Trial reportThe American journal of clinical nutrition2023
Smartphone prediction of skeletal muscle mass: model development and validation in adults.
Trial report in The American journal of clinical nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Optical Body Composition and Health Assessment (Shape Up! Adults) Study
Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Skeletal Muscle and Cardiometabolic Health in Older Adults-REALPA Breaks in Sedentary Time Pilot Study
Evaluation of the Accuracy of a Novel Smartwatch Bioimpedance Device for Assessment of Body Composition
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Smartphone and tablet-based 3D scanner for anthropometric assessments in adults: a systematic review of reliability, validity, and accuracy.Biomedical engineering online · 2025Pooled it
- What is measured and what is estimated in body composition? A clarification framework for terminology and interpretation.Reviews in endocrine & metabolic disorders · 2026Review
- Advancing sarcopenia assessment with wearable and app-based technology: a scoping review.The journal of nutrition, health & aging · 2026Article
- Exploration of an interpretable machine learning-based screening manner for low muscle mass among Chinese community-dwelling older adults using routine physical examination information.BMC geriatrics · 2026Article
- Development of a metabolic syndrome prediction model using smartphone-derived digital anthropometry.The British journal of nutrition · 2026Article
- Predicting anthropometric body composition variables using 3D optical imaging and machine learning.Frontiers in bioinformatics · 2026Article
- The Critical Role of Body Composition Assessment in Advancing Research and Clinical Health Risk Assessment across the Lifespan.Journal of obesity & metabolic syndrome · 2025Review
- D3BT: Dynamic 3D Body Transformer for Body Fat Percentage Assessment.IEEE journal of biomedical and health informatics · 2025Article
- The impact of different body surface area prediction equations on ventricular dilatation prevalence in youth soccer players.Frontiers in cardiovascular medicine · 2025Article
- A predictive model for body water and fluid balance using 3D smartphone anthropometry.Frontiers in physiology · 2025Article
- Innovative digital approaches to characterize core factors of patients with late-stage knee osteoarthritis: a cross-sectional study.Frontiers in digital health · 2025Article
- Assessing alternative strategies for measuring metabolic risk.NPJ digital medicine · 2024Article
- Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.International journal of obesity (2005) · 2024Article
- Prediction of total and regional body composition from 3D body shape.NPJ digital medicine · 2024Article
- Detection of body shape changes in obesity monitoring using image processing techniques.Scientific reports · 2024Article
- Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification.Clinical nutrition (Edinburgh, Scotland) · 2024Article
- The Associations between Depression and Sugar Consumption Are Mediated by Emotional Eating and Craving Control in Multi-Ethnic Young Adults.Healthcare (Basel, Switzerland) · 2024Article
- Predicting Nonalcoholic Fatty Liver Disease in Obese Populations with 3D Body Scans.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.European journal of clinical nutrition · 2024Article
- Equations for smartphone prediction of adiposity and appendicular lean mass in youth soccer players.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSkeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes.
objectivesThis study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity.
methodsAppendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application.
resultsLASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R
conclusionsSmartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.