SANPSY, UMR 6033, University of Bordeaux, 33076 Bordeaux, France.
School of Nursing, Department of Nursing, University of Murcia, 30120 Murcia, Spain.
J Am Med Inform Assoc. 2023 Nov 17;30(12):1934-1942. doi: 10.1093/jamia/ocad163.
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.
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).
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).
Users' adherence is motivated by positive perceptions of the app's features and pre-intervention improvements.
Determinants of adherence should be assessed, and targeted, to increase the impact of fully automated digital interventions.
全自动化数字干预措施有望推广基于证据的策略来管理失眠投诉。然而，一个经常被忽视的重要概念是，用户在多大程度上将这些方案中的建议应用于日常生活中。我们的目标是评估用户对应用程序中提供的行为建议的遵守程度，并探讨用户对应用程序的看法是否会影响他们的遵守行为。
对 2020 年 12 月至 2022 年 9 月期间通过虚拟代理完成全自动化失眠管理方案的个体进行病例系列研究。主要结果是自我报告对提供的行为建议的遵守程度。使用可接受性电子量表和 ECA-Trust 问卷来衡量对应用程序和虚拟代理的看法。在基线（第 1 阶段）、睡眠监测后 7 天（第 2 阶段）和干预后（第 3 阶段）使用失眠严重程度指数评估失眠。
共纳入 824 名用户，其中 62.7%为女性，平均年龄 51.85（±12.55）岁。其中，32.7%的人报告至少遵守了一项建议。用户对虚拟代理的信任和对应用程序的接受度与失眠严重程度的干预前效应有关（第 2 阶段）。反过来，较大的干预前改善预测了更好的遵守。中介分析表明，对虚拟代理的信任度更高和对应用程序的接受度更好对遵守行为产生了显著的积极影响（β=0.007，95%置信区间，0.001-0.017 和 β=0.003，95%置信区间 0.0004-0.008）。
用户的遵守是由对应用程序功能和干预前改善的积极看法所驱动的。
应评估和针对遵守的决定因素，以提高全自动化数字干预措施的效果。
