Changes in BMI over 6 years: the role of demographic and neighborhood characteristics

Berry, Tanya R.; Spence, John C.; Blanchard, Chris M.; Cutumisu, Nicoleta; Edwards, John N.; Nykiforuk, Candace I. J.

doi:10.1038/ijo.2010.36

Cited by 74 publications

(89 citation statements)

References 39 publications

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“…Previous studies have adopted a variety of approaches to quantify the difference in BMI or body weight at different time points. 19,20 We categorized changes in BMI, which is consistent with a number of recent studies. We coded the change in BMI into the following increase in body weight of 5% (this is considered clinically significant).…”

mentioning

confidence: 55%

Occupational Factors Associated With 4-Year Weight Gain in Australian Adults

Magee

Caputi²,

Stefanic³

et al. 2010

Journal of Occupational &Amp; Environmental Medicine

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Objective: This article examined whether occupational factors predicted 4-year change in body mass index (BMI) in a sample of full-time Australian employees. Methods:Data from 1670 full-time Australian employees were collected through the Longitudinal Study of Australian Children. Multinomial logistic regression was used to examine whether several occupational factors at baseline predicted changes in BMI at 4-year follow up; several health and demographic covariates were controlled.Results: Inflexible working hours (odds ratio = 1.54, 95% confidence interval [1.14 to 2.09]) and weekend work (odds ratio = 1.33, 95% confidence interval [1.04 to 1.68]) significantly predicted increased BMI. Conclusions: This article demonstrates that certain occupational factors (ie, inflexible work hours andweekend work) significantly predicted increased BMI. Targeting these factors may play a role in combating obesity and related health problems among employees. Learning Objectives* Discuss previous studies of the possible contribution of working conditions to the development of obesity.* Identify the two work-related factors associated with weight gain in this longitudinal study.* Review the possible mechanisms of these associations and the implications for preventing obesity in employees.

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mentioning

confidence: 55%

Occupational Factors Associated With 4-Year Weight Gain in Australian Adults

Magee

Caputi²,

Stefanic³

et al. 2010

Journal of Occupational &Amp; Environmental Medicine

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“…Nonetheless, it was not clear from this study how the relationship between neighborhood-SES and increasing BMI over time was attenuated by perceptions about traffic. (Berry et al, 2010) In prior work from DHS, we found that unfavorable perceptions of heavy traffic in one’s neighborhood were also associated with 20–40% greater likelihood of prevalent obesity. (Powell-Wiley et al, 2013) However, these perceptions may not play a predominant role in explaining weight change in the DHS cohort.…”

Section: Discussionmentioning

confidence: 82%

“…(Berry et al, 2010; Coogan et al, 2010; Mujahid et al, 2005; Ruel et al, 2010; Stafford et al, 2010) Results have been mixed, with some showing no significant associations between neighborhood SES and weight trajectory, (Mujahid et al, 2005; Ruel et al, 2010) and others demonstrating that lower neighborhood SES can predict weight gain and incident obesity. (Coogan et al, 2010; Stafford et al, 2010) Lack of access to resources for physical activity and a healthful diet, the influence of neighborhood characteristics on psychosocial stress, and the impact of social norms across neighborhood environments of varying socioeconomic levels are potential mechanisms by which neighborhood-level SES may influence weight change.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood-level socioeconomic deprivation predicts weight gain in a multi-ethnic population: Longitudinal data from the Dallas Heart Study

Powell‐Wiley

Ayers

Agyemang

et al. 2014

Preventive Medicine

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Objective To examine relationship between neighborhood-level socioeconomic deprivation and weight change in a multi-ethnic cohort from Dallas County, Texas and whether behavioral/psychosocial factors attenuate the relationship. Methods Non-movers (those in the same neighborhood throughout the study period) aged 18–65 (N=939) in Dallas Heart Study (DHS) underwent weight measurements between 2000–2009 (median 7-year follow-up). Geocoded home addresses defined block groups; a neighborhood deprivation index (NDI) was created (higher NDI=greater deprivation). Multi-level modeling determined weight change relative to NDI. Model fit improvement was examined with adding physical activity and neighborhood environment perceptions (higher score=more unfavorable perceptions) as covariates. A significant interaction between residence length and NDI was found (p-interaction=0.04); results were stratified by median residence length (11 years). Results Adjusting for age, sex, race/ethnicity, smoking, education/income, those who lived in neighborhood>11 years gained 1.0 kilograms (kg) per one-unit increment of NDI (p=0.03), or 6 kg for those in highest NDI tertile compared with those in the lowest tertile. Physical activity improved model fit; NDI remained associated with weight gain after adjustment for physical activity and neighborhood environment perceptions. There was no significant relationship between NDI and weight change for those in their neighborhood≤11 years. Conclusions Living in more socioeconomically deprived neighborhoods over a longer time period was associated with weight gain in DHS.

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“…Moreover, the perceived microscale measures, which have associations with walking behavior, include perceived pedestrian infrastructure (such as presence of sidewalks, crosswalks, and street lights) [21,39], perceived aesthetics [16,39], and perceived traffic safety [14,21,39,40].…”

Section: Measures Of Physical Environment In Relation To Physicalmentioning

confidence: 99%

Walking beyond the Socioeconomic Status in an Objectively and Perceptually Walkable Pedestrian Environment

Toker

2015

Urban Studies Research

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An extensive body of literature suggests that physical environment, physical activity, and socioeconomic status (SES) are intrinsically linked to each other and to weight related health problems. In this study, the role of objective and perceived pedestrian environment characteristics (microscale measures) was explored in relation to people's recreational walking patterns in two neighborhoods with opposite SES. A total of 441 street segments were assessed and a total of 133 questionnaires were conducted. The findings suggest that recreational walking can take place beyond a neighborhood's suggested SES when objective and especially perceived microscale characteristics (pedestrian environment) are favorable.

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Changes in BMI over 6 years: the role of demographic and neighborhood characteristics

Cited by 74 publications

References 39 publications

Occupational Factors Associated With 4-Year Weight Gain in Australian Adults

Occupational Factors Associated With 4-Year Weight Gain in Australian Adults

Neighborhood-level socioeconomic deprivation predicts weight gain in a multi-ethnic population: Longitudinal data from the Dallas Heart Study

Walking beyond the Socioeconomic Status in an Objectively and Perceptually Walkable Pedestrian Environment

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