Exploration of Digital Interventions for Vaping Cessation: Scoping Review
Bibliographic record
Abstract
Background: Digital interventions have emerged as a promising approach to support vaping cessation, particularly among youth and young adults. Mobile apps, text messaging programs, telehealth-delivered contingency management, and web-based or social media interventions offer scalable and accessible alternatives to traditional cessation methods. However, there is considerable variation in how these interventions are designed, implemented, and evaluated, with inconsistencies in engagement strategies, theoretical frameworks, and long-term effectiveness. Objective: This scoping review aimed to map the current landscape of digital interventions for vaping cessation and identify key strategies, effectiveness outcomes, and implementation challenges. The following questions were addressed: (1) What digital interventions have been developed or evaluated for vaping cessation? (2) What evidence exists regarding the effectiveness of these digital interventions in promoting vaping cessation and user engagement? (3) What key barriers and facilitators influence the adoption, adherence, and success of digital vaping cessation interventions? (4) What gaps remain in the literature, and what areas should future research prioritize to enhance the design and effectiveness of digital vaping cessation tools? Methods: This scoping review followed the Joanna Briggs Institute methodology and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted in CINAHL, MEDLINE, and PsycINFO using search terms related to vaping cessation and digital health interventions. Studies examining mobile apps, text messaging programs, social media or web-based interventions, and telehealth coaching explicitly designed for vaping cessation were included. A narrative synthesis was conducted to identify common themes, barriers, and facilitators. Results: Sixteen studies were identified, including SMS text messaging programs, mobile apps, telehealth-delivered contingency management, and web-based or social media interventions. Many interventions reported moderate to high abstinence rates. Programs incorporating personalized messaging, behavioral tracking, and social and interactive features demonstrated greater retention and cessation success. However, minimal application of evidence-based behavior change frameworks, inconsistent reporting of engagement metrics, reliance on self-reported abstinence, and scalability limitations were noted. Conclusions: Digital interventions show promise for vaping cessation, particularly among youth and young adults, but current evidence highlights both opportunities and limitations. Effective interventions leverage personalization and social support to enhance engagement and quit outcomes. However, challenges such as high dropout rates, accessibility barriers, and limited use of rigorous evaluation methods persist. Future research should prioritize hybrid approaches that combine digital support with human interaction, apply equity-focused design principles, and adopt pragmatic, theory-driven evaluation methods to accelerate translation from pilot success to sustainable public health impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".