NCT03706612

Shape Up! Kids Study

Completed Last updated 23 January 2023

What this trial tests

trial in Healthy in 430 participants. Completed in 4 January 2023.

Timeline

1 April 2017

Primary endpoint
4 January 2023

4 January 2023

Quick facts

Lead sponsor	University of Hawaii
Status	Completed
Study type	OBSERVATIONAL
Enrollment	430
Start date	1 April 2017
Primary completion	4 January 2023
Estimated completion	4 January 2023
Sites	3 locations across United States

Conditions studied

• Healthy — all drugs for Healthy →

Sponsor

University of Hawaii

Who can join

Adults 5 to 17, any sex, with Healthy. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Identify the unique associations of body shape to body composition and bone density indices in a pediatric population that represents the variance of sex, age, BMI-Z, and ethnicity found in the US population.

Describe the precision and accuracy of optical scans to monitor change in body composition, bone density and metabolic health interventions.

Estimate the level of association of optical to common health indicators including metabolic risk factors (glucose, triglycerides, HDL-cholesterol, blood pressure, VAT, WC and strength) by gender, race, age, and BMI-Z.

Investigate holistic, high-resolution descriptors of 3D body shape as direct predictors of body composition and metabolic risk using statistical shape models and Latent Class Analysis.

Publications & conference data

8 peer-reviewed publications reference this trial (live from Europe PMC):

1. Children and Adolescents' Anthropometrics Body Composition from 3-D Optical Surface Scans.
   Wong MC, Ng BK, Kennedy SF, Hwaung P, et al · · 2019 · cited 46× · PMID 31689009 · DOI 10.1002/oby.22637
2. Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use.
   Kennedy S, Hwaung P, Kelly N, Liu YE, et al · · 2020 · cited 16× · PMID 31551533 · DOI 10.1038/s41430-019-0501-2
3. Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans.
   Garber AK, Bennett JP, Wong MC, Tian IY, et al · · 2023 · cited 8× · PMID 37598747 · DOI 10.1016/j.ajcnut.2023.08.004
4. Emergence of the adolescent obesity epidemic in the United States: five-decade visualization with humanoid avatars.
   Bennett J, Wong MC, McCarthy C, Fearnbach N, et al · · 2022 · cited 8× · PMID 35610336 · DOI 10.1038/s41366-022-01153-9
5. Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry.
   Kennedy S, Smith B, Sobhiyeh S, Dechenaud ME, et al · · 2022 · cited 7× · PMID 34040201 · DOI 10.1038/s41430-021-00938-x
6. Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.
   Marazzato F, McCarthy C, Field RH, Nguyen H, et al · · 2024 · cited 5× · PMID 38142263 · DOI 10.1038/s41430-023-01396-3
7. Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations.
   Tian IY, Wong MC, Nguyen WM, Kennedy S, et al · · 2023 · cited 4× · PMID 37481870 · DOI 10.1016/j.clnu.2023.07.012
8. 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.
   Tian IY, Liu J, Wong MC, Kelly NN, et al · · 2025 · cited 2× · PMID 39894882 · DOI 10.1038/s41746-025-01469-6