Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for
Norito Kawakami1, Kotaro Imamura1, Kazuhiro Watanabe2
1Department of Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
JMIR Research Protocols
|August 30, 2021
Summary
This study investigates an AI-guided internet-based cognitive behavioral therapy (iCBT) program to reduce depression in workers. The randomized controlled trial will assess the effectiveness of this novel, automated stress management approach.
Area of Science:
- Digital mental health interventions
- Artificial intelligence in healthcare
- Cognitive behavioral therapy
Background:
- Unguided internet-based cognitive behavioral therapy (iCBT) for stress may be improved with artificial intelligence (AI) guidance.
- AI can enhance participant engagement and adherence to iCBT programs.
Purpose of the Study:
- To describe a research protocol for a novel AI-guided iCBT stress management program.
- To investigate the program's effect on depression in healthy workers during the COVID-19 pandemic.
Main Methods:
- A two-arm, parallel, randomized controlled trial with 1400 participants.
- Development of SMART-CBT, an internet-based program using machine learning and deep learning for guided exercises.
- Primary outcome (depression) measured by Beck Depression Inventory II at baseline and 3- and 6-month follow-ups.
Main Results:
- Participant recruitment was underway at the time of submission.
- Data analysis for the primary outcome is scheduled to begin in January 2022.
- Results are anticipated for publication in 2022 or 2023.
Conclusions:
- This is the first randomized controlled trial of a fully automated, machine-guided iCBT program for subthreshold depression in workers.
- The study explores the potential for scalable, low-cost online stress management programs in the post-COVID-19 era.
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