Determinants of Dropout From a Virtual Agent-Based App for Insomnia Management in a Self-Selected Sample of Users...

Fully automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important aspect is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities. However, a major challenge to their successful implementation is the phenomenon of users dropping out early.

The purpose of this study was to pinpoint the factors influencing early dropout in a sample of self-selected users of a virtual agent (VA)-based behavioral intervention for managing insomnia, named KANOPEE, which is freely available in France.

From January 2021 to December 2022, of the 9657 individuals, aged 18 years or older, who downloaded and completed the KANOPEE screening interview and had either subclinical or clinical insomnia symptoms, 4295 (44.5%) dropped out (ie, did not return to the app to continue filling in subsequent assessments). The primary outcome was a binary variable: having dropped out after completing the screening assessment (early dropout) or having completed all the treatment phases (n=551). Multivariable logistic regression analysis was used to identify predictors of dropout among a set of sociodemographic, clinical, and sleep diary variables, and users' perceptions of the treatment program, collected during the screening interview.

The users' mean age was 47.95 (SD 15.21) years. Of those who dropped out early and those who completed the treatment, 65.1% (3153/4846) were women and 34.9% (1693/4846) were men. Younger age (adjusted odds ratio [AOR] 0.98, 95% CI 0.97-0.99), lower education level (compared to middle school; high school: AOR 0.56, 95% CI 0.35-0.90; bachelor's degree: AOR 0.35, 95% CI 0.23-0.52; master's degree or higher: AOR 0.35, 95% CI 0.22-0.55), poorer nocturnal sleep (sleep efficiency: AOR 0.64, 95% CI 0.42-0.96; number of nocturnal awakenings: AOR 1.13, 95% CI 1.04-1.23), and more severe depression symptoms (AOR 1.12, 95% CI 1.04-1.21) were significant predictors of dropping out. When measures of perceptions of the app were included in the model, perceived benevolence and credibility of the VA decreased the odds of dropout (AOR 0.91, 95% CI 0.85-0.97).

As in traditional face-to-face cognitive behavioral therapy for insomnia, the presence of significant depression symptoms plays an important role in treatment dropout. This variable represents an important target to address to increase early engagement with fully automated insomnia management programs. Furthermore, our results support the contention that a VA can provide relevant user stimulation that will eventually pay out in terms of user engagement.