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Does the built environment influence the effectiveness of behavioral weight management interventions?

ABSTRACT: Outcomes of behavioral lifestyle interventions for promoting weight loss vary widely across participants. The effectiveness of a weight management intervention may depend on a person's environmental context. This study compared short- and longer-term effects of a structured nationwide weight management program for people living in neighborhoods with different levels of walkability and different access to recreational places (parks, fitness facilities). Drawing on the health production model, we tested competing hypotheses for whether treatment effects of the program complement environmental supports or substitute for environmental constraints. We studied the US Department of Veterans Affairs (VA) MOVE! weight management program using VA electronic heath record data (2009-2014) and a difference-in-differences design with an inverse propensity score matched comparison group. A total of 114,256 program participants and 498,494 non-participants comprised the sample. Built environment features were measured within one-mile of each person's home. We estimated program effects on body mass index (BMI) for subgroups with different built environments at 6-, 12-, 18-, and 24-month follow-up using linear regressions with person and year fixed effects. At 6?months, the program reduced BMI by 0.4-0.6?kg/m2 among men and 0.3-0.5?kg/m2 among women. The effect diminished at 12, 18, and 24?months. The program effect did not vary significantly across subgroups with different walkability, park access, or fitness facility access. The MOVE! program was not sensitive to environmental context. Results did not lend support to either hypothesis that the MOVE! program complements or substitutes for a person's built environment to affect weight management outcomes.

SUBMITTER: Zenk SN

PROVIDER: S-EPMC6878977 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Does the built environment influence the effectiveness of behavioral weight management interventions?

Zenk Shannon N SN Tarlov Elizabeth E Wing Coady C Slater Sandy S Jones Kelly K KK Fitzgibbon Marian M Powell Lisa M LM

Preventive medicine 20190719

Outcomes of behavioral lifestyle interventions for promoting weight loss vary widely across participants. The effectiveness of a weight management intervention may depend on a person's environmental context. This study compared short- and longer-term effects of a structured nationwide weight management program for people living in neighborhoods with different levels of walkability and different access to recreational places (parks, fitness facilities). Drawing on the health production model, we ...[more]

PMID: 31330154

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Project description:BackgroundFor economic models to be considered fit for purpose, it is vital that their outputs can be interpreted with confidence by clinicians, budget holders and other stakeholders. Consequently, thorough validation of models should be carried out to enhance confidence in their predictions. Here, we present results of external dependent and independent validations of the Core Obesity Model (COM), which was developed to assess the cost-effectiveness of weight management interventions.ObjectiveThe aim was to assess the external validity of the COM (version 6.1), in line with best practice guidance from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.MethodsFor validation, suitable sources and outcomes were identified, and used to populate the COM with relevant inputs to allow prediction of study outcomes. Study characteristics were entered into the COM to replicate either the studies used to develop the model (dependent validation) or those not included in the model (independent validation). The concordance between predicted and observed outcomes was then assessed using established statistical methods and generation of mean error estimates.ResultsFor most outcomes, the predictions of the COM showed good linear correlation with observed outcomes, as evidenced by the high coefficients of determination (R2 values). The independent validation revealed a degree of underestimation in predictions of cardiovascular (CV) disease and mortality, and type 2 diabetes.ConclusionThe predictions generated by the risk equations used in the COM showed good concordance both with the studies used to develop the model and with studies not included in the model. In particular, the concordance observed in the external dependent validation suggests that the COM accurately predicts obesity-related event rates observed in the studies used to develop the model. However, the impact of existing CV risk, as well as mortality, is a key area for future refinement of the COM. Our results should increase confidence in the estimates derived from the COM and reduce uncertainty associated with analyses using this model.

Project description:Researchers investigating relationships between the neighbourhood environment and health first need to decide on the spatial extent of the neighbourhood they are interested in. This decision is an important and ongoing methodological challenge since different methods of defining and delineating neighbourhood boundaries can produce different results. This paper explores this issue in the context of a New Zealand-based study of the relationship between the built environment and multiple measures of physical activity. Geographic information systems were used to measure three built environment attributes-dwelling density, street connectivity, and neighbourhood destination accessibility-using seven different neighbourhood definitions (three administrative unit boundaries, and 500, 800, 1000- and 1500-m road network buffers). The associations between the three built environment measures and five measures of physical activity (mean accelerometer counts per hour, percentage time in moderate-vigorous physical activity, self-reported walking for transport, self-reported walking for recreation and self-reported walking for all purposes) were modelled for each neighbourhood definition. The combination of the choice of neighbourhood definition, built environment measure, and physical activity measure determined whether evidence of an association was detected or not. Results demonstrated that, while there was no single ideal neighbourhood definition, the built environment was most consistently associated with a range of physical activity measures when the 800-m and 1000-m road network buffers were used. For the street connectivity and destination accessibility measures, associations with physical activity were less likely to be detected at smaller scales (less than 800 m). In line with some previous research, this study demonstrated that the choice of neighbourhood definition can influence whether or not an association between the built environment and adults' physical activity is detected or not. This study additionally highlighted the importance of the choice of built environment attribute and physical activity measures. While we identified the 800-m and 1000-m road network buffers as the neighbourhood definitions most consistently associated with a range of physical activity measures, it is important that researchers carefully consider the most appropriate type of neighbourhood definition and scale for the particular aim and participants, especially at smaller scales.

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Does the built environment influence the effectiveness of behavioral weight management interventions?

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Does the built environment influence the effectiveness of behavioral weight management interventions?

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