In recent years there has been considerable interest in tracking a variety of health-related data via a growing number of ubiquitous devices, smartphones and wearable devices. This phenomenon is bundled by the so-called “Quantified Self” (QS) movement, an Internet community focusing on self-quantification through technological aids. The Quantified Self movement promises “self knowledge through numbers” and its adherents are proponents of self-tracking in many forms, including the use of wearable devices, blood testing, genetic testing, and journal recording. A variety of relevant health parameters are now being captured via an ecosystem of consumer-oriented wearable devices, smartphone apps and related services. Techniques from information science, sociology, psychology, statistics, machine learning and data mining are applied to analyze collected data. These techniques provide new opportunities to enrich understanding of individual and population health. Self-tracking data can provide better measures of everyday behavior and lifestyle and can complement more traditional clinical data collection, towards a comprehensive picture of health. The aims of the workshop are to engage researchers from both Healthcare and Quantified Self communities to discuss key issues, opportunities and obstacles for personal health data research. These include challenges of capturing, summarizing, presenting and retrieving relevant information from heterogeneous sources to support a new vision of pervasive personal healthcare.