Associations between Children’s Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review
Abstract
:1. Introduction
2. Methods
2.1. Information Sources, Search Terms, and Search Strategy
2.2. Eligibility Criteria
2.3. Selection of Sources of Evidence
2.4. Quality Assessment
2.5. Data Charting and Synthesis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Author (Year); Country | No. of Participants; Sex (% Female); Age in Years (y) | Socio-Economic and Ethnicity Characteristics | Physical Activity Outcome(s) | Key Findings * | MMAT Score |
---|---|---|---|---|---|
Boone-Heinonen and Gordon-Larsen [34]; USA | 12,701 at both time points; 51%; 11–22 y in wave 1 (1994/1995), 18–26 y in wave 3 (2001/2002) | Parental household income at wave 1 (mean ± SD): USD43,100 ± 1500; Ethnicity: White 68%, Black 16%, Asian 4%, Hispanic 12%; Highest parental education: Some college or higher 54% | MVPA (self-reported weekly frequency of skating, cycling, exercise, and active sports at wave 1, modified at wave 3 to include age-appropriate activities) | MVPA was positively related to landscape diversity in all participants, and negatively related to street connectivity in females only. | 4 |
Boone-Heinonen, Guilkey [35]; USA | 12,701 at both time points; 51%; 11–22 y in wave 1 (1994/1995) 18–26 y in wave 3 (2001/2002) | Parental household income at wave 1 (mean ± SD): USD43,100 ± 1500 ; Ethnicity: White 68%, Black 16%, Asian 4%, Hispanic 12%; Highest parental education: <High school 15%, High school/GED 31%, Some college 29%, College or greater 25% | MVPA (self-reported weekly frequency of skating, cycling, exercise, and active sports at wave 1, modified slightly at wave 3 to include age-appropriate activities) | MVPA was higher with greater PA pay facilities in males only. | 5 |
Boone-Heinonen, Popkin [36]; USA | 17,659; 50%; 11–22 y | Significant differences in income tertile by urbanicity, higher proportion of tertile 1 and lower proportion of tertile 3 in high-urban compared with lower-urbanized areas (direction of tertiles was unclear); Urbanicity: non-urban 39%, low-urban 36%, high-urban 24%. Significant differences in education level by urbanicity, lower education levels in high-urban compared with lower-urbanized areas | MVPA (self-reported weekly frequency of skating, cycling, exercise, and active sports) | MVPA was associated with intersection density in 1 km buffer (all in non-urban areas, males in high-urban areas), and count of PA resources in 3 km buffer (all in low-urban areas). Associations with weighted counts were similar to counts within 1–5 km. | 5 |
Bringolf-Isler, Grize [37]; Switzerland | 1081; 49% (children), 54% (adolescents); 6–7 y, 9–10 y, 13–14 y | Maternal education: low 16%, medium 48%, high 36%; Car ownership: none 20%, one 66%, two or more 16% | Vigorous outdoor play (parent-reported, daily average) | Vigorous outdoor play was negatively associated with main street density, population, and building density in the 100 m home buffer. Comparable results were observed using 100 m, 200 m, and 500 m buffers. | 2 |
Buck, Kneib [38]; Germany | 400; 52%; 2–9 y | Children living in urban environments only | MVPA (min/day, accelerometer) | MVPA was associated with the availability of public open spaces (school girls, pre-school children), public transit (school girls), and higher street connectivity (school girls). Stable results were found within a network-distance using kernel intensity measures from 750 m up to 1.5 km for school children and from 500 m up to 1 km for pre-school children. Different results were observed by buffer size using the simple intensity approach. | 4 |
Cain, Millstein [39]; USA | 955; 50%; 6–16 y | Based on the recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: non-White 31% (children) and 33% (adolescents); Parent education: college degree 68% (children) and 64% (adolescents) | Walking and cycling to specified locations (parent-reported for children, self-reported for adolescents; average of usual frequency of trips) Average daily minutes of MVPA (accelerometer, non-school hours) Average daily minutes of MVPA in the neighborhood (children only, linking parent-completed daily location logs to accelerometer data, non-school hours) Neighborhood PA (parent-reported for children, self-reported for adolescents) | After adjusting for GIS-defined walkability, numerous observed microscale environmental variables were related to active travel and neighborhood PA. | 3 |
Carlson, Mitchell [40]; USA | 528; 50%; 12–16 y | 50% resided in high-income neighborhoods; Ethnicity: White non-Hispanic 70%; Home neighborhood walkability: high-walkability 46% | Time and MVPA in specified locations (GPS and accelerometer) | MVPA in one location was mainly independent of MVPA in other locations (i.e., no compensation effect), except for higher at-school MVPA (less at home and other location MVPA) and higher home neighborhood MPVA (more at home MVPA). | 4 |
Carlson, Saelens [41]; USA | 690; 51%;12–16 y | Based on the recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: White non-Hispanic 69%; Highest parental education: college degree 64%; Marital status: Parent married or living with partner 84.0%; Household car ownership (mean ± SD): 2.5 ± 1.0 | Walking and cycling travel time (min/day, GPS and accelerometer) Active travel mode share (daily walking + cycling minutes/total daily travel time × 100, GPS and accelerometer) | Walking time and active travel mode share were associated with residential density, intersection density, entertainment density, and walkability. Cycling time was associated with intersection density and walkability. | 3 |
Carlson, Sallis [42]; USA | 294; 47%; 12–16 y | Based on the recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: Non-Hispanic Caucasian 69%; Highest parental education: college degree 62%; Marital status: married or living with partner 85%; Parental employment status: full-time 53%; Vehicles/driver in household (mean ± SD): 1.07 ± 0.39 | Active travel to/from school on average school week (self-reported), classified as none, occasional (1–4 trips), and habitual (5–10 trips) | Active travel to/from school was positively associated with street connectivity around home, residential density around home, and residential density around school; and negatively associated with distance to school. The odds of travelling actively occasionally or habitually reduced to 0.60 and 0.24, respectively, for every additional km in distance to school. | 4 |
Carroll-Scott, Gilstad-Hayden [43]; USA | 1048; 53%; Mean ± SD: 10.9 ± 0.8 y | Free/reduced lunch eligibility 77%; Not food-secure 11%; Ethnicity: Non-white 89%; Primary language at home: not English 38% | Frequency of exercise (self-reported, PACE PA item) Number of hours of weekday screen time (usual duration of TV, video game, and computer (for fun) time on weekdays) | Screen time was negatively associated with living in more affluent neighborhoods. | 4 |
Carver, Timperio [44]; Australia | 446 at both time points; 49% (children), 57% (adolescents); 8–9 y and 13–15 y in 2004; 10–11 y and 15–17 y in 2006 | Primary language at home: not English 38% | Frequency of walking/cycling trips per week, change in walking/cycling trips per week over time (parent-reported for children, self-reported for adolescents) Mean minutes per day of MVPA and change over time (accelerometer) | Change in active travel was associated with the number of traffic/pedestrian lights (in younger girls), length of walking tracks (younger and adolescent girls), and intersection density (adolescent boys). Change in MVPA was associated with slow points (younger boys before school) and speed humps (adolescent boys after school). | 3 |
Carver, Timperio [45]; Australia | 640 (411 primary school-aged, 229 secondary school-aged); 51%; Mean ± SD: 11.6 ± 2.0 y | Primary-school-aged: urban 72% urban; Secondary-school-aged: urban 50% | Cycling at least once per week (self-reported) | The odds of cycling at least once per week were negatively associated with the number of sports facilities within the 5 km buffer and positively associated with living in neighborhoods with the top tertile of length of bike paths in 5 km buffer. | 3 |
Coughenour and Burns [46]; USA | 71 (26 children aged 6–18 y); 66%; 6–94 y | Annual household income (N = 42 out of 44, 95%): <USD75,000; Ethnicity: Hispanic 39%; Black 27%; Caucasian 25% | MVPA (parent-reported moderate vigorous PA, such as “pushing a vacuum or climbing 1 flight of stairs” and “running, lifting heavy objects, and strenuous sports”) | No significant difference was observed in those meeting weekly PA recommendations by opportunities for PA in the neighborhood. | 3 |
Dalton, Longacre [47]; USA | 1552; 52%; 12–17 y | Annual household income: >USD$75,000 38%; Ethnicity: Non-Hispanic Caucasian 92%; Highest parental education: bachelor degree or higher 38%; Single-parent household 19% | Active travel to/from school, defined as walking or biking to or from school at least 1 day per week during one or more season (self-reported) | Active school travel was associated with higher residential and intersection densities and lower distance to school (81% who lived within 1 mile of school were active travelers versus 30% of those who lived 2–3 miles from school). | 4 |
De Meester, Van Dyck [48]; Belgium | 637; 50%; 13–15 y | Education level: college degree or higher 61%; Employment status: both parents employed 69% | MVPA and average counts/min (accelerometer), duration of PA behaviors in specific contexts and school-related active travel (self-reported) | MVPA and average PA counts/min were associated with walkability in adolescents residing in low SES areas only. Walking for transport during leisure time was negatively associated with neighborhood SES. | 3 |
Dessing, de Vries [49]; The Netherlands | 184; 53%; 8–12 y | NR | Built environment characteristics (comparison of variables between route measures) Active travel to school | Children mainly traveled through residential areas on their way to school (>80% of the route). Actual walking routes had less traffic area, greater % water along route, fewer street lights/km, fewer zebra crossings, and lower % sidewalk along the route than estimated routes. Actual cycling routes had greater % recreational area, % water along route, traffic lights/km, junctions/km, % residential streets, fewer trees/km, street lights/km, street bumps/km, and zebra crossings/km, and lower % sidewalk along route and % pedestrian path than estimated routes. | 3 |
DeWeese, Ohri-Vachaspati [50]; USA | 404; 48%; 3–18 y | Ethnicity: Non-Hispanic black 49%, Hispanic 44%, Non-Hispanic Caucasian 7%; Highest parental education: some college or higher education 31%; Average block group median income (mean ± SD): ~USD36,900 ± 16,200 | Parent reported PA behaviors (categorized as 60-min of PA on 7 days per week vs. <7 days; ever walked or biked to school vs. never; walked to destinations often vs. sometimes, rarely, or never) | Three classes were identified and characterized: (1) “Low PA-Low Food” (N = 72, 17% of sample) had the lowest probability for above-median residential dwellings and intersections, and for the presence of a PA facility, supermarket, small grocery store, convenience store, and fast-food restaurant and a high probability of the presence of a large park; (2) “High Intersection & Parks-Moderate Density & Food” (34%) had the highest probability for above median intersections and for the presence of large parks, and low probabilities of having a PA facility, a supermarket, and a small grocery store; (3) “High Density- Low Parks-High Food” (49%) had the highest probability of above-median residential dwellings and the presence of PA facilities, supermarkets, small grocery stores, convenience stores, and fast-food restaurants, and had the lowest probability for a large park presence. Children in the High Density-Low Parks-High Food class had higher odds of walking or biking to school and to other destinations compared to children in the Low PA-Low Food class, before adjusting for covariates. Neither healthy nor unhealthy food intake differed across classes. | 5 |
Helbich, Emmichoven [51]; The Netherlands | 97; 60%; 6–11 y | NR | Active travel to/from school (estimated from GPS), environmental characteristics of school route travelled | Active school travel was negatively associated with distance when only personal, traffic safety, and weather features were considered. After adjusting for urban environments, the distance to school was not significant; well-connected streets and % cycling lanes were positively associated with active school travel. | 2 |
Hinckson, Cerin [52]; Aotearoa New Zealand | 524; 55%; 12–18 y | 50% resided in high-income neighborhoods; Ethnicity: Māori (indigenous to Aotearoa New Zealand) 3%, New Zealand European 70%, Pacific 2%, Asian 12%, Other 13%; Household highest educational attainment: post-school qualification or higher 70% | MVPA and sedentary time (average min/day, accelerometer) | MVPA was associated with residential density and number of parks within 2 km from home independently, and also when combined into an objective environmental index of activity-friendliness. | 3 |
Ikeda, Hinckson [53]; Aotearoa New Zealand | 542; 51%; 8–13 y | Ethnicity: Māori 12.9%, New Zealand European 52.7%, Pacific 15.3%, Asian 15.0%; Education level: bachelor’s degree or higher 30.0%; Car ownership: >1 63.8% | Usual mode of travel to school (self-reported and dichotomized to active or passive travel) | Active school travel was negatively associated with the distance to school. Full mediation of the association between the active mobility environment occurred through the distance to school. All indicators of the active mobility environment were negatively correlated with the distance to school. | 4 |
Islam, Moore [54]; Bangladesh | 109; 39%; 9–14 y | Monthly household incomes: Taka25,001–40,000 41% (urban average household monthly income: Taka9878); Education level: bachelor’s degree (father) 59%, bachelor’s degree (mother) 50%; Average residency: 6.5 y | Children’s outdoor activities (self-reported average time outdoors in past week, calculated using reported start and end times of outdoor activities within the neighborhood) | Average time outdoors on weekdays was negatively associated with total building footprint area within the neighborhood. | 4 |
Jauregui, Soltero [55]; Mexico | 1191; 53%; 6–14 y | Household income: Mexican peso <5000 50% (income data available for 59% of participants) | Active school travel (parent-reported usual mode of travel to school, walking or biking classified as active school travel) | Active school travel was associated with lower walkability in the 400 m buffer only. | 3 |
Kyttä, Broberg [15]; Finland | 1837; 49%; 10–15 y | Housing: detached house 37%, apartment building 33%, terraced house 30%; Household car ownership: 92% | Active school travel (self-reported walking or cycling normally used on journeys both to and from school), parental licenses for their child’s independent mobility, and territorial range (distance to the furthest marked place the child travelled to independent of parental supervision), dichotomized as above versus below the within-age group mean | Active school travel was positively related to residential density, and negatively related to the proportions of green space and child population. The distance from home to children’s meaningful places decreased as the residential density increased and increased as the proportion of green space and child population increased. Meaningful places of children were located close to home: 16.6% were ≥ 50 m from home, 24.8% were within 100 m, and 53.3% within 0.5 km of home. The size of the territorial range was significantly higher in more green areas, and lower in areas with a higher child population. Children had significantly more limitations on mobility licenses if the child’s home was in a more densely built area. A significant correlation was found between the number of marked destinations and school travel mode. The size of territorial range was positively correlated with active school travel mode. | 3 |
Laxer and Janssen [56]; Canada | 6626; 50%; 11–15 y | Household SES: high 24%, medium-high 32%, low-medium 35%, low 10%; Ethnicity: Caucasian 73%, Other 27% | PA (self-reported usual and past 7 days number of days physically active for ≥60 min/day, dichotomized as physically inactive (≤4 days/week) or physically active (>4 days/week)) | Physical inactivity was higher in neighborhoods with higher walkability, lower cul-de-sac density, and moderate to high park space. An estimated 23% of physical inactivity within the population was attributable to living in walkable neighborhoods, 16% was attributable to living in neighborhoods with a low density of cul-de-sacs, and 15% was attributable to living in neighborhoods with a moderate to high amount of park space. | 3 |
McGrath, Hinckson [57]; Aotearoa New Zealand | 226; 51%; 5–13 y | Income per adult (mean ± SD): NZD39,000 ± 2000; Ethnicity: Māori/Polynesian 17%, European/other 72%, Asian 11% | School travel mode (parent-reported 7-day travel log), classified as passive (car or bus) or active (walk, bicycle, skateboard, or scooter); MVPA, step-based MVPA, hourly step counts (accelerometer) | MVPA steps on non-school days were associated with living in neighborhoods with more green space (positive) and food outlet density (negative). | 5 |
Mecredy, Pickett [58]; Canada | 8535; NR; 11–15 y | NR | MVPA outside of school (self-reported usual hours of exercise in free time) categorized as ≥4 h/week or <4 h/week. | Higher MVPA was associated with residing in neigborhoods with the highest street connectivity quartile. | 5 |
Mitchell, Clark [59]; Canada | 435; 59%; 9–14 y | Median family income: CAD71,758 | Average daily MVPA during non-school hours (accelerometer) | MVPA out of school hours was associated with parks with sports fields and multi-use path space at both buffers in grouped analyses. Significant associations were observed between boys’ MVPA and parks with sports fields (positive) and parks with playgrounds (negative) at both buffers (although the magnitudes were greater for 800 m), and girls’ MVPA and parks with sports fields (positive, 800 m buffer only). | 3 |
Mölenberg, Noordzij [60]; The Netherlands | 1841 (N = 1607 for outdoor play, N = 1545 for sedentary behavior); 56% (intervention group), 49% (control group); Mean 6 y at wave 1, 9.7 y at wave 2 | Net household income/month: ≤€2000 18% (intervention) and 15% (control), >€2000–€3200 34% (intervention) and 27% (control), >€3200 48% (intervention) and 58% (control); Ethnicity: Dutch 60% (intervention) and 70% (control), Other Western 13% (intervention) and 12% (control), Non-Western 27% (intervention) and 18% (control); Maternal education: mid-high 54% (intervention) and 63% (control); Paternal education: mid-high 54% (intervention) and 62% (control) | Outdoor play (parent-reported exercise at school and outside school hours for an average week, calculated as mean min/week playing outdoors), sedentary behavior (parent-reported television viewing and computer game use for an average week, calculated as mean min/week watching television and computer gaming | The introduction of a dedicated PA space within 600 m from home, and the reduction in the distance per 100 m, did not affect outdoor play or sedentary behaviors. | 4 |
Nordbø, Raanaas [61]; Norway | 21,146; 49%; 8 y | Maternal university education: 78% | Leisure-time PA (parent-reported time in PA outside school hours), classified as ≥5 h/week or ≤ 4 h/week. Organized PA participation (parent-reported days/week participation in any kind of organized leisure PA), classified as ≥2 days/week or once a week or less. Informal social activity with friends and peers (parent-reported time with friends and peers, excluding school hours and organized activities), classified as ≥2 days/week or once a week or less. | Leisure-time PA was associated with having a park within 800 m from home (summer) and living in a neighborhood with a higher proportion of green space (winter). Participation in organized and social activities was associated with population density and access to facilities. | 4 |
Oliver, Badland [62]; Aotearoa New Zealand | 217; 49%; 6–15 y | Average annual household income: <NZD60,000 39%, NZD60,001–100,000 25%, >NZD100,000 25%; Ethnicity: Māori 24%, Asian 15%, New Zealand European/Other 60%; Unlimited car access: 87%; Residing in school zone: 56%; Parent neighborhood self-selection: prefer high walkable and live low walkable 31%, prefer high walkable and live high walkable 20%, prefer low walkable and live low walkable 31%, prefer low walkable and live high walkable 19% | Active school travel (self-reported walking or cycling normally used on journeys both to and from school), parental licenses for their child’s independent mobility, and territorial range (distance to the furthest marked place the child travelled to independently) | Active school travel was significantly associated with the city a child lived in and neighborhood self-selection (children who lived in a low-walkable neighborhood, but whose parents preferred a highly walkable neighborhood were three times less likely to have active school travel than their counterparts), and negatively associated with distance to school. | 3 |
Sallis, Cain [63]; USA | 3677 (758 children aged 6–11 y and 897 adolescents aged 12–16 y (findings for these age groups presented here)); NR; 6 y and older | Based on recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: Non-Caucasian 31% (children) and 33% (adolescents) | MVPA (accelerometer; mean daily hours out-of-school hours for adolescents, mean daily MVPA in neighborhood for children (via temporal matching of accelerometer and parent reported times in neighborhood locations)). Active travel to common locations (parent-reported for children, self-reported for adolescents). | Controlling for GIS-derived macro-level walkability, total microscale environment scores were significantly related to active travel in both groups, and with leisure-time PA and accelerometer measures in children. | 3 |
Sallis, Conway [64]; USA | 928; 50%; 12–16 y | Based on the recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: Non-Hispanic Caucasian 66%; Parent education: college degree or higher 74%; Time living at current address (mean ± SD): 12.6 ± 7.0 y; Motor vehicles/licensed driver (mean ± SD): 1.1 ± 0.38 | MVPA, sedentary time (accelerometer), self-reported active travel to school and non-school destinations (e.g., recreation facility, friend’s house, park, food outlet), leisure-time PA in specified locations, number of days accumulated 60 min PA, number of sports and PA classes outside of school, usual time/day in sedentary behaviors | Walkability was positively related to objectively measured PA and walking for transportation. Self-reported sedentary time and television time were negatively related to walkability. The time in vehicles was negatively related to walkability only among those living in higher income census blocks. | 5 |
Tucker, Irwin [65]; Canada | 811; 50%; 11–13 y | Household income: <CAD50,000 21%, CAD50,000–69,999 13%, >CAD70,000 32%; Ethnicity: Caucasian 75%, Black 2%, Latin-American 7%, Asian 6%, Other 9%; Paternal education: college or higher 70%; Maternal education: college or higher 70% | PA (self-reported type and intensity of activity in 30 min blocks throughout the afternoon and evening of the previous day (15:00–23:00) plus blocks of time for morning and afternoon recess, lunch time, and physical education class (with 15 min blocks allocated for the morning and afternoon recess and 30 min blocks allotted for lunch hour and physical education class)). | MVPA was associated with having ≥2 recreational opportunities in the neighborhood | 4 |
van Loon, Frank [66]; Canada | 366; 53%; 8–11 y | Ethnicity: European/North American 44%, East/Southeast Asian 29%, South Asian 11%, Mixed/other 16% | MVPA (daily average, accelerometer) | MVPA was positively associated with commercial density, residential density, number of parks, and intersection density; and negatively associated with the distance to school and recreation sites. When entered as a composite index, these measures accounted for 4.4% in the variation in MVPA for the full sample. Sex-stratified models better explained the relationships between the neighborhood environment and PA. For boys, built and social environment characteristics of neighborhoods accounted for 8.7% of the variation in MVPA, and for girls, neighborhood factors explained 7.2% of the variation. Sex stratified models also point towards distinct differences in factors associated with PA, with MVPA of boys associated with wider-ranging neighborhood characteristics than MVPA of girls. For girls, two safety-related neighborhood features were found to be significantly associated with MVPA: cul-de-sac density and proportion of low speed limit streets. | 4 |
Villanueva, Giles-Corti [67]; Australia | 926; 50%; 10–12 y | School-level SES: low 28%, medium 34%, high 38%; Maternal education: less than secondary education 28%, secondary education/trade/diploma 56%, Bachelor degree or higher 16% | Activity spaces: associations included average daily steps (pedometer) and leisure-time PA (parent-reported time in leisure-time PA in previous week) | Activity space size was positively associated with the confidence to travel independently and negatively associated with utilitarian destination availability. For boys, activity spaces were larger if they owned a bike. For girls, activity space size was positively associated with being independently mobile, leisure time PA, and parent confidence in their ability to travel independently, and negatively associated with parents reporting living on a busy road. | 3 |
Wang, Conway [68]; USA | 928; 50%; 12–16 y | Based on the recruitment strategy, approximately half of the participants should be from low income neighborhoods, and half from high income neighborhoods; Ethnicity: Non-Hispanic Caucasian 66%; Parent education: college degree or higher 74%; Time living at current address (mean ± SD): 12.6 ± 7.0 y; Motor vehicles/licensed driver (mean ± SD): 1.1 ± 0.38 | Active travel to/from school and non-school destinations (e.g., recreation facility, friend’s house, park, food outlet), active transport index (sum of z scores for active travel to school and non-school destinations) | GIS-derived neighborhood walkability and the count of nearby parks and recreation facilities (as well as audited streetscape quality using MAPS) had significant main effects in the direction of more PA-supportive built environments associated with more active travel. Significant two-way interactions with GIS-based measures were observed: self-efficacy × GIS- based walkability index, and self-efficacy × GIS-based counts of parks and recreation facilities. In each two-way interaction, the highest active travel was found among adolescents, with the combination of the PA-supportive built environment and positive psychosocial characteristics. | 4 |
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Smith, M.; Mavoa, S.; Ikeda, E.; Hasanzadeh, K.; Zhao, J.; Rinne, T.E.; Donnellan, N.; Kyttä, M.; Cui, J. Associations between Children’s Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review. Int. J. Environ. Res. Public Health 2022, 19, 1033. https://doi.org/10.3390/ijerph19031033
Smith M, Mavoa S, Ikeda E, Hasanzadeh K, Zhao J, Rinne TE, Donnellan N, Kyttä M, Cui J. Associations between Children’s Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review. International Journal of Environmental Research and Public Health. 2022; 19(3):1033. https://doi.org/10.3390/ijerph19031033
Chicago/Turabian StyleSmith, Melody, Suzanne Mavoa, Erika Ikeda, Kamyar Hasanzadeh, Jinfeng Zhao, Tiina E. Rinne, Niamh Donnellan, Marketta Kyttä, and Jianqiang Cui. 2022. "Associations between Children’s Physical Activity and Neighborhood Environments Using GIS: A Secondary Analysis from a Systematic Scoping Review" International Journal of Environmental Research and Public Health 19, no. 3: 1033. https://doi.org/10.3390/ijerph19031033