Evaluating Augmented Reality for 'real Life' Teaching of Food Portion Concepts

Overview

Journal J Hum Nutr Diet

Date 2022 Apr 14

PMID 35419898

Authors

Ioannis Mellos

Yasmine Probst

Affiliations

Soon will be listed here.

Abstract

Background: Estimation of food portions is a vital skill for dietitians, which is developed during formal nutrition training. Skill development is often accomplished by training with food portion estimation tools. These tools can vary in design but evaluations often reveal them to be limited in their effectiveness and generally impractical for everyday use. The aim of this study was to develop and evaluate an augmented reality (AR) tool for the estimation food portions.

Methods: An online, quasi-experimental, randomised pre-test post-test study was conducted to evaluate the effectiveness of three food portion tools with nutrition students. These tools consisted of an online, AR, and an infographic tool (control). Students tested 10 different food images and were asked to estimate food portion sizes with and without assistance of a portion tool to determine absolute error, relative error, and overall improvement in estimation.

Results: A total of 33 participants enrolled in the study with 26 (72.0%) completing the study. The mean absolute error was lowest in the online group (53.0%), followed by AR (59.5%) and control (64.0%). Relative error scores revealed higher accuracy for the AR group (45.5%) followed by online (43.5%), and control group (29.0%). Overall improvement in estimation was highest in the AR group (+12.2%) followed by the online (+11.6%) tool with a decrease seen for the infographic (-1.7%) tool.

Conclusions: The use of technology, notably AR technology, may provide some advantage when training nutrition students in food portion estimation, although further investigation is advised.

Citing Articles

Enhancing consumer sensory science approach through augmented virtuality.

Zulkarnain A, Moskowitz H, Kokai Z, Gere A Curr Res Food Sci. 2024; 9:100834.

PMID: 39286427 PMC: 11403449. DOI: 10.1016/j.crfs.2024.100834.

Evaluating augmented reality for 'real life' teaching of food portion concepts.

Mellos I, Probst Y J Hum Nutr Diet. 2022; 35(6):1245-1254.

PMID: 35419898 PMC: 9790224. DOI: 10.1111/jhn.13016.

References

Rollo M, Bucher T, Smith S, Collins C . ServAR: An augmented reality tool to guide the serving of food. Int J Behav Nutr Phys Act. 2017; 14(1):65. PMC: 5429537. DOI: 10.1186/s12966-017-0516-9. View

Maunder K, Walton K, Williams P, Ferguson M, Beck E . eHealth readiness of dietitians. J Hum Nutr Diet. 2018; 31(4):573-583. DOI: 10.1111/jhn.12542. View

Arroyo M, Martinez de la Pera C, Ansotegui L, Rocandio A . A short training program improves the accuracy of portion-size estimates in future dietitians. Arch Latinoam Nutr. 2007; 57(2):163-7. View

Faulkner G, Livingstone M, Kirsty Pourshahidi L, Spence M, Dean M, OBrien S . An evaluation of portion size estimation aids: precision, ease of use and likelihood of future use. Public Health Nutr. 2016; 19(13):2377-87. PMC: 10270801. DOI: 10.1017/S1368980016000082. View

Wansink B, van Ittersum K . Portion size me: downsizing our consumption norms. J Am Diet Assoc. 2007; 107(7):1103-6. DOI: 10.1016/j.jada.2007.05.019. View

Japur C, Diez-Garcia R . Food energy content influences food portion size estimation by nutrition students. J Hum Nutr Diet. 2010; 23(3):272-6. DOI: 10.1111/j.1365-277X.2010.01042.x. View

Faulkner G, Livingstone M, Kirsty Pourshahidi L, Spence M, Dean M, OBrien S . An evaluation of portion size estimation aids: Consumer perspectives on their effectiveness. Appetite. 2017; 114:200-208. DOI: 10.1016/j.appet.2017.03.027. View

Robinson E, Kersbergen I . Portion size and later food intake: evidence on the "normalizing" effect of reducing food portion sizes. Am J Clin Nutr. 2018; 107(4):640-646. DOI: 10.1093/ajcn/nqy013. View

Steenhuis I, Poelman M . Portion Size: Latest Developments and Interventions. Curr Obes Rep. 2017; 6(1):10-17. PMC: 5359369. DOI: 10.1007/s13679-017-0239-x. View

10.

Mellos I, Probst Y . Evaluating augmented reality for 'real life' teaching of food portion concepts. J Hum Nutr Diet. 2022; 35(6):1245-1254. PMC: 9790224. DOI: 10.1111/jhn.13016. View

11.

Foster E, Matthews J, Nelson M, Harris J, Mathers J, Adamson A . Accuracy of estimates of food portion size using food photographs--the importance of using age-appropriate tools. Public Health Nutr. 2006; 9(4):509-14. DOI: 10.1079/phn2005872. View

12.

Kroeze W, Rongen F, Eykelenboom M, Heideman W, Bolleurs C, Govers E . A Process Evaluation of a Multi-Component Intervention in Dutch Dietetic Treatment to Improve Portion Control Behavior and Decrease Body Mass Index in Overweight and Obese Patients. Nutrients. 2018; 10(11). PMC: 6265690. DOI: 10.3390/nu10111717. View

13.

Huang K, Ball C, Francis J, Ratan R, Boumis J, Fordham J . Augmented Versus Virtual Reality in Education: An Exploratory Study Examining Science Knowledge Retention When Using Augmented Reality/Virtual Reality Mobile Applications. Cyberpsychol Behav Soc Netw. 2019; 22(2):105-110. PMC: 6389766. DOI: 10.1089/cyber.2018.0150. View

14.

Byrd-Bredbenner C, Schwartz J . The effect of practical portion size measurement aids on the accuracy of portion size estimates made by young adults. J Hum Nutr Diet. 2004; 17(4):351-7. DOI: 10.1111/j.1365-277X.2004.00534.x. View

15.

Biltoft-Jensen A, Holmgaard Nielsen T, Hess Ygil K, Christensen T, Fagt S . Accuracy of food photographs for quantifying food servings in a lunch meal setting among Danish children and adults. J Hum Nutr Diet. 2017; 31(1):131-140. DOI: 10.1111/jhn.12490. View

16.

Ayala G . An experimental evaluation of a group- versus computer-based intervention to improve food portion size estimation skills. Health Educ Res. 2005; 21(1):133-45. PMC: 3724534. DOI: 10.1093/her/cyh049. View

17.

Almiron-Roig E, Solis-Trapala I, Dodd J, Jebb S . Estimating food portions. Influence of unit number, meal type and energy density. Appetite. 2013; 71:95-103. PMC: 3857597. DOI: 10.1016/j.appet.2013.07.012. View

18.

Subar A, Crafts J, Zimmerman T, Wilson M, Mittl B, Islam N . Assessment of the accuracy of portion size reports using computer-based food photographs aids in the development of an automated self-administered 24-hour recall. J Am Diet Assoc. 2010; 110(1):55-64. PMC: 3773715. DOI: 10.1016/j.jada.2009.10.007. View

19.

Yang Y, Jia W, Bucher T, Zhang H, Sun M . Image-based food portion size estimation using a smartphone without a fiducial marker. Public Health Nutr. 2018; 22(7):1180-1192. PMC: 8115205. DOI: 10.1017/S136898001800054X. View

20.

McArdle P, Greenfield S, Avery A, Adams G, Gill P . Dietitians' practice in giving carbohydrate advice in the management of type 2 diabetes: a mixed methods study. J Hum Nutr Diet. 2017; 30(3):385-393. DOI: 10.1111/jhn.12436. View