JMIR Publications

ABSTRACT

Background:

Fully-automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important value 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.

Objective:

The purpose of this study was to pinpoint the factors influencing early dropout in a sample of self-selected users of a virtual agent-based behavioural therapy for managing insomnia named Kanopee, which is freely available in France.

Methods:

From January 2021 to December 2022, of the 10,889 individuals, aged 18 or older, who downloaded and completed the Kanopee screening interview, 4,949 dropped out (i.e. did not return to the app to continue filling in subsequent assessments). Of these, 4,295 had either subclinical or clinical insomnia symptoms. The primary outcome was a binary variable: 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.

Results:

The users’ mean age was 47.95 years (SD: 15.21) (65.1% women). 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 degree: AOR 0.35, 95% CI 0.23-0.52; Master degree: 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-credibility of the virtual agent decreased the odds of dropout (AOR 0.91, 95% CI: 0.85- 0.97).

Conclusions:

As in traditional face-to-face cognitive-behavioural therapy for insomnia, perceptions of treatment credibility and the presence of significant depression symptoms play an important role in treatment dropout. These variables represent targets to be addressed in order to increase the outreach of fully automated insomnia therapy. Furthermore, these results highlight the added value of using virtual agents to deliver digital behavioural interventions.