Taxi ROADmAP (Realizing Optimization Around Diet And Physical activity) - ABSTRACT Taxi ROADmAP (Realizing Optimization Around Diet And Physical activity) is an effectiveness-implementation hybrid type 1 design using the Multiphase Optimization Strategy (MOST) to address the overweight and obesity crisis in a growing essential worker population: taxi and for-hire vehicle (FHV) drivers (Lyft, Uber, etc.). MOST, an innovative framework, involves highly efficient randomized experimentation to assess the effects of individual treatment components to guide assembly of an optimized treatment package that achieves target outcomes with the lowest resource consumption. Hybrid trials, which blend effectiveness and implementation studies, can lead to more rapid translational uptake and more effective implementation. There are over 750,000 licensed taxi and FHV drivers in in the U.S. and over 185,000 in New York City (NYC). They have higher rates of overweight/obese range body mass index (BMI) than New Yorkers in general (77% vs 56%) and have high rates of elevated waist circumference, sedentary behavior, and poor diets. Obesity contributes to cardiovascular disease and cancer. Modifiable factors, such as physical inactivity, compound drivers’ likelihood of obesity and related diseases. While there is much evidence on effective multicomponent lifestyle interventions, such as the Diabetes Prevention Program (DPP) and Look AHEAD, focused on weight loss in some populations, these programs were not designed to be translatable to community settings and are considered too expensive to be widely disseminated. There is a paucity of data on how to optimize such approaches for all people, to reduce costs but still lead to meaningful weight loss. Even less literature addresses implementation potential and strategies for such interventions. ROADmAP builds on our unique preliminary work and uses a hybrid type 1 design and MOST, to address these gaps. ROADmAP will test 4 evidence- and theory-based (Social Cognitive Theory [SCT]) behavior change intervention components, which we developed and piloted, and which include DPP and Look AHEAD features. We will use MOST to identify which of the 4 components contribute most significantly and cost-effectively to weight loss among NYC drivers recruited at workplace health fairs (HFs) and virtually. Objectives are to apply MOST to design an optimized version of a scalable lifestyle intervention for taxi/FHV drivers, and then to conduct a mixed methods multistakeholder process evaluation to facilitate widespread intervention implementation.
