Understanding Dropout in Digital Psychosocial Interventions for Substance Use
The global burden of illicit drug use remains high, and digital psychosocial interventions have emerged as a flexible alternative to traditional therapy. This article distills evidence from a comprehensive systematic review and meta-analysis examining dropout rates and the factors that influence retention among adults with illicit substance use who participate in digital interventions. By clarifying how engagement, adherence, and dropout interrelate, we aim to guide researchers and clinicians in designing more effective, patient-centered digital treatments.
What the Evidence Says About Dropout Rates
Across 41 randomized trials involving nearly 9,700 participants, the analysis found a pooled dropout rate in the digital intervention groups of about 22% at posttreatment and 28% at the longest follow-up. While these figures suggest digital delivery can sustain participation somewhat better than traditional face-to-face programs (where dropout often hovers around 30%), the studies exhibited substantial heterogeneity. Differences in population characteristics, intervention design, recruitment strategies, and reporting quality all contributed to variable retention outcomes.
Posttreatment vs. Longest Follow-Up
At the end of treatment, digital interventions showed a modest advantage: lower dropout than controls (about 22% vs. 26%). Over longer follow-up periods, dropout edged up (29–28%), illustrating that retention challenges persist beyond active treatment. These patterns underscore the need for ongoing engagement strategies and adaptive support as participants transition from structured programs to maintenance phases.
Key Predictors of Dropout: What Matters Most
The review identified a set of multidimensional predictors spanning four major categories: participant demographics, baseline clinical characteristics, therapist or provider factors, and intervention design elements. Importantly, the findings emphasize that no single variable dictates dropout; instead, complex interactions shape retention risk at different stages of treatment.
Demographics and Social Context
Early dropouts were more common among those with employment, suggesting that work-related demands and perceived competing priorities can disrupt participation. Conversely, being single in longer follow-up periods was associated with lower dropout in some analyses, potentially reflecting different social dynamics and online engagement patterns. These results hint at the need for flexible scheduling, reminders, and social support modules that align with varied life circumstances.
Baseline Clinical Characteristics
Baseline mental health comorbidity and certain substance-use profiles were linked to higher dropout risk. In particular, participants with cocaine use at baseline demonstrated greater disengagement risks than those using cannabis or opioids, likely reflecting distinct withdrawal patterns and adherence challenges. Tailored supplementary therapies and phased engagement plans may help address these substance-specific risks.
Intervention Characteristics
Intervention frequency—i.e., how often participants received contact or content—showed a meaningful association with retention. More frequent contact tended to reduce dropout, indicating that sustained therapeutic engagement strengthens alliance and commitment. Recruitment channels also influenced dropout: online recruitment was linked to higher attrition in some studies, while campus-based recruitment showed lower dropout. These patterns suggest that recruitment context shapes initial engagement and ongoing participation, calling for mixed online-offline strategies and careful tailoring of online modules to participants recruited via web channels.
Measurement and Reporting: Why Clarity Matters
Although the evidence points to actionable predictors, the field suffers from inconsistent reporting about digital modalities, levels of human support, and program design details. Analyses that lumped together studies with unreported digitalization status produced unstable estimates, highlighting the need for standardized reporting. Future work should preregister intervention components, provide transparent descriptions of digital features, and share modulation strategies that reliably influence retention.
Clinical and Research Implications
The findings offer several practical implications. Clinically, integrating higher-frequency touchpoints, crisis-on-demand support, and personalized feedback may strengthen retention across diverse populations. For researchers, the results advocate a multidimensional approach to dropout, prioritizing interactions between participant characteristics and intervention design. Large-scale trials with rigorous reporting, participant-level data, and harmonized outcomes will improve our understanding of who benefits most from digital psychosocial interventions and how to minimize attrition.
Concluding Thoughts
Digital psychosocial interventions hold promise for expanding access to treatment for adults with illicit substance use, but dropout remains a key challenge. By recognizing the dynamic, multifactorial nature of retention and emphasizing standardized reporting, the field can develop more effective, user-centered digital tools that support sustained recovery.
