Validation of Artificial Intelligence-based Digital Anthropometry Applications for Estimating Body Composition.

NCT ID: NCT07003516

Last Updated: 2025-06-04

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

RECRUITING

Total Enrollment

130 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-01-11

Study Completion Date

2025-12-31

Brief Summary

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Anthropometric measurements are commonly employed to evaluate body composition, morphology, and health-related parameters across diverse populations. While a cost-effective and field-friendly method, the COVID-19 pandemic has spurred research on digital anthropometry worldwide. Machine learning, a fusion of artificial intelligence and data mining, holds promise for enhancing data collection and analysis in Kinanthropometry applications. Rather than replacing traditional methods, digital anthropometry presents a significant opportunity to enhance accuracy, validity, practicality, and the implementation of self-monitoring procedures under professional guidance. The CyberMetron Project by DBSS aims to perform additional research and increase scientific literacy among practitioners for public awareness of digital anthropometry.

Detailed Description

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Anthropometric measurements are commonly used to assess body composition, morphology, and health-related parameters in diverse populations. Although it is an economical and practical method in the field, the COVID-19 pandemic has propelled worldwide research on digital anthropometry. Machine learning, a fusion of artificial intelligence and data mining, promises to enhance the collection and analysis of data in applications that rely on cineanthropometric data.

Instead of replacing traditional methods, digital anthropometry represents a significant opportunity to improve the precision, validity, practicality, and implementation of self-monitoring procedures under professional supervision. The CyberMetron Project by DBSS aims to conduct additional research and increase scientific literacy among professionals to raise awareness about digital anthropometry.

Data will be collected from residents of both genders and with different levels of physical activity. The population sample will be obtained through internal calls to both students and administrative staff at participating universities and through an open call. Anthropometric variables of restricted and complete profiles established by ISAK will be measured, along with digital images taken at different distances from the lens. Regarding the sample size, it will be conducted by convenience (non-probabilistic), primarily considering university students, administrative staff, and other potentially eligible adults who respond to the study announcement and sign the informed consent.

Finally, all statistical analyses will be performed within the statistical computing environment R v4.2.3, with a statistical significance of P\<0.05. Pearson's correlation coefficient (r), adjusted determination coefficient (aR²), and Lin's concordance correlation coefficient (ρc) will be used for comparative analysis between main variables taken by conventional anthropometry and digital anthropometry. The coefficient of repeated measures correlation (rrm) will be employed to assess the strength of the linear association between variables, while the intraclass correlation coefficient (ICC), with its corresponding 95% confidence interval (95% CI), and Finn's coefficient (rF) will be used to evaluate reliability between evaluators. Additionally, the Bland-Altman analysis will be applied for the concordance analysis between conventional and digital anthropometry.

Conditions

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Body Composition

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Sedentary

It will be obtained by open call for both students and administrative staff at the CES University.

No interventions assigned to this group

Physically active

DBSS will open a call for participation to get a sample of personal trainers and physically active population that want to be involved as sample participants.

No interventions assigned to this group

Athletes

Under the collaboration framework between DBSS international SAS and ARTHROS Physiotherapy and Exercise Center, a sample of professional athletes from different sports disciplines will be invited to participate.

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* People over 18 years of age (under 60 years of age)
* Persons born and living in the cities of the participating universities
* Signed informed consent to undergo evaluation of anthropometric measurements.

Exclusion Criteria

* Body mass ≥ 160 kg
* Amputations
* Pregnant women.
* People with implants or prostheses.
Minimum Eligible Age

18 Years

Maximum Eligible Age

60 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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CES University

OTHER

Sponsor Role collaborator

Universidad de Córdoba

OTHER

Sponsor Role collaborator

ARTHROS Centro de Fisioterapia y Ejercicio

UNKNOWN

Sponsor Role collaborator

Dynamical Business and Science Society - DBSS International SAS

NETWORK

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Diego A Bonilla, PhD

Role: PRINCIPAL_INVESTIGATOR

Research Division, DBSS International SAS.

Jorge L Petro, PhD

Role: STUDY_CHAIR

Research Division, DBSS International SAS

Locations

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Universidad CES

Medellín, Antioquia, Colombia

Site Status RECRUITING

Fundación Universitaria del Área Andina

Bogotá, Bogota D.C., Colombia

Site Status RECRUITING

Fundación Universitaria del Área Andina

Pereira, Risaralda Department, Colombia

Site Status RECRUITING

Countries

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Colombia

Central Contacts

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Diego A Bonilla

Role: CONTACT

+573203352050

Facility Contacts

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Katherine Franco-Hoyos, MSc

Role: primary

+573002719536

Luis A. Cardozo, MSc

Role: primary

+573133270327

Juan C Granados, MSc

Role: primary

+573174609496

Related Links

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https://doi.org/10.1016/j.jcgg.2014.01.006

Interrelationships between body mass to waist circumference ratio, body mass index, and total body muscularity in older women

https://www.semanticscholar.org/paper/Relative-fat-mass-is-a-better-tool-to-diagnose-high-Corr%C3%AAa-Formolo/6dda903bea801b92e38020306f29c934fead27bf

Relative fat mass is a better tool to diagnose high adiposity when compared to body mass index in young male adults: A cross-section study.

Other Identifiers

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CyberMetron_DBSS

Identifier Type: -

Identifier Source: org_study_id

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