The Benefits of Crowdsourcing to Seed and Align an Algorithm in an mHealth Intervention for African American and Hispanic Adults: Survey Study

• Published in: Journal of medical Internet research 2022-06, Vol.24 (6), p.e30216-e30216
• Main Authors: Sehgal, Neil Jay, Huang, Shuo, Johnson, Neil Mason, Dickerson, John, Jackson, Devlon, Baur, Cynthia
• Format: Article
• Language: English
• Subjects: Adults; African Americans; Algorithms; Avoidable; Bias; Bilingualism; Crowdsourcing; Cultural sensitivity; Customization; Datasets; Demography; Disease prevention; Health information; Health literacy; Health promotion; Internet; Medical libraries; Objectives; Original Paper; Personal health; Polls & surveys; Preventive medicine; Respondents; Telemedicine
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/30216

Background: The lack of publicly available and culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults’ disease prevention and health promotion priorities presents a major challenge for researchers and developers who want to create and test personalized tools built on and aligned with those priorities. Personalization depends on prediction and performance data. A recommender system (RecSys) could predict the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users via a novel smartphone app. However, early in a user’s experience, a RecSys can face the “cold start problem” of serving untailored and irrelevant content before it learns user preferences. For underserved African American and Hispanic populations, who are consistently being served health content targeted toward the White majority, the cold start problem can become an example of algorithmic bias. To avoid this, a RecSys needs population-appropriate seed data aligned with the app’s purposes. Crowdsourcing provides a means to generate population-appropriate seed data. Objective: Our objective was to identify and test a method to address the lack of culturally specific preventative personal health data and sidestep the type of algorithmic bias inherent in a RecSys not trained in the population of focus. We did this by collecting a large amount of data quickly and at low cost from members of the population of focus, thereby generating a novel data set based on prevention-focused, population-relevant health goals. We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American/Black adult respondents, using Amazon Mechanical Turk (MTurk). Methods: MTurk provided the crowdsourcing platform for a web-based survey in which respondents completed a personal profile and a health information–seeking assessment, and provided data on family health history and personal health history. Respondents then selected their top 3 health goals related to preventable health conditions, and for each goal, reviewed and rated the top 3 information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. This paper reports the article ratings because our intent was to assess the benefits of crowdsourcing to seed a RecSys. The analysi