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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:BackgroundBehavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss.ObjectiveEvaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention.MethodsParticipants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months.ResultsGrocery store density was inversely associated with change in weight (β = -0.95; p = 0.02; R 2 = 0.062) and WC (β = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (β = 0.39; p = 0.046; R 2 = 0.051).ConclusionEnvironmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

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.

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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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