UMIN-ICDS Clinical Trial
| Unique ID issued by UMIN | UMIN000059442 |
|---|---|
| Receipt number | R000067780 |
| Scientific Title | Developing Algorithms for Severity Prediction in Mental Health Management Applications |
| Date of disclosure of the study information | 2025/10/17 |
| Last modified on | 2025/10/17 13:59:08 |
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Basic information
Public title
Developing Algorithms for Severity Prediction in Mental Health Management Applications
Acronym
Developing Algorithms for Severity Prediction in Mental Health Management Applications
Scientific Title
Developing Algorithms for Severity Prediction in Mental Health Management Applications
Scientific Title:Acronym
Developing Algorithms for Severity Prediction in Mental Health Management Applications
Region
| Japan |
Condition
Condition
depression
Classification by specialty
| Psychiatry |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The final objective of this research is to create an application that enables daily visualization of mental health and provides tailored interventions, both digital content and medical care, based on the individual's condition. As a preparatory step, we will identify practical and sustainable indicators for monitoring mental health and develop severity prediction algorithms using these indicators.
Basic objectives2
Others
Basic objectives -Others
Mental health problems in the workplace, from mild to severe, negatively affect individuals and society. Yet, tools that allow daily monitoring, early detection, and timely intervention remain insufficient. Because the invisible state of mental health lacks clear criteria for visualization, there is a pressing need to establish practical and sustainable indicators and to develop severity prediction algorithms that make use of them.
Trial characteristics_1
Exploratory
Trial characteristics_2
Developmental phase
Not applicable
Assessment
Primary outcomes
Mental health problems in the workplace, from mild to severe, negatively affect individuals and society. Yet, tools that allow daily monitoring, early detection, and timely intervention remain insufficient. Because the invisible state of mental health lacks clear criteria for visualization, there is a pressing need to establish practical and sustainable indicators and to develop severity prediction algorithms that make use of them.
Key secondary outcomes
We will examine whether the severity of mental health states, classified as mild, moderate, or severe based on psychological measures related to current and future sleep, anxiety, and depression, can be predicted using machine learning algorithms applied to other simple indicators.
We will evaluate implementation and adherence rates of each application and questionnaire to identify tools that are feasible for daily use.
We will also assess which background factors strongly influence the primary algorithmic outcomes, thereby evaluating which approaches are most effective.
Furthermore, we will investigate whether there are correlations between daily questionnaires, heart rate variability, voice measurements, and weekly follow-up results, as well as their temporal fluctuations.
In addition, we will explore whether combining other indicators can predict both the "current state" and "subsequent changes" in mental health.
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
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
| 65 | years-old | >= |
Gender
Male and Female
Key inclusion criteria
1. Individuals aged between 18 and 65 years at the start of data collection
2. No restriction on sex
Key exclusion criteria
1. Difficulty in responding to questionnaires in Japanese
2. Not owning a smartphone capable of running the applications required for data collection
3. History of arrhythmia
4. Currently undergoing treatment with a pacemaker or antiarrhythmic medication
Target sample size
200
Research contact person
Name of lead principal investigator
| 1st name | Tomoyuki |
| Middle name | |
| Last name | Miyazaki |
Organization
Yokohama City University
Division name
Research and Industry-Academia Collaboration Division
Zip code
220-0012
Address
3-7-1 Minatomiari, Nishi-ku, Yokohama-shi, Kanagawa
TEL
05035757535
johney@yokohama-cu.ac.jp
Public contact
Name of contact person
| 1st name | Mizuki |
| Middle name | |
| Last name | Ohashi |
Organization
Yokohama City University
Division name
Research and Industry-Academia Collaboration Division
Zip code
220-0012
Address
3-7-1 Minatomiari, Nishi-ku, Yokohama-shi, Kanagawa
TEL
05035757535
Homepage URL
mizukion@belle.shiga-med.ac.jp
Sponsor or person
Institute
Yokohama City University
Institute
Department
Personal name
Funding Source
Organization
Japan Science and Technology Agency
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
Yokohama City University Ethics Committee
Address
3-9 Fukuura, Kanazawa-ku, Yokohama-shi, Kanagawa
Tel
045-370-7627
rinri@yokohama-cu.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
| 2025 | Year | 10 | Month | 17 | 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
Open public recruiting
Date of protocol fixation
| 2025 | Year | 07 | Month | 14 | Day |
Date of IRB
| 2025 | Year | 07 | Month | 14 | Day |
Anticipated trial start date
| 2025 | Year | 10 | Month | 01 | Day |
Last follow-up date
| 2026 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Observational items: questionnaire survey, heart rate measurement via smartphone application, and HAM-D (Hamilton Depression Rating Scale).
Management information
Registered date
| 2025 | Year | 10 | Month | 17 | Day |
Last modified on
| 2025 | Year | 10 | Month | 17 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000067780
