Next steps: eliminating disparities in diabetes and obesity

Developing priorities to achieve health equity through diabetes translation research: a concept mapping study

INTRODUCTION

The goal of diabetes translation research is to advance research into practice and ensure equitable benefit from scientific evidence. This study uses concept mapping to inform and refine future directions of diabetes translation research with the goal of achieving health equity in diabetes prevention and control.

RESEARCH DESIGN AND METHODS

This study used concept mapping and input from a national network of diabetes researchers and public health practitioners. Concept mapping is a mixed-method, participant-based process. First, participants generated statements by responding to a focus prompt ("To eliminate disparities and achieve health equity in the prevention and treatment of diabetes, research should…"). Participants then sorted statements by conceptual similarity and rated each statement on importance and feasibility (Likert scale of 1-5). A cluster map was created using multidimensional scaling and hierarchical cluster analysis; statements were plotted by average importance and feasibility.

RESULTS

Ten clusters were identified containing between 6 and 12 statements from 95 total generated statements. The ranges of average importance and feasibility ratings for clusters were fairly high and narrow (3.62-4.09; 3.10-3.93, respectively). Clusters with the most statements in the "go-zone" quadrant (above average importance/feasibility) were community and partner engagement (n=7), dissemination and implementation principles (n=4), and enrichment and capacity building (n=4). Clusters with the most statements in the "innovative-targets" quadrant (above average importance, below average feasibility) included next generation interventions (n=6), policy approaches (n=4), and interventions for specific populations (n=4).

CONCLUSIONS

This study created a framework of 10 priority areas to guide current and future efforts in diabetes translation research to achieve health equity. Themes rated as highly important and feasible provide the basis to evaluate current research support. Future efforts should explore how to best support innovative-targets, those rated highly important but less feasible.

Assessing Diabetes-Relevant Data Provided by Undergraduate and Crowdsourced Web-Based Survey Participants for Honesty and Accuracy

Background

To eliminate health disparities, research will depend on our ability to reach select groups of people (eg, samples of a particular racial or ethnic group with a particular disease); unfortunately, researchers often experience difficulty obtaining high-quality data from samples of sufficient size.

Objective

Past studies utilizing MTurk applaud its diversity, so our initial objective was to capitalize on MTurk’s diversity to investigate psychosocial factors related to diabetes self-care.

Methods

In Study 1, a “Health Survey” was posted on MTurk to examine diabetes-relevant psychosocial factors. The survey was restricted to individuals who were 18 years of age or older with diabetes. Detection of irregularities in the data, however, prompted an evaluation of the quality of MTurk health-relevant data. This ultimately led to Study 2, which utilized an alert statement to improve conscientious behavior, or the likelihood that participants would be thorough and diligent in their responses. Trap questions were also embedded to assess conscientious behavior.

Results

In Study 1, of 4165 responses, 1246 were generated from 533 unique IP addresses completing the survey multiple times within close temporal proximity. Ultimately, only 252 responses were found to be acceptable. Further analyses indicated additional quality concerns with this subsample. In Study 2, as compared with the MTurk sample (N=316), the undergraduate sample (N=300) included more females, and fewer individuals who were married. The samples did not differ with respect to race. Although the presence of an alert resulted in fewer trap failures (mean=0.07) than when no alert was present (mean=0.11), this difference failed to reach significance: F_1,604=2.5, P=.11, ƞ²=.004, power=.35. The modal trap failure response was zero, while the mean was 0.092 (SD=0.32). There were a total of 60 trap failures in a context where the potential could have exceeded 16,000.

Conclusions

Published studies that utilize MTurk participants are rapidly appearing in the health domain. While MTurk may have the potential to be more diverse than an undergraduate sample, our efforts did not meet the criteria for what would constitute a diverse sample in and of itself. Because some researchers have experienced successful data collection on MTurk, while others report disastrous results, Kees et al recently identified that one essential area of research is of the types and magnitude of cheating behavior occurring on Web-based platforms. The present studies can contribute to this dialogue, and alternately provide evidence of disaster and success. Moving forward, it is recommended that researchers employ best practices in survey design and deliberately embed trap questions to assess participant behavior. We would strongly suggest that standards be in place for publishing the results of Web-based surveys—standards that protect against publication unless there are suitable quality assurance tests built into the survey design, distribution, and analysis.