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jurnal

1. The Triple Burden of Malnutrition Among Adolescents in Indonesia
Psychosocial, Eating Behavior,
and Lifestyle Factors
Influencing Overweight and
Obesity in Adolescents
Rina Agustina, MD, PhD
1,2
, Meilianawati, MD
1
,
Fenny, MD
1
, Atmarita, PhD
3
, Suparmi, MKM
3
,
Kun A. Susiloretni, PhD
4
, Wiji Lestari, MD
1
,
Kirana Pritasari, MQIH
5
, and Anuraj H. Shankar, DSc
6
Abstract
Background:Adolescent overweight and obesity (AOO) is a global public health problem and risk
for noncommunicable diseases. Understanding context-specific risks is crucial for interventions.
Objective:Determine the prevalence of AOO in the Indonesian National Health Survey (INHS)
2013, assess the 5-year trend from 2013 to 2018, and identify risks.
Methods:We selected adolescents aged 10 to 19 years (n¼174 290) from the INHS 2013 and used
hierarchical logistic regression to identify gender-specific risks for those aged 15 to 19 years (n¼77
534). Change in AOO was assessed by comparison to INHS 2018 reports.
Results:The national AOO prevalence increased over 5 years by 48% in young adolescents (13-15
years) and 85% in older ones (16-18 years). High prevalence areas included the urban location of
Jakarta (20.9%) and the remote rural region of Papua (19.4%). Overall, AOO risks were being
sedentary, male, lower education, married, younger adolescent, and school enrollment, with urban
residence and higher wealth being persistent risks for all analyses. Data for depressive symptoms were
available for older adolescents whose additional risks were being sedentary, depressive symptoms, and
high-fat diet. Male risks were being sedentary and lower education, and female risks were being
1
Department of Nutrition, Faculty of Medicine, Universitas Indonesia—Dr. Cipto, Mangunkusumo General Hospital, Jakarta,
Indonesia
2
Human Nutrition Research Center, Indonesian Medical Education and Research Institute (HNRC-IMERI), Faculty of Medicine,
Universitas Indonesia, Jakarta, Indonesia
3
National Institute of Health Research and Development (NIHRD), Ministry of Health, Jakarta, Indonesia
4
Semarang Health Polytechnic Ministry of Health—Poltekkes Kemenkes Semarang, Indonesia
5
Directorate of Public Health, Ministry of Health, Jakarta, Indonesia
6
Centre for Tropical Medicine and Global Health, Oxford University, Oxford, UK
Corresponding Author:
Rina Agustina, Department of Nutrition, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mengunkusumo General
Hospital; and Human Nutrition Research Center, Indonesian Medical Education and Research Institute (HNRC-IMERI),
Faculty of Medicine, Universitas Indonesia, IMERI Building Tower A, 10th Floor, Jl Salemba Raya No.6, Central Jakarta 10430,
Indonesia.
Emails: [email protected]; [email protected]
Food and Nutrition Bulletin
2021, Vol. 42(1S) S72-S91
ªThe Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0379572121992750
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2. married, depressive symptoms, high-fat intake, and lower education. Higher intake of fruits and
vegetables and fewer sweets did not protect against AOO if a high-fat diet was consumed.
Conclusions:Adolescent overweight and obesity in Indonesia is rapidly increasing, especially in older
adolescents and males, and with gender-specific risks. Customized multisectoral interventions to
identify strategies for lifestyle change are urgently needed.
Keywords
adolescents, overweight, obesity, nutrition, depressive symptoms
Introduction
Obesity is a global public health concern and con-
tributes to 2.6 million deaths worldwide every
year.
1
The prevalence of obesity has escalated
in many countries, including those in East and
South Asia, where an accelerated rise in body
mass index (BMI) has been reported.
2
Further-
more, the coexistence of obesity and malnutrition
in many low- and middle-income countries
(LMIC) presents a challenging double burden.
3
Moreover, around 10%of young people aged
5 to 19 years are overweight or obese.
4
Specifi-
cally, adolescent overweight and obesity (AOO)
is considered particularly serious due to its life-
long effects,
5
making it among the top public
health challenges of the 21st century.
4
Obesity is associated with significant health
risks and comorbidities,such as cardiovascular
disease, hypertension, hyperlipidemia, type 2
diabetes, and certain cancers.
1
It is associated
with an increased risk of morbidity and mortal-
ity, as well as reduced life expectancy. Com-
pared with individuals of healthy weight, life
expectancy from age 40 years was 4.2 years
shorter in obese men and 3.5 years shorter in
obese women.
6
High BMI contributed to an esti-
mated 4 million deaths globally in 2015.
7
Sev-
eral studies and meta-analyses have found strong
associations between BMI and nearly all causes
of mortality, an exception being transportation-
related accidents.
6
These associations with mor-
tality are stronger at younger ages than at older
ones and often exhibit a U-shape with minimum
death in the healthy BMI range.
6,8,9
Unfortu-
nately, the past 2 decades have witnessed an
increase in AOO and related health issues and
health care costs.
10
Indonesia is one of the countries facing the
double burden of malnutrition, and with an
increasing prevalence of overweight and obesity,
and AOO. Household survey data from the Indo-
nesian National Health Survey (INHS) 2013
showed that 18.8%of children and adolescents
aged 5 to 12 years were overweight or obese, as
were 10.8%of adolescents aged 13 to 15 years,
and 7.3%of adolescents aged 16 to 18 years.
10
By
2018, these prevalences had increased to 20%for
children aged 5 to 12 years, 16%for adolescents
aged 13 to 15 years, and 13.5%for adolescents
aged 16 to 18 years. The national prevalence of
AOO is expected to increase 2-fold by 2030, as
seen in other countries,
1
if attention and interven-
tions are not prioritized.
The etiology of AOO is multifactorial. Inter-
actions between genetic, neuroendocrine, meta-
bolic, psychological, environmental, behavioral,
and sociocultural factors are evident.
11
However,
the interactions between multifactorial determi-
nants and the prevalence of obesity have rarely
been assessed in countries experiencing the
diverse challenges of overweight and obesity,
malnutrition, infectious diseases, and rapid eco-
nomic and social transition. A better understand-
ing of the prevalence of AOO, along with its
determinants, is crucial for the development of
effective prevention strategies and will aid
decision-making to optimize and implement a
range of policies and strategies.
3
Given that Indo-
nesia is the fourth most populous nation and that 1
in 7 persons with AOO resides therein, solutions
from Indonesia may have global implications.
This study aimed to assess the national and
subnational prevalence of AOO in Indonesia and
to identify determinants by analysis of the 2013
INHS. The results may facilitate policymakers
Agustina et al S73

3. and multiple stakeholders to prioritize and
address obesity among Indonesian adolescents
and potentially other countries facing similar
challenges.
Methods
This study used data from the INHS, known as
Riskesdasin Indonesia, for the year 2013.Riskes-
dasis a nationally representative household
health survey conducted every 5 years since
2007 by the National Institute of Health Research
and Development, Ministry of Health, Republic
of Indonesia. A multistage systematic random
sampling method was used to select partici-
pants.
12
Details on the methodology of data col-
lection and sampling procedures are provided
elsewhere.
10,12
In brief, the INHS 2013 data
include 1 027 763 individuals from 294 959
households from all 34 provinces and all 497 dis-
tricts.
10
The analyses herein were restricted to
adolescents as defined by the World Health Orga-
nization (WHO) as individuals aged 10 to
19 years. The INHS 2013 included adolescents
aged 10 to 19 years and composed of 199 149
adolescents (96 673 females and 102 476 males)
from 34 provinces.
The outcomes of this study were the preva-
lence of AOO in INHS 2013,
10
the 5-year trend
in prevalence based on comparison with reports
on AOO from INHS 2018,
13
and identification of
determinants of AOO stratified by gender. The
trend of AOO was assessed by comparison to
reported indicators from the INHS 2018 for ado-
lescents aged 13 to 18 years. As indicated in the
INHS report 2013,
10
anthropometry data for
weight and height on INHS 2013 were obtained
from the primary measurement conducted by
trained nutritionists. Body weight was measured
using a portable digital scale (Fesco brand), and
body height was measured using an aluminum
portable stadiometer. All anthropometric mea-
surements were assessed for accuracy and preci-
sion and reviewed by validators in the field. The
demographic variables on gender, date of birth,
date of current assessment, and weight and height
were entered into WHO AnthroPlus-2007 soft-
ware to calculate BMI for agez-score. Over-
weight and obesity were classified as BMI for
age >þ1SD and >þ2SD, respectively, based on
the WHO definitions. The AOO was coded as
“1,” and normal adolescents (BMI for ageþ1to
2SD) as “0” or reference. Adolescents with
BMI <2SD (thinness) or flagged as invalid, that
is,5SD orþ5SD, were excluded.
We assessed previously identified
1,14
determi-
nants or risk factors that were gathered with the
INHS questionnaires, which were obtained from
a face-to-face interview by INHS 2013. As shown
in Figure 1, the variables were age, gender, mar-
ital status, school enrollment or working status,
educational level, family income, mental health
(depression), adolescent smoking behavior, life-
style factors such as sedentary activity, and diet-
ary patterns such as consumption of sweetened
beverages and snacks, high-fat diets, as well as
fruits and vegetables intake.
Family income was classified into 5 groups:
lowest to highest quintile based on the Indonesia
Wealth Index. Marital status was grouped into
2 categories: never married and married. Adoles-
cent smoking status was grouped into 2 cate-
gories: never smoked and smoking/ex smoker.
Education level was divided into 2 groups: 9 years
or less and more than 9 years based on education
regulations in Indonesia that mandate 9 years of
schooling. Depressive symptoms were measured
by a mental health questionnaire
15
used for INHS
2013, and persons were classified as depressed if
a “yes” response was elicited for any of 3 ques-
tions: the participant feels worthless, loss of inter-
est in anything, having suicidal thoughts.
Depressive symptom data were available for
older adolescents aged 15 to 19 years, but not in
younger adolescents (10-14 years). Sedentary
activity was measured by a questionnaire based
on the reported duration of activities, such as sit-
ting, lying down, watching television, playing
games, and reading.
16
The participant was con-
sidered sedentary if the total sedentary activities
were more than 120 min/d.
17
The residence was
divided into rural or urban areas.
The frequency of food or drink consumption
was based on respondent recall and categorized
by interviewers as either: once or more per day,
once a day, 3 to 6 times per week, less than 3 times
per month, and never. we further categorized
these into 2 groups: we coded as “0” a group
S74 Food and Nutrition Bulletin 42(1S)

4. comprising less than once per day, once a day, 3 to
6 times per week, less than 3 times per month, and
never; and coded as “1” once or more per day.
Fruits and vegetables were categorized into con-
sumption of less than 5 portion per day and coded
as “1”, and 5 portion or more per day coded as “0.”
Data were analyzed using the Statistical Pack-
age for Social Sciences, version 20. Descriptive
and multivariable logistic regression analyses
were performed. Descriptive analyses provided
bivariate association of explanatory and outcome
variables for further exploration of key determi-
nants of AOO. Hierarchical logistic regression
with survey weights was used to determine the
adjusted associations with AOO in the overall
group of surveyed adolescents, with available
determinants being valid anthropometry data, and
stratified by gender, for age, marital status, school
enrollment or working status, educational level,
residence, mental health, adolescent smoking
behavior, and the lifestyle factors of sedentary
activity and eating behaviors. Two models were
run to assess determinants of overweight/obesity:
the crude and adjusted models. The crude model
assessed the association between overweight/obe-
sity and each of the lifestyle, psychosocial, eating
behavior, individual/social demography, and
environmental factors independently, while the
adjusted model assessed the association between
overweight/obesity for those factors, which were
potential confounders (P< .25). The adjusted
model with inclusion of psychosocial factors was
performed on the age range of 15 to 19 years,
while the full adjusted model without inclusion
of these factors was done in 10 to 19 years old
adolescents.
Sampling probabilities for each household were
provided by the Central Bureau of Statistics. These
were used to weight observations so that correct
population-based inference could be made, given
the finite population represented by the survey.
10
Descriptive data were presented as mean and SD;
and adjusted odds ratios (aORs) were reported
along with their 95%CIs, and aPvalue of <.05
was considered statistically significant.
Results
The age and anthropometric characteristics of the
study population are summarized in Table 1.
Among the total of 199 149 adolescents aged 10
to 19 years in the INHS 2013 data set, 193 795
Figure 1.Theoretical framework for determinants driving adolescent overweight and obesity.
Agustina et al S75

5. (97.3%) with complete and valid anthropometric
assessment had an average age of 14.1+2.8
years and BMI-zscore of0.4+1.3. The pre-
valence of AOO was 13.2%and was 13.8%for
males compared to 12.5%for females. Female
adolescents had significantly higher BMI-zscores
than males.
Age- and gender-specific analyses by province
can be observed in Figure 2. Among the areas
with the highest prevalence were Jakarta
(20.9%), the capital city and a highly urbanized
region, and Papua (19.4%), a remote area with a
low human development index. Meanwhile, the
lowest prevalence was observed in East Nusa
Tenggara province (5.5%), the region with the
highest stunting prevalence.
10
The prevalence of
young adolescents with overweight and obesity in
Jakarta was higher in males, whereas in older
adolescents, the prevalence was higher in
females. When compared to the report of INHS
2018, the findings indicate that within 5 years, the
prevalence of AOO rose 5.2 percentage points
(48%increase) in younger age adolescents
(13-15 years) and up to 6.2 percentage points
(85%increase) in older adolescents aged 16 to
18 years (Figure 3).
Table 2 presents that among 174 290 (87.5%)
adolescents, most were aged 10 to 14 years and
categorized as early adolescents (55.5%). More
than half were male (50.3%) or lived in rural
areas (54.7%), and most reported never being
married (97.4%). Although married adolescents
were minimal, we included marital status in the
regression analyses as the raw number of those
married enabled inference. The majority were
still at school or not working (63.6%), and most
reported education for 9 years or less (89.8%).
The income quintiles revealed limited differences
between household characteristics of participants.
Interactions with gender were assessed for
selected significant covariates and are presented
in Tables 2 and 3. For all adolescents (Table 2),
the interaction analysis showed risk factors for
AOO were sedentary activity (aOR¼1.06,
95%CI, 1.03-1.15) and high-fat consumption
once or more per day (aOR¼1.05, 95%CI,
1.00-1.11). Furthermore, adolescents who were
married (aOR¼1.81, 95%CI, 1.66-1.97) and
Table 1.Age and Anthropometric Measurements of Indonesian Adolescents Participating in the National Health
Survey 2013.
Variables
Overall
adolescents
(aged 10-19
years)
Older adolescents (aged 15-19 years)
All Males Females
Total participants, n (%)
a,b
193 795 (97.3) 84 905 43 453 (51.2) 41 452 (48.8)
Age
Mean+SD 14.1 +2.79 16.8 +1.38 16.9 +1.38 16.8 +1.38
Median (25th, 75th
percentiles)
14 (12, 16) 17 (16, 18) 17 (16, 18) 17 (16, 18)
BMI-z
b
Mean+SD 0.4+1.3 0.48+1.13 0.57+1.19 0.37+1.07
Median (25th, 75th
percentiles)
0.4 (1.2, 0.43)0.45 (1.17, 0.22)0.55 (1.31, 0.17)0.36 (1.01, 0.27)
Nutrition status (%)
b
Thin 19 505 (10.1) 7 371 (8.7) 4 824 (11.1) 2 547 (6.1)
Normal 148 743 (76.8) 70 500 (83.0) 35 170 (80.9) 35 330 (85.2)
Overweight 18 508 (9.6) 5 500 (6.5) 2 667 (6.1) 2 833 (6.8)
Obese 7 039 (3.6) 1 534 (1.8) 792 (1.8) 742 (1.8)
Abbreviation: BMI, body mass index.
a
Participant with available and valid BMI-zmeasurements (within BMI-z5 and 5).
b
Analyzed with weighted data.
S76 Food and Nutrition Bulletin 42(1S)

6. in a higher income quintile, were male (aOR¼
1.23, 95%CI, 1.19-1.26), not working (aOR¼
1.16, 95%CI, 1.11-1.22), with educational back-
ground of less than 9 years (aOR¼1.11, 95%CI,
1.04-1.17), and living in an urban area (aOR¼
1.15, 95%CI, 1.11-1.18) were significantly more
likely to be overweight or obese. Adolescents
who were older (aOR 0.48, 95%Cl¼0.457,
0.498), smoked (aOR 0.75, 95%CI, 0.71-0.80)
or consumed fruits less than 1 portion per day
(aOR¼0.87, 95%CI, 0.84-0.91), consumed
vegetables less than 1 portion per day
Figure 3.Trends of adolescent overweight and obesity prevalence by age and period assessed by comparison of
the Indonesian National Health Survey Report in 2013
10
and 2018.
13
Figure 2.Prevalence of overweight and obesity in Indonesian adolescents by province in 2013 (n¼193 795).
Agustina et al S77

7. Table 2.
Logistic Regression Analysis Between Determinants and Prevalence of Overweight and Obesity in Indonesian Adolescents Aged 10 to 19 Years (n
¼
174 290).
Characteristics Number of Persons, n (%)
Overweight and Obesity in Adolescents
f
Yes, n (%) Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
Individual lifestyle/behavioral
Smoking behavior
Not smoking 157 890 (90.6) 24 139 (15.3) 1 1 Smoking/ex smoker 16 400 (9.4) 1 408 (8.6) 0.520 (0.492, 0.551) <.001 0.750 (0.705, 0.797) <.001
a
Sedentary activity
No 53 843 (30.3) 7 255 (13.7) 1 1 Yes 121 447 (69.7) 18 292 (5.1) 1.114 (1.082, 1.147) <.001 1.058 (1.027, 1.090) <.001
c
Depressive symptoms
d
No 74 233 (95.7) 6 702 (9.0) 1 Yes 3 301 (4.3) 332 (10.1) 1.127 (1.003, 1.226) .040 – –
Eating behavior
Sweetened beverages and snacks (time/d)
Less than once 86 021 (49.4) 12 440 (14.5) 1 1 Once or more 88 269 (50.6) 13 107 (14.8) 1.031 (1.004, 1.059) .022 0.968 (0.941, 0.995) .020
a
High-fat diet (times/d)
Less than once 115 058 (66.0) 16 702 (14.5) 1 1 Once or more 59 232 (34.0) 8 845 (14.9) 1.034 (1.005, 1.063) .020 1.004 (0.975 1.033) .812
Fruits (portion/d)
Once or more 2 254 (12.8) 3 939 (13.7) 1 1 Less than once 152 036 (87.2) 21 608 (14.2) 0.770 (0.742, 0.800) <.001 0.871 (0.837, 0.906) <.001
c
Vegetables (portion/d)
Once or more 102 214 (58.6) 15 244 (14.9) 1 1 Less than once 72 076 (41.4) 10 303 (14.3) 0.952 (0.926, 0.978) <.001 0.949 (0.923, 0.976) <.001
c
Sociodemographic factors
Age (years)
10-14 96 756 (55.5) 18 513 (19.1) 1 15-19 77 534 (44.5) 7 034 (9.1) 0.422 (0.410, 0.434) <.001 0.477 (0.457, 0.498) <.001
c
Gender
Female 86 625 (49.7) 11 797 (13.6) 1 1 Male 87 665 (50.3) 13 750 (15.7) 1.180 (1.149, 1.212) <.001 1.227 (1.193, 1.262) <.001
a
(continued)
S78

8. Table 2.
(continued)
Characteristics Number of Persons, n (%)
Overweight and Obesity in Adolescents
f
Yes, n (%) Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
Educational background (years)
>9 17 834 (10.2) 1 549 (8.7) 1 1
9 156 456 (89.8) 23 998 (15.3) 1.905 (1.805, 2.010) <.001 1.105 (1.039, 1.175) .001
b
School enrollment
No (working) 63 257 (36.4) 6 221 (9.8) 1 1 Yes (not working) 110 763 (63.6) 19 325 (17.4) 1.947 (1.889, 2.007) <.001 1.162 (1.112, 1.215) <.001
c
Social/household factors
Marital status
Not married 169 836 (97.4) 24 826 (14.6) 1 1 Married 4 454 (2.6) 721 (16.2) 1.128 (1.041, 1.223) .003 1.810 (1.665, 1.969) <.001
c
Income quintile
Lowest 32 664 (18.7) 3 770 (11.5) 1 1 Low 33 748 (19.4) 4 053 (12.0) 1.046 (0.998, 1.097) .062 1.054 (1.004, 1.106) .032
a
Middle 34 470 (19.8) 4 675 (13.6) 1.203 (1.149, 1.259) <.001 1.208 (1.151, 1.267) <.001
c
High 36 350 (20.9) 5 917 (16.3) 1.490 (1.426, 1.557) <.001 1.472 (1.404, 1.544) <.001
c
Highest 37 058 (21.3) 7 132 (19.2) 1.827 (1.750, 1.906) <.001 1.769 (1.687. 1.855) <.001
c
Environmental factors
Residence
Rural 95 257 (54.7) 12 478 (13.1) 1 1 Urban 79 033 (45.7) 13 069 (16.5) 1.314 (1.280, 1.350) <.001 1.145 (1.111, 1.180) <.001
c
a
Significant at
P
< .05.
b
Significant at
P
< .01.
c
Significant at
P
< .001.
d
Depressive symptoms data were available for older adolescents aged 15 to 19 years (n
¼
84 905).
e
Analyzed using survey data weights.
fOverweight and obesity were determined by BMI for age >
þ
1SD and >
þ
2SD. Included participants with available and valid BMI-
z
measurements (within BMI-
z
score

5 and 5).
S79

9. Table 3.
Logistic Regression Analysis Between Determinants and Prevalence of Overweight and Obesity in Indonesian Adolescents Aged 15 to 19 Years (n
¼
77 534).
Characteristics Number of Persons (n
¼
77 534)
e
, n (%)
Overweight and Obesity in Adolescents
f
Yes, n (%) Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
Individual lifestyle/behavioral
Smoking behavior
Not smoking 63 048 (81.3) 5 900 (9.4) 1 1 Smoking/ex smoker 14 486 (18.7) 1 134 (7.8) 0.823 (0.770, 0.879) <.001
c
0.825 (0.766, 0.889) <.001
c
Sedentary activity
No 24 385 (31.5) 2 052 (8.4) 1 1 Yes 53 149 (68.5) 4 982 (9.4) 1.126 (1.067, 1.188) <.001
c
1.083 (1.026, 0.143) .004
a
Depressive symptoms
d
No 74 233 (95.7) 6 702 (9.0) 1 1 Yes 3 301 (4.3) 332 (10.1) 1.127 (1.003 1.266) .044
a
1.107 (0.984, 1.246) .089
Eating behavior
Sweetened beverages and snack (times/d)
Less than once 38 974 (50.3) 3 589 (9.2) 1 1 Once or more 38 560 (49.7) 3 445 (8.9) 0.967 (0.921, 1.016) .183 0.913 (0.868, 0.960) <.001
c
High-fat diet (times/d)
Less than once 51 226 (66.1) 4 478 (8.7) 1 1 Once or more 26 308 (33.9) 2 556 (8.9) 1.123 (1.068, 1.182) <.001
c
1.090 (1.034, 1.149) .001
c
Fruits intake (portion/d)
Once or more 10 059 (13.0) 452 (10) 1 1 Less than once 67 475 (99.4) 6 201 (8.2) 0.738 (0.690, 0.789) <.001
c
0.837 (0.781, 0.897) <.001
c
Vegetables intake (portion/d)
Once or more 46 807 (60.4) 4 358 (9.3) 1 1 Less than once 30 727 (39.6) 2 676 (8.7) 0.929 (0.884, 0.977) .004
a
0.961 (0.913, 1.012) .134
Individual sociodemographic factors
Gender
Female 38 905 (50.2) 3 575 (9.2) 1 1 Male 38 629 (49.8) 3 459 (9.0) 0.972 (0.925, 1.021) .255 1.092 (1.033, 1.155) .002
c
Educational background (years)
>9 65 638 (77.2) 5 491 (8.4) 1 1 <9 19 414 (22.8) 1 550 (8.0) 1.064 (1.003, 1.128) .090 1.127 (1.058, 1.201) <.001
(continued)
S80

10. Table 3.
(continued)
Characteristics Number of Persons (n
¼
77 534)
e
, n (%)
Overweight and Obesity in Adolescents
f
Yes, n (%) Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
School enrollment
No (working) 37 843 (44.5) 3 106 (8.2) 1 1 Yes (not working) 47 209 (55.5) 3 935 (13.5) 1.017 (0.968, 1.068) .502 0.891 (0.838, 0.946) <.001
Social/household factors
Marital status
Not married 73 797 (95.2) 6 479 (8.8) 1 1 Married 3 737 (4.8) 555 (14.9) 1.812 (1.650, 1.990) <.001
c
2.008 (1.824, 2.211) <.001
c
Income quintile
Lowest 13 488 (17.4) 960 (7.1) 1 1 Low 14 840 (19.1) 1 097 (7.4) 1.042 (0.9S2, 1.140) .374 1.011 (0.923, 1.108) .810 Middle 15 875 (20.5) 1 324 (8.3) 1.187 (1.089, 1.295) <.001
c
1.115 (1.018, 1.220) .019
a
High 16 612 (21.4) 1 677 (10.1) 1.465 (1.349, 1.592) <.001
c
1.344 (1.229, 1.471) <.001
c
Highest 16 719 (21.6) 1 976 (11.9) 1.749 (1.613, 1.896) <.001
c
1.571 (1.435, 1.720) <.001
c
Environmental factors
Residence
Rural 40 901 (52.8) 3 081 (7.5) 1 1 Urban 36 633 (42.7) 3 953 (10.8) 1.485 (1.413, 1.550) <.001
c
1.330 (1.259, 1.405) <.001
c
a
Significant at
P
< .05.
b
Significant at
P
< .01.
c
Significant at
P
< .001.
d
Depressive symptoms data were available for 86 231 participants.
e
Analyzed using survey data weights.
fOverweight and obesity were determined by BMI for age >
þ
1SD and >
þ
2SD. Included participants with available and valid BMI-
z
measurements (within BMI-
z

5 and 5).
S81

11. (aOR¼0.95, 95%CI, 0.92-0.98), and consumed
sweetened beverages and snacks once or more per
day (aOR¼0.97, 95%CI, 0.94-0.99) were less
likely to be overweight or obese.
Forlateadolescentsaged15to19years
(Table 3), the risk factors for AOO were related
to depressive symptoms (aOR¼1.11, 95%CI,
0.98-1.25), high-fat diet (aOR¼1.09, 95%CI,
1.03-1.15), and sedentary activity (aOR¼1.08,
95%CI, 1.03-0.14). In addition, late adolescents
who were married (aOR¼2.01, 95%CI,
1.82-2.21) and in the middle to highest income
quintiles (aOR¼1.12, 95%CI, 1.02-1.22;
aOR¼1.34, 95%CI, 1.23-1.47; aOR¼1.57,
95%CI, 1.44-1.72 respectively), lived in an urban
area (aOR¼1.33, 95%CI, 1.26-1.41), had an
educational background less than 9 years
(aOR¼1.13, 95%CI, 1.06-1.20), and male
(aOR¼1.09, 95%CI, 1.03-1.16) were more
likely to have AOO. Several factors related to
lowerriskofAOOweresmokingbehavior
(aOR¼0.83, 95%CI, 0.77-0.89), consumed
fruits less than 1 portion per day (aOR¼0.84,
95%CI, 0.78-0.90), not working (aOR¼0.89,
95%CI, 0.84-0.95), and consumed sweetened
beverages and snacks once or more per day (aOR
¼0.91, 95%CI, 0.87-0.96).
The following factors were associated with
increased odds of AOO among males following
multivariate adjustment (Table 4): high and high-
est socioeconomic quintiles (aOR¼1.45, 95%
CI, 1.27-1.65 and aOR¼1.82, 95%CI,
1.60-2.02, respectively), sedentary activity
(aOR¼1.10, 95%CI, 1.02-1.19), being married
(aOR¼1.32, 95%CI, 1.05-1.64), living in an
urban area (aOR¼1.31, 95%CI, 1.21-1.42), with
school enrollment or not working (aOR¼1.10,
95%CI, 1.01-1.20), and an educational back-
ground less than 9 years (aOR¼1.11, 95%CI,
1.02-1.22). Conversely, risk of AOO was signif-
icantly less for male smokers or ex smokers (aOR
¼0.84, 95%CI, 0.78-0.91), consumed sweetened
beverages and snacks once or more per day
(aOR¼0.93, 95%CI, 0.87-1.00), and consumed
fruits less than 1 portion per day (aOR¼0.81,
95%CI, 0.74-0.90).
The following factors were associated with
increased odds of AOO among females
(Table 4): being married (aOR¼2.19, 95%CI,
1.96-2.44), high or highest socioeconomic quin-
tiles (aOR¼1.26, 95%CI, 1.11-1.43 and
aOR¼1.37, 95%CI, 1.20-1.55, respectively),
an educational background less than 9 years
(aOR¼1.14, 95%CI, 1.05-1.25), living in an
urban area (aOR¼1.34, 95%CI, 1.25-1.46),
having depressive symptoms (aOR¼1.17, 95%
CI, 1.02-1.34), and consumed high-fat diet once
or more per day (aOR¼1.10, 95%CI, 1.02-
1.19). In contrast, AOO among females was sig-
nificantly less likely for those who consumed
sweetened beverages and snacks once or more per
day (aOR¼0.90, 95%CI, 0.84-0.96) and con-
sumed fruits less than 1 portion per day (aOR¼
0.86, 95%CI, 0.78-0.95).
Discussion
The prevalence of AOO in Indonesia for 2013 was
already high. Over 5 years, by 2018, it had
increased substantially. The burden of AOO was
slightly higher for males than females. Overall,
factors associated with an increased risk of AOO
included being sedentary, male, lower education,
married, younger adolescent, school enrollment or
not working, residing in an urban area, and within
a family in the top wealth quintiles. Being seden-
tary and with lower education was a risk factor
specific to male adolescents, while depressive
symptoms, being married, lower education, and
high-fat intake were risk factors specific to female
adolescents. Higher intake of fruits and vegetables
and fewer sweets did not protect against AOO if a
high-fat diet was consumed.
Our study documented a higher AOO burden
in male compared to female adolescents. This is
consistent with findings in some studies in chil-
dren and adolescents in Asian countries (eg,
China and Japan) and in the Central European
region (ie, Poland).
18-20
However, these differed
from findings from England, other Western Eur-
opean countries (eg, Ireland and Luxembourg),
and the United States, where higher obesity rates
were reported among female compared to male
adolescents.
21,22
Lower female AOO may occur
because females may pay more attention to foods
as a way to influence health and are more likely to
perceive themselves as being overweight than
males. For example, female adolescents were
S82 Food and Nutrition Bulletin 42(1S)

12. Table 4.
Determinants Associated With Overweight and Obesity Among Indonesian Adolescents Aged 15 to 19 Years Stratified by Gender From the National Health
Survey Using Logistic Regression Analysis (n
¼
77 534).
Characteristics
Males (n
¼
38 629) Females (n
¼
38 905)
Yes, % Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value Yes, % Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
Individual lifestyle
Smoking
Not smoking 2 360 (9.7) 1 1 3 540 (9.2) 1 Smoking/ ex smoker 1 099 (7.8) 0.786 (0.730, 0.848) <.001 0.841 (0.779, 0.907) <.001 35 (11.4) 1.274 (0.895, 1.815) .179 1.155 (0.808, 1.652) .429
Sedentary activity
No 1 007 (8.2) 1 1 1 045 (8.6) 1 1 Yes 2 452 (9.3) 1.143 (1.058, 1.234) .001 1.103 (1.021, 1.192) .013 2 530 (9.5) 1.102 (1.057, 1.150) .007 1.060 (0.982, 1.144) .134
Depressive symptoms
No 3 378 (9.0) 1 1 3 324 (9.1) 1 1 Yes 81 (8.0) 0.879 (0.699, 1.106) .271
d
0.918 (0.728, 1.158) .470 251 (11.0) 1.235 (1.078, 1.415) .002 1.169 (1.019, 1.342) .026
Eating behavior
Sweetened beverages and snack (times/d)
Less than once 1 689 (9.0) 1 1 1 900 (9.4) 1 Once or more 1 770 (8.9) 0.987 (0.920, 1.058) .704
d
0.930 (0.866, 1.000) .050 1 675 (9.0) 0.951 (0.887, 1.018) .150 0.899 (0.838, 0.965) .003
High-fat diet (times/d)
Less than once 2 246 (8.7) 1 1 2 232 (8.8) 1 1 Once or more 1 213 (9.5) 1.104 (1.206, 1.188) .008 1.072 (0.994, 1.157) .072 1 343 (9.9) 1.141 (1.062, 1.225) <.001 1.102 (1.023, 1.186) .010
Fruit (portion/d)
Once or more 585 (12.0) 1 1 568 (11.0) 1 1 Less than once 2 874 (8.5) 0.685 (0.623, 0.753) <.001 0.813 (0.737, 0.897) <.001 3 007 (8.9) 0.793 (0.721, 0.872) <.001 0.858 (0.778, 0.947) .002
Vegetables (portion/d)
Once or more 2 135 (9.3) 1 1 2 223 (9.4) 1 1 Less than once 1 324 (8.5) 0.908 (0.845, 0.976) .008 0.950 (0.882, 1.022) .169 1352 (8.9) 0.951 (0.886, 1.021) .168 0.975 (0.907, 1.048) .492
Individual sociodemographic factors
Educational background (years)
>9 737 (8.9) 1 1 812 (8.5) 1 1
9 2 722 (9.0) 1.006 (0.923, 1.095) .897
d
1.113 (1.016, 1.220) .022 2763 (9.4) 1. .006 1.143 (1.047, 1.247) .003
Work status
Yes 2 497 (9.1) 1 1 2 646 (8.9) 1 No 962 (8.7) 0.955 (0.883, 1.032) .245 1.102 (1.012, 1.199) .025 929 (10.1) 1.142 (1.056, 1.236) .001 1.142 (1.048, 1.245) .003
Social/household factors
Marital status
Not married 3 365 (9.2) 1 1 3 114 (8.6) 1 1 Married 94 (10.7) 1.219 (0.981, 1.514) .074 1.319 (1.050, 1.641) .013 461 (16.1) 2.037 (1.831, 2.265) <.001 2.189 (1.961, 2.442) <.001
(continued)
S83

13. Table 4.
(continued)
Characteristics
Males (n
¼
38 629) Females (n
¼
38 905)
Yes, % Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value Yes, % Bivariate OR (95% CI)
P
value Multivariate aOR (95% CI)
P
value
Income quintiles
Lowest 441 (6.4) 1 1 519 (7.8) 1 1 Low 502 (6.7) 1.045 (0.916, 1.193) .513
d
1.001 (0.875, 1.144) .989 595 (8.1) 1.037 (0.918, 1.173) .558
d
1.020 (0.900, 1.156) .761
Middle 616 (7.8) 1.230 (1.084, 1.396) .001 1.118 (0.980, 1.275) .097 708 (8.9) 1.148 (1.020, 1.292) .022 1.117 (0.986, 1.266) .081 High 842 (10.3) 1.662 (1.474, 1.874) <.001 1.451 (1.275, 1.652) <.001 835 (9.9) 1.300 (1.159, 1.458) <.001 1.259 (1.111, 1.427) <.001 Highest 1 058 (12.9) 2.162 (1.925, 2.428) <.001 1.819 (1.598, 2.017) <.001 918 (10.7) 1.418 (1.267, 1.587) <.001 1.369 (1.205, 1.555) <.001
Environmental factors
Residence
Rural 1 502 (7.2) 1 1 579 (7.8) 1 1 Urban 1 957 (11.0) 1.579 (1.472, 1.694) <.001 1.313 (1.214, 1.419) <.001 1 996 (10.6) 1.398 (1.304, 1.498) <.001 1.349 (1.249, 1.458) <.001
a
Significant at
P
< .05.
b
Significant at
P
< .01.
c
Significant at
P
< .001.
d
Variables with
P
value > .25 were not included in multivariate analysis.
Sastroasmoro S, Ismael S. Dasar-dasar metodologi penelitian klinis (in English: Basic methodology for clinical research). Jakarta: Sagung Seto. 2
016.
S84

14. more likely to report dissatisfaction with their
weight.
19
It should be noted that distortion of
weight perception may have detrimental influ-
ences on the psychological development of ado-
lescents, although it strongly correlates with a
desire to lose weight.
19
In a study conducted by
Wong et al,
23
it was determined that the factors
affecting eating attitudes are gender, body
weight, body dissatisfaction, and the expected
shape of the body. Toro et al
24
carried out a study
in adolescents wherein the highest Eating Atti-
tude Test scores, with high being worse, were
obtained from girls, older adolescents, over-
weight adolescents, adolescents imposing restric-
tive diets, and people who thought that they were
obese and wanted to change their body size.
Adolescent age was identified to be an overall
determinant of AOO. Older adolescence was
protective against overweight and obesity.
Lower odds of being overweight or obese in
older adolescents might be explained by
increased consciousness of body image and
appearance, or by more recent changes in food
consumption patterns seen in younger adoles-
cents.
25
It is also of interest that for young ado-
lescents compared to older ones, the risk of
obesity (excluding those overweight) was
higher. This may be caused by an obesogenic
environment or an increase in exposure to adver-
tisements, even at a young age.
We found that length of education, that is,
more than 9 years, and school enrollment or not
working were protective for AOO, regardless of
gender. This confirms other reports that adoles-
cents lacking in education have a higher risk of
becoming obese as compared to well-educated
ones, perhaps because those who are higher edu-
cated may be earlier adopters of healthy food
preferences.
1
A study in South Africa
1
found that
white African female adolescents who were both
more highly educated and with a higher family
income did not have increased odds of obesity;
however, male adolescents did. School enroll-
ment is also a primary factor in children’s nutri-
tion. This is because most spend*6 hours a day,
eatatleast1meal,andperformmostoftheir
physical activities at school.
14
The presence of
fast-food establishments within 0.1 miles of a
school was associated with a 5%increase in
obesity rates among students, whereas further dis-
tances were not associated.
14
Investment in
school-based programs and fast-food policies are,
therefore, central to addressing obesity. More-
over, the benefits of female education on mater-
nal and child health outcomes have been well
documented.
1
In our study, marriage is a risk factor for
AOO in female adolescents, but not in males.
In contrast, the South Africa study observed that
marriage was a risk factor for obesity in male
adolescents.
1
Other studies have also reported
a higher risk of obesity in married persons.
26,27
This could be due to married couples spending
more time eating together and more frequently
consuming less healthy processed or fast food.
They may also spend more time being sedentary
and watching television and exercising less.
Conversely, unmarried individuals may spend
more time exercising and eating less, in part to
make themselvesmore attractive.
26
Interven-
tions for married couples could include premar-
ital nutrition education programs, couple-based
physical activities, and broader mass communi-
cation programs emphasizing healthy eating
patterns.
26
Many studies in high-income countries
reported that higher socioeconomic status (SES)
is associated with a reduced risk of obesity.
28-31
However, a systematic review of studies in chil-
dren indicated that 35%of studies found no asso-
ciation between SES and obesity among girls, and
41%found no association among boys.
29
In many
the reverse has been shown, that is increased SES
is associated with an increased risk of obe-
sity.
1,32,33
This is consistent with findings from
our study, which demonstrate an elevated risk of
AOO among the higher socioeconomic groups
after adjustment for sociodemographics, resi-
dence, marital status, lifestyle, and depression.
Highly educated female adolescents had signifi-
cantly increased odds of AOO, like males. It has
been noted that as a family’s economic status and
earnings improve, they tend to adoptWestern
lifestyles that include a decrease in physical
activity levels.
31,34
Increased wealth can also
contribute to poorer food choices, such as
increased energy density, bigger portion sizes,
more frequent intake of processed foods high in
Agustina et al S85

15. animal fat, sugar, and salt, and reduced intake of
fruits, vegetables, and grains. All have been
attributed to rising overweight and obesity levels
in LMIC.
31,35
A decrease in physical activity and the adop-
tion of a more sedentary lifestyle are major fac-
tors in the increased rate of AOO.
1,36
However,
we observed that being physically inactive is
only part of the problemas inactive female ado-
lescents did not have increased odds of AOO
after adjustment for several confounding factors.
The previous INHS 2013 report suggested that
almost 30%of the Indonesian population was
physically inactive.
10
There was a dose-
dependent correlation between television watch-
ing and being overweight. Those who watched 4
or more hours of television per school day were
the most likely to be overweight. In contrast,
those reporting less than 1 hour of television
watching per day were approximately 1.4 times
less likely to be overweight. Those who were
inactive were at 1.6 times higher risk of over-
weight than those who were sufficiently
active.
37
Pearson and Biddle
38
conducted a sys-
tematic review across all ages that showed a
clear association between screen time and
unhealthy dietary intake, particularly high con-
sumption of energy-dense snacks and drinks,
total energy intake, and fast foods and low intake
of fruits and vegetables. Adolescents in Indone-
sia participated in a number of sedentary beha-
viors, from television and movie watching to
playing video games and reading.
A global analysis of physical activity levels
published inThe Lancetshowed that in 2016,
more than 80%of school-going adolescents aged
11 to 17 years did not meet recommendations.
Bangladesh and India reported the lowest preva-
lence of inadequate activity in girls, potentially
explained by societal factors such as required
domestic and other activities around the home.
39
However, measuring levels of physical activity in
children and adolescents can be challenging,
especially in younger children. Many studies
relied upon self-reported questionnaires or inter-
views. Although these may be easier, they are less
reliable than direct measurements of activity.
31
Younger children might not be able to report their
activities appropriately, leading to recall bias.
Our study demonstrated that residing in urban
areas increases the risk for AOO. This is similar
to the results of most countries in which children
living in urban areas were more likely to be over-
weight obese than in rural areas.
32
Indeed, urba-
nization and globalization are fueling the
nutrition transition.
36
The increase in obesogenic
urban environments is likely a major driver of the
obesity epidemic.
1
Urbanization generally results
in the adoption of a moreWesternizedlifestyle, as
described above, which is conducive to obesity
and cardiometabolic risk factors.
36
Unhealthy
dietary choices increase the risk and prevalence
of obesity and are part of the nutritional transition
from rural to urban areas.
1
Many studies have assessed the association
between sugar-sweetened beverages and body
weight, BMI, or body fat content. Results have
ranged from a strong association to no associa-
tion.
14
In our study, adolescents who consumed
sweetened beverages and snacks once or more per
day did not show an association with the risk of
AOO. However, we acknowledge this could be
affected by misclassification of sweetened bev-
erages and snack consumption, thereby limiting
inferences for sugar consumption. Studies on the
association between dietary fat, including fast-
food consumption and obesity in children, have
also shown conflicting results.
40
Lieb et al
31
reported that from 1980 to 2006, American ado-
lescents increased consumption of fat and carbo-
hydrates by 4%and 15%, respectively, and
obesity rates tripled. In our study, consumption
of fat that is known to be high in saturated and
trans fats was associated with AOO.
Most people who use tobacco begin during
adolescence.
41
Globally, over 4.7 million middle-
and high-school students currently use tobacco.
Some studies found that cigarette smokers
weighed less than nonsmokers and that former
smokers were of similar weight as nonsmokers.
Studies examining the relationship between BMI
and smoking in adults show that cigarette smo-
kers had a lower BMI than nonsmokers or “never
smokers.”
42
When similar analyses were
conducted among young, middle, and older ado-
lescents, the relationship between smoking fre-
quency and lower BMI became stronger over
time in males, but less so in females.
43
In this
S86 Food and Nutrition Bulletin 42(1S)

16. study, we note that the prevalence of smoking in
male adolescents was much higher than that for
females, and we found an inverse association
between smoking behavior and AOO. This asso-
ciation persists in male adolescents after adjust-
ment for age, educational level, marital status,
residence, family wealth index, sedentary activ-
ity, and eating behavior.
The pathophysiologic mechanisms for the
association between smoking and weight are
unknown.
43
One explanation may involve
changes in glucocorticoid metabolism and psy-
chosocial stress associated with smoking.
43
Nicotine has small metabolic effects and can sup-
press appetite.
42
In addition, smoking has a
reported antiestrogenic effect in young people,
which may reduce fat deposition, leading to
weight loss. However, it is important to note that
the impact of smoking on body weight may dis-
sipate over time. Long-term smokers are
heavier than never, or former smokers, and
high-frequency smokers are more likely to be
obese than other smokers and nonsmokers. It has
been proposed that long-term smoking interferes
with glucose tolerance, insulin sensitivity, and
insulin resistance and leads to hyperinsuline-
mia.
44
Studies of smoking to reduce weight in
young people
45,46
suggest more harmful and few,
if any, beneficial effects. Indeed, the adverse
effects on metabolic and hormonal changes due
to smoking contribute to multiple noncommunic-
able diseases. As such, smoking in adolescence is
a risk factor for developing abdominal obesity
among both genders and for overweight in
women in adulthood. The prevention of smoking
during adolescence is important to promote
healthy weight and to decrease morbidity related
to abdominal obesity.
43
Our study also confirmed the association
between obesity and depressive symptoms in ado-
lescents, especially in females. This increased
vulnerability to both depression and obesity sug-
gests a possible bidirectional association. Possi-
ble mechanisms include behavioral and lifestyle
factors as well as biological and genetic factors.
47
Adolescents who are depressed may change their
appetite, dietary, and sleep patterns, which can
affect psychomotor retardation, anxiety, or
somatization
48
and influence weight gain or loss.
Adolescents are more inclined to favor
carbohydrate-rich food, which may provide plea-
sure or comfort and can increase sedentary activ-
ity and affect sleeping disorders. These factors
increase the risk of obesity. Subsequently, obese
adolescents may experience stigmatization, poor
body image, and low self-esteem, which exacer-
bate their vulnerability to depression.
49
A meta-
analysis of prospective studies found that the
association between obesity and depression was
greater in adolescents than other ages, particu-
larly for females.
50
Marmorstein et al
51
reported
an association of greater depression with obesity
primarily for adolescent females. They found that
obese female adolescents were at higher risk for
depressive symptoms in adulthood and that
female adolescents with depressive symptoms
were at higher risk of obesity in adulthood. Man-
nan and colleagues
47
found that more females
were dissatisfied with their bodies, regardless of
their actual weight, whereas males were dissatis-
fied when objectively overweight. Adverse life
events, such as bullying, as well as reduced
self-esteem, can produce stress, which may, in
turn, increase the risk of obesity and depressive
symptoms in adolescent females more than in
males.
49
An understanding of the determinants of
AOO is critical to developing the best policies
and strategies for intervention. There is an
urgent need to evaluate population-based inter-
ventions for obesity, including changes to envi-
ronmental determinants. Such efforts must be
based on a detailed scientific understanding of
the multiple interlinked risk factors. Prevention
of AOO is essential and likely more effective
compared to its treatment and related complica-
tions. However, preventive and health protection
programs for young children and adolescents are
lacking and require attention.
52
This is espe-
cially important, given that AOO is a high risk
for noncommunicable diseases later in life.
Future analyses should define the attributable
fraction of AOO for other adverse health and
psychosocial conditionsto help determine prio-
rities for intervention.
The major strength of the present study is a
large number of participants and adjusted analyses
for multiple covariates. A limitation relates to the
Agustina et al S87

17. cross-sectional design that precludes the inference
of directionality and causality. As this was second-
ary data analysis, several key determinants were
not part of the primary data collection, including
total energy and fat consumption, birth weight,
pubertal status, ethnicity, obesity in the family, and
smoking behavior in the family. Data for swee-
tened beverages and high-fat diet were based on
reported frequency of daily consumption, not by
measuring amounts consumed. These data should
be collected in future surveys focused on AOO.
Conclusions
The prevalence of overweight and obesity among
Indonesian adolescents has rapidly risen over the
past 5 years, especially in older adolescents and
males, and with gender-specific risks. This
increase is potentially linked to specific determi-
nants, such as being sedentary, male, lower edu-
cation, married, younger age, school enrolment or
not working, residing in urban areas, higher fam-
ily wealth, and more frequent consumption of
fatty foods.
Common and different risks exist for males
and females. A sedentary lifestyle and lower
education were specific risk factors for male ado-
lescents, while depression, being married, and
high-fat intake were specific to female adoles-
cents. Higher intake of fruits and vegetables and
fewer sweets did not protect against AOO if a
high-fat diet was consumed. Educational attain-
ment more than 9 years was a protective factor for
overweight and obesity in both males and
females. There is urgent need for customized
gender-specific multisectoral interventions and
trials to identify strategies for lifestyle change.
Our findings call for more customized prevention
efforts and a multisectoral strategy to reduce the
risk of obesity and associated disorders.
Acknowledgments
The authors thank the National Institute for Research
and Development and the Ministry of Health for their
approval to utilize the national data set that was pro-
vided to Atmarita. The authors also thank Wanda
Lasepa, Atikah, and Hanifa, who helped the authors
arrange the administrative work for this study.
Authors’ Note
RA, F, M, A, and AHS conceptualized the study. F, M,
and RA cleaned and analyzed the data. RA, A, F, M,
and AHS reviewed the tables and figures. RA, F, and M
drafted the manuscript. RA, A, KAS, S, WL, and AHS
reviewed the draft manuscript. RA coordinated the
team and was responsible for making the final decision
on the manuscript. All authors read and gave the final
approval to the paper.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest
with respect to the research, authorship, and/or publi-
cation of this article.
Funding
The author(s) disclosed the following financial support
for the research, authorship, and/or publication of this
article: This study was supported by a grant from the
Directorate of Research and Community Services, Uni-
versitas Indonesia through Q1Q2 grant no. NKB-0233/
UN2.R3.1/HKP.05.00/2019. This study was also
funded by the Ministry of Research, Technology and
Higher Education of the Republic of Indonesia (no. 1/
E1/KP.PTNBH/2019 and 234/PKS/R/UI/2019.
ORCID iDs
Rina Agustinahttps://orcid.org/0000-0002-8464-
1037
Wiji Lestarihttps://orcid.org/0000-0003-1638-5147
Kun Aristiati Susiloretni
https://orcid.org/0000-
0002-9858-4336
Suparmi iD
https://orcid.org/0000-0002-1319-0961
Anuraj H Shankar
https://orcid.org/0000-0001-
7268-6708
Highlights
The increase in overweight and obesity in Indonesian
adolescents is alarming, especially in younger adoles-
cents and males. These findings call for innovative and
customized interventions, particularly for dietary shifts
and lifestyle changes.
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