Local descriptive norms for overweight/obesity and physical inactivity, features of the built environment, and 10-year change in glycosylated haemoglobin in an Australian population-based biomedical cohort

Carroll, Suzanne J.; Paquet, Catherine; Howard, Natasha; Coffee, Neil; Taylor, Anne; Niyonsenga, Théophile; Daniel, Mark

doi:10.1016/j.socscimed.2016.08.031

Cited by 12 publications

(18 citation statements)

References 34 publications

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“…Similarly, this study considers POS in terms of count, area, and greenness; it does not include other features relating to POS quality that may influence behaviour and health outcomes [60]. The environmental characteristics assessed may have changed during the cohort follow-up period, however there was little evidence of area gentrification during this time period [61]. Though this study concurrently assessed multiple residential environment features in relation to overweight/obesity, it did not assess any interactions effects; the influence of environmental characteristics may be modified by other environmental characteristics or by individual-level factors.…”

Section: Discussionmentioning

confidence: 99%

Contributions of Multiple Built Environment Features to 10-Year Change in Body Mass Index and Waist Circumference in a South Australian Middle-Aged Cohort

Carroll

Dale

Taylor

et al. 2020

IJERPH

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Residential areas may shape health, yet few studies are longitudinal or concurrently test relationships between multiple residential features and health. This longitudinal study concurrently assessed the contributions of multiple environmental features to 10-year change in clinically measured body mass index (BMI) and waist circumference (WC). Longitudinal data for adults (18+ years of age, n = 2253) from the north-west of Adelaide, Australia were linked to built environment measures representing the physical activity and food environment (expressed for residence-based 1600 m road-network buffers) and area education. Associations were concurrently estimated using latent growth models. In models including all environmental exposure measures, area education was associated with change in BMI and WC (protective effects). Dwelling density was associated with worsening BMI and WC but also highly correlated with area education and moderately correlated with count of fast food outlets. Public open space (POS) area was associated with worsening WC. Intersection density, land use mix, greenness, and a retail food environment index were not associated with change in BMI or WC. This study found greater dwelling density and POS area exacerbated increases in BMI and WC. Greater area education was protective against worsening body size. Interventions should consider dwelling density and POS, and target areas with low SES.

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Section: Discussionmentioning

confidence: 99%

Contributions of Multiple Built Environment Features to 10-Year Change in Body Mass Index and Waist Circumference in a South Australian Middle-Aged Cohort

Carroll

Dale

Taylor

et al. 2020

IJERPH

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“…Recent research has implicated spatially variable, local descriptive norms as environmental influences on cardiometabolic risk [ 33 , 34 , 35 ]. Descriptive norms are what most people do , as opposed to injunctive norms which are the ‘shared rules of conduct’ informing on what ought to be done [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…Descriptive norms can be expressed for social groups (i.e., subjective descriptive norms) as well as within spatial settings apart from the social connections (or lack thereof) amongst individuals in that setting (i.e., local descriptive norms) [ 37 , 38 , 39 ]. Operationally, the local-area prevalence of a behavior or trait defines a local descriptive norm [ 34 , 35 ]. The importance of such influences is highlighted by a longitudinal study indicating that the neighborhood prevalence of overweight/obesity predicted normal weight individuals becoming overweight/obese over 13 years of follow up, independent of individual-level factors and neighborhood SES [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…The importance of such influences is highlighted by a longitudinal study indicating that the neighborhood prevalence of overweight/obesity predicted normal weight individuals becoming overweight/obese over 13 years of follow up, independent of individual-level factors and neighborhood SES [ 33 ]. Two recent reports using the same population cohort as analyzed here found that greater local descriptive norms for overweight/obesity, physical inactivity and poor fruit intake each predicted increasing cardiometabolic risk (glycosylated hemoglobin, HbA 1c ) over 10 years of follow up, accounting for covariates including individual-level factors, local-area built environment features and area-level SES [ 34 , 35 ]. These studies did not assess, however, the mechanism (i.e., individual-level health-related behavior) through which local descriptive norms influence cardiometabolic risk.…”

Section: Introductionmentioning

confidence: 99%

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Does Physical Activity Mediate the Associations Between Local-Area Descriptive Norms, Built Environment Walkability, and Glycosylated Hemoglobin?

Carroll

Niyonsenga

Coffee

et al. 2017

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Associations between local-area residential features and glycosylated hemoglobin (HbA1c) may be mediated by individual-level health behaviors. Such indirect effects have rarely been tested. This study assessed whether individual-level self-reported physical activity mediated the influence of local-area descriptive norms and objectively expressed walkability on 10-year change in HbA1c. HbA1c was assessed three times for adults in a 10-year population-based biomedical cohort (n = 4056). Local-area norms specific to each participant were calculated, aggregating responses from a separate statewide surveillance survey for 1600 m road-network buffers centered on participant addresses (local prevalence of overweight/obesity (body mass index ≥25 kg/m2) and physical inactivity (<150 min/week)). Separate latent growth models estimated direct and indirect (through physical activity) effects of local-area exposures on change in HbA1c, accounting for spatial clustering and covariates (individual-level age, sex, smoking status, marital status, employment and education, and area-level median household income). HbA1c worsened over time. Local-area norms directly and indirectly predicted worsening HbA1c trajectories. Walkability was directly and indirectly protective of worsening HbA1c. Local-area descriptive norms and walkability influence cardiometabolic risk trajectory through individual-level physical activity. Efforts to reduce population cardiometabolic risk should consider the extent of local-area unhealthful behavioral norms and walkability in tailoring strategies to improve physical activity.

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“…Dietary habits are often determined as much by cultural traditions as they are by nutritional needs and family economics (Anderson and Whitaker, 2010;Anderson, 2012;Hughes et al, 2010;Lhila, 2011;Mata et al, 2017;Redsell et al, 2010;Vizireanu and Hruschka, 2018). Cultural factors may therefore underlie local differences in obesity and diabetes rates, which exhibit effects of local neighborhood and its built environment (Alvarado, 2016;Carroll et al, 2016;Mullan et al, 2017;Kowaleski-Jones et al, 2017), family size (Datar, 2017), ethnic group and age group (Cook et al, 2017).…”

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confidence: 99%

Recent origin and evolution of obesity-income correlation across the United States

2018

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From a gene-culture evolutionary perspective, the recent rise in obesity rates around the Developed world is unprecedented; perhaps the most rapid population-scale shift in human phenotype ever to occur. Focusing on the recent rise of obesity and diabetes in the United States, we consider the predictions of human behavioral ecology (HBE) versus the predictions of social learning (SL) of obesity through cultural traditions and/or peer-to-peer influence. To isolate differences that might discriminate these different models, we first explore temporal and geographic trends in the inverse correlation between household income and obesity and diabetes rates in the U.S. Whereas by 2015 these inverse correlations were strong, these correlations were non-existent as recently as 1990. The inverse correlations have evolved steadily over recent decades, and we present equations for their time evolution since 1990. We then explore evidence for a "social multiplier" effect at county scale over a ten-year period, as well as a social diffusion pattern at state scale over a 26-year period. We conclude that these patterns support HBE and SL as factors driving obesity, with HBE explaining ultimate causation. As a specific "ecological" driver for this human behavior, we speculate that refined sugar in processed foods may be a prime driver of increasing obesity and diabetes.

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Local descriptive norms for overweight/obesity and physical inactivity, features of the built environment, and 10-year change in glycosylated haemoglobin in an Australian population-based biomedical cohort

Cited by 12 publications

References 34 publications

Contributions of Multiple Built Environment Features to 10-Year Change in Body Mass Index and Waist Circumference in a South Australian Middle-Aged Cohort

Contributions of Multiple Built Environment Features to 10-Year Change in Body Mass Index and Waist Circumference in a South Australian Middle-Aged Cohort

Does Physical Activity Mediate the Associations Between Local-Area Descriptive Norms, Built Environment Walkability, and Glycosylated Hemoglobin?

Recent origin and evolution of obesity-income correlation across the United States

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