BACKGROUND: Visualizations of the emerging obesity epidemic, such as with serial US color prevalence maps, provide graphic images that extend informative public health messages beyond those in written communications. Advances in low-cost 3D optical technology now allow for development of large image databases that include participants varying in race/ethnicity, body mass, height, age, and circumferences. When combined with contemporary statistical methods, these data sets can be used to create humanoid avatar images with prespecified anthropometric features. OBJECTIVES: The current study aimed to develop a humanoid avatar series with characteristics of representative US adults extending over the past 6 decades. METHODS: 3D optical scans were conducted on a demographically diverse sample of 570 healthy adults. Image data were converted to principal components and manifold regression equations were then developed with body mass, height, age, and waist circumference as covariates. Humanoid avatars were generated for representative adults with these 4 characteristics as reported in CDC surveys beginning in 1960-1962 up to 2015-2018. RESULTS: There was a curvilinear increase in adult US population body mass, waist circumference, and BMI in males and females across the 9 surveys spanning 6 decades. A small increase in average adult population age was present between 1960 and 2018; height changes were inconsistent. A series of 4 avatars developed at ∼20-y intervals for representative males and females reveal the changes in body size and shape consistent with the emergence of the obesity epidemic. An additional series of developed avatars portray the shapes and sizes of males and females at key BMI cutoffs. CONCLUSIONS: New mathematical approaches and accessible 3D optical technology combined with increasingly available large and diverse data sets across the life span now make unique visualization of body size and shape possible on a previously unattainable scale. This study is registered at https://clinicaltrials.gov/ct2/show/NCT03637855 as NCT03637855.
BACKGROUND: Visualizations of the emerging obesity epidemic, such as with serial US color prevalence maps, provide graphic images that extend informative public health messages beyond those in written communications. Advances in low-cost 3D optical technology now allow for development of large image databases that include participants varying in race/ethnicity, body mass, height, age, and circumferences. When combined with contemporary statistical methods, these data sets can be used to create humanoid avatar images with prespecified anthropometric features. OBJECTIVES: The current study aimed to develop a humanoid avatar series with characteristics of representative US adults extending over the past 6 decades. METHODS: 3D optical scans were conducted on a demographically diverse sample of 570 healthy adults. Image data were converted to principal components and manifold regression equations were then developed with body mass, height, age, and waist circumference as covariates. Humanoid avatars were generated for representative adults with these 4 characteristics as reported in CDC surveys beginning in 1960-1962 up to 2015-2018. RESULTS: There was a curvilinear increase in adult US population body mass, waist circumference, and BMI in males and females across the 9 surveys spanning 6 decades. A small increase in average adult population age was present between 1960 and 2018; height changes were inconsistent. A series of 4 avatars developed at ∼20-y intervals for representative males and females reveal the changes in body size and shape consistent with the emergence of the obesity epidemic. An additional series of developed avatars portray the shapes and sizes of males and females at key BMI cutoffs. CONCLUSIONS: New mathematical approaches and accessible 3D optical technology combined with increasingly available large and diverse data sets across the life span now make unique visualization of body size and shape possible on a previously unattainable scale. This study is registered at https://clinicaltrials.gov/ct2/show/NCT03637855 as NCT03637855.
Authors: Bennett K Ng; Markus J Sommer; Michael C Wong; Ian Pagano; Yilin Nie; Bo Fan; Samantha Kennedy; Brianna Bourgeois; Nisa Kelly; Yong E Liu; Phoenix Hwaung; Andrea K Garber; Dominic Chow; Christian Vaisse; Brian Curless; Steven B Heymsfield; John A Shepherd Journal: Am J Clin Nutr Date: 2019-12-01 Impact factor: 7.045
Authors: Michael C Wong; Bennett K Ng; Samantha F Kennedy; Phoenix Hwaung; En Y Liu; Nisa N Kelly; Ian S Pagano; Andrea K Garber; Dominic C Chow; Steven B Heymsfield; John A Shepherd Journal: Obesity (Silver Spring) Date: 2019-11 Impact factor: 5.002
Authors: Isaac Y Tian; Bennett K Ng; Michael C Wong; Samantha Kennedy; Phoenix Hwaung; Nisa Kelly; En Liu; Andrea K Garber; Brian Curless; Steven B Heymsfield; John A Shepherd Journal: Med Phys Date: 2020-10-20 Impact factor: 4.071
Authors: Marco Alessandro Minetto; Angelo Pietrobelli; Chiara Busso; Jonathan P Bennett; Andrea Ferraris; John A Shepherd; Steven B Heymsfield Journal: J Pers Med Date: 2022-05-30
Authors: Manfred J Müller; Anja Bosy-Westphal; Wiebke Braun; Michael C Wong; John A Shepherd; Steven B Heymsfield Journal: Nutrients Date: 2022-04-06 Impact factor: 5.717