Patterns of Walkability, Transit, and Recreation Environment for Physical Activity

Marc Adams, Michael Todd, Jonathan Kurka, Terry L. Conway, Kelli L. Cain, Lawrence D. Frank, James F. Sallis

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Introduction Diverse combinations of built environment (BE) features for physical activity (PA) are understudied. This study explored whether patterns of GIS-derived BE features explained objective and self-reported PA, sedentary behavior, and BMI. Methods Neighborhood Quality of Life Study participants (N=2,199, aged 20-65 years, 48.2% female, 26% ethnic minority) were sampled in 2001-2005 from Seattle / King County WA and Baltimore MD / Washington DC regions. Their addresses were geocoded to compute net residential density, land use mix, retail floor area ratio, intersection density, public transit, and public park and private recreation facility densities using a 1-km network buffer. Latent profile analyses (LPAs) were estimated from these variables. Multilevel regression models compared profiles on accelerometer-measured moderate to vigorous PA (MVPA) and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2013-2014. Results Seattle region LPAs yielded four profiles, including low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); moderately high walkability/transit/recreation (MH-MH-MH); and high walkability/transit/recreation (H-HH). All measures were higher in the HHH than the LLL profile (difference of 17.1 minutes/day for MVPA, 146.5 minutes/week for walking for transportation, 58.2 minutes/week for leisure-time PA, and 2.2 BMI points; all p

Original languageEnglish (US)
Pages (from-to)878-887
Number of pages10
JournalAmerican Journal of Preventive Medicine
Volume49
Issue number6
DOIs
StatePublished - Dec 1 2015

Fingerprint

Recreation
Geographic Mapping
Baltimore
Leisure Activities
Walking
Cluster Analysis
Buffers
Quality of Life

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Epidemiology

Cite this

Patterns of Walkability, Transit, and Recreation Environment for Physical Activity. / Adams, Marc; Todd, Michael; Kurka, Jonathan; Conway, Terry L.; Cain, Kelli L.; Frank, Lawrence D.; Sallis, James F.

In: American Journal of Preventive Medicine, Vol. 49, No. 6, 01.12.2015, p. 878-887.

Research output: Contribution to journalArticle

Adams, Marc ; Todd, Michael ; Kurka, Jonathan ; Conway, Terry L. ; Cain, Kelli L. ; Frank, Lawrence D. ; Sallis, James F. / Patterns of Walkability, Transit, and Recreation Environment for Physical Activity. In: American Journal of Preventive Medicine. 2015 ; Vol. 49, No. 6. pp. 878-887.
@article{88fb43d766b549148b1e749c617b6fad,
title = "Patterns of Walkability, Transit, and Recreation Environment for Physical Activity",
abstract = "Introduction Diverse combinations of built environment (BE) features for physical activity (PA) are understudied. This study explored whether patterns of GIS-derived BE features explained objective and self-reported PA, sedentary behavior, and BMI. Methods Neighborhood Quality of Life Study participants (N=2,199, aged 20-65 years, 48.2{\%} female, 26{\%} ethnic minority) were sampled in 2001-2005 from Seattle / King County WA and Baltimore MD / Washington DC regions. Their addresses were geocoded to compute net residential density, land use mix, retail floor area ratio, intersection density, public transit, and public park and private recreation facility densities using a 1-km network buffer. Latent profile analyses (LPAs) were estimated from these variables. Multilevel regression models compared profiles on accelerometer-measured moderate to vigorous PA (MVPA) and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2013-2014. Results Seattle region LPAs yielded four profiles, including low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); moderately high walkability/transit/recreation (MH-MH-MH); and high walkability/transit/recreation (H-HH). All measures were higher in the HHH than the LLL profile (difference of 17.1 minutes/day for MVPA, 146.5 minutes/week for walking for transportation, 58.2 minutes/week for leisure-time PA, and 2.2 BMI points; all p",
author = "Marc Adams and Michael Todd and Jonathan Kurka and Conway, {Terry L.} and Cain, {Kelli L.} and Frank, {Lawrence D.} and Sallis, {James F.}",
year = "2015",
month = "12",
day = "1",
doi = "10.1016/j.amepre.2015.05.024",
language = "English (US)",
volume = "49",
pages = "878--887",
journal = "American Journal of Preventive Medicine",
issn = "0749-3797",
publisher = "Elsevier Inc.",
number = "6",

}

TY - JOUR

T1 - Patterns of Walkability, Transit, and Recreation Environment for Physical Activity

AU - Adams, Marc

AU - Todd, Michael

AU - Kurka, Jonathan

AU - Conway, Terry L.

AU - Cain, Kelli L.

AU - Frank, Lawrence D.

AU - Sallis, James F.

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Introduction Diverse combinations of built environment (BE) features for physical activity (PA) are understudied. This study explored whether patterns of GIS-derived BE features explained objective and self-reported PA, sedentary behavior, and BMI. Methods Neighborhood Quality of Life Study participants (N=2,199, aged 20-65 years, 48.2% female, 26% ethnic minority) were sampled in 2001-2005 from Seattle / King County WA and Baltimore MD / Washington DC regions. Their addresses were geocoded to compute net residential density, land use mix, retail floor area ratio, intersection density, public transit, and public park and private recreation facility densities using a 1-km network buffer. Latent profile analyses (LPAs) were estimated from these variables. Multilevel regression models compared profiles on accelerometer-measured moderate to vigorous PA (MVPA) and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2013-2014. Results Seattle region LPAs yielded four profiles, including low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); moderately high walkability/transit/recreation (MH-MH-MH); and high walkability/transit/recreation (H-HH). All measures were higher in the HHH than the LLL profile (difference of 17.1 minutes/day for MVPA, 146.5 minutes/week for walking for transportation, 58.2 minutes/week for leisure-time PA, and 2.2 BMI points; all p

AB - Introduction Diverse combinations of built environment (BE) features for physical activity (PA) are understudied. This study explored whether patterns of GIS-derived BE features explained objective and self-reported PA, sedentary behavior, and BMI. Methods Neighborhood Quality of Life Study participants (N=2,199, aged 20-65 years, 48.2% female, 26% ethnic minority) were sampled in 2001-2005 from Seattle / King County WA and Baltimore MD / Washington DC regions. Their addresses were geocoded to compute net residential density, land use mix, retail floor area ratio, intersection density, public transit, and public park and private recreation facility densities using a 1-km network buffer. Latent profile analyses (LPAs) were estimated from these variables. Multilevel regression models compared profiles on accelerometer-measured moderate to vigorous PA (MVPA) and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2013-2014. Results Seattle region LPAs yielded four profiles, including low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); moderately high walkability/transit/recreation (MH-MH-MH); and high walkability/transit/recreation (H-HH). All measures were higher in the HHH than the LLL profile (difference of 17.1 minutes/day for MVPA, 146.5 minutes/week for walking for transportation, 58.2 minutes/week for leisure-time PA, and 2.2 BMI points; all p

UR - http://www.scopus.com/inward/record.url?scp=84949725044&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949725044&partnerID=8YFLogxK

U2 - 10.1016/j.amepre.2015.05.024

DO - 10.1016/j.amepre.2015.05.024

M3 - Article

C2 - 26232902

AN - SCOPUS:84949725044

VL - 49

SP - 878

EP - 887

JO - American Journal of Preventive Medicine

JF - American Journal of Preventive Medicine

SN - 0749-3797

IS - 6

ER -