Evidence map›Paper›PMID 36304916›Full record

ArticleProceedings of the ACM on human-computer interaction2021

Automated vs. Human Health Coaching: Exploring Participant and Practitioner Experiences.

Elliot G Mitchell, Rosa Maimone, Andrea Cassells, Jonathan N Tobin, Patricia Davidson, Arlene M Smaldone, Lena Mamykina

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ACM on human-computer interaction, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. Pooled it
  2. Trial
  3. Trial
  4. Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026
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  16. Examining AI Methods for Micro-Coaching Dialogs.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022
    Article
  17. Review
  18. T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-Management.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
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  19. 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

7 authors.

Elliot G MitchellColumbia University, Department of Biomedical Informatics, USA.
Rosa MaimoneFondazione Bruno Kessler, Trento, Italy.
Andrea CassellsClinical Directors Network (CDN), USA.
Jonathan N TobinClinical Directors Network (CDN) and The Rockefeller University, USA.
Patricia DavidsonWest Chester University, USA.
Arlene M SmaldoneSchool of Nursing, Columbia University, USA.
Lena MamykinaColumbia University, Department of Biomedical Informatics, USA.

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

Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.

Indexed as

chatbotsconversational agentsHealth coachingself-managementtype 2 diabeteswizard-of-oz

Identifiers

PMID36304916
PMCPMC9605038

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.