ArticleDigital health
Predicting non-initiation of care and dropout in a blended care CBT intervention: Impact of early digital engagement, sociodemographic, and clinical factors.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study.JMIR human factors · 2024Trial
- Risk Factors for Noninitiation and Dropout in Blended Therapy in Inpatient Psychiatric Patients: Retrospective Cohort Study.JMIR human factors · 2026Observational
- The present and future of blended care: current research and introduction to the B-FIT framework.NPJ digital medicine · 2026Review
- Defining and reporting treatment dropout in blended therapy for mental health: scoping review and analysis.NPJ digital medicine · 2026Article
- Exploring psychotherapists' experiences in delivering effective treatment for major depression in Saudi Arabia: a qualitative study.Scientific reports · 2025Article
- The Role of Early Engagement in a Self-Directed, Digital Mental Health Intervention for Adolescent Anxiety: Moderated Regression Analysis.JMIR pediatrics and parenting · 2025Article
- Predictors of dropout in self-guided internet-delivered cognitive behaviour therapy for obsessive-compulsive disorder: An exploratory study.The British journal of clinical psychology · 2025Article
- Engagement With an Internet-Administered, Guided, Low-Intensity Cognitive Behavioral Therapy Intervention for Parents of Children Treated for Cancer: Analysis of Log-Data From the ENGAGE Feasibility Trial.JMIR formative research · 2025Article
- Comparing the Effectiveness of a Web-Based Application With a Digital Live Seminar to Improve Safe Communication for Pregnant Women: 3-Group Partially Randomized Controlled Trial.JMIR pediatrics and parenting · 2023Article
- The development of a smartphone app to enhance post-traumatic stress disorder treatment in high-risk workers.Digital healthArticle
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study examines predictors of non-initiation of care and dropout in a blended care CBT intervention, with a focus on early digital engagement and sociodemographic and clinical factors. Methods: This retrospective cohort analysis included 3566 US-based individuals who presented with clinical levels of anxiety and depression and enrolled in a blended-care CBT (BC-CBT) program. The treatment program consisted of face-to-face therapy sessions via videoconference and provider-assigned digital activities that were personalized to the client's presentation. Multinomial logistic regression and Cox proportional hazard survival analysis were used to identify predictors of an increased likelihood of non-initiation of therapy and dropout. Results: Individuals were more likely to cancel and/or no-show to their first therapy session if they were female, did not disclose their ethnicity, reported poor financial status, did not have a college degree, endorsed more presenting issues during the onboarding triage assessment, reported taking antidepressants, and had a longer wait time to their first appointment. Of those who started care, clients were significantly more likely to drop out if they did not complete the digital activities assigned by their provider early in treatment, were female, reported more severe depressive symptoms at baseline, reported taking antidepressants, and did not disclose their ethnicity. Conclusions: Various sociodemographic and clinical predictors emerged for both non-initiation of care and for dropout, suggesting that clients with these characteristics may benefit from additional attention and support (especially those with poor early digital engagement). Future research areas include targeted mitigation efforts to improve initiation rates and curb dropout.
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