Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants
Yichen Han, Christopher Bo Han, Chen Chen, Peng Wei Lee, Michael, Hogarth, Alison A. Moore, Nadir Weibel, Emilia Farcas

TL;DR
This paper explores visualizing time-series EMA health data on standalone voice-first virtual assistants like Amazon Echo Show to improve accessibility for older adults, through a prototype and expert interviews.
Contribution
It introduces a prototype system for visualizing EMA data on standalone IVAs and highlights key considerations from expert feedback for designing effective visualizations.
Findings
Three key considerations for visualization design identified
Prototype demonstrates feasibility of EMA data visualization on IVAs
Expert feedback informs future design improvements
Abstract
Population aging is an increasingly important consideration for health care in the 21th century, and continuing to have access and interact with digital health information is a key challenge for aging populations. Voice-based Intelligent Virtual Assistants (IVAs) are promising to improve the Quality of Life (QoL) of older adults, and coupled with Ecological Momentary Assessments (EMA) they can be effective to collect important health information from older adults, especially when it comes to repeated time-based events. However, this same EMA data is hard to access for the older adult: although the newest IVAs are equipped with a display, the effectiveness of visualizing time-series based EMA data on standalone IVAs has not been explored. To investigate the potential opportunities for visualizing time-series based EMA data on standalone IVAs, we designed a prototype system, where older…
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