The credo of the Quantified Self movement promises “self-knowledge through numbers”; these numbers arising from often automatically collected and collated data about, for example, heart rate, sleep quality, caloric intake, or money spent. Through analysis and visualization, determinants of parameters of interest come to light (e.g., late dinners lead to sleepless nights, alcohol aggravates hay fever symptoms), thereby suggesting opportunities for improvement (e.g. eating earlier, avoiding alcohol in pollen season).

However, in spite of proliferation of self-tracking wearables and apps, such systems are typically abandoned quickly, sometimes because success is achieved, but more often because the systems fail to be of use. To date, there is little evidence that data leads to insights that foster (long-term) behavior change. Understanding what the data mean can be difficult in itself, and when insight does arise it is rarely actionable – that is, it does not become grounds for a set of concrete future actions that might be beneficial to the user. Data remains rhetoric, top-down, and just another way of stating the sometimes obvious, without much support to take action.

In addition, the aspects measured (and thereby promoted) by Quantified Self systems are typically an easy-to-assess, but reductive form of what people actually want to know and improve: steps are equated with healthy exercise, calories with a healthy diet, causing users to improve their scores rather than their lives.

Furthermore, numbers may seem neutral or impartial, but many systems implicitly make assumptions about what constitutes ‘good’ behavior. Self-tracking devices can easily configure ideals of health in ways that reproduce normative stereotypes. Such a focus on the ‘good’ and ‘bad’ ignores and possibly even undermines other meanings of the things being tracked, like the simple joy of the activity itself, or associated joys like having time to oneself while exercising or socializing during meal times.

Finally, self-tracking data forces users to confront what may be a less-than-perfect image of themselves. While this may motivate some users to take action, for others such negative feedback can also lead to demotivation, disengagement, or even shame and guilt when faced with the reality of their data.

Overall, there is a need for new, perhaps more critical, approaches and perspectives on the use of self-quantification for behavior change. The objective of this workshop is to take a critical perspective on self-quantification, pinpointing current challenges and proposing new approaches to harness the power of self-quantification for personal change.

In the workshop we aim to bring together researchers from various fields (Quantified Self/personal informatics, m-Health, recommender systems, persuasive technology, and more broadly information sciences, psychology, medicine, industrial design, ethics of technology) with an interest in behavior change support systems, to compile an interdisciplinary perspective on the topic of using self-quantification as a tool for behavior change.

The workshop “Self-Quantification for Behavior Change” is hosted by MobileHCI 2017, the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. For more information on MobileHCI 2017 see https://mobilehci.acm.org/2017/