1
Child and Adolescent Obesity in the United States
Belkis Mejia de Castano
Columbia Southern University
PUH: Applied Biostatistics
Dr. Bakali Mukasa
January 11th,2021
2
Child and Adolescent Obesity in the United States
Thesis: With the increase in childhood obesity which concerns public health, different studies
have been carried out to prevent and manage obesity, but which of these interventions are more
effective?
I.
Childhood and adolescent obesity in the US.
A. Childhood and adolescent obesity have reached scary rates in the past decades in
the US (Sanyaolu et al., 2019).
B. Definition: Obesity in childhood and adolescents “was defined as a BMI of
greater than or equal to the age- and sex-specific 95th percentile and overweight
with a BMI between the 85th and 95th percentiles” (Sanyaolu et al., 2019 p. 3).
C. Prevalence of obesity in childhood and adolescence in the U.S
1.Overall prevalence among children is 20.6% (Sanyaolu et al., 2019).
2.Pre-school prevalence is 13.9%, while school aged children’s prevalence
is 20.4% (Sanyaolu et al., 2019).
D. Etiology and risk factors.
1.There are different factors associated with obesity in children and
adolescent populations (Sanyaolu et al., 2019).
2.Excess calorie intake, sedentary life, lack of physical activities are the
main factors (Sanyaolu et al., 2019).
3.Other factors associated are family related such as the mother’s BMI
during pregnancy, ethnicity, and maternal education (Sanyaolu et al.,
2019).
3
II.
Issues associated with childhood obesity.
A. Health Effect of childhood obesity.
1. Increases risk of developing heart diseases, diabetes, cancer, and high
blood pressure (Schwarz & Peterson, 2010).
2.Evidence showed an association between obesity in childhood and asthma
(Sanyaolu et al., 2019).
3. Researchers have reported that obesity causes chronic inflammation in
tissue predisposing children to a high risk of chronic diseases (Sanyaolu et
al., 2019).
B. Psychological consequences
1. “A study by Britz et al found that high rates of mood, anxiety,
somatoform, and eating disorders were detected among children with
obesity” (Sanyaolu et al., 2019 p. 6).
2.In different countries, one common problem that affects obese children
and adolescents is bullying (Sanyaolu etal., 2019).
C. Disparities among race/ethnicity
1.Mexican-American and non-Hispanic black children/ adolescents have the
highest rate of obesity and overweight (Schwarz & Peterson, 2010).
2.Black American girls have a rate of 29.2% higher than the black American
boys with a rate of 19.8% (Schwarz & Peterson, 2010).
3. Mexican American boys have rates of obesity of 25.5 % (Schwarz &
Peterson, 2010).
D. Economic Consequences
4
1.Obesity costs the US approximately 6 to 10 percent of healthcare
expenditure (Schwarz & Peterson, 2010).
2. “The obesity epidemic exceeds $140 billion annually” (Schwarz &
Peterson, 2010, p. 2).
III.
Challenges preventing childhood and adolescent obesity.
A. Food insecurities and limited access to healthy and affordable food.
1.People in low-income communities have a lack of access to healthy and
fresh food (Schwarz & Peterson, 2010).
2.Investigators believe when people lack money and have food insecurities,
they made poor food choices (Schwarz & Peterson, 2010).
B. Environmental challenges
1.More access to fast food, due to the increase in fast-food restaurants
(Schwarz & Peterson, 2010).
2.Lack of regulation on nutrition programs outside of government settings
(Schwarz & Peterson, 2010).
C. Poor eating habits and food marketing
1. Children and adolescents consume more sugary drinks and unhealthy
beverages now than in the past (Schwarz & Peterson, 2010).
2.Food marketing focuses more on unhealthy food and uses social media, tv,
the internet, and others to advertise their products (Schwarz & Peterson,
2010).
D. Sedentarism and decreased physical activity.
5
1.There has been an increase in the time spent in front of the TV or
computer during the past few years (Schwarz & Peterson, 2010).
2.There has been a decrease in the use of bicycles and walking as the
method of transportation for adolescents going to school (Schwarz &
Peterson, 2010).
IV.
Prevention and treatment of obesity.
A. Behavioral and community-based prevention programs
1.Behavior-oriented prevention that is focused on individuals, educating the
participants in nutrition, and encouraging physical activity through school
lessons (Weihrauch-Blüher et al., 2018).
2.Community/environment-based prevention is focused on changing
environmental conditions such as access to the playground as well as
improving schools’ nutrition (Weihrauch-Blüher et al., 2018).
B. Lifestyle modification intervention program.
1.Focus on obesity prevention.
i.
Results in the analysis showed a reduction of BMI when a
combined diet and exercise intervention are applied (Salam et al.,
2020).
ii.
A reduction of BMI was noticed with behavioral therapy, and the
combination of this one with the previous one (Salam et al., 2020).
iii.
There was no difference between the control group and
intervention for diet only or exercise only intervention (Salam et
al., 2020).
6
2.Focus on obesity management.
i.
Results are of obesity prevention, results showed no difference
with exercise or diet only intervention (Salam et al., 2020).
ii.
A combination of diet and exercise, as well as behavioral therapy,
can reduce BMI for obesity management (Salam et al., 2020).
C. School nutrition program.
1.Some studies have shown that a school with a stronger food policy can help in
the decrease in calorie consumption (Sildén, 2018).
2. There are controversies in the results regarding school-based programs, some
showed no differences between interventions and control groups, others have
shown mild differences with long term intervention (Sobol-Goldberg et al., 2013)
D. Conclusions.
1.Obesity in childhood and adolescents, a public health concern.
2.Obesity is one that can cause many health problems, including
psychological issues.
3.There are various programs being carried out to improve obesity problems
in children and adolescents.
4.Studies have shown differences in results regarding the effectiveness of
the programs or interventions in preventing or managing obesity.
7
References
Salam, R. A., Padhani, Z. A., Das , J. K., Shaikh , A. Y., Hoodbhoy , Z., Masroor Jeelani, S., …
Bhutta, Z. A. (2020). Effects of Lifestyle Modification Interventions to Prevent and
Manage Child and Adolescent Obesity: A Systematic Review and Meta-Analysis.
Nutrients, 12(8). https://doi.org/10.3390/nu12082208
Schwarz, S. W., & Peterson, J. (2010). Adolescent Obesity in the United States: Facts for
Policymakers. National Center for Children in Poverty .
file:///C:/Users/bel_k/Downloads/text_977.pdf.
Sanyaolu, A., Okorie, C., Qi, X., Locke, J., & Rehman, S. (2019). Childhood and Adolescent
Obesity in the United States: A Public Health Concern. Global Pediatric Health, 6.
https://doi.org/10.1177/2333794x19891305
Sildén, K. E. (2018). Impact of competitive foods in public schools on child nutrition: effects on
adolescent obesity in the United States an integrative systematic literature review. Global
Health Action, 11(1), 1477492. https://doi.org/10.1080/16549716.2018.1477492
Sobol-Goldberg, S., Rabinowitz, J., & Gross, R. (2013). School-based obesity prevention
programs: A meta-analysis of randomized controlled trials. Obesity, 21(12), 2422–2428.
https://doi.org/10.1002/oby.20515
Weihrauch-Blüher, S., Kromeyer-Hauschild, K., Graf, C., Widhalm, K., Korsten-Reck, U.,
Jödicke, B., … Wiegand, S. (2018). Current Guidelines for Obesity Prevention in
Childhood and Adolescence. Obesity Facts, 11(3), 263–276.
https://doi.org/10.1159/000486512
8
1
Running head: CHILD AND ADOLESCENT OBESITY IN THE UNITED STATES
Child and adolescent obesity in the United States
Belkis Mejia de Castano
Columbia Southern University
CHILD AND ADOLESCENT OBESITY IN THE UNITED STATES
Child and Adolescent Obesity in the United States
Ash, T., Agaronov, A., Young, T. L., Aftosmes-Tobio, A., & Davison, K. K. (2017). Familybased childhood obesity prevention interventions: a systematic review and quantitative
content analysis. International Journal of Behavioral Nutrition and Physical Activity,
14(1).
Ash et, al compiled and analyzed the data collected from different studies to
review family-based obesity prevention, as well as to include methods to analyze
differences in the knowledge. The importance of this review relies on the high rates of
childhood obesity. “While reviews convey extensive information around intervention
effectiveness, they cannot reveal gaps in the knowledge base” (p.2). 159 articles met the
eligibility criteria and were analyzed. The results showed gaps were found in intervention,
demographics, children’s age, and others. Although most studies assess childhood obesity
interventions, this review focuses on the gaps.
Chamay-Weber, C., Farpour-Lambert , N. J., Saunders , C., Martin, X. E., Gal , C., & Maggio ,
A. B. (2016). Obesity Management in Adolescents: Comparison of a Low-Intensity Faceto-Face Therapy Provided by a Trained Paediatrician with an Intensive Multidisciplinary
Group Therapy, 9, 112–120.
In this study, Chamay-Weber et al., had the purpose to compare two different
interventions to see which one is more effective for obesity management in adolescents.
The study included 233 adolescents from 11 to 18 years of age, they chose which therapy
they wanted to be in. The therapies were low-intensity face-to-face therapy sessions where
CHILD AND ADOLESCENT OBESITY IN THE UNITED STATES
“cognitive and behavioral management techniques were used to promote lifestyle changes
in adolescents and their parents” or intensive group therapy which “the main goal was to
encourage lifestyle changes over a 1-year period. It consisted of psycho-educative sessions
of 90 min with a dietician and a psychologist certified in cognitive-behavioral therapy, and
sessions of 90 min of physical activity with a sport teacher specialized in adapted physical
education” (p.114). They took anthropometric measures at the beginning of the study and
the follow up for four years. Results showed no differences between therapies, also there
was a reduction of BMI scores in both groups.
Hammond, R., Athanasiadou, R., Curado, S., Aphinyanaphongs, Y., Abrams, C., Messito, M. J.,
… Elbel, B. (2019). Correction: Predicting childhood obesity using electronic health
records and publicly available data. Plos One, 14(10).
As obesity in childhood and adolescence have increased in the past decades,
different interventions and treatment have been studied, however, not many interventions
have been proven to be effective in managing obesity. In this study, Hammond et al, “used
EHR and machine learning algorithms to identify young children with a high risk of
developing obesity that could be specifically targeted for intervention” (p.11). This study
aimed to predict obesity in early childhood using the aforementioned method. The
significance of the results of this study is that they were able to predict obesity in infants at
the age of five, also “using EHR data could improve the ability of clinicians and
researchers to drive future policy intervention design, and the decision-making process in a
clinical setting” (p.2).
CHILD AND ADOLESCENT OBESITY IN THE UNITED STATES
Meo, S. A., Altuwaym, A. A., Alfallaj, R. M., Alduraibi, K. A., Alhamoudi, A. M., Alghamdi, S.
M., & Akram, A. (2019). Effect of Obesity on Cognitive Function among School
Adolescents: A Cross-Sectional Study. Obesity Facts, 12(2), 150–156.
One of the biggest concerns of public health regarding childhood and adolescent
obesity are the health consequences that they could lead to. In this study, Meo et al, aimed
to determine if there was an association between cognitive function and obesity. The study
was conducted on adolescents in school for over a year. To evaluate cognitive function the
students were divided into two groups, 233 obese and 177 non-obese for a total of 400
students in the study. “Cognitive functions were recorded as per study tool of the
Cambridge Neuropsychological Test Automated Battery (CANTAB)” (p.150). “These tests
assess the three domains of cognitive health-processing speed, sustained attention, and
executive functions'' (p.151). “The findings of this study support the concept that a high
BMI affects attention, memory, and recognition, as found in other studies'' (p154).
Singh, G. K., Yu, S. M., & Kogan, M. D. (2013). Health, Chronic Conditions, and Behavioral
Risk Disparities among U.S. Immigrant Children and Adolescents. Public Health Reports,
128(6), 463–479.
It is known that social determinants of health, as well as ethnicity, can influence
in the way that an individual develops. Some races/ethnicities are associated with chronic
conditions and obesity is not an exception. In this study Singh et al, investigated and
analyzed the “difference in the prevalence of parent-reported health, chronic condition, and
behavioral indicators among children of immigrants and U.S.-born parents” (p.463). The
method used to collect the data was the 2007 National Survey of Children’s Health, and the
CHILD AND ADOLESCENT OBESITY IN THE UNITED STATES
results showed that immigrant children have better health outcomes than native-born
children, however, obesity and lack of physical activity is higher in Hispanic children.
Another finding of this study was that the prevalence of obesity increased in the later
generation in comparison with native-born children.
Ash et al. International Journal of Behavioral Nutrition and Physical Activity
(2017) 14:113
DOI 10.1186/s12966-017-0571-2
REVIEW
Open Access
Family-based childhood obesity prevention
interventions: a systematic review and
quantitative content analysis
Tayla Ash1,2* , Alen Agaronov1, Ta’Loria Young3, Alyssa Aftosmes-Tobio2 and Kirsten K. Davison1,2
Abstract
Background: A wide range of interventions has been implemented and tested to prevent obesity in children.
Given parents’ influence and control over children’s energy-balance behaviors, including diet, physical activity,
media use, and sleep, family interventions are a key strategy in this effort. The objective of this study was to profile
the field of recent family-based childhood obesity prevention interventions by employing systematic review and
quantitative content analysis methods to identify gaps in the knowledge base.
Methods: Using a comprehensive search strategy, we searched the PubMed, PsycIFO, and CINAHL databases to
identify eligible interventions aimed at preventing childhood obesity with an active family component published
between 2008 and 2015. Characteristics of study design, behavioral domains targeted, and sample demographics
were extracted from eligible articles using a comprehensive codebook.
Results: More than 90% of the 119 eligible interventions were based in the United States, Europe, or Australia. Most
interventions targeted children 2–5 years of age (43%) or 6–10 years of age (35%), with few studies targeting the
prenatal period (8%) or children 14–17 years of age (7%). The home (28%), primary health care (27%), and community
(33%) were the most common intervention settings. Diet (90%) and physical activity (82%) were more frequently
targeted in interventions than media use (55%) and sleep (20%). Only 16% of interventions targeted all four behavioral
domains. In addition to studies in developing countries, racial minorities and non-traditional families were also
underrepresented. Hispanic/Latino and families of low socioeconomic status were highly represented.
Conclusions: The limited number of interventions targeting diverse populations and obesity risk behaviors beyond
diet and physical activity inhibit the development of comprehensive, tailored interventions. To ensure a broad
evidence base, more interventions implemented in developing countries and targeting racial minorities, children at
both ends of the age spectrum, and media and sleep behaviors would be beneficial. This study can help inform future
decision-making around the design and funding of family-based interventions to prevent childhood obesity.
Keywords: Childhood obesity, Diet, Physical activity, Media use, Sedentary behavior, Sleep, Family-based
Background
Childhood obesity continues to be a pervasive global
public health issue as children worldwide are significantly heavier than prior generations [1]. Over the past
few decades, the prevalence of obesity among children
and adolescents has risen by 47% [2]. Increases have
* Correspondence: Tra775@mail.harvard.edu
1
Harvard T.H. Chan School of Public Health, Department of Social and
Behavioral Sciences, SPH-2 655 Huntington Avenue, Boston 02115, USA
2
Harvard T.H. Chan School of Public Health, Department of Nutrition, Kresge
Building 677 Huntington Avenue, Boston 02115, USA
Full list of author information is available at the end of the article
been seen in both developed and developing countries,
with recent prevalence estimates of 23 and 13%, respectively [2]. Despite evidence of a plateau in the rates of
obesity, at least among young children in developed
countries, current levels are still too high, posing shortand long-term impacts on children’s physical, psychological, social, and economic well-being [2–5]. Of equal,
if not greater concern, racial/ethnic and socioeconomic disparities appear to be widening in some
countries [5–8]. Given the extensive disease burden,
treatment resistance of obesity, and lack of signs of
© The Author(s). 2017 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, and
reproduction 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.
Ash et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:113
attenuation for rates in the developing world, scientists, clinicians, and practitioners are working hard to
devise and test interventions to prevent childhood
obesity and reduce associated disparities [2, 9].
One category of interventions to prevent childhood
obesity that has grown considerably in recent years is
family-based interventions. This was in part due to a
number of key reports published in 2007, including an
Institute of Medicine (IOM) report on the recent progress of childhood obesity prevention [10] and a report
from a committee of experts representing 15 professional organizations appointed to make evidence-based
recommendations for the prevention, assessment, and
treatment of childhood obesity [11, 12]. In both reports,
parents are described as integral targets in interventions,
given their highly influential role in supporting and
managing the four behaviors that affect children’s energy
balance (diet, physical activity, media use, and sleep)
[13–15]. This includes not only parenting practices and
rules, but also the environments to which children are
exposed, and the adoption of parents’ own behavioral
habits by children [15–19].
Since the release of these reports, there has been a
proliferation of family-based interventions to prevent
and treat childhood obesity as documented in at least
five published reviews of this literature in the past decade [20–24]. While these reviews convey extensive information around intervention effectiveness, they cannot
reveal gaps in the knowledge base. Quantitative content
analysis [25–27] can be used to code intervention and
participant characteristics, and a review of the resulting
data can reveal areas and populations receiving a great
deal of attention, as well as those where few or no studies exist, thereby highlighting knowledge gaps. With a
focus on childhood obesity interventions, pertinent
questions to address include: whether interventions have
continued to focus primarily on diet and physical activity, neglecting the more recently established predictors
of media use and sleep [28–30]; whether some behaviors
are more likely to be targeted among certain age groups
or settings than others; and whether there are gaps with
regard to the populations targeted by interventions to
date, in particular, the representation of vulnerable populations (e.g. families living in developing countries, those
of low socioeconomic status, racial and ethnic minorities,
immigrants, and non-traditional families) [2, 31–37]. In
addition to ethical reasons, from a pragmatic viewpoint, it
is difficult to identify best practices to prevent childhood
obesity in vulnerable populations when few interventions
have focused on that population [38, 39].
The goal of this study is to profile family-based interventions to prevent childhood obesity published since
2008 to identify gaps in intervention design and methodology. In particular, we use quantitative content analysis
Page 2 of 12
to systematically document intervention and sample
characteristics with the goal of directing future research
to address the identified knowledge gaps.
Methods
We used a multistage process informed by the Preferred
Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines to identify family-based
childhood obesity prevention interventions that were written in English and published between January 1, 2008 and
December 31, 2015 [40]. Using an a priori defined protocol, we identified relevant articles and systematically
screened articles against inclusion and exclusion criteria.
The systematic review protocol was registered in the
PROSPERO database (CRD42016042009).
Following the identification of eligible studies, we conducted a quantitative content analysis to profile recent interventions for childhood obesity prevention. Content
analysis, originally used in communication sciences but
increasingly utilized in public health, is a research method
used to generate objective, systematic, and quantitative
descriptions of a topic of interest [25–27]. Our research
team has previously employed this technique to survey
observational studies on parenting and childhood obesity
published between 2009 and 2015 [41, 42].
Search strategy and initial screening
With the help of a research librarian, two authors (TA,
AA) searched three databases (PubMed, PsycINFO, and
CINAHL) using individually tailored search strategies
most appropriate for each database. The selected databases are the three most common databases used in recent systematic reviews. Our search strategy consisted of
search strings composed of terms targeting four concepts: (1) family (e.g. family, mother, father, home), (2)
intervention (e.g. prevention, promotion), (3) children
(e.g. child, infant, youth), and (4) obesity (e.g. overweight, body mass) (see Additional file 1 for full search
strategy for one database). We searched title, abstract,
and medical subject headings (MeSH) or descriptor subjects (DE) term fields. Animal studies (e.g. rats), nonoriginal research articles (e.g. commentaries, editorials,
case reports), studies written in languages other than
English and studies focused on populations older than
18 years were excluded using search limits and NOT
terms. We restricted the search to articles published
since January 1, 2008, to capture interventions implemented after the release of the IOM and expert committee reports. Furthermore, a start point of January 2008
ensured the feasibility of this study given the labor and
time intensive process to screen and code studies. In a
recent systematic review of family-based interventions
for the treatment and prevention of childhood obesity,
more than 80% of eligible studies were published since
Ash et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:113
2008 [43]. Thus, a start date of 2008 appropriately
balances feasibility of implementation and the validity of
the resulting information. The search end date was
December 31, 2015.
The search yielded 12,274 hits, representing 9152
unique articles after removing duplicates (see Fig. 1).
Following a review of titles by three authors (TA, AA,
TY) and one research assistant, 7451 articles were
removed based on exclusion criteria, resulting in 1701
articles that proceeded to abstract review. Articles were
removed during title review if they were not written in
English or published in the designated time frame, were
not original research articles, did not include human
subjects, did not target children, were observational
studies, were not relevant to the topic of childhood
obesity (e.g. papers about Anorexia Nervosa), or included special clinical populations.
Application of eligibility criteria
Three authors (TA, AA, TY) and one research assistant
screened articles against the eligibility criteria during
abstract review, while two authors (TA, AA) screened
during full-text review, applying the aforementioned exclusion criteria. Eligible studies included family-based interventions for childhood obesity prevention published
Page 3 of 12
since 2008. We defined family-based interventions as
those involving active and repeated involvement in intervention activities from at least one parent or guardian
[19]. Examples of intervention activities that qualify as
active parent involvement include workshops and counseling. Examples of passive involvement, which were
excluded, include sending home brochures for parents,
or simply inviting parents to a single event, but not involving them in the intervention in an integral way. We
defined obesity interventions as those that reported at
least one weight-related outcome (weight, body mass
index, etc.) or which self-identified as an obesity intervention. We defined interventions as preventive if they
did not explicitly focus on weight loss or management,
or if they did not recruit only children with obesity. The
final inclusion criterion was that the intervention was
designed with the intent of benefiting children (child
being defined as 89%), and given that kappa coefficients are difficult to interpret when variability is low
[45, 46], which would result from a category (e.g. inclusion
of children 14–17 years) being infrequently coded or endorsed, they were retained in the analyses. Coders were
retrained on the three variables prior to coding the remainder of the articles.
Data synthesis and analysis
Both inter-rater reliability and all other analyses were
conducted in STATA 13 [StataCorp LP, College Station,
TX, USA]. One coder (TA) cleaned the data. The majority of missing data was not reported (i.e., were missing
by design) and therefore coded as ‘0’ (no/not sure).
Where data were missing, one of the coders (TA)
returned to the full-text article to confirm and correct
any errors.
Intervention characteristics
Eligible articles described 119 unique interventions.
Table 1 summarizes additional intervention characteristics for eligible interventions. For more than a fourth of
these interventions (n = 34, 29%), only an intervention
protocol was identified (i.e., no published outcomes were
available). More than half (n = 66, 56%) of the interventions were based in the U.S. Studies based in Europe/
United Kingdom (n = 30, 25%), Australia/New Zealand
(n = 10, 8%), and Canada (n = 6, 5%) comprised 38%.
Few interventions were conducted in countries in
Central America, South America, Asia, Africa, the
Middle East, or the Caribbean.
Less than a third of interventions were implemented for
a year or more (n = 33, 28%). Interventions that were implemented in-person (n = 101, 85%) were more common
than those delivered using technology (n = 27, 23%).
Fourteen (12%) of interventions had both in-person and
technology components. Five interventions (4%) had neither an in-person nor a technology component; these
Ash et al. International Journal of Behavioral Nutrition and Physical Activity (2017) 14:113
Table 1 Intervention characteristics of family-based childhood
obesity prevention interventions published from 2008 to 2015
(n = 119)
Page 6 of 12
Table 1 Intervention characteristics of family-based childhood
obesity prevention interventions published from 2008 to 2015
(n = 119) (Continued)
n (%)
Geographic Region
Corporate
21 (18)
University
23 (19)
8 (7)
Unites States
66 (56)
Unclear
Europe/United Kingdom
30 (25)
Type of paper
Australia/New Zealand
10 (8)
Outcome evaluation
85 (71)
Canada
6 (5)
Protocol only
34 (29)
Othera
7 (6)
b
Theory
Age of target childb
Social Cognitive Theory
49 (41)
Prenatal
10 (8)
Parenting Styles
20 (17)
0–1 years (toddler)
29 (24)
Ecological Framework
20 (17)
2–5 years (preschool-kindergarten)
51 (43)
Transtheoretical Model of Behavior Change
10 (8)
6–10 years (elementary school)
42 (35)
Health Belief Model
8 (7)
11–13 years (middle school)
25 (21)
Theory of Planned Behavior
6 (5)
14–17 years (high school)
8 (7)
Other
23 (19)
Unclear
34 (29)
Settingb
Home
33 (28)
Primary care/health clinic
32 (27)
Community-based
39 (33)
School
21 (18)
Childcare/preschool
11 (9)
Multi-setting
24 (20)
Not setting specific/Unclear
11 (9)
Length of intervention
< 13 weeks (
Purchase answer to see full
attachment