ArticleJournal of clinical densitometry : the official journal of the International Society for Clinical Densitometry
Predicting Bone Mineral Content from Smartphone Digital Anthropometrics: Evaluation of an Existing Application and the Development of New Prediction Models.
Article in Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of a metabolic syndrome prediction model using smartphone-derived digital anthropometry.The British journal of nutrition · 2026Article
- Effects of a 12-week increased hip flexion gait exercise intervention in individuals with obesity and knee osteoarthritis: a randomized crossover feasibility study.Frontiers in sports and active living · 2026Article
- Advancing Precision Diagnosis of Sarcopenic Obesity Through Digital Technologies, Wearables and Omics Data.Life (Basel, Switzerland) · 2025Review
- Clinical Thresholds for Visceral Adiposity Accumulation: A Comparative Analysis in Sex-, Age-, and BMI-Matched Black and White Adults.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Comparing digital anthropometrics from mobile applications to reference methods: a scoping review.European journal of clinical nutrition · 2025Article
- Bone mineral content determined by energy X-ray absorptiometry correlates with handgrip strength in Latin American divers.Frontiers in public health · 2025Article
- A predictive model for body water and fluid balance using 3D smartphone anthropometry.Frontiers in physiology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
INTRODUCTION/
backgroundBone 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.
resultsNeither BMC estimates (R
conclusionsSmartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.
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