Choice Architecture Cueing to Healthier Dietary Choices and Physical Activity at the Workplace:Implementation and Feasibility Evaluation

Redesigning choice environments appears a promising approach to encourage healthier eating and physical activity, but little evidence exists of the feasibility of this approach in real-world settings. The aim of this paper is to portray the implementation and feasibility assessment of a 12-month mixed-methods intervention study, StopDia at Work, targeting the environment of 53 diverse worksites. The intervention was conducted within a type 2 diabetes prevention study, StopDia. We assessed feasibility through the fidelity, facilitators and barriers, and maintenance of implementation, building on implementer interviews (n = 61 informants) and observations of the worksites at six (t1) and twelve months (t2). We analysed quantitative data with Kruskall–Wallis and Mann–Whitney U tests and qualitative data with content analysis. Intervention sites altogether implemented 23 various choice architectural strategies (median 3, range 0–14 strategies/site), employing 21 behaviour change mechanisms. Quantitative analysis found implementation was successful in 66%, imperfect in 25%, and failed in 9% of evaluated cases. These ratings were independent of the ease of implementation of applied strategies and reminders that implementers received. Researchers’ assistance in intervention launch (p = 0.02) and direct contact to intervention sites (p < 0.001) predicted higher fidelity at t1, but not at t2. Qualitative content analysis identified facilitators and barriers related to the organisation, intervention, worksite environment, implementer, and user. Contributors of successful implementation included apt implementers, sufficient implementer training, careful planning, integration into worksite values and activities, and management support. After the study, 49% of the worksites intended to maintain the implementation in some form. Overall, the choice architecture approach seems suitable for workplace health promotion, but a range of practicalities warrant consideration while designing real-world implementation

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution

Novel loci achieving genome-wide significance (P<5x10^-8) in meta-analyses for PA-adjusted SNP main effect (P_adjPA) or the joint test of SNP main effect and SNP-PA interaction (P_joint).

<p>Novel loci achieving genome-wide significance (P<5x10^-8) in meta-analyses for PA-adjusted SNP main effect (P_adjPA) or the joint test of SNP main effect and SNP-PA interaction (P_joint).</p

Genes of biological interest within 500 kb of lead SNPs associated with WC_adjBMI or WHR_adjBMI.

<p>Genes of biological interest within 500 kb of lead SNPs associated with WC_adjBMI or WHR_adjBMI.</p

Genes of biological interest within 500 kb of lead SNPs associated with BMI.

<p>Genes of biological interest within 500 kb of lead SNPs associated with BMI.</p

Power to identify PA-adjusted main, joint or GxPA interaction effects in 200,000 individuals (45,000 inactive, 155,000 active).

<p>The plots compare power to identify genome-wide significant main effects (P_adjPA<5x10^-8, dashed black), joint effects (P_JOINT<5x10^-8, dotted green) or GxPA interaction effects (P_INT<5x10^-8, solid magenta) as well as the power to identify Bonferroni-corrected interaction effects (P_INT<0.05/number of loci, solid orange) for the SNPs that reached a genome-wide significant PA-adjusted main effect association (P_adjPA<5x10^-8). The power computations were based on analytical power formulae provided elsewhere [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006528#pgen.1006528.ref050" target="_blank">50</a>] and were conducted a-priori based on various types of known realistic BMI effect sizes [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006528#pgen.1006528.ref051" target="_blank">51</a>]. <b>Panels A, C, E</b>: Assuming an effect in inactive individuals similar to a small (, comparable to the known BMI effect of the <i>NUDT3</i> locus), medium (, comparable to the known BMI effect of the <i>BDNF</i> locus) and large (, comparable to the known BMI effect of the <i>FTO</i> locus) realistic effect on BMI and for various effects in physically active individuals (varied on the x axis); <b>Panels B,D,F</b>: Assuming an effect in physically active individuals similar to the small, medium and large realistic effects of the <i>NUDT3</i>, <i>BDNF and FTO</i> loci on BMI and for various effects in inactive individuals (varied on x axis).</p