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| ID | Type | Description | Link |
|---|---|---|---|
| 1R34CA287720-01A1 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
| Advanced Bionics | INDUSTRY |
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The goal of this quasi-experimental study is to test if a smartphone app can help adolescents aged 14-20 quit e-cigarettes. The main questions it aims to answer are:
Researchers will compare two groups: an immediate-intervention group that starts using the app right away and a delayed-intervention group that begins after three months, to see if the timing of app access influences outcomes in e-cigarette cessation.
Participants will:
This study aims to create an accessible, personalized tool to help adolescents reduce or quit e-cigarette use, exploring its feasibility as a broader intervention model.
This quasi-experimental study aims to develop and evaluate an AI-enhanced smartphone app designed to support adolescents aged 14-20 in quitting e-cigarettes. Given the high prevalence of e-cigarette use among youth, this app-based intervention focuses on providing personalized, real-time support for cravings and motivation to quit. The app integrates interactive features to engage users and is designed for scalability, enabling wide-reaching impact in various settings such as schools, clinics, and communities.
Study Phases and Objectives
Phase 1: Development and Usability Testing Phase 1 focuses on refining an existing beta version of the app. In this formative stage, the app's design, content, and features will be adjusted based on adolescent feedback to ensure it is user-friendly and engaging. Participants will test the app and provide insights through usability surveys and interviews, which will inform necessary changes.
Key activities in this phase include:
Phase 2: Clinical Feasibility Testing In Phase 2, the app's effectiveness will be tested using a quasi-randomized design with two groups: one group of participants will begin using the app immediately, while the second group will start after a three-month delay. This approach will help determine if earlier access to the intervention leads to improved outcomes in terms of e-cigarette cessation.
The study will assess how the app impacts participants' readiness to quit, actual quitting attempts, and ongoing motivation over time. Engagement levels with the app's features, such as real-time craving support and AI-driven educational modules, will also be tracked to evaluate the intervention's overall feasibility and appeal.
App Features and Personalization
The app's core features include:
These AI-driven tools are customized according to user data and interactions within the app, ensuring the intervention feels personal and responsive to each user's progress.
Data Collection and Analysis Data will be collected on app usage, engagement with specific features, and changes in e-cigarette use over time. Analysis will include both user feedback and statistical evaluation of the app's impact on participants' quitting success. Insights from this data will contribute to the ongoing refinement of the app and inform its potential for broader use as an adolescent-focused e-cigarette cessation tool.
Anticipated Impact This study aims to create a user-friendly, scalable app that leverages AI to support adolescents in quitting e-cigarettes effectively. If successful, this digital intervention could be a valuable resource for youth cessation programs and serve as a model for similar health-related app-based interventions.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Immediate-Intervention Group | Experimental | Participants in this arm will begin using the AI-enhanced smartphone app immediately after enrollment. This arm serves to assess the initial impact and feasibility of the app as a tool for e-cigarette cessation among adolescents. |
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| Delayed-Intervention Group | Active Comparator | Participants in this arm will wait three months after enrollment before using the AI-enhanced smartphone app. This arm serves as a delayed control, allowing comparison with the immediate-intervention group to understand the impact of timing on quitting success. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-enhanced smartphone app | Behavioral | A smartphone app has been developed and is in keeping with guideline recommendations for the treatment of e-cigarette products. This app has a user-friendly Graphic User Interface (GUI) to allow users to build their own accounts and individualized contents conveniently, based on the input the users initially provide including e-cigarette use patterns, readiness to quit e-cigarette, beliefs about e-cigarette, nicotine addiction, self-efficacy, other substance use status, and parental or peer e-cigarette use status. The proposed AI model in this app will learn information from the input data, including progress toward e-cigarette cessation (e,g, changes of readiness of quitting, quit attempts), and additional data including emotional status, stress level, feedback to the previous learning modules, and then predict the result on the fly. Based on the predicted result, the app will send in-time motivational messages and mindfulness training modules. |
| Measure | Description | Time Frame |
|---|---|---|
| Usability | The usability of the intervention will be assessed using the mHealth App Usability Questionnaire, a 21-item instrument designed for interactive mobile health apps. It measures three domains: ease of use and satisfaction, system information arrangement, and usefulness. Each item is rated on a 5-point Likert scale, yielding a total score range of 21 to 105, with higher scores indicating better usability. An open-ended question will also be included at the end of the survey to gather suggestions for app improvement. | 30-Day Follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| Engagement - Frequency of App Use | Automatically recorded log data from the app will track each login event by a participant. | 30-Day and 3-Month Follow-up |
| Engagement - Minutes of App Use | Cumulative minutes of app usage will be captured via app log data. |
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Inclusion Criteria
Exclusion Criteria
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Eunhee Park, PhD, RN | Contact | 716-829-3701 | eunheepa@buffalo.edu |
| Name | Affiliation | Role |
|---|---|---|
| Eunhee Park, PhD, RN | University at Buffalo | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University at Buffalo, School of Nursing | Buffalo | New York | 14214 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32149716 | Background | Hebert ET, Ra CK, Alexander AC, Helt A, Moisiuc R, Kendzor DE, Vidrine DJ, Funk-Lawler RK, Businelle MS. A Mobile Just-in-Time Adaptive Intervention for Smoking Cessation: Pilot Randomized Controlled Trial. J Med Internet Res. 2020 Mar 9;22(3):e16907. doi: 10.2196/16907. | |
| 23961782 | Background | Billingham SA, Whitehead AL, Julious SA. An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database. BMC Med Res Methodol. 2013 Aug 20;13:104. doi: 10.1186/1471-2288-13-104. |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Aug 11, 2024 | Jun 30, 2026 | Prot_001.pdf |
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| ID | Term |
|---|---|
| D014029 | Tobacco Use Disorder |
| ID | Term |
|---|---|
| D019966 | Substance-Related Disorders |
| D064419 | Chemically-Induced Disorders |
| D001523 | Mental Disorders |
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The study involves two groups: an immediate-intervention group that starts using the app immediately and a delayed-intervention group that begins after three months.
Each group is assigned to its respective intervention timeline at the start and follows it independently of the other group, without switching or crossover.
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| AI-enhanced smartphone app, but with delayed access | Behavioral | Participants in the control group will be placed on a three-month waitlist. After this period, they will receive access to the same app-based intervention as the immediate intervention group, allowing a comparison between immediate and delayed access. |
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| 30-Day and 3-Month Follow-up |
| E-cigarette Use | Self-reported number of days participants used e-cigarettes in the past 30 days. Responses range from 0 to 30 days. | 30-day and 3-month follow up |
| Quit attempts | Self-reported number of quit attempts in the past 30 days, along with the longest duration of abstinence. | 30-Day and 3-Month Follow-up |
| Readiness to Quit | Readiness to quit will be assessed using a modified Contemplation Ladder. The Contemplation Ladder ranges from 0 (no thought of quitting) to 10 (taking action to quit). Higher scores indicate greater readiness. | 30-Day and 3-Month Follow-up |
| Nicotine Dependence | Measured using the Penn State Electronic Cigarette Dependence Index (PSECDI), a 10-item scale with a total score range of 0 to 20. Higher scores indicate higher levels of nicotine dependence. | 30-Day and 3-Month Follow-up |
| Beliefs About E-Cigarettes | Assessed using a questions from the National Youth Tobacco Survey about e-cigarette harm, addictiveness, and benefits. | 30-Day and 3-Month Follow-up |
| 29403314 | Background | Bell ML, Whitehead AL, Julious SA. Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes. Clin Epidemiol. 2018 Jan 18;10:153-157. doi: 10.2147/CLEP.S146397. eCollection 2018. |
| 30355563 | Background | Baskerville NB, Struik LL, Guindon GE, Norman CD, Whittaker R, Burns C, Hammond D, Dash D, Brown KS. Effect of a Mobile Phone Intervention on Quitting Smoking in a Young Adult Population of Smokers: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2018 Oct 23;6(10):e10893. doi: 10.2196/10893. |
| 31351341 | Background | Audrain-McGovern J, Rodriguez D, Pianin S, Alexander E. Initial e-cigarette flavoring and nicotine exposure and e-cigarette uptake among adolescents. Drug Alcohol Depend. 2019 Sep 1;202:149-155. doi: 10.1016/j.drugalcdep.2019.04.037. Epub 2019 Jul 19. |