Summary There exists a large body of literature examining the association between built environment factors and dietary intake, physical activity, and weight status; however, synthesis of this literature has been limited. To address this gap, we conducted a scoping review of reviews and identified 74 reviews and meta‐analyses that investigated the association between built environment factors and dietary intake, physical activity, and/or weight status. Results across reviews were mixed, with heterogeneous effects demonstrated in terms of strength and statistical significance; however, preliminary support was identified for several built environment factors. For example, quality of dietary intake appeared to be associated with the availability of grocery stores, higher levels of physical activity appeared to be most consistently associated with greater walkability, and lower weight status was associated with greater diversity in land‐use mix. Overall, reviews reported substantial concern regarding methodological limitations and poor quality of existing studies. Future research should focus on improving study quality (e.g., using longitudinal methods, including natural experiments, and newer mobile sensing technologies) and consensus should be drawn regarding how to define and measure both built environment factors and weight‐related outcomes.

Background Greater sensitivity to food rewards and higher levels of impulsivity (and an interaction between these variables, termed “reinforcement pathology”) have been associated with obesity in cross-sectional studies. Less is known regarding how these constructs may impact attempts at weight loss or longer-term weight loss maintenance. Methods We provided 75 adults (69%Female, 84%White, age = 50.8y, BMI = 31.2kg/m2) with a 3-month Internet-based weight loss program and assessed weight, food reward sensitivity (via the Power of Food Scale [PFS]), and impulsivity (via Go No-Go [GNG] and Delay Discounting [DD] computer tasks) at baseline and at Months 3, 6, 9, and 12. No additional intervention was provided Months 3–12. Multi-level mixed-effect models were used to examine changes in PFS, GNG, and DD over time and associations between these measures and weight loss/regain. Results Participants lost 6.0±1.1kg Months 0–3 and regained 2.4±1.1kg Months 3–12. Across time points, higher PFS scores were associated with higher weight, p = .007; however, there were no significant associations between GNG or DD and weight nor between the interactions of PFS and GNG or DD and weight, ps>.05. There were significant decreases from Months 0–3 in PFS, GNG, and DD, ps < .05; however, neither baseline values nor changes were significantly associated with weight change and there were no significant associations between the interactions of PFS and GNG or DD and weight change, ps>.05. Conclusion Results demonstrated an association between food reward sensitivity and weight. Further, decreases in both food reward sensitivity and impulsivity were observed during an initial weight loss program, but neither baseline levels nor improvements were associated with weight change. Taken together, results suggest that the constructs of food reward sensitivity, impulsivity, and reinforcement pathology may have limited clinical utility within behavioral weight management interventions. Future intervention studies should examine whether food-related impulsivity tasks lead to a similar pattern of results.

Self-monitoring of weight, dietary intake, and physical activity is a key strategy for weight management in adults with obesity. Despite research suggesting consistent associations between more frequent self-monitoring and greater success with weight regulation, adherence is often suboptimal and tends to decrease over time. New technologies such as smartphone applications, e-scales, and wearable devices can help eliminate some of the barriers individuals experience with traditional self-monitoring tools, and research has demonstrated that these tools may improve self-monitoring adherence. To improve the integration of these tools in clinical practice, the current narrative review introduces the various types of self-monitoring technologies, presents current evidence regarding their use for nutrition support and weight management, and provides guidance for optimal implementation. The review ends with a discussion of barriers to the implementation of these technologies and the role that they should optimally play in nutritional counseling and weight management. Although newer self-monitoring technologies may help improve adherence to self-monitoring, these tools should not be viewed as an intervention in and of themselves and are most efficacious when implemented with ongoing clinical support.

No abstract