Bjertnaes et al. BMC Pediatrics(2019) 19:431
https://doi.org/10.1186/s12887-019-1790-2

RESEARCH ARTICLE

Open AccessSex-related change in BMI of 15- to 16year-old Norwegian girls in cross-sectional
studies in 2002 and 2017
Asborg A. Bjertnaes1,2* , Jacob H. Grundt3, Petur B. Juliusson4,5,6, Trond J. Markestad7, Tor A. Strand7 andMads N. Holten-Andersen1,2

Abstract
Background: The prevalence of overweight and obesity (OWOB) has stabilized in some countries, but a portion ofchildren with high body mass index (BMI) may have become heavier. This study aimed to describe the distributionsof BMI and the point prevalence of OWOB in Norwegian adolescents in 2002 and 2017.
Methods: A cross-sectional study involving 15- to 16-year-old adolescents in Oppland, Norway, was undertaken in
2002 and 2017. We calculated their BMI, BMI z-scores (BMIz), and the prevalence of OWOB.
Results: The mean BMI increased from 20.7 to 21.4 (p < 0.001) for girls but remained unchanged at 21.5 vs 21.4
(p = 0.80) for boys. The prevalence of OWOB increased from 9 to 14% among girls (difference 5, 95% CI: 2, 8) andfrom 17 to 20% among boys (difference 3, 95% CI: − 1, 6%). The BMI density plots revealed similar shapes at bothtime points for both sexes, but the distribution for girls shifted to the right from 2002 to 2017.
Conclusion: Contrary to previous knowledge, we found that the increase in OWOB presented a uniform shift in theentire BMI distribution for 15–16-year-old Norwegian girls and was not due to a larger shift in a specific subpopulationin the upper percentiles.
Keywords: Adolescent, Body mass index, Body mass index distribution, Obesity, Overweight, Sex differences

Background
The relationship between body mass index (BMI) in adolescence and subsequent health in adulthood is well established [1–3], and both overly low and overly high BMI
values are of concern [4]. The prevalence of adolescentoverweight and obesity (OWOB) has increased over the last
decades [5], and studies have found that this change is primarily due to increasing BMI in subgroups in the upper
percentiles of the BMI distribution [6].
Population changes in BMI distributions over time
have been studied in many countries [7–9], including
the US [10]. However, relatively few European studies
have addressed this issue in adolescents, and even fewer
are based on data from the last decade when the obesity

epidemic is said to have stabilized in some countries[11].
Adolescents with obesity have a high risk of becoming
adults with obesity [12]. As both the biology of OWOB
[13] and comorbidities due to central fat distribution differ by sex [14], sex-related trends in adolescent OWOB
are important to elucidate for public health reasons.
In this study, we compared BMI distributions and theprevalence of OWOB in Norwegian adolescents in 10th
grade (15–16 years of age) at 15-year intervals stratified
by sex. Our aim was to explore whether an increasing
mean BMI and prevalence of OWOB was due to increasing BMI within a subgroup of adolescents.

Methods* Correspondence: Asborg.Aanstad.Bjertnaes@sykehuset-innlandet.no1
Department of Paediatric and Adolescent Medicine, Innlandet Hospital
Trust, Anders Sandvigs gate 17, 2609 Lillehammer, Norway
2
Department of Clinical Medicine, University of Oslo, Oslo, Norway
Full list of author information is available at the end of the article

Subjects

This cross-sectional study was based on questionnaires
answered by 10th grade students (15–16 years old) inhigh schools in the district of Oppland, Norway, in© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Bjertnaes et al. BMC Pediatrics(2019) 19:431

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Fig. 1 Flowchart

April–June 2002 (n = 2085) and in April–May 2017 (n =2233) (Fig. 1). Oppland is one of 18 counties in Norway
and had a population of 183,000 in 2002 and 190,000 in
2017. The county is predominantly rural but has two
major cities, each with populations of 25,000-30,000.

three outcome variables were calculated: BMI, BMI zscore (BMIz), and OWOB vs. under-weight and normalweight. For both 2002 and 2017-data, we based BMIz on
updated Norwegian growth reference data [16] and defined OWOB according to the International Obesity
Task Force (IOTF) [17].

Protocol and measures

The Norwegian Institute of Public Health conducted thefirst survey in 2002 [15]. We conducted the second in2017 in collaboration with the County Governor of Oppland, the supreme authority of all high schools in the
county.
The survey was a paper-questionnaire in 2002 and a
web-based questionnaire in 2017. Central questions including health, nutrition, activity, and perceived familial
socioeconomic status from 2002, were repeated in 2017.
Current weight and height measurements were selfreported in both surveys. The questionnaire used in2017 was piloted among 842 students in 22 schools in
2015–2016. The 2002 questionnaire lacked the date ofheight and weight measurements. This date was needed
to calculate z-scores; thus, the date of questionnairecompletion was used.
Variables
Outcome variables

Anthropometric measurements included self-reported
weight (to the nearest kg) and height (to the nearestcm). Based on the self-reported anthropometric data,

Background data (Table 1)

Information on sex and age was available for all participants. The following background data were alsocollected.
Socio-demography of the family: We asked the adolescents if they lived with any siblings, how they classified
their family economy compared to other families (poor,
average, good, very good), if the parents lived together,and if the parents had full-time employment (full-time/
part-time/ unemployed or receiving social security services/ housewife/ student/ dead). We also included aquestion on frequency of teeth brushing (< every secondday, every second day, once daily, >once daily), as higherfamily socioeconomic status is associated with greater
odds of teeth brushing twice a day or more [18]. As ameasure of rural living, place of residence was dichotomized into more or less than 20, 000 inhabitants. Smoking: We recorded smoking habits of the adolescents(never, used to but quit, sometimes, daily) and of theirparents (yes/no). Mental health: We asked the adolescents if they had sought help for mental health problems
in the past 12 months (yes/no). Activity: We recorded(2019) 19:431

Bjertnaes et al. BMC Pediatrics

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Table 1 Descriptive statistics of the background variables
2002

2017
OWOB %a

N

%

1675

823

Girl

823

49.1

Boy

852

50.9

1675

(15.9) 0.3

Not living with siblings

1631

17.0

Poor family economy

1656

3.5

Parents not living together

1664

Father working full time

1644

Sex

Age, years (mean) SD

OWOB %a

N

%

1580

814

9.0

814

51.5

14.0

17.0

766

48.5

20.0

1580

(15.8) 0.4

14.4

1566

16.5

22.1

24.1

1572

4.0

23.8

27.1

13.1

1573

32.1

20.0

84.2

12.2

1560

84.5

16.4

Sociodemography of the family

Mother working full time

1654

57.8

11.9

1569

70.2

16.0

Teeth brushing ≤ once daily

1670

25.7

18.4

1573

24.1

24.0

Rural living

1675

74.1

13.0

1580

72.3

18.7

Smoking
Never smoked

1671

62.4

13.2

1567

87.0

16.3

Parental smoking

1675

35.8

14.0

1580

12.4

23.0

1573

5.2

14.6

1541

13.0

21.9

Spare-time physical activity < 4 times weeklyc

1570

56.9

14.3

1576

55.9

19.3

Screen time > 2 h/daily

1666

57.6

14.8

1571

68.7

17.6

Participates in organized spare-time sports

1659

43.5

9.3

1563

56.8

12.2

Walking or riding bike to school

1665

35.6

11.6

1577

39.9

14.6

Educational plans > 12 years

1659

46.6

11.7

1573

61.0

13.0

Best or second-best gradesd

1555

55.3

11.2

1506

67.8

14.7

1642

68.9

11.9

1565

78.9

16.2

Mental health issues
Sought help for mental health issuesb
Activity

Student educatione

Positive opinion on education
Nutrition
Daily breakfast

1673

65.8

13.9

1576

62.9

15.3

Drinking sugar-containing soda ≥ daily

1664

37.5

14.4

1561

13.4

16.3

Eating candy ≥ daily

1660

17.7

9.9

1566

8.2

14.8

a

% overweight and obesity (OWOB) within the given categorybduring the last 12 monthscactivity generating sweating or heavy breathingdin ≥ 1 of 4 subjects: Norwegian writing, mathematics, social science, EnglisheAnswered yes to ≥1 of the questions “my education is interesting and I learn a lot”, “good grades are important to me”, and “my parents findeducation important”

how frequently the adolescents participated in sparetime activities that generated heavy breathing or sweating (never, once per week, 2–3 times per week, 4–6
times per week, daily), how long they watched a screen(phone, computer, TV, tablet) daily during out-of-school
hours (< 1 h, 1–2 h, 3–5 h, > 5 h), if they attended organized spare-time sport activities (yes/no) and if they rodea bike or walked to school (yes/no).
We asked the adolescents to describe student education by three proxies: educational plans (planning for an

education for 9 years, 11 years, 12 years, college or university degree), achievement of good grades (best or
second-best grade in ≥1 of the following subjects: Norwegian writing, mathematics, English, social science),
and whether they had a positive opinion on education(answered agree/partly agree to ≥one of the three questions “my education is interesting, and I learn a lot”,“good grades are important to me”, and “my parents findeducation important”). The adolescents also answeredquestions regarding nutrition by reporting how often

Bjertnaes et al. BMC Pediatrics(2019) 19:431

they ate breakfast (seldom/never, 1–2 times per week,
3–4 times per week, 5–6 times per week, daily), dranksugar-sweetened soda (seldom/never, 1–6 glasses a week,
1 glass daily, 2–3 glasses daily, ≥4 glasses daily) and how
often they consumed candy (seldom/never, 1–3 times
monthly, 1–3 times weekly, 4–6 times weekly, 1–2 times
daily, ≥3 times daily).
Statistical analyses

We calculated percentages, means and standard deviations
for all included variables. The following background variables were dichotomized in the descriptive analysis (Table
1): Family economy into poor vs other, parental employment into full-time employment vs other, teeth brushinginto ≤ once daily vs other, smoking habits into never vsother, spare-time physical activity ≥4 times per week vsother, daily screen time > 2 h daily vs other, educational
plans > 12 years vs other, good grades into best or secondbest grade in ≥1 of 4 subjects: Norwegian writing, mathematics, social science or English vs other, drinking sugarcontaining soda ≥ daily vs other and consumption of
candy ≥ daily vs other.
We calculated mean differences by using student’s ttests, and risk differences by the cohort study commandin STATA.
Data were analyzed using STATA 15.0 software(STATA, College Station, TX, United States: StataCorp,
2017). The 95% CI of the difference in various percentiles between the two time points was calculated usingbootstrap resampling with 1000 replicates. The distributions were created with Epanechnikov kernel density
plots in R Version 3.4.2. Vienna, Austria: R Foundationfor Statistical Computing, 2017, www.R-project.org).

Results
The mean age was 15.9 years (SD 0.3) in 2002 (n = 1675)
and 15.8 years (SD 0.4) in 2017 (n = 1580). The proportions of boys were 50.9% in 2002 and 48.5% in 2017
(Table 1).
The 2017 cohort differed from the 2002 cohort in that
more mothers worked full time, and that fewer parents
smoked cigarettes. Further, fewer adolescents smoked
and brushed their teeth ≤ once daily, but a larger portion

Page 4 of 9sought help for mental health problems in 2017. Moreadolescents had screen time > 2 h daily, but more also
participated in organized spare-time sports in 2017.
There were more adolescents with a positive attitude towards higher education, and more adolescents achieved
better grades and had plans for education beyond 12
years in 2017. Fewer adolescents consumed candy andsugar-containing soda daily in 2017 (Table 1).
The prevalence of OWOB increased by most background variables, including the sociodemographic variables, when comparing 2002 and 2017. (Table 1).
For girls, the mean BMI increased from 20.7 to 21.4
(mean difference 0.70, 95% CI: 0.40, 0.99, p < 0.001),
while the mean BMI, at 21.5–21.4, was stable among
boys (p = 0.80, Table 2).
The prevalence of OWOB increased from 9 to 14%
among girls (difference 5 percentage points%, 95 CI: 2, 8)
and from 17 to 20% among boys (difference 3 percentage
point, 95% CI: − 1, 6) (Table 3).
The shapes of the BMI density plots for both boys andgirls were similar in 2002 and 2017 (Fig. 2). The mean
BMIz increased significantly from − 0.07 to 0.22 (meandifference 0.29, 95% CI: 0.18, 0.39) among girls, while
the numbers were stable at 0.19 (mean difference 0.00,
95% CI (− 0.10,0.10) among boys. For girls, a persistentmean difference in BMIz between 0.21 and 0.35 was
found across all percentiles (5th -95th). For boys, meandifferences per percentile ranged between − 0.06 and0.09 (Table 4).

Discussion
The mean BMI and the prevalence of OWOB increased
among Norwegian adolescent girls from 2002 to 2017.
This change was due to an increase throughout the BMI
distribution and is opposed to both our hypothesis andsome previous findings [8, 9, 19]. No such change wasseen for boys.
We found that the percentage of OWOB increased
from 2002 to 2017 for almost all background variables,
including the sociodemographic indicators. This findingis also supported by other studies [20] and it could bespeculated that behavior has changed across socio-

Table 2 Mean anthropometric measurements of the participants, mean differenceGirls
Height, cm

Mean difference, 95% CI

p-value

Boys

2002

2017

2002

2017

166.4

166.7

176.4

177.5

Mean difference, 95% CI

p-value

Weight, kg

57.5

59.7

67.0

67.7

BMIa

20.7

21.4

0.7 (0.37,0.95)<0.001

21.5

21.4−0.04 (− 0.35,0.27)

0.8

−0.07

0.22

0.29 (0.18,0.39)<0.0010.19

0.19

0.00(−0.10,0.10)

0.5

b

BMIz
a

Body Mass Index (BMI)b
BMI z- score (BMIz)

(2019) 19:431

Bjertnaes et al. BMC Pediatrics

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Table 3 Anthropometric measurements of the participants, risk difference
Girls
OWOBa %b

OB %Risk Difference, 95% CI

2002

2017

9

14

5 (2,8)1.8

2.5

0.7 (−0.7,2)

p-value

Risk difference, 95% CI

p-value20

3 (−1,6)

0.18

2.7

0.2 (−1,2)

0.73

Boys
2002

2017

0.002

17

0.35

2.5

a

OWOB = overweight and obesity, age and sex-adjusted BMI > 25b
OB = obesity, age- and sex-adjusted BMI > 30

demographic levels towards a lifestyle favoring weightgain [21].
Public health promotion strategies and health-related
habits are comparable between Norway and other European countries in many aspects. All children pay visits tothe school nurse at 6, 8 and 13 years of age with additional
visits for vaccines. The diet in Norway is generally varied[22] and adherence to nutritional guidelines among adolescents resemble that of other European countries [23].
Finally, the percentage of Norwegian adolescents meeting
recommendations for daily physical activity corresponds
to results from other European studies on adolescents [24][25]. Still, the prevalence of OWOB is increasing among
both Norwegian adolescents and adults [22], as in manyother European countries [26].
Our finding of increased OWOB prevalence in girls is
supported by a nationwide Norwegian report carried out
during the same period [24]. There is a possibility that a

sex-related increase in BMI appeared among boys before
our study; mean weights for boys entering the military
muster at age 17 increased between 1995 and 2008 andseemed to stabilize and decrease thereafter [27]. A regional study also revealed a higher BMI and an increasing prevalence of overweight and higher BMI values
above the upper percentiles among adolescent Norwegian boys between 1966 and 1969 and 1995–1997 [28].
International, long-term studies of adolescents haveshown mixed results; the mean BMI increased moreamong European girls than European boys between
1975 and 2016 [29], whereas global trends of OWOB between 1980 and 2013 displayed only small sex-relateddifferences [30]. However, national and international
trends in adolescent BMI and OWOB are difficult tocompare due to low numbers of studies and differencesin methodologies and results. This point is illustrated by
the latter two studies where different growth-curves are

Fig. 2 BMI distribution of boys and girls. BMI distribution of boys and girls. (solid, vertikal black line) mean BMI (2002), (solid, vertical grey line) mean
BMI (2017) (discontinuous, vertical line) OWOB (Overweight or obesity, age adjusted BMI > 25),(solid, grey line in distribution) BMI (2017)(solid black line in distribution) BMI (2002),

Bjertnaes et al. BMC Pediatrics(2019) 19:431

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Table 4 Mean differences (change in BMIz from 2002 to 2017by percentile)
Difference

95% CI5

0.21(0.02, 0.44)

10

0.31

(0.12, 0.51)

25

0.33

(0.19, 0.47)

50

0.29

(0.15, 0.43)

75

0.35

(0.19, 0.51)

90

0.30

(0.10, 0.51)

95

0.22

(0.15, 0.58)

5

0.09

(−0.14,0.32)

10

−0.06

(−0.22, 0.11)

25

−0.05

(−0.20,0.09)

Girls
Percentile

Boys
Percentile

50

−0.03

(−0.14, 0.09)

75

0.06

(−0.08, 0.21)

90

0.00

(−0.18,0.17)

95

0.09

(−0.11,0.29)

used, resulting in different cut-points for overweight andobesity.
The average BMIs for girls in our study (20.7 and21.4) are in the normal-weight range for both timepoints. Nevertheless, the increase in mean BMI is ofclinical value, as changes in the mean value of a trait ofa disease have established consequences for the frequency of illness [31]. Further, this will have consequences for the future prevalence of OWOB. Anotherimportant point is that the entire BMI-distribution forgirls has shifted upwards on the BMI-scale from 2002 to
2017. This is underlined by the equal average increases in BMIz across percentiles for girls. This finding is concerning since girls, due to biologicaldifferences, gain increased fat mass compared to boys
during adolescence [32].
For adolescent girls, our finding of an increased BMI
throughout the total distribution may reveal a sexspecific obesogenic effect at the population level, andearlier studies have shown sex-related differences inweight gain due to both biological, behavioral and traumatic experiences [13, 33–35]. Due to the limitations of
the cross-sectional design and the lack of other bodymeasurements and biological tests, we were not able toexplore changes in important risk factors that could explain the shift in OWOB and BMI scores.
Nevertheless, some perspectives regarding the increased BMI among girls seem relevant to consider.
First, the adolescents in this study were exposed to theobesity epidemic both pre-, peri- and postnatally andwere born prior to (1986) and at the height (2001) of aperiod of increasing birthweights in Norway [36]. A
higher birthweight is correlated with an increased risk
for later overweight [37, 38], although not with central
adiposity or fat mass per se [39, 40]. The crossing of percentiles during the period from birth to adiposity rebound at 5–6 years of age has been seen as a critical
period for later obesity, but might reflect increased
growth in children that are already heavier instead [41].
Girls with higher BMI also tend to have earlier menarche, but the directionality of this relationship remains
unclear [42]. In sum, children with high birthweight arevulnerable to subsequent higher BMI, but no clear pathway from high birthweight through adiposity rebound in
pre-school age, early menarche and subsequent OWOB
has been established.
Second, the obesity epidemic is a rather recent
phenomenon that began 3 to 4 decades ago. Disentangling of the possible biological, societal, and environmental contributors to the etiology of obesity is ongoing.
An example is the relatively newly gained knowledge ofsex-specific increases in BMI and a higher risk of overweight in relation to dioxin exposure [43]. The main human sources of dioxins are foods, including meat, fatty
fish, and dairy products, but dioxins are also concentrated in breast milk [44, 45]. We do not have a detailed
record of food-intake, and therefore no measure of dioxin exposure in our study. Still, 90% of the adolescent cohort from 2017 had been breastfed, [46] andexclusive breastfeeding in Norway increased between
1998 and 2006 [47]. The possibility of breastfeeding
as a mediator of adolescent OWOB contradicts the
traditional view of breastfeeding as a protective factor
from later overweight [48].
A strength of this study was that we explored the entire BMI distribution. This provides more extensive information than only BMI means or OWOB percentages.
We used BMI, as this is currently the recommended
screening test for obesity. We are not aware of any recent studies exploring secular change in BMI distributions in adolescents in other populations.
A notable weakness of our study was that height andweight were self-reported. We assume, however, that
self-reporting may have reduced the number of refusals.
A meta-analysis on self-reported BMI revealed an underestimation of the prevalence of overweight and obesity
among girls and older children [49]. In addition, a Norwegian study found that adolescent girls significantly
underestimated their BMI [50], yet with a high degree ofagreement between self-reported and measured
anthropometrics measured by intraclass correlation(intra-class coefficient for BMI was 0.87 in girls). Online registration of self-reported height and weight has

Bjertnaes et al. BMC Pediatrics(2019) 19:431

also been found to have high validity when compared toclinical examination [51]. This may imply a risk that our
results underestimate the real BMI levels especially ingirls, but likely so in both populations.
Another weakness is the lack of other metrics to explore overweight and obesity, i.e., waist circumference orpercentage of body fat. BMI tends not to reflect percentage of body fat accurately [32], and especially among
girls, an increase in waist circumference that is not explained by increase in BMI has been found [52].
Our study also lacks a measure of pubertal status. Females gain relatively more fat mass than boys during puberty and on average start puberty 2 years prior to boys.
As the mean age of menarche in Norway has been stable
at 13.2 years for the last 70 years [53] most girls in ourstudy at both time points will have reached puberty. It is
unlikely that puberty could explain the change in BMI
for girls from 2002 compared to 2017.
The 2002 questionnaire lacked a date for when height
and weight were measured, this may have led to modestly less precise calculations of BMIz. As both datacollections were conducted during the same months ofthe year, we again consider the datasets comparable.
A selection bias caused by a lower response rate
among a larger group in the upper percentiles in 2017
cannot be completely ruled out. However, we have no
specific indications of differences amongst the twogroups of non-responders, and response rates of 80%(2002) and 70% (2017) are comparable to earlier observational studies on childhood OWOB [54].
We found sex-related trends in BMI and OWOB among
Norwegian 15- to 16-year-olds. Girls had an increasing
prevalence of OWOB and an increased mean BMI over thelast 15 years represented by a uniform right shift in the entire
BMI distribution. Thus, a shift of the entire BMI distribution
in girls is explaining the increased prevalence of OWOB.
Using OWOB to describe how a population is affected by anobesogenic environment accordingly has inherent limitations
as the number of individuals above this cutoff vastly underestimates the number affected. Although the Norwegian rates
of OWOB for children and adolescents are low compared tothose in other European countries [26], we know that increasing BMI in late adolescence increases the risk of deathfrom coronary heart disease in adulthood [1]. As cardiovascular disease is a common cause of death, especially inwomen, the impact of our observed trend on future health
may be significant.

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finding may have significant implications on future
health in Norwegian women.
Abbreviations
BMI: Body Mass Index; BMIz: BMI z-score; OWOB: Overweight or obesity
Acknowledgements
The assistance provided by Stian Hauge (County Governor of Oppland) inthe collection of data was greatly appreciated. We also want to thank ournurses/research assistants, Anne Berit Klakegg Sundby, Ragnhild Gunstad,and Line Hovstein, who were present in each school class to answer
questions and resolve technical problems during the 2017 data-collection.
Authors’ contributions
AAB contributed to the conception and design of the study, analyzed andinterpreted the data, drafted and completed the manuscript. JHG
contributed to the acquisition of data and revised the manuscript. PBJ
revised the manuscript. TJM revised the manuscript. TAS contributed to theconception and design of the study, analysis and interpretation of data anddrafting and completing the manuscript. MNHA contributed to theconception and design of the study, contributed to the collection of data
and drafted and completed the manuscript. All authors read and approved
the final manuscript.
Funding
This study was supported by unrestricted grants from the Innlandet Hospital
Trust. The funding source did not play any role in the design and implementationof the study; collection, management, analysis or interpretation of the data; andpreparation, review or approval of the manuscript.
Availability of data and materials
The part of the data collected in 2002 that support the findings of this study
are available from the Norwegian Public Health Institute, but restrictions
apply to availability of these data, which were used under the license for thecurrent study, and so are not publicly available. Data collected in 2017 are
however available from the authors upon reasonable request, and datacollected in 2002 are available with permission of the Norwegian Public
Health Institute.
Ethics approval and consent to participate
Written consent by parents for students who were younger than 16 years
and by students who were older than 16 years was obtained on bothoccasions. The Regional Committee for Medical Research Ethics; Region
South East, (University of Oslo), approved the study in both 2002 and 2017(2017 project number: 2016/1755).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interest.
Author details
1
Department of Paediatric and Adolescent Medicine, Innlandet Hospital
Trust, Anders Sandvigs gate 17, 2609 Lillehammer, Norway. 2Department ofClinical Medicine, University of Oslo, Oslo, Norway. 3Department ofPaediatrics, Oslo University Hospital, Oslo, Norway. 4Department of HealthRegistries, Norwegian Institute of Public Health, Oslo, Norway. 5Departmentof Clinical Science, University of Bergen, Bergen, Norway. 6Department ofPaediatrics, Haukeland University Hospital, Bergen, Norway. 7Department ofResearch, Innlandet Hospital Trust, Brumunddal, Norway.
Received: 24 May 2019 Accepted: 16 October 2019Conclusion
We found that the increase in OWOB among 15–16year old Norwegian girls presented a uniform shift in theentire BMI distribution, and was not due to a larger shift
in a specific subpopulation in the upper percentiles. This

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