Chatbot for Online Support Groups to Treat Tobacco Addiction
NCT ID: NCT06952725
Last Updated: 2025-05-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
120 participants
INTERVENTIONAL
2026-12-01
2028-06-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
NONE
Study Groups
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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.
online quit-smoking support group with intelligent chatbot
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.
control: support group with unintelligent bot
The unintelligent chatbot will not respond to participants' posts; it will merely post a daily discussion topic.
online quit-smoking support group with unintelligent bot
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.
Interventions
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online quit-smoking support group with intelligent chatbot
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.
online quit-smoking support group with unintelligent bot
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.
Eligibility Criteria
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Inclusion Criteria
* 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
* 5+ cigarettes per day
* Not an illicit drug user
* Not a daily marijuana/cannabis user
18 Years
75 Years
ALL
Yes
Sponsors
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University of California, Irvine
OTHER
Responsible Party
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Cornelia Pechmann
Professor
Principal Investigators
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Cornelia A. Pechmann, PhD
Role: PRINCIPAL_INVESTIGATOR
University of California, Irvine
Central Contacts
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References
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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.
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.
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.
Other Identifiers
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919
Identifier Type: -
Identifier Source: org_study_id
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