UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000049729 |
|---|---|
| Receipt number | R000056636 |
| Scientific Title | Verification of predictability of medical examination results by AI (artificial intelligence) and usefulness for specific health guidance method |
| Date of disclosure of the study information | 2022/12/09 |
| Last modified on | 2022/12/08 15:18:27 |
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Basic information
Public title
Verification of predictability of medical examination results by AI (artificial intelligence) and usefulness for specific health guidance method
Acronym
Verification of predictability of medical examination results by AI (artificial intelligence) and usefulness for specific health guidance method
Scientific Title
Verification of predictability of medical examination results by AI (artificial intelligence) and usefulness for specific health guidance method
Scientific Title:Acronym
Verification of predictability of medical examination results by AI (artificial intelligence) and usefulness for specific health guidance method
Region
| Japan |
Condition
Condition
non-communicable diseases
Classification by specialty
| Medicine in general |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The AI-based test value prediction software "Simulation for Prediction of Health Examination Results" developed by NEC has the potential to predict test values for three years this year, next year, and the year after that, based on test values for the past two years. Data from the Niigata Association of Occupational Health was used in the development of this program, which was developed after being selected as a "candidate for consignee to conduct AI/IoT utilization business creation demonstration in Niigata Prefecture in fiscal year 2008.
The specific health checkups and specific health guidance started in April 2008, and the third phase of specific health guidance started in 2018. Already, more than 10 years have passed. Regarding the issue of this specific health guidance, Tsushita et al. pointed out that the challenges of this specific health guidance are as follows: Difficulties in dealing with repeaters who continue to receive the guidance in the following year after its implementation and insufficient power of specialists and the need to improve health guidance techniques. They also feel the need to improve health guidance techniques. Therefore, by utilizing this "health checkup result prediction simulation," it is possible to use AI to show examinees how much their numerical values will improve if they improve their lifestyle habits as asked in the questionnaire in the specified health checkup. This "visualization" can provide examinees with targets for lifestyle improvement that have an impact on them, and can also be a tool for professionals in charge of health guidance to fill in the gaps between individuals.
Based on the above, we will verify the predictive ability of the prediction program by comparing the specific health guidance group (Group A) using the "health checkup result prediction simulation" and the conventional specific health guidance group (Group B).
Basic objectives2
Safety,Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Waist circumference, body weight, BMI, degree of improvement in health interest questionnaire after 3 months
Key secondary outcomes
Base
Study type
Interventional
Study design
Basic design
Parallel
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Active
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
2
Purpose of intervention
Prevention
Type of intervention
| Behavior,custom |
Interventions/Control_1
For the intervention group, the target to achieve weight loss recorded in the "Lifestyle Improvement Record Chart" will be shown to the examinee in actual improvement of laboratory values due to the lifestyle improvement indicated by the AI. With reference to this, the public health nurse and the subject consult and decide on the target.
Interventions/Control_2
In the control group, conventional guidance is provided without using AI.
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
| 40 | years-old | <= |
Age-upper limit
| 74 | years-old | >= |
Gender
Male and Female
Key inclusion criteria
Screening of examinees who are healthy persons who will undergo physical checkups from April 2020 to March 2021 and who have undergone tests for the required items and who are eligible for active support for specific health guidance are those who are eligible for active support.
Key exclusion criteria
Since AI predicts test values for the next year from this year's and last year's data, those who do not have last year's health checkup data are not eligible. Those who could not participate in health guidance due to reasons such as workplace changes (dropout cases), and those who refused informed consent (refusal cases) are considered.
Target sample size
600
Research contact person
Name of lead principal investigator
| 1st name | Kiminori |
| Middle name | |
| Last name | Kato |
Organization
Niigata University Graduate School of Medical & Dental Sciences
Division name
Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup
Zip code
951-8510
Address
1-754 Asahimachi, Niigata-City Niigata 951-8510 Japan
TEL
+81-25-227-2117
kkato48@med.niigata-u.ac.jp
Public contact
Name of contact person
| 1st name | Kiminori |
| Middle name | |
| Last name | Kato |
Organization
Niigata University Graduate School of Medical & Dental Sciences
Division name
Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup
Zip code
951-8510
Address
1-754 Asahimachi, Niigata-City Niigata 951-8510 Japan
TEL
+81-25-227-2117
Homepage URL
kkato48@med.niigata-u.ac.jp
Sponsor or person
Institute
Niigata University Graduate School of Medical & Dental Sciences
Institute
Department
Personal name
Funding Source
Organization
Niigata University Graduate School of Medical & Dental Sciences
Organization
Division
Category of Funding Organization
Government offices of other countries
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Ethics Committee of Niigata University
Address
1-754 Asahimachi, Niigata-City Niigata 951-8510 Japan
Tel
025-227-2625
ethics@adm.niigata-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
| 2022 | Year | 12 | Month | 09 | 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
1210
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
No longer recruiting
Date of protocol fixation
| 2020 | Year | 09 | Month | 27 | Day |
Date of IRB
| 2020 | Year | 09 | Month | 27 | Day |
Anticipated trial start date
| 2020 | Year | 09 | Month | 27 | Day |
Last follow-up date
| 2024 | 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
| 2022 | Year | 12 | Month | 08 | Day |
Last modified on
| 2022 | Year | 12 | Month | 08 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000056636
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