UMIN-ICDS Clinical Trial
| Unique ID issued by UMIN | UMIN000052394 |
|---|---|
| Receipt number | R000059428 |
| Scientific Title | AI-based Online Cognitive Bias Modification and Cognitive Behavioral Therapy for Depression: A Randomized Controlled Trial |
| Date of disclosure of the study information | 2023/12/01 |
| Last modified on | 2024/04/03 09:21:37 |
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Basic information
Public title
AI-based Online Cognitive Intervention for Depression
Acronym
Online Cognitive Intervention for Depression
Scientific Title
AI-based Online Cognitive Bias Modification and Cognitive Behavioral Therapy for Depression: A Randomized Controlled Trial
Scientific Title:Acronym
AI-based Online Cognitive Bias Modification and Cognitive Behavioral Therapy for Depression
Region
| Japan |
Condition
Condition
Depressive symptoms
Classification by specialty
| Psychosomatic Internal Medicine | Psychiatry | Adult |
Classification by malignancy
Others
Genomic information
YES
Objectives
Narrative objectives1
Depressive disorder is a highly prevalent and debilitating mental disorder and is the leading cause of global disease burden. Efficient delivery of treatment and prevention to the population is more pressing than ever. Evidence from cognitive science indicates that cognitive bias plays a critical role in the psychopathology of depression. Cognitive bias is a cognitive-behavioral tendency characterized by preferential attentional focus on and interpretation of emotionally negative information and difficulties remembering non-negative information (specifically, contextual memory loss). Cognitive bias modification (CBM) and cognitive behavioral therapy (CBT), effective psychological interventions for depression, alleviate the cognitive bias; however, CBM targets all levels of the biases, including negative attention and contextual memory loss, while CBT mainly targets negative interpretation. Online delivery of a combined program of CBM and CBT might benefit a wider population with higher efficacy. However, previous findings on the efficacy of online CBM and CBT remain inconsistent partly because they lack appropriate guidance and feedback. Therefore, this study aims to evaluate the efficacy of an AI-based online CBM and CBT program for depressive symptoms, using Artificial Intelligence (AI) technology that overcomes the disadvantages of online CBM/CBT with an interactive feedback system.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Depressive symptoms
Key secondary outcomes
- Anxiety symptoms
- Physical symptoms
- Cognitive bias
- Personality traits (anxiety/depression-related)
- Biomarkers (including neuroimaging and endocrine/immune markers)
Base
Study type
Interventional
Study design
Basic design
Parallel
Randomization
Randomized
Randomization unit
Individual
Blinding
Double blind -all involved are blinded
Control
Placebo
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
3
Purpose of intervention
Educational,Counseling,Training
Type of intervention
| Behavior,custom | Other |
Interventions/Control_1
Cognitive Behavioral Therapy and Cognitive Bias Modification Training (targeted at diminution of contextual memory)
Interventions/Control_2
Cognitive Behavioral Therapy and Sham-Cognitive Bias Modification Training
Interventions/Control_3
Waiting list (individuals before participating in the study who are followed up for one month)
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 18 | years-old | <= |
Age-upper limit
| 60 | years-old | > |
Gender
Male and Female
Key inclusion criteria
- 18 years of age or older and less than 60 years of age
- Have scored 5 or more and less than 20 points on PHQ-9 for at least one month
Key exclusion criteria
- No schizophrenia, bipolar disorder, personality disorder, substance abuse, obsessive-compulsive disorder, or mental retardation
- No changes expected in pharmacotherapy during participating in the study period
- No other psychotherapy received or expected to receive during the study period
- No major medical illnesses
- No severe cognitive deficit such as due to neurodegeneration and brain injury/trauma
- No history of brain injury/trauma with loss of consciousness over 10 min
- No metal or medical appliance on/inside the body
- No habitual intake of excessive caffeine (> 400 mg/day) and Acetaminophen/Loxoprofen
Target sample size
160
Research contact person
Name of lead principal investigator
| 1st name | Yuko |
| Middle name | |
| Last name | Hakamata |
Organization
Kitasato University
Toyama University
National Center of Neurology and Psychiatry (NCNP)
Division name
School of Allied Health Sciences (Kitasato University)/ Faculty of Medicine (Toyama University)/ National Institute of Mental Health (NCNP)
Zip code
930-0194
Address
Sugitani 2630, Toyama
TEL
076-434-7566
hakamata@med.u-toyama.ac.jp
Public contact
Name of contact person
| 1st name | Yuko |
| Middle name | |
| Last name | Hakamata |
Organization
Natinal Institute of Mental Health, National Center for Neurology and Psychiatry
Division name
Department of Behavioral Medicine
Zip code
187-8553
Address
Ogawahigashi 4-1-1, Kodaira, Tokyo
TEL
042-346-1986
Homepage URL
cbmmri.research@gmail.com
Sponsor or person
Institute
Kitasato University
Institute
Department
Personal name
Funding Source
Organization
Toyama University
Organization
Division
Category of Funding Organization
Japanese Governmental office
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
The Kitasato University Medical Ethics Organization
Address
Kitasato 1-15-1, Minamiku, Sagamihara, Kanagawa
Tel
042-778-8273
rinrib@med.kitasato-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 12 | Month | 01 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2023 | Year | 09 | Month | 27 | Day |
Date of IRB
| 2023 | Year | 11 | Month | 27 | Day |
Anticipated trial start date
| 2023 | Year | 12 | Month | 09 | Day |
Last follow-up date
| 2028 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Management information
Registered date
| 2023 | Year | 10 | Month | 03 | Day |
Last modified on
| 2024 | Year | 04 | Month | 03 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000059428
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