Perceived acceptability and feasibility of HIV self-testing and app-based data collection for HIV prevention research with transgender women in the United States.
Motolani AkinolaAndrea L WirtzAeysha ChaudhryErin CooneySari L Reisnernull nullPublished in: AIDS care (2021)
In the United States, transgender women are disproportionately burdened by HIV infection. Research aimed at curbing the HIV epidemic for this population may benefit from innovative technology to engage participants in research. Adult transgender women (n = 41) from six cities in the southern and eastern United States participated in seven online focus groups between August 2017 and January 2018. Analyses focused on perceived acceptability of novel technologies for research purposes, particularly HIV self-testing (HIVST) and remote data collection through a mobile app. While participants noted a number of benefits to HIVST and remote study participation, including increased participant engagement and sentiments of agency, they also expressed concerns that may impact HIVST and remote participation including housing instability, inconsistent access to technology, and confidentiality. Study findings provide insight into gaps that must be addressed when using technology-enhanced methods to support HIV testing and research participation among transgender women in the US. Substantial effort is required on the part of investigators to ensure equitable access across subgroups and, thus, minimize bias to avoid reproducing health disparities in research.
Keyphrases
- hiv testing
- men who have sex with men
- hiv positive
- polycystic ovary syndrome
- physical activity
- antiretroviral therapy
- pregnancy outcomes
- human immunodeficiency virus
- mental health
- healthcare
- cervical cancer screening
- social media
- depressive symptoms
- electronic health record
- hiv infected
- public health
- social support
- big data
- health information
- hiv aids
- breast cancer risk
- south africa
- skeletal muscle
- climate change
- adipose tissue
- young adults
- metabolic syndrome
- artificial intelligence
- data analysis
- risk assessment
- machine learning