Fostering engagement in technology-mediated stress management: A comparative study of biofeedback designs. Issue 140 (August 2020) - Sun, Zhida; Reani, Manuele; Li, Quan; Ma, Xiaojuan
Highlights: We propose five representative biofeedback visual designs for guided stress management by employing a series of design activities. We conduct a comprehensive empirical study to compare the effect of five different visual designs on promoting engagement during guided stress management training. We investigate the strengths and weaknesses of each design and discuss considerations for designing technology-mediated stress management interfaces. Abstract: The prevalence of functional and affordable wearable biosensors has led to an increase in stress management applications for learning about and improving one's mental health. Previous research suggests that different types of graphical interfaces may evoke diverse user experiences with biofeedback interpretations and related interventions. In this research, we systematically investigate the effect of various biofeedback visual designs on supporting user engagement during a guided stress management practice. Specifically, we hypothesize that certain visual designs will affect user engagement and, thus, the training attention and physiological measures, due to their specific design features. Following a series of design studies, we assess five different styles of visual design and, for each style, we identify a suitable representative ( i.e., raw numbers for the numerical style, bar charts for the analytical style, waves for the illustrative style, butterfly drawings for the artistic style, and table lamps for the ambient style). In a controlled within-subject experiment with 35 participants, we assess these five designs through a biofeedback-assisted stress management practice. We find that, overall, visual representation of data increases engagement and, thus, better facilitates stress management training when compared to a numerical display. The analysis through the results suggests that a certain level of abstraction in the interpretation of data encoding helps users move away from being judgmental; whereas animations and transitions augment the experience of being in the present moment.