Exploring UX issues in Quantified Self technologies

The Quantified Self is a movement that promotes the use of technology for self-tracking various kinds of personal information, such as physical activities and energy consumption. In this paper, we study the user reviews of quantified self tools, as reported on a quantified self community website. We perform a content analysis to categorize tracking tools, and to explore user experience (UX) issues related to quantified self technologies. From this analysis, we find various tracking categories, including body state (e.g., physical and physiological), psychological state and traits, activities (e.g., exercise, eating, sleep), social interactions, and environmental and property states. Furthermore, we find the key UX issues associated with quantified self technologies, which include data controllability, data integration, data accuracy, data visualization, input complexity, sharing/privacy, design/aesthetics, and engagement. The UX issues reported in this paper have significant implications for the design of quantified self technologies.

[1]  Jodi Forlizzi,et al.  A stage-based model of personal informatics systems , 2010, CHI.

[2]  John Zimmerman,et al.  Toss 'n' turn: smartphone as sleep and sleep quality detector , 2014, CHI.

[3]  Sean A. Munson,et al.  Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[4]  Paul Mcfedries.  Tracking the quantified self [Technically speaking] , 2013 .

[5]  Matthew Chalmers,et al.  Personal tracking as lived informatics , 2014, CHI.

[6]  Celementina R. Russo,et al.  The Quantified Self , 2015, HCI.

[7]  Tammy Toscos,et al.  DRAFT 1 Using Behavior Modification Theory to Understand and Guide Technological Interventions , 2008 .

[8]  Thomas Zimmermann,et al.  Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness , 2014, CHI.

[9]  Frank Bentley,et al.  Mobile Health Mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[10]  Karen Holtzblatt,et al.  Contextual design , 1997, INTR.

[11]  Inseok Hwang,et al.  SocioPhone: everyday face-to-face interaction monitoring platform using multi-phone sensor fusion , 2013, MobiSys '13.

[12]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[13]  Melanie Swan,et al.  The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery , 2013, Big Data.

[14]  Hilary Johnson,et al.  Proceedings of the seventh ACM conference on Creativity and cognition , 2009 .

[15]  TractinskyNoam,et al.  Assessing dimensions of perceived visual aesthetics of web sites , 2004 .

[16]  Tammy Toscos,et al.  Using Behavior Change Theory to Understand and Guide Technological Interventions. , 2010 .

[17]  N. McNamara,et al.  Defining usability: quality of use or quality of experience? , 2005, IPCC 2005. Proceedings. International Professional Communication Conference, 2005..

[18]  Wanda Pratt,et al.  Understanding quantified-selfers' practices in collecting and exploring personal data , 2014, CHI.

[19]  Kasper Hornbæk,et al.  Old wine in new bottles or novel challenges: a critical analysis of empirical studies of user experience , 2011, CHI.

[20]  Valentin Zacharias,et al.  Applying quantified self approaches to support reflective learning , 2012, LAK.

[21]  Virpi Roto,et al.  User experience evaluation methods: current state and development needs , 2010, NordiCHI.

[22]  Ernest A. Edmonds,et al.  Directed and emergent play , 2009, C&C '09.

[23]  Jakob Grue Simonsen,et al.  Extracting usability and user experience information from online user reviews , 2013, CHI.

[24]  David F. Redmiles,et al.  Extracting usability information from user interface events , 2000, CSUR.