| Autor: |
Willie Njoroge, Rachel Maina, Frank Elena, Lukoye Atwoli, Zhenke Wu, Anthony Ngugi, Srijan Sen, Jian Wang, Stephen Wong, Jessica Baker, Eileen Haus, Linda Khakali, Andrew Aballa, James Orwa, Moses Nyongesa, Zul Merali, Karim Akbar, Amina Abubakar |
| Rok vydání: |
2023 |
| DOI: |
10.21203/rs.3.rs-2458763/v1 |
| Popis: |
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: |
OpenAIRE |
| Externí odkaz: |
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