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Journal of Sport and Health Science logoLink to Journal of Sport and Health Science
. 2018 Nov 16;8(2):153–169. doi: 10.1016/j.jshs.2018.11.003

Impact of built environment on physical activity and obesity among children and adolescents in China: A narrative systematic review

Ruopeng An a,⁎, Jing Shen b, Qiuying Yang c, Yan Yang a
PMCID: PMC6451055  PMID: 30997262

Highlights

  • •

    We systematically reviewed evidence regarding the impact of built environment on physical activity and obesity among children and adolescents in China.

  • •

    Twenty studies, including 16 cross-sectional, 3 longitudinal, and 1 descriptive studies, met the predetermined selection criteria and were included in the review.

  • •

    Availability and accessibility in proximity to greenspace, park, recreational facility, and sidewalks were found to be associated with increased physical activity level, reduced sedentary behavior, and/or active commuting among Chinese children and adolescents.

  • •

    In contrast, lacking bike lanes and living in higher residential density area were associated with an increased likelihood of childhood overweight and obesity.

Keywords: Body weight, Chinese, Exercise, Literature review, Local environment, Neighborhood environment, Physical environment

Abstract

Background

Neighborhood built environment may profoundly influence children's physical activity (PA) and body weight. This study systematically reviewed scientific evidence regarding the impact of built environment on PA and obesity among children and adolescents in China.

Methods

A keyword and reference search was conducted in Active Living Research, Cochrane Library, PubMed, and Web of Science. Studies that met all of the following criteria were included in the review: (1) study designs—experimental studies, observational studies, and qualitative studies; (2) study subjects—Chinese children and/or adolescents aged ≤17 years; (3) exposures—neighborhood built environment; (4) outcomes—PA and/or body weight status; (5) article type—peer-reviewed publications; (6) time window of search—from the inception of an electronicbibliographic database to May 31, 2018; (7) country—China; and (8) language—articles written in English.

Results

A total of 20 studies, including 16 cross-sectional studies, 3 longitudinal studies, and 1 descriptive study, met the predetermined selection criteria and were included in the review. A total of 13 studies adopted subjective built environment measures reported by parents and/or children,2 adopted objective measures (e.g., geographical information system, field observations), and 5 adopted both objective and subjective measures. PA behaviors included PA, physical inactivity, sedentary behavior, active/passive commuting from/to school, and park visits. Among the 16 studies that provided some quantitative estimates of the influence of built environment on PA and body weight status, all reported a statistically significant relationship in the expected direction. Availability and accessibility in proximity to greenspaces, parks, recreational facilities, and sidewalks were found to be associated with increased PA levels, reduced sedentary behaviors, and/or active commuting among Chinese childrenand adolescents. In contrast, the absence of bike lanes and living in higher density residential areas were associated with increased likelihood of childhood overweight and obesity.

Conclusion

Neighborhood built environment plays an important role in Chinese children's PA engagement and weight outcomes. Building new exercise facilities and enhancing the accessibility of existing facilities hold the potential to enhance PA engagement among Chinese children and adolescents. In addition, urban designs that incorporate sidewalks, bike lanes, walking paths, less motorized traffic, and lower residential density are likely to promote PA and prevent childhood obesity in China.

1. Introduction

Obesity is a leading cause of morbidity and premature mortality worldwide.1, 2, 3, 4 Childhood obesity is linked to various immediate and long-term adverse health outcomes such as sleep apnea, hypertension, heart disease, stroke, type 2 diabetes, osteoarthritis, and certain types of cancer, and it leads to social and psychological problems such as stigmatization and poor self-esteem.5, 6 In 2010, 43million children worldwide (35million in developing countries) were estimated to be overweight or obese.7 By 2020, the prevalence of childhood overweight/obesity is projected to reach 9%, or 60million children worldwide.8 Based on the 2017 Report on Childhood Obesity in China, the prevalence of childhood overweight increased from 2.1% in 1985 to 12.2% in 2014 among Chinese children aged ≥7 years, and the prevalence of childhood obesity increased from 0.5% to 7.3% during the same time period, resulting in an increase in overweight/obese children from 6.2million to 35.0 million nationwide.9 Based on the Physical Activity and Fitness in China—the Youth Study, the prevalence of obesity among Chinese school children and adolescents aged 9–17years reached 12%, and approximately 37% of them did not meet screen time viewing recommendations.10 It is projected that the prevalence of childhood overweight/obesity will reach 28%, or 49.5million children, in 2030.9

An active lifestyle helps to decrease the risk of obesity in both adults and children.11 In contrast, physical inactivity is a leading risk factor for morbidity and mortality.12, 13, 14 A vast majority of children and adults in both developed and developingcountries fall short of the meeting guidelines-recommendedlevels for physical activity (PA).15 The relationship between built environment and PA among children in general has been extensively documented.16,17 Accumulating evidence suggests that the availability of parks, playgrounds, gyms, and other facilities, as well as other neighborhood features such as walkability and safety, profoundly shape children's daily PA patterns.18, 19 Roemmich et al.20 found that residential locations closer to parks were associated with higher PA levels in young children than those farther away from parks. Reimers et al.21 reported that adolescent girls residing far away from a gym were less likely to engage in indoor sports activities than those residing near a gym. Molnar et al.22 found that poorer neighborhood safety was negatively associated with PA among youth aged 11–16years.

The majority of studies on the relationship between built environment and PA focus on adults and children in developed countries (e.g., the United States),16, 17, 18, 19 whereas relevant studies on the child population in developing countries remain limited. The characteristic and quality of various types of neighborhood built environment can noticeably differ between developed and developing countries,23 which may differentially impact the PA and obesity patterns among local residents. In the coming decades, China is expected to undergo an unprecedented urbanization and urban expansion.24 This circumstance creates a critical opportunity for urban planners to design smart cities and environments conducive to an active lifestyle among residents, including children and adolescents.24 However, this grand goal would not be accomplished without a profound understanding of the relationship between built environment and PA. Moreover, cultural norms in China may play an important role in children's PA patterns.25 For example, Confucian ideology regards sport and PA participation as predominantly masculine in nature, which results in Chinese women engaging in low levels of PA during childhood and adolescence.25 Owing to the potential mediating effect of Chinese culture, the relationship between the built environment and PA in China might not resemble that in Western countries and thus warrants targeted research. One study that reviewed the built environmental correlates of PA in China, primarily among working-age and older adults, documented increased levels of PA in relation to proximal nonresidential locations, pedestrian-friendly infrastructure, aesthetics, and recreational amenities.26

This study is, to our knowledge, the first that systematically reviewed the existing literature regarding the impact of neighborhood built environment on PA and body weight status among children and adolescents in China. Findings from this review can be informative to policymakers and stakeholders in their efforts to design and modify certain features of the neighborhood built environment that promote PA, decrease sedentary behavior, and contribute to healthy weight among the Chinese child population. The study also identified limitations and gaps in this field that warranted future research.

2. Methods

2.1. Study selection criteria

Studies that met all of the following criteria were included in our review: (1) study designs—experimental studies (e.g., randomized, controlled trials or pre–post studies), observational studies (e.g., longitudinal studies, case-control studies, or cross-sectional studies), and qualitative studies; (2) study subjects—Chinese children and/or adolescents aged ≤17 years; (3) exposures—neighborhood built environment (e.g., sports facilities,parks, greenspaces, bike lanes, and sidewalks); (4) outcomes—PA and/or body weight status; (5) article type—peer-reviewed publications; (6) time window of search—from the inception of an electronic bibliographic database to May 31, 2018; (7) country—China; and (8) language—articles written in English.

Studies that met any of the following criteria were excluded from the review: (1) studies that incorporated no outcome pertaining to PA and/or body weight status; (2) studies that evaluated an area substantially larger than a neighborhood, such as a city or province; (3) studies that evaluated a resident's transportation mode but that had no evaluation of the neighborhood's transportation system; (4) studies that did not include human participants; (5) controlled experiments conducted in manipulated rather than naturalistic settings; (6) articles not written in English; (7) letters, editorials, study/review protocols, or review articles; or (8) studies involving Chinese participants who lived outside the Mainland of China, Hong Kong, China, or Macao, China.

2.2. Search strategy

A keyword search was performed in 4 electronic bibliographic databases: Active Living Research (ALR), Cochrane Library, PubMed, and Web of Science. Administered by the University of California, San Diego, ALR was founded in 2001 by an interdisciplinary team with research-and-practice expertise in public health, transportation, planning, parks and recreation, school activity programs, behavioral science, and obesity prevention. Over the past 2 decades, ALR has established a rich database of articles covering community designs that promote PA, physical/mental health, economic vitality, and environmental sustainability. The Cochrane Library is a collection of databases in medicine and other health specialties.

The search algorithm included all possible combinations of keywords associated with the 4 groups: (1) “environment”, “environments”,“environmental”,“neighborhood”, “neighbourhood”, “neighborhoods”, “neighbourhoods”, “place”, “places”, “residence”, “residential”, “area”, “areas”, “space”, “spaces”, “spatial”, “greenspace”, “land”, “lands”, “landscape”, “landscaping”, “park”, “parks”, “community”, “communities”, “municipal”, “garden”, “gardens”, “wood”, “woods”, “forest”, “forests”, “facility”, “facilities”, “gym”, “gyms”, “gymnasium”, “gymnasiums”, “path”, “paths”, “trail”, “trails”, “resource”, “resources”, “amenity”, “amenities”, “urban”, “city”, “cities”, “town”, “towns”, “county”, “counties”, “walkability”, “built”, “building”, “buildings”, “street”, “streets”, “streetscape”, “road”, “roads”, “highway”, “highways”, “freeway”, “freeways”, “expressway”, “expressways”, “sidewalk”, “sidewalks”, “footway”, “footways”, “footpath”, “footpaths”, “track”, “tracks”, “walkway”, “walkways”, “passage”, “passages”, “passageway”, “passageways”, “block”, “blocks”, “transport”, “transportation”, “transit”, “geography”, “geographies”, “geographic”, or “geographical”; (2) “motor activity”, “motor activities”, “sport”, “sports”, “physical fitness”, "physical exertion”, “physical activity”, “physical activities”, “physical inactivity”, “sedentary behavior”, “sedentary behaviors”, “sedentary behaviors”, “sedentary behaviors”, “sedentary lifestyle”, “sedentary lifestyles”, “inactive lifestyle”, “inactive lifestyles”, “exercise”, “exercises”, “active living”, “active lifestyle”, “active lifestyles”, “play”, “outdoor activity”, “outdoor activities”, “step”, “steps”, “walk”, “walking”, “run”, “running”, “bike”, “biking”, “bicycle”, “bicycling”, “cycle”, “cycling”, “stroll”, “strolling”, “active transport”, “active transportation”, “active transit”, “active commuting”, “travel mode”, “mode of travel”, “physically active”, “physically inactive”, “obesity”, “obese”, “adiposity”, “overweight”, “body mass index”, “BMI”, “body weight”, “waist circumference”, “waist to hip”, “waist-to-hip”, or “body fat”; (3) “China” or “Chinese”; and (4) “child”, “children”, “childhood”, “juvenile”, “pubescent”, “pubertal”, “puberty”, “adolescent”, “adolescents”, “adolescence”, “youth”, “teen”, “teens”, “teenage”, “teen-age”, “teenaged”, “teen-aged”, “teenager”, “teenagers”, “teen-ager”, “teen-agers”, “kid”, “kids”, “youngster”, “youngsters”, “minor”, “minors”, “student”, “students”, “preschooler”, or “preschoolers”. The MeSH terms “environment design”, “exercise”, “overweight”, “obesity”, “child”, “adolescent”, “China”, and “Chinese” were included in the PubMed search. All keywords in PubMed were searched with the “[All fields]” tag, which are processed using Automatic Term Mapping.27 The search function TS = Topic was used in Web of Science, which launches a search for topic terms in the fields of title, abstract, keywords, and Keywords Plus®.28 Titles and abstracts of the articles identified through the keywords search were screened against the study selection criteria. Potentially relevant articles were retrieved for evaluation of the full text. Two coauthors of this review (JS and QY) independently conducted title and abstract screening and identified potentially relevant articles. Inter rater agreement was assessed using the Cohen's kappa (κ = 0.74). Discrepancies were resolved through discussion with a third coauthor (RA).

A reference list search (i.e., backward reference search) and a cited reference search (i.e., forward reference search) were conducted based on the full-text articles meeting the study selection criteria that were identified from the keyword search. Articles identified from the backward and forward reference searches were further screened and evaluated using the same study selection criteria. The reference search was repeated on newly identified articles until no additional relevant articles were found.

2.3. Data extraction and preparation

A standardized data extraction form was used to collect the following methodologic and outcome variables from each article included authors, publication year, country, study design, sample size, age range, proportion of female participants, samplecharacteristics, statistical model, nonresponse rate, geographicalcoverage, setting, type of built environment measure, detailed measure of built environment, type of PA measure, detailed measure of PA, type of body weight status measure, detailed measure of body weight status, estimated effects of built environment on PA or body weight status, andkey findings on the relationship between built environment and PA or body weight status.

2.4. Data synthesis

A tabulation of extracted data by 2 coauthors of this review (YY and RA) revealed that no studies used the same measures for built environment, PA, and body weight to provide quantitative estimates for the impact of neighborhood built environment on PA and/or body weight status. This finding precluded a meta-analysis. Therefore, in this article we have summarized the common themes and findings of the included studies narratively. The data extraction, theme identification, and narrative summarization were independently conducted by 2 coauthors of this review (JS and QY). Discrepancies were resolved through discussion with a third coauthor (RA).

2.5. Study quality assessment

We used the National Institutes of Health's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to assess the quality of each included study.29 This assessment tool rates each study based on 14 criteria (Table1). For each criterion, a score of 1 was assigned if “yes” was the response, whereas a score of 0 was assigned otherwise (i.e., if an answer of “no”, “not applicable”, “not reported”, or “cannot determine” resulted). A study-specific global score ranging from 0 to 14 was calculated by summing the scores across all criteria. The study quality assessment helped measure the strength of scientific evidence but was not used to determine the inclusion of studies. Two coauthors of this review (JS and QY) independently conducted the study quality assessment, with discrepancies resolved through discussion with a third coauthor (RA).

Table1.

Study quality assessment.

Study ID
Criterion 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1. Was the research question or objective in this paper clearly stated? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
2. Was the study population clearly specified and defined? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
3. Was the participation rate of eligible persons ≥50%? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
5. Was a sample size justification, power description, or variance and effect estimates provided? Y N N N N N N N N N N N N N N N N N N N
6. For the analyses in this paper, were the exposure(s) of interest measured before the outcome(s) being measured? N N N N N Y N N N N N Y N N N N N N N Y
7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? N N N N N Y N N N N N Y N N N N N N N Y
8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (e.g., categories of exposure, or exposure measured as continuous variable)? N N N Y Y N N N N N N Y Y Y Y Y N Y Y Y
9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? N N N Y Y N Y N N N N N N Y N Y N Y N N
10. Was the exposure(s) assessed more than once over time? N N N N N Y N N N Y N Y N N N N N N N Y
11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? N N N N N N N N N N N N N N N Y Y N N N
12. Were the outcome assessors blinded to the exposure status of participants? N N N N N N N N N N N N N N N N N N N N
13. Was loss to follow-up after baseline ≤20%? Y Y Y Y Y N Y Y Y Y Y N Y Y Y Y Y Y Y N
14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? Y Y Y Y Y Y Y Y N Y Y Y Y Y N Y Y N Y N
Total score 7 6 6 8 8 8 7 6 5 7 6 9 7 8 6 9 7 7 7 8
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Notes: This study quality assessment tool was adopted from the National Institutes of Health's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.28 For each criterion, a score of 1 was assigned if “yes” was the response, whereas a score of 0 was assigned otherwise. A study-specific global score, ranging from 0 to 14, was calculated by summing up scores across all 14 criteria. Study quality assessment helped measure strength of scientific evidence, but was not used to determine whether a study was included in the review.

Abbreviations: N = no; Y = yes.

3. Results

3.1. Identification of studies

Fig.1 shows the study selection flowchart. We identified 8286 articles total through the keywords and references search. After removing duplications, 7922 unique articles underwent title and abstract screening, after which 7879 articles were excluded. The full texts of the remaining 43 articles were reviewed against the study selection criteria. Of these, 23articles were excluded. The primary reasons for exclusion were lack of neighborhood environment measures, no outcome reported on PA and childhood obesity, and adult sample instead of children and/or adolescents. The remaining 20 studies that examined the relationship between built environment and children's PA and/or body weight status were included in our review.30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49

Fig.1.

Fig.1.

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Study selection flowchart.

3.2. Basic characteristics of the included studies

Table2 summarizes the basic characteristics of the 20 included studies. A total of 5 studies were conducted in Hong Kong, China, and the remaining studies were conducted in the Mainland of China. All included studies were published in or after 2006 — 1 each in 2006, 2008, 2009, 2011, 2012, and 2016; 2 each in 2010 and 2015; 4 in 2017; and 6 in 2014. A total of 14 studies exclusively focused on PA, 2 studies exclusively focused on body weight status, and the remaining 4 studies examined both PA and body weight status. A single study adopted a qualitative study design, 3 adopted a longitudinal study design, and the remaining 16 studies adopted a cross-sectional study design. Sample sizes were generally large but varied substantially across studies. A total of 4 studies had a sample size between 20 and 99; 5 had a sample size between 100 and 999; 9 had a sample size between 1000 and 9999; and the remaining 2 studies had a sample size of >10,000. The mean and median sample sizes were 7498 and 1359, respectively, with a standard deviation of 18,545 and a range from 20 to 80,928. The majority of studies (n = 12) recruited adolescents aged 11–17years, 4 recruited children aged 6–10years, and the remaining 4 recruited both children and adolescents. All included studies recruited both girls and boys, with the 2 sexes largely equally distributed in the samples. A variety of statistical models were applied across studies, including generalized estimating equation, logistic regression, hierarchical logistic regression, mixed-effects logistic regression, ordinal logistic regression, generalized linear model, hierarchical linear regression, multilevel Poisson regression, and multilevel path modeling. The majority of studies (n = 15) adjusted for some individual sociodemographics in the statistical analysis. A total of 9 studies adjusted for certain environment and/or PA characteristics (e.g., greenspace and sedentary behavior) in the statistical analysis.

Table2.

Basic characteristics of the studies included in the review.

Study ID First author (year) Region Study design Sample size Age (year) Female (%) Sample characteristics Statistical model Nonresponse rate (%) Geographical coverage Setting
1 Li etal. (2006)30 The Mainland of China Cross-sectional 1787 11–17 49.8 Junior high school students Hierarchical logistic regression 0.9 Household, school and community in a city Urban
2 Li etal. (2008)31 The Mainland of China Cross-sectional 1792 11–17 49.8 Junior high school students Hierarchical logistic regression 0.7 Household, school and community in a city Urban, suburban, and rural
3 Wong etal. (2010)32 Hong Kong, China Cross-sectional 29,139 Boys: 14.5 ± 0.1 56.0 Grades 7–13 students Logistic regression 15.3 Home and neighborhood in a city Urban
Girls: 14.6 ± 0.1
4 Xu etal. (2010)33 The Mainland of China Cross-sectional 2375 13–15 53.8 Grades 7–9 junior high school students Mixed effects logistic regression 10.7 Residential density and green space in a city Urban
5 Xu etal. (2010)34 The Mainland of China Cross-sectional 2375 13–15 53.8 Grades 7–9 junior high school students Mixed effects logistic regression 10.7 Residential density in a city Urban
6 Cui etal. (2011)35 The Mainland of China Longitudinal 6935 6–18 Urban: 49.0 Children and adolescents Generalized estimating equations 3.9 Household and community in 9 provinces Urban and rural
Rural: 46.0
7 Huang etal. (2013)36 Hong Kong, China Cross-sectional 280 Boys: 11.1 ± 0.9 52.8 Grades 4–6 primary school students Hierarchical linear regression 7.6 Home and neighborhood in a city Urban
Girls: 11.2 ± 0.9
8 An etal. (2014)37 The Mainland of China Cross-sectional 487 <18 51.4 Residents Multivariate regression NA Home proximity to an exercise facility in 10 provinces Urban and rural
9 He etal. (2014)38 Hong Kong, China Qualitative 34 10–11 50.0 Grades 5–6 primary school students NA NA Household and neighborhood in a city Urban and rural
10 Jia etal. (2014)39 The Mainland of China Cross-sectional 1528 15–19 50.8 Community residents Logistic regression 15.1 Community and park in a city Suburban
11 Li etal. (2014)40 The Mainland of China Cross-sectional 497 8–10 48.3 Grade 3 primary school-aged students Logistic regression 10.3 Family and neighborhood in 2 cities Urban
12 Wong etal. (2014)41 Hong Kong, China Longitudinal 9993 14.0 ± 1.7 63.2 Grades 7–12 secondary school students Generalized linear model 22.7 Neighborhood in a city Urban
13 Guo etal. (2014)42 The Mainland of China Cross-sectional 463 <18 51.2 Permanent residents Logistic regression 21.5 Community in 10 provinces Urban and rural
14 Liu etal. (2015)43 The Mainland of China Cross-sectional 29 11–20 51.6 Community residents Hierarchical linear regression 32.9 Community and park in a city Urban and suburban
15 Zhang etal. (2015)44 The Mainland of China Cross-sectional 75 15–20 53.0 Permanent residents Ordinal logistic regression 58.3 Green space in a city Urban and suburban
16 Wong etal. (2016)45 Hong Kong, China Cross-sectional 1189 8–12 46.0 Grades 3–5 primary school students Generalized linear mixed model 6.0 Home and neighborhood in a city Urban
17 Gomes etal. (2017)46 The Mainland of China Cross-sectional 552 9–11 46.9 School students Multilevel Poisson model 6.7 School in a city Urban
18 Liu etal. (2017)47 The Mainland of China Cross-sectional 20 11–20 51.5 Community residents Hierarchical linear regression 61.5 Community and park in a city Urban and suburban
19 Wang etal. (2017)48 The Mainland of China Cross-sectional 80,928 13.71 ± 2.94 50.9 Grades 4–12 school-aged students Multilevel path modeling 32.9 School and community nationwide Urban and rural
20 Yang etal. (2017)49 The Mainland of China Longitudinal 9487 6–17 47.1 School-age students Logistic regression NA School and community nationwide Urban, suburban, and rural
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Abbreviation: NA = not applicable.

Table3 summarizes the measures for built environment, PA, and body weight status used in the included studies. The majority (n = 13) of the studies adopted subjective built environment measures reported by parents and/or the children themselves, 2 studies adopted objective built environment measures, and 5 studies used both objective and subjective built environment measures. Specific measures of the built environment varied substantially across studies. Parent- and/or self-reported subjective built environment measures included both general questions (e.g., “Whether or not you have sports facilities in your neighborhood?” or “How long does it take to walk to the nearest exercise facility?”) and more specific questions pertaining to the built environment (e.g., accessibility and availability of sports/recreational facilities, greenspace, parks, playgrounds, and video game shops, as well as questions about neighborhood aesthetics and attractive natural sights in the neighborhood). Among the subjective built environment measures, only 2 studies used measures known to be validated in previous research (e.g., the Neighborhood Environment Walkability Scale).39, 40 Objective built environment measures (e.g., population density, road density, street intersection density, and distance to nearest park and/or recreational facility) were constructed based on geographical information systems, whereas other built environment measures such as walking paths, playgrounds, jogging/cycling lanes, transportation, schools, and parks were tabulated in field observations by trained observers in the community under study.

Table3.

Measures of built environment, PA, and body weight status in the studies included in the review.

Study ID First author (year) Type of built environment measure Detailed measure of built environment Type of PA measure Detailed measure of PA Type of body weight status measure Detailed measure of body weight status
1 Li etal. (2006)30 Self-report questionnaire 1. Recreational facilities in the community2. Places around the home for children to play3. Transportation4. Urbanicity
5. Sidewalks around house
6. Video game shops around house		 Self-report questionnaire 1. Intensity of PA
2. Duration of PA
3. Physical inactivity		 Objective measure 1. Height
2. Weight
3. BMI
2 Li etal. (2008)31 Self-report questionnaire Houses with bike lanes nearby Self-report questionnaire (Adolescents Physical Activity Recall Questionnaire) Sedentary time
1. Watching TV
2. Playing computer games or video games
3. Doing homework after school
4. Listening to music
5. Other sedentary habits		 Objective measure 1. Height
2. Weight
3. BMI
3 Wong etal. (2010)32 Self-report questionnaire 1. Sport grounds
2. Cycling/jogging lanes
3. Table tennis courts
4. Swimming pools accessibility		 Self-report questionnaire The frequency of participation in moderate-to-vigorous nonschool leisure-time PA Self-report 1. Height
2. Weight
3. BMI
4 Xu etal. (2010)33 Objective measure Residential density Self-report questionnaire 1. Time spent in recreational PA
2. Sedentary behavior time		 Self-report 1. Height
2. Weight
3. BMI
5 Xu etal. (2010)34 Objective measure Residential density Self-report questionnaire 1. Time spent in recreational PA
2. Sedentary behavior time		 Self-report 1. Weight
2. Height
3. BMI
6 Cui etal. (2011)35 Self-report questionnaire 1. Schools in the local community
2. Bus stops in the neighborhood		 Self-report questionnaire Commuting type
1. Walking
2. Bicycle use
3. Motorized transportation
7 Huang etal. (2013)36 Self-report questionnaire
Objective measure		 1. Sports facilities in the neighborhood (self-report questionnaire)
2. Population density of residence districts (objective measure)		 1. Self-report questionnaire (Children's Leisure Activities Study Survey, Chinese version)
2. Parent-report questionnaire		 1. Time spent in physical activities (self-report questionnaire)
2. Time spent in screen-based behaviors (parent-report questionnaire)		 Objective measure 1. Height
2. Weight
3. BMI
8 An etal. (2014)37 Self-report questionnaire Walking distance to the nearest exercise facility Self-report questionnaire Leisure-time PA
9 He etal. (2014)38 Focus group of children Neighborhood environment Focus group of children 1. All body movements including exercise
2. Lifestyle activities
10 Jia etal. (2014)39 Observation self-report questionnaire 1. Walking paths in the community and park (observation)
2. Accessibility to services (questionnaire)
3. Neighborhood surroundings/aesthetics (questionnaire)		 Self-report questionnaire (IPAQ) 1. Time spent in PA
2. Time spent sitting		 Self-report 1. Height
2. Weight
11 Li etal. (2014)40 Parent-report questionnaire Walkability of the neighborhood environment Parent-report questionnaire (Godin leisure-time exercise questionnaire) 1. Sedentary
2. Light, moderate, and vigorous activities		 Objective measure 1. Height
2. Weight
3. BMI
12 Wong etal. (2014)41 Self-report questionnaire 1. Sport facility accessibility
2. Sport facility availability		 Self-report questionnaire Frequency of moderate-to-vigorous leisure-time PA Self-report 1. Weight
2. Height
3. BMI
13 Guo etal. (2014)42 Self-report questionnaire Sport facility proximity Self-report questionnaire Frequency of moderate or vigorous PA
14 Liu etal. (2015)43 Self-report questionnaire
Objective measure (Internet and Gaode map)		 1. Proportion of residential greenspace (objective measure)
2. Park accessibility (self-report questionnaire and objective measure)		 Self-report questionnaire Park visitation
15 Zhang etal. (2015)44 Self-report questionnaire 1. Quality vegetation
2. Time to park
3. Availability and accessibility of urban greenspaces		 Self-report questionnaire Use of urban greenspaces for physical activities
16 Wong etal. (2016)45 Objective measure (Geographic Information System)
Parent-report questionnaire		 1. Accessibility of facilities
2. Availability of sports facilities
3. Attractive natural sights
4. Local destinations
5. Road density
6. Street intersections		 1. Objective measure (accelerometers)
2. Parent-report questionnaire		 MVPA Objective measure 1. Height
2. Weight
17 Gomes etal. (2017)46 Self-report questionnaire 1. Access to school facilities
2. Indoor and outdoor facilities
3. Playground and sport equipment		 Objective measure (accelerometers) Time spent in MVPA Objective measure 1. Standing height
2. Sitting height
3. Weight
4. BMI
18 Liu etal. (2017)47 Self-report questionnaire
Objective measure (Gaode map)		 1. Park accessibility
2. Proportion of residential greenspace
3. Perceived quality of the nearest park		 Self-report questionnaire (IPAQ) 1. Time spent being physically active
2. Vigorous-intensity activity
3. Moderate-intensity activity
4. Walking activity
5. Sitting
19 Wang etal. (2017)48 Self-report questionnaire 1. Access to PA facilities
2. Availability of sports clubs and organizations		 Self-report questionnaire (IPAQ) 1. Time spent sitting
2. Walking
3. Moderate-intensity and vigorous-intensity activities		 Objective measure 1. Height
2. Weight
3. BMI
20 Yang etal. (2017)49 Self-report questionnaire Distance to school Self-report questionnaire 1. Active travel to school
2. PA
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Abbreviations: BMI = body mass index; IPAQ = International Physical Activity Questionnaire; MVPA = moderate-to-vigorous physical activity; PA = physical activity.

PA behaviors measured in the studies included PA (n = 13), physical inactivity (n = 2), sedentary behavior (n = 2), active/passive commuting from/to school (n = 2), and park visits (n = 1). Most studies (n = 17) measured PA levels using questionnaires administered to parents and/or children, whereas 1 study adopted an objective measure by having participants wear an accelerometer, 1 study adopted an objective measure through both a parent-reported questionnaire and participant use of a wearable accelerometer, and 1 study collected qualitative data from a focus group of children. Parent- and/or self-reported PA questionnaires included both standardized questionnaires (e.g., the International Physical Activity Questionnaire (IPAQ)) and general questions (e.g., “How often do you do exercises continuously for at least 30min?”). Among the studies that adopted subjective PA measures, 8 used measures known to be validated in previous researches (i.e., the Adolescent Physical Activity Recall Questionnaire, Physical Activity Questionnaire for Children, IPAQ, IPAQ Short Form, and Godin Exercise Leisure-time Questionnaire). A total of 18studies included body weight status measured as body mass index (n = 12), overweight (n = 4), and obesity (n = 2). Body mass index (BMI)-related measures were based on both objectively measured (n = 7) and self-reported (n = 5) height and weight.

3.3. Key findings

Table4 summarizes the estimated effects of built environment on PA and/or body weight status among Chinese children and adolescents. Among the 16 studies that provided some quantitative estimates of the relationship between built environment measures and PA, all of them reported a statistically significant relationship in the expected direction. A lack of recreational facilities in a community was found to be positively associated with physical inactivity among local children.30 In contrast, perceived availability of sport facilities in a neighborhood was positively associated with leisure time overall PA level32,41 as well as moderate-to-vigorous PA level.36,48 Commuting time to the nearest sport facility played an essential role in PA engagement. Longer commuting time was found to be negatively associated with the likelihood of meeting the PA recommendation42 and using walking paths.39 In contrast, children living within a 10-min walking distance and/or 500m of an exercise facility tended to engage in more leisure-time PA.37,47

Table 4.

Estimated effects of built environment on PA and body weight status in the studies included in the review.

Estimated effects of built environment
 Main findings of study
Study ID First author (year) PA Body weight status PA Body weight status
1 Li et al. (2006)30 1. The access to public facilities (OR = 1.4, 95%CI: 1.0–1.9 for moderate and OR = 1.7, 95%CI: 1.2–2.4 for difficult) were positively associated with inactivity.2. Adolescents living in a neighborhood without sidewalks were 1.3 times more likely to be inactive (OR = 1.3, 95%CI: 1.0–1.6).3. Adolescent boys living in surroundings without vacant fields were 1.7 times (95%CI: 1.2–2.5) more likely to be inactive.4. Unavailability of video game shops near their home was associated with inactivity in boys by 50% (OR = 1.5, 95%CI: 1.1–2.1). 1. Lack of sidewalks and facilities in the community were significantly associated with physical inactivity.2. Having video game shops in the community was associated with increased PA in boys.
2 Li et al. (2008)31 1. The risk of overweight and obesity in adolescents was associated with place of residence. In comparison with those living in a rural area, adolescents in suburban areas (OR = 2.5, 95%CI: 1.8–3.4) or urban areas (OR = 4.0, 95%CI: 2.7–6.0) had greater risks of being overweight/obese.2. Boys living in houses without bike lanes nearby were 1.6 times (95%CI: 1.04–2.40) more likely to be overweight and obese. In urban and suburban areas, adolescents were more likely to be overweight or obese.
3 Wong et al. (2010)32 1. Perceived availability of sport facilities was positively (ORboys = 1.17, ORgirls = 1.26) associated with being sufficiently active.2. A significant interaction effect of perceived availability of neighborhood sport facilities and video game console on the odds of being physically active was found (p = 0.046 for boys and p = 0.049 for girls).3. Perceived availability of sport facilities in the neighborhood was positively associated with the odds of being physically active only in the absence of video game consoles in the home (ORboys = 1.26, 95%CI: 1.10–1.46, p = 0.001; ORgirls = 1.34, 95%CI: 1.15–1.56, p < 0.001).4. No significant associations were found between perceived availability of sport facilities and being physically active in the presence of video game console (ORboys = 1.01, 95%CI: 0.84–1.21, p = 0.94; ORgirls = 1.06, 95%CI: 0.81–1.41, p = 0.63). 1. Perceived availability of sport facilities in the neighborhood may have a positive impact on adolescents’ level of PA.2. Having a computer/Internet may cancel out the effects of active opportunities in the neighborhood.
4 Xu et al. (2010)33 1. Compared with students from the lower residential density tertile, students in the higher and middle residential tertile had lower odds (OR = 0.64, 95%CI: 0.42–0.97; OR = 0.83, 95%CI: 0.59–1.18, respectively) of being in the higher PA category, after adjusting for potential confounding variables including sedentary behavior time and greenspace.2. Students in the higher residential density tertile spent significantly less time on PA but more time on sedentary behaviors (p < 0.01). There may be a negative association between population density and recreational PA for adolescents in a rapidly expanding urban area of China. Students in the higher residential population density tertile were also more likely to be overweight compared with their counterparts in the low population density areas.
5 Xu et al. (2010)34 Students in the higher and middle tertiles of residential density had a 1.87-fold (95%CI: 1.23–2.85) and 1.71-fold (95%CI: 1.12– 2.61) higher likelihood of being overweight, respectively, compared with those in the lower tertile. Students in the higher residential density tertile spent significantly less time on PA and more time on sedentary behaviors than their counterparts in the lowest residential density areas. Residential density was positively associated with overweight among urban Chinese adolescents.
6 Cui et al. (2011)35 1. In urban areas, after adjusting for other cofactors, children whose school was not in the community, were nearly twice (aOR = 1.94, 95%CI: 1.42–2.66) as likely to passively commute as were children whose school was in the local community.2. In rural areas, children whose school was not in the community were nearly 4 times more likely (aOR = 3.73, 95%CI: 2.81–4.96) to passively commute to school than were children whose school was in the community. Children whose school was not in the local community were more likely to commute passively.
7 Huang et al. (2013)36 1. For girls, positive associations with MVPA were found in sports facilities in the neighborhood in the univariable analyses (β = 0.37, 95%CI: 0.01–0.73, p < 0.05).2. For boys, higher density was associated with higher TV viewing time in the crude model (β = 1.02, 95%CI: –0.05 to 2.10, p < 0.1), density was positively associated with Internet use and e-games playing in hierarchical model (β = 1.16, 95%CI: 0.24–2.08, p < 0.05). 1. There were no significant associations between neighborhood environmental variables and children's PA.2. Boys living in a neighborhood with higher population density reported more time using the Internet and playing e-games.
8 An et al. (2014)37 Compared with those living further away, individuals within 10-min walking distance to an exercise facility were 6.79% (95%CI: 3.67–10.01) more likely to have some leisure-time PA. Proximity to an exercise facility was found to be positively associated with leisure time PA.
9 He et al. (2014)38 Factors perceived as PA facilitators included sufficient lighting, bridge or tunnel, convenient transportation, subway station, recreation grounds, shopping malls with air conditioning, and perfume shops. Factors perceived as PA barriers included hard to find toilets in shopping malls and too many people in recreation grounds.
10 Jia et al. (2014)39 1. Roads and aesthetics scores were significantly positively correlated with walking path use (OR = 1.044, 95%CI: 1.017– 1.072).2. Participants who reported the distance from neighborhood to a park >1 km were less likely to use the walking paths (OR = 0.703, 95%CI: 0.530–0.933).3. In males, the roads and aesthetics scores were positively and significantly correlated with walking path use (OR = 1.078, 95%CI: 1.036–1.123). Those who reported a distance to a park <1km and those who perceived better roads and aesthetics were more likely to have used walking paths.
11 Li et al. (2014)40 There was a relationship between land-use-mix access and childhood overweight (OR = 1.22, 95%CI: 0.85–1.75, p > 0.05). No evidence was found for associations between perceived neighborhood environmental characteristics and children's weight status and obesogenic behaviors in this study.
12 Wong et al. (2014)41 Increased perceived availability of sport facilities from baseline to follow-up predicted more leisure-time PA at follow-up (β = 1.029, 95%CI: 1.011–1.047, p < 0.01) overall. This effect was modified by baseline PA, with a significant effect observed only among those who had engaged in leisure-time PA >3 times a week (β = 1.068, 95%CI: 1.022–1.116, p < 0.01). Increasing awareness of neighborhood sport facilities or building more such facilities may help active adolescents to maintain or increase their leisure-time PA. No significant interactions of sex, school grade, and weight status were found with the perceived availability of neighborhood sport facilities.
13 Guo et al. (2014)42 Participants who spent ≥30 min in commuting time to the nearest sport facility had 80% odds (OR = 0.80, 95%CI: 0.65–0.98) of meeting the PA recommendation compared with those who spent <10 min. For every 10-min increment in commuting time, the odds were decreased by 6% (OR = 0.94, 95%CI: 0.88–0.99). An inverse association between the commuting time to the nearest sport facility and the likelihood of meeting the PA recommendation was found.
14 Liu et al. (2015)43 Associations and relative importance of proportion of residential greenspace on citizens’ park visitation (β = −0.17, p = 0.010), shortest road distance to nearest park on citizens’ park visitation (β = −0.12, p = 0.020), walking time to nearest park on citizens’ park visitation (β = −0.11, p = 0.010). The proportion of residential greenspace, walking time to the nearest park, and distance grade (with the grade increasing with the distance of the shortest route between the residence and the nearest park) were negatively correlated with monthly park visitation.
15 Zhang et al. (2015)44 1. The less time to reach a park, the higher the satisfaction level of use of urban greenspace for PA (β = −0.42, SE = 0.07).2. The more availability of greenspaces and parks, the higher the satisfaction level (β = 0.19, SE = 0.09).3. The higher the quality of the vegetation, the higher the satisfaction level (β = 1.49, SE = 0.09). Quality of vegetation, accessibility of greenspaces, and availability of greenspaces and parks significantly affected the residents’ satisfaction levels and use of urban greenspaces for PA.
16 Wong et al. (2016)45 Attractive natural sights in the neighborhood were associated with objectively assessed %MVPA (coefficient = 0.101, SE = 0.042). 1. Children living in a neighborhood farther away from a park and with fewer trees were more likely to be obese. (β = 0.863, 95%CI: 0.210 to −1.517; β = −0.345, 95%CI: −0.655 to −0.035).2. The influence of neighborhood variables seemed to exist only for children with less educated parents. Children living in a neighborhood with more attractive buildings or natural sights spent more time in PA. 1. The aesthetics of the neighborhood environment (presence of trees) were related to a reduced risk of being obese.2. The likelihood of being obese was positively associated with nearest distance to a park in the neighborhood, but only in children with parents who had completed a secondary education.
17 Gomes et al. (2017)46 Children with access to a gymnasium outside the school hours complied more with the MVPA guidelines (RR = 1.14). The availability of a school gymnasium outside of school hours was positively associated with children's MVPA compliance.
18 Liu et al. (2017)47 The number of parks within 500 m was associated with PA (β = 1.2, St. β = 0.1, p = 0.046). Park accessibility was significantly and positively correlated with residents’ PA.
19 Wang et al. (2017)48 Children who reported high scores on availability of sports clubs and organizations (β = 4.489, p < 0.001) and convenient access to PA facilities (β = 5.278, p < 0.001) were significantly more likely to report high levels of MVPA. Children's perceptions of availability of clubs and organizations and convenient access to PA facilities were associated with a high level of participation in MVPA
20 Yang et al. (2017)49 Distance to school was strongly associated with ATS with a dose-response relationship and statistical significance (p < 0.001). Compared with children who lived >2 miles away from school, those who lived the closest (<0.5 miles), the second closest (0.5–1.0 mile), and the third closest (1–2 miles) were 41, 26, and 8 times as likely to engage in ATS, respectively (OR = 40.6, 95%CI: 24.7–67.0; OR = 26.0, 95%CI: 18.6–36.4, and OR = 8.10, 95%CI: 6.33–10.40). 1. The decrease of ATS was concurrent with the increase of children living a farther distance from school.2. Children who were living in a metropolitan area were less likely to report ATS.
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Abbreviations: aOR = adjusted odds ratio; ATS = active travel to and from school; CI = confidence interval; OR = odds ratio; MVPA = moderate-to-vigorous physical activity; PA = physical activity; RR = risk ratio; SE = standard error.

In addition to exercise facilities, other environmental characteristics were found to be associated with PA. For instance, adolescents living in residential neighborhoods without sidewalks or without vacant fields were 30% and 70% more likely to be physically inactive, respectively.30 The proportion of residential greenspace was found to be negatively correlated with monthly park visits.43 Adolescents living in a neighborhood with a higher residential density were more likely to spend less time on PA and more time on sedentary behaviors such as television viewing.33,36 Among the 3 studies that estimated the longitudinal relationship between the built environment and body weight status, 2 reported a statistically significant association, whereas the remaining study reported no association. Boys living in places lacking bike lanes were 60% more likely to be overweight or obese.31 Adolescents living in a higher residential density area were 87% more likely to be overweight.34 In contrast, no association was found between neighborhood mixed land use and childhood overweight.40

3.4. Study quality assessment

Table1 reports criterion-specific and global ratings from the study quality assessment. The included studies on average scored 7 out of 14 (range: 5–9). All studies included in the review clearly stated the research question/objective, specified and defined the study population, had a participation rate of ≥50%, recruited participants from the same or similar populations during the same time period, and prespecified and uniformly applied inclusion and exclusion criteria to all potential participants. Most studies measured and statistically adjusted key potential confounding variables for their impact on the relationship between exposures and outcomes (n = 16), had an attrition rate of ≤20% (n = 17), and examined different levels of the exposure in relation to the outcome (n = 10). In contrast, none of the studies had the outcome assessors blinded to the exposure status of the participants. Only 1 study provided a sample size justification using power analysis. Exposures of interest (e.g., built environment and other environment characteristics) before the outcomes were measured in 3 studies. A total of 3 studies had a reasonably long follow-up period that was sufficient for changes in outcomes to be observed, 4 studies assessed the exposures more than once during the study period, and 6 studies implemented valid and reliable exposure measures.

4. Discussion

This analysis systematically reviewed scientific evidence regarding the influence of neighborhood built environment on PA and body weight status among Chinese children and adolescents. A total of 20 studies, including 16 cross-sectional, 3 longitudinal, and 1 descriptive, met the selection criteria and were included in this review. A total of 13 studies adopted subjective built environment measures reported by parents and/or children, 2 adopted objective measures (e.g., geographical information systems, field observations), and 5 adopted both objective and subjective measures. PA behaviors included PA, physical inactivity, sedentary behavior, active/passive commuting from/to school, and park visits. Body weight status included BMI overweight, and obesity. The 16 studies that provided some quantitative estimates of the influence of built environment on PA and body weight status all reported a statistically significant relationship in the expected direction.

Findings from this review confirmed the documented relationship between neighborhood built environment and PA among children and adults in developed countries. For example, Reed and Phillips50 reported increased duration and intensity of PA among American college students residing close to exercise facilities. Jilcott et al.51 found that perceived distance to gyms was negatively associated with PA among low-income, middle-aged women in the United States. Hanibuchi et al.52 documented that proximity to parks or greenspaces was positively associated with PA frequency among older Japanese adults. Edwards et al.53 reported that proximity to parks and beaches was positively associated with use of those environments for PA among Australian adolescents. Findings from this review add a data point to the literature on the importance of designing new physical environments and/or modifying existing environments in a way that promotes an active lifestyle for children and adolescents in China.

Childhood obesity in China has reached an alarmingly high level. It has become critical that interventions be designed to target the root causes of obesity and its risk factors. Physical inactivity is a major modifiable risk factor for childhood obesity.54 A majority of children and adolescents in China fail to meet the recommended guidelines for PA levels.55–57 Although the traditional perspective mainly blamed individuals themselves for their sedentary lifestyles, increasing attention has now shifted to the environmental determinants of health behavior, in particular, how neighborhood built environments may impact local residents’ engagement with PA. Two systematic reviews have investigated the associations between various built environment attributes and childhood obesity.56, 58 Neighborhood features such as walkability, bikeability, and proximity to parks and recreational facilities were found to be associated with increased PA and active commuting. Interventions providing safe, walkable neighborhood with access to necessary destinations have been recommended in an effort to combat the epidemic of childhood obesity. A recent study reviewed the built environmental correlates of PAin China and documented a positive association between PA and proximal nonresidential locations, pedestrian infrastructure, aesthetics, and recreational facilities, as well as a negative association between PA and living in an urban setting.26 Findings from that review are largely consistent with ours, except that our review focused more on the built environment as it pertained to locations for children's daily activities (e.g., parks, greenspaces, and recreational facilities), whereas the afore mentioned review, which predominantly examined PA among Chinese adults aged ≥18 years, focused more on work-related built environments PA (e.g., transportation systems and proximity to the workplace).26 Our review adds to the scientific evidence by systematically identifying and synthesizingliterature regarding the impact of neighborhood environments on child and adolescent populations in China.

Improving the availability of local exercise facilities in communities has become a national policy, as highlighted in China's 13th Five-Year Plan (2016–2020) for the Sport Industry.59 The plan includes building new exercise facilities (e.g., community fitness centers, parks, roadside open spaces with exercise equipments) and enhances the accessibility of existing facilities (e.g., extending the operating hours of school gyms/playgrounds and opening them to the public). Findings from this review suggest that these policy-level interventions can enhance PA engagement among Chinese children and adolescents. In the meantime, urban designs that incorporate sidewalks, bike lanes, and walking paths, reduce motorized traffic, and encourage lower population densities are likely to promote PA and prevent childhood obesity in China.

A few limitations pertaining to this review and the studies it includes should be noted. All studies adopted an observational study design. The absence of experimental designs prevents a causal interpretation regarding the impact of neighborhood built environment on PA and body weight status; therefore, the estimated relationships are correlational in nature. In addition, only 3 of the 20 studies included in this review adopted a longitudinal study design, which is necessary to examine the change in PA or body weight in response to changes in built environment. No studies included in this review reported quantitative estimates focusing on the same built environments, physical activities, and body weight measures, which presented a situation that precluded meta-analysis. The literature search identified articles written in English only and excluded articles written in Chinese and other languages. This decision was made in part owing to concerns about potential discrepancies in study quality, as well as concerns about the lack of heterogeneity in built environment measures and study designs existing in the literature written in English versus Chinese. Also, this review only included published literature; thus, useful and relevant unpublished studies may have been overlooked. Future reviews could explore the unpublished, or “grey” literature to determine whether it is useful in building on our findings.

5. Conclusion

This study systematically reviewed the scientific evidence regarding the influence of neighborhood built environment on PA and body weight status among Chinese children and adolescents. A total of 20 studies met the selection criteria and were included in the review. Availability, accessibility, andproximity to greenspaces, parks, recreational facilities, and sidewalks were associated with increased PA, reduced sedentary behavior, and/or active commuting. In contrast, the absence of bike lanes and living in higher density residential areas were associated with increased likelihood of childhood obesity. Research on the environmental correlates of PA in China and other countries is rapidly increasing, so future reviews may be able to analyze more studies in this field. Future reviews should also include articles written in Chinese and other languages, as well as unpublished studies in the “grey” literature. Longitudinal or experimental study designs and objective environmental and PA measures should also be part of future research into the influence of the neighborhood built environment on PA and body weight status among children and adolescents in China.

Authors’ contributions

RA conceived of the study, conducted the systematic review, and wrote the manuscript; JS and QY contributed to literature review, data synthesis, and manuscript drafting; YY contributed to data synthesis and manuscript drafting. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Peer review under responsibility of Shanghai University of Sport.

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