Evidence map›Paper›PMID 36454205›Full record

ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2022

Examining AI Methods for Micro-Coaching Dialogs.

Elliot G Mitchell, Noémie Elhadad, Lena Mamykina

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021
    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

3 authors.

Elliot G MitchellColumbia University, Department of Biomedical Informatics, New York, New York.
Noémie ElhadadColumbia University, Department of Biomedical Informatics, New York, New York.
Lena MamykinaColumbia University, Department of Biomedical Informatics, New York, New York.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data - a Randomized Controlled Trial.R01DK113189 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MAMYKINA, OLENA · 2019 to 2023
$3.3M
NIDDK NIH HHS R01 DK113189NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable

Indexed as

chatbotsconversational agentsHealth coachingreinforcement learningself-management

Identifiers

PMID36454205
PMCPMC9707294

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.