Evidence map›Paper›PMID 40041394›Full record

ArticleDigital health

Enhancing physical activity through a relational artificial intelligence chatbot: A feasibility and usability study.

Yoo Jung Oh, Kai-Hui Liang, Diane Dagyong Kim, Xuanming Zhang, Zhou Yu, Yoshimi Fukuoka, Jingwen Zhang

Registry-linked trialAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05794308 (A Relational Artificial Intelligence), which is not on this 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.

NCT05794308 naunknown statusnot on this map

A Relational Artificial Intelligence (AI) Chatbot for App-Based Physical Activity Promotion Intervention Among Adults With a Sedentary Lifestyle

TypeinterventionalSponsorUniversity of California, DavisRan2023 to 2024Enrolled140ConditionsBehavior, HealthArmsNon-Relational Chatbot Physical Activity Intervention, Relational Chatbot Physical Activity Intervention
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Yoo Jung OhDepartment of Communication, Michigan State University, East Lansing, Michigan, USA.ORCID https://orcid.org/0000-0002-7829-8535
Kai-Hui LiangDepartment of Computer Science, Columbia University, New York, USA.
Diane Dagyong KimDepartment of Communication, University of California, Davis, California, USA.
Xuanming ZhangDepartment of Computer Science, Columbia University, New York, USA.
Zhou YuDepartment of Computer Science, Columbia University, New York, USA.
Yoshimi FukuokaDepartment of Physiological Nursing, University of California, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-2245-9264
Jingwen ZhangDepartment of Communication, University of California, Davis, California, USA.ORCID https://orcid.org/0000-0003-1733-6857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study presents a pilot randomized controlled trial to assess the usability, feasibility, and initial efficacy of a mobile app-based relational artificial intelligence (AI) chatbot (Exerbot) intervention for increasing physical activity behavior. Methods: The study was conducted over a 1-week period, during which participants were randomized to either converse with a baseline chatbot without relational capacity (control group) or a relational chatbot using social relational communication strategies. Objectively measured physical activity data were collected using smartphone pedometers. Results: The study was feasible in enrolling a sample of 36 participants and with a 94% retention rate after 1 week. Daily engagement rate with the AI chatbot reached over 88% across the groups. Findings revealed that the control group experienced a significant decrease in steps on the final day, whereas the group interacting with the relational chatbot maintained their step counts throughout the study period. Importantly, individuals who engaged with the relational chatbot reported a stronger social bond with the chatbot compared to those in the control group. Conclusions: Leveraging AI chatbot and the relationship-building capabilities of AI holds promise in the development of cost-effective, accessible, and sustainable behavior change interventions. This approach may benefit individuals with limited access to conventional in-person behavior interventions. Clinical trial registrations: ClinicalTrials.gov; NCT05794308; https://clinicaltrials.gov/ct2/show/NCT05794308.

Indexed as

Artificial intelligencechatbothuman–AI communicationmobile interventionphysical activity

Identifiers

PMID40041394
PMCPMC11877463

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.