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干预设计对失眠症数字自助治疗的参与度和效果的影响——析因随机对照试验

Effects of intervention design on engagement and outcomes in digital self-help for insomnia - factorial RCT.

作者信息

Hentati Amira, Hentati Isacsson Nils, Rosén Ann, Jernelöv Susanna, Kaldo Viktor, Ljótsson Brjánn, Forsell Erik, Lindefors Nils, Kraepelien Martin

机构信息

Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.

Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

出版信息

NPJ Digit Med. 2025 Jul 8;8(1):416. doi: 10.1038/s41746-025-01839-0.

Abstract

Digital self-help can improve access to mental health care, but poor engagement limits effectiveness. This single-blind 2 x 2 x 2 factorial randomized controlled trial examined whether an optimized graphical user interface (GUI), automated reminders (AR), and an adaptive treatment strategy (ATS) improved engagement and outcomes in a digital self-help insomnia intervention. Adults (N = 447) with moderate to severe insomnia were randomized to combinations of the factors. The GUI improved self-rated engagement, sleep log activity, login frequency, and usability. AR increased sleep log activity and logins, while ATS improved satisfaction. All three combined significantly improved insomnia symptoms (d = 0.50). No severe adverse effects were reported. Clinicians spent 13.74 min on average on the ATS. Statistical analyses included linear and multilevel regression. Factors were effect coded. Intervention design can enhance engagement and outcomes, requiring minimal clinician time. Pre-registered 2023-04-11 (ClinicalTrials.gov, NCT05826002). Funded by the Swedish Ministry of Health and Social Affairs, grant number: S2018/03855/FS.

摘要

数字自助可以改善心理健康护理的可及性,但参与度低会限制其效果。这项双盲2×2×2析因随机对照试验研究了优化的图形用户界面(GUI)、自动提醒(AR)和自适应治疗策略(ATS)是否能改善数字自助失眠干预中的参与度和治疗效果。患有中度至重度失眠的成年人(N = 447)被随机分配到各因素的组合中。GUI提高了自我报告的参与度、睡眠日志活动、登录频率和可用性。AR增加了睡眠日志活动和登录次数,而ATS提高了满意度。所有三项结合显著改善了失眠症状(d = 0.50)。未报告严重不良反应。临床医生在ATS上平均花费13.74分钟。统计分析包括线性和多层回归。因素采用效应编码。干预设计可以提高参与度和治疗效果,所需临床医生时间最少。于2023年4月11日预先注册(ClinicalTrials.gov,NCT05826002)。由瑞典卫生和社会事务部资助,资助编号:S2018/03855/FS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bfb/12238518/3e6b4d4a784b/41746_2025_1839_Fig1_HTML.jpg

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本文引用的文献

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