Project Summary
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, at-risk, multilingual low socioeconomic
status (SES), hard-to-reach, predominantly immigrant and minority 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 and participant burden. 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, poor diets, and health care services underutilization.
Obesity, a shared cardiovascular disease and cancer risk, and its consequences affect minority
and low SES populations disproportionately. Modifiable factors, such as physical inactivity,
compound drivers’ obesity risk. While there is much evidence on effective multicomponent lifestyle
interventions focused on weight loss in non-minority populations, such as the Diabetes Prevention
Program (DPP) and Look AHEAD, these programs were not designed to be translatable to
community settings and are considered too expensive and burdensome to be widely disseminated.
There is a paucity of data on how to optimize such approaches for minorities (who have benefitted
less from weight loss programs than non-Hispanic whites), to reduce participant burden and 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, developed and piloted by the Memorial Sloan Kettering Immigrant Health and Cancer
Disparities Center, 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.