Clinical trial NCT07003516: Validation of Artificial Intelligence-based Digital Anthropometry Applications for Estimating Body Composition.
Official title: Validation and Development of Digital Anthropometry Methodologies.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Body Composition
Keywords: Kinanthropometry
Artificial intelligence
Data mining
Machine learning
Mobile Applications
3D Image
Lead sponsor: Dynamical Business and Science Society - DBSS International SAS
Sponsor class: NETWORK
Collaborators: name: CES University
name: Universidad de Córdoba
name: ARTHROS Centro de Fisioterapia y Ejercicio
First posted: 2025-06-04
Enrollment: 130
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The population sample is obtained through internal calls to both students and administrative staff of the participating universities, as well as through an open call, taking advantage of the collaboration agreement between DBSS International and Smart Fit.

Brief summary: 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: 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Anthropometric measurements
timeFrame: 40 Minutes
measure: Body composition and morphology by digital anthropometry
timeFrame: 15 Minutes
Secondary outcomes:
measure: Measurements of dual-energy X-ray absorptiometry
timeFrame: 10 Minutes
Other outcomes:
Locations:
city: Medellin
country: Colombia
facility: Universidad CES
state: Antioquia
city: Pereira
country: Colombia
facility: Fundación Universitaria del Área Andina
state: Risaralda
city: Bogota
country: Colombia
facility: Fundación Universitaria del Área Andina
Study officials:
affiliation: Research Division, DBSS International SAS.
name: Diego A Bonilla, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Research Division, DBSS International SAS
name: Jorge L Petro, PhD
role: STUDY_CHAIR
References: