Despite strong and growing interest in self-tracking data to capture a more complete, accurate, and longer-term understanding of an individual's health (e.g., activity / symptom journals, wearable sensor data), patients and health providers struggle to gain insights from such data. Technologies often fail to deliver benefits, as people are limited by data silos, abandon tracking due its burdens, fail to track potential symptom triggers or relevant context, and reach flawed conclusions due to a lack of scientific rigor. Providers often question data, lack tools to analyze it, and feel unequipped to interpret it. When people seek assistance from providers, commercial self-tracking tools generally lack support for sharing and patient-provider collaboration in analysis and interpretation. The long-term goal is to empower patients and providers to leverage the unprecedented potential of self-tracking data in moving from population-level understanding to personalized insights in self-management and clinical care. The overall objective in this application is: (1) to develop open tools that support innovation and research in self-tracking for self-management and clinical care, and (2) to inform, develop, and evaluate these tools with patients and providers seeking personalized insights through self-tracking data in irritable bowel syndrome, chronic headaches, and juvenile idiopathic arthritis. In the five specific aims of the research: (1) Proposed open tools will support the end-to-end process of self-tracking, from collecting to managing to interpreting many types of self-tracked health data. (2) Proposed self-tracking applications and underlying tools will be informed, developed, and evaluated in participatory research with patients and providers, including focus groups, technology probes, and pilot studies with patients and providers. (3) Proposed tool support for self-experimentation will be developed, helping patients use data to move from population-level understanding (e.g., that caffeine can trigger symptoms) to individualized understanding (e.g., whether caffeine appears to cause this individual patient?s symptoms), as informed, developed, and evaluated in participatory research with patients and providers. (4) Proposed tool support for patient-provider collaboration using self-tracked health data will be developed, supporting end-to-end collaboration in planning, collecting, analyzing, and interpreting self-tracked health data, as informed, developed, and evaluated in participatory research with patients and providers. (5) Proposed design patterns and underlying open tools will support extension of these innovations to additional health contexts. The approach is innovative in extensive participatory research with patients and providers examining effective uses of self-tracked health data, in development of self-tracking designs in the contexts of irritable bowel syndrome, chronic headaches, and juvenile idiopathic arthritis, and in distilling design patterns to extend innovation into new contexts. The research is significant because of the potential for new understanding and open tools to transform the role of self-tracked health data in self-management and clinical care, within and well beyond the contexts of the current research.
The proposed research is relevant to public health through its long-term goal of empowering patients and providers to leverage the unprecedented potential of self-tracking data in moving from population-level understanding to personalized insights in self-management and clinical care. The project develops a suite of open tools for end-to-end support of self-tracked health data, informed and evaluated in participatory research with patients and providers in three symptom management contexts (i.e., irritable bowel syndrome, chronic headaches, and juvenile idiopathic arthritis). The project therefore advances the mission of personal health libraries by developing self-tracking solutions that empower patients seeking personalized insights from their data, developing design patterns for self-experimentation and patient-provider collaboration with self-tracking data, and developing open tools for extending these approaches to rapid innovation and research in additional self-tracking contexts.
