Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study

Clary, Christelle; Lewis, Daniel; Limb, Elizabeth; Nightingale, Claire M; Ram, Bina; Rudnicka, Alicja R.; Procter, Duncan; Cooper, Ashley R; Ellaway, Anne; Giles‐Corti, Billie; Whincup, Peter H.; Cook, Derek G; Owen, Christopher G.; Cummins, Steven

doi:10.1371/journal.pone.0237323

Cited by 10 publications

(6 citation statements)

References 58 publications

(85 reference statements)

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“…Moreover, youth living in rural farm or urban environments generally have better access to sports facilities compared to rural desert youth. Moreover, these results are consistent with a range of previous studies in children and young people indicating associations between built environmental factors, PA participation and weight status (Rodriguez and Vogt, 2009, Saelens et al, 2012, Sallis et al, 2018, Clary et al, 2020, Arellana et al, 2020. Although the current findings contribute novelty and can be generalisable in other context of environments and cultures, it is important to note that inactivity and obesity prevalence are highly prevalent amongst demographic groups in Al-Ahsa.…”

Section: Discussionsupporting

confidence: 91%

“…A systematic review conducted by (Larson et al, 2009) found that neighbourhood residents who have access to supermarkets and limited access to convenience stores tend to have healthier diets and lower levels of obesity. Moreover, an increasing number of studies examining the association between PA and built environment has been documented, but most studies were conducted in Western countries, particularly in the USA and UK (Rodriguez and Vogt, 2009, Saelens et al, 2012, Sallis et al, 2018, Clary et al, 2020, Arellana et al, 2020. To the best of researchers' knowledge, no study conducted in Saudi Arabia to determine the effects of the built environment on PA and obesity.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Environment on Physical Activity Levels and Obesity among Saudi Arabia Youth: Comparison of Urban; Rural Farm and Rural Desert Geographical Locations

Al-Nuaim

Safi

2022

Physical Activity and Health

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Background:The inactive lifestyle is becoming prevalent in many developed and developing countries including Saudi Arabia. Thus, the aim of this study was to evaluate the impact of neighborhoods' characteristics on physical activity (PA) levels and body mass index (BMI) in Saudi adolescents. Methods:The anthropometric measurement of 380 secondary-school (boys = 199; girls = 181) from different geographical locations such as urban, rural farm and rural desert was taken using the Seca digital scales for weight and Seca portable measure for height. The BMI was calculated using the formula kg/m 2 followed by participants wearing piezoelectric, New-Lifestyles NL-2000 PA Monitor, and completing the international physical activity short form questionnaire. Results:The findings indicated, an average boys recorded 9180 steps per day compared to girls 5580 and the univariate ANOVA revealed a significant difference between genders steps per day in three geographical areas (F 1,334 = 70.01, p < 0.001). The BMI results demonstrated that participant from rural farm had lower BMI (mean = 21.01 kg/m 2 ) compared to urban location (mean = 24.12 kg/m 2 ) and rural desert youth (mean = 25.58 kg/m 2 ) indicating significant differences in BMI status in geographical locations (F 2,379 = 16.40, p < 0.001). Conclusion:The inactivity and obesity prevalence are prevalent amongst demographic groups in Al-Ahsa. Therefore, future policies and interventions could target this populations especially the rural desert youth in which the health risk could be higher.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

The Impact of Environment on Physical Activity Levels and Obesity among Saudi Arabia Youth: Comparison of Urban; Rural Farm and Rural Desert Geographical Locations

Al-Nuaim

Safi

2022

Physical Activity and Health

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“…Geographic patterns of MVPA on weekdays were similar to the patterns on weekends. However, we observed a higher concentration of individuals in MVPA clusters during weekdays, which may highlight different physical activity interactions of individuals with their built environment depending on the day of the week [49, 50]. The comparison of the GGIR algorithm with the PAMPRO methodology to calculate MVPA showed some similarities but also some differences in their MVPA spatial distribution.…”

Section: Discussionmentioning

confidence: 94%

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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“…Geographic patterns of MVPA on weekdays were similar to the patterns on weekends. However, we observed a higher concentration of individuals in MVPA clusters during weekdays, which may highlight different physical activity interactions of individuals with their built environment depending on the day of the week [ 54 , 55 ]. The comparison of the GGIR algorithm with the PAMPRO methodology to calculate MVPA showed some similarities but also some differences in their MVPA spatial distribution.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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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Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study

Cited by 10 publications

References 58 publications

The Impact of Environment on Physical Activity Levels and Obesity among Saudi Arabia Youth: Comparison of Urban; Rural Farm and Rural Desert Geographical Locations

The Impact of Environment on Physical Activity Levels and Obesity among Saudi Arabia Youth: Comparison of Urban; Rural Farm and Rural Desert Geographical Locations

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

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