Toward a Unified Metadata Schema for Ecological Momentary Assessment with Voice-First Virtual Assistants

TL;DR

This paper introduces a unified metadata schema for ecological momentary assessment (EMA) that leverages voice-first virtual assistants to reduce user burden and improve data collection in natural environments.

Contribution

The paper proposes a novel, flexible metadata schema for EMA questions that enables efficient voice interface deployment without extensive coding, facilitating rapid prototyping.

Findings

Schema supports rendering on multiple voice devices

Enables rule-based question rendering at runtime

Reduces development effort for voice EMA platforms

Abstract

Ecological momentary assessment (EMA) is used to evaluate subjects' behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today's intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.