Evangelos Karapanos

Evangelos Karapanos, Ruben Gouveia

We contrast three perspectives on engagement from three projects on the design of Digital Behavior Change Interventions (DBCIs), all conducted as part of the PhD thesis of the second author. We provide a reflection on this work with respect to engagement, discussing the motivation, the assumed effects of engagement, the measures of engagements and key insights of each project, as the well as the strategies employed to increase engagement.

Evangelos Karapanos

The behavior change process is a dynamic journey with different informational and motivational needs across its different stages; yet current technologies for behavior change are static. In our recent deployment of Habito, an activity tracking mobile app, we found individuals "readiness" to behavior change (or the stage of behavior change they were in) to be a strong predictor of adoption. Individuals in the contemplation and preparation stages had an adoption rate of 56%, whereas individuals in precontemplation, action or maintenance stages had an adoption rate of only 20%. In this position paper we argue for behavior change technologies that are tailored to the different stages of behavior change.

Davide Neves, Donovan Costa, Marcio Oliveira et al.

We describe Hydroprompt, a prototype for sensing and motivating healthy water intake in work environments. In a 3-week field deployment of Hydroprompt, we evaluate the effectiveness of three approaches to behavior change: historical information enabling users to compare their water intake lev- els across different times of day and days of week, implicit feedback providing subtle cues to users on the current hydration levels, and explicit prompting at- tempting to remind participants when hydration falls below acceptable levels or when substantial amount of time has elapsed since the last sip.

Rui Alves, Veranika Lim, Evangelos Niforatos et al.

This paper introduces the use of electroencephalography (EEG) and eye tracking in exploring customer experiences in service design. These tools are expected to allow designers to generate customer journeys from empirical data leading to new visualization methods and therefore improvements in service design deliverables.

Evangelos Karapanos

Experience Sampling has been considered the golden standard of in-situ measurement, yet, at the expense of high burden to participants. In this paper we propose Technology-Assisted Reconstruction (TAR), a methodological approach that combines passive logging of users' behaviors with use of these data in assisting the reconstruction of behaviors and experiences. Through a number of recent and ongoing projects we will discuss how TAR may be employed for the evaluation of personal informatics systems, but also, conversely, how ideas from the field of personal informatics may contribute towards the development of new methodologies for in-situ evaluation.

José Rodrigues, Rúben Gouveia, Olga Lyra et al.

We introduce Senseμ (pronounced "sense me"), a mobile application that aims at supporting awareness in parent- child relationships through the sensing capabilities of mobile devices. We discuss the relevance of three types of awareness information: physical activity inferred from accelerometers, verbal activity during class hours inferred from microphones, and social activity inferred from Bluetooth pair-wise proximity sensing. We describe how we attempt to contextualize these sensing data with the goal of supporting parents' awareness of the educational performance and social wellbeing of their children, as well as motivating and sustaining a two-way communication between parents and teachers over the long term.

Rúben Gouveia, Evangelos Niforatos, Evangelos Karapanos

With the increasing emphasis on how mobile technologies are experienced in everyday life, researchers are increasingly emphasizing the use of in-situ methods such as Experience Sampling and Day Reconstruction. In our line of research we explore the concept of Technology-Assisted Reconstruction, in which passively logged behavior data assist in the later reconstruction of daily experiences. In this paper we introduce Footprint tracker, a web application that supports participants in reviewing lifelogs and reconstructing their daily experiences. We focus on three kinds of data: visual (as captured through Microsoft's sensecam), location, and context (i.e., SMS and calls received and made). We describe how Footprint Tracker supports the user in reviewing these lifelogs and outline a field study that attempts to inquire into whether and how this data support reconstruction from memory.