PREDICTIVE ABILITY OF A 3D BODY SCANNING MOBILE APPLICATION FOR METABOLIC HEALTH RISK

<p>There is an increasing prevalence of obesity within the US and rising rates of metabolic syndrome among those aged 20-39 concurrent with a decrease in the reception of primary care. Limitations to healthcare including access, cost, and availability, highlighting the need for simple, efficient, and accessible cardiometabolic health risk screening. Given the surge in smartphone ownership over the last decade, this study sought to determine the predictive ability of a mobile 3D-optical (3DO) body composition assessment application in determining metabolic health risk. A total of 62 participants (female: 36) underwent traditional anthropometric measurements, 3DO body scanning using a smartphone application, and the collection of chronic health biomarkers from capillary blood. Metabolic syndrome risk scores (MSs) were determined using previously generated sex- and race/ethnicity-specific equations. Three prediction models were produced using variables extracted from the 3DO scans (anthropometric, body composition, and combined models), with the final models produced by backwards regression. Sex specific models were also generated. The combined model including both body composition and anthropometric variables provided the strongest predictor of MSs (R² = 0.64, p < 0.001), with performance improving when separated into female (R² = 0.77, p < 0.001) and male specific models (R² = 0.87, p = 0.002). The combined sex-specific models did not reveal significant proportional biases (female: coefficient = 0.138, p = 0.123, male: coefficient = -0.072, p = 0.142). The findings of this study provide preliminary evidence for the use of mobile 3DO scanning for cardiometabolic health risk screening. Thus, mobile 3DO scanning may provide an affordable, accessible, and easy to use tool that can be deployed remotely to improve healthcare access.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2023

Author dc:creator

• Brandner, Caleb

Contributors dc:contributor

• Dr. Austin Graybeal
• Dr. Jon Stavres
• Dr. Riley Galloway

• 3D body scanning
• remote healthcare
• metabolic syndrome
• antrhopometrics
• body composition
• telehealth
• Exercise Science
• Telemedicine

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/983

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-2056