Adapting an evidence-based digital health intervention (HealthMpowerment) to address unique mental health needs - Research Plan. Asian American (AA) lesbian, gay, and bisexual (LGB) individuals represent an understudied LGB population with substantial mental health needs for whom there are documented disparities in access and engagement in mental health interventions. Lifetime prevalence estimates of depression among AA LGB are as high as 97%, approximately 20% have been diagnosed with depression compared to 7% of AA heterosexual individuals, and suicide remains the leading cause of death among AA young adults. Despite these needs, AA are the least likely racial/ethnic group in the United States to receive mental healthcare, with less than 9% receiving any type of mental health care compared to 18% of the general U.S. population. AA LGB experience multiple forms of stressors (including social- and mental health-related stigma) that contribute to depression and anxiety. Resilience, the process of coping with stressful life experiences, has been found to buffer the impact of stigma related to social experiences and mental health. HealthMpowerment, a CDC-designated evidence-based digital health intervention, has shown effectiveness in reducing stigma among Black and Latino LGB individuals by providing tailored content, decision tools, and skills to foster resilience. However, engagement of AA LGB in large scale iterations of HealthMpowerment has been limited, with only 0.9% of more than 3,000 enrolled participants identifying as AA LGB in the HMP LITE2 cohort study, highlighting the need to systematically adapt HealthMpowerment to improve its reach and engagement among AA LGB. Consistent with meta-analytic evidence demonstrating that culturally adapted interventions improve engagement and treatment outcomes among AA populations, this project will systematically adapt HealthMpowerment for AA LGB while generating a rigorous adaptation framework that can inform future adaptation of evidence-based digital mental health interventions for other populations. Specifically, leveraging the HealthMpowerment digital structure, this K01 proposes to conduct formative research to understand how multiple sources of stress experienced by AA LGB influence mental health outcomes (AIM 1) to inform the adaptation of intervention components designed to strengthen resilience and social connectedness that will follow an iterative community-engaged process guided by the ADAPT-ITT model, to reflect the cultural, social, environmental, and psychological factors influencing mental health in this population (AIM 2). The adapted intervention will be pilot tested to assess preliminary efficacy and implementation outcomes such as participant engagement, and to develop all procedures that will inform and lead the subsequent R01 trial as a fully powered RCT while generating a rigorous, reproducible adaptation framework to optimize the reach and engagement of evidence-based digital mental health interventions among populations less likely to seek or engage in mental health care (AIM 3). Career Development. As an emerging scholar, Dr. Lee will leverage USF’s training infrastructure to build her professional independence and expertise to develop a high-impact community-engaged research agenda focused on addressing mental health disparities through innovative and multidimensional digital health approaches. Specifically, Dr. Lee seeks to expand her repertoire of methodological skills in mental health research in the key areas of 1) the application of relevant frameworks to understand how stress attributed to multiple factors experienced by AA LGB contribute to mental health problems; 2) community-engaged research methods; 3) adaptation of digital mental health intervention components; and 4) integrating implementation considerations in digital mental health intervention research. Scientific results from this K01 will provide crucial data, training, and methodological expertise to make Dr. Lee competitive for a future R01 to test the efficacy and implementation of the adapted Heal
