Literature DB >> 33430147

Integration of an Image-Based Dietary Assessment Paradigm into Dietetic Training Improves Food Portion Estimates by Future Dietitians.

Dang Khanh Ngan Ho1, Wan-Chun Chiu1,2, Yu-Chieh Lee3, Hsiu-Yueh Su1,4, Chun-Chao Chang5,6, Chih-Yuan Yao7, Kai-Lung Hua7, Hung-Kuo Chu8, Chien-Yeh Hsu9,10, Jung-Su Chang1,11,12,13.   

Abstract

The use of image-based dietary assessments (IBDAs) has rapidly increased; however, there is no formalized training program to enhance the digital viewing skills of dieticians. An IBDA was integrated into a nutritional practicum course in the School of Nutrition and Health Sciences, Taipei Medical University Taiwan. An online IBDA platform was created as an off-campus remedial teaching tool to reinforce the conceptualization of food portion sizes. Dietetic students' receptiveness and response to the IBDA, and their performance in food identification and quantification, were compared between the IBDA and real food visual estimations (RFVEs). No differences were found between the IBDA and RFVE in terms of food identification (67% vs. 71%) or quantification (±10% of estimated calories: 23% vs. 24%). A Spearman correlation analysis showed a moderate to high correlation for calorie estimates between the IBDA and RFVE (r ≥ 0.33~0.75, all p < 0.0001). Repeated IBDA training significantly improved students' image-viewing skills [food identification: first semester: 67%; pretest: 77%; second semester: 84%) and quantification [±10%: first semester: 23%; pretest: 28%; second semester: 32%; and ±20%: first semester: 38%; pretest: 48%; second semester: 59%] and reduced absolute estimated errors from 27% (first semester) to 16% (second semester). Training also greatly improved the identification of omitted foods (e.g., condiments, sugar, cooking oil, and batter coatings) and the accuracy of food portion size estimates. The integration of an IBDA into dietetic courses has the potential to help students develop knowledge and skills related to "e-dietetics".

Entities:  

Keywords:  dietetic training; image-based dietary assessment; portion size estimation

Mesh:

Year:  2021        PMID: 33430147      PMCID: PMC7827495          DOI: 10.3390/nu13010175

Source DB:  PubMed          Journal:  Nutrients        ISSN: 2072-6643            Impact factor:   5.717


  33 in total

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Authors:  Courtney K Wilson; June I Matthews; Jamie A Seabrook; Paula D N Dworatzek
Journal:  Appetite       Date:  2016-10-11       Impact factor: 3.868

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Journal:  Nutrients       Date:  2018-06-07       Impact factor: 5.717

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Authors:  Ayob Ainaa Fatehah; Bee Koon Poh; Safii Nik Shanita; Jyh Eiin Wong
Journal:  Nutrients       Date:  2018-07-27       Impact factor: 5.717

10.  Interrater reliability: the kappa statistic.

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  2 in total

1.  A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Authors:  Wenyan Jia; Yiqiu Ren; Boyang Li; Britney Beatrice; Jingda Que; Shunxin Cao; Zekun Wu; Zhi-Hong Mao; Benny Lo; Alex K Anderson; Gary Frost; Megan A McCrory; Edward Sazonov; Matilda Steiner-Asiedu; Tom Baranowski; Lora E Burke; Mingui Sun
Journal:  Sensors (Basel)       Date:  2022-02-15       Impact factor: 3.576

2.  COVID-19 and Virtual Nutrition: A Pilot Study of Integrating Digital Food Models for Interactive Portion Size Education.

Authors:  Dang Khanh Ngan Ho; Yu-Chieh Lee; Wan-Chun Chiu; Yi-Ta Shen; Chih-Yuan Yao; Hung-Kuo Chu; Wei-Ta Chu; Nguyen Quoc Khanh Le; Hung Trong Nguyen; Hsiu-Yueh Su; Jung-Su Chang
Journal:  Nutrients       Date:  2022-08-12       Impact factor: 6.706

  2 in total

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