Spatial distribution and physical activity: implications for prevention of cardiovascular diseases

Mena, Carlos; Fuentes, Eduardo; Ormazábal, Yony; Triana, Jhon; Palomo, Iván

doi:10.1007/s11332-017-0349-6

Cited by 3 publications

(2 citation statements)

References 103 publications

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“…This is of major importance as it supports the development of public policies promoting physically active behaviors at the community level. To facilitate healthy environments and target areas for intervention, it is important to identify and understand the spatial distribution of health outcomes and environmental characteristics [ 15 , 16 ]. Spatial statistics and related clustering methods are of great help to reveal non-random geographic patterns of a given outcome and have become popular in recent years in epidemiological studies due to the availability of high-resolution spatial data [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

et al. 2022

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“…This is of major importance as it supports the development of public policies promoting physically active behaviors at the community level. To facilitate physically active environments and target areas for intervention, it is important to identify and understand their spatial distribution and environmental characteristics [12]. Spatial statistics and related clustering methods are of great help to reveal non-random geographic patterns of a given outcome and have become popular in recent years in epidemiological studies [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

Vallarta-Robledo

Joost

Ruas

et al. 2021

Preprint

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BackgroundEvidence suggests that the built environment can influence the intensity of physical activity. However, most of the studies do not consider the geographic context of this association. Studies assessing the spatial dependence of physical activity are limited by an aggregated geographic scale or self-reported physical activity. 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.MethodsCross-sectional data from the second follow-up of CoLaus|PsyCoLaus (2014-2017), a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure daily MVPA using accelerometers. We ran Local Moran’s I to assess the spatial dependence of MVPA. This method compares the behavior (MVPA) of participants to that of other individuals located in the same neighborhood. It classifies individuals into four categories: high MVPA clusters, low MVPA clusters, low MVPA outliers in high MVPA clusters, and high MVPA outliers in low MVPA clusters. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were described.ResultsData from 1,889 participants (median age 63 years, 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: 36.9 mins; 3% of the studied population) and low MVPA (median: 10.1 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 with a higher density of parks, recreational, public, and commercial & industrial areas, preferential pedestrian zones, population density, cycling pathways, public transport accessibility and interconnected streets than low MVPA clusters.ConclusionsAlthough with few individuals within clusters, MVPA clusters were identified. The built environment may influence 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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Semi-structured physical activity intervention in daily life: a good compromise between effectiveness and feasibility

et al. 2018

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Spatial distribution and physical activity: implications for prevention of cardiovascular diseases

Cited by 3 publications

References 103 publications

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

Semi-structured physical activity intervention in daily life: a good compromise between effectiveness and feasibility

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