ArticleJournal of medical Internet research2022
Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Understanding Adherence to Digital Health Technologies: Systematic Review of Predictive Factors.Journal of medical Internet research · 2025Pooled it
- Adherence, Acceptability, and Sexual Health Outcomes of the Odeya App-Based Intervention for Sexual Distress in Women With Endometriosis: Randomized Controlled Mixed Methods Trial.Journal of medical Internet research · 2026Trial
- Web-Based Video Intervention and Associated Factors for the Uptake of the Catch-Up Human Papillomavirus Vaccination in Japan: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Predictors of dropout in cognitive behavior and interpersonal online brief psychotherapies for essential professionals during the COVID-19 pandemic.Scientific reports · 2024Trial
- Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study.JMIR human factors · 2024Trial
- Effectiveness of Virtual Exercise Intervention in Adults With Major Depressive Disorder: A Systematic Review.Health science reports · 2026Review
- Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs.Healthcare (Basel, Switzerland) · 2026Article
- User engagement in the tuberculosis treatment support tools intervention and its impact on treatment outcomes: A secondary analysis of a pragmatic trial.PLOS digital health · 2026Article
- Who gets included? Equity in digital and decentralised mental health and neurodevelopmental trials: A systematic review.PLOS digital health · 2026Article
- Defining and reporting treatment dropout in blended therapy for mental health: scoping review and analysis.NPJ digital medicine · 2026Article
- Meeting people where they are: Crowdsourcing goal-specific personalized wellness practices.PLOS digital health · 2024Article
- Persuasive technologies design for mental and behavioral health platforms: A scoping literature review.PLOS digital health · 2024Article
- Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks.Proceedings of the ... International Joint Conference on Autonomous Agents and Multiagent Systems : AAMAS. International Joint Conference on Autonomous Agents and Multiagent Systems · 2024Article
- Internet-based self-administered intervention to reduce anxiety and depression symptomatology and improve well-being in 7 countries: protocol of a randomized control trial.Frontiers in psychology · 2024Article
- The Effects of a Digital, Transdiagnostic, Clinically and Peer-Moderated Treatment Platform for Young People With Emerging Mental Health Complaints: Repeated Measures Within-Subjects Study.JMIR mHealth and uHealth · 2023Article
- Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder.Internet interventions · 2023Article
- Exploring dropout in internet-delivered cognitive behavioral therapy for insomnia: A secondary analysis of prevalence, self-reported reasons, and baseline and intervention data as predictors.International journal of clinical and health psychology : IJCHPArticle
- 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings.
objectiveThis study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment.
methodsData were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants' baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants' baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression.
resultsFrom participants' baseline characteristics, lower level of education (odds ratio [OR] 3.33, 95% CI 1.51-7.32) and both lower and higher age (a quadratic effect; age: OR 0.62, 95% CI 0.47-0.82, and age
conclusionsDropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.