Predictors of users' adherence to a fully automated digital intervention to manage insomnia complaints

Abstract

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.

Keywords: insomnia; mobile health; treatment adherence.

Figure 1.

Flow of the intervention with examples of interfaces of Kanopee. (A) Screenshot of Louise questioning the Insomnia Severity Index (ISI); (B) screenshot of sleep diary; (C) screenshot of a sleep recommendation given by Louise during Phase 2; and (D) screenshot of visual feedback provided by the app on the completion of each day of sleep diary.

Figure 2.

Flow of participants.

Figure 3.

Distribution of usability, satisfaction, benevolence, and credibility perceptions among users completing the Acceptability E-Scale and the ECA-Trust Questionnaire (n = 500).

Figure 4.

Path diagram of relationship of users’ perceptions, pre-intervention improvement, and adherence (n = 473).