The use of self-quantification systems for personal health information: big data management activities and prospects.

BACKGROUND: Self-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes. In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'.

OBJECTIVES: In this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities.

METHOD: We reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used.

FINDINGS: We found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities.

CONCLUSIONS: Self-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals).