Evidence map›Paper›PMID 36350760›Full record

ArticleJMIR human factors2023

Design Validation of a Relational Agent by COVID-19 Patients: Mixed Methods Study.

Ashraful Islam, Beenish Moalla Chaudhry

Abstract read
In one paragraph

Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Chatbots and COVID-19: Taking Stock of the Lessons Learned.Journal of medical Internet research · 2024
    Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ashraful IslamSchool of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, United States.ORCID https://orcid.org/0000-0003-2367-2013
Beenish Moalla ChaudhrySchool of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, United States.ORCID https://orcid.org/0000-0002-0437-6924

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRelational agents (RAs) have shown effectiveness in various health interventions with and without doctors and hospital facilities. In situations such as a pandemic like the COVID-19 pandemic when health care professionals (HCPs) and facilities are unable to cope with increased demands, RAs may play a major role in ameliorating the situation. However, they have not been well explored in this domain.

objectiveThis study aimed to design a prototypical RA in collaboration with COVID-19 patients and HCPs and test it with the potential users, for its ability to deliver services during a pandemic.

methodsThe RA was designed and developed in collaboration with people with COVID-19 (n=21) and 2 groups of HCPs (n=19 and n=16, respectively) to aid COVID-19 patients at various stages by performing 4 main tasks: testing guidance, support during self-isolation, handling emergency situations, and promoting postrecovery mental well-being. A design validation survey was conducted with 98 individuals to evaluate the usability of the prototype using the System Usability Scale (SUS), and the participants provided feedback on the design. In addition, the RA's usefulness and acceptability were rated by the participants using Likert scales.

resultsIn the design validation survey, the prototypical RA received an average SUS score of 58.82. Moreover, 90% (88/98) of participants perceived it to be helpful, and 69% (68/98) of participants accepted it as a viable alternative to HCPs. The prototypical RA received favorable feedback from the participants, and they were inclined to accept it as an alternative to HCPs in non-life-threatening scenarios despite the usability rating falling below the acceptable threshold.

conclusionsBased on participants' feedback, we recommend further development of the RA with improved automation and emotional support, ability to provide information, tracking, and specific recommendations.

Indexed as

chatbotCOVID-19design validationdesign validation surveydigital health interventionhealth carehealth care professionalhealth promotionheuristicmental well-beingmHealthrelational agentself-isolation

Identifiers

PMID36350760
PMCPMC10263105

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.