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

ObjectiveThis 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.ApproachThe 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.ExpectationThis 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.ConclusionA mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

Automated summary

This study aims to predict the near-term risk of major depressive episodes and future mood among healthcare workers in Kenya by identifying and validating weighted sensor stream signatures. The study will use a mobile application platform and advanced data science analytic approaches to identify predictors of mental health disorders. The findings could inform precise interventions to mitigate depression and ensure a healthy, resilient healthcare workforce for sustainable economic growth in Kenya, East Africa, and neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches.