Nisa Kelly

Clinical Research Coordinator

Background in biochemistry and medical health sciences; coordinated the Shape Up! Studies. Co-author on publications examining optical imaging for pediatric body composition.

Publications (24)

• 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology
  Tian IY, Liu J, Wong MC, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
  npj Digital Medicine, 2025· doi:10.1038/s41746-025-01469-6
• Evaluation of body shape as a human body composition assessment in isolated conditions and remote environments
  Wong MC, Bennett JP, Leong LT, Liu YE, Kelly NN, Cherry JA, Kloza K, Li B, Iuliano S, Sibonga JD, Sawyer A, Ayton J, Shepherd J
  npj Microgravity, 2024· doi:10.1038/s41526-024-00412-5
• Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification
  Bennett JP, Wong MC, Liu YE, Quon BK, Kelly NN, Garber AK, Heymsfield SB, Shepherd J
  Clinical Nutrition, 2024· doi:10.1016/j.clnu.2024.09.028
• Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans
  Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
  Communications Medicine, 2024· doi:10.1038/s43856-024-00434-w
• Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations
  Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, González MC, Heymsfield SB, Shepherd J
  Clinical Nutrition ESPEN, 2024· doi:10.1016/j.clnesp.2024.07.009
• Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis
  Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, Schoeller DA, Kelly TL, Heymsfield SB, Shepherd J
  Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.12.009
• Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations
  Tian IY, Wong MC, Nguyen WM, Kennedy S, McCarthy C, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
  Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.07.012
• 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, Maskarinec G, Kennedy S, McCarthy C, Kelly NN, Liu YE, Machen VI, Heymsfield SB, Shepherd J
  American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.08.004
• Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study
  Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly TL, Schoeller DA, Heymsfield SB, Shepherd J
  Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.11.040
• Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity
  Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
  American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.07.010
• Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry
  Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Wong JM, Ebbeling CB, Ludwig DS, Irving BA, Scott MC, Stampley J, Davis B, Johannsen NM, Matthews R, Vincellette C, Garber AK, ...
  American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.02.006
• A device‐agnostic shape model for automated body composition estimates from 3D optical scans
  Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
  Medical Physics, 2022· doi:10.1002/mp.15843
• Three‐dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults
  Bennett JP, Liu YE, Quon BK, Kelly NN, Leong LT, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
  Obesity, 2022· doi:10.1002/oby.23470
• Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample
  Bennett JP, Liu YE, Kelly NN, Quon BK, Wong MC, McCarthy C, Heymsfield SB, Shepherd J
  American Journal of Clinical Nutrition, 2022· doi:10.1093/ajcn/nqac200
• Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry
  Kennedy S, Smith B, Sobhiyeh S, Dechenaud M, Wong MC, Kelly NN, Shepherd J, Heymsfield SB
  European Journal of Clinical Nutrition, 2021· doi:10.1038/s41430-021-00938-x
• A pose‐independent method for accurate and precise body composition from 3D optical scans
  Wong MC, Ng BK, Tian IY, Sobhiyeh S, Pagano I, Dechenaud M, Kennedy S, Liu YE, Kelly NN, Chow DC, Garber AK, Maskarinec G, Pujades S, Black MJ, Curless B, Heymsfield SB, Shepherd J
  Obesity, 2021· doi:10.1002/oby.23256
• Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner
  Bennett JP, Liu YE, Quon BK, Kelly NN, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
  Clinical Nutrition, 2021· doi:10.1016/j.clnu.2021.11.031
• Predictors of liver fat among children and adolescents from five different ethnic groups
  Maskarinec G, Garber AK, Wong MC, Kelly NN, Kazemi L, Buchthal SD, Fearnbach N, Heymsfield SB, Shepherd J
  Obesity Science & Practice, 2020· doi:10.1002/osp4.459
• Diet Quality and Visceral Adiposity among a Multiethnic Population of Young, Middle, and Older Aged Adults
  Panizza C, Wong MC, Kelly NN, Liu YE, Shvetsov YB, Lowe DA, Weiss EJ, Heymsfield SB, Kennedy S, Boushey CJ, Maskarinec G, Shepherd J
  Current Developments in Nutrition, 2020· doi:10.1093/cdn/nzaa090
• Predicting 3D body shape and body composition from conventional 2D photography
  Tian IY, Ng BK, Wong MC, Kennedy S, Hwaung P, Kelly NN, Liu E, Garber AK, Curless B, Heymsfield SB, Shepherd J
  Medical Physics, 2020· doi:10.1002/mp.14492
• Effects of Time-Restricted Eating on Weight Loss and Other Metabolic Parameters in Women and Men With Overweight and Obesity
  Lowe DA, Wu N, Rohdin-Bibby L, Moore AH, Kelly NN, Liu YE, Philip EJ, Vittinghoff E, Heymsfield SB, Olgin JE, Shepherd J, Weiss EJ
  JAMA Internal Medicine, 2020· doi:10.1001/jamainternmed.2020.4153
• Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use
  Kennedy S, Hwaung P, Kelly NN, Liu YE, Sobhiyeh S, Heo M, Shepherd J, Heymsfield SB
  European Journal of Clinical Nutrition, 2019· doi:10.1038/s41430-019-0501-2
• Children and Adolescents’ Anthropometrics Body Composition from 3‐D Optical Surface Scans
  Wong MC, Ng BK, Kennedy S, Hwaung P, Liu EY, Kelly NN, Pagano I, Garber AK, Chow DC, Heymsfield SB, Shepherd J
  Obesity, 2019· doi:10.1002/oby.22637
• Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies
  Ng BK, Sommer MJ, Wong MC, Pagano I, Nie Y, Fan B, Kennedy S, Bourgeois B, Kelly NN, Liu YE, Hwaung P, Garber AK, Chow DC, Vaisse C, Curless B, Heymsfield SB, Shepherd J
  American Journal of Clinical Nutrition, 2019· doi:10.1093/ajcn/nqz218