Evidence map›Paper›PMID 35799198›Full record

ArticleThe international journal of behavioral nutrition and physical activity2022

Using combined Global Position System and accelerometer data points to examine how built environments and gentrification are associated with physical activity in four Canadian cities.

Caislin L Firth, Yan Kestens, Meghan Winters, Kevin Stanley, Scott Bell, Benoit Thierry, Kole Phillips, Zoé Poirier-Stephens, Daniel Fuller

Abstract read
In one paragraph

Article in The international journal of behavioral nutrition and physical activity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Caislin L FirthUniversity of Washington, 1959 NE Pacific St, Seattle, WA, 98195, United States.
Yan KestensUniversité de Montréal/Centre de Recherche du CHUM, Pavillon S, 850 rue St-Denis, Montréal, QC, H2X 0A9, Canada.
Meghan WintersFaculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
Kevin StanleyUniversity of Saskatchewan, 105 Administration Place, Saskatoon, S7N 5A2, Canada.
Scott BellUniversity of Saskatchewan, 105 Administration Place, Saskatoon, S7N 5A2, Canada.
Benoit ThierryUniversity of Saskatchewan, 105 Administration Place, Saskatoon, S7N 5A2, Canada.
Kole PhillipsUniversity of Saskatchewan, 105 Administration Place, Saskatoon, S7N 5A2, Canada.
Zoé Poirier-StephensUniversité de Montréal/Centre de Recherche du CHUM, Pavillon S, 850 rue St-Denis, Montréal, QC, H2X 0A9, Canada.
Daniel FullerUniversity of Saskatchewan, 105 Administration Place, Saskatoon, S7N 5A2, Canada. dfuller@mun.ca.ORCID 0000-0002-2015-2955

Funding

CIHR IP2-1507071CCIHR PJT-165955
6 · The paper itself

Abstract

backgroundBuilt and social environments are associated with physical activity. Global Positioning Systems (GPS) and accelerometer data can capture how people move through their environments and provide promising tools to better understand associations between environmental characteristics and physical activity. The purpose of this study is to examine the associations between GPS-derived exposure to built environment and gentrification characteristics and accelerometer-measured physical activity in a sample of adults across four cities.

methodsWe used wave 1 data from the Interventions, Research, and Action in Cities Team, a cohort of adults living in the Canadian cities of Victoria, Vancouver, Saskatoon, and Montreal. A subsample of participants wore a SenseDoc device for 10 days during May 2017-January 2019 to record GPS and accelerometry data. Two physical activity outcomes were derived from SenseDoc data: time spent in light, moderate, and vigorous physical activity; and time spent in moderate or vigorous physical activity. Using corresponding GPS coordinates, we summarized physical activity outcomes by dissemination area-a Canadian census geography that represents areas where 400 to 700 people live- and joined to built (active living space, proximity to amenities, and urban compactness) and gentrification measures. We examined the associations between environmental measures and physical activity outcomes using multi-level negative binomial regression models that were stratified by city and adjusted for covariates (weekday/weekend), home dissemination area, precipitation, temperature) and participant-level characteristics obtained from a survey (age, gender, income, race).

resultsWe found that adults spent more time being physically active near their homes, and in environments that were more walkable and near parks and less time in urban compact areas, regardless of where participants lived. Our analysis also highlighted how proximity to different amenities was linked to physical activity across different cities.

conclusionsOur study provides insights into how built environment and gentrification characteristics are associated with the amount of time adults spend being physically active in four Canadian cities. These findings enhance our understanding of the influence that environments have on physical activity over time and space, and can support policies to increase physical activity.

Indexed as

ExerciseResidence CharacteristicsAccelerometryAdultBuilt EnvironmentCanadaCitiesEnvironment DesignGeographic Information SystemsHumansAccelerometryGentrificationGlobal positioning systemsPhysical activityUrban sprawlWalkability

Identifiers

PMID35799198
PMCPMC9261044

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.