Quantifying the Activities of Self-quantifiers: Management of Data, Time and Health.

Current self-quantification systems (SQS) are limited in their ability to support the acquisition of health-related information essential for individuals to make informed decisions based on their health status. They do not offer services such as data handling and data aggregation in a single place, and using multiple types of tools for this purpose complicates data and health self-management for self-quantifiers. An online survey was used to elicit information from self-quantifiers about the methods they used to undertake key activities related to health self-management. This paper provides empirical evidence about self-quantifiers' time spent using different data collection, data handling, data analysis, and data sharing tools and draws implications for health self-management activities.

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).

Designing a mobile augmented reality tool for the locative visualisation of biomedical knowledge.

Mobile augmented reality (MAR) may offer new and engaging ways to support consumer participation in health. We report on design-based research into a MAR application for smartphones and tablets, intended to improve public engagement with biomedical research in a specific urban precinct. Following a review of technical capabilities and organizational and locative design considerations, we worked with staff of four research institutes to elicit their ideas about information and interaction functionalities of a shared MAR app. The results were promising, supporting the development of a prototype and initial field testing with these staff. Evidence from this project may point the way toward user-centred design of MAR services that will enable more widespread adoption of the technology in other healthcare and biomedical research contexts.