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Geographic Clusters of Objectively Measured Physical Activity and the Characteristics of Their Built Environment in a Swiss Urban Area

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Journal PLoS One
Date 2022 Feb 23
PMID 35196322
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Abstract

Introduction: Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA.

Methods: Cross-sectional data from the second follow-up (2014-2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran's I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared.

Results: Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity.

Conclusions: Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.

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Li Y, Zhao G, Su L, Fu J, Sun S, Chen R BMC Public Health. 2024; 24(1):1522.

PMID: 38844937 PMC: 11154994. DOI: 10.1186/s12889-024-19035-2.

References
1.
Dillon C, Fitzgerald A, Kearney P, Perry I, Rennie K, Kozarski R . Number of Days Required to Estimate Habitual Activity Using Wrist-Worn GENEActiv Accelerometer: A Cross-Sectional Study. PLoS One. 2016; 11(5):e0109913. PMC: 4858250. DOI: 10.1371/journal.pone.0109913. View

2.
Schule S, Bolte G . Interactive and independent associations between the socioeconomic and objective built environment on the neighbourhood level and individual health: a systematic review of multilevel studies. PLoS One. 2015; 10(4):e0123456. PMC: 4388459. DOI: 10.1371/journal.pone.0123456. View

3.
Joost S, Duruz S, Marques-Vidal P, Bochud M, Stringhini S, Paccaud F . Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study. BMJ Open. 2016; 6(1):e010145. PMC: 4716152. DOI: 10.1136/bmjopen-2015-010145. View

4.
Auchincloss A, Gebreab S, Mair C, Diez Roux A . A review of spatial methods in epidemiology, 2000-2010. Annu Rev Public Health. 2012; 33:107-22. PMC: 3638991. DOI: 10.1146/annurev-publhealth-031811-124655. View

5.
Mayne D, Morgan G, Jalaludin B, Bauman A . The contribution of area-level walkability to geographic variation in physical activity: a spatial analysis of 95,837 participants from the 45 and Up Study living in Sydney, Australia. Popul Health Metr. 2017; 15(1):38. PMC: 5627488. DOI: 10.1186/s12963-017-0149-x. View