Evidence map›Paper›PMID 39027154›Full record

ArticleProceedings of the 3rd Conference on Conversational User Interfaces (CUI 2021) : 27-29th July 2021, virtual event. Conference on Conversational User Interfaces (3rd : 2021 : Online)2021

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

Chen Chen, Khalil Mrini, Kemeberly Charles, Ella T Lifset, Michael Hogarth, Alison A Moore, Nadir Weibel, Emilia Farcas

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Article in Proceedings of the 3rd Conference on Conversational User Interfaces (CUI 2021) : 27-29th July 2021, virtual event. Conference on Conversational User Interfaces (3rd : 2021 : Online), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Chen ChenComputer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Khalil MriniComputer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Kemeberly CharlesSchool of Medicine, University of California San Diego, La Jolla, CA, USA.
Ella T LifsetBiological Sciences, University of California San Diego, La Jolla, CA, USA.
Michael HogarthSchool of Medicine, University of California San Diego, La Jolla, CA, USA.
Alison A MooreSchool of Medicine, University of California San Diego, La Jolla, CA, USA.
Nadir WeibelComputer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Emilia FarcasQualcomm Institute, University of California San Diego, La Jolla, CA, USA.

Funding

SCH: INT: VOLI: Voice Assistant for Quality of Life and Healthcare Improvement in Aging Populations R56AG067393 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FARCAS, EMILIA · 2019 to 2020
$599k
NIA NIH HHS R56 AG067393
6 · The paper itself

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.

Indexed as

Data ModellingEcological Momentary AssessmentHealthcareVoice AssistantVoice First Interface

Identifiers

PMID39027154
PMCPMC11257172

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.