The importance of how research participants think they are perceived: results from an electronic monitoring study of antiretroviral therapy in Uganda.
Jeffrey I CampbellAngella MusiimentaBridget BurnsSylvia NatukundaNicholas MusinguziJessica E HabererNir EyalPublished in: AIDS care (2018)
Novel monitoring technologies in HIV research, such as electronic adherence monitors (EAMs), have changed the nature of researcher-participant interactions. Yet little is known about how EAMs and the resulting interaction between researchers and participants affect research participation and the data gathered. We interviewed participants and research assistants (RAs) in an observational cohort study involving EAMs for HIV antiretroviral therapy (ART) in Uganda. We qualitatively explored interviewees' views about ethical issues surrounding EAMs and assessed data with conventional and directed content analysis. Participants valued their relationships with RAs and were preoccupied with RAs' perceptions of them. Participants were pleased when the EAM revealed regular adherence, and annoyed when it revealed non-adherence that contradicted self-reported pill-taking behavior. For many, the desire to maintain a good impression incentivized adherence. But some sought to creatively conceal non-adherence, or refused to use the EAM to avoid revealing non-adherence to RAs. These findings show that participants' perceptions of the study staff's perceptions of them affected the experience of being monitored, study participation, and ultimately the data gathered in the study. Investigators in monitoring-based research should be aware that social interactions between participants and study staff could affect both the practical and ethical conduct of that research.
Keyphrases
- antiretroviral therapy
- hiv infected
- healthcare
- human immunodeficiency virus
- hiv positive
- primary care
- physical activity
- hiv aids
- mental health
- type diabetes
- hepatitis c virus
- electronic health record
- metabolic syndrome
- men who have sex with men
- big data
- wild type
- hiv infected patients
- depressive symptoms
- single cell
- south africa
- artificial intelligence
- hiv testing
- deep learning
- long term care