Highlight
- The average dropout rate in digital psychosocial interventions for adult illicit drug users is approximately 22% during treatment and increases modestly to 28% at longest follow-up.
- Dropout rates are influenced by participant demographics (e.g., employment, marital status), clinical characteristics (baseline diagnosis, drug use type, medication frequency), and intervention features (frequency, recruitment method, and digitalization level).
- High heterogeneity exists across trials, underscoring the complexity of retention challenges and the need for personalized, precision approaches in digital substance use disorder (SUD) interventions.
- The study highlights the potential of digital interventions but emphasizes that dropout remains a significant barrier that must be addressed to optimize clinical outcomes.
Background
Illicit substance use represents a major global public health burden, contributing substantially to morbidity, mortality, and social disruption. Traditional psychosocial interventions remain cornerstone treatments, but access and adherence represent ongoing challenges. Digital psychosocial interventions—including apps, web-based platforms, and telehealth—offer innovative, scalable approaches with potential to expand treatment reach while adapting to patient needs. Despite promising efficacy signals, dropout from digital interventions remains a critical issue limiting real-world effectiveness. Understanding dropout prevalence and predictors among adult illicit drug users is essential to designing better engagement strategies and tailoring treatment delivery.
Study Design
This systematic review and meta-analysis adhered to the PRISMA guidelines and synthesized data from 41 randomized controlled trials (RCTs), encompassing 9693 adult illicit drug users. Trials were selected based on the availability of dropout data related to digital psychosocial interventions. Databases searched included Web of Science, PubMed, PsycINFO, Embase, and Cochrane Controlled Trials Register up to August 27, 2025. Two independent reviewers screened studies, extracted relevant data, and assessed bias risk using the Cochrane ROB 2.0 tool. Meta-analyses were performed using random-effects models via Comprehensive Meta-Analysis software (CMA 4.0), incorporating heterogeneity testing, sensitivity analyses, and publication bias assessment. Moderator analyses investigated factors potentially explaining variability in dropout rates.
Key Findings
The pooled dropout rate in intervention arms during treatment (18 studies) was 22% (95% CI, 13% to 36%), notably lower than the control group’s dropout rate of 26% (95% CI, 16% to 39%). Follow-up dropout rates (30 studies) rose modestly to 28.2% (95% CI, 19% to 39%) in the intervention groups and were similar in controls at 27.8% (95% CI, 20% to 37%).
Substantial between-study heterogeneity was evident throughout (I² > 95%, P<.001), prompting moderator analyses which revealed influential factors in dropout variability:
- Participant Demographics: Employment rates correlated with dropout during treatment, while single marital status predicted dropout at follow-up.
- Clinical Characteristics: Baseline psychiatric diagnoses and type of illicit drug used affected dropout during treatment; medication frequency at baseline influenced follow-up dropout.
- Intervention Attributes: Intervention frequency was linked to dropout variation during treatment. At follow-up, recruitment methods and degree of digitalization were significant moderators, highlighting the role of engagement strategies and technological delivery methods.
Publication bias and sensitivity analyses indicated robust results with minimal distortion from potential unpublished negative findings or outliers.
Expert Commentary
This comprehensive meta-analysis elucidates the multifactorial nature of dropout in digital psychosocial interventions for illicit drug use, emphasizing interplay between patient socio-demographic profiles, baseline clinical characteristics, and intervention design. The findings resonate with broader addiction literature underscoring that retention is not merely a function of treatment modality but also patient context and intervention tailoring. Engagement strategies that consider employment status or social support networks may enhance retention. Moreover, tailoring digital content frequency and modality to patient needs could mitigate dropout.
The high heterogeneity reflects both population diversity and evolving digital intervention formats, cautioning against one-size-fits-all approaches. Though digital interventions hold promise for increasing treatment access and flexibility, dropout remains a formidable barrier to effectiveness. Future research should focus on precision digital therapeutics, integrating real-time engagement metrics and adaptive interventions responsive to user behavior and clinical complexity.
Limitations include variability in outcome reporting across trials and potential confounding unmeasured factors such as severity of substance use or comorbidities. Nonetheless, the rigorous methodology and breadth of included studies provide valuable evidence to inform both clinical deployment and future research.
Conclusion
This meta-analysis highlights a meaningful dropout rate in digital psychosocial interventions for adult illicit substance users, with approximately one in four participants discontinuing treatment or follow-up. Dropout patterns are influenced by a complex interplay of demographic, clinical, and intervention-related factors, underscoring the need for personalized, adaptive treatment models in digital addiction care. These insights not only inform the development of more engaging and responsive digital interventions but also emphasize the importance of considering patient context to improve adherence and ultimately, treatment outcomes. Continued research is imperative to optimize digital care frameworks that can effectively address this global public health challenge.
Funding and Registration
Details on study funding and clinical trials registration were not provided in the source article.
References
Li J, Liu X, Du X, Mi T, Ren Z. Prevalence of Dropout and Influencing Factors in Digital Psychosocial Intervention Trials for Adult Illicit Substance Users: Systematic Review and Meta-Analysis. J Med Internet Res. 2025 Oct 10;27:e77853. doi: 10.2196/77853. PMID: 41072041; PMCID: PMC12513713.
