Kinect Equations for Body Indices and Body Composition

NCT ID: NCT04969588

Last Updated: 2021-08-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

240 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-09-01

Study Completion Date

2024-07-31

Brief Summary

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This perspective observational study will recruited 240 participants for investigating the feasibility of using Microsoft Azure Kinect developer kit as a method for body indices and body composition. Reference methods will be dual energy X-ray absorptiometry and bioelectrical impedance analysis.

Detailed Description

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The purpose of this research is to validate the precision and accuracy of Microsoft Azure Kinect Developer Kit in the application of body anthropometric index and body composition measures. This study assumes that depth camera along with skeletal tracking package can estimate the 3D joint position and skeletal length. A dual energy X-ray absorptiometry scanner, a medical device with a daily background dose of radiation will be used as the gold standard.

This study aims to investigate the correlation and agreement of using Kinect in estimating skeletal length and body composition in human.A total of 240 participants including children and adults will be recruited for measurements in a 3-year period.

Conditions

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Anthropometry Body Composition

Keywords

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Anthropometry Body Composition

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Study Groups

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Children and adult

Participants aged 6-65 years old are recruited for the measurements of depth camera, bioelectrical impedance analysis and dual energy X-ray absorptiometry.

Microsoft Azure Kinect Developer Kit

Intervention Type DIAGNOSTIC_TEST

3D images and 3D joint coordinates are obtained.

Bioelectrical impedance analyzer

Intervention Type DIAGNOSTIC_TEST

Fat mass, fat-free mass and percentage body fat in total body and body segments are estimated.

Dual Energy X-ray Absorptiometry

Intervention Type DIAGNOSTIC_TEST

Skeletal length and body composition are estimated.

Interventions

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Microsoft Azure Kinect Developer Kit

3D images and 3D joint coordinates are obtained.

Intervention Type DIAGNOSTIC_TEST

Bioelectrical impedance analyzer

Fat mass, fat-free mass and percentage body fat in total body and body segments are estimated.

Intervention Type DIAGNOSTIC_TEST

Dual Energy X-ray Absorptiometry

Skeletal length and body composition are estimated.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Healthy children and adult
* Age 6-65 years

Exclusion Criteria

* Chronic illness or chronic medication use (\> 3 months)
* Pregnant
* Metal implant(s) or splint(s)
* Pacemaker implantation
* Limb defect(s) or injury
Minimum Eligible Age

6 Years

Maximum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ministry of Science and Technology, Taiwan

OTHER_GOV

Sponsor Role collaborator

Chang Gung Memorial Hospital

OTHER

Sponsor Role lead

Responsible Party

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Li-Wen Lee

Consultant Radiologist

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Li-Wen Lee, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Chang Gung Memorial Hospital, Chiayi, Taiwan

Locations

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Chang Gung Memorial Hospital

Chiayi City, , Taiwan

Site Status RECRUITING

Countries

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Taiwan

Central Contacts

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Li-Wen Lee, MD, PhD

Role: CONTACT

Phone: +886 5 3621 000

Email: [email protected]

Facility Contacts

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Li-Wen Lee, MD, PhD

Role: primary

Other Identifiers

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202002212A3

Identifier Type: -

Identifier Source: org_study_id