Predicting bone mineral content from smartphone digital anthropometrics: evaluation of an existing application and the development of new prediction models

Austin J Graybeal; Sydney H Swafford; Abby T Compton; Megan E Renna; Tanner Thorsen; Jon Stavres

doi:10.1016/j.jocd.2024.101537

Predicting bone mineral content from smartphone digital anthropometrics: evaluation of an existing application and the development of new prediction models

J Clin Densitom. 2024 Oct 24;28(1):101537. doi: 10.1016/j.jocd.2024.101537. Online ahead of print.

Authors

Austin J Graybeal¹, Sydney H Swafford², Abby T Compton², Megan E Renna³, Tanner Thorsen², Jon Stavres²

Affiliations

¹ School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA. Electronic address: austin.graybeal@usm.edu.
² School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA.
³ School of Psychology, University of Southern Mississippi, Hattiesburg, MS, USA.

PMID: 39509826
DOI: 10.1016/j.jocd.2024.101537

Abstract

Introduction/background: Bone mineral content (BMC) is most commonly evaluated using dual-energy X-ray absorptiometry (DXA), but there are several challenges that limit use of DXA during routine care. Breakthroughs in digital imaging now allow smartphone applications to automate important anthropometrics that can predict several body composition components. However, it is unknown whether the anthropometrics automated using smartphone applications can predict DXA-derived BMC.

Methodology: A total of 214 participants (129 F, 85 M) had BMC measurements collected from an existing proprietary prediction equation, embedded within a smartphone application (MeThreeSixty), and evaluated against DXA. LASSO regression was then used to develop a new BMC prediction equation using the anthropometric estimates produced by the smartphone application in a portion of the participants (n = 174), which was subsequently evaluated against DXA in the remaining sample (n = 40). BMC z-scores were calculated and used to identify the prevalence of low BMC for the existing and newly developed smartphone prediction equations and evaluated against DXA-derived z-scores.

Results: Neither BMC estimates (R²: 0.72; RMSE: 376 g) nor BMC z-scores (R²: 0.55; RMSE: 1.09 SD) produced from the existing propriety prediction equation demonstrated equivalence with DXA in the combined sample. Moreover, the existing prediction equation had a 69.6 % accuracy of identifying low BMC. LASSO regression for the newly developed smartphone prediction model produced the following equation: BMC (g) = -2020.769 + 60.902(Black=1, 0=all other races) - 180.364(Asian=1, 0=all other races) + 24.433(height) + 1.702(weight) + 2.92(shoulder circumference) + 0.258(arm surface area) - 715.29(waist circumference/(BMI^2/3 x height^1/2)). BMC (R²: 0.91; RMSE: 209 g) and BMC z-scores (R²: 0.85; RMSE: 0.61) produced from the newly developed equation in the testing sample demonstrated equivalence with DXA and had a 92.5 % accuracy of identifying low BMC.

Conclusions: Smartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.

Keywords: 3D; Body composition; Bone mineral; DXA; Digital anthropometrics; Smartphone application.