Background: Latina breast cancer survivors experience poorer health-related quality of life (HRQoL), greater symptom burden, and more psychosocial needs compared to non-Latina breast cancer survivors. eHealth platforms such as smartphone apps are increasingly being used to deliver psychosocial interventions to cancer survivors. However, few psychosocial eHealth interventions have been developed specifically for Latina breast cancer survivors. Further, little is known about how Latinas, in general, engage with eHealth interventions and whether specific participant characteristics are associated with app use in this population. We evaluated the use of 2 culturally informed, evidence-based smartphone apps for Latina breast cancer survivors—one that was designed to improve HRQoL and reduce symptom burden (My Guide) and the other to promote healthy lifestyle behaviors (My Health). Objective: The objectives of our study were to explore the patterns of use of the My Guide intervention app and My Health attention-control app among Latina breast cancer survivors. Methods: Eighty Latina breast cancer survivors were randomized to use the My Guide or My Health app for 6 weeks. Assessments were collected at baseline (T1), immediately after the 6-week intervention (T2), and 2 weeks after T2 (T3). Specific study outcomes included subdomains of HRQoL, symptom burden, cancer-specific distress, cancer-relevant self-efficacy, and breast cancer knowledge. Results: On average, participants used their assigned app for more than 1 hour per week. Sociodemographic or psychological characteristics were not significantly associated with app use, except for employment status in the My Health group. Content related to common physical and emotional symptoms of breast cancer survivors as well as recommendations for nutrition and physical activity were most frequently accessed by My Guide and My Health participants, respectively. Lastly, clinically meaningful improvements were demonstrated in breast cancer well-being among low app users (ie, <60 minutes of use/week) of My Guide and social well-being among high app users (ie, ≥60 minutes of use/week) of My Health. Conclusions: The favorable rates of participant use across both apps suggest that Latina breast cancer survivors are interested in the content delivered across both My Guide and My Health. Furthermore, since sociodemographic variables, excluding employment status, and baseline HRQoL (psychological variable) were not related to app use, My Guide and My Health may be accessible to diverse Latina breast cancer survivors. Trial Registration: ClinicalTrials.gov NCT03645005; https://clinicaltrials.gov/ct2/show/NCT03645005

Latina breast cancer patients in the USA report significantly worse cancer-related symptom burden and health-related quality of life than non-Hispanic whites. However, health literacy (e.g. knowledge about cancer, coping skills and communication) has been found to improve quality of life. In this paper, we present a case study of the methodology used to design Mi Guía (My Guide), a mobile application that aims to improve symptom burden and health-related quality of life among Hispanic women who have completed active treatment for breast cancer by increasing their health literacy. We developed a community-supported approach to building the application, which involved: (1) eliciting feedback from community leaders such as support group organizers and facilitators who are bilingual in Spanish and English, prioritize patients’ preferences and best interests and have a unique knowledge of the women and their needs; (2) conducting a formal evaluation of design principles based on previous interaction design research and user responses; (3) incorporating feedback from potential future users. In this paper, we discuss our methodology, and the challenges and benefits of this approach. We believe that future studies that aim to develop mobile technologies for underserved populations may benefit from a community-supported approach to design.

The My Guide smartphone application was developed to improve quality of life and symptom burden (primary outcomes) for Latina breast cancer survivors (BCS) and tested in a pilot randomized controll...

The potential of AI to improve people’s lives is unlimited. However, designing AI-infused systems to realize this potential for all is challenging, and it is particularly challenging within under-served minority communities. Revisiting algorithms and data sources to eliminate biases in AI is an important step toward expanding its benefits. However, this leads to the realization that some applications, although unbiased, may simply not be what marginalized communities need. Marginalized communities live in a unique socioeconomic and cultural context and have difficulties taking advantage of traditional AI-powered systems due to language, digital literacy, or other barriers. Additionally, the systems that currently exist usually fail to address the unique challenges in these communities. Therefore, designers and researchers in AI must understand that a fundamental dimension of fairness and ethics is to empower and lift the lives of under-served populations through purposeful human-AI research. This research not only recognizes the unique intersectionality and needs present in each community, but also treats them as the primary audience by overcoming traditional barriers between researchers and underserved communities. This SIG proposal aims to initiate a multidisciplinary discussion around the design of AI systems that are purposefully targeted to marginalized populations. Within this discussion, our objective is to better understand how to conduct research to support and serve these communities – particularly if we are not members of such communities; and challenge the effectiveness of techniques like user-centered design in the context of AI-infused systems when the designer is not a member of the user community. In addition, we will delve into ways of translating research into practical applications to create a positive impact within these communities and narrow the disparities that exist in the optimal utilization of AI to improve lives for different communities.