Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study
This study provides independent validation of the use of first-week engagement to predict early dropout, demonstrating that the method generalizes across intervention modalities and engagement metrics. As digital intervention researchers continue to address the challenges of low engagement and early dropout, these results suggest that first-week engagement is a useful construct with predictive validity that is robust across interventions and definitions. Future research should explore the applicability and efficiency of this model to develop interventions to increase retention and improve health behavioral outcomes.
Authors
- Linda Q. Yu (ORCID: https://orcid.org/0000-0002-4658-9100)
- Michael S. Amato (ORCID: https://orcid.org/0000-0002-9769-957X)
- Sarah Cha (ORCID: https://orcid.org/0000-0003-3505-3164)
- George D. Papandonatos (ORCID: https://orcid.org/0000-0001-6770-932X)
- Amanda L. Graham (ORCID: https://orcid.org/0000-0003-3036-9653)
Institutions
- American Legacy Foundation (US)
- Mayo Clinic (US)
- Georgetown University (US)
- Brown University (US)
- Georgetown University Medical Center (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-10-14
- DOI
- https://doi.org/10.2196/54248
- Citations
- 2
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.82