Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05794308 |
| Other study ID # |
2033031 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
April 5, 2023 |
| Est. completion date |
December 1, 2024 |
Study information
| Verified date |
September 2023 |
| Source |
University of California, Davis |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
This study aims to empirically test the theoretical mechanisms of relational perceptions in
the context of building and testing a relational artificial intelligence (AI) chatbot for
improving physical activity (PA) behaviors among a sedentary adult population in the U.S.
The aim of the study is to build and experimentally test relational capacities of AI chatbot
in inducing positive human-AI relationship and leading to higher PA behavior change
intention. During the 7-day intervention, the relational chatbot will educate participants on
physical activity using 5 types of relational messages during a PA intervention including 1)
social dialogue, 2) empathy, 3) self-disclosure, 4) meta-relational communication, and 5)
humor. On the other hand, the non-relational chatbot will only deliver PA intervention
messages, without relational cues. Relational chatbot condition will be compared to the
non-relational chatbot condition to assess its effectiveness.
The objective of this study is to test the efficacy of the mobile app intervention leveraging
chatbots in increasing participants' relationship perception and physical activity behavior
change.
Description:
Despite the recognition of the importance of communication for relationship and trust
building in healthcare programs (Ward, 2018), theoretical work and empirical testing of
relational capacities in AI chatbot designed for changing health behaviors is lacking in
previous research. In our systematic review of AI chatbot-based interventions on PA, diet,
and weight loss outcomes, it was found that although majority of studies programmed AI
chatbots to engage in relational communication behaviors (e.g., personalized greetings,
showing empathy and compassion), none of these studies tested whether AI chatbot's relational
capacities contributed to human-AI trust and relationship building (Oh et al., 2021). Beyond
knowing some chatbots were perceived to be useful and friendly, we do not know theoretical
mechanisms for what relational conversational strategies contribute to higher quality
relationship perception and behavior outcomes.
By developing an AI chatbot that provides access to informational, motivational, and
socio-emotional aspects of care will open new opportunities for delivering accessible
interventions to improve sedentary population's PA behaviors. More broadly, the test of the
AI Chatbot Behavior Change Model (Zhang et al., 2020) will provide the first empirical
evidence on the model's utility and working mechanisms accounting for how relational AI
chatbot can change health behaviors. If successful, the theoretical model and design of the
relational chatbot will be able to generalize to related behavior change fields.
This field experiment will randomly assign participants to relational chatbot condition or
non-relational (control) chatbot condition. Both conditions will involve an information
component addressing physical activity education sessions.
The objective of this study is to test the efficacy of the mobile app intervention leveraging
chatbots in increasing participants' relationship perception and physical activity behavior
change.
