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Walkability indices and childhood obesity: A review of epidemiologic evidence

Authors:
  • West China School of Public Health, Sichuan University

Abstract

The lack of an active neighbourhood living environment can impact community health to a great extent. One such impact manifests in walkability, a measure of urban design in connecting places and facilitating physical activity. Although a low level of walkability is generally considered to be a risk factor for childhood obesity, this association has not been established in obesity research. To further examine this association, we conducted a literature search on PubMed, Web of Science and Scopus for articles published until 31 December 2018. The included literature examined the association between measures of walkability (e.g., walkability score and walkability index) and weight‐related behaviours and/or outcomes among children aged under 18 years. A total of 13 studies conducted in seven countries were identified, including 12 cross‐sectional studies and one longitudinal study. The sample size ranged from 98 to 37 460, with a mean of 4971 ± 10 618, and the age of samples ranged from 2 to 18. Eight studies reported that a higher level of walkability was associated with active lifestyles and healthy weight status, which was not supported by five studies. In addition to reviewing the state‐of‐the‐art of applications of walkability indices in childhood obesity studies, this study also provides guidance on when and how to use walkability indices in future obesity‐related research.
SUPPLEMENT ARTICLE
Walkability indices and childhood obesity: A review of
epidemiologic evidence
Shujuan Yang
1,2
| Xiang Chen
3
| Lei Wang
1
| Tong Wu
4,2
| Teng Fei
5,2
|
Qian Xiao
6,2
| Gang Zhang
7
| Yi Ning
8,9,2
| Peng Jia
10,11,2
1
West China School of Public Health and West
China Fourth Hospital, Sichuan University,
Chengdu, China
2
International Institute of Spatial Lifecourse
Epidemiology (ISLE), Hong Kong, China
3
Department of Geography, University of
Connecticut, Storrs, Connecticut, USA
4
Research Center for Eco-Environmental
Sciences, Chinese Academy of Sciences,
Beijing, China
5
School of Resources and Environmental
Science, Wuhan University, Wuhan, China
6
Department of Epidemiology, Human
Genetics, and Environmental Sciences, The
University of Texas Health Science Center at
Houston, Houston, Texas, USA
7
Sichuan Provincial Hospital for Women and
Children (Affiliated Women and Children's
Hospital of Chengdu Medical College),
Chengdu, China
8
Peking University Health Science Center
Meinian Public Health Research Institute,
Beijing, China
9
Meinian Institute of Health, Beijing, China
10
Department of Land Surveying and Geo-
Informatics, The Hong Kong Polytechnic
University, Hong Kong, China
11
Faculty of Geo-information Science and
Earth Observation, University of Twente,
Enschede, The Netherlands
Correspondence
Peng Jia, PhD, Director, International Institute
of Spatial Lifecourse Epidemiology (ISLE);
Department of Land Surveying and Geo-
Informatics, The Hong Kong Polytechnic
University, Hong Kong, China.
Email: jiapengff@hotmail.com
Summary
The lack of an active neighbourhood living environment can impact community
health to a great extent. One such impact manifests in walkability, a measure of
urban design in connecting places and facilitating physical activity. Although a low
level of walkability is generally considered to be a risk factor for childhood obesity,
this association has not been established in obesity research. To further examine this
association, we conducted a literature search on PubMed, Web of Science and
Scopus for articles published until 31 December 2018. The included literature exam-
ined the association between measures of walkability (e.g., walkability score and
walkability index) and weight-related behaviours and/or outcomes among children
aged under 18 years. A total of 13 studies conducted in seven countries were identi-
fied, including 12 cross-sectional studies and one longitudinal study. The sample size
ranged from 98 to 37 460, with a mean of 4971 ± 10 618, and the age of samples
ranged from 2 to 18. Eight studies reported that a higher level of walkability was
associated with active lifestyles and healthy weight status, which was not supported
by five studies. In addition to reviewing the state-of-the-art of applications of
walkability indices in childhood obesity studies, this study also provides guidance on
when and how to use walkability indices in future obesity-related research.
KEYWORDS
built environment, child, obesity, walkability index
Shujuan Yang and Xiang Chen contributed equally to this work.
Received: 24 June 2020 Accepted: 25 June 2020
DOI: 10.1111/obr.13096
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2020 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation
Obesity Reviews. 2021;22(S1):e13096. wileyonlinelibrary.com/journal/obr 1of11
https://doi.org/10.1111/obr.13096
[Correction added on 3 February 2021, after
first online publication: Peng Jia's
correspondence details have been updated.
Also, affiliations 9 and 10 were interchanged.]
Yi Ning, PhD, Executive Director, Peking
University Health Science Center Meinian
Public Health Research Institute, Huanyuan
Road 38, Haidian, Beijing, China.
Email: yi.ning@meinianresearch.com
Funding information
National Key R&D Program 'Precision
Medicine Initiative' of China, Grant/Award
Number: 2017YFC0907304; Sichuan Science
and Technology Program, Grant/Award
Number: 2019YJ0148
[Correction added on 3 February 2021, after
first online publication: Funding Information
has been revised.]
1|INTRODUCTION
Obesity is a leading cause of morbidity and premature mortality world-
wide. One major challenge of the global rise in obesity is its adverse
effects on children. According to the World Health Organization
(WHO), the obesity rate among children and adolescents was less than
1% in 1975, but after nearly 40 years of economic development and
nutritional improvement, the global obesity rate rose to 8% among boys
and 6% among girls in 2016, leading to over 340 million children and
adolescents with obesity.
1
Among developed countries, the United
States has been the largest victim of the obesity epidemic, where nearly
one-third of all children and adolescents have overweight or obesity.
2
Additionally, childhood obesity has become an emerging issue in devel-
oping and underdeveloped countries and has become extremely critical
in Asia.
3
For example, nearly half of Asian children under the age of
5 were diagnosed with obesity or overweight,
4
which implies a soaring
obesity trend among the Asian population.
Childhood obesity is a chronic health outcome that can be intro-
duced by a complex array of factors, including environment, genetics
and ecological effects.
46
The etiology leading to childhood obesity is
extremely complex: for example, the overconsumption of calories
among children can be an intrinsic outcome of unhealthy diets, which
could be driven by family and social influences, such as feeding styles
7
and the popularity of sugar-sweetened beverages.
8
Another widely
discussed contributor to childhood obesity is the built environment.
Research on public health shows strong evidence that the built envi-
ronment can shape the quality of individual life and the community's
overall health by promoting physical activity (PA), providing proper
nutrition and reducing toxic exposure.
9
Specifically, unfavourable built
environments (e.g., the prevalence of fast-food outlets and the lack of
PA sites) play an obesogenic role by encouraging unbalanced diets
and a sedentary lifestyle.
10
However, the connection between the
built environment and childhood obesity remains convoluted as the
change of weight status is inseparable from the dynamics of physical
growth (e.g., height and weight), the early onset of genetic syndromes
and the unshaped eating behaviours in child development.
4
Although it is extremely difficult to disentangle the myriad
obesogenic factors in the built environment that implicitly contribute
to childhood obesity, one underexplored metric is walkability. Although
the term walkablehas been used since the 18th century, its extension
to walkabilitywas relatively recent and also lacks clarity.
11
There are
three clusters of definitions of walkability, focusing on the means or
conditions to achieve a walkable environment, the outcomes or perfor-
mance of having a walkable environment and the proxy for measuring
the quality of a walkable environment.
11
The U.S. Centers for Disease
Control and Prevention (CDC) adopts the third definition, considering
walkability as the idea of quantifying the safety and desirability of the
walking routes.
12
This conceptualization of walkability, stemming from
the scientific evidence that walking can boost metabolism, lower blood
sugar and improve mental health,
13
has become a quantifiable variable
to study health-promoting effects of the built environment.
However, there are two existing challenges in elucidating the
effects of walkable environments on childhood obesity. The first chal-
lenge is incongruities in methodology, as the metrics used to quantify
walkability vary across studies.
14,15
One widely used walkability met-
ric refers to the Walkability Audit Tool developed by the CDC, which
is a seven-step audit tool to evaluate outdoor walking surfaces.
12
The
method evaluates an individual work environment with a relatively
subjective, labour-intensive nature; therefore, it cannot be effectively
applied to large-scale assessments. The advancement of geospatial
technologies, particularly Geographical Information Systems (GIS), has
facilitated the development of walkability metrics for large-scale
observations.
16,17
These GIS methods can be roughly split between
two categories. One group of studies employs area-based metrics,
such as the density of restaurants,
18,19
food retailers
20,21
and built
environmental features
6
within statistical units (e.g., census tracts and
postal zones). The other group of studies employs network-based
metrics, considering walkability as a measure of accessibility to nearby
amenities (e.g., stores, public transits and greenspaces) from residen-
tial locations or workplaces.
22,23
One popular proximity measure,
called the Walk Score,
24
evaluates the walkability of more than
10 000 neighbourhoods in over 2800 cities in the United States,
2of11 YANG ET AL.
which further supports public inquiry about the livability of
neighbourhoods. To date, there has been a lack of consensus in the
selection of metrics for walkability assessment.
The second challenge is the lack of consistency in defining
weight-related behaviours and outcomes when evaluating the effects
of walkability on childhood obesity. While living in a walkable commu-
nity could promote engagement in PA and thus reduce the risks of
obesity, this association cannot be elucidated without refining the
choice of mediator variables. Variables used to characterize weight-
related behaviours and outcomes vary across childhood obesity stud-
ies. For example, for weight-related behaviours, studies have exam-
ined PA,
25
moderate-vigorous physical activity (MVPA)
26
and active
commuting to school (ACS);
27
for weight-related outcomes, studies
have examined the obesity rate,
28
weight status
29
and body mass
index (BMI) values.
30
In addition to these different variable choices,
variations in study areas and age groups among children add another
layer of uncertainty over the correlation analysis.
Because of these methodological uncertainties, associations
between walkability and childhood obesity are rather inconsistent.
For example, although the walkability score (e.g., intersection density
and land use mix) was calculated and identified as positively corre-
lated with PA among children in Spain,
25,27
Australia,
26
the United
States
31,32
and New Zealand,
29
this correlation was not found in two
other studies conducted in Scotland
30
and Germany.
33
Furthermore,
the correlation between walkability scores and obesity is far from con-
clusive: Although the negative correlation between walkability scores
and the childhood obesity index (e.g., BMI) was found to be significant
in the United States
34,35
and Malaysia,
36
this correlation was not sig-
nificant in Germany.
37
In addition, one study showed that the
walkability score was positively associated with the risk of overweight
or obesity in England.
38
Existing reviews on the relationships between walkability and
weight-related behaviours and outcomes are all focused on
adults.
39,40
There have been no reviews of such relationships in chil-
dren. To this end, we conducted a systematic review of existing litera-
ture focused on the walkability-weight status behaviour/outcome
relationship among children and adolescents. We first compiled an
inclusive list of measures of walkability employed for studying child-
hood obesity. Then, we reviewed and categorized these studies with
respect to the measure of walkability, weight-related behaviour and
weight-related outcome. This review has important public health
implicationsby identifying the attributes and major findings of case
studies, future research on childhood obesity can choose appropriate
models and significant metrics to define walkability as one important
built environmental variable. The summarized evidence about the
effects of walkability can guide research on childhood obesity and
solidify its scientific underpinnings.
2|METHODS
A systematic review and meta-analysis were conducted in accordance
with the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses.
41
2.1 |Study selection criteria
Studies meeting all of the following criteria were included in the
review: (a) study designs: longitudinal and cross-sectional studies;
(b) study subjects: children and adolescents aged under 18 years
(studies with subjects aged under 19 years are partially included with
explanations); (c) study outcomes: weight-related behaviours (e.g., PA,
sedentary behaviour and eating behaviour) and/or outcomes
(e.g., weight status, BMI, waist circumference, waist-to-hip ratio and
body fat); (d) article types: peer-reviewed original research articles;
(e) time of publication: from the inception of the electronic biblio-
graphic database to 31 December 2018; and (f ) language: articles
written in English.
2.2 |Search strategy
A keyword search was performed in three electronic bibliographic
databases: PubMed, Web of Science and Scopus. The search strategy
included all possible combinations of keywords from the three groups
related to measures of walkability, children and weight-related behav-
iours or outcomes. The specific search strategy is provided in Appen-
dix S1.
The titles and abstracts of the articles identified through the key-
word search were screened against the study selection criteria. Poten-
tially relevant articles were retrieved for an evaluation of the full text.
Two reviewers independently conducted the title and abstract screen-
ing and identified potentially relevant articles for the full-text review.
Discrepancies were compiled by A and screened by a third reviewer.
Three reviewers jointly determined the list of articles for the full-text
review through a discussion. Then, two reviewers independently
reviewed the full texts of all the articles in the list and determined the
final pool of articles included in the review.
2.3 |Data extraction and preparation
A standardized data extraction form was used to collect methodologi-
cal and outcome variables from each selected study, including author
names, year of publication, country, sampling strategy, sample size,
age at baseline, follow-up years, number of repeated measures, sam-
ple characteristics, statistical model, attrition rate, measures of
walkability, measures of weight-related behaviours, measures of
body-weight status and key findings on the association between
walkability and weight-related behaviours and/or outcomes. Two
reviewers independently extracted data from each study included in
the review, and discrepancies were resolved by the third reviewer.
2.4 |Study quality assessment
We used the National Institutes of Health's Quality Assessment Tool
for Observational Cohort and Cross-Sectional Studies
42
to assess the
YANG ET AL.3of11
quality of each included study. This assessment tool rates each study
based on a 14-question criterion (Appendix S2). For each question, a
score of one was assigned if yeswas the response, whereas a score
of zero was assigned otherwise (i.e., an answer of no,not applica-
ble,not reportedor cannot determine). A study-specific global
score ranging from 0 to 14 was calculated by summing up scores
across all questions. The quality assessment helped measure the
strength of scientific evidence but was not used to determine the
inclusion of studies.
3|RESULTS
3.1 |Study selection
Figure 1 shows the flowchart of the study selection procedure. We
identified a total of 368 articles through the keyword search. After
undergoing title and abstract screening, 311 articles were excluded.
The full texts of the remaining 55 articles were reviewed against the
study selection criteria, after which 42 articles were further excluded.
The remaining 13 studies that examined the relationship between
walkability and children's weight-related behaviours and/or outcomes
were included in this review.
3.2 |Study characteristics
Table 1 summarizes the basic characteristics of the 13 included stud-
ies, including one longitudinal study and 12 cross-sectional studies.
The articles included in this review were from seven different coun-
tries, including the United States (n= 6), Spain (n= 2) and one each
from the United Kingdom, Canada, New Zealand, Germany and
Malaysia. The sample size ranged from 98 to 37 460, with a mean of
4971 ± 10 618, and the age of the samples ranged from 2 to 18. The
statistical models used for analysis included linear regressions (n= 5),
correlation analyses (n= 4), logistic regressions (n= 2), separate
regressions (n= 2), mixed models (n= 2) and generalized estimates
equations (n= 1).
3.3 |Measures of walkability
The measures of walkability and weight-related behaviours and/or
outcomes in the included studies were summarized (Table 2). The
level of walkability was calculated by using different statistical units,
such as census blocks (n= 2), buffer zones around homes or work-
places (n= 5) and the school enrolment zone (n= 1). Four studies
measured walkability by using the scoring criterion of the Neighbor-
hood Environment Walkability Scale for Youth (NEWS-Y).
29,36,43,44
Specifically, the NEWS-Y is an aggregate measure with nine scoring
components: diversity of the land use mix, neighbourhood recreation
facilities, residential density, accessibility measures of the land use
mix, street connectivity, walking/cycling facilities, neighbourhood aes-
thetics, pedestrian and road traffic safety and crime safety.
43
Seven
studies evaluated walkability by some of these nine components. One
study measured walkability by calculating the density of convenience
stores, fast-food restaurants, grocery stores, fitness facilities and parks
within a 0.5-mile radius of the school.
45
Another study evaluated the
walkability of home addresses based on the distance-weighted prox-
imity to categorized amenities, including education, recreational, food,
retail and entertainment.
46
3.4 |Measures of weight-related behaviours and
outcomes
With respect to weight-related behaviours, PA and MVPA were the
most common behavioural measures (Table 2).
27,29,3133,36,43,44,47
FIGURE 1 Flowchart of the
study selection procedure
4of11 YANG ET AL.
Nine studies measured PA or MVPA through accelerometers or self-
reporting. Two studies measured ACS.
25,27
One study measured phys-
ical fitness using a maximal multistage 20-m shuttle run test to deter-
mine the maximal aerobic power.
36
One study measured the number
of sports teams or PA classes outside of school.
30
With respect to weight-related outcomes, BMI and BMI z-score
were the most common health outcome measures.
25,27,3133,36,43,4547
Eleven studies measured the BMI based on objectively measured or
self-reported heights and weights, whereas five of these studies
used BMI as the criterion to determine obesity (i.e., BMI greater
than 95% quantile) or overweight status. One study derived body
fat percentage (%BF) through bioelectrical impedance analysis and
dichotomized the measure into low/high categories using the thresh-
olds of 25% for boys and 30% for girls.
30
Waist circumference
43
and
the sum of skinfolds
32
were also employed as health outcome
measures.
3.5 |Associations between walkability and weight-
related behaviours and outcomes
Out of the 13 studies, seven studies reported a significant association
between measures of walkability and weight-related behaviours
(Table S1). Four studies reported a positive association between the
TABLE 1 Characteristics of the studies included in the review
First author
(year) Study area (scale)
a
Study
design
b
Sample
size
Age at baseline (years,
range and/or mean
± SD)
c
Sample characteristics Statistical models
Cheah
(2012)
36
Kuching, Sarawak
(C)
C 316 1416 School children Univariate correlation
analysis
Molina-García
(2017)
27
Valencia, Spain (C) C 325 1418 (16.4 ± 0.8) in
20132015
School children Separate mixed effects
regression models;
generalized linear mixed
models
Shahid
(2015)
46
Calgary, Canada
(C)
L 37 460 4.56 in 20052008 School children (followed
up from 2005 to 2008
with PHANTIM
database)
Correlation;
cross-correlation analysis
Slater
(2013)
34
US (N) C 11 041 Public school students
at grades 8, 10 and
12 in 2010
School children Multivariable logistic
regression
Molina-García
(2017)
25
Spain (N) C 310 1012 in 2015 School children Mixed model regression
Hinckson
(2017)
29
Auckland and
Wellington, NZ
(C2)
C 524 1218 (15.78 ± 1.62) in
2013 and 2014
School children Generalized additive mixed
models
Noonan
(2015)
43
Liverpool, UK (C) C 194 9 and 10 in 2014 School children Analysis of covariance;
linear regression
Rosenberg
(2009)
44
Boston, Cincinnati
and San Diego,
US (C3)
C 458 518 in 2005 School children Single measure intraclass
correlation coefficients;
one-way analysis of
covariance
Lovasi
(2011)
32
New York City,
US (C)
C 428 25 in 20032005 Preschool children Generalized estimates
equations
Graziose
(2016)
47
New York City,
US (C)
C 952 10.6 in 2012 and 2013 School children Multilevel linear models
Buck (2014)
33
Delmenhorst,
German (C)
C 400 29 in 2007 and 2008 Preschool and school
children
Gamma log regression
Model
Kligerman
(2006)
31
San Diego
County, US (CT)
C 98 14.617.6 in 2005 School children Multiple linear regression;
Pearson correlation;
separate regression
Wasserman
(2014)
45
Kansas, US (S) C 12 118 412 (8.22 ± 1.77) in
2008 and 2009
School children Hierarchical linear
modelling
a
Study area: (N)National, (CT)County or equivalent unit, (CTn)ncounties or equivalent units, (C)City; (Cn)ncities.
b
Sample age: Age in baseline year for cohort studies or mean age in survey year for cross-sectional studies.
YANG ET AL.5of11
TABLE 2 Measures of walkability and weight-related behaviors and/or outcomes in the included studies
First Author
(year) Walkability indices
Other environmental factors
adjusted for in the model
Measures of weight-related
behavior
Detailed measures of weight-
related outcomes
Cheah
(2012)
36
The sum of z-scores of each of
the nine perceived
categories (residential
density, land-use mix
diversity, land-use mix
access, street connectivity,
infrastructure for walking,
aesthetics, traffic safety,
safety from crime, and
neighborhood satisfaction)
in the neighborhood on the
basis of a modified
questionnaire adapted from
the NEWS-Y
NA PA (time spent outdoors per
day collected through self-
reporting)
Physical fitness (using a
maximal multistage 0.02-km
shuttle run test to determine
the maximal aerobic power)
BMI based on measured
height and weight
Overweight (between 85th
percentile and 95th
percentile)
Obesity (95th percentile)
Molina-
García
(2017)
27
(z-score of intersection
density) + (z-score of net
residential density) + (z-
score of land use mix) within
a census block
Days per week living at the
primary address
Distance to school (km)
Driver license (yes or no)
Number of children < 18
years old living in the
household
Number of motor vehicles
per licensed driver
Years at current address
Exercise equipment in or
around home
MVPA (measured by
ActiGraph accelerometers;
1148 counts per 30-second
epoch, MVPA; and 50
counts per 30-second
epoch, ST)
Physically active 60 min/
day outside of school (days
per week)
ACS (trips per week)
Number of sports teams or
PA classes outside of school
BMI based on measured
height and weight
Overweight (between 85th
percentile and 95th
percentile)
Obesity (95th percentile)
%BF analyzed by
bioelectrical impedance, %
BF dichotomized as low/
high (using the cut points of
25% for boys and 30% for
girls)
Shahid
(2015)
46
Walkscoreindex: the sum of
the weighted straight-line
distances to the closest
facilities in each of the five
categories (education,
recreational, food, retail, and
entertainment), with a
normalized value ranging
from 0 to 100 (0 is the least
walkable, and 100 is the
most walkable)
NA NA BMI z-score based on self-
reported height and weight
Overweight (between 85th
percentile and 95th
percentile)
Obesity (95th percentile)
Slater
(2013)
34
The proportion of streets in a
community that have
walkable features (mixed
land use, sidewalks, sidewalk
buffers, sidewalk/street
lighting, other side-walk
elements, traffic lights,
pedestrian signal at the
traffic light, marked
crosswalks, pedestrian
crossings and other signage,
and public transit)
NA NA BMI based on self-reported
height and weight (age- and
gender-specific)
Overweight (between 85th
percentile and 95th
percentile)
Obesity (95th percentile)
Molina-
García
(2017)
25
(z-score of net residential
density) + (z-score of land
use mix) + (z-score of road
intersection density) within a
census block
NA ACS (the number of trips per
week to and from school by
walking, cycling or
skateboarding)
BMI based on measured
height and weight
(calculated by the 2000 CDC
growth charts)
BMI percentile adjusted for
age and sex
Hinckson
(2017)
29
The sum of z-scores of gross
residential density and
number of parks within a 2-
km home buffer
NA PA (the GT3X+ Actigraph
accelerometer was used to
estimate the minutes of PA
and ST over a 7-day period)
NA
6of11 YANG ET AL.
TABLE 2 (Continued)
First Author
(year) Walkability indices
Other environmental factors
adjusted for in the model
Measures of weight-related
behavior
Detailed measures of weight-
related outcomes
The sum of z-scores of
perceived land use mix-
diversity, street connectivity,
and aesthetics
Average minutes per day of
MVPA and ST
Noonan
(2015)
43
The sum of z-scores of each of
the nine perceived
categories (land use mix-
diversity, neighborhood
recreation facilities,
residential density, land-use
mix-access, street
connectivity, walking/cycling
facilities, neighborhood
aesthetics, pedestrian and
road traffic safety, and crime
safety) perceived in the
neighborhood on the basis
of NEWS-Y
NA PA (assessed using the PA
questionnaire)
BMI based on measured
height and weight
Waist circumference
Rosenberg
(2009)
44
The sum of z-scores of each of
the nine perceived
categories in the
neighborhood, including
eight standard categories
(land use mix-diversity,
pedestrian and automobile
traffic safety, crime safety,
neighborhood aesthetics,
walking/cycling facilities,
street connectivity, land use
mix-access, and residential
density) on the basis of
NEWS-Y and one additional
category (recreation facilities
within a 10-min walk from
home)
Income PA (walking to/from school
at least once per week, Y/N)
PA (doing physical activity in
the street at least once per
week, Y/N)
PA (walking to a park at least
once per week, Y/N)
PA (walking to shops at least
once per week, Y/N)
PA (doing physical activity in
a park at least once per
week, Y/N)
MVPA (participant meeting
the criterion of 60 min of
activity for 5 days per week,
Y/N)
NA
Lovasi
(2011)
32
Five different measures within
a 0.5-km neighborhood
buffer: population density of
the census block group, land
use mix constructed using
the parcel-level data (0:
single land use; 1: mix uses),
subway stop density, bus
stop density, and
intersection density
Number of rooms in the
household
Neighborhood
characteristics
Season
PA (assessed through placing
Acti-Watch accelerometers
and using a 6-day PA recall)
BMI z-score based on
measured height and weight
Sum of skinfolds
Graziose
(2016)
47
The sum of z-scores of four
environmental measures in
school neighborhood (land
use mix, intersection
density, residential
population density, and
retail floor area density)
NA PA (using FHC-Q to access) BMI-for-age percentile and
BMI z-score based on
measured height and weight
Buck
(2015)
33
The sum of z-scores of three
measures (residential
density, land use mix, and
intersection density) within a
1-km home street-network
buffer
Hours of valid weartime
Season of the
accelerometer
measurement
MVPA (using accelerometer
measurements)
Age- and sex-specific BMI z-
score
Weight status
(Continues)
YANG ET AL.7of11
walkability score and one of the following weight-related behaviours:
MVPA (p= 0.035),
27
MVPA (β= 0.278, p< 0.01),
31
ACS (p< 0.001)
25
or independent mobility (β= 0.25, p< 0.01).
43
Other studies, albeit
not explicitly targeting walkability, found that walkability-related envi-
ronmental factors are also associated with weight-related behaviours.
It was found that the density of public transit (β= 0.037, p= 0.01) and
intersections (β= 0.003, p= 0.04)
33
was positively associated with the
MVPA of school children. The diversity of the land use mix (β= 1.049,
p= 0.010) and street connectivity (β= 1.063, p= 0.010) was also
found to be positively associated with objectively measured MVPA.
29
Another study also found that land use mix was positively associated
with PA (β= 26, p= 0.015).
32
The associations between walkability and weight-related out-
comes were mixed. Three studies reported a null association. Three
other studies reported a negative association between the walkability
score with one of the following weight-related outcomes: the preva-
lence of overweight (odds ratio [OR] = 0.98, 95% confidence interval
[CI]: 0.95, 0.99) and obesity (OR = 0.97, 95% CI: 0.95, 0.99),
34
obesity
(p< 0.05)
46
and BMI z-scores (d= 0.3, p< 0.01) and waist circumfer-
ence (d= 0.3, p< 0.001).
27
Two studies employed alternative mea-
sures of walkability for the correlation analysis: one study focused on
the association between the density of subway stops and adiposity
(β=1.2, p= 0.001),
32
and the other study considered walkability as
the number of parks within the 1-mile buffer of the household and
then correlated it with overweight (OR = 0.94, 95% CI: 0.90, 0.98)
and risk of overweight (OR = 0.95, 95% CI: 0.92, 0.99).
45
3.6 |Study quality assessment
Table S2 summarizes the scoring results of the study quality assess-
ment based on the National Institutes of Health's Quality Assessment
Tool for Observational Cohort and Cross-Sectional Studies.
42
The
studies included in the review scored 9.2 out of 14 on average, with a
range from 7 to 11.
4|DISCUSSION
Although the accumulated evidence supported associations between
walkability and childhood obesity, few studies have reviewed such
associations. In this study, we systematically reviewed 13 studies that
evaluated the statistical relationships between walkability and weight-
related behaviours and/or outcomes among children and adolescents.
Our review corroborates the conclusions of previous reviews. For
instance, Rahman
48
concluded that children's built environment
impacts their engagement in PA, which eventually lowers the risks of
obesity; walkability, as one neighbourhood feature, plays an indis-
pensable role in increasing the use of activity-inducing amenities.
Additionally, Booth
49
found that neighbourhoods with sufficient PA
resources such as sidewalks are more likely to promote an active life-
style. These previous reviews of obesity prevention factors, although
relating to walkability,
48,49
have not systematically examined the
effects on childhood obesity; it is this gap which our study aims to fill.
TABLE 2 (Continued)
First Author
(year) Walkability indices
Other environmental factors
adjusted for in the model
Measures of weight-related
behavior
Detailed measures of weight-
related outcomes
The sum of z-scores of four
measures (residential
density, land use mix,
intersection density, and
public transit density) within
a 1-km home street-network
buffer
Kligerman
(2007)
31
The sum of z-scores of each of
the four categories (land use
mix, retail floor area ratio or
retail density, intersection
density, and residential
density) within a 0.8-km
home street-network buffer
NA MVPA (average daily
minutes collected by the
Actigraph uniaxial
accelerometer for a 7-day
period)
Height was measured with a
portable stadiometer and
weight on a calibrated digital
scale
BMI based on measured
height and weight
Wasserman
(2014)
45
The density of convenience
stores, fast-food restaurants,
grocery stores, and fitness
facilities within a 0.8-km
school buffer and of parks
within a 1.6-km school
buffer, by referring to
Walkscorewebsite
State of residence NA BMI based on measured
height and weight
Overweight (95th
percentile)
At risk of overweight (85th
percentile)
a
ACS active commuting to school; BF body fat; BMI body mass index; CDC Center for Disease Control and Prevention; GIS Geographic Informa-
tion Systems; MVPA moderate-vigorous physical activity; NA not available; NEWS-Y Neighborhood Environment Walkability Scale-Youth; PA phys-
ical activity; ST sedentary time.
8of11 YANG ET AL.
Although the majority of the studies in our review reported that
higher levels of walkability in the built environment were associated
with active lifestyles and healthy weight statuses,
25,27,3133,36,46
other
studies did not support this association.
29,4345,47
These mixed find-
ings, coupled with regional heterogeneity and a relatively small pool
of qualified literature, do not permit us to draw a solid conclusion
about the health-promoting effects of living in walkable environments.
This contraction can be explained by two methodological biases. First,
actual environmental influences on human behaviour and health sta-
tus have uncertainties, which arises as community-based attributes,
such as walkability, cannot entirely dictate people's daily activities,
because a given community's influence is uncertain in both spatial and
temporal scales.
50
It has been observed that people tend to travel out
of their neighbourhoods for daily activities, so that factors affecting
people's PA and weight status could exist beyond their immediate liv-
ing environments.
51
One relevant example is that participants pre-
ferred to exercise in an activity-inducing environment (e.g., inside a
gym) rather than walking in their neighbourhood.
36
Second, the asso-
ciation with a walkability score alone does not permit us to justify the
role of the built environment in facilitating walking. This statistical
issue, known as the omitted-variable bias,
52
occurs when one or more
relevant variables are ignored in statistical analyses. Specifically, quan-
tifying walkability using a predefined rubric cannot articulate other
important qualitative variables, such as the aesthetics of the land-
scape, the presence of sidewalks or the quality of stores along the
street. All of these variables could significantly affect young people's
willingness to walk and exercise.
27,46
Also, another important factor
omitted in some of these correlation analyses is the community's food
environment, which could be health-promoting (e.g., supermarkets) or
health-damaging (e.g., fast-food restaurants).
53
For example, the inun-
dation of unhealthy food provisioning in a community can offset the
health benefits derived from PA.
46
The opportunities for and enjoy-
ment of outdoor activities could also be affected by weather, season-
ality and, more broadly, climate change.
54
This review has limitations. First, the majority of the walkability
studies included in the review was cross-sectional, with only one longi-
tudinal study. This limitation on study inclusion weakens the ability to
draw causal inferences to weight-related behaviours and outcomes.
55
Second, because of the variety of walkability definitions, analysis
methods and sample characteristics, we only summarized major find-
ings in the review instead of adopting meta-analysis in a comprehensive
manner. Also, multiple statistical methods (e.g., generalized estimating
equations,
33
linear regression
29,45,48,49
and logistic regression
25,34
)
were used to examine the associations differed across studies, which
may lead to different results. These aspects can be improved by
adopting rigorous reporting guidelines in the future.
56
Third, some
studies
29,31,32,43
used self-reported measures rather than objective
measures to quantify weight-related behaviours (e.g., PA, MVPA and
sedentary time), which is prone to recall errors.
39
Fourth, confounding
factors (e.g., family income, educational attainment, race and living con-
ditions) varied across studies and could lead to the heterogeneity of
correlation results. Although part of the confounders has been adjusted
in the review, the adjustment could not be exclusive and could affect
the accuracy of the results. Finally, studies included in the review only
covered the United States and Europe; existing etiology about child-
hood obesity is mostly drawn from the evidence in urban areas of
developed countries. Therefore, the conclusions found in these studies
cannot be applied to the rural areas or regions in developing or under-
developed countries, which often face a rising prevalence of obesity.
57
We expect this review to provide a sound reference for future
studies on the associations between walkability and weight-related
behaviours and outcomes, thus helping to justify the health effects of
community design in alleviating obesity. Future studies on childhood
obesity should focus on ensuring consistency in measuring walkability
to improve the quality of reporting. Also, longitudinal studies focused
on a selected population group (e.g., African-Americans) and areas
with the greatest obesity challenges (e.g., developing countries) should
be prioritized to justify public health interventions for improving
neighbourhood walkability.
ACKNOWLEDGEMENTS
We thank the National Key R&D Program Precision Medicine Initiative
of China (2017YFC0907304), Sichuan Science and Technology Program
(2019YJ0148), and the International Institute of Spatial Lifecourse Epi-
demiology (ISLE) for research support. [Correction added on 3 February
2021, after first online publication: Acknowledgements have been
revised.]
CONFLICT OF INTEREST
We declare no conflicts of interest.
ORCID
Xiang Chen https://orcid.org/0000-0002-5045-9253
Qian Xiao https://orcid.org/0000-0002-8388-1178
Peng Jia https://orcid.org/0000-0003-0110-3637
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of this article.
How to cite this article: Yang S, Chen X, Wang L, et al.
Walkability indices and childhood obesity: A review of
epidemiologic evidence. Obesity Reviews. 2021;22(S1):e13096.
https://doi.org/10.1111/obr.13096
YANG ET AL.11 of 11
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Purpose Striking disparities persist in cardiovascular disease risk factors among minority youth. We examined the association between multiple indicators of neighborhood quality and minority youth fitness. Methods The primary exposure was the Child Opportunity Index (COI), a measure comprised of indicators that facilitate healthy child development. Outcome data were drawn from the 2018-2019 Fit2Play Study (Miami-Dade County, FL). Hotspot analysis evaluated COI spatial clustering. Generalized linear mixed models examined cross-sectional COI-fitness associations. Results The sample included 725 youth (53% Black, 43% Hispanic; 5-17 years). Significant neighborhood quality spatial clusters were identified (Gi*z-score=-4.85-5.36). Adjusting for sociodemographics, walkability was associated with lower percentiles in body mass index (BMI) and diastolic blood pressure percentiles (DBP) (β=-5.25, 95% CI:-8.88,-1.62 and β=-3.95, 95% CI:-7.02,-0.89, respectively) for all, lower skinfold thickness (β=-4.83, 95% CI:-9.97, 0.31 and higher sit-ups (β=1.67, 95% CI:-0.17,3.50) among girls, and lower systolic blood pressure percentiles (SBP) (β=-4.75, 95% CI:-8.99,-0.52) among boys. Greenspace was associated with higher BMI (β=6.17, 95% CI:2.47, 9.87), SBP (β=3.47, 95% CI:-0.05, 6.99), and DBP (β=4.11, 95% CI:1.08, 7.13). Conclusions COI indicators were positively associated with youth fitness. Disparities in youth cardiovascular disease risk may be modifiable through community interventions and built environment initiatives targeting select neighborhood factors.
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The project ‘Obesogenic Environment and Childhood Obesity’ (OBECHO), carried out under the leadership of the International Institute of Spatial Lifecourse Epidemiology (ISLE), has reviewed all sufficiently reported studies of obesogenic environmental determinants published prior to 1 January 2019. Findings of the OBECHO project have formed the unprecedentedly inclusive evidence for policy‐making and the establishment of the future research agenda regarding the obesogenic environment. Furthermore, the outbreak of the coronavirus disease 2019 (COVID‐19) pandemic has made this evidence become an important benchmark record of how youths have interacted with the obesogenic environment in the pre‐COVID‐19 era. The implementation of lockdown measures worldwide for curbing COVID‐19 transmission has been affecting not mere youth's lifestyle behaviours and weight status but, more fundamentally, obesogenic environments and hence youth‐environment interaction patterns. However, COVID‐19, although causing unfavoured changes, will speed up the transformation of the research landscape from traditional to modern modes for more reliable evidence. We should closely track and study those abnormalities caused by COVID‐19 and the accompanying interventions.
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In recent years, built environmental characteristics have been linked to childhood overweight, but the results remain inconsistent across studies. The present study examines associations between several built environmental features and body weight status (BMI) z-score among a large sample of preschool children in the city of Hannover, Germany. Walkability (Index), green space availability, and playground availability related to preschool children’s home environments were measured using data from OpenStreetMap (OSM). These built environment characteristics were linked to the data from the 2010–2014 school entry examinations in the Hannover city (n = 22,678), and analysed using multilevel linear regression models to examine associations between the built environment features and the BMI z-score of these children (4–8 years old). No significant associations of built environmental factors on children’s BMI were detected, but the effect between green space availability and BMI was modified by the parental educational level. In children with lower compared to higher educated parents, a higher spatial availability of greenspace was significantly associated with reduced body weight. Future research should continue to monitor the disparities in diverse built environment features and how these are related to children’s health.
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Background: Living in urban or rural environments may influence children's levels of physical activity and sedentary behaviours. We know little about variations in device-measured physical activity and sedentary levels of urban and rural children using nationally representative samples, or if these differences are moderated by socioeconomic factors or seasonal variation. Moreover, little is known about the influence of 'walkability' in the UK context. A greater understanding of these can better inform intervention strategies or policy initiatives at the population level. Methods: Country-wide cross-sectional study in Scotland in which 774 children (427 girls, 357 boys), aged 10/11 years, wore an accelerometer on one occasion for at least four weekdays and one weekend day. Mean total physical activity, time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA), per day were extracted for weekdays, weekend days, and all days combined. Regression analyses explored associations between physical activity outcomes, urban/rural residence, and a modified walkability index (dwelling density and intersection density); with interactions fitted for household equivalised income and season of data collection. Sensitivity analyses assessed variation in findings by socioeconomic factors and urbanicity. Results: Rural children spent an average of 14 min less sedentary (95% CI of difference: 2.23, 26.32) and 13 min more in light intensity activity (95% CI of difference, 2.81, 24.09) per day than those from urban settlements. No urban-rural differences were found for time spent in MVPA or in total levels of activity. Our walkability index was not associated with any outcome measure. We found no interactions with household equivalised income, but there were urban/rural differences in seasonal variation; urban children engaged in higher levels of MVPA in the spring months (difference: 10 mins, p = 0.06, n.s) and significantly lower levels in winter (difference: 8.7 mins, p = 0.036). Conclusions: Extrapolated across one-year, rural children would accumulate approximately 79 h (or just over 3 days) less sedentary time than urban children, replacing this for light intensity activity. With both outcomes having known implications for health, this finding is particularly important. Future work should prioritise exploring the patterns and context in which these differences occur to allow for more targeted intervention/policy strategies.
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Background: Geographical inequalities in overweight and obesity prevalence among children are well established in cross-sectional research. We aimed to examine how environmental area characteristics at birth are related to these outcomes in childhood. Methods: Anonymised antenatal and birth data recorded by University Hospital Southampton linked to school-measured weight and height data for children within Southampton, UK, were utilised (14,084 children at ages 4-5 and 5637 at ages 10-11). Children's home address at birth was analysed at the Lower and Middle layer Super Output Area (LSOA/MSOA) levels (areas with average populations of 1500 and 7000, respectively). Area-level indices (walkability, relative density of unhealthy food outlets, spaces for social interaction), natural greenspace coverage, supermarket density and measures of air pollution (PM2.5, PM10 and NOx) were constructed using ArcGIS Network Analyst. Overweight/obesity was defined as a body mass index (BMI; kg/m2) greater than the 85th centile for sex and age. Population-average generalised estimating equations estimated the risk of being overweight/obese for children at both time points. Confounders included maternal BMI and smoking in early pregnancy, education, ethnicity and parity. We also examined associations for a subgroup of children who moved residence between birth and outcome measurement. Results: There were mixed results between area characteristics at birth and overweight/obesity at later ages. MSOA relative density of unhealthy food outlets and PM10 were positively associated with overweight/obesity, but not among children who moved. LSOA greenspace coverage was negatively associated with the risk of being overweight/obese at ages 10-11 in all children (relative risk ratio 0.997, 95% confidence interval 0.995-0.999, p = 0.02) and among children who moved. Conclusions: Local access to natural greenspaces at the time of birth was inversely associated with becoming overweight or obese by age 10-11, regardless of migration. Increased access/protection of greenspace may have a role in the early prevention of childhood obesity.
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The prevalence of obesity is still rising among Chinese adults and may be attributed to environmental factors, which, however, has only been examined in western countries before. This study aimed to estimate associations between obesogenic environments and adult obesity in China, on the basis of the official 2013-4 nationally representative survey. General and abdominal obesity were defined by body mass index and waist circumference, respectively, according to both the Chinese and international criteria. The mean summer/winter temperature in provinces, the mean fine particulate matter (PM2.5) concentration, gross domestic product per capita, and education level in districts/counties, and the densities of fast-food restaurants, full-service restaurants, grocery stores, and supermarkets in subdistricts/towns were calculated. Five-level logistic regression models were used to estimate their associations with obesity, also in urban and rural regions separately. Both general and abdominal obesity in men were associated with the highest PM2.5 concentration, summer temperature, and density of full-service restaurants and grocery stores, as well as the lowest winter temperature. These associations were also observed in women except for summer temperature and density of full-service restaurants with abdominal obesity. Some associations varied by urban-rural regions. Also, the higher regional education level was associated with general and abdominal obesity in men. Additionally, obesity was associated with the increasing number of coexisting obesogenic environmental factors. Our findings call for more attention to citizens living in certain environments in China, such as cold winters and with more full-service restaurants and grocery stores. This is the first national, comprehensive obesogenic environment study in China, which generated evidence-based hypotheses for future longitudinal research and interventions on obesogenic environments in China.
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Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2–3 years from its release.
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Excessive access to fast‐food restaurants (FFRs) in the neighbourhood is thought to be a risk factor for childhood obesity by discouraging healthful dietary behaviours while encouraging the exposure to unhealthful food venues and hence the compensatory intake of unhealthy food option. A literature search was conducted in the PubMed, Web of Science, and Embase for articles published until 1 January 2019 that analysed the association between access to FFRs and weight‐related behaviours and outcomes among children aged younger than 18. Sixteen cohort studies and 71 cross‐sectional studies conducted in 14 countries were identified. While higher FFR access was not associated with weight‐related behaviours (eg, dietary quality score and frequency of food consumption) in most studies, it was commonly associated with more fast‐food consumption. Despite that, insignificant results were observed for all meta‐analyses conducted by different measures of FFR access in the neighbourhood and weight‐related outcomes, although 17 of 39 studies reported positive associations when using overweight/obesity as the outcome. This systematic review and meta‐analysis revealed a rather mixed relationship between FFR access and weight‐related behaviours/outcomes among children and adolescents.
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Background: Attributes of the built environment, such as neighborhood walkability, have been linked to increased physical activity and reduced obesity risk. This relationship, however, has primarily been documented in adults; less is known about neighborhood walkability and youth obesity, as limited prior research has produced mixed findings. The purpose of this study was to examine the association between neighborhood walkability and youth obesity, including differences by urbanicity. Methods: Data were collected in 2013 from youth aged 7-14 years (n = 13,469) in a Southeastern county school district. Height and weight were objectively measured and utilized to calculate body mass index (BMI) z-scores. Youth demographic characteristics and addresses were obtained, and a Walk Score® was gathered for each youth's home address. Multilevel linear regression analysis, accounting for nesting within census block groups, was conducted to examine the association between Walk Score and BMI z-score and to test for the moderating effect of urbanicity. Separate multilevel analyses examined Walk Score and BMI z-score among urban, urban-rural mixed, and rural youth subsamples. Results: Overall, as Walk Score increased, youth BMI z-score decreased. Walk Score was positively associated with BMI z-score among urban youth and negatively associated with BMI z-score among rural youth; no relationship was observed between Walk Score and youth in urban-rural mixed areas. Conclusions: Neighborhood walkability may impact youth differently across geographic areas. Further study is warranted about how youth utilize a walkable environment and mechanisms through which walkability influences youth physical activity and obesity risk.
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Introduction Physical inactivity is a risk factor for many non‐communicable diseases. As reported by the World Health Organisation, 81% of children worldwide are physically inactive. Environmental factors, such as neighbourhood walkability, can shape people’s physical activity (PA) behaviour. This study explored the association between neighbourhood walkability and after‐school PA among Australian schoolchildren. Methods The Department for Education and Child Development (DECD) distributed the survey to 189 schools across South Australia to assess the health and well‐being of schoolchildren aged between 8 and 14 years. Neighbourhood was defined as an area corresponding to a four digit postcode, and its walkability was measured using Walk Score®. The association between neighbourhood walkability and after‐school PA was analysed using multinomial logistic regression adjusted for age, gender, SEIFA score, number of days of TV watching, number of times of eating junk food, neighbourhood safety and children’s weight status. Results Children residing in highly walkable areas (walker’s paradise) compared to car‐dependent areas had higher odds (OR(95%CI)) of engaging in after‐school PA three (1.216 (1.029, 1.436), p = 0.021), four (1.287 (1.064, 1.557), p = 0.009) and five times a week (1.230 (1.030, 1.133), p = 0.022) compared to children never participating in PA. Conclusion Living in highly walkable areas (walker’s paradise), compared to living in car‐dependent areas was associated with higher levels of after‐school PA. So what Creating walkable neighbourhoods with greater access to amenities, services and public transportation may help increase after‐school PA among schoolchildren.