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Data ecosystem in self‐tracking health and wellness apps
Author(s) -
Trace Ciaran B.,
Cruz Katherine,
Yonemaru Daiki,
Zhang Yan
Publication year - 2017
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2017.14505401169
Subject(s) - tracking (education) , computer science , data quality , health data , data science , android (operating system) , data sharing , smartphone app , world wide web , psychology , medicine , health care , engineering , metric (unit) , operations management , alternative medicine , pathology , economics , economic growth , operating system , pedagogy
To understand information behavior associated with self‐tracking and to facilitate exploration of various issues associated with quantified‐self data, such as data quality and data sharing, to name a few, we logged the data in self‐tracking health and wellness apps. Specifically, we selected 10 of the most popular health and wellness self‐tracking apps for both iOS and Android platforms, used these apps for a period of time, and logged data input into, generated by and exported out of the apps. The data were then grouped into categories and mapped to form a data ecosystem that can be queried to reveal latent research opportunities.