SHOULD PARENTS DETERMINE FAMILY EATING HABITS WHEN THE CHILD IS OBESE?"
boopier
134748490.pdf
420
Childhood obesity is a major health concern in the United States (US) with significant disparities among racial/ethnic minority subgroups1 and children with lower socioeconomic status.2 National Health and Nutrition Examina- tion Survey data from 2013-2014 indicate that the prevalence of obesity is higher and occurs at a younger age among black and Hispanic youth with
the highest prevalence (25%) found in elemen- tary school-age Hispanic youth.3 Standard of care4 to address pediatric obesity indicate that pediat- ric primary care providers (PCPs) should provide family-based counseling that includes culturally appropriate dietary and physical activity recom- mendations for behavioral change. With almost 80% of US children receiving one or more well-
Beth A. Conlon, Postgraduate Student, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. Aileen P. McGinn, Associate Professor, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. Carmen R. Isasi, Associate Professor, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. Remaining coauthors are listed on page 434. Correspondence Dr Conlon; [email protected]
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
Beth A. Conlon, PhD, RDN Aileen P. McGinn, PhD Carmen R. Isasi, MD, PhD Yasmin Mossavar-Rahmani, PhD, RDN David W. Lounsbury, PhD Mindy S. Ginsberg, BA Pamela M. Diamantis, MD Adriana E. Groisman-Perelstein, MD Judith Wylie-Rosett, EdD, RDN
Objectives: Home environment may influence children’s health behaviors associated with obe- sity. In this study, we examined home environment factors associated with diet and physical ac- tivity behaviors of overweight or obese youth. Methods: We analyzed baseline data from child and parent/caregiver dyads enrolled in an urban family weight management program. Multi- variable logistic regression examined how home environment (parenting practices, family meal habits, and home availability of fruits/vegetables, sugar-sweetened beverages (SSBs), screen media, and physical activity resources) are related to children’s intake of fruit, vegetables, and SSBs, and moderate-vigorous physical activity and sedentary time (ST) after adjusting for poten- tial confounders. Results: Children were more likely to consume fruit if their families frequent- ly ate meals together and infrequently watched TV during meals, and more likely to consume vegetables with high fruit/vegetable availability and low SSB availability. Children were more likely to engage in ST if parents practiced monitoring and frequently watched TV during meals. Conclusions: Overweight or obese children appear to have healthier habits if their families eat meals together without watching TV and if healthy food choices are available in the home. En- couraging parents to focus these practices may promote healthier body weight in children.
Key words: obesity; children; home environment; nutrition; physical activity; parenting Am J Health Behav.™ 2019;43(2):420-436 DOI: https://doi.org/10.5993/AJHB.43.2.17
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 421 DOI: https://doi.org/10.5993/AJHB.43.2.17
child care visits annually,5 PCPs are well positioned to address parenting behaviors related to obesity. Prior research suggests that parents view PCPs as highly valued weight management advisors6,7 re- lying on the PCP to initiate weight management counsel.8 PCPs need to guide parents to become positive behavior-change agents in the prevention and treatment of childhood obesity. This includes empowering parents to create healthier home food environments and develop effective parenting prac- tices related to food and physical activity.4,9
However, there are important gaps in our under- standing of how PCPs can leverage parenting and home environment factors to promote health be- havior change,4 particularly among high risk racial/ ethnic minority populations. Limited but grow- ing evidence among Hispanic/Latino youth sug- gests that targeting parenting practices/skills and the home environment among preschool and early elementary-aged (kindergarten-2nd grade) children is effective for addressing pediatric obesity dispari- ties.10-13 However, whether parenting and home environment variables are applicable to older chil- dren, who face increased exposure to external social and environmental determinants of obesity, needs elucidation. Findings from our pediatric ambulato- ry care-based family intervention trial suggest that semi-structured PCP counseling, which includes several targets of parenting behavior, can achieve a modest improvement in body mass index (BMI) Z-scores among overweight and obese elementary school-age youth (ages 7-12 years old).14
The present study focuses on a more in-depth examination of baseline data from this cohort of predominantly Hispanic/Latino and underserved children and their parent or primary caregiver (here- after referred to as parent). The aim was to evaluate cross-sectional associations between children’s diet and physical activity behaviors and components of the home environment, including parenting prac- tices, family meal habits, home availability of fruits/ vegetables, home availability of sugar-sweetened beverages, and home availability of screen-media and physical activity resource. Results can contrib- ute to clinical practice research gaps and inform the development of primary care-based obesity preven- tion or treatment interventions that aim to target home environments of families to promote weight management, particularly among high-risk His- panic/Latino youth.
METHODS Study Design, Setting, Participants, and Procedures
The present study is a cross-sectional analysis of baseline data of the Family Weight Manage- ment (FWM) study (registered at www.clinicaltri- als.gov, NCT00851201; also known as the Fun Healthy Families Study).14 The FWM study14 was a 12-month randomized controlled trial of a pedi- atric weight management intervention conducted through a Bronx municipal hospital’s safety-net (with predominantly Medicaid coverage) primary care pediatric practice. Bronx County, New York, is a predominantly ethnic minority (53.9% His- panic/Latino; 30% non-Hispanic/Latino black or African American),15,16 low-income, urban commu- nity with an estimated childhood overweight and obesity prevalence of 40%,17,18 which exceeds state and national levels.1 Participants were child-parent dyads. Children (ages 7 to 12 years old) were over- weight or obese (BMI ≥85th for age and sex). We previously reported that our participants had high cardiometabolic risk.19 Recruitment and enrollment occurred from July 2009 through December 2011. Inclusion/exclusion criteria have been published elsewhere.19,20 One parent (or primary caregiver) per child were enrolled in the study. Two to 3 visits were used to obtain baseline data. Families could begin completing questionnaires after providing consent/ assess, complete additional questionnaires during pre-randomization visit for obtaining fasting blood specimens, and could finish completing question- naires during a third visit in which eligibility was verified prior to randomization.
Independent survey data were not available for siblings, limiting this study to 301 parent-child (in- dex) dyads that had complete anthropometric and survey measurements assessed in our analyses. As previously described,14 536 children were assessed for eligibility, of which 170 were excluded for the following reasons, which were not mutually exclu- sive; overall, 23 did not meet inclusion criteria, 80 declined to participate, and 76 did not complete baseline assessments. A total of 360 eligible children assented. In the case that the child recruited into the study (called the index child) had sibling(s) that met eligibility criteria, the sibling(s) was invited to participate (N = 42); however, surveys used in this analyses were for the index child only.
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
422
Dependent Variables Children’s dietary intake. Children’s fruit, veg-
etable, and sugar-sweetened beverage (SSB) intake, defined in Table 1, were assessed using the Block Kids 2004 Food Frequency Questionnaire (Block Kids FFQ) Spanish version (NutritionQuest, Berkeley, CA). The Spanish version contains food items of typical Mexican-Americans diets that were selected using nationally representative surveys.21 The Block Kids FFQ is a widely accepted reliable instrument22 with a third-grade reading level. It has been validated for use among racial/ethnic minor- ity children ages 8 to 17 years of age.23 In addition, it is a valid estimate of beverage intake among chil- dren ages 7 to 9 years,24 and has been used to mea- sure sugar-sweetened beverage intake in children as young as 3 to 5 years old.28
The Block Kids FFQ,25 available in English or Spanish languages, was obtained during an in- person interview at baseline conducted by trained, bilingual (English/Spanish) research staff with chil- dren and parental assistance, as needed for clari- fication. The Block Kids FFQ25 Spanish version consists of 84 food/beverage line items and takes approximately 25 minutes to complete. Instruc- tions on the Block Kids FFQ asked children (and assisting parents), “How many days last week?” did they eat a specific food item(s), and to include foods eaten at home, at school, from fast food, or from a restaurant. Responses were recorded using a 6-point frequency of intake scale: none, 1 day, 2 days, 3–4 days, 5–6 days, or every day. Portion sizes for each item were determined using a hand- out with pictures of serving sizes to enhance the accuracy of quantification.
Block Kids FFQs25 were mailed to Nutrition- Quest (Berkeley, CA) for data processing and anal- yses. Data output provided daily average intake of food groups based upon the USDA MyPyramid Equivalents Database (MPED) version 2.0.26 Table 1 provides definitions of children’s dietary intake measures and selected cut-points for data coding in bivariate and logistic regression analyses. We assessed fruit and vegetable intake, respectively, in cup equivalents, which is consistent with the 2015 Dietary Guidelines for Americans (DGAs).27 Both fruit and vegetable intake were dichotomized for analyses as < 1 cup equivalent per 1000 kcal/ day versus ≥ 1 cup equivalent per 1000 kcal/day,
based on the 2015 DGAs27 intake recommenda- tions for children. We assessed SSB intake by the total number of kilocalories (kcal) per day from SSB, calculated as (kcal/day from SSB)/(total kcal/ day). This method adjusts for differences in total energy intake to facilitate comparisons across sex and age groups, and has been used to estimate na- tional trends in SSB intake.28 SSB intake was di- chotomized for analyses as < 5% kcal/day versus ≥ 5% kcal/day. This cut-point was selected based on DGA27 recommendations to limit added sugar and solids fats be to 5%-15% of total daily calories.27 Most added sugars in the US diet are from sugar- sweetened beverages (soda, energy drinks, sports drinks, and fruit drinks).28 Children who did not complete the Block Kids FFQ (N = 8) and children with implausible reported energy intakes of ≤ 500 kcal/day were excluded (N = 16) from analyses, limiting the sample size for diet-related outcomes to 277 parent-child dyads.
Children’s physical activity. Children’s seden- tary time and moderate-vigorous physical activity (MVPA) were objectively measured using the Acti- graph GT3X accelerometer (ActiGraph, Pensacola, FL). Accelerometers are activity monitors that cap- ture the intensity of an individual’s daily physical activities in units of measurement called counts. ActiGraph accelerometers have established validity for use among children ages 6 to 16 years old.29-32
According to standardized procedures,33 chil- dren were instructed to wear the accelerometer on their hips at waistline for 5 days, excluding during bedtime, bathing, and swimming. To be included in this analysis, children must have had 10 hours of valid wear time for 3 or more of the 5 days. Non-wear periods were defined as 60 minutes of consecutive zero counts with an allowance for up to 2 minutes of nonzero counts.34,35 Data from the accelerometer were downloaded, processed and screened for wear time.
Epoch lengths (sampling intervals) of 60 seconds were used to determine activity cut-point values.36 The GT3X device has an inbuilt inclinometer that detects standing, lying, sitting and “off.” The Ac- tiGraph algorithm classifies counts >100 counts min−1 as standing. If the counts are below 100, the data from the axes is used to classify sitting or lying or “off.” Thus, this analysis defined sedentary time as <100 counts min−1.
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 423 DOI: https://doi.org/10.5993/AJHB.43.2.17
Whereas several definitions of sedentary time have been proposed, there is no standard definition of the term. It is recommended to use cut-points that are consistent with unit of analysis (eg, epoch length), instrumentation.37 Our analysis defined sedentary time using < 100 counts min−1 because threshold has been calibrated30 and validated29,38 to measure sedentary time and physical activity with the ActiGraph accelerometer in young children (ages ≥ 3 years old), and is consistent with pub- lished literature using the ActiGraph GT3X.19,34
A MET is an estimate of relative intensity such that 1 MET represents the energy expenditure for an individual at rest; whereas a 5 MET activ- ity requires 5 times that amount. Average time (minutes/day) spent in MVPA was calculated by using an age- and sex- specific energy expenditure prediction equation, developed by Freedson et al39 and used by others31,33,40 for count cut-points that correspond to levels of PA measured in metabolic equivalents (METS): 2.757 + [0.00153 x counts/ minute] – [0.0896 x age (years)] – [0.000038 x counts/minute x age (years)]. Table 1 provides defi- nitions of children’s sedentary time and MVPA, and selected cut-points for data coding in bivariate and logistic regression analyses. Sedentary time was di- chotomized for analyses as < 8 hours/day versus ≥ 8 hours/day, based on the average time that American youth spend engaged in sedentary time, which in- cludes hours during and outside of school. Specific recommendations for total time spent in sedentary time are not available.41,42 MVPA was dichoto- mized for analyses as < 60 minutes/day versus ≥ 60 minutes/day, based on the DGAs27 and American Academy of Pediatric recommendations4,41,42 for children to perform 60 minutes or more of physi- cal activity per day, of which most of the minutes should be either moderate- or vigorous-intensity.
The biggest barrier to accelerometer data collec- tion was children forgetting to put on the acceler- ometer, resulting in non-wear periods. We excluded 88 (29.2%) children from this analysis due to too few (< 3) valid accelerometer wear days. Compared to children with ≥ 3 valid wear days (data not shown), these children had a greater proportion of parents that obtained more than a high school education (34.1% vs 23.0%; p = .05). No other statistically significant differences in child/parent characteristics, HE, or anthropometric measures
assessed in this study were observed between the 2 groups. This limited the sample size when sed- entary time or MVPA were dependent variables to 213 parent-child dyads.
Independent Variables Sociodemographic and home environment sur-
veys (available in English and Spanish) were admin- istered face-to-face by trained, bilingual (English/ Spanish) research staff. Parental and child sociode- mographic and home environment questionnaires were answered by the parent. Additional details of the HE measures assessed in this analysis have been published elsewhere.20
Anthropometric. Parents’ and children’s standing heights and weights were objectively measured by trained research staff in a private patient examina- tion room using a stadiometer and digital scale, following standardized procedures.43,44 Participants wore light clothing without shoes and emptied their pockets. For children, BMI percentiles and z-scores were calculated using the SAS Program for the 2000 CDC Growth Charts (ages 0 to < 20 years),45 and categorized as overweight (BMI 85th to 94.9th % for age and sex) or obese (≥95th % for age and sex). Parents’ BMI was calculated using the formula BMI = (Weight, kilograms)/(Height, me- ters2) and categorized as normal/overweight (BMI 18.5 < BMI < 30 ) or obese (BMI ≥ 30.0).46 One parent was underweight (BMI = 16.9) and catego- rized as normal/overweight for analyses.
Sociodemographics. Due to a large proportion of Hispanics/Latino children (74.8%) in our popu- lation, race and ethnicity were collapsed: Hispanic/ Latino, Non-Hispanic black, and other. Child characteristics included age, sex, BMI percentile, weight category, and race/ethnicity. Familial char- acteristics included parent age, sex, BMI, weight category, and proportion of years spent living in the US (calculated as [(years living in the US)/ (age in years]).47 Additional sociodemographic de- tails of the study population have been published elsewhere.20
Parenting practices. The Parenting Practices for Eating and Activity Scale (PEAS) is a 26-item instrument that was developed and validated by Larios et al48,49 among Latina/Mexican-American mothers of elementary-aged children to assess par- enting strategies related to children’s dietary and
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
424
Ta bl
e 1
D efi
n it
io n
s an
d Se
le ct
ed C
u t-
p oi
n ts
o f
C h
il dr
en ’s
D ie
t an
d P
h ys
ic al
A ct
iv it
y B
eh av
io rs
( D
ep en
de n
t V ar
ia bl
es )
A ss
es se
d in
B iv
ar ia
te a
n d
L og
is ti
c R
eg re
ss io
n A
n al
ys es
C hi
ld re
n’ s
W ei
gh t-
re la
te d
B eh
av io
rs
(D ep
en de
nt
V ar
ia bl
es )
In st
ru m
en t
U ni
t o f A
na ly
si s
Se le
ct ed
C
ut -p
oi nt
fo r
A na
ly se
s D
efi ni
ti on
U se
d in
O ur
S tu
dy S
am pl
e
F ru
it I
nt ak
e
B lo
ck 2
00 4
Fo od
F re
qu en
cy
Q ue
st io
nn ai
re , S
pa ni
sh V
er si
on
(N ut
ri tio
nQ ue
st ; B
er ke
le y,
C A
)
C up
e qu
iv al
en ts
p er
1 00
0 kc
al p
er
da y
(U SD
A M
yP yr
am id
E qu
iv al
en ts
D
at ab
as e
ve rs
io n
2. 0)
< 1
cu p
eq ui
va le
nt p
er
10 00
k ca
l/d ay
v er
su s
≥ 1
cu p
eq ui
va le
nt p
er
10 00
k ca
l/d ay
B an
an as
; a pp
le s
or p
ea rs
; o ra
ng es
o r t
an ge
ri ne
s (e
xc lu
de s
ju ic
es );
s tr
aw be
rr ie
s or
o th
er b
er ri
es ; a
pp le
sa uc
e,
fr ui
t c oc
kt ai
l o r p
in ea
pp le
s lic
es ; j
el ly
o r j
am ; a
ny o
th er
fr ui
t l ik
e gr
ap es
, p ea
ch es
, w at
er m
el on
, c an
ta lo
up e,
or
fr ui
t r ol
l- up
s; re
al o
ra ng
e ju
ic e
(e xc
lu de
s Su
nk is
t o r o
th er
o ra
ng e
so da
s) ; a
ny o
th er
re al
fr ui
t j ui
ce s
lik e
ap pl
e ju
ic e
or g
ra pe
ju ic
e (i
nc lu
de s
ju ic
e bo
xe s)
, a dd
iti on
al m
ix ed
fo od
sa
V eg
et ab
le
In ta
ke
B lo
ck 2
00 4
Fo od
F re
qu en
cy
Q ue
st io
nn ai
re , S
pa ni
sh V
er si
on
(N ut
ri tio
nQ ue
st ; B
er ke
le y,
C A
)
C up
e qu
iv al
en ts
p er
1 00
0 kc
al p
er
da y
(U SD
A M
yP yr
am id
E qu
iv al
en ts
D
at ab
as e
ve rs
io n
2. 0)
a
< 1
cu p
eq ui
va le
nt p
er
10 00
k ca
l/d ay
v er
su s
≥ 1
cu p
eq ui
va le
nt p
er
10 00
k ca
l/d ay
Sa la
d w
ith le
ttu ce
, g re
en s
al ad
; a vo
ca do
, g ua
ca m
ol e;
g re
en b
ea ns
, s tr
in g
be an
s, o
r p ea
s; p
in to
, c hi
li w
ith
be an
s, o
r b ea
n bu
rr ito
; r ef
ri ed
b ea
ns ; s
pa gh
et ti,
ra vi
ol i,
or la
sa gn
a w
ith to
m at
o sa
uc e;
v eg
et ab
le s
ou p,
ve
ge ta
bl e
be ef
s ou
p, o
r t om
at o
so up
; a ny
o th
er s
ou p
or s
te w
; g re
en s
lik e
co lla
rd s,
m us
ta rd
g re
en s
or
sp in
ac h;
b ro
cc ol
i; ca
rr ot
s, c
ar ro
t s tic
ks , o
r c oo
ke d
ca rr
ot s;
F re
nc h
fr ie
s, T
at er
T ot
s, h
as h
br ow
ns o
r h om
e fr
ie s;
a ny
o th
er k
in d
of p
ot at
oe s,
li ke
m as
he d,
b ak
ed , o
r b oi
le d;
s w
ee t p
ot at
oe s,
s w
ee t p
ot at
o, o
r p um
pk in
pi
e; a
ny o
th er
v eg
et ab
le s
lik e
sq ua
sh , c
au lifl
ow er
, a sp
ar ag
us , n
op al
es ; k
et ch
up , s
al sa
, o r b
ar be
cu e
sa uc
e,
ad di
tio na
l m ix
ed fo
od sb
Sp an
is h
ad de
d fo
od s:
ta m
al es
o r t
am al
e pi
e; c
oo ke
d gr
ee n
ch ile
p ep
pe rs
; c oo
ke d
gr ee
n pe
pp er
s, c
hi le
re
lle no
s, o
r g re
en c
hi le
s te
w ; o
th er
s ta
rc hy
v eg
et ab
le s
lik e
yu cc
a or
p la
nt ai
ns
Su ga
r- sw
ee te
ne d
B ev
er ag
e (S
SB )
In ta
ke
B lo
ck 2
00 4
Fo od
F re
qu en
cy
Q ue
st io
nn ai
re , S
pa ni
sh V
er si
on
(N ut
ri tio
nQ ue
st ; B
er ke
le y,
C A
)
Pe rc
en ta
ge o
f t ot
al e
ne rg
y in
ta ke
, kc
al /d
ay , f
ro m
S SB
c
SS B
in ta
ke <
5 %
k ca
l/ da
y ve
rs us
≥ 5
% k
ca l/
da y
So da
s lik
e C
ok e,
D r.
Pe pp
er , 7
-U p,
S pr
ite , S
un ki
st , O
ra ng
e C
ru sh
(e xc
lu de
s di
et );
s lu
rp ee
s, s
no w
c on
es ,
po ps
ic le
s (n
ot ic
e cr
ea m
); H
aw ai
ia n
Pu nc
h™ , K
oo l-
A id
™ , S
un ny
D el
ig ht
™ , G
at or
ad e™
, i ce
d te
a,
Sn ap
pl e™
; H i-
C ™
, T an
g, T
am pi
co ™
, M r.
Ju ic
y™ , S
si ps
p un
ch ™
Se de
nt ar
y T
im e
A ct
ig ra
ph G
T 3X
A cc
el er
om et
er
(P en
sa co
la , F
L )
m in
ut es
/d ay
< 8
ho ur
s/ da
y ve
rs us
≥
8 ho
ur s/
da y
T he
a m
ou nt
o f t
im e
sp en
t i n
pe ri
od s
of a
ct iv
ity w
ith c
ou nt
s th
at c
or re
sp on
de d
to <
1. 00
M E
Ts (o
r < 10
0 co
un ts
/m in
ut e;
a ls
o eq
ui va
le nt
to <
1 M
E T
in o
ur s
am pl
e) . S
ed en
ta ry
ti m
e m
ea su
re s
al l d
ai ly
a ct
iv iti
es th
at
do n
ot in
cr ea
se e
ne rg
y ab
ov e
th e
re st
in g
le ve
l, su
ch a
s si
tti ng
, l ay
in g
do w
n, a
nd s
ta nd
in g
st ill
. T im
e sp
en t
sl ee
pi ng
w as
n ot
m ea
su re
d. C
hi ld
re n
w er
e in
st ru
ct ed
to re
m ov
e th
e m
on ito
r w he
n th
ey w
en t t
o be
d at
ni
gh t,
ba th
ed , o
r s w
am .
M od
er at
e- to
- V
ig or
ou s
P hy
si ca
l A ct
iv it
y (M
V PA
)
A ct
ig ra
ph G
T 3X
A cc
el er
om et
er (P
en sa
co la
, F L
)
m in
ut es
/d ay
< 60
m in
ut es
/d ay
ve
rs us
≥ 6
0 m
in ut
es /
da y
T he
a m
ou nt
o f t
im e
sp en
t i n
pe ri
od s
of a
ct iv
ity w
ith c
ou nt
s th
at c
or re
sp on
de d
to ≥
4. 00
M E
Ts . E
xa m
pl es
o f
M V
PA in
cl ud
e br
is k
w al
ki ng
, b ic
yc lin
g, a
er ob
ic s,
a nd
p la
yi ng
s po
rt s.
N ot
e. A
bb re
vi at
io ns
: k ca
l, ki
lo ca
lo ri
e; M
E Ts
, m et
ab ol
ic e
qu iv
al en
ts ; U
SD A
, U ni
te d
St at
es D
ep ar
tm en
t o f A
gr ic
ul tu
re
a: T
he U
SD A
M yP
yr am
id E
qu iv
al en
ts D
at ab
as e2
6 v er
si on
2 .0
d efi
ne s
1 cu
p eq
ui va
le nt
a s
1 cu
p of
r aw
, c oo
ke d,
o r
ca nn
ed fr
ui t o
r ve
ge ta
bl es
; 2 c
up s
of r
aw le
af y
gr ee
n ve
ge ta
bl es
; 1 c
up
o
f 1 00
% fr
ui t v
eg et
ab le
ju ic
e; ½
c up
o f d
ri ed
fr ui
t o r
ve ge
ta bl
es ; o
r 4
ou nc
es o
f d ry
b ea
ns a
nd p
ea s.
b:
M ix
ed fo
od s
ar e
di sa
gg re
ga te
d in
to in
di vi
du al
in gr
ed ie
nt s
an d
as si
gn ed
in to
a pp
ro pr
ia te
M yP
yr am
id fo
od g
ro up
s. 27
F or
e xa
m pl
e, a
pp le
p ie
is d
is ag
gr eg
at ed
b ef
or e
fo od
g ro
up
a
ss ig
nm en
ts a
re m
ad e.
T hu
s, c
er ta
in m
ix ed
fo od
s ar
e in
cl ud
ed in
th e
fr ui
t a nd
v eg
et ab
le g
ro up
s bu
t c on
tr ib
ut e
ne gl
ig ib
le a
m ou
nt s
to th
e ca
lc ul
at io
ns a
nd a
re n
ot c
on si
de re
d m
aj or
fo od
g ro
up c
on tr
ib ut
or s.
c:
S ug
ar -s
w ee
te ne
d be
ve ra
ge e
ne rg
y in
ta ke
is e
st im
at ed
u si
ng th
e fo
llo w
in g
st an
da rd
s er
vi ng
s iz
es : 1
2 ou
nc es
o f s
od a;
5 o
un ce
s of
S lu
rp ee
, s no
w c
on es
, p op
si cl
es (n
ot ic
e cr
ea m
);
8
o un
ce s
of H
aw ai
ia n
P un
ch ™
, K oo
l- A
id ™
, S un
ny D
el ig
ht ™
, G at
or ad
e™ , i
ce d
te a,
S na
pp le
™ ; H
i- C
™ , T
an g,
T am
pi co
™ , M
r. J
ui cy
™ , S
si ps
p un
ch ™
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 425 DOI: https://doi.org/10.5993/AJHB.43.2.17
activity-related behaviors. The PEAS has dem- onstrated good internal consistency in additional populations.50-52 To examine the factor structure of the PEAS instrument in our population, we con- ducted exploratory principal component analysis. Methods and results of our PCA analysis have been published previously.20 Six factors were revealed: monitoring, discipline, limit setting of soda/snacks, limit setting of sedentary behavior, pressure to eat, and reinforcement. Parents rated each item (eg, “My child should always eat all the food on his/ her plate”) using a 5-point Likert-type scale with responses ranging from 1 = never to 5 = always or 1 = strongly disagree to 5 = strongly, as appropriate. Mean construct scores (possible range of 1 to 5) were calculated for each factor, demonstrating good internal consistency in our sample with Cronbach’s alpha ranging from 0.67 to 0.87. Higher mean construct scores indicate higher engagement in the parenting practice.
Family meal habits. The frequency of family meals and frequency of watching TV during family meals, respectively, were assessed using 2-items:53,54 “How many times does your family sit down to- gether for dinner?” and “How many times does your family have meals in front of the TV?” Re- sponse scales were never, once per month, 2-3 times per month, 1-3 times per week, and 4+ times per week. Response options were collapsed into more frequent ≥ 1-3 times per week) and less frequent (≤ 2-3 times/month).
Home fruit/vegetable and SSB availability. Parents were asked to answer the following items to indicate the frequency of fruit and vegetable avail- ability in the home and at dinner: (1) How often would you say fruits and vegetables are available in your home? (2) How often are vegetables served at dinner? (3) How often is fruit served at dinner?”55,56 Parents were asked to answer the following items to indicate the frequency of sugar-sweetened bev- erage (SSB) availability in the home and at meals: (1) How often is juice (like apple or orange) served at meals in your home? (2) How often are other drinks (like iced tea, lemonade, fruit punch, Kool- AidTM, Capri SunTM, Sunny DelightTM, SnappleTM, GatoradeTM, Vitamin WaterTM) served at meals in your home? (3) How often is regular soda served at meals in your home?” These items have been previously reported in the literature.55-59 Response
scales were always, usually, sometimes, or never. Response options were collapsed into always/usu- ally and sometimes/never.
Home screen-media availability. The number of TV’s in the home and the presence of a TV in the child’s bedroom were assessed by parent-response to 2-items:58,60,61 “How many TVs do you have at home?” (numeric response) and (2) “Is there a TV in your child’s bedroom?” (yes/no).
Home physical activity resource availabil- ity. Availability of active video games and sports equipment in the home, respectively, were assessed by parent-response to 2 items:58,62,63 “Now, we would like to know if your child or somebody else at home has: (1) Active video games (like Dance Dance RevolutionTM, WiiTM etc)?” and (2) “Sports equipment (balls, rackets, bats, sticks)?” Response options were yes/no.
Data Analysis Descriptive statistics are presented as N (%) for
categorical variables, mean ± standard deviation (SD) for normally distributed continuous vari- ables, or median (interquartile range, IQR) for skewed continuous variables (Supplementary Ta- bles 1 and 2). The distributions of variables were assessed for normality visually by histograms and quantitatively with tests of skewness and kurtosis. The distributions of the 5 dependent variables of interest (children’s weight-related behaviors: fruit intake, vegetable intake, sugar-sweetened beverage intake, sedentary time, and MVPA) were non-nor- mally distributed. Because this was an exploratory analysis, we first conducted non-parametric uni- variate analysis using Wilcoxon Rank-Sum test or chi-square test to identify independent variables (home environment components) that were as- sociated with children’s weight-related behaviors (dependent variables). Any variable with a p < .25 in bivariate analysis was subsequently assessed in a multiple logistic regression model.
Logistic regression was used because relation- ships between independent and dependent vari- ables were not linear, as determined by analysis of standardized residuals from linear regression analy- ses (data not shown). Respective multiple logistic regression models were constructed for each of the 5 children’s weight-related behaviors (dependent variables). Based on clinical judgement and previ-
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
426
ously published data from the FWM study,19,20 it was decided a priori to adjust models for the fol- lowing potential confounders: child sex (10-12 vs 7-9 years old), gender (male vs female), ethnicity (Hispanic/Latino vs non-Hispanic/Latino), and BMI z-scores; and parent age, sex, BMI, and pro- portion of parent years spent living in the US (cal- culated as = (parent time spent living in the US in years)/(parent age in years). Using backward stepwise elimination procedures, the least statisti- cally significant variables (p > .05) were removed, one-by-one, until there were no additional vari- ables, other than a priori adjustment factors, that were non-significant. Each removed variable was added back to the model, one-by-one, to assess for confounding. The importance of each home en- vironment component in the model was assessed using the likelihood ratio test. Assumptions of the models were tested using methods consistent with logistic regression (Hosmer and Lemeshow Good- ness-of-Fit Test, influential covariate patterns, and multicollinearity). A p-value ≤ .05 was considered statistically significant. Data were analyzed using STATA (version 13.1, 2014, StataCorp LP, College Station, TX).
RESULTS Characteristics of the Sample and Children’s Diet and Physical Activity Behaviors
Median child age was 10 (8.4, 11.4) years. Children were predominantly Hispanic/Latino (74.8%) and obese (76.4%) (Table 2). Median (IQR) reported energy intake on the Block Kids FFQ was 1040.7 (768, 1452) kcal/day (Table 3). About one-half of children consumed ≥ 1 cup fruit equivalents/1000 kcal/day, whereas less than one-third met the recommended intake of ≥ 1 cup vegetable equivalents/1000 kcal/day. The ma- jority (61%) of children consumed < 5% of to- tal energy intake from SSBs. Less than one-half (45.5%) achieved ≥ 60 minutes/day of MVPA. Most (56.8%) of children engaged in < 8 hours/ day of sedentary time. Median parent age was 36.0 (31.0, 42.0) years (Table 2), and most were female (92.4%), obese (62.5%), and lived in the US for ≤ 67% of their lives. Additional characteristics of the sample population and descriptive statistics of home environment measures are described in detail elsewhere.14,19,20
Associations between the Home Environment and Children’s Dietary and Physical Activity Behaviors
Children’s fruit intake. In bivariate analysis (Supplementary Table 1), children’s fruit intake was associated at the p < .25 level with the variables limit setting of sedentary behavior (p = .13), fre- quency of family meals (p = .003), frequency of TV watching during family meals (p = .02), and TV in child’s bedroom (p = .14). These variables were evaluated in the backward stepwise logistic regres- sion procedure. Results of the backward stepwise logistic regression (Table 4, Outcome 1) indicated that family meal frequency and TV watching dur- ing family meals were independently associated with children’s fruit intake in a final multivariable model (Hosmer and Lemeshow Goodness-of-Fit test p = .84). Children were more likely to have fruit intake ≥ 1 cup equivalent/1000 kcal/day ver- sus < 1 cup equivalent/1000 kcal/day when the family reported frequent family meals (OR = 1.84; 95% CI 1.0, 3.39; p = .05) compared to a family who does not have frequent family meals. Children were less likely to have fruit intake ≥ 1 cup equiva- lent/1000 kcal/day versus < 1 cup equivalent/1000 kcal/day when parents reported more frequent TV watching during family meals (OR = 0.59; 95% CI 0.36, 0.98; p = .04) compared to parents who reported less frequent TV watching during family meals.
Children’s vegetable intake. In bivariate analy- sis (Supplementary Table 1), children’s vegetable intake was associated at the p < .25 level with the variables frequency of TV watching during family meals (p = .13), availability of FV in the home (p = .01), availability of SSB in the home (p = .02), TV in child’s bedroom (p = .15), and owning ac- tive video games (p = .22). These variables were evaluated in the backward stepwise logistic regres- sion procedure. Results of backward stepwise logis- tic regression (Table 4, Outcome 2) indicated that FV availability and SSB availability were indepen- dently associated with children’s vegetable intake (Hosmer and Lemeshow Goodness-of-Fit test p = .35). Children were more likely to have vegetable intake ≥ 1 cup equivalent/1000 kcal/day versus < 1 cup equivalent/1000 kcal/day when parents reported high home FV availability (OR = 1.90; 95% CI 1.0, 3.6; p = .04) compared to parents
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 427 DOI: https://doi.org/10.5993/AJHB.43.2.17
who reported low home FV availability. Compared to parents with low home SSB availability, parents with high home SSB availability had children who were more likely to consume < 1 cup equivalent vegetables/1000 kcal/day compared to ≥ 1 cup equivalent vegetables/1000 kcal/day (OR = 0.44; 95% CI 0.20, 0.94; p = .03)
Children’s SSB intake. In bivariate analysis (Supplementary Table I), children’s SSB intake was associated at the p < .25 level with the variables monitoring (p = .003), discipline (p = .03), limit
setting soda/snacks (p = .03), limit setting of sed- entary behavior (p = .21), reinforcement (p = .21), frequency of TV watching during family meals (p = .02), availability of FV in the home (p = .02), availability of SSB in the home (p = .07) and own- ing active video games (p = .09). These variables were evaluated in the backward stepwise logistic regression procedure. Results of backward stepwise logistic regression (Table 4, Outcome 3) indicated that monitoring (OR = 0.75; 95% CI 0.55, 1.04; p = .08) and limit setting of soda/snacks (OR =
Table 2 Characteristics of the Sample Population
(N = 301 Parent/Child Dyads)
Variable Summary Statistic
Child Characteristics
Age (years), median (IQR) 10 (8.4, 11.4)
Age category (years),a N(%)
7-9 153 (50.8) 10-12 148 (49.2)
Sex, N(%) Female 162 (53.8) Male 139 (46.2)
BMI percentile,b median(IQR) 97.8 (95.3, 98.9)
Weight Category, N(%) Overweight (BMI ≥85th to <95th %) 71 (23.6) Obese (BMI ≥95th percentile) 230 (76.4)
Race/ethnicity, N(%) Hispanic or Latino 225 (74.8) Non-Hispanic Black 55 (18.2) Othera 21 (7.0)
(continued on next column)
Parent Characteristics Age (years),b median(IQR) 36.0 (31.0, 42.0)
Age category (years),b N(%)
22-36 153 (51.3) 37-67 145 (48.7)
Sex, N(%) Female 278 (92.4) Male 23 (7.6)
BMIb median (IQR) 32.7 (7.0)
Weight Category,c N(%) Normal/Overweight (18.5 < BMI < 30) 112 (29.1)
Obese (BMI ≥ 30.0) 187 (62.5)
Proportion years living in the USb,c)
≤ 0.33 86 (28.9) 0.34 – 0.67 127 (42.6) > 0.67 85 (28.5)
Table 2 (continued) Characteristics of the Sample Population
(N = 301 Parent/Child Dyads)
Variable Summary Statistic
Note. Abbreviations: BMI, body mass index; IQR, interquartile range; US, United States of America a: Child other race/ethnicity (N) = Asian (N = 6); Caucasian or white (N = 5); Pakistani (N = 2); Sri Lankan (N = 1); Bangladeshi (N = 1); East Indian (N = 1); Bengali (N = 1); Egyptian (N = 1); Arabic, unspecified (N = 1); Dominican/ Puerto Rican, not self-identified as Hispanic/Latino or black (N = 1); Afro-Caribbean (N = 1) b: For parent age, N = 298 parents/caregivers (3 parents/caregivers did not report birth dates). c: Proportion of parent years living in the United States = (parent time spent living in the US in years)/(parent age in years)
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
428
0.78; 95% CI 0.58, 1.06; p = .11) were not in- dependently associated with children’s SSB Intake, but the relationship between children’s SSB intake and monitoring approached statistical significance (Hosmer and Lemeshow Goodness-of-Fit test p = .11) Table 4, Outcome 3). Monitoring confounded the relationship between SSB Intake and limit set- ting of soda/snacks (percent change in Beta coef- ficient > 10%), and therefore, was included in the final model. The interaction term for (monitoring) X (limit sitting of soda/snacks) was not statistically significant (data not shown).
Children’s sedentary time. In bivariate analysis (Supplementary Table 2), children’s sedentary time was associated at the p < .25 level with the variables monitoring (p = .003), limit setting soda/snacks (p = .11), control (p = .11), reinforcement (p = .15), frequency of TV watching during family meals (p = .03), availability of FV in the home (p = .14) and owning active video games (p = .05). These variables were evaluated in the backward stepwise logistic regression procedure. Results of backward stepwise logistic regression (Table 4, Outcome 4) indicated that parent monitoring and the fre-
quency of watching TV during meals were inde- pendently associated with children’s sedentary time (Hosmer and Lemeshow Goodness of-Fit test p = .22). Children were more likely to engage in ≥ 8 hours/day versus < 8 hours/day of sedentary time when parents reported higher engagement in mon- itoring (OR = 1.95, 95% CI 1.36, 2.77; p = .001), and when parents reported frequently TV watch- ing during family meals (OR = 2.17; 95% CI 1.16, 4.0; p = .02) compared to parents who reported less frequent TV watching during family meals.
Children’s moderate-vigorous activity. In bi- variate analysis (Supplementary Table 2), children’s MVPA was associated at the p < .25 level with the variables limit setting of sedentary behaviors (p = .09), control (p = .03), availability of SSB in the home (p = .04), number of TVs at home (p = .14), and owning active video games (p = .04). These variables were evaluated in the backward stepwise logistic regression procedure. Results of the backward stepwise logistic regression (Table 4, Outcome 5) indicated that owning active video games was independently associated with children’s MVPA (Hosmer and Lemeshow Goodness-of-Fit
Table 3 Summary Statistics of Children’s Weight-related Behaviors
Weight-related Behaviors Summary Statistic
Dietary Intake (N = 277) Median (IQR) or N(%)
Total kilocalories/day 1040.7 (768, 1452)
Fruits, cup equivalents/1000 kcal/day 1 (0.6, 1.7) ≥ 1 cup fruit equivalents/1000 kcal/day 144 (52.0%) Vegetables, cup equivalents/1000 kcal/day 0.7 (0.4, 1.0) ≥ 1 cup vegetable equivalents/1000 kcal/day 75 (27%) Sugar-sweetened beverages, % kcal/day 3.2 (0, 7.5) < 5% kcal/day 169 (61%)
Physical Activity (N = 213) Sedentary time 458.1 (376.5, 538.2) < 8 hours/day 121 (56.8%) Moderate-vigorous physical activity 56 (34.9, 88.1) ≥ 60 minutes/day 97 (45.5%)
Note. Abbreviations: IQR, interquartile range; kcal, kilocalorie
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 429 DOI: https://doi.org/10.5993/AJHB.43.2.17
test p = .84). Children were more likely to engage in ≥60 mins/day versus <60 mins/day of MVPA when parents reported owning active video games
(OR = 0.39; 95% CI 0.18, 0.84; p = .01) com- pared to parents who did not report owning ac- tive video games. Additionally, the relationship
Table 4 Multivariable Logistic Regression Analysis of Associations between Home Environment
Measures (Independent Variables) and Children’s Health Behaviors (Dependent Variables)a Children’s Health Behaviors
Dietary Intake Physical Activity
Home Environment Measure
Outcome 1
Fruit Intakeb
(N = 271)
Outcome 2
Vegetable Intakec
(N = 271)
Outcome 3
SSB Intaked
(N = 271)
Outcome 4
Sedentary Timee
(N = 209)
Outcome 5
Moderate- Vigorous PAf
(N = 209)
Odds Ratio (95% Confidence Interval)
Parenting Practices
Monitoring -- -- 0.75 (0.55, 1.04)ǂ 1.95
(1.36, 2.77)** --
Limit setting soda/snacks -- -- 0.78 (0.58, 1.06) -- --
Family Meal Habits
Family meals (more frequent)e 1.84 (1.0, 3.39)* -- -- -- --
TV during meals (more frequent)e 0.59 (0.36, 0.98)* -- -- 2.17
(1.16, 4.0)* --
Food Availability
Availability FVs (high)g -- 1.90 (1.0, 3.6)* -- -- --
Availability SSBs (high)g -- 0.44 (0.20, 0.94)* -- 2.42
(0.96, 6.0)ǂ PA Resource Availability
Own active video games (yes)h -- -- -- -- 0.39 (0.18, 0.84)*
ǂp < .10, *p ≤ .05, **p < .001
Note. Abbreviations: FVs, fruits and vegetables; SSB, sugar-sweetened beverages; SA, sedentary activity; PA, physical activ- ity; TV, television -- Variable not included in final model because it did not contribute to overall significance or confound associations between the independent and dependent variables. a: Each column represents one of the 5 dependent variables, respectively. All models included the following covariates: child age (10-12 vs 7-9 years old), gender (male vs female), race/ethnicity (other and non-Hispanic black vs Hispanic/ Latino), and BMI percentile category (≥ 95th vs < 95th); and parent age (> 36 years vs ≤ 36 years), sex (male vs female), BMI category (≥ 30 vs < 30), and proportion of years spent living in the US (> 0.67 and 0.34-0.67 vs ≤ 0.33) b: Categorized as ≥ 1 cup equivalent per 1000 kcal/day versus <1 cup equivalent per 1000 kcal/day c: Categorized as ≥ 5.0% of total kilocalories from sugar-sweetened beverages/day versus < 5.0% of total kilocalories from sugar-sweetened beverages/day d: Categorized as ≥ 8 hours of sedentary time/day versus < 8 hours of sedentary time/day e: Categorized as ≥ 60 minutes of moderate-vigorous PA/day versus < 60 minutes of moderate-vigorous PA/day f: Categorized as More frequent (≥ 1-3 times per week) versus less frequent (≤ 2-3 times/month) g: Categorized as High (always/usually) versus low (sometimes/never) h: Categorized as Yes versus No
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
430
between SSB availability and MVPA approached statistical significance (p = .06). SSB availability did not confound the relationship between MVPA and owning active video games, but it was kept in the final model because the likelihood ratio test indicated that that the model fit better with both SSB availability and owning active video games as independent variables (LR = 3.66; pr > LR = .05).
DISCUSSION Our study identified several measures of the home
environment that were independently associated with children’s diet and physical activity behaviors, including the parenting practice monitoring, fre- quency of family meals, frequency of watching TV during meals, availability of fruits and vegetables in the home, availability of sugar-sweetened bever- ages in the home, and owning active video games. The standard care recommendations for pediatric weight management encourage pediatricians in pri- mary care settings to provide family-focused weight management counsel.41,42 However, low-income familes commonly experience barriers to lifestyle changes, including time, financial costs, and physi- cal environmental challenges (eg, lack of safe places to play). Despite these barriers, Cason-Wilkerson et al64 found that engaging low-income children in a family-oriented obesity treatment program that teaches specific skills/strategies, such as increasing family mealtimes, can attenuate barriers to change and promote the adoption of healthful diet and physical activity behaviors. Thus, weight manage- ment counseling by PCPs should extend beyond BMI to address specific learning skills to enhance efficacy of the PCP-patient interaction.65
In this analysis, higher frequency of family meals increased the odds of children consuming ≥ 1 cup equivalent fruit/1000 kcal/day, whereas higher fre- quency of watching TV during meals decreased these odds. In a cross-sectional analysis of Latino children and caregivers at baseline, Andaya et al54 similarly observed positive effects of an increased frequency of family meals and decreased frequency of watching TV during family meals on the fruit and vegetable intakes of elementary-aged Latino children. However, we assessed fruit and vegetable intake separately, and found that food availability, rather than family meals, was associated with chil- dren’s vegetable intake. Parents who reported higher
FV availability in the home, and lower availability of SSBs in the home, had children with increased odds of consuming ≥ 1 cup vegetables/1000kcal/ day. In a cohort study among a predominantly white, middle class population in the Neighbor- hood Impact on Kids Study, Couch et al66 simi- larly reported that children’s diet quality increased as healthful home food availability increased. Al- though slightly more than one-half of our sample achieved the recommended30 fruit intake of ≥ 1 cup fruit equivalents/1000 kcal/day, few (27.0%) met this recommendation for vegetables. Corroborat- ing myriad evidence that fruit and vegetable intake and availability/accessibility is an important target for pediatric weight management among low-in- come families.
In addition, we observed that the higher report of parent monitoring and limit setting of soda/ snacks was associated, although not statistically sig- nificantly, with reduced odds of children consum- ing > 5% total energy from SSBs. The majority of our sample (61.0%) consumed within the recom- mended target of 5% or less of total energy intake from added sugars.67 Given that sugar-sweetened beverage reduction is a major public health recom- mendation, this observation may be due to social desirability bias or the fact that our participants had enrolled in a family family weight management program and already changed their SSB intake.
We also found that parent monitoring was asso- ciated with increased odds of children engaging in ≥ 8 hours/day sedentary time. This is in contrast to the findings of Arredondo et al68 who report- ed that monitoring was positively associated with physical activity levels in cross-sectional analysis of the Aventuras para Niños study. It is possible that our data reflects parent concern about their child’s weight55,69 if they engaged in frequent sedentary be- haviors, resulting in a greater need for monitoring. Whereas we did not measure concern in this study, future investigations should consider exploring this association. Watching TV during meals was also as- sociated with increased odds of children’s sedentary time. Watching TV during meals has been previ- ously associated with obesity,70,71 but data relating it to sedentary time, as measured by accelerometry, is limited. Pediatricians’ promotion of more frequent family meals, with an emphasis on turning off the TV, may help children achieve recommendations
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 431 DOI: https://doi.org/10.5993/AJHB.43.2.17
of one hour of physical activity per day, with limits of ≤2 hours of screen time per day.41,42
Interestingly, owning active video games (eg, Nintendo Wii™ , Dance Dance Revolution™, etc) was associated with decreased odds of children en- gaging in 60 minutes/day of moderate-vigorous physical activity. Although several studies support positive benefits of active video games on children’s physical activity,72,73 other studies have shown no effects.74,75 One explanation may be that active video games are not played intensely enough to achieve moderate -vigorous levels of physical activ- ity. Future studies should assess this association to help clarify the relationship.
Whereas our study has many strengths, several limitations should be noted. Our sample consisted of overweight/obese, predominantly Hispanic/La- tino children in a safety-net primary care setting,14 and may not be representative of other populations. This was a cross-sectional analysis, and temporal relationships cannot be established. Recall and so- cial desirability bias may affect parent-report of the home environment and child-report of the Block Kids FFQ. The more accurate 24-hour multiple pass recall method76 was not used in our study due to costs and time restraints. Although the Block Kids FFQ is a widely used and validated instru- ment,21,23,24 FFQs are susceptible to underreporting and social desirability.77 To minimize underreport- ing, we excluded participants with ≤ 500 total re- ported kcals/day from this analysis. Whereas we used the Spanish version of the Block Kids FFQ, food items may not reflect the specific food prefer- ences of Bronx families. For example, aguas fres- cas, a popular Mexican-American sugar-sweetened beverage, was not measured. Lastly, FFQs rely on children to remember foods from a previous time, introducing recall bias.
Moreover, our use of accelerometry to measure children’s physical activity is susceptible to under- estimating activity spent engaged in upper body movements, cycling, and swimming.78 A large number of children (about 30% our sample) did not adhere to accelerometry protocols due to chil- dren forgetting to put on the accelerometer, result- ing in non-wear periods. This reduced the sample size for analysis. Future investigations to improve accelerometry adherence in our study population would be beneficial.
CONCLUSIONS The home environment is an important compo-
nent of pediatric weight management. Our findings elucidated several home environment components for PCPs to encourage parents to focus that may fa- cilitate increasing fruit and vegetable intake and de- creasing sedentary time to promote healthier body weight in children. More specifically, overweight or obese and predominantly Hispanic/Latino chil- dren appear to have healthier habits if their fami- lies eat meals together without watching TV and if healthy food choices are available in the home. Fu- ture studies are needed to investigate how changes in the home environment influence changes in children’s weight-related behaviors over time, and implications for long-term weight management.
Human Subjects Approval Statement The Institutional Review Boards of Albert Ein-
stein College of Medicine (FWA #00023382) and Tufts University approved all study procedures (IRB # 2005-582). Each participating parent/care- giver provided written informed consent, and chil- dren provided assent.
Conflict of Interest Disclosure Statement The authors have no conflicts of interest to
disclose.
Acknowledgments This investigation received support from the Na-
tional Institutes of Health (NIH) R18DK075981, P30DK111022 UL1 TR001073, TL1 TR001072, and KL2 TR001071 from the National Center for Advancing Translational Sciences (NCATS), a component of the NIH. The content is solely the responsibility of the authors and does not neces- sarily represent the official views of the NIH. Ad- ditional doctoral student support was received through the Commission on Dietetic Registration Doctoral Scholarship and the Pediatric Nutrition Dietetic Practice Group, respectively, of the Acad- emy of Nutrition and Dietetics.
References 1. Ogden C, Carroll M, Kit B, Flegal K. Prevalence of child-
hood and adult obesity in the United States, 2011-2012. JAMA. 2014;311(8):806-814.
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
432
2. Taveras EM, Gillman MW, Kleinman KP, et al. Reduc- ing racial/ethnic disparities in childhood obesity: the role of early life risk factors. JAMA Pediatr. 2013;167(8):731- 738.
3. Ogden CL, Carroll MD, Lawman HG, et al. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. JAMA. 2016;315(21):2292-2299.
4. Daniels SR, Hassink SG, Committee On Nutrition. The role of the pediatrician in primary prevention of obesity. Pediatrics. 2015;136(1):e275-e292.
5. US Department of Health and Human Services, Health Resources and Services Administration. Child Health USA 2014. Available at: https://mchb.hrsa.gov/chusa14/ dl/chusa14.pdf. Published March 2015. Accessed July 1, 2018.
6. Hernandez RG, Cheng TL, Serwint JR. Parents’ healthy weight perceptions and preferences regarding obesity counseling in preschoolers:pediatricians matter. Clin Pe- diatr (Phila). 2010;49(8):790-798.
7. Nelson JM, Vos MB, Walsh SM, et al. Weight manage- ment-related assessment and counseling by primary care providers in an area of high childhood obesity prevalence: current practices and areas of opportunity. Child Obes. 2015;11(2):194-201.
8. Lupi JL, Haddad MB, Gazmararian JA, Rask KJ. Parental perceptions of family and pediatrician roles in childhood weight management. J Pediatr. 2014;165(1):99-103. e102.
9. Faith MS, Van Horn L, Appel LJ, et al. Evaluating parents and adult caregivers as “agents of change” for treating obese children: evidence for parent behavior change strategies and research gaps: a scientific state- ment from the American Heart Association. Circulation. 2012;125(9):1186-1207.
10. Haines J, McDonald J, O’Brien A, et al. Healthy Habits, Happy Homes: randomized trial to improve household routines for obesity prevention among preschool-aged children. JAMA Pediatr. 2013;167(11):1072-1079.
11. Ayala GX, Elder JP, Campbell NR, et al. Longitudinal intervention effects on parenting of the Aventuras para Ninos study. Am J Prev Med. 2010;38(2):154-162.
12. Crespo NC, Elder JP, Ayala GX, et al. Results of a multi- level intervention to prevent and control childhood obe- sity among Latino children: the Aventuras Para Ninos Study. Ann Behav Med. 2012;43(1):84-100.
13. Janicke DM, Steele RG, Gayes LA, et al. Systematic re- view and meta-analysis of comprehensive behavioral fam- ily lifestyle interventions addressing pediatric obesity. J Pediatr Psychol. 2014;39(8):809-825.
14. Wylie-Rosett J, Groisman-Perelstein AE, Diamantis PM, et al. Embedding weight management into safety-net pe- diatric primary care: randomized controlled trial. Int J Behav Nutr Phys Act. 2018;15(1):12.
15. US Census Bureau. QuickFacts: Bronx County (Bronx Borough), New York. Available at: https://www.census. gov/quickfacts/fact/table/bronxcountybronxborough- newyork/PST045217. Accessed July 1, 2018.
16. US Census Bureau. ACS Demographic and Housing Es- timates 2009-2013, American Community Survey 5-Year Estimates. Available at: https://factfinder.census.gov/ faces/tableservices/jsf/pages/productview.xhtml?src=CF.
Accessed September 14, 2018. 17. Isasi CR, Whiffen A, Campbell E, et al. High preva-
lence of obesity among inner-city adolescent boys in the Bronx, New York: forgetting our boys. Prev Chronic Dis. 2011;8(1):A23.
18. Matte T, Ellis J, Bedell J, et al. Obesity in the South Bronx: A Look across Generations. New York, NY: New York City Department of Health and Mental Hygiene; 2007.
19. Khan UI, McGinn AP, Isasi CR, et al. Differences in cardiometabolic risk between insulin-sensitive and insu- lin-resistant overweight and obese children. Child Obes. 2015;11(3):289-296.
20. Conlon B, McGinn A, Lounsbury D, et al. The role of parenting practices in the home environment among un- derserved youth. Child Obes. 2015;11(4):394-405.
21. Garcia-Dominic O, Trevino RP, Echon RM, et al. Improv- ing quality of Food Frequency Questionnaire response in low-income Mexican American children. Health Promot Pract. 2012;13(6):763-771.
22. Block G, Thompson FE, Hartman AM, et al. Compari- son of two dietary questionnaires validated against mul- tiple dietary records collected during a 1-year period. J Am Diet Assoc. 1992;92(6):686-693.
23. Cullen KW, Watson K, Zakeri I. Relative reliability and validity of the Block Kids Questionnaire among youth aged 10 to 17 years. J Am Diet Assoc. 2008;108(5):862- 866.
24. Marshall TA, Eichenberger Gilmore JM, Broffitt B, et al. Relative validity of the Iowa Fluoride Study targeted nutrient semi-quantitative questionnaire and the block kids’ food questionnaire for estimating beverage, calci- um, and vitamin D intakes by children. J Am Diet Assoc. 2008;108(3):465-472.
25. NutritionQuest. Assessment and Analysis Services: Ques- tionnaires and Screeners. Available at: https://nutrition- quest.com/assessment/list-of-questionnaires-and-screen- ers/. Accessed July 11, 2018.
26. Bowman S, Friday J, Moshfegh A. MyPyramid Equiva- lents Database, 2.0 for USDA Survey Foods, 2003-2004. Available at: http://www.ars.usda.gov/ba/bhnrc/fsrg. Ac- cessed September 14, 2015.
27. Bowman SA, Friday JE, Moshfegh AJ. MyPyramid Equiv- alents Database, 2.0 for USDA Survey Foods, 2003-2004: Documentation and User Guide. Available at: https:// www.ars.usda.gov/ARSUserFiles/80400530/pdf/mped/ mped2_doc.pdf. Accessed September 14, 2018.
28. Slining MM, Mathias KC, Popkin BM. Trends in food and beverage sources among US children and adolescents: 1989-2010. J Acad Nutr Diet. 2013;113(12):1683-1694.
29. Hanggi JM, Phillips LR, Rowlands AV. Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. J Sci Med Sport. 2013;16(1):40-44.
30. Puyau MR, Adolph AL, Vohra FA, Butte NF. Validation and calibration of physical activity monitors in children. Obes Res. 2002;10(3):150-157.
31. Trost SG, Ward DS, Moorehead SM, et al. Validity of the computer science and applications (CSA) activity moni- tor in children. Med Sci Sports Exerc. 1998;30(4):629- 633.
32. Trost SG, Pate RR, Sallis JF, et al. Age and gender differ- ences in objectively measured physical activity in youth. Med Sci Sports Exerc. 2002;34(2):350-355.
Colon et al
Am J Health Behav.™ 2019;43(2):420-436 433 DOI: https://doi.org/10.5993/AJHB.43.2.17
33. Kriska A, Delahanty L, Edelstein S, et al. Sedentary be- havior and physical activity in youth with recent onset of type 2 diabetes. Pediatrics. 2013;131(3):e850-e856.
34. Kaminsky LA, Ozemek C. A comparison of the Actigraph GT1M and GT3X accelerometers under standardized and free-living conditions. Physiol Meas. 2012;33(11):1869- 1876.
35. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification al- gorithm. Med Sci Sports Exerc. 2011;43(2):357-364.
36. ActiGraph. How are cut points calculated? Available at: https://actigraph.desk.com/customer/portal/arti- cles/2515802. Accessed July 1, 2018.
37. Aguilar-Farias N, Brown WJ, Peeters GM. ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport. 2014;17(3):293-299.
38. Jimmy G, Seiler R, Maeder U. Development and valida- tion of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children. J Phys Act Health. 2013;10(7):1057-1067.
39. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc accelerometer. Med Sci Sports Exerc. 1998;30(5):777-781.
40. Martinez-Gomez D, Eisenmann JC, Tucker J, et al. As- sociations between moderate-to-vigorous physical activity and central body fat in 3-8-year-old children. Int J Pediatr Obes. 2011;6(2-2):e611-e614.
41. Spear BA, Barlow SE, Ervin C, et al. Recommendations for treatment of child and adolescent overweight and obe- sity. Pediatrics. 2007;120(Suppl 4):S254-S288.
42. Barlow SE. Expert committee recommendations regard- ing the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120(Suppl 4):S164-S192.
43. Sorlie PD, Aviles-Santa LM, Wassertheil-Smoller S, et al. Design and implementation of the Hispanic Com- munity Health Study/Study of Latinos. Ann Epidemiol. 2010;20(8):629-641.
44. Stevens J, Murray DM, Baggett CD, et al. Objectively assessed associations between physical activity and body composition in middle-school girls: the Trial of Activity for Adolescent Girls. Am J Epidemiol. 2007;166(11):1298- 1305.
45. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC Growth charts for the United States: methods and devel- opment. Vital Health Stat 11. 2002(246):1-190.
46. Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol. 2014;63(25 Pt B):2985-3023.
47. Sussner KM, Lindsay AC, Greaney ML, Peterson KE. The influence of immigrant status and acculturation on the development of overweight in Latino families: a qualita- tive study. J Immigr Minor Health. 2008;10(6):497-505.
48. Larios SE, Ayala GX, Arredondo EM, et al. Development and validation of a scale to measure Latino parenting strategies related to children’s obesigenic behaviors. The Parenting Strategies for Eating and Activity Scale (PEAS). Appetite. 2009;52(1):166-172.
49. Larios SE, Ayala GX, Baquero B, et al. Development of survey that measures home environmental factors con- tributing to obesity. Poster presented at: 132nd Annual Meeting of American Public Health Association; Novem- ber 2004; Washington, D.C. Available at: https://apha. confex.com/apha/132am/techprogram/paper_82894. htm. Accessed December 13, 2018.
50. Lloyd AB, Lubans DR, Plotnikoff RC, et al. Maternal and paternal parenting practices and their influence on chil- dren’s adiposity, screen-time, diet and physical activity. Appetite. 2014;79:149-157.
51. Schneider EM, Wilson DK, Kitzman-Ulrich H, et al. The associations of parenting factors with adolescent body mass index in an underserved population. J Obes. 2013;2013:715618.
52. Ayala GX, Carnethon M, Arredondo E, et al. Theoretical foundations of the Study of Latino (SOL) Youth: implica- tions for obesity and cardiometabolic risk. Ann Epidemiol. 2014;24(1):36-43.
53. Neumark-Sztainer D, Hannan PJ, Story M, et al. Fam- ily meal patterns: associations with sociodemographic characteristics and improved dietary intake among ado- lescents. J Am Diet Assoc. 2003;103(3):317-322.
54. Andaya AA, Arredondo EM, Alcaraz JE, et al. The asso- ciation between family meals, TV viewing during meals, and fruit, vegetables, soda, and chips intake among La- tino children. J Nutr Educ Behav. 2011;43(5):308-315.
55. Boutelle KN, Birkeland RW, Hannan PJ, et al. Asso- ciations between maternal concern for healthful eating and maternal eating behaviors, home food availabil- ity, and adolescent eating behaviors. J Nutr Educ Behav. 2007;39(5):248-256.
56. Neumark-Sztainer D, Wall M, Perry C, Story M. Cor- relates of fruit and vegetable intake among adolescents. Findings from Project EAT. Prev Med. 2003;37(3):198- 208.
57. Arcan C, Neumark-Sztainer D, Hannan P, et al. Parental eating behaviours, home food environment and adoles- cent intakes of fruits, vegetables and dairy foods: longi- tudinal findings from Project EAT. Public Health Nutr. 2007;10(11):1257-1265.
58. Bauer KW, Neumark-Sztainer D, Fulkerson JA, Story M. Adolescent girls’ weight-related family environments, Minnesota. Prev Chronic Dis. 2011;8(3):A68.
59. Hanson NI, Neumark-Sztainer D, Eisenberg ME, et al. Associations between parental report of the home food environment and adolescent intakes of fruits, vegetables and dairy foods. Public Health Nutr. 2005;8(1):77-85.
60. Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics. 2002;109(6):1028-1035.
61. Feng D, Reed DB, Esperat MC, Uchida M. Effects of TV in the bedroom on young Hispanic children. Am J Health Promot. 2011;25(5):310-318.
62. O’Connor TM, Chen TA, Baranowski J, et al. Physical activity and screen-media-related parenting practices have different associations with children’s objectively measured physical activity. Child Obes. 2013;9(5):446-453.
63. Rosenberg DE, Sallis JF, Kerr J, et al. Brief scales to assess physical activity and sedentary equipment in the home. Int J Behav Nutr Phys Act. 2010;7:10.
Home Environment Factors and Health Behaviors of Low-income, Overweight, and Obese Youth
434
64. Cason-Wilkerson R, Goldberg S, Albright K, et al. Factors influencing healthy lifestyle changes: a qualitative look at low-income families engaged in treatment for overweight children. Child Obes. 2015;11(2):170-176.
65. Ashman F, Sturgiss E, Haesler E. Exploring self-efficacy in Australian General Practitioners Managing Patient Obesity: a qualitative survey study. Int J Family Med. 2016;2016:8212837.
66. Couch SC, Glanz K, Zhou C, et al. Home food environ- ment in relation to children’s diet quality and weight sta- tus. J Acad Nutr Diet. 2014;114(10):1569-1579 e1561.
67. World Health Organization. Healthy Diet. Available at: http://www.who.int/mediacentre/factsheets/fs394/en/. Accessed July 11, 2018.
68. Arredondo EM, Elder JP, Ayala GX, et al. Is parenting style related to children’s healthy eating and physical activity in Latino families? Health Educ Res. 2006;21(6):862-871.
69. Peyer KL, Welk G, Bailey-Davis L, et al. Factors associ- ated with parent concern for child weight and parenting behaviors. Child Obes. 2015;11(3):269-274.
70. Roos E, Pajunen T, Ray C, et al. Does eating family meals and having the television on during dinner correlate with overweight? A sub-study of the PRO GREENS project, looking at children from nine European countries. Public Health Nutr. 2014;17(11):2528-2536.
71. Feldman S, Eisenberg ME, Neumark-Sztainer D, Story M. Associations between watching TV during family
meals and dietary intake among adolescents. J Nutr Educ Behav. 2007;39(5):257-263.
72. Graf DL, Pratt LV, Hester CN, Short KR. Playing active video games increases energy expenditure in children. Pe- diatrics. 2009;124(2):534-540.
73. Lanningham-Foster L, Jensen TB, Foster RC, et al. Energy expenditure of sedentary screen time com- pared with active screen time for children. Pediatrics. 2006;118(6):e1831-e1835.
74. Baranowski T, Abdelsamad D, Baranowski J, et al. Impact of an active video game on healthy children’s physical ac- tivity. Pediatrics. 2012;129(3):e636-e642.
75. Baranowski T, Baranowski J, O’Connor T, et al. Is en- hanced physical activity possible using active videogames? Games Health J. 2012;1(3):228-232.
76. Burrows TL, Martin RJ, Collins CE. A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. J Am Diet Assoc. 2010;110(10):1501-1510.
77. Miller TM, Abdel-Maksoud MF, Crane LA, et al. Effects of social approval bias on self-reported fruit and vegeta- ble consumption: a randomized controlled trial. Nutr J. 2008;7:18.
78. Hansen BH, Bortnes I, Hildebrand M, et al. Validity of the ActiGraph GT1M during walking and cycling. J Sports Sci. 2014;32(6):510-516.
Yasmin Mossavar-Rahmani, Associate Professor, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. David W. Lounsbury, Assistant Professor, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. Mindy S. Ginsberg, Senior Associate, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY. Pamela M. Diamantis, Attending Physician, Department of Pediatrics, Children’s Health Services Jacobi Medical Center, Bronx, NY. Adriana E. Groisman- Perelstein, Assistant Director, Department of Pediatric, Children’s Health Services Jacobi Medical Center, Bronx, NY. Judith Wylie-Rosett, Professor, Albert Einstein College of Medicine, Department of Epidemiology and Population Health, Bronx, NY.
Remaining authors
Copyright of American Journal of Health Behavior is the property of PNG Publications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
