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Lucero Robert's Picture

Lucero Robert

Associate Professor of Nursing

NIH T32 Co-Director, Translational Science Training to Reduce the Impact of Alcohol on HIV Infection

Associate Director (Community Engagement), Southern HIV/AIDS Research Consortium

College of Nursing

Family, Community and Health Systems Science

Dr. Lucero’s research is on the cutting edge of participatory design approaches that require consumer engagement. His Consumer-centered Participatory Design approach not only focuses on consumer’s technology needs but also takes into account core public health functions and outcomes such as informing, educating, and empowering individuals about health issues; mobilizing community partnerships; linking people to health services; and conducting health-related research to develop insights and innovative solutions. This approach has tremendous potential for overcoming social and cultural barriers that can impede the adoption of health information technology. The Consumer-centered Participatory Design approach has wide-scale applicability for real-world health-related challenges, which has been endorsed through peer-reviewed publications and NIH/NINR funding, and has been applied in different populations and clinical domains, including community-based, falls prevention among Latino older adults, Latino family caregivers of people living with Alzheimer’s disease and related dementias, family caregivers of children living with obesity and asthma, and African-American women living with HIV. In addition to his community-based research, Dr. Lucero has been advancing a data science research program to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. Launched in 2015 with partners from UFHealth Shands Hospital, this research aims to leverage the unique electronic data and health care resources at UF including structured and text data. Primarily, this research will contribute to the development of additional research-data infrastructure, in the form of text-mining methods and resources, to optimize studies in critical aging topics that can disproportionately affect vulnerable populations including Latinos.