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
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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
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One study that used AI in cognitive behavioral therapy to alleviate workers' depression guided the program in a group format, which did not account for individual characteristics.
“One study that used AI in cognitive behavioral therapy to alleviate workers' depression, guided the program in a group format (Kawakami et al. 2021), which did not account for individual characteristics.”
From <scp>AI</scp>‐Assisted Tailored Intervention for Nurse Burnout: A Three‐Group Randomized Controlled Trial (2025) · cites this paper as Kawakami et al. 2021
The Methods > Intervention passage describes personalized predictions and messages based on each participant’s stressors and emotional reactions, contrary to the claim that the program did not account for individual characteristics. The paper describes an AI-guided CBT program for workers, but its Abstract > Objective identifies it as a trial protocol, not a completed demonstration that it alleviated depression. The cited passages have no page numbers in our copy. (LLM, conf=0.89)
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Experimental — accuracy not yet measuredOne study used AI in stress management programs.
“However, 1 study used AI in stress management programs [65].”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [65]
In that study, AI algorithms were used only to guide programs and programs were conducted in group settings, thus overlooking diversity.
“AI algorithms were used only to guide programs and programs were conducted in group settings, thus overlooking the aspect of diversity.”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [65]
No previous study has incorporated AI to provide tailored programs to users to reduce burnout.
“To the best of our knowledge, no previous study has incorporated AI to provide tailored programs to users to reduce burnout.”
From Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial (2024) · cites this paper as [65]
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- Authors
- Kawakami, N., Imamura, K., Watanabe, K., Sekiya, Y., Sasaki, N., Sato, N., & SMART-CBT Project Team
- Affiliations
- The University of Tokyo (JP)
- Kitasato University (JP)
- Corresponding author
- Kawakami, Norito
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2021-09-29
- Volume
- 10
- Issue
- 9
- Pages
- e30305
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC8515231
- OpenAlex
- W3191520835
- Cited by
- 12
- References
- 54
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (18H04072, 20K19671, 17K15849)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
- Keywords
- internet-based cognitive behavioral therapy, stress management, depression, subthreshold depression, artificial intelligence, deep learning, machine learning, workers, workplace mental health, COVID-19 pandemic, universal prevention, online intervention
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