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RESEARCH ARTICLE
Prevalence of overweight among Dutch
primary school children living in JOGG and
non-JOGG areas
Annita KobesID*, Tina Kretschmer, Margaretha C. Timmerman
Faculty of Behavioural and Social Sciences, Department of Pedagogical and Educational Sciences,
University of Groningen, Groningen, The Netherlands
*a.kobes@rug.nl
Abstract
Background
One of the most influential integrated approaches towards reducing childhood obesity is
EPODE, a program that has been translated to over 20 different countries worldwide.
Aim
The goal of this study was to explore how JOGG–the Dutch EPODE adaptation–might
reduce overweight prevalence among children.
Methods
To compare whether overweight prevalence was different in JOGG areas vs. non-JOGG
areas, in long-term JOGG areas vs. short-term JOGG areas, and in low SES JOGG areas
vs. middle/high SES JOGG areas, secondary anthropometric and personal data of 209,565
Dutch children were mapped onto publicly available JOGG data.
Results
Findings showed that overweight prevalence decreased from 25.17% to 16.08% in JOGG-
areas, and from 32.31% to 18.43% in long-term JOGG areas. However, when taking into
account SES, the decrease in prevalence was mainly visible in low SES long-term JOGG
areas.
Conclusion
JOGG appeared to be successful in targeting areas where overweight was most prevalent.
Low SES areas that had implemented JOGG for a longer period of time, i.e., six years,
appeared to be successful in decreasing overweight prevalence.
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PLOS ONE | https://doi.org/10.1371/journal.pone.0261406 December 17, 2021 1 / 14
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OPEN ACCESS
Citation: Kobes A, Kretschmer T, Timmerman MC
(2021) Prevalence of overweight among Dutch
primary school children living in JOGG and non-
JOGG areas. PLoS ONE 16(12): e0261406. https://
doi.org/10.1371/journal.pone.0261406
Editor: Bidhubhusan Mahapatra, Population
Council, INDIA
Received: March 16, 2021
Accepted: December 1, 2021
Published: December 17, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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https://doi.org/10.1371/journal.pone.0261406
Copyright: ©2021 Kobes et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
the results presented in the study are available
from the Dutch Center for Youth Health at the
expense of a fee, secretariaat@ncj.nl. The authors
of this paper confirm that no special privileges
Introduction
Among the most promising childhood obesity interventions are interventions that target over-
weight across multiple settings (e.g., school, home), levels (e.g., individual, neighborhood), and
angles (e.g., diet, physical activity) and are called integrated approaches [1]. One of the most
influential integrated approaches is the Ensemble,Prévenons l’Obésité Des Enfants (EPODE)
program, which was piloted in 2004 in two French communities and has since been intro-
duced in over 20 countries (see here). One of these countries is the Netherlands, which intro-
duced “Jongeren Op Gezond Gewicht” (JOGG, Youth at Healthy Weight) in 2010 [2].
The foundation of EPODE, JOGG, and other adaptations is based on findings of the Fleur-
baix Laventie Ville Sante
´(FLVS) intervention study conducted in the 1990s [3]. Reports on
FLVS findings focus predominantly on changes in intervention town inhabitants over time
[4–10], whereas comparisons with control town inhabitants have been disseminated to the
international scientific community in the form of two publications, suggesting that 1) children
in intervention towns had better nutritional knowledge and consumed 6.8% fewer calories per
day than children in control towns post-intervention [11], and 2) overweight prevalence was
lower in intervention compared to control towns, although the decrease was only observed
after eight years [12]. Notably, the prevalence of overweight among families with lower SES
was significantly higher in control towns than in intervention towns post-intervention.
Insights from the FLVS study resulted in the design of EPODE, an approach that advocates
the installment of stakeholders at two levels: the central level and the local level [3]. At the cen-
tral level, EPODE advises to organize a Central Coordination Team (CCT) responsible for the
program’s overall management. Industry partners can show commitment to the EPODE pro-
gram, but are not meant to intervene in the program’s content. At the local level, a project
manager activates stakeholders such as school boards, dietitians, and early parenthood consul-
tation clinics to implement EPODE components. Examples of such components are installing
water taps at schools, or distributing recipes and storybooks to families to promote healthy
food choices. Components can be implemented continuously, or for a specific period of time
and can be implemented all at once or in a specific order. Local project managers are free to
implement any and all interventions they deem suitable for their community. Thus, which
components are implemented differs not only by country, but also between municipalities, cit-
ies, and neighborhoods.
EPODE demands substantial investments in terms of money, time, and effort from all
stakeholders, which makes their acclaim remarkable given the absence of systematic evidence
for the approach’s effectiveness. Multiple study protocols have been published [13–15], but
only four studies describe the adaptations’ effectiveness. An evaluation of the Belgian adapta-
tion showed a trend towards a decrease in overweight (p= .05), and overweight + obesity (p=
.06) in pilot towns compared to the general population [16]. Two other studies–evaluations of
OPAL in Australia [17] and TCHP in Spain [18]–showed no statistically significant decreases
in overweight prevalence and BMI z-score after 2–3 years and 18 months post-intervention,
respectively. The fourth study is another evaluation of the Spanish EPODE adaptation and sur-
veyed children living in intervention areas between 2009 and 2019. During this period of time,
overweight and obesity prevalence decreased, however, the systematic effectiveness of the pro-
gram is difficult to conclude as there was no comparison with a control group [19].
JOGG, the Dutch EPODE adaptation, has been implemented since 2010. Like EPODE,
JOGG targets a child’s entire community and aims to reduce the prevalence of overweight
among children [20], however, no evaluations of its effectiveness have been published in the
scientific literature yet.
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were received in accessing the data from the Dutch
Center for Youth Health that other researchers
would not have.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Current study
Integrated approaches are promising [1,21–24], nonetheless, it is meaningful to explore how
they might reduce obesity. In this study, we attempt such an exploration using data that are
structurally collected among Dutch children in the context of school-based health check-ups.
A FLVS study on the change in overweight prevalence suggests that intervention effects were
only visible after a long period of time [12], therefore, we take into account trends in over-
weight prevalence depending on the implementation duration of JOGG.
Given that intervention effects of integrated approaches are often small [23,24], and the
evaluations of Belgian, Australian and Spanish EPODE adaptations resulted in decreasing
trends at best [16–18], our hypotheses were conservative. In detail, in order for us to conclude
that JOGG is successful, (i) JOGG areas should show a less steep increase in the prevalence of
overweight among children than non-JOGG areas. The trend in overweight prevalence among
children would thus vary as a function as to whether the area had adopted JOGG or not. More-
over, we expected that (ii) JOGG areas that had implemented the program for a longer period
of time (i.e., six years) would show a less steep increase in the prevalence of children with over-
weight than areas that had adopted the program for a shorter period of time (i.e., three years)
or had not adopted the program at all. Thus, the trend in the prevalence of overweight among
children was dependent on the duration of the program’s implementation.
Children growing up in low SES families are more likely to become overweight/obese [25–
27]. FLVS results showed that overweight prevalence among low SES groups was significantly
lower in intervention towns than control towns post-intervention [12], and the JOGG
approach advises municipalities to focus on low SES neighborhoods [2]. We will visualize
exploratorily whether low SES JOGG areas show a different development in overweight preva-
lence than middle/high SES JOGG areas.
This research’s statistical analysis plan was pre-registered on Open Science Framework
(here). Modifications to this plan were described in amendments (here).
Methods
Ethics approval
The research institute at which this research was conducted does not require ethical approval
for secondary data analysis.
The JOGG program
To implement JOGG, a local government signs a three-year contract with the organization
that can be extended for additional three-year periods. JOGG can be implemented in entire
municipalities, selected villages or cities, or even in specific neighborhoods. Municipal govern-
ments pay the national JOGG organization an annual fee and additionally appoint a local
“JOGG-manager” for at least 16 hours per week, whose task is to encourage local institutions
in implementing JOGG components, such as realizing healthier breakfasts in family homes, or
organizing sports competitions to increase physical activity [28]. The central JOGG organiza-
tion advises on, but does not carry out these components, which are to be organized and
financed by the local governments in addition to paying the annual fee [20]. In other words,
JOGG, like EPODE, is an organizational structure that is dependent on substantial invest-
ments in time, energy and money by stakeholders. Currently, 40% of Dutch municipalities
implement JOGG (n= 143), and JOGG reaches over one million children [2]. JOGG does not
structurally evaluate its impact on the level of the individual, which means that children are
not usually assessed individually by the JOGG organization. However, if municipalities decide
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to collect individual data for evaluation purposes, informed consent of parents is needed. For
the present analyses, no such data was used.
Participants
Dutch children are invited by their local public health service center (GGD) to participate in
periodical school-based health check-ups, for which parents receive an information letter via
(e-)mail. Parents are furthermore asked to fill in a questionnaire concerning their child’s
health, and are given the option to opt-out of the health check-up. On the day(s) of the health
check-up, a school nurse employed by the local public health service center is present at the
school and sees all children individually. Participation in the health check-ups is encouraged,
and 25% of GGDs had a response rate of >95% in 2009 [29]. One health check-up takes place
in year seven of primary education when children are usually 9–11 years old. A school nurse
measures–among other health indicators–children’s height and weight. Data are stored at
GGDs. Since 2013, many GGDs or affiliated institutions (N= 29) shared data with the Dutch
Center for Youth Health (NCJ) (Fig 1). For this study, the NCJ shared data on children’s
height, weight, sex, and age from 2013–2018.
We received data from 209,571 children. Three entries missed information on the child’s
sex and were excluded from analyses, resulting in a data set consisting of 209,568 children with
103,776 girls (49.5%) and 105,792 boys (50.5%). 28,083 children were 9 years old (13.4%),
95,635 children were aged 10 (45.6%), 80,453 children were aged 11 (38.4%), and 5,397 chil-
dren were aged 12 (2.6%). Table 1 shows the sample size per year. We estimated how many of
the entire population of Dutch children were likely in year seven of primary education based
on data of Statistics Netherlands [30], and compared that number to our sample size (Table 1).
Our sample likely contained 5.7% of the population in 2013, 10.9% in 2014, 21.7% in 2015,
26.3% in 2016, 26.6% in 2017, and 17.6% in 2018. Table 1 shows how overweight prevalence in
our sample compared to overweight prevalence according to data from Statistics Netherlands.
Group sizes for JOGG/non-JOGG comparisons were generally large, the smallest being the
JOGG cohort measured in 2013 (n= 437). The smallest group in the non-JOGG/short-term
JOGG/long-term JOGG comparisons was the long-term JOGG cohort measured in 2014
Fig 1. Graphical display of how data were obtained. Visual representation of how the anthropometric data was
obtained. GGDs send a nurse to schools to measure examine the health of children in year seven of primary education.
Some GGDs have shared their data with NCJ (A, B, D) between 2013–2018, but might not have done so for the entire
period, e.g., GGD B shared data of 2014, 2015, and 2017 with NCJ.
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(n= 155). The middle/high SES JOGG cohort in 2013 was too small to take into account in sta-
tistical analyses (n= 5), as were the middle/high SES long-term JOGG cohorts measured in
2013 (n= 1), and 2014 (n= 2). S1 Table shows all sample sizes per group and year.
Measures
BMI. Height and weight were winsorized at the 97.5
th
percentile to correct for extreme
values [31]. Height, weight and sex were used to compute BMI-for-age and corresponding cut-
off scores to indicate weight status; 0 = normal weight, 1 = overweight [32]. Weight status was
used instead of continuous BMI, because JOGG aims to reduce the prevalence of children with
overweight (i.e., not obesity). Continuous BMI was used as a sensitivity check.
JOGG implementation status. The JOGG monitor [2] lists which municipalities imple-
ment JOGG, however, some implement JOGG in certain cities within their municipality, or
even within specific neighborhoods. All JOGG-managers were contacted to verify the informa-
tion in the JOGG monitor (response rate = 100%). We registered whether JOGG was imple-
mented before 2013, in 2013, 2014, 2015, 2016, 2017 and 2018. Implementation duration was
categorized as follows: non-JOGG areas, short-term JOGG areas (i.e., three years), and long-
term JOGG areas (i.e., six years). These 3- and 6-year-periods are contractual periods assigned
by the national JOGG organization. We identified JOGG cohorts that continued implementa-
tion up to the final measurement in 2018. Three children lived in areas that stopped imple-
menting JOGG after the first cycle and could thus not be organized into a JOGG cohort. These
were excluded from further analyses.
SES. The Netherlands Institute for Social Research (SCP) ranked each area inhabited by at
least one hundred people according to SES between 1998–2017. The SCP’s SES-rank is based
on average income, percentage of people with low income, percentage of people with low edu-
cational attainment, and percentage of unemployed people, and summarized into a factor
score [33]. The most recent SES-rank was used to categorize JOGG areas into groups: low SES
JOGG areas, consisting of the 25% lowest SES ranks, high SES JOGG areas, consisting of the
25% highest SES ranks, and middle SES JOGG areas, consisting of the remaining 50% SES
ranks. Since JOGG–and EPODE–explicitly target low SES groups/areas, it seems sensible to
compare low SES group to non-low SES groups, i.e., compare low SES to middle/high SES.
Thus, we compared the 25% lowest SES ranks to the 75% middle/highest SES ranks. Table 2
provides an overview of the data frame structure.
Table 1. Representativeness of the sample regarding size and overweight prevalence.
2013 2014 2015 2016 2017 2018
Number of children in year seven of primary education
Sample N 11,526 21,870 42,546 50,273 50,246 33,107
Statistics Netherlands data N�202,130 200,194 196,237 191,356 188,574 187,677
% children in sample 5.70% 10.92% 21.68% 26.27% 26.64% 17.64%
Overweight prevalence among children
Sample prevalence 14.56% 13.42% 15.68% 16.78% 15.14% 14.32%
Statistics Netherlands prevalence�� 12.2% 11.6% 12.2% 11.9% 13.3% 12.3%
�This number is an estimation of the total number of Dutch children in year seven of primary education based on the composition of children’s age in year seven in our
sample. Based on our sample, 13.4% of children were 9 years old, 45.6% were 10 years old, 38.4% were 11 years old, and 2.6% were 12 years old. Accordingly, we
calculated the total number of children in year seven in the Netherlands by taking 13.4% of all Dutch 9-year-olds, 45.6% of 10-year-olds, et cetera.
��Statistics Netherlands surveys annually among the Dutch population to provide an overview of the population’s health and health behavior. The percentages reported
here represent the percentage of Dutch children aged 4–12 with overweight. Statistics Netherlands provides overweight prevalence data accurate to one decimal.
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Statistical analysis
To explore whether JOGG areas showed a less steep increase in overweight prevalence among
children than non-JOGG areas (hypothesis (i)), we calculated overweight prevalence per
cohort. We tested whether there were differences between JOGG and non-JOGG cohorts by
means of z-tests, because overweight prevalence per JOGG cohort was expressed in percent-
ages, z-tests were the most appropriate statistical procedure. Overweight prevalence was
expressed as a percentage between 0 and 100 and functioned as dependent variable. Whether
or not JOGG was implemented functioned as independent variable. Next, to explore whether
long-term JOGG areas showed a less steep increase in overweight prevalence than short-term
JOGG areas or non-JOGG areas (hypothesis (ii)), we calculated overweight prevalence per
cohort. The dependent variable was overweight prevalence among children as a percentage
between 0 and 100 and JOGG implementation duration functioned as independent variable.
Power calculation. Power was calculated for z-tests comparing JOGG and non-JOGG
areas. Effect sizes are expressed in Cohen’s h, a measure for determining differences between
percentages [34]. At α= .05 and power of 90%, effect sizes of h= .16 (2013), h = .05 (2014), h =
.03 (2015, 2016, 2017), and h = .04 (2018) could be detected. For power calculations comparing
non-JOGG areas with short-term and long-term JOGG areas, we assumed that all groups were
as large as the smallest group. At α= .05 and power of 90%, effect sizes of h= .25 (2013), h=
.29 (2014), h= .06 (2015), h= .07 (2016, 2017, 2018) could be detected.
Sensitivity analyses. We conducted sensitivity analyses by combining data of children liv-
ing in areas that implemented JOGG since 2013 with data of children living in areas that had
implemented JOGG before 2013, i.e., we extended the long-term JOGG cohort to a very long-
term JOGG cohort. If long-term JOGG areas truly show a steeper decrease in overweight prev-
alence, extending the cohort to a very long-term cohort should amplify the results. Further-
more, we conducted sensitivity analyses by altering the dependent variable from a discrete
variable, i.e., overweight y/n, to continuous BMI. We conducted t-tests and ANOVAs to test
whether BMI significantly differed between cohorts.
Exploratory analyses. We re-computed the analyses for hypothesis (i) and hypothesis (ii), while
this time distinguishing between low SES areas and middle/high SES areas by means of z-tests.
Data availaibility
The data of children’s height, weight, age, and sex used in this manuscript were provided by the
Dutch Center for Youth Health at the expense of a fee. We are contractually obliged to not share
these data openly. However, data requests may be sent directly to the NCJ (secretariaat@ncj.nl).
Results
Hypothesis 1: JOGG and non-JOGG areas
Overweight prevalence in JOGG areas decreased from 25.17% in 2013 to 16.08% in 2018.
Overweight prevalence in non-JOGG areas remained fairly stable with 14.14% in 2013
Table 2. Overview of the data frame’s structure.
ID Sex Age Height Weight JOGG <2013 JOGG 2013 JOGG 2014 . . . JOGG 2018 SES rank
1 2 11 1.34 25 1 1 1 . . . 1 234
2 1 10 1.19 22 0 0 1 . . . 1 118
n. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ID, sex, age, height, and weight were provided by the NCJ. JOGG <2013-JOGG 2018 were constructed by AK, and SES rank was extracted from the publicly available
information from the SCP. Based on sex, age, height, and weight, we were able to calculate BMI-for-age and corresponding weight status.
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compared to 12.61% in 2018 (Fig 2). Furthermore, overweight prevalence remained consistently
higher in JOGG areas than in non-JOGG areas. The difference in prevalence was largest in 2013
(11.03%) and smallest in 2014 (1.03%). Z-tests indicated significant differences between JOGG and
non-JOGG areas in 2013 (X
2
(1) = 41.1, p<.001), 2015 (X
2
(1) = 208.4, p<.001), 2016 (X
2
(1) =
311.4, p<.001), 2017 (X
2
(1) = 160.8, p<.001), and 2018 (X
2
(1) = 81.4, p<.001) (Fig 2).
Hypothesis 2: Non-JOGG, short-term JOGG, and long-term JOGG areas
Overweight prevalence in long-term JOGG areas decreased from 32.31% in 2013 to 18.43% in
2018 (Fig 3). Overweight prevalence among children living in short-term and non-JOGG
areas remained fairly stable (Fig 3). Z-tests indicated significant differences between the groups
in all years (2013: X
2
(2) = 53.0, p<.001; 2014: X
2
(2) = 28.7, p<.001; 2015: X
2
(2) = 12.4, p<
.01; 2016: X
2
(2) = 44.5, p<.001; 2017: X
2
(2) = 55.5, p<.001; 2018: X
2
(2) = 74.8, p<.001).
Results of post-hoc analyses are shown in Fig 3 and indicated that, generally, overweight preva-
lence in non-JOGG areas and long-term areas significantly differed, as did overweight preva-
lence in short-term and long-term JOGG areas.
Fig 2. Prevalence of overweight in JOGG areas and non-JOGG areas. Visual representation of overweight prevalence among children in non-JOGG areas and JOGG
areas between 2013 and 2018. �p<.05; �� p<.01; ��� p<.001.
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Fig 3. Prevalence of overweight in long-term JOGG areas, short-term JOGG areas and non-JOGG areas. Visual representation of overweight prevalence among
children in non-JOGG areas, short-term JOGG areas, and long-term JOGG areas between 2013–2018. The statistical significance of the difference between the three
groups is expressed below the bars; the results of post-hoc analyses are visualized in the Figure. �p<.05; �� p<.01; ��� p<.001.
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Sensitivity analyses
We conducted sensitivity analyses to examine whether results would be affected by extending
the long-term JOGG cohort, i.e., areas that started implementing JOGG in 2013, with data
from areas that implemented JOGG before 2013. The decrease in overweight prevalence for
long-term JOGG areas visible in Fig 3 disappeared (S1 Fig), implying that extending the 2013
JOGG cohort yielded different results. Furthermore, all analyses were conducted with BMI
instead of overweight prevalence as an additional sensitivity check. Patterns of results did not
change when altering the dependent variable from overweight prevalence to BMI.
Exploratory analyses
Exploratory analyses examining the role of SES were conducted. We re-examined hypothesis
(i) while distinguishing between overweight prevalence for low SES areas and middle/high SES
areas. Fig 4 shows overweight prevalence for low SES JOGG and non-JOGG areas, and mid-
dle/high SES JOGG and non-JOGG areas. Overweight prevalence in middle/high SES areas
ranged from 10.79–14.26%, while prevalence in low SES areas ranged from 15.72–27.07%.
Overweight prevalence in middle/high SES JOGG and non-JOGG areas was fairly equal, while
the prevalence in low SES JOGG areas was consistently higher than in low SES non-JOGG
areas. What became furthermore apparent, was that the prevalence of 25.17% for JOGG areas
in 2013 (Fig 2) consisted entirely of children living in low SES areas (Fig 4).
Next, we re-examined hypothesis (ii) while again distinguishing between low SES and mid-
dle/high SES areas. Overweight prevalence in middle/high SES groups appeared to be fairly
comparable (Fig 5). However, the overweight prevalence in low SES long-term JOGG areas
Fig 4. Prevalence of overweight in JOGG areas and non-JOGG areas, separated by SES. Visual representation of overweight prevalence in non-JOGG areas and JOGG
areas between 2013–2018, separated by SES. On the left, low SES groups are visualized, while middle/high SES groups are visualized on the right. �p<.05; �� p<.01; ���
p<.001.
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Fig 5. Prevalence of overweight in short-term JOGG areas, long-term JOGG areas and non-JOGG areas, separated by SES. Visual representation of overweight
prevalence of non-JOGG areas, short-term JOGG areas, and long-term JOGG areas between 2013–2018, separated by SES. On the left, low SES groups are visualized,
while middle/high SES groups are visualized on the right. The statistical significance of the difference between the three groups is expressed below the bars; the results of
post-hoc analyses are visualized in the Figure. �p<.05; �� p<.01; ��� p<.001.
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was consistently higher than the prevalence in low-SES non-JOGG and low-SES short-term
JOGG areas. The prevalence in long-term JOGG areas in 2013 (= 32.31%) and 2014 (=
27.74%) (Fig 3) consisted entirely of children living in low SES areas (Fig 5).
Discussion
This study examined variation in overweight prevalence over time in JOGG and non-JOGG
areas. We hypothesized that overweight prevalence would overall increase, however, this
appeared not to be true. Overweight prevalence in JOGG areas decreased over time compared
to non-JOGG areas, and overweight prevalence in long-term JOGG areas decreased over time
compared to short-term JOGG, and non-JOGG areas. When the long-term cohort, i.e., six
years implementation, was extended to the longer-term cohort, i.e., >6 years implementation,
the trend vanished, which might suggest that a longer duration of the program might not nec-
essarily lead to more impact. It might also imply that the duration of implementation in this
evaluation has not been long enough to lead to a consistent decrease in overweight prevalence,
which would align with results of the longest-term evaluation of EPODE, for which it took
eight years to result in a decrease of overweight prevalence [12].
The most remarkable result, perhaps, is that the decreases in overweight prevalence in
JOGG areas and long-term JOGG areas are potentially explained by SES, rather than the
approach’s success: the JOGG cohort in 2013 consisted entirely of children living in low SES
areas–where obesity is generally more severe–while the JOGG cohort in 2014 consisted of chil-
dren living in low SES and middle/high SES areas. Similarly, SES could also explain the
decrease in overweight prevalence in long-term JOGG areas between 2014 and 2015. When
taking SES into account, the low SES long-term JOGG areas form the only cohort in which
overweight prevalence decreased.
It is unclear why this decrease is visible in low SES long-term JOGG areas and not in other
JOGG areas, and our data contain insufficient information to speculate on the basis of find-
ings. Compared to other EPODE evaluations, our results were surprising. No other EPODE
evaluations compared overweight prevalence in low SES and middle/high SES areas, however,
a Spanish evaluation showed that overweight/obesity prevalence was higher among public
school students, which are generally from lower SES families, than among charter and private
schools, which are generally populated by higher SES students [19]. Other EPODE adaptation
evaluation studies have controlled for maternal education [18], or a SES measure similar to
ours [17]. These studies showed that the proportion of children with a healthy weight did not
change over a 2-3-year intervention period in Australia [17], and that the Spanish EPODE
adaptation did not significantly affect weight development, obesity incidence, or diet quality
and physical activity after 15 months compared to control cities [18]. An explanation for our
findings could lie in JOGG’s implementation; perhaps municipal governments of low SES
JOGG areas apply more funding to combatting childhood obesity, or JOGG managers in these
areas install more, or more effective interventions. However, this does not explain why the
decrease in overweight prevalence is only visible in low SES long-term JOGG areas. It could be
that the areas that were first to adopt JOGG had the most severe childhood overweight issues,
and were thus most committed to decreasing the issue in their areas. Or, a more wry explana-
tion could be that in areas where the issue is most severe, it is easier to achieve results, just like
childhood obesity treatment is generally more effective than obesity prevention [35].
The results of EPODE evaluations–including the present study–should be interpreted with
caution. Previous research has emphasized the difficulty of translating a program-as-intended
to the everyday practice [36]. The effectiveness of JOGG could be affected by how components
are carried out by stakeholders, which adds a level of subjectivity that could not be
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incorporated in this and other evaluations [17,18]. Furthermore, previous studies argued that
evaluations of EPODE adaptations should incorporate longer-term effects [16–18]. While this
is the longest-term EPODE evaluation thus far, it still has not exceeded the period of time it
took for the FLVS intervention to show an effect, i.e., eight years [12].
The evaluation of integrated approaches such as EPODE and JOGG is still in its infancy,
and is complicated by often poor- to moderate-quality monitoring and evaluation [37] due to
a lack of motivation, resources, time, and knowledge [38,39]. Furthermore, target groups are
often not thoroughly assessed prior to designing the intervention, which makes it more diffi-
cult to reach and engage with the target group and achieve the intended change [37]. Depend-
ing on what would be regarded as JOGG’s target group, different conclusions would be drawn:
JOGG as an approach for all children in all Dutch communities does not appear to have
remarkable impact on overweight prevalence, which is in line with previous EPODE adapta-
tion evaluations [16–18]. However, if one would regard JOGG as an approach specifically for
communities where the issue is most severe, then this study shows first signs of its success.
JOGG appears to be very successful in reaching areas where the issue is most severe, i.e., low
SES areas, and appears to be also successful in decreasing the prevalence of children with over-
weight in these areas.
Limitations and implications for practice and future research
Data used for these analyses are complex (Fig 1). Many, but not all GGDs shared their data
with the NCJ, and which GGDs shared their data differed per year. Therefore, we withheld
from applying repeated measures analyses. Furthermore, we cannot draw firm conclusions
about the representativeness of the data used for this study. Comparisons of data of Statistics
Netherlands and our data gave no reason to believe that our sample is a specific subsample of
the population of Dutch children in year seven of primary education (Table 1), however, it
might be that some subpopulations were more likely than others to participate in the health
check-ups. For example, if children in low SES areas are less likely to participate, and even
more so if they have overweight, this might have impacted our results. Table 1 shows a com-
parison between the overweight prevalence among children in our sample, and the overweight
prevalence according to Statistics Netherlands, based on a representative sample of children
aged 4–12. The overweight prevalence in our sample is higher, making it less likely that our
sample contains an underrepresentation of the number of Dutch children with overweight.
Another limitation of this study is that we were not able to include a variety of factors that
might have influenced our results. For example, we could not control for the availability and
implementation of other interventions than JOGG, which could mean that families living in
non-JOGG areas might have participated in obesity-related interventions other than JOGG.
When looking at Fig 5, the decrease of obesity prevalence in long-term low SES JOGG areas is
clearly visible, while the pattern of overweight prevalence of long-term low SES non-JOGG
areas looks much more stable. Thus, even if a plethora of obesity-related interventions would
have been implemented in these areas, one could argue that their impact is not as visible as the
impact of JOGG, implying that JOGG might have additional value compared to other inter-
ventions, which would not seem unreasonable to believe because JOGG, in contrast to many
other obesity-related programs, offers an organizational structure instead of a fixed type of
intervention.
Future research should further investigate the role of SES. While FLVS findings showed
that overweight prevalence among low SES groups was lower in intervention towns than con-
trol towns post-intervention, our findings showed contradicting results; overweight prevalence
was much higher in low SES JOGG areas than non-JOGG areas. EPODE and its adaptations
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Overweight prevalence in JOGG and non-JOGG areas
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intend to focus on low SES groups, and our findings suggest that JOGG might have a different
impact on different SES levels. It is not only important to know how JOGG affects areas, but
also why JOGG has a different impact on different areas; are JOGG-managers motivated more
strongly in certain JOGG areas, do local governments provide more funding for certain areas
than others? Many JOGG municipalities collect BMI data (78%), however, in 2018, evaluations
of only 35 JOGG municipalities were known to the JOGG organization, which corresponds to
25% of the total number of JOGG municipalities in 2018 [28]. In the most recent JOGG moni-
tor, JOGG municipalities are advised to evaluate JOGG processes and effects more carefully,
because insights in what works why is missing. Future effectiveness research should combine
local effectiveness evaluations with local process evaluations to see whether JOGG-as-intended
is translated to the everyday practice, and whether or not the effectiveness of JOGG depends
on what is implemented and by whom at the local level.
With regards to the evaluation of such complex programs, first steps have been taken to
work towards a systematic appraisal of such integrated approaches [37]. Previous studies have
shown that decisions pertaining to design and methodology are difficult in evaluating inte-
grated approaches, for example, randomization is often difficult which might introduce the
risk of sample bias [40]. Another factor which makes it difficult to conduct a randomized con-
trolled trial, is that the inclusion of large numbers of individuals–which is often the case in the
evaluation of integrated approaches–is costly [41]. Alternatives to randomized controlled trials
have been suggested, such as pair-matched randomization methods or historical controls, or
alternative research designs that may permit more longitudinal analysis, such as extended time
series designs [41].
Conclusion
With respect to this paper’s overarching aim–exploring how JOGG might reduce overweight
prevalence–we showed that JOGG might work well for a particular subpopulation of children,
namely children living in low SES areas where overweight is a severe problem. Taken this and
previous research [16–18] together, these studies do not yet provide strong evidence for the
effectiveness of EPODE adaptations, assuming their aim is to reach all children in a commu-
nity. This underscores the need for more research on the effectiveness of EPODE and its adap-
tations: over 20 countries are investing time, money, and energy into combatting childhood
obesity by means of an approach which effectiveness has not yet been scientifically demon-
strated enough.
Supporting information
S1 Table. Number of children per group and per year. S1A Table shows the number of chil-
dren for each group. Overweight prevalence in these groups is visualized in Figs 2and 3. S1B
Table shows the number of children for each group, separated by SES. Overweight prevalence
in these groups is visualized in Figs 4and 5.
(DOCX)
S1 Fig. Visual representation of overweight prevalence in non-JOGG areas, short-term
JOGG areas, and very long-term JOGG areas, i.e., the <2013 and 2013 cohorts combined,
between 2013–2018. The statistical significance of the difference between the three groups is
expressed below the bars; the results of post-hoc analyses are visualized in the Figure. �
p<0.05; �� p<0.01; ��� p<0.001.
(DOCX)
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Overweight prevalence in JOGG and non-JOGG areas
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Author Contributions
Conceptualization: Annita Kobes, Tina Kretschmer, Margaretha C. Timmerman.
Formal analysis: Annita Kobes.
Methodology: Annita Kobes, Tina Kretschmer.
Supervision: Tina Kretschmer, Margaretha C. Timmerman.
Visualization: Annita Kobes.
Writing – original draft: Annita Kobes.
Writing – review & editing: Tina Kretschmer, Margaretha C. Timmerman.
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