Chatbot for Online Support Groups to Treat Tobacco Addiction
Intelligent Chatbot for Online Support Groups to Treat Tobacco Addiction
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Cornelia A Pechmann, PhD
- Phone Number: 3108920619
- Email: cpechman@uci.edu
Study Contact Backup
- Name: Ian G. Harris, PhD
- Phone Number: 9498248842
- Email: harris@ics.uci.edu
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Cigarette smokers (can also use e-cigarettes)
- Ages 18-75 years
- English speaking
- Smart phone with unlimited data
- 100 cigarettes lifetime
- Prepared to quit smoking within 10 days of study start
- Active text and email
- Use of social media or group messaging
- Home address provided
- Contact information for a collateral provided
- Setup of a GroupMe account for study
Exclusion Criteria:
- No NRT health contraindications
- 5+ cigarettes per day
- Not an illicit drug user
- Not a daily marijuana/cannabis user
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Experimental: test: support group with intelligent chatbot
When participants post to their support groups, the intelligent chatbot will detect relevant post types and generate responses which will be posted back to them and their group, if no human responds to the post within about 10 seconds.
The intelligent chatbot will also post a daily discussion topic.
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In the intervention arm (N=60), each quit-smoking peer support group will be connected to an intelligent chatbot running on a secure local server as a trained LLM (large language model).
This chatbot will monitor all posts in the group and seek to comprehend these posts using the training it has been provided.
If a group member makes a post and no one responds with about 10 seconds, the chatbot will respond using one of its 25 response libraries created from knowledge bases, which contain over 1k responses in total.
In effect, the intelligent chatbot will function as an additional member of the GroupMe support group, but a member that only responds if no human does so.
|
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Active Comparator: control: support group with unintelligent bot
The unintelligent chatbot will not respond to participants' posts; it will merely post a daily discussion topic.
|
In the control arm (N=60), the support groups will be connected to our original automated message-posting bot running on our secure local server.
This automated message-posting bot will lack the response capabilities of the intelligent chatbot; it will not respond to posts if no human group member does but, instead, remain silent.
However, it will post the same daily discussion topic, and at the same time of day, as the intelligent chatbot.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Overall posts per participant
Time Frame: End of 90-day intervention
|
Each night, our study website will download each group's past 24-hour posts in both the test and control conditions labeled by date and poster.
|
End of 90-day intervention
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Bio-confirmed smoking abstinence
Time Frame: End of 90-day intervention
|
We will measure smoking abstinence at 90-day intervention end, bioconfirmed with a saliva test, to conduct a power analysis for a larger RCT.
|
End of 90-day intervention
|
|
NRT use at 1-month
Time Frame: 30 days after intervention start
|
In a survey (using email, text or phone), 30 days after intervention start, we will measure past 7-day use of Nicotine Replacement Therapy or NRT, for descriptive purposes, e.g. to ensure no adverse effects on NRT use.
|
30 days after intervention start
|
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nrt use at intervention end
Time Frame: End of 90-day intervention
|
In a survey (using email, text or phone) at the end of the 90-day intervention, we will measure past 7-day use of Nicotine Replacement Therapy or NRT, for descriptive purposes, e.g. to ensure no adverse effects on NRT use.
|
End of 90-day intervention
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Cornelia A. Pechmann, PhD, University of California, Irvine
Publications and helpful links
General Publications
- Pechmann C, Delucchi K, Lakon CM, Prochaska JJ. Randomised controlled trial evaluation of Tweet2Quit: a social network quit-smoking intervention. Tob Control. 2017 Mar;26(2):188-194. doi: 10.1136/tobaccocontrol-2015-052768. Epub 2016 Feb 29.
- Giyahchi T, Singh S, Harris I, Pechmann C. Customized training of pretrained language models to detect post intents in online health support groups. In: Shaban-Nejad A, Michalowski M, Bianco S, eds. Multimodal AI in Healthcare Studies in Computational Intelligence. Springer Nature; 2023:59-76:chap 14.
- Prochaska JJ, Vogel EA, Chieng A, Kendra M, Baiocchi M, Pajarito S, Robinson A. A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study. J Med Internet Res. 2021 Mar 23;23(3):e24850. doi: 10.2196/24850.
- Phillips C, Pechmann C, Calder D, Prochaska JJ. Understanding Hesitation to Use Nicotine Replacement Therapy: A Content Analysis of Posts in Online Tobacco-Cessation Support Groups. Am J Health Promot. 2023 Jan;37(1):30-38. doi: 10.1177/08901171221113835. Epub 2022 Jul 11.
- Pechmann CC, Yoon KE, Trapido D, Prochaska JJ. Perceived Costs versus Actual Benefits of Demographic Self-Disclosure in Online Support Groups. J Consum Psychol. 2021 Jul;31(3):450-477. doi: 10.1002/jcpy.1200. Epub 2020 Oct 19.
- Pechmann C, Pan L, Delucchi K, Lakon CM, Prochaska JJ. Development of a Twitter-based intervention for smoking cessation that encourages high-quality social media interactions via automessages. J Med Internet Res. 2015 Feb 23;17(2):e50. doi: 10.2196/jmir.3772.
- Lakon CM, Pechmann C, Wang C, Pan L, Delucchi K, Prochaska JJ. Mapping Engagement in Twitter-Based Support Networks for Adult Smoking Cessation. Am J Public Health. 2016 Aug;106(8):1374-80. doi: 10.2105/AJPH.2016.303256. Epub 2016 Jun 16.
- Esmaeeli A, Pechmann CC, Prochaska JJ. Buddies as In-Group Influencers in Online Support Groups: A Social Network Analysis of Processes and Outcomes. J Interact Market. 2022 May;57(2):198-211. doi: 10.1177/10949968221076144. Epub 2022 Apr 26.
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 919
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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