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COVID-19 and Virtual Nutrition: A Pilot Study of Integrating Digital Food Models for Interactive Portion Size Education.
Ho, Dang Khanh Ngan; Lee, Yu-Chieh; Chiu, Wan-Chun; Shen, Yi-Ta; Yao, Chih-Yuan; Chu, Hung-Kuo; Chu, Wei-Ta; Le, Nguyen Quoc Khanh; Nguyen, Hung Trong; Su, Hsiu-Yueh; Chang, Jung-Su.
  • Ho DKN; School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 110, Taiwan.
  • Lee YC; Smart Surgery Co., Ltd., Taipei 110, Taiwan.
  • Chiu WC; School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 110, Taiwan.
  • Shen YT; Department of Nutrition, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan.
  • Yao CY; Smart Surgery Co., Ltd., Taipei 110, Taiwan.
  • Chu HK; Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
  • Chu WT; Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan.
  • Le NQK; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
  • Nguyen HT; Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.
  • Su HY; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan.
  • Chang JS; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan.
Nutrients ; 14(16)2022 Aug 12.
Article in English | MEDLINE | ID: covidwho-1987906
ABSTRACT
BACKGROUND AND

AIMS:

Digital food viewing is a vital skill for connecting dieticians to e-health. The aim of this study was to integrate a novel pedagogical framework that combines interactive three- (3-D) and two-dimensional (2-D) food models into a formal dietetic training course. The level of agreement between the digital food models (first semester) and the effectiveness of educational integration of digital food models during the school closure due to coronavirus disease 2019 (COVID-19) (second semester) were evaluated.

METHOD:

In total, 65 second-year undergraduate dietetic students were enrolled in a nutritional practicum course at the School of Nutrition and Health Sciences, Taipei Medical University (Taipei, Taiwan). A 3-D food model was created using Agisoft Metashape. Students' digital food viewing skills and receptiveness towards integrating digital food models were evaluated.

RESULTS:

In the first semester, no statistical differences were observed between 2-D and 3-D food viewing skills in food identification (2-D 89% vs. 3-D 85%) and quantification (within ±10% difference in total calories) (2-D 19.4% vs. 3-D 19.3%). A Spearman correlation analysis showed moderate to strong correlations of estimated total calories (0.69~0.93; all p values < 0.05) between the 3-D and 2-D models. Further analysis showed that students who struggled to master both 2-D and 3-D food viewing skills had lower estimation accuracies than those who did not (equal performers 28% vs. unequal performers16%, p = 0.041), and interactive 3-D models may help them perform better than 2-D models. In the second semester, the digital food viewing skills significantly improved (food identification 91.5% and quantification 42.9%) even for those students who struggled to perform digital food viewing skills equally in the first semester (equal performers 44% vs. unequal performers 40%).

CONCLUSION:

Although repeated training greatly enhanced students' digital food viewing skills, a tailored training program may be needed to master 2-D and 3-D digital food viewing skills. Future study is needed to evaluate the effectiveness of digital food models for future "eHealth" care.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Simulation Training / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Nu14163313

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Simulation Training / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Nu14163313