Endometriosis is a chronic condition which is estimated to affect 10% of women in reproductive age. It has a very high burden on quality of life and productivity, and the self-management needs of women living with the disease are multiple. This project aims to design, develop, and evaluate a data-science enabled personal health library called PhendoPHL to support the self-management needs of women living with endometriosis. Grounded in self-determination theory, and informed by user-centered design methods PhendoPHL will enable exploration of health patterns through interactive visualizations of integrated clinical and self-tracked data, identify temporal personalized patterns and comparison to population norms through novel data-science methods, and provide actionable visualizations of data for shared decision making during patient-provider encounters. PhendoPHL builds on our existing work in novel informatics methods for endometriosis, and the extensive experience of our research team in designing and evaluating novel informatics interventions. The proposed work also fills a research gap in personal health informatics: the development and validation of novel computational methods to identify personalized and population- based patterns in clinical and self-monitoring data; both types of data which are critical to successful self- management and challenging from a computational standpoint because they are temporal, heterogeneous, and sparse. Using a mixed-methods evaluation study (standardized surveys, logfile analysis, Critical Incident Technique interviews, focus groups), we will study PhendoPHL?s usability, assess the factors critical to user engagement and perceived impact on self-determination and shared decision making, and the generalizability to other reproductive chronic conditions in women?s health.
This project aims to design, develop, and validate a data-science enabled personal health library called PhendoPHL to support the self-management needs of women living with endometriosis. Grounded in self- determination theory, PhendoPHL will enable exploration of health patterns through interactive visualizations of integrated clinical and self-tracked data, identify temporal personalized patterns and comparison to population norms through novel data-science methods, and provide actionable visualizations of data for shared decision making during patient-provider encounters.
