Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial

Norito Kawakami; Kotaro Imamura; Kazuhiro Watanabe; Yuki Sekiya; Natsu Sasaki; Nana Sato; SMART-CBT Project Team

doi:10.2196/30305

Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial

JMIR Res Protoc. 2021 Sep 29;10(9):e30305. doi: 10.2196/30305.

Authors

Norito Kawakami¹, Kotaro Imamura¹, Kazuhiro Watanabe², Yuki Sekiya¹, Natsu Sasaki¹, Nana Sato¹; SMART-CBT Project Team¹

Affiliations

¹ Department of Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
² Department of Public Health, Kitasato University School of Medicine, Sagamihara, Japan.

PMID: 34460414
PMCID: PMC8515231
DOI: 10.2196/30305

Abstract

Background: The effect of an unguided internet-based cognitive behavioral therapy (iCBT) stress management program on depression may be enhanced by applying artificial intelligence (AI) technologies to guide participants adopting the program.

Objective: The aim of this study is to describe a research protocol to investigate the effect of a newly developed iCBT stress management program adopting AI technologies on improving depression among healthy workers during the COVID-19 pandemic.

Methods: This study is a two-arm, parallel, randomized controlled trial. Participants (N=1400) will be recruited, and those who meet the inclusion criteria will be randomly allocated to the intervention or control (treatment as usual) group. A 6-week, six-module, internet-based stress management program, SMART-CBT, has been developed that includes machine-guided exercises to help participants acquire CBT skills, and it applies machine learning and deep learning technologies. The intervention group will participate in the program for 10 weeks. The primary outcome, depression, will be measured using the Beck Depression Inventory II at baseline and 3- and 6-month follow-ups. A mixed model repeated measures analysis will be used to test the intervention effect (group × time interactions) in the total sample (universal prevention) on an intention-to-treat basis.

Results: The study was at the stage of recruitment of participants at the time of submission. The data analysis related to the primary outcome will start in January 2022, and the results might be published in 2022 or 2023.

Conclusions: This is the first study to investigate the effectiveness of a fully automated machine-guided iCBT program for improving subthreshold depression among workers using a randomized controlled trial design. The study will explore the potential of a machine-guided stress management program that can be disseminated online to a large number of workers with minimal cost in the post-COVID-19 era.

Trial registration: UMIN Clinical Trials Registry（UMIN-CTR) UMIN000043897; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050125.

International registered report identifier (irrid): PRR1-10.2196/30305.

Keywords: deep learning; depression; machine learning; unguided intervention; universal prevention; workplace.

©Norito Kawakami, Kotaro Imamura, Kazuhiro Watanabe, Yuki Sekiya, Natsu Sasaki, Nana Sato, SMART-CBT Project Team. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.09.2021.