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      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

      research-article
      , MD, PhD 1 , , , PhD 1 , , PhD 2 , , PhD 1 , , MD 1 , , MPH 1 , SMART-CBT Project Team 1
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Research Protocols
      JMIR Publications
      deep learning, unguided intervention, universal prevention, workplace, depression, machine learning

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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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          Most cited references57

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          Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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            Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

            Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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              SPIRIT 2013 statement: defining standard protocol items for clinical trials.

              The protocol of a clinical trial serves as the foundation for study planning, conduct, reporting, and appraisal. However, trial protocols and existing protocol guidelines vary greatly in content and quality. This article describes the systematic development and scope of SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013, a guideline for the minimum content of a clinical trial protocol.The 33-item SPIRIT checklist applies to protocols for all clinical trials and focuses on content rather than format. The checklist recommends a full description of what is planned; it does not prescribe how to design or conduct a trial. By providing guidance for key content, the SPIRIT recommendations aim to facilitate the drafting of high-quality protocols. Adherence to SPIRIT would also enhance the transparency and completeness of trial protocols for the benefit of investigators, trial participants, patients, sponsors, funders, research ethics committees or institutional review boards, peer reviewers, journals, trial registries, policymakers, regulators, and other key stakeholders.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                September 2021
                29 September 2021
                : 10
                : 9
                : e30305
                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
                Author notes
                Corresponding Author: Norito Kawakami nkawakami@ 123456m.u-tokyo.ac.jp
                Author information
                https://orcid.org/0000-0003-1080-2720
                https://orcid.org/0000-0003-2584-0666
                https://orcid.org/0000-0002-3342-6142
                https://orcid.org/0000-0003-4529-6119
                https://orcid.org/0000-0002-6097-5948
                https://orcid.org/0000-0002-2806-9777
                Article
                v10i9e30305
                10.2196/30305
                8515231
                34460414
                68ba1d19-4c7d-437b-a8d5-bdb3eb8f3354
                ©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.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 21 May 2021
                : 24 June 2021
                : 27 July 2021
                : 9 August 2021
                Categories
                Protocol
                Protocol

                deep learning,unguided intervention,universal prevention,workplace,depression,machine learning

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