Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

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Activity sensing in the wild: a field trial of ubifit garden

People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and/or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people's lives to enrich the value of systems.

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A stage-based model of personal informatics systems

Methodological Review: Healthcare in the pocket: Mapping the space of mobile-phone health interventions

Researchers have studied how people use self-tracking technologies and discovered a long list of barriers including lack of time and motivation as well as difficulty in data integration and interpretation. Despite the barriers, an increasing number of Quantified-Selfers diligently track many kinds of data about themselves, and some of them share their best practices and mistakes through Meetup talks, blogging, and conferences. In this work, we aim to gain insights from these "extreme users," who have used existing technologies and built their own workarounds to overcome different barriers. We conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what they did, how they did it, and what they learned. We highlight several common pitfalls to self-tracking, including tracking too many things, not tracking triggers and context, and insufficient scientific rigor. We identify future research efforts that could help make progress toward addressing these pitfalls. We also discuss how our findings can have broad implications in designing and developing self-tracking technologies.

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Understanding quantified-selfers' practices in collecting and exploring personal data

In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and other tactics that should be supported by persuasive technologies that target long-term discretionary use throughout everyday life. We used these strategies to design and build a system that encourages people to lead a physically active lifestyle. Results from two field studies of the system - a three-week trial and a three-month experiment - have shown that the system was successful at helping people maintain a more physically active lifestyle and validate the usefulness of the strategies.

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Theory-driven design strategies for technologies that support behavior change in everyday life

A sedentary lifestyle is a contributing factor to chronic diseases, and it is often correlated with obesity. To promote an increase in physical activity, we created a social computer game, Fish'n'Steps, which links a player's daily foot step count to the growth and activity of an animated virtual character, a fish in a fish tank. As further encouragement, some of the players' fish tanks included other players' fish, thereby creating an environment of both cooperation and competition. In a fourteen-week study with nineteen participants, the game served as a catalyst for promoting exercise and for improving game players' attitudes towards physical activity. Furthermore, although most player's enthusiasm in the game decreased after the game's first two weeks, analyzing the results using Prochaska's Transtheoretical Model of Behavioral Change suggests that individuals had, by that time, established new routines that led to healthier patterns of physical activity in their daily lives. Lessons learned from this study underscore the value of such games to encourage rather than provide negative reinforcement, especially when individuals are not meeting their own expectations, to foster long-term behavioral change.

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Henry B. Strub

Papers

Fish'n'Steps: encouraging physical activity with an interactive computer game