JMIR Publications

ABSTRACT

Background:

The effect of an unguided cognitive-behavioral therapy-based (CBT) stress management program on depression may be enhanced by applying artificial intelligence (AI) technologies to guide participants’ learning.

Objective:

The objective of this study is to propose a research protocol to investigate the effect of a newly developed machine-guided CBT stress management program on improving depression among workers during an outbreak of COVID-19.

Methods:

This study is a two-arm, parallel randomized control trial. Participants (N = 1,400) will be recruited and those who meet the inclusion criteria will be randomly allocated to the intervention or control (treatment as usual) group. A six-week, six-module Internet-based stress management program, SMART-CBT, has been developed that includes machine-guided exercises to help participants acquire CBT skills, applying machine learning and deep learning technologies. The intervention group will participate in the program for 10 weeks. Depression as the primary outcome will be measured using the Beck Depression Inventory II at baseline and in 3- and 6-month follow-up surveys. 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 the participants at the time of submission. The data analysis of the primary outcome will start in January 2022, and the results could be published in 2022.

Conclusions:

This is the first study to investigate the effectiveness of a fully-automated, machine-guided iCBT program on improving subthreshold depression among workers using a RCT 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. Clinical Trial: Trial registration number: UMIN000043897 (May 31, 2021).