  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.
