Case Number 7 Student’s Name Institution Affiliation Case Number 7. The case of physician do not heal thyself Questions 1. Have you recently engaged in risky behaviors such as binge eating, unsafe sex, gambling, drug and substance abuse, or risky driving? 1. How would you describe your relationships with people such as your spouse, friends, neighbors, colleagues, and strangers while considering aspects of anger, irritability, and violence? 1. Do you have a recurring problem of variant moods that result to interpersonal stress, feeling of emptiness, and other challenges that are stress-related and they push you towards suicidal thoughts? People to speak to It is crucial to identify the right people to provide essential details for the assessment of the patient. Some of the most important people include the spouses, siblings, family friends, personal friends, and neighbors. Furthermore, the patient’s colleagues can provide important information regarding the behaviors of the patient and help in identifying issues that the patient could be hiding. Speaking to the people to whom the patient exercises authority is important in attaining the true image of the person. Physical exam and diagnostic test The disorder is mental, but it can be assessed through physical exams that indicate how the brain is working in relation to actions ( Stahl 2013). Fixing a puzzle would be an effective way of testing the patient and how stable they can be. The other approach is engaging the patient in a physical exercise and observing their participation. Physical exams provide a diagnostic insight to test how the patient relates with others. Diagnoses Personality Disorder Mood Disorder Depression with psychotic features Pharmacological agents Application of antidepressants Use of antipsychotics Administering mood-stabilizing drugs Contradictions or Alterations It is a complex situation to treat a complex and long-term unstable disorder of mood because the patients experience different emotions even during therapy (Yasuda & Huang 2008). It becomes difficult to separate mood disorder from personality disorder especially for difficult patient like in this case. Furthermore, there are no specific drugs that can be used for treatment without additional therapy since this patient is able to adjust or play with their own treatment as a physician. The mental condition observed in the patient requires a careful approach due to the delicate situations involving suicidal thoughts and aggression. Lessons Learned In the case study “The case of physician do not heal thyself,” the lessons include the importance of conducting a complete assessment of the patient and including other people who interact with the patient. It would be more effective to treat such conditions if the patients had stable emotions, but strategic approaches can help to streamline the treatment process ( Stahl 2014b). References Stahl, S. M. (2013). Stahl’s essential psychopharmacol.

1. Case Number 7
Student’s Name
Institution Affiliation
Case Number 7. The case of physician do not heal thyself
Questions
1. Have you recently engaged in risky behaviors such as binge
eating, unsafe sex, gambling, drug and substance abuse, or risky
driving?
1. How would you describe your relationships with people such
as your spouse, friends, neighbors, colleagues, and strangers
while considering aspects of anger, irritability, and violence?
1. Do you have a recurring problem of variant moods that result
to interpersonal stress, feeling of emptiness, and other
challenges that are stress-related and they push you towards
suicidal thoughts?
People to speak to
It is crucial to identify the right people to provide essential
details for the assessment of the patient. Some of the most
important people include the spouses, siblings, family friends,
personal friends, and neighbors. Furthermore, the patient’s
colleagues can provide important information regarding the
behaviors of the patient and help in identifying issues that the
patient could be hiding. Speaking to the people to whom the
patient exercises authority is important in attaining the true
image of the person.

2. Physical exam and diagnostic test
The disorder is mental, but it can be assessed through physical
exams that indicate how the brain is working in relation to
actions ( Stahl 2013). Fixing a puzzle would be an effective way
of testing the patient and how stable they can be. The other
approach is engaging the patient in a physical exercise and
observing their participation. Physical exams provide a
diagnostic insight to test how the patient relates with others.
Diagnoses
Personality Disorder
Mood Disorder
Depression with psychotic features
Pharmacological agents
Application of antidepressants
Use of antipsychotics
Administering mood-stabilizing drugs
Contradictions or Alterations
It is a complex situation to treat a complex and long-term
unstable disorder of mood because the patients experience
different emotions even during therapy (Yasuda & Huang 2008).
It becomes difficult to separate mood disorder from personality
disorder especially for difficult patient like in this case.
Furthermore, there are no specific drugs that can be used for
treatment without additional therapy since this patient is able to
adjust or play with their own treatment as a physician. The
mental condition observed in the patient requires a careful
approach due to the delicate situations involving suicidal
thoughts and aggression.
Lessons Learned
In the case study “The case of physician do not heal thyself,”
the lessons include the importance of conducting a complete
assessment of the patient and including other people who
interact with the patient. It would be more effective to treat
such conditions if the patients had stable emotions, but strategic
approaches can help to streamline the treatment process ( Stahl
2014b).

3. References
Stahl, S. M. (2013). Stahl’s essential psychopharmacology:
Neuroscientific basis and practical applications (4th ed.). New
York, NY: Cambridge University Press.
Stahl, S. M. (2014b). The prescriber’s guide (5th ed.). New
York, NY: Cambridge University Press.
Yasuda, S.U., Zhang, L. & Huang, S.-M. (2008). The role of
ethnicity in variability in response to drugs: Focus on clinical
pharmacology studies. Clinical Pharmacology &
Therapeutics, 84( 3), 417–423. Retrieved from
https://web.archive.org/web/20170809004704/https://www.fda.g
ov/downloads/Drugs/ScienceRe search/.../UCM085502.pdf
Stand-Biased Versus Seated Classrooms and
Childhood Obesity: A Randomized Experiment
in Texas

4. Monica L. Wendel, DrPH, MA, Mark E. Benden, PhD, CPE,
Hongwei Zhao, PhD, and Christina Jeffrey, MS
Objectives.To measure changes in body mass index (BMI)
percentiles among third- and
fourth-grade students in stand-biased classrooms and traditional
seated classrooms in
3 Texas elementary schools.
Methods. Research staff recorded the height and weight of 380
students in 24
classrooms across the 3 schools at the beginning (2011–2012)
and end (2012–2013) of
the 2-year study.
Results. After adjustment for grade, race/ethnicity, and gender,
there was a statisti-
cally significant decrease in BMI percentile in the group that
used stand-biased desks for
2 consecutive years relative to the group that used standard
desks during both years.
Mean BMI increased by 0.1 and 0.4 kilograms per meter
squared in the treatment and
control groups, respectively. The between-group difference in
BMI percentile change
was 5.24 (SE = 2.50; P = .037). No other covariates had a

5. statistically significant impact
on BMI percentile changes.
Conclusions. Changing a classroom to a stand-biased
environment had a significant
effect on students’ BMI percentile, indicating the need to
redesign traditional classroom
environments. (Am J Public Health. 2016;106:1849–1854.
doi:10.2105/AJPH.2016.303323)
See also Galea and Vaughan, p. 1730.
Despite considerable attention, resourceinvestment, and effort,
obesity—in
particular childhood obesity—remains one
of the prominent public health issues in the
United States. Although overall obesity rates
seem to have stabilized, the prevalence of
childhood obesity is still alarmingly high. In
their longitudinal analysis of national data,
Ogden et al. found that 16.9% of children
aged 2 to 19 years were obese in 2012, and
another 14.9% were overweight.1 Obese
children are at significantly increased risk for
chronic diseases, including diabetes, cardio-
vascular disease, hypertension, osteoarthritis,
stroke, and several types of cancer.2,3 In ad-
dition, children who are overweight or obese
are more likely to have low self-esteem,
perform worse in school, and be victims
of bullying.4–6 Obese children are more
likely than their normal-weight peers to
become obese adults, and the long-term

6. implications include increased risk of
disease, disability, and early death.7,8
At the most basic level, childhood obesity
is caused by energy imbalance, or the con-
sumption of more calories than are used by
the body over an extended period of time.9
However, myriad social and environmental
factors contribute to childhood obesity, such
as poverty, neighborhood safety, and low cost
of nutritionally poor foods.10,11 These factors
complicate the development and imple-
mentation of effective population-level
strategies to combat childhood obesity.
Given that the vast majority of children
spend between 7 and 9 hours of their 14 to
16 hours of awake time at school each day,
many public health initiatives, such as the
National Football League’s “Play 60” and
Michelle Obama’s “Let’s Move!” campaign,
have focused on schools as a key setting for
obesity-related interventions.12 Many
school-based initiatives have primarily aimed
to reduce caloric intake through compre-
hensive school-based nutrition services out of
concern that initiatives aimed at increasing
physical activity in schools take away from
time for academic instruction.13,14 A greater
focus on standardized test scores has created
pressure on teachers and administrators and
contributed to decreased requirements for
students to participate in physical activity
during the school day.14,15 This situation has

7. also led to significant amounts of prolonged
sedentary behaviors among students, and
these behaviors are associated with a signifi-
cant risk of chronic disease and measurable
metabolic changes.16,17
A variety of interventions designed to
reduce sitting or sedentary behavior, increase
physical activity, or increase passive caloric
expenditures have been tested, primarily
among office workers. One systematic review
showed that standing, stand-biased, and ad-
justable work stations decreased sitting time
and increased caloric expenditures, as well as
improving posture and decreasing pain.18 In
addition, the use of stand-biased desks in
office settings has been shown to mitigate the
biological effects of sitting.19 Although results
among adults are promising, relatively little
ABOUT THE AUTHORS
Monica L. Wendel is with the Department of Health Promotion
and Behavioral Sciences, University of Louisville School of
Public Health & Information Sciences, Louisville, KY. Mark E.
Benden is with the Department of Environmental and
Occupational Health, Texas A&M School of Public Health,
College Station. Hongwei Zhao is with the Department of
Epidemiology and Biostatistics, Texas A&M School of Public
Health. Christina Jeffrey is with the Department of Educational
Psychology, Texas A&M University.
Correspondence should be sent to Mark E. Benden, PhD, CPE,
1266 TAMU, College Station, TX 77843-1266 (e-mail:
[email protected]). Reprints can be ordered at
http://www.ajph.org by clicking the “Reprints” link.

8. This article was accepted June 13, 2016.
doi: 10.2105/AJPH.2016.303323
October 2016, Vol 106, No. 10 AJPH Wendel et al. Peer
Reviewed Research 1849
AJPH RESEARCH
mailto:[email protected]
http://www.ajph.org
research has been conducted in classroom
settings to explore whether stand-biased desks
yield similar effects among children. The
studies published in the peer-reviewed lit-
erature thus far have been limited to pilot
investigations.20–24
In view of the aforementioned concerns
with respect to in-school activity-promoting
initiatives, school-based physical activity in-
terventions, if they are to be practical and
scalable, must be simple and affordable and
must require minimal instructional or staff
time. Hence, in this study, we tested the
effectiveness of activity-permissive learning
environments as a means of meeting academic
as well as health goals. The intervention
assessed involved changing classroom envi-
ronments from traditional seated desks to
stand-biased desks, which are set at a height at
which children can work at their desk while
standing but are also outfitted with a stool so
that they can sit if they so choose. Changing
classroom environments is relatively simple,

9. the equipment is comparable in cost to that
of traditional classroom desks, and the in-
tervention requires no instructional time.
Several earlier investigations established
evidence foundational for the current study.
In 2009, we conducted a laboratory study
confirming that the Sensewear Armband was
a sufficiently sensitive device to measure ca-
loric expenditures among elementary school
children.25 In the 2009–2010 academic year,
we launched a small pilot study to examine
whether use of stand-biased desks in first-
grade classrooms increased caloric expendi-
tures. That study’s findings not only indicated
that caloric expenditures indeed increased
in the treatment classrooms but also pro-
vided anecdotal evidence that standing
improved students’ behavioral classroom
engagement.22,26,27
In 2011, our research team began ex-
ploring ideal stand-biased desk designs for
classrooms. Partnering with Stand2Learn
(a small, ergonomically focused school fur-
niture design company) and supported by
a small business innovation research grant
from the Centers for Disease Control and
Prevention, the team developed desks and
tested them to ensure that they were af-
fordable and ergonomically correct, with
a small footprint and adequate storage. The
purpose of the 2-year study described here
was to determine the impact on students’
body mass index (BMI) of altering elementary

10. school classroom environments from tradi-
tional to stand-biased environments.
METHODS
We approached 24 teachers in 3 Texas
schools (8 in each school), informed them of
the study’s purpose and protocol, and offered
them a financial incentive for their partici-
pation. All 24 teachers consented to take part
in the study, and 4 in each school were
randomly assigned to treatment conditions
and 4 to control conditions. In August 2011,
research staff members attended the parent
orientation events held at each of the schools
and presented study information to parents.
A total of 480 students were eligible for
participation in our 2-year study (which
encompassed the 2011–2012 and 2012–2013
school years), and parental consent and
child assent were obtained for 380 of them.
Two of the sample classrooms used exercise
balls as chairs instead of the traditional
layout and thus failed to meet the study’s in-
clusion criteria; as a result, 37 students were
removed from the initial sample. At the start of
the first semester of the study, 6 students
dropped out of the study owing to behavioral
issues or switching to a different school.
Therefore, the final sample at the beginning of
the study consisted of 337 students. Parental
consent (or child assent) was not obtained for
any new children after this time frame.
Because our research was conducted in

11. a school environment, many factors were
outside of our control. School administrators
and teachers were incredibly helpful and
gracious, but they were unable to accom-
modate all research requests. For example, in
the transition from year 1 to year 2 of the
study, students were assigned to different
classrooms (as is the case at almost all public
elementary schools); also, the stand-biased
desks had to stay with the original teachers,
who typically remained in the same grade. As
a result, the student cohorts were not wholly
maintained in the transition from year 1 to
year 2; that is, some students who were in
a control condition in year 1 were assigned to
a treatment classroom in year 2, and vice versa.
Thus, 4 distinctgroups emerged from the final
sample: those who remained in treatment
conditions for both years of the study (the
T-T group), those who remained in a con-
trol condition for both years of the study
(the C-C group), those who switched from
a control to a treatment condition (the C-T
group), and those who switched from a treat-
ment to a control condition (the T-C group).
One grade at one of the schools was also
excluded from data collection in the second
year of the study as a result of students
switching to classrooms that were not par-
ticipating in the study. Thus, the final
sample size for our analyses was 193. (Data on
overall attrition across the study period are
shown in Figure A, available as a supplement
to the online version of this article at http://

12. www.ajph.org.)
Intervention
In each of the participating schools, the
control classrooms were left unchanged,
outfitted identically to the rest of the class-
rooms in the school, with traditional seated
desks (FBBK Series Model 2200, Scholar
Craft Products, Birmingham, AL) and ac-
companying chairs (9000 Classic Series, Virco
Inc., Torrance, CA). The treatment class-
rooms were outfitted completely with
Stand2learn LLC (College Station, TX)
stand-biased desks and stools (models S2LK04
and S2LS04, respectively). It is important
to note that all desks in the treatment class-
rooms were changed to stand-biased desks,
regardless of parental or student consent
to participate in the study; consent was
relevant solely to data collection.
Data Collection
After completion of the consent process,
researchers organized trips to each classroom
early in the first semester of the academic year
to record students’ height, weight, gender,
birth date, and age. These data were used
to calculate each student’s BMI, BMI per-
centile, and BMI category, according to the
Centers for Disease Control and Prevention
guidelines (https://nccd.cdc.gov/dnpabmi/
calculator.aspx). This process was repeated at
the conclusion of the 2-year study, late in the
spring semester. Teachers received $50 per

13. semester after data collection as an incentive
for their participation. (We also used Sense-
wear Armbands to collect data on caloric
expenditures; these findings are being ana-
lyzed and will be reported separately.)
AJPH RESEARCH
1850 Research Peer Reviewed Wendel et al. AJPH October
2016, Vol 106, No. 10
http://www.ajph.org
http://www.ajph.org
https://nccd.cdc.gov/dnpabmi/calculator.aspx
https://nccd.cdc.gov/dnpabmi/calculator.aspx
Statistical Analyses
At the beginning of the study, treatment
group randomization (traditional desks vs
stand-biased desks) was performed at the
classroom level in each of the 3 schools.
However, the classroom formation could not
be maintained in the second study year be-
cause students had different classroom as-
signments as they transitioned to the next
grade level. Thus, although desirable, a mul-
tilevel analysis with classrooms as the units of
analysis was not possible. Another study
feature is that weight and height measure-
ments were made at the beginning of the
study, before stand-biased desks were in
use, and later toward the end of the study,
after these desks had been in use for about
2 academic years. As a result, the most ap-

14. propriate strategy involved data analysis of
changes in BMI percentiles in the 4 treatment
groups (T-T, T-C, C-T, and C-C) described
earlier.
Initially, box plots were used to identify
obvious outliers. Next, we examined de-
scriptive statistics with respect to the char-
acteristics of students in each treatment group.
We conducted c2 comparison tests (for cat-
egorical variables) to ensure that the 4 treat-
ment groups were similar in terms of baseline
characteristics. For each treatment group, raw
BMI measures, BMI percentiles, and BMI
categories (normal or underweight, over-
weight, obese) were used to summarize BMI
measurements taken at the beginning and end
of the study and BMI changes over the study
period. Because the percentage of students
with changes in BMI categories over the
2-year study period was quite small, we de-
cided to use BMI percentile (which involves
more information than BMI category and
takes into consideration natural increases in
BMI among growing children) as the primary
outcome variable.
The main focus of our analysis was the
impact of stand-biased desks on BMI per-
centile changes over the 2-year period. We
first calculated students’ BMI percentile
change scores. We then fit an ordinary linear
regression model to the data with BMI per-
centile change score as the dependent variable
and treatment, grade, gender, and race/
ethnicity as the covariates. The C-C group

15. served as the reference group in comparisons
of each of the other 3 treatment types. We also
considered interactions between covariates
(grade, gender, and race/ethnicity) and
treatment types. The statistical significance
level was set at .05. In addition, because
students from 3 different schools were en-
rolled in the study, we fit a multilevel
linear mixed-effect model to the data with
the same covariates just mentioned as fixed
effects and school as a random effect. A
likelihood ratio test (assessing whether the
variance of the random effect was equal to
zero) was conducted to examine the necessity
of including school as a random effect.
RESULTS
In general, the sample was almost equally
made up of male and female students, with
a mean age of 8.8 years. The majority of
participating students were White (75%);
approximately 8% were Hispanic, 7% were
African American, and roughly 10% were of
Asian or Native American descent. According
to the weight percentiles for children set forth
by the Centers for Disease Control and
Prevention, approximately 79% of the stu-
dents were in the normal-weight category,
12% were overweight, and 9% were obese at
the start of the study.28 Table 1 shows de-
scriptive statistics for participants in each
treatment group.
As a result of the aforementioned attrition

16. and participant exclusion, treatment and
control group sample sizes were dispropor-
tionate across schools and grades. Despite
these discrepancies, there were no significant
differences in baseline characteristics such as
race/ethnicity, gender, and BMI category
(Table 1). Table 2 shows BMI and BMI
percentile means and standard deviations for
all of the treatment groups during each study
year, as well as changes during the 2 years of
the study in BMI, BMI percentile, and BMI
category. The largest decrease in BMI per-
centile across both years occurred in the T-T
group; there was also an increase in BMI
percentile in the C-C group.
To evaluate the effects of stand-biased
desks on students’ body weight, we fit
a linear regression model with BMI per-
centile changes over the 2 study years as the
outcome variable and grade, race/ethnicity,
gender, and their treatment group in-
teractions as the covariates. None of the
interaction terms were statistically signifi-
cant, and these terms were consequently
removed from the final model. The results
are summarized in Table 3.
After adjustment for grade, race/ethnicity,
and gender, there was a statistically significant
decrease in BMI percentile in the group that
used stand-biased desks for 2 consecutive
years relative to the group that used standard
desks during both years. The estimated dif-
ference in BMI percentile change between

17. these groups was 5.24 (SD = 2.50, P = .037).
There were no significant differences be-
tween the group that used stand-biased desks
for 2 consecutive years and the 2 other groups
that used stand-biased desks for only 1 year of
the study (P values not shown). No other
covariates had a significant impact on changes
in BMI percentiles.
We also fit a multilevel linear mixed-
effect model to the data with treatment
group, grade, race/ethnicity, and gender
as fixed effects and school as a random
effect. The treatment effect for the T-T
group relative to the C-C group was re-
duced, with an estimated difference of 3.89
(P = .075). The effects for the other 2
treatment groups (T-C and C-T) were
similar to the effects obtained with the linear
regression model. The likelihood ratio test
assessing the variance of the random effect
produced a nonsignificant result, indicating
that it was not necessary to include school as
a random effect.
DISCUSSION
The results of this study indicate that simply
changing a classroom to a stand-biased envi-
ronment had a significant effect on students’
BMI percentile. The greatest impact occurred
among students who were in treatment class-
rooms (T-T) in both study years. However,
the other 2 groups that had stand-biased
desks for least 1 year (T-C and C-T) experi-
enced smaller (nonsignificant) BMI percentile

18. changes than the group that was in a control
classroom(C-C) during both years. In addition,
there were no statistically significant in-
teractions according to gender or race/
ethnicity, suggesting that this 2-year in-
tervention benefitted our elementary school
study population equivalently across de-
mographic groups. Consistent with our pilot
AJPH RESEARCH
October 2016, Vol 106, No. 10 AJPH Wendel et al. Peer
Reviewed Research 1851
study findings amongfirst graders, an age group
in which many habits are being formed, the
intervention resulted in a marked decrease in
students’ BMI percentiles. Our findings are also
consistent with what has been found among
adults using stand-biased desks in workplaces.
As noted by Dunstan et al., “prolonged
sitting has been engineered into our lives
across many settings.”16(p368) The norm for
TABLE 2—Body Mass Index (BMI) Measures for Participating
Students: 3 Texas Schools, 2011–2013
Variable
T-T Group (n = 62),
% or Mean (SD)
T-C Group (n = 59),

19. % or Mean (SD)
C-T Group (n = 23),
% or Mean (SD)
C-C Group (n = 49),
% or Mean (SD)
BMI category statusa
Moved down 1 category 6.5 0.0 8.7 2.0
Maintained category 88.7 94.9 87.0 85.7
Moved up 1 category 4.8 5.1 4.4 12.2
BMI
Year 1 16.9 (2.2) 18.0 (3.5) 16.9 (3.2) 17.3 (2.9)
Year 2 17.0 (2.5) 18.3 (4.1) 17.0 (3.5) 17.7 (3.0)
Change 0.1 (1.2) 0.3 (1.0) 0.1 (0.7) 0.4 (1.1)
BMI percentile
Year 1 52.7 (27.4) 54.8 (30.4) 45.9 (32.1) 55.6 (26.6)
Year 2 49.7 (29.5) 53.3 (34.9) 44.9 (32.5) 57.4 (27.8)
Change –3.1 (14.5) –1.5 (10.0) –1.0 (10.3) 1.8 (14.6)
Note. Treatment groups are as follows: students who remained
in a treatment condition for both years of the study (T-T),
students who remained in a control
condition for both years of the study (C-C),students who

20. switched from a control to a treatment condition (C-T), and
students who switched from a treatment to
a control condition (T-C). BMI, BMI percentile, and BMI
category were determined according to the Centers for Disease
Control and Prevention guidelines
(https://nccd.cdc.gov/dnpabmi/calculator.aspx).
aIndicates whether children moved up from, moved down from,
or maintained their original BMI category.
TABLE 1—Baseline Characteristics of Participating Students: 3
Texas Schools, 2011–2013
Characteristic T-T Group (n = 62), % T-C Group (n = 59), % C-
T Group (n = 23), % C-C Group (n = 49), % Total (n = 193), %
P a
School < .001
School 1 (n = 35) 33.9 23.7 0.0 0.0 18.1
School 2 (n = 107) 35.5 57.6 47.8 81.6 55.4
School 3 (n = 51) 30.7 18.6 52.2 18.4 26.4
Gender .88
Female (n = 97) 46.8 50.9 56.5 51.0 50.3
Male (n = 96) 53.2 49.2 43.5 49.0 49.7
Grade .005
Grade 2 (n = 103) 59.7 37.3 78.3 53.1 53.4
Grade 3 (n = 90) 40.3 62.7 21.7 46.9 46.6
Race/ethnicity .42

21. White (n = 144) 77.4 76.3 82.6 65.3 74.6
Hispanic (n = 15) 8.1 8.5 8.7 6.1 7.8
Black (n = 14) 4.8 10.2 0.0 10.2 7.3
Other (n = 20) 9.7 5.1 8.7 18.4 10.4
Body mass index categoryb .07
Normal or underweight (n = 153) 82.3 72.9 82.6 81.6 79.3
Overweight (n = 23) 14.5 8.5 8.7 14.3 11.9
Obese (n = 17) 3.2 18.6 8.7 4.1 8.8
Note. Treatment groups are as follows: students who remained
in a treatment condition for both years of the study (T-T),
students who remained in a control
condition for both years of the study (C-C), students who
switched from a control to a treatment condition (C-T), and
students who switched from a treatment
to a control condition (T-C).
aP values determined by Pearson c2 test.
bBody mass index category was determined according to the
Centers for Disease Control and Prevention guidelines
(https://nccd.cdc.gov/dnpabmi/calculator.aspx).
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2016, Vol 106, No. 10
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22. general public school classrooms is seated
instruction; they were designed that way.
However, with a growing body of evidence
that prolonged sitting greatly increases one’s
risk not only for obesity but also for metabolic
issues and chronic diseases, is it time to
reengineer classrooms? Our society is ripe
with examples of using scientific findings to
shape policy.29 Perhaps the more important
question is can we choose not to redesign the
classroom environment, knowing that we are
doing long-term harm to children by con-
ditioning them to prolonged sitting?
Limitations
A few limitations of our study warrant
attention. First, measuring children’s BMIs is
complex; because BMI is based on height and
weight, both of which are expected to in-
crease as children grow and develop, child
BMI results must be interpreted carefully and
in light of what is developmentally normal.
Examining changes in BMI percentile is one
way of balancing this issue, because growth
charts account for anticipated increases in
height and weight. In addition, our
measurements were taken over a 2-year pe-
riod, thus allowing time to balance out
fluctuations related to episodic growth spurts.
A second limitation is that, although our
intervention was provided to all of the students

23. in treatment classrooms, we were able to collect
data only for those children who assented and
whose parents provided consent. Thus, our
resultsdonotincludeeveryonewhowastreated.
Wedid not observe specific differences between
children whodidanddidnotparticipate,butit is
possible that small differences existed.
Finally, our research was challenged by its
implementation in real school environments,
where many factors were out of our control.
For example, some teachers themselves stood
more than others and consequently influ-
enced classroom dynamics; although our total
of 24 classroom interventions is not sufficient
to thoroughly examine teacher effects, it is
sufficient to account for classroom variations.
Ultimately, implementation in actual school
settings was a benefit of the study, as the results
suggest what effects might be expected if
the intervention were replicated.
Public Health Implications
Changing classroom environments to
stand-biased environments has the potential
to affect millions of children; according to the
National Center for Education Statistics, 49.8
million students were enrolled in public
schools in fall 2014.30 Stand-biased classrooms
can interrupt sedentary behavior patterns
among students in kindergarten through
grade 12 (and beyond) during the hours they
spend at school, and this can be done simply,
at a low cost, and without disrupting class-
room instruction time.

24. Research solely based on 2 hours of in-
structional time each day indicates that
stand-biased classrooms have measurable ef-
fects on elementary school students. Con-
sidering the increase in seated instructional
time as students move to higher grade levels,
the potential impact could be even greater
among secondary school students. Additional
research should examine actual effects on
older students as their instructional contexts
change and they progress with respect to
physiological development.
CONTRIBUTORS
M. L. Wendel was the co–principal investigator of the
study, contributed to the study design and data analysis,
and led the writing of the article. M. E. Benden was the
principal investigator of the study, led the study design,
and contributed to the writing of the article. H. Zhao led
the statistical design and analysis of data and contributed to
the results section of the article. C. Jeffrey led the data
collection for the study and contributed to the back-
ground and methods sections of the article.
ACKNOWLEDGMENTS
This study was supported by the Eunice Kennedy Shriver
National Institute of Child Health and Human Devel-
opment (grant 5R21HD068841).
M. E. Benden declares a financial conflict of interest
associated with this research since his US patented designs
for standing height school desks have been licensed by
Texas A&M University to Stand2Learn LLC, a faculty led
startup company, of which he owns stock and whose desks

25. were included in the treatment groups used in this study.
M. E. Benden’s COI is managed by a TAMU approved
plan and his involvement was at the experimental design
stage and not the data collection or analysis phases.
We thank the College Station (TX) Independent
School District for its partnership in this project, the 24
teachers whograciouslyallowedusintotheirclassroomsto
collect data over the 2-year study period, and the children
who taught us so much, kept us laughing, and reminded us
why we do this work.
Note. The conclusions presented are those of the
authors and do not necessarily represent the official po-
sition of the National Institutes of Health.
HUMAN PARTICIPANT PROTECTION
This study was approved bythe institutional review boards
of Texas A&M University and the College Station In-
dependent School District. Written informed consent was
obtained from parents or guardians, and verbal assent was
obtained from students, prior to data collection.
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TABLE 3—Changes in Body Mass Index
Percentiles Associated With Stand-Biased
vs Seated Classrooms: Students in 3 Texas
Schools, 2011–2013
Variable b (95% CI) SE
Intercept 3.93 (–0.89, 8.75) 2.44
Treatment group

27. T-T –5.24 (–10.16, –0.31) 2.50
T-C –2.96 (–7.97, 2.05) 2.54
C-T –3.94 (–10.56, 2.68) 3.35
Male gender –1.08 (–4.81, 2.65) 1.89
Grade 3 –2.41 (–6.27, …
RESEARCH ARTICLE Open Access
Enrolment of families with overweight
children into a program aimed at reducing
childhood obesity with and without a
weight criterion: a natural experiment
Emma Esdaile1* , Emely Hernandez1, Carly Jane Moores2 and
Helen Anna Vidgen1
Abstract
Background: Difficulties engaging families with overweight
children to enrol into programs aimed at reducing
childhood obesity have been well documented. During the
implementation of the Parenting, Eating and Activity for
Child Health Program (PEACH™) over a large geographical
area (Queensland (QLD), Australia), a natural experiment
developed. This experiment provided an opportunity to observe
if there was a difference in enrolment for families
with overweight children with a weight criterion (referred to as
the period with a Targeted Eligibility Criterion (TEC))
compared to when a weight criterion was removed (the period
referred to as Universal Eligibility Criterion (UEC)). We

28. also examined the eligibility criterion’s relationship with
attendance, parental concern about their child’s weight,
estimation of overweight and obesity from parent-reported data.
Methods: A secondary analysis of baseline data from 926
overweight/obese children from 817 families enrolled in
PEACH™ QLD was performed. Analyses were adjusted to
control for the presence of clustered data. Bivariate statistics
were performed using Pearson chi-square test with the second-
order Rao-Scott correction, and Mann–Whitney U-test
for non-parametric continuous variables. Generalized
Estimating Equations (GEE) explored the association between
weight status-based eligibility criteria and enrolment of
overweight children. GEE were adjusted for sex, age and
socioeconomic index and stratified for weight category.
Results: Compared to obese children, overweight children were
almost twice as likely to be enrolled when the
program did not have weight status-based eligibility criteria
(during UEC period) (OR = 1.90 (CI 95% 1.35–2.68, p <
0.001)). Parents of overweight children enrolled during the UEC
period were more likely to regard their child’s weight
as less of a concern than during the TEC period (UEC 67% vs.
TEC 45%, p = 0.036). Children whose parent-reported data
underestimated their weight category were more likely to be
enrolled while the program did not have weight-related
eligibility criteria OR = 2.27 (CI 1.38–3.70, p < 0.01). Program
session attendance did not appear to be impacted by the
changes in eligibility criteria.
Conclusions: The omission of weight criteria for healthy
lifestyle programs is a consideration for health professionals
and
decision-makers alike when encouraging the enrolment of
children who are overweight into healthy lifestyle programs.

29. Trial registration: ACTRN12617000315314. Retrospectively
registered 28 February 2017.
Keywords: Childhood obesity, Primary school, Healthy lifestyle
program, Engagement, Eligibility
© The Author(s). 2019 Open Access This article is distributed
under the terms of the Creative Commons Attribution 4.0
International License
(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, 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.
* Correspondence: [email protected]
1School of Exercise & Nutrition Sciences, Faculty of Health,
Queensland
University of Technology, Level 4, A Wing, O Block, Kelvin
Grove, Brisbane,
Queensland Qld 4059, Australia
Full list of author information is available at the end of the
article
Esdaile et al. BMC Public Health (2019) 19:756
https://doi.org/10.1186/s12889-019-6894-y
http://crossmark.crossref.org/dialog/?doi=10.1186/s12889-019-
6894-y&domain=pdf
http://orcid.org/0000-0002-9166-1001
https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?i
d=372258
http://creativecommons.org/licenses/by/4.0/

30. http://creativecommons.org/publicdomain/zero/1.0/
mailto:[email protected]
Background
Elevated obesity prevalence rates are an international
phenomenon, and one in four Australian children aged 5–
17 years are overweight or obese [1]. Body Mass Index
(BMI) categories of excess weight reflect the different
levels of risk of chronic illness experienced by overweight
and obese individuals. Obesity contributes to multiple
co-morbidities in childhood and adulthood, as well as with
all-cause mortality when compared to normal or over-
weight status [2–5], and increases lifetime risk of develop-
ing chronic disease [6]. As such, returning children to
their healthy weight range is likely to have the greatest
health benefits relative to the risks associated with lifelong
excess weight. While childhood weight management pro-
grams likely have benefits irrespective of the child’s weight
status at enrolment, those who are overweight, rather than
obese, are more likely to shift their weight status category
into the healthy range [7], giving them the healthiest foun-
dation for adulthood [8, 9]. This reinforces the importance
of early identification and intervention.
The Parenting, Eating and Activity for Child Health
(PEACH™) Program is an evidence-based [7, 10] healthy
lifestyle program that was scaled up and delivered state-
wide in Queensland (a geographically large state of
Australia) to parents of primary school-aged children who
were above their healthy weight range. In Queensland, the
prevalence of overweight in children aged 5–17 years is
19%, while the obesity prevalence is 7% [11]. Despite the
higher proportion of overweight compared to obese chil-
dren in the general population, the children who enrolled
into PEACH™ Queensland (PEACH™ QLD) and were

31. above a healthy weight were predominantly obese (79%)
rather than overweight (21%) [10]. Other studies show dif-
ficulties in recruiting families with overweight children
into weight management programs in Australia; when par-
ents enrol, their children tend to be disproportionately
obese, rather than overweight [10, 12]. Despite these diffi-
culties PEACH™ QLD was interested in increasing enrol-
ments among children who were overweight, in order to
better reflect the target population.
From a public health perspective weight loss among
obese and overweight children is significant. However, the
probability of returning to a healthy weight is higher [7]
while avoiding the development of co-morbidities is lower
[13] for children who are overweight (compared to obese)
and so maximising their enrolments into healthy lifestyle
programs is a key prevention strategy. Early qualitative re-
search undertaken among parents who enrolled into
PEACH™ QLD identified that parents sought out a range
of other methods to act on their child’s weight before they
considered enrolling into a weight management program
[14], suggesting there are factors which delay enrolment
as children continue to gain excess weight. Continuous
quality improvement during the scaling up of PEACH™
QLD (described in detail elsewhere [15]) provided the op-
portunity for a retrospective natural experiment to explore
whether parents of overweight children were more likely
to enrol into the program when the weight status eligibil-
ity criterion was removed.
Our research aim was to investigate if a weight criterion
acts as a barrier to enrolment into healthy lifestyle pro-
grams aimed at reducing childhood obesity. Three key
themes emerge from the literature as the primary barriers
for parents of overweight children not enrolling into a

32. weight management program. These barriers include per-
ceived stigma [16–18], inability of parents to recognise
their child is above their healthy weight [19, 20] and par-
ents not considering weight to be an immediate health
issue [20, 21]. While stigma was not measured, our evalu-
ation framework collected data that relates to the other
identified barriers. These were the extent of agreement be-
tween parent-reported and facilitator-measured anthropo-
metric data and the extent to which parents were
concerned about the seriousness of obesity for their child.
We hypothesised that the removal of a weight criterion
would lower barriers to entry and proportionately more
parents with overweight children would enrol, rather than
delaying seeking external support for their child. To ex-
plore this, we compared the proportion of overweight and
obese children enrolled into the program before and after
the removal of weight status-based eligibility criterion to
be above a healthy weight. We compared factors that re-
lated to identified barriers to enrolment, where data were
available. We also compared enrolment and attendance
before and after the weight-status eligibility criterion to
observe if there was a difference in these characteristics in
order to inform recruitment for future programs.
Methods
PEACH™ Queensland
The Queensland Government contracted Queensland
University of Technology to deliver the PEACH™ Program
using a license from its creators [10], this project is re-
ferred to as PEACH™ QLD [15]. We have previously de-
scribed implementation learnings [15], evaluation [22] and
program outcomes [7] of PEACH™ QLD elsewhere.
Briefly, PEACH™ consists of ten 90-min group-based face-
to-face sessions delivered by a trained facilitator over a
six-month period. The parent group sessions focus on

33. parenting skills training to improve the healthy lifestyle
behaviours (diet, physical activity and sedentary behaviour
including screen time) of children [23]. Consistent with
clinical practice guidelines [24], PEACH™ QLD focused on
healthy lifestyle messages using whole-of-population mes-
sages from the Australian Dietary Guidelines [25] and
Australia’s Physical Activity & Sedentary Behaviour Guide-
lines for Children [26] rather than focus on weight reduc-
tion explicitly. As such, its content was suitable for all
Esdaile et al. BMC Public Health (2019) 19:756 Page 2 of
12
children irrespective of weight status. Facilitators then de-
veloped parents’ skills in recognising the obesogenic envir-
onment and developing strategies to guide their family in
adopting healthy behaviours. Children participate in con-
current facilitated sessions which include non-competitive
physical activities and a brief healthy lifestyle activity de-
signed to complement the content of the parent sessions.
The primary objectives of PEACH™ QLD were to enrol
1100 children and to deliver the program as broadly
across the state of Queensland as possible [15]. Eligibility
to enrol into the program originally required children to
reside in Queensland, be 5–11 years of age, and be above
a healthy weight range for their age and gender. The
PEACH™ QLD project was delivered in five waves across
more than 3 years and changes to eligibility are sum-
marised in Table 1.
Waves one and two (October 2013 to April 2015) were
primarily focused on the piloting of the program across
different healthcare settings, with a secondary focus on

34. geographical reach. Waves three and four (February 2015
to April 2016) heavily focused on geographical reach, and
smaller towns reported finding it more difficult to recruit
participants [15]. Between 2014 and late 2015 several
meetings were held with program facilitators and informal
discussions were held with participants as part of the qual-
ity improvement cycle to identify strategies for improving
program delivery. Also, health professionals in towns with
close-knit communities identified that the removal of a
weight criterion could reduce the potential stigma of at-
tending a weight management program and may encour-
age more families to enrol and attend sessions. There
were also concerns that the program was not reaching
those most likely to successfully transition to their healthy
weight range. After consideration of this feedback and in
consultation with the funder, the eligibility criteria for
child weight status was removed for the final roll out
phase, Wave 5 (February to September 2016). In this
paper, we refer to Waves one to four as the TEC phase
and Wave five as Universal Eligibility Criterion (UEC)
phase in relation to these differences in eligibility relating
to child weight status, as in Table 1.
Messages within the PEACH™ Program focused on
healthy lifestyle and not specifically on weight loss, so
program content was not changed and the removal of
the weight-based eligibility criterion meant that the pro-
gram acted as both a primary and secondary prevention
intervention in the UEC groups. Marketing and advertis-
ing materials were also changed: original marketing ma-
terials during the TEC made reference to ‘healthy
weight’ while later UEC marketing messages focused on
‘healthy lifestyle’ with no reference to weight.
Measures

35. Demographics
Demographic data, including child age, gender, height and
weight, postcode and source of referral, were collected at
the first point of contact with parents in order to assess
eligibility for enrolment. Parent Indigeneity was also deter-
mined at enrolment, in line with best practice [27]. Social
disadvantage, and accessibility and remoteness were deter-
mined using the postcode-based scores: Index of Relative
Socio-Economic Disadvantage (IRSD) [28] and the Acces-
sibility/Remoteness Index of Australia (ARIA2011+) [29],
respectively. The IRSD is a component of the Socioeco-
nomic Indexes for Areas (SEIFA) [28], where the highest
IRSD quintile indicates a relative lack of disadvantage in
general, and the lowest quintile indicates a greater level of
disadvantage in general [28]. ARIA2011+ is a measure of
geographical remoteness or accessibility for every location
in Australia, and it can be classified as five categories, that
range from highly accessible to very remote. Both
measures were used as categorical and continuous vari-
ables in this study.
Recruitment and weight status
Referrals into the program came in two ways: either parents
self-referred or they were referred by the health sector
(health professionals or directly from a hospital wait list,
see [15] for a more detailed description of these referral
methods). Health professional-reported or parent-reported
child height and weight were collected for the duration of
the program. During the phase of the program with a
weight status eligibility criterion (referred to as the Target
Eligibility Criterion (TEC) phase) these reported child mea-
sures were used to assess the eligibility for enrolment into
the program, set at or above the 85th percentile for child
Table 1 Summary of PEACH™ QLD timeline with targeted
versus universal eligibility criteria

36. Targeted Eligibility Criterion (TEC) Universal Eligibility
Criterion (UEC)
Program Wave Waves 1–4 Wave 5
Date range October 2013–April 2016 February – September
2016
Age criterion 5–11 years Primary school ageda
Weight criterion Above healthy weight only All weight
categories were accepted
Residence Families must reside in Queensland
aIn Queensland, children are able to commence primary school
from 4 years, depending on when their date of birth falls in the
calendar year. The age criteria (5–
11 years) meant that some primary school aged children were
not eligible to enrol. In Wave 5 the extension to ‘primary school
age’ was made to streamline
recruitment strategies [16]
Esdaile et al. BMC Public Health (2019) 19:756 Page 3 of
12
age and gender, calculated using US-CDC BMI growth
charts [30]. Self-referral was initiated through a compre-
hensive marketing strategy whose messages were modified
as a result of change in eligibility criteria (details reported
elsewhere [15]).
Children also had their height, weight, and waist circum-
ference measured by trained child facilitators when they

37. attended their first session. The method of weight meas-
urement used for the PEACH™ QLD program has been de-
scribed previously [7]. Children with biologically
implausible values for anthropometric data, defined by the
US-CDC [30], were excluded from all analyses as described
in a previous paper [7]. These 17 cases (1.8%) were all gen-
erated by parent-reported child measures. Facilitator-mea-
sured data were available for 700 children who attended
the program (75.6% of enrolled children above their
healthy weight). In the present study, parent- or health
professional-reported anthropometric data obtained at en-
rolment were used for children without facilitator-mea-
sured height and weight (n = 226).
The agreement between parent-reported data at enrol-
ment, and facilitator-measured data at program sessions
was explored for the subsample with available data (n =
551). These measures were compared to determine the re-
lationship of parental reporting biases (i.e., if they were
aware of their child’s current weight and height) with
enrolment into the program. For this analysis, the
parent-reported anthropometric data was classified as
over-reporting, under-reporting or agreement. These clas-
sifications refer to parent reporting of child height and
weight, not the reporting of child weight status. Over-
reporting was defined as parent-reported height and
weight resulting in a weight status category higher than
that derived from facilitator-measured data (i.e.,
parent-reported data was calculated to result in an obese/
overweight category, when the facilitator-measured data
was calculated to result in overweight/ normal weight cat-
egory). In contrast, under-reporting was defined as
parent-reported height and weight resulting in a weight
status category lower than that derived from
facilitator-measured data (i.e., parent-reported data was
calculated to result in underweight/ normal weight/ over-

38. weight, when facilitator-measured data was calculated to
result in normal weight/ overweight/ obese result). Finally,
agreement meant that the information parents provided at
enrolment, when calculated, was in agreement with the
measured data.
Program attendance
Trained facilitators who delivered PEACH™ recorded
parent attendance at each session and classified children
as ‘attended’ if their parents attended at least one session
and ‘never attended’, if parents did not attend any ses-
sions. Families were able to enrol until the third session.
For those who attended, the total number of sessions
attended was calculated as a range from 1 to 10.
Parental perceptions
Parent’s perceptions regarding the severity of their child’s
weight status were captured at baseline. Parents were
asked: “Do you think that your child’s weight is a serious
health condition?” Parents responded to each item using a
5-point Likert scale (end points: not serious, very serious).
These responses were re-coded into 3 categories: not ser-
ious (answers 1 and 2), serious (answer 3), very serious
(answers 4 and 5).
Sample and statistical analysis
Children above their healthy weight at enrolment were
selected in order to directly compare differences be-
tween participants in PEACH™ QLD with UEC and
TEC. These children were classified as overweight or
obese (including children classified as obese/morbidly
obese according to IOTF extended) [31].
The program allowed for multiple children from the
same family to be enrolled into the program. This led to

39. a mixture of children enrolled alone (without siblings),
and children enrolled with one or more siblings. In these
cases, observations from children from the same family
were not independent from one another for some vari-
ables. Consequently, our methods have been adapted to
account for potential cluster effect of the presence of
siblings in the dataset. Specifically, Pearson chi-square
test with the second-order Rao-Scott correction and
Generalized Estimating Equations (GEE) were used in-
stead of conventional Pearson chi-square and logistic re-
gression, respectively.
The proportion of categorical variables were compared
by enrolment phase (TEC vs UEC) using Pearson
chi-square test with the second-order Rao-Scott correc-
tion. Total number of sessions attended, a non-parametric
continuous variable, was compared by the enrolment of
TEC and UEC phase using the Mann–Whitney U-test.
Chi-square analyses conducted to compare the proportion
of children enrolled in the TEC and UEC stages of the
program in terms of their parents’ perceptions of the ser-
iousness of their weight, included adjusted residuals which
indicate the magnitude of the difference between observed
and expected counts. Analyses evaluating attendance and
parental concern for their child’s weight were performed
in the whole sample as well as stratified for weight cat-
egory, in order to detect possible confounding effects.
Family-level data from the subset of 99 families that
enrolled more than one child was evaluated with the
aim of determining if overweight children were recruited
along with obese siblings, as opposed to being independ-
ently recruited. A conventional Pearson chi-square ana-
lysis was used to compare the proportions of families
Esdaile et al. BMC Public Health (2019) 19:756 Page 4 of

40. 12
that enrolled exclusively overweight siblings, exclusively
obese siblings or a combination of both weight categor-
ies during the UEC and TEC phases.
To account for clustering of siblings at the family level,
GEE logistic regression analyses with robust standard er-
rors, were used to assess the relationship between UEC
enrolment and weight category, as well as the agreement
of weight category calculated from parent-reported data
with categories derived from measures taken by facilita-
tors. GEE has been shown to be an appropriate analysis
strategy for datasets with intra-cluster dependence and
small, unbalanced clusters [32–34]. GEE models were
computed using an exchangeable correlation matrix
structure with a binomial probability distribution and a
logit link function. Both children and families were en-
tered as subject variables in the model.
The GEE logistic regression analyses were conducted in
several steps. First, we estimated the unadjusted odds ra-
tios for each relationship. Potential confounding variables
for this relationship were selected a priori based on the lit-
erature and their association with the predictor and the
outcome, and then sequentially included in the model.
Evaluated confounders were: age, sex, and SEIFA score.
Data were entered and analysed in SPSS version 21
(SPSS Inc., Chicago, Ill., USA) where test results with
p ≤ 0.05 were considered to be statistically significant.
Results
A total of 926 children above a healthy weight were en-

41. rolled in PEACH™ QLD from 817 families.
Characteristics of children are presented in Table 2.
Overall, most children were obese, from highly accessible
geographical areas and from areas with a relative lack of
disadvantage. Over two-thirds (76%) of children were
self-referred into the program by their parents or carers.
In order to assess the effect of modifying the program eli-
gibility criteria to include all weight categories, we com-
pared children enrolled when the program had weight
category eligibility criteria (TEC, waves 1–4) with children
enrolled during the time when the program did not have
weight category eligibility criteria (UEC, wave 5), see Table
1. When compared to children enrolled at the time of
TEC, children enrolled at the time of UEC were more
likely to be overweight (30% vs. 20%, p = 0.001), male (57%
vs. 43%, p < 0.001), living in highly accessible areas
(ARIA) (82% vs. 69%, p = 0.012), referred to the program
by a health professional (21% vs. 14%, p < 0.001) and have
parents identify as Aboriginal or Torres Strait Islander
(7% vs. 5%, p < 0.001), see Table 2. With the exception of
accessibility index (ARIA), these differences stayed signifi-
cant when data from children with facilitator-measured
anthropometry were analysed (Table 2).
To explore whether the enrolment of overweight sib-
lings of obese children was a contributing factor to the
greater proportion of overweight children during UEC,
analysis of a subset of families (n = 99 families) with
more than one child enrolled and available weight data,
was performed (data not shown). During UEC a greater
proportion of families with only overweight children
were enrolled (UEC 15% vs. TEC 2%, p = 0.001). On the
other hand, a greater percentage of families with a com-
bination of obese and overweight children enrolled dur-

42. ing UEC (UEC 57% vs. TEC 37%, p = 0.001), at the
expense of a smaller proportion of families that only en-
rolled obese siblings (UEC 27% vs. TEC 61%, p = 0.001).
A total of 551 children had both parent-reported an-
thropometric data at enrolment and facilitator-measured
height and weight from sessions. IOTF weight categories
derived from parent-reported data were compared to
those obtained from facilitator-measured anthropometric
measurements to estimate the agreement of these mea-
surements. A median period of 42 days (IQR = 19–86)
elapsed between parent-reported and facilitator-measured
anthropometric data. For the majority of children (73.3%,
n = 404), parent-reported data was in agreement with
facilitator-measured data. Weight category calculated
from parent-reported data was underestimated in 16.9%
(n = 93) and overestimated in 9.8% (n = 54) of children in
the subsample with these available data.
Table 3 shows the results of GEE logistic regression ana-
lyses performed to determine if being enrolled during the
UEC stage of the program was associated with weight sta-
tus. After adjusting for sex, age, and a socioeconomic
index (SEIFA IRSD), children who were overweight were
nearly twice as likely to enrol during the UEC stage of the
program than during the TEC, when compared to chil-
dren who were obese (OR = 1.90 (CI 95% 1.35–2.68, p <
0.001)). Exclusion of children without facilitator-measured
anthropometric data from the regression model did not
change these results substantially (Table 4).
Further, children whose parent-reported anthropometric
data yielded a lower weight category than the one ob-
tained from facilitator-measured data (i.e., under-reported
their child’s weight category), were two times more likely
to enrol during the UEC stage, when compared to chil-

43. dren whose weight category derived from parent-reported
data showed agreement with facilitator-measured weight
category (OR = 2.27 (CI 1.38–3.70, p < 0.01), Table 4).
Attendance
A significantly higher percentage of children above their
healthy weight enrolled at the time of UEC did not attend
any sessions when compared to those enrolled in groups
with TEC (27.6% vs. 20.4%, p = 0.042). After stratifying for
weight status category, this association appeared to be sta-
tistically significant for obese children (26.9% vs. 19.3%,
p = 0.050), but not for overweight children (29.2% vs.
24.4%, p = 0.481) (Fig. 1).
Esdaile et al. BMC Public Health (2019) 19:756 Page 5 of
12
For children that attended one or more sessions (n =
720), a Mann-Whitney U-test indicated that the total
number of sessions attended was marginally, but signifi-
cantly greater for children enrolled in groups with UEC
(Mdn = 7, IQR = 4.25–9, Mean Rank = 387) than for chil-
dren enrolled in groups with TEC (Mdn = 7, IQR = 3–9,
Mean Rank = 352), U = 43,178.5, p = 0.049 two-tailed.
Parental perceptions about child weight
A significant relationship was observed between the type
of eligibility criteria at the time of a child’s enrolment and
the parental concern about their child’s weight (Table 5).
During the UEC stage, parents were significantly more
likely to consider their child’s weight less of a concern (‘not
serious’: UEC 35% vs. TEC 25%, p = 0.044). After stratifying
Table 2 General characteristics of overweight and obese

44. children enrolled in PEACH™ QLD, and their families
Anthropometric data collection
All participants with available
anthropometric dataa (n = 926)
Participants with anthropometric
data measured by facilitator (n = 700)
All Targeted Eligibility
Criterion
Universal Eligibility
Criterion
p All Targeted Eligibility
Criterion
Universal Eligibility
Criterion
p
Child characteristics n (%) n (%) n (%) n (%) n (%) n (%)
Sex
Boys 435 (47.0) 297 (43.5) 138 (56.8) < 0.001 319 (45.6) 229
(43.3) 90 (52.6) 0.037
Girls 491 (53.0) 386 (56.5) 105 (43.2) 381 (54.4) 300 (56.7) 81
(47.4)
Age at baseline

45. < 8 years old 302 (32.6) 212 (31.0) 90 (37.0) 0.185 234 (33.4)
174 (32.9) 60 (35.1) 0.845
≥ 8 -< 10 years old 297 (32.1) 222(32.5) 75 (30.9) 236 (33.7)
179 (33.8) 57 (33.3)
≥ 10 years old 327 (35.3) 249 (36.5) 78 (32.1) 230 (32.9) 176
(33.3) 54 (31.6)
Weight category (IOTF 2012)b
Overweight 207 (22.4) 135 (19.8) 72 (29.6) 0.001 553 (79.0) 98
(18.5) 49 (28.7) 0.005
Obese and morbidly
obese
719 (77.6) 548 (80.2) 171 (70.4) 147 (21.0) 431 (81.5) 122
(71.3)
ARIA
Highly accessible 675 (72.9) 475 (69.5) 200 (82.3) 0.012 505
(72.1) 369 (69.8) 136 (79.5) 0.204
Accessible 91 (9.8) 73 (10.7) 18 (7.4) 69 (9.9) 56 (10.6) 13
(7.6)
Moderately accessible 120 (13.0) 100 (14.6) 20 (8.2) 93 (13.3)
75 (14.2) 18 (10.5)
Remote/Very remote 40 (4.3) 35 (5.1) 5 (2.1) 33 (4.7) 29 (5.5) 4
(2.3)
SEIFA quintile (IRSD)

46. Most disadvantaged 113 (12.2) …