Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

Willie Njoroge, Aga Khan University
Rachel Maina, Aga Khan University
Lukoye Atwoli, Aga Khan University
Elena Frank, University of Michigan, USA
Zhenke Wu, University of Michigan, USA
Anthony Ngugi, Aga Khan University
Srijan Sen, Aga Khan University
Linda Khakali, Aga Khan University
Andrew Aballa, Aga Khan University
James Orwa, Aga Khan University
Zul Merali, Aga Khan University


Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.

Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya.

Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches.

Conclusion: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.