Young peoples data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom
Sieberts, S.; Marten, C.; Bampton, E.; Björling, E. A.; Burn, A.-M.; Carey, E.; Carlson, S.; Fernandes, B.; Kalha, J.; Lindani, S.; Masomera, H.; Neelakantan, L.; Pasquale, L.; Ranganathan, S.; Scanlan, J.; Shah, H.; Sibisi, R.; Sumant, S.; Suver, C.; Thungana, Y.; Tummalacherla, M.; Velloza, J.; Collins, P.; Fazel, M.; Ford, T.; Freeman, M.; Pathare, S.; Zingela, Z.; The MindKind Consortium, ; Doerr, M.
Show abstract
Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young peoples preferences for how their data are governed and quantifies potential participants willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16-24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the "right people" access their data. Throughout the study, we recognized young peoples commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research.
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