Use of Mobile Technology to Identify Behavioral Mechanisms Linked to Mental Health Outcomes in Kenya: Protocol for Development and Validation of a Predictive Model.

Autor:	Njoroge W; Aga Khan University Nairobi., Maina R; Aga Khan University Nairobi., Elena F; University of Michigan., Atwoli L; Aga Khan University Nairobi., Wu Z; University of Michigan., Ngugi A; Aga Khan University Nairobi., Sen S; University of Michigan., Wang J; Dalhousie University., Wong S; Aga Khan University Nairobi., Baker J; University of Michigan., Haus E; University of Michigan., Khakali L; Aga Khan University Nairobi., Aballa A; Aga Khan University Nairobi., Orwa J; Aga Khan University Nairobi., Nyongesa M; Aga Khan University Nairobi., Merali Z; Aga Khan University Nairobi., Akbar K; University of Michigan., Abubakar A; Neurosciences Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2023 Jan 16. Date of Electronic Publication: 2023 Jan 16.
DOI:	10.21203/rs.3.rs-2458763/v1
Abstrakt:	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 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.
Databáze:	MEDLINE
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