Browsing by Subject "Mobile health"

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    Determinants of dropout from a virtual agent–based app for insomnia management in a self-selected sample of users with insomnia symptoms: longitudinal study
    (JMIR Publications, 2023) Sánchez Ortuño, María Montserrat; Pecune, Florian; Coelho, Julien; Micoulaud Franchi, Jean Arthur; Salles, Nathalie; Auriacombe, Marc; Serre, Fuschia; Levavasseur, Yannick; De Sevin, Etienne; Sagaspe, Patricia; Philip, Pierre; Enfermería
    Background: 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. 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 (VA)–based behavioral intervention for managing insomnia, named KANOPEE, which is freely available in France. Methods: 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. Results: 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). Conclusions: 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.
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    Predictors of users’ adherence to a fully automated digital intervention to manage insomnia complaints
    (Oxford University Press, 2023-12) Sánchez Ortuño, María Montserrat; Pecune, Florian; Coelho, Julien; Micoulaud Franchi, Jean Arthur; Salles, Nathalie; Auriacombe; Serre, Fuschia; Levavasseur, Yannick; Sevin, Etienne de; Sagaspe, Patricia; Philip, Pierre; Enfermería
    Objective Fully automated digital interventions show promise for disseminating evidence-based strategies to manage insomnia complaints. However, an important concept often overlooked concerns the extent to which users adopt the recommendations provided in these programs into their daily lives. Our objectives were evaluating users’ adherence to the behavioral recommendations provided by an app, and exploring whether users’ perceptions of the app had an impact on their adherence behavior. Material and methods Case series study of individuals completing a fully automated insomnia management program, conducted by a virtual agent, during December 2020 to September 2022. Primary outcome was self-reported adherence to the behavioral recommendations provided. Perceptions of the app and of the virtual agent were measured with the Acceptability E-Scale and ECA-Trust Questionnaire. Insomnia was evaluated with the Insomnia Severity Index at baseline (phase 1), after 7 days of sleep monitoring (phase 2) and post-intervention (phase 3). Results A total of 824 users were included, 62.7% female, mean age 51.85 (±12.55) years. Of them, 32.7% reported having followed at least one recommendation. Users’ trust in the virtual agent and acceptance of the app were related to a pre-intervention effect in insomnia severity (phase 2). In turn, larger pre-intervention improvements predicted better adherence. Mediational analyses showed that higher levels of trust in the virtual agent and better acceptance of the app exerted statistically significant positive effects on adherence (β = 0.007, 95% CI, 0.001-0.017 and β = 0.003, 95% CI 0.0004-0.008, respectively). Discussion Users’ adherence is motivated by positive perceptions of the app’s features and pre-intervention improvements. Conclusions Determinants of adherence should be assessed, and targeted, to increase the impact of fully automated digital interventions.