Tobacco DependenceNCT06952725
Chatbot for Online Support Groups to Treat Tobacco Addiction
A study testing online quit-smoking support group with intelligent chatbot and online quit-smoking support group with unintelligent bot in people with Tobacco Dependence and Tobacco Use Disorder, run by University of California, Irvine.
Trial summary
Investigators will conduct a pilot RCT to test the efficacy of an intelligent chatbot to aid small, private, quit-smoking peer support groups. Participants will be randomized to an intervention arm (chatbot-enhanced support group), or a control arm (support group only). Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Tobacco Dependence, Tobacco Use Disorder
- What is being tested?
- online quit-smoking support group with intelligent chatbot and online quit-smoking support group with unintelligent bot
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- No sites listed yet
- Study dates
- 1 Dec 2026to 1 Jun 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Show all 11Show fewer
- 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
Who cannot join
- No NRT health contraindications
- 5+ cigarettes per day
- Not an illicit drug user
- Not a daily marijuana/cannabis user
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Intelligent Chatbot for Online Support Groups to Treat Tobacco Addiction
Investigators will conduct a pilot RCT to test the efficacy of an intelligent chatbot to aid small, private, quit-smoking peer support groups. Participants will be randomized to an intervention arm (chatbot-enhanced support group), or a control arm (support group only). In the intervention arm (N=60), each support group will be connected to an intelligent chatbot running on a secure local server as a trained LLM (large language model). 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. In the control arm (N=60), the support groups will be connected to an automated message-posting bot running on a secure local server. This automated message-posting bot will lack the response capabilities of the intelligent chatbot. But both the intelligent chatbot and the automated message-posting bot will post a pre-written daily discussion topic to encourage participants to discuss issues known to facilitate tobacco cessation or group bonding.
Read the detailed description
Investigators will conduct a pilot RCT to test the efficacy of an intelligent chatbot to aid small, private, quit-smoking peer support groups. For this pilot RCT, investigators will use a 2-armed parallel group trial design with an active treatment concurrent control. Participants will be randomized to the intervention arm (chatbot-enhanced support group), or the control arm (support group only). All groups will run on GroupMe, a free group chat platform. Investigators will recruit cohorts of 30 smokers, in 4 waves (N=120). After recruitment is complete, a statistician will randomly assign 15 participants to the intervention arm and 15 participants to the control arm. Investigators will place 15 people in each support group arm to ensure a critical mass even with the expected participant attrition. After randomization, each participant will stay in their assigned arm for study duration. Prior to study start, each participant will receive 8 weeks of combination NRT, including nicotine patch and gum or lozenges (their choice), consistent with clinical practice guidelines for tobacco cessation. In the intervention arm (N=60), each support group will be connected to the 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. Furthermore, every day at 5 PM Pacific, 8 PM Eastern, the chatbot will post a pre-written daily discussion topic, to encourage participants to discuss issues known to facilitate tobacco cessation or group bonding. In the control arm (N=60), the support groups will be connected to an automated message-posting bot running on a 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. Each night, investigators will download each group's past 24-hour posts. These downloads will show each post verbatim along with the datetime stamp and poster username (including posts by the chatbot and automated message-posting bot). Using these downloaded posts, investigators will measure the primary outcome: the total number of posts made by each human participant, excluding the chatbot or bot. In addition, investigators will measure participants' bio-confirmed smoking abstinence and their NRT usage at 1-month and at 3-month intervention end. Trained research staff who are blind to study arm will contact participants via email, text and phone. They will ask participants the following 4 questions: Over the past 7 days, how many cigarettes have they smoked? Over the past 7 days, how many times have they vaped or use e-cigarettes with nicotine? Over the past 7 days, how many times have they used tobacco products other than cigarettes, e.g., cigars, pipe, snuff, chew, snus, or hookah? Over the past 7 days, how many times have they used Nicotine Replacement Therapy or NRT, e.g., patches, gum or lozenges? At 3-month intervention end, investigators will seek to bioconfirm self-reported smoking abstinence. If a participant reports no use of tobacco and no NRT use over the past 7 days, or refuses to answer, investigators will mail out a cotinine saliva test and set up a video (e.g., zoom or FaceTime) appointment at a convenient time for them to take it. Each participant's saliva test will be observed by trained research staff blind to study arm. If a participant recently used NRT, the saliva test will pick up that NRT, and so the test will not be a valid indicator of their smoking status. Therefore, investigators will not give a saliva test current NRT users and instead record them as NRT users. Investigators will incentivize participants to take the saliva test with gift cards.
- Conditions
- Tobacco Dependence, Tobacco Use Disorder
- Being tested
- online quit-smoking support group with intelligent chatbot, online quit-smoking support group with unintelligent bot
- Keywords
- tobacco use addiction, online support group, chatbot
- Medical subject headings
- Tobacco Use Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
online quit-smoking support group with intelligent chatbotBehavioural
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 botBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| test: support group with intelligent chatbot | Experimental | 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. Receives: Behavioral: online quit-smoking support group with intelligent chatbot |
| control: support group with unintelligent bot | Active comparator | The unintelligent chatbot will not respond to participants' posts; it will merely post a daily discussion topic. Receives: Behavioral: online quit-smoking support group with unintelligent bot |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall posts per participant
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.
Measured: End of 90-day intervention
Secondary outcomes (3)
Bio-confirmed smoking abstinence
We will measure smoking abstinence at 90-day intervention end, bioconfirmed with a saliva test, to conduct a power analysis for a larger RCT.
Measured: End of 90-day intervention
NRT use at 1-month
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.
Measured: 30 days after intervention start
nrt use at intervention end
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.
Measured: End of 90-day intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2025
- Study start1 Dec 2026 (estimated)
- Primary completion1 Jun 2028 (estimated)
- Study completion1 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Tobacco DependenceStudies of the same condition, open and under way.
- More trials run by University of California, IrvineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, Irvine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Cornelia Pechmann, University of California, Irvine
- Investigators
- Cornelia A. Pechmann, PhD, Principal investigator, University of California, Irvine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06952725
- Sponsor's protocol ID
- 919
- Status last verified
- May 2026
- First submitted
- 23 Apr 2025
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33755028Phillips 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.
PubMed 35817548Pechmann 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.
PubMed 36276230Pechmann 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.
PubMed 25707037Pechmann 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.
PubMed 26928205Lakon 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.
PubMed 27310342Esmaeeli 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.
PubMed 35656556
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Chatbot for Online Support Groups to Treat Tobacco Addiction
- ClinicalTrials.gov ID
- NCT06952725
- Acronym / sponsor ID
- 919
- Conditions
- Tobacco Dependence, Tobacco Use Disorder
- Keywords
- tobacco use addiction, online support group, chatbot
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- University of California, IrvineUniversity, hospital or other
- Study start
- 1 Dec 2026 (estimated)
- Primary completion
- 1 Jun 2028 (estimated)
- Study completion
- 1 Jun 2028 (estimated)
- First posted
- 1 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Investigators will conduct a pilot RCT to test the efficacy of an intelligent chatbot to aid small, private, quit-smoking peer support groups. Participants will be randomized to an intervention arm (chatbot-enhanced support group), or a control arm (support group only). In the intervention arm (N=60), each support group will be connected to an intelligent chatbot running on a secure local server as a trained LLM (large language model). 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. In the control arm (N=60), the support groups will be connected to an automated message-posting bot running on a secure local server. This automated message-posting bot will lack the response capabilities of the intelligent chatbot. But both the intelligent chatbot and the automated message-posting bot will post a pre-written daily discussion topic to encourage participants to discuss issues known to facilitate tobacco cessation or group bonding.
Detailed description
Investigators will conduct a pilot RCT to test the efficacy of an intelligent chatbot to aid small, private, quit-smoking peer support groups. For this pilot RCT, investigators will use a 2-armed parallel group trial design with an active treatment concurrent control. Participants will be randomized to the intervention arm (chatbot-enhanced support group), or the control arm (support group only). All groups will run on GroupMe, a free group chat platform. Investigators will recruit cohorts of 30 smokers, in 4 waves (N=120). After recruitment is complete, a statistician will randomly assign 15 participants to the intervention arm and 15 participants to the control arm. Investigators will place 15 people in each support group arm to ensure a critical mass even with the expected participant attrition. After randomization, each participant will stay in their assigned arm for study duration. Prior to study start, each participant will receive 8 weeks of combination NRT, including nicotine patch and gum or lozenges (their choice), consistent with clinical practice guidelines for tobacco cessation. In the intervention arm (N=60), each support group will be connected to the 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. Furthermore, every day at 5 PM Pacific, 8 PM Eastern, the chatbot will post a pre-written daily discussion topic, to encourage participants to discuss issues known to facilitate tobacco cessation or group bonding. In the control arm (N=60), the support groups will be connected to an automated message-posting bot running on a 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. Each night, investigators will download each group's past 24-hour posts. These downloads will show each post verbatim along with the datetime stamp and poster username (including posts by the chatbot and automated message-posting bot). Using these downloaded posts, investigators will measure the primary outcome: the total number of posts made by each human participant, excluding the chatbot or bot. In addition, investigators will measure participants' bio-confirmed smoking abstinence and their NRT usage at 1-month and at 3-month intervention end. Trained research staff who are blind to study arm will contact participants via email, text and phone. They will ask participants the following 4 questions: Over the past 7 days, how many cigarettes have they smoked? Over the past 7 days, how many times have they vaped or use e-cigarettes with nicotine? Over the past 7 days, how many times have they used tobacco products other than cigarettes, e.g., cigars, pipe, snuff, chew, snus, or hookah? Over the past 7 days, how many times have they used Nicotine Replacement Therapy or NRT, e.g., patches, gum or lozenges? At 3-month intervention end, investigators will seek to bioconfirm self-reported smoking abstinence. If a participant reports no use of tobacco and no NRT use over the past 7 days, or refuses to answer, investigators will mail out a cotinine saliva test and set up a video (e.g., zoom or FaceTime) appointment at a convenient time for them to take it. Each participant's saliva test will be observed by trained research staff blind to study arm. If a participant recently used NRT, the saliva test will pick up that NRT, and so the test will not be a valid indicator of their smoking status. Therefore, investigators will not give a saliva test current NRT users and instead record them as NRT users. Investigators will incentivize participants to take the saliva test with gift cards.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| test: support group with intelligent chatbot | Experimental | Behavioral: online quit-smoking support group with intelligent chatbotWhen 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. |
| control: support group with unintelligent bot | Active comparator | Behavioral: online quit-smoking support group with unintelligent botThe unintelligent chatbot will not respond to participants' posts; it will merely post a daily discussion topic. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (11 listed)
- Cigarette smokers (can also use e-cigarettes)
- Ages 18-75 years
- English speaking
- Smart phone with unlimited data
- 100 cigarettes lifetime
Key exclusion criteria (4 listed)
- No NRT health contraindications
- 5+ cigarettes per day
- Not an illicit drug user
- Not a daily marijuana/cannabis user
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| online quit-smoking support group with intelligent chatbotIn 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. | Behavioural | test: support group with intelligent chatbot |
| online quit-smoking support group with unintelligent botIn 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. | Behavioural | control: support group with unintelligent bot |
Primary outcome
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.
3 secondary outcomes
Bio-confirmed smoking abstinence
Time frame: End of 90-day intervention
NRT use at 1-month
Time frame: 30 days after intervention start
nrt use at intervention end
Time frame: End of 90-day intervention
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 May 2026.
- BackgroundGiyahchi 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.
- Background: PubMed 33755028Prochaska 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.
- Background: PubMed 35817548Phillips 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.
- Background: PubMed 36276230Pechmann 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.
- Background: PubMed 25707037Pechmann 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.
- Background: PubMed 26928205Pechmann 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.
- Background: PubMed 27310342Lakon 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.
- Background: PubMed 35656556Esmaeeli 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Cornelia A. Pechmann, PhD principal investigator
University of California, Irvine
Questions about this trial
What is this clinical trial studying?
A study testing online quit-smoking support group with intelligent chatbot and online quit-smoking support group with unintelligent bot in people with Tobacco Dependence and Tobacco Use Disorder, run by University of California, Irvine.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 11 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Cornelia A Pechmann, PhD (3108920619, cpechman@uci.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall posts per participant (time frame: End of 90-day intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06952725, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 May 2026.