AI-Driven Digital Therapeutic for Insomnia: Development and Clinical Validation

Condition(s) studied

Investigational drug(s) / intervention(s)

Digital BBTI-Based Sleep Management: Participants will receive a four-week digital sleep management intervention delivered through two independent platforms: a BBTI sleep diary website and a BBTI mobile application. Participants will record their sleep patterns daily through the website. The mobile application provides BBTI-related sleep education, behavioral sleep management information, and relaxation techniques to support insomnia management.

AI-Assisted Sleep Education and Personalized Guidance: Participants in the experimental group will additionally use an independent AI-powered website for four weeks. The website allows participants to ask questions about insomnia and sleep management, access sleep-related educational information, and receive AI-generated behavioral suggestions. The AI platform is intended to provide interactive and personalized guidance to support sleep management. It operates independently of the BBTI sleep diary website and mobile application.

Study summary

Insomnia is a prevalent global health problem, with approximately one in five Taiwanese adults chronically affected. Prolonged use of sedative-hypnotic medications carries risks of adverse effects, dependence, and unnecessary healthcare utilization. Although cognitive behavioral therapy for insomnia (CBT-I) is internationally recommended as first-line treatment, workforce shortages and suboptimal patient adherence limit its reach. This project will develop an artificial intelligence-driven mobile application for insomnia that implements the CBT-I components of stimulus control and sleep restriction. Using retrieval-augmented generation and large language models, we will build a localized sleep knowledge base to deliver personalized, interactive sleep guidance. A pilot randomized controlled trial will assess the intervention's effectiveness in reducing insomnia severity and sedative-hypnotic use. We will subsequently pursue a large-scale clinical trial and iterative model optimization, aiming to deliver the first clinically validated AI-enabled prescription digital therapeutic for insomnia and to advance smart healthcare in sleep care.

Eligibility

Primary outcome measure(s)

• Insomnia Severity Index (ISI) Score — Baseline and immediately after the 4-week intervention
  Insomnia severity will be assessed using the Chinese version of the Insomnia Severity Index (C-ISI). The ISI consists of 7 items assessing the severity and impact of insomnia symptoms. Each item is rated from 0 to 4, yielding a total score ranging from 0 to 28, with higher scores indicating greater insomnia severity.

Trial sites (1)

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