Evidence map›Paper›PMID 37750107›Full record

ArticleProceedings : Annual International Computer Software and Applications Conference. COMPSAC2023

A Survey of Conversational Agents and Their Applications for Self-Management of Chronic Conditions.

Min Sook Park, Paramita Basak Upama, Adib Ahmed Anik, Sheikh Iqbal Ahamed, Jake Luo, Shiyu Tian, Masud Rabbani, Hyungkyoung Oh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings : Annual International Computer Software and Applications Conference. COMPSAC, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Min Sook ParkSchool of Information Studies, University of Wisconsin-Milwaukee, WI, U.S.A.
Paramita Basak UpamaDepartment of Computer Science, Ubicomp Lab, Marquette University, Milwaukee, WI, U.S.A.
Adib Ahmed AnikDepartment of Computer Science, Ubicomp Lab, Marquette University, Milwaukee, WI, U.S.A.
Sheikh Iqbal AhamedDepartment of Computer Science, Ubicomp Lab, Marquette University, Milwaukee, WI, U.S.A.
Jake LuoCollege of Health Sciences, University of Wisconsin-Milwaukee, WI, U.S.A.
Shiyu TianDepartment of Computer Science, Ubicomp Lab, Marquette University, Milwaukee, WI, U.S.A.
Masud RabbaniDepartment of Computer Science, Ubicomp Lab, Marquette University, Milwaukee, WI, U.S.A.
Hyungkyoung OhCollege of Nursing, University of Wisconsin-Milwaukee, WI, U.S.A.

Funding

Self-Monitoring Support Application for Adults with Diabetes and HypertensionR21NR019707 · NINR · UNIVERSITY OF WISCONSIN MILWAUKEE · PI OH, HYUNKYOUNG · 2021 to 2022
$441k
NINR NIH HHS R21 NR019707
6 · The paper itself

Abstract

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

Indexed as

chronic conditionsConversational agentsintelligent agents architectureself-management

Identifiers

PMID37750107
PMCPMC10519706

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.