section
Breech presentation
Apgar score at 5 min <6
NICU admission
High maternal educational level 
(university degree or higher)*
Very preterm birth (<32 0/7 weeks)
SGA
LGA
Maternal age 
Hypertensive disorders of pregnancy 
Gestational diabetes
Nulliparity
Twin pregnancy
Maternal pre-pregnancy BMI*
Cesarean section
Breech presentation
Apgar score at 5 min <6
NICU admission
0.102
0.516
0.310
0.266
0.011
0.278
0.234
0.105
0.270
0.012
0.145
0.236
0.737
0.248
0.255
1.266
0.773
0.666
0.029
0.682
0.574
0.261
0.677
0.031
0.365
0.586
2.024
0.618
<0.001
0.091
0.773
0.106
0.150
0.226
0.677
0.159
0.659
<0.001
0.878
0.378
0.376
0.718
<0.001
0.719
0.342
0.409
0.121
0.743
0.241
<0.001
0.405
<0.001
0.541
0.851
0.687
0.020
-0.484
-0.873
0.089
0.430
-0.016
0.337
-0.098
0.147
-0.119
0.153
-0.022
-0.208
0.653
-0.090
1.598
-0.455
-0.735
-0.550
-0.044
0.224
-0.672
-1.528
0.564
-0.105
0.223
-0.110
-0.815
-1.432
-0.091
-0.036
0.006
0.031
-0.029
0.022
-0.008
0.028
-0.009
0.241
-0.003
-0.017
0.017
-0.009
0.120
-0.008
-0.018
-0.016
-0.031
0.006
-0.021
-0.115
0.017
-0.066
0.012
-0.004
-0.008
-0.056
SE
SE
p
p
Unstandardized B
Unstandardized B
Standardized B
Standardized B
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85
4 DISCUSSION
Our findings emphasize the importance of maternal 
education and lifestyle on children’s motor and physical 
development. Higher maternal education and lower pre-
pregnancy maternal BMI were associated with lower BMI 
and higher physical fitness of children at age 8. Children’s 
physical fitness was also linked to parity (first-borns 
having lower physical fitness than their siblings) and NICU 
admission irrespective of gestational age. 
These results are in accordance with several previously 
published studies. Finger et al. showed lower parental 
education level to be associated with higher BMI and 
lower fitness capacity among adolescents (34). Parental 
education and occupation were more strongly related 
to adolescents’ physical activity and fitness outcomes 
than to family income (31). Two other studies reported 
similar results, but they were both based on surveys and 
not on objective fitness capacity measurements (35, 36). 
Children from families with lower socioeconomic status 
have also been shown to have higher rates of metabolic 
syndrome, impaired fasting glucose and type 2 diabetes 
in adulthood (37). Special preventive measures for these 
families with programs focused on healthy nutrition and 
fitness stimulation could effectively decrease the burden 
of metabolic syndrome in adulthood.
Very preterm birth (at <32 weeks’ gestation) was not an 
independent risk factor for increased child’s BMI at the age 
of eight years. This is not in accordance with the results 
from Vasylyeva et al., who showed preterm birth to be 
associated with the highest risk for excessive weight gain 
during childhood (10). However, late preterm birth, and 
not very preterm birth, was the main factor contributing 
to excessive weight gain in their study (10). A possible 
explanation for this could be that altered appetite 
regulation or altered insulin secretion are responsible 
for childhood obesity only in preterm infants with higher 
birth mass, i.e., those born in the late preterm period 
(11, 12). Physical fitness was also not associated with very 
preterm delivery. However, admission to the NICU was 
an independent risk factor for decreased physical fitness 
at school-age. This may be explained by increased pain 
sensitivity, increased avoidance behaviour, and social 
hypervigilance, which are all possible consequences 
of untreated NICU associated pain in early infancy and 
are unrelated to gestational age at NICU admission (13). 
Decreasing stress in the NICU and active encouraging of 
parents to stimulate their NICU children to participate 
in regular childhood physical activities and sports could, 
therefore, be very important to decrease the negative 
influence of NICU on physical fitness in childhood.
The association between birth order and physical fitness 
has not been well studied to date. We found first-born 
children to have lower exercise capacities, in contrast to 
Barclay and Myrskyla, who reported the opposite, i.e., 
better physical fitness in first-borns (38). It is important to 
notice that they analysed only males, who were born in a 
different time period (1965–1977). They interpreted their 
findings as being the consequence of social upbringing 
within the family rather than prenatal experience. Our 
results seem to suggest that older siblings positively 
influence the physical activity of their younger brother 
or sister. The important influence of peer stimulation is 
supported by the fact that children who attended higher 
school grades at the same age had better PFI in our study. 
The main limitation of the study is its retrospective 
observational nature, which does not allow accounting for 
all potential confounders. It also does not allow deriving 
definitive conclusions on causal relations between factors 
studied and children’s BMI and physical fitness. Moreover, 
we were not able to match the data of all children from 
the SLOfit database and NPIS. The group in which we 
were unable to link children’s data from SLOfit to NPIS 
differed in many maternal and neonatal characteristics 
from the group for which data from these databases could 
be matched. Many of these differences were statistically 
important due to large numbers, but clinically irrelevant, 
e.g., the 94 g difference in birth mass between the two 
groups. Other differences, such as higher proportion of 
nulliparous women in the non-matched group, however, 
could indicate a systemic bias. Our results should, 
therefore, be interpreted with caution. To allow this, 
we decided to present also maternal and neonatal 
characteristics of pregnancies of the non-matched 
data. We have not stratified BMIs in subgroups, but only 
determined a correlation between maternal and child’s 
BMI and other factors. Further studies will, therefore, be 
needed in order to more exactly determine which BMIs 
(in which subgroups) are correlated with which outcomes. 
We believe, however, that our results highlight the 
importance of maternal pre-pregnancy BMI and will lead 
to additional studies in this field. 
In conclusion, our results seem to suggest that factors such 
as high maternal pre-pregnancy BMI and low maternal 
education level seem to be among the most important 
determinants of worse child’s physical health at school-
age. These public health domain factors seem to be of 
greater importance for child’s long-term development 
than many perinatal outcomes, such as very preterm 
birth, SGA, LGA or low Apgar scores, on which perinatal 
medicine is currently mostly focused. Finally, our study 
shows that social factors seem to significantly determine 
the developmental outcome of children, which gives lots 
of opportunities for effective interventions in the pre-
school and school age.
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CONFLICTS OF INTEREST 
The authors declare that no conflicts of interest exist.
FUNDING
The study was not financed.
ETHICAL APPROVAL
Received from the National Medical Ethics Committee 
(approval number 102/03/15).
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