People Like Me

Abstract

Increases in data complexity in personal informatics systems require new ways of contextualizing personal data to facilitate meaningful reflection. An emerging approach for providing such context includes augmenting one's personal data with the data of others "like them" to help individuals make sense of their data. However, we do not yet understand how an individual's self-reflection process is affected when the data of others is made available. In this paper, we investigate how people reflect on three types of personal data when presented alongside a large set of aggregated data of multiple cohorts. We conducted personal and cohort data reviews using a subset of participants from a mobile-sensing study that collected physical activity, digital social activity, and perceived stress, from 47 students over three weeks. Participants preferred to use characteristics of the data (e.g., maxima, minima) and graphical presentation (e.g., appearance of trends) along with demographic identities (e.g., age, gender) when relating to cohorts. We further characterize how participants incorporated cohort data into their self-reflection process, and conclude with discussion of the implications for personal informatics systems that leverage the data of "people like me" to enable meaningful reflection.

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

“…As shown in Figure 4, studies have adopted various techniques to satisfy individual requirements, such as descriptive feedback, gaps between progress and goals, just-in-time reminders, and role model. However, most of the responses only present data through various types of visualized graphics [36][37][38]103,104]. For instance, similar types of user data are applied to facilitate individual reflection on their behavior [104].…”

“…However, most of the responses only present data through various types of visualized graphics [36][37][38]103,104]. For instance, similar types of user data are applied to facilitate individual reflection on their behavior [104]. Peaks and valleys are used to demonstrate the extreme value that may explain the reasons for their behavior.…”

Eliciting Meaningful Collaboration Metrics: Design Implications for Self-Tracking Technologies at Work

“…Group tracking is an emerging trend in PI in recent years [16,42] and is a valuable opportunity for collective sense-making [9] in interpersonal contexts such as collaboration. On the one hand, an aggregated cohort data sharing may promote social discovery of common experiences [18], which were deemed essential for several participants. On the other hand, group-tracking would ideally differ from the simple aggregation of data from individuals, but adequate trackable group elements remain to be identified.…”

Data@Hand: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction

“…Personal visualizations equipped with interactivity enable visual data exploration, making it easy for people to understand and refect on their data. As such, many Personal Informatics systems (e.g., [6,18,26,28,38,72]) support visual data exploration to empower people to gain personal insights.…”