Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants

• URL: http://arxiv.org/abs/2208.00301v1
• Date: Sat, 30 Jul 2022 20:03:15 GMT
• Title: Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants
• Authors: Yichen Han, Christopher Bo Han, Chen Chen, Peng Wei Lee, Michael Hogarth, Alison A. Moore, Nadir Weibel, Emilia Farcas
• Abstract summary: We designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.
• Score: 12.563166620724248
• License: http://creativecommons.org/licenses/by/4.0/
• 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 adults are able to query and examine the time-series EMA data on Amazon Echo Show - a widely used commercially available standalone screen-based IVA. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.

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