Ptakauskaite, N;
Cox, AL;
Musolesi, M;
Mehrotra, A;
Cheshire, J;
Garattini, C;
(2018)
Personal Informatics Tools Benefit from Combining Automatic and Manual Data Capture in the Long-Term.
In:
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2018).
Association for Computing Machinery (ACM): New York, NY, USA.
(In press).
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Abstract
Harnessing the research opportunities provided by the large datasets generated by users of self-tracking technologies is a challenge for researchers of both human-computer interaction (HCI) and data science. While HCI is concerned with facilitating the insights gathered from data produced by self-tracking systems, data scientists rely on the quality of such data for training more accurate predictive models, which can sustain the flow of insightful data even after manual self-tracking is abandoned. In this position paper we consider the complementary roles that manual and automated data capture methods hold and argue that interdisciplinary collaborations are vital for advancing long-term self-tracking, the research and intervention opportunities that come with it, and provide a concrete example of where such collaborations would fit.
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