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QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform

Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
This paper is an extended version of paper published in the International Conference on Wireless Mobile Communication and Healthcare in 2018.
Sensors 2018, 18(4), 1097;
Received: 20 February 2018 / Revised: 25 March 2018 / Accepted: 2 April 2018 / Published: 5 April 2018
PDF [1298 KB, uploaded 3 May 2018]


Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experiments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing. View Full-Text
Keywords: single-case experimental design; mobile health; wearable sensors; self-experiment; self-tracking single-case experimental design; mobile health; wearable sensors; self-experiment; self-tracking

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Taylor, S.; Sano, A.; Ferguson, C.; Mohan, A.; Picard, R.W. QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform. Sensors 2018, 18, 1097.

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