Menetelmällinen kehitys mielenterveyden digitaalisessa fenotyypityksessä

Digital phenotyping is an evolving field that fuses the disciplines of data science, behavioral science, and medicine. Digital phenotyping research utilizes data from personal digital devices and online platforms to investigate various behavioral, social, and health-related aspects of an individual's life. By analyzing these data, research aims to find novel insights into behavior, health, and well-being. These insights may lead to new complementary methods and applications for more effective healthcare solutions. As a new field, several factors hinder digital phenotyping from reaching its full potential. This thesis aims to advance the field of digital phenotyping through two perspectives: methodological and practical. From a methodological perspective, the thesis focuses on the barriers due to technical and research methodology-related challenges. In turn, the practical perspective evaluates the potential of using digital phenotyping to monitor and predict depressive symptom severity using data collected from outpatients diagnosed with ongoing depressive episodes. This thesis comprises five research articles, two of which focus on advancing the methodology, and the remaining three focus on practical aspects. The first article introduces Niimpy, an open-source Python toolbox for analyzing behavioral data. The second article outlines a datadriven workflow that facilitates research and improves its generalizability. The third article examines how accurately digital phenotyping, using smartphone-sensed data, can assess depression severity. The fourth article inspects the feasibility of digital phenotyping by analyzing the behavioral differences between the healthy control and patient cohorts. The fifth article explores the inherent variability in depressive symptomology and associated mobile-sensed behaviors. Taken together, these articles provide standardized tools and data analysis pipelines that facilitate behavioral data analysis, thereby lowering the barrier to entry into digital phenotyping research. This thesis proposes concrete, actionable guidelines for improving research replicability and reproducibility through the use of analysis workflows. The work demonstrates that machine learning models, using digital phenotyping data, can predict future depression severity. In addition, the feasibility of using smartphone data is supported by both study groups, healthy controls and patients diagnosed with depression, providing behavioral data with no differences in participation adherence or data quantity. Finally, the thesis demonstrates how behavioral differences, both between and within the participants diagnosed with depression, are connected with their self-reported symptoms. To conclude, this thesis advances digital phenotyping research by introducing tools and workflows to facilitate analysis. Furthermore, by building on previous work in the field, the thesis contributes to the research by presenting research outcomes obtained through the analysis of depressive outpatient behavioral data.