Journal List > Korean J Community Nutr > v.22(6) > 1038603

TOOLS
Kim and Chung: Development and User Satisfaction of a Mobile Phone Application for Image-based Dietary Assessment
Research Article

Korean Journal of Community Nutrition 2017; 22(6): 485-494.

Published online: 31 December 2017

DOI: https://doi.org/10.5720/kjcn.2017.22.6.485

Development and User Satisfaction of a Mobile Phone Application for Image-based Dietary Assessment

Seo-Yoon Kim, Sang-Jin Chung

Department of Foods & Nutrition, Kookmin University, Seoul, Korea.

Corresponding author: Sang-Jin Chung. Department of Foods and Nutrition, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Korea. Tel: (02) 910-4777, Fax: (02) 910-5249, schung@kookmin.ac.kr

Received 7 December 2017       Revised 25 December 2017       Accepted 25 December 2017

Copyright © 2017 The Korean Society of Community Nutrition

The Korean Society of Community Nutrition

(open-access):

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives

The objective of this study was to develop mobile phone application for image-based dietary assessment and evaluate satisfaction regarding respondent's use of the mobile phone application.

Methods

We developed a mobile phone application to assess dietary intakes using 24 hour dietary recall. After initial development, application was reviewed by ten adults and revised based on their comments. We recruited 192 volunteers (92 males, 100 females) to use the mobile phone application and to respond to a satisfaction survey. Participants were instructed to use the mobile phone application with fiducial marker five centimeter in width, length and two centimeter height at each eating occasion during designated 4 days, capturing 45° angle and 90° angle images of all food and beverage items before and after consumption. After using the mobile phone application for 4 days, participants were asked to complete an online questionnaire on the satisfaction of the mobile phone app. User satisfaction items composed of 12 questions of application user interface, 8 questions of emotional response, 9 questions of eating behavior in 5 likert scale. Participants were also asked to provide additional open-ended comments on the use of mobile phone application. Statistical analysis was performed by using the SPSS 23.0 (Statistical Package for the Social Science).

Results

The average user interface score was 2.82 ± 1.08, which was close to the ‘normal’ response. Responses for emotion and eating behavior also were borderline to the ‘normal’.

Conclusions

This study found that the mobile phone application using 24-hour recall was acceptable to be used to assess dietary intakes for several days. However, there should be a need for such technology to be user-oriented instead of researcher-oriented. Easy and cost-effective new technology is needed for estimating the amounts of food eaten automatically when the photos are taken.

Keywords: 24-hour recall, dietary assessment, mobile application, image-based, satisfaction

Figures and Tables

Fig. 1

Main screens of the 24-hour recall application tool

kjcn-22-485-g001
Table 1

General characteristics of the study subjects

kjcn-22-485-i001

Mean±SD

Table 2

Satisfaction assessment mean values of questions on interface

kjcn-22-485-i002

The participants used a 5-point Likert scales.

(1. strongly disagree, 2. disagree, 3. neutral, 4. agree, 5. strongly agree).

Table 3

Satisfaction assessment mean values of questions on emotional reaction

kjcn-22-485-i003

The users used a 5-point Likert scales.

(1. strongly disagree, 2. disagree, 3. neutral, 4. agree, 5. strongly agree).

Table 4

Satisfaction assessment mean values of questions on eating behavior

kjcn-22-485-i004

The users used a 5-point Likert scales.

(1. strongly disagree, 2. disagree, 3. neutral, 4. agree, 5. strongly agree).

Table 5

Correlation between the items of the satisfaction assessment

kjcn-22-485-i005

**: p<0.01

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2059985)

References

1. Kim MK, Lee SS, Ahn YO. Reproducibility and validity of a self-administered semiquantitative food frequency questionnaire among middle-aged men in Seoul. Korean J Community Nutr. 1996; 1(3):376–394.
2. Kim HR. A study on the association of diet quality and risk of mortality and major chronic diseases from nationally representative longitudinal data. Health Soc Welf Rev. 2013; 33(3):5–30.
3. Kang H, Jung HJ, Paik HY. Analysis of foods and nutrients intake obtained at the final probing step in 24-hour recall method. Korean J Nutr. 2009; 42(2):158–170.
4. Cameron ME, Van Staveren WA. Manual on methodology for food consumption studies. 1st revision. Boston: Oxford University Press;1988. p. 33.
5. Willet W. Nutritional epidemiology. 1st revision. New York: Oxford University Press;1990. p. 100.
6. Schatzkin A, Kipnis V, Carroll RJ, Midthune D, Subar AF, Bingham S, et al. A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: Results from the biomarker-based observing protein and energy nutrition (OPEN) study. Int J Epidemiol. 2003; 32(6):1054–1062.
7. Lee SY, Ju DL, Paik HY, Shin CS, Lee HK. Assessment of dietary intake obtained by 24-hour recall method in adults living in Yeonchon area (1): assessment based on nutrient intake. Korean J Nutr. 1998; 31(3):333–342.
8. Dwyer JT, Krall EA, Coleman KA. The problem of memory in nutritional epidemiological research. J Am Diet Assoc. 1987; 87(11):1509–1512.
9. Jobe JB, Mingay DJ. Cognitive research improves questionnaires. Am J Public health. 1989; 79(8):1053–1055.
10. Willet W. Nutritional epidemiology. 2nd ed. New York: Oxford University Press;1995. p. 111.
11. Krall EA, Dwyer JT, Coleman KA. Factors influencing accuracy of dietary recall. Nutr Res. 1988; 8(7):829–841.
12. Lee JE, Ahn Y, Kimm K, Park C. Study on the associations of dietary variety and nutrition intake level by the number of survey days. Korean J Nutr. 2004; 37(10):908–916.
13. Chang UJ, Ko SA. A study on the dietary intake survey method using a cameraphone. Korean J Community Nutr. 2007; 12(2):198–205.
14. Kim SB, Kim SK, Kim SN, Cho YS, Kim MH. Establishment of one portion size of dishes frequently consumed by Korean adults using 2010 KNHANES and its comparison with the one portion size using 2005 KNHANES: focusing on rice, noodles, soups, and stews. Korean J Food Nutr. 2013; 26(4):745–752.
15. Nelson M, Atkinson M, Darbyshire S. Food photography I: the perception of food portion size from photographs. Br J Nutr. 1994; 72(5):649–663.
16. Nelson M, Atkinson M, Darbyshire S. Food photography II: use of food photographs for estimating portion size and the nutrient content of meals. Br J Nutr. 1996; 76(1):31–49.
17. Turconi G, Guarcello M, Berzolari FG, Carolei A, Bazzano R, Roggi C. Evaluation of a colour food photography atlas as a tool for quantifying food portion size in epidemiological dietary survey. Eur J Clin Nutr. 2005; 59(8):923–931.
18. Ovaskainen ML, Paturi M, Reinivuo H, Hannila ML, Sinkko H, Lehtisalo J. Accuracy in the estimation of food servings against the portions in food photographs. Eur J Clin Nutr. 2008; 62(5):674–681.
19. Lazarte CE, Encinas ME, Alegre C, Granfeldt Y. Validation of digital photographs, as a tool in 24-h recall, for the improvement of dietary assessment among rural populations in developing countries. Nutr J. 2012; 11:61.
20. Kim HS, Lee EY, Kim K, Kim KW, Pyun J, Chung SJ. Survey on dietary behaviors and intakes of instant noodle (Ramyeon) soup among college students. Korean J Community Nutr. 2013; 18(4):365–371.
21. Wang DH, Kogashiwa M, Ohta S, Kira S. Validity and reliability of dietary assessment method: The application of a digital camera with a mobile phone card attachment. J Nutr Sci Vitaminol. 2002; 48(6):498–504.
22. Williamson DA, Allen HR, Martin PD, Alfonso AJ, Gerald B, Hunt A. Comparison of digital photography to weighed and visual estimation of portion size. J Am Diet Assoc. 2003; 103(9):1139–1145.
23. Wang DH, Kogashiwa M, Kira S. Development of a new instrument for evaluating individuals' dietary intakes. J Am Diet Assoc. 2006; 106(10):1588–1593.
24. Kwon JS, Kim K, Kim HK. A study on application of food photographs for estimating individual' dietary intake. Korean J Community Nutr. 2010; 15(6):760–775.
25. Six BL, Schap TE, Zhu FM, Mariappan A, Bosch M, Delp EJ. Evidence-based development of a mobile telephone food record. J Am Diet Assoc. 2010; 110(1):74–79.
26. Stumbo PJ. New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc. 2013; 72(1):70–76.
27. Hongu N, Pope BT, Bilgic P, Orr BJ, Suzuki A, Kim AS. Usability of a smartphone food picture app for assisting 24-hour dietary recall: a pilot study. Nutr Res Pract. 2015; 9(2):207–212.
28. Choi H, Choi YJ. The impact of smartphone application quality factors on trust and the users' continuance intention according to gender. J Korea Ind Inf Syst Res. 2011; 16(4):151–162.
29. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US department of agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr. 2003; 77(5):1171–1178.
30. Chang WS, Ji YG. Usability evaluation for smart phone augmented reality application user interface. J Soc e-Business Stud. 2011; 16(1):35–47.
31. Eiin Wong Jyh, Chen Yoke San, Othman Nor Effendy, Poh Bee Koon. Development of and electronic food diary to assess dietary intake among young adults. In : Proceedings of Asian congress of nutrition 12th; 2015 May 14; Japan. p. p. 145.
32. Daugherty BL, Schap TE, Ettienne-Gittens R, Zhu FM, Bosch M, Delp EJ. Novel technologies for assessing dietary intake: Evaluating the usability of a mobile telephone food record among adults and adolescents. J Med Internet Res. 2012; 14(2):e58.
33. Lee HJ, Lee JS. Supporting sustainable food logging by adjusting the degree of data input structure. In : Proceedings of HCI KOREA; 2016 Jan 27; Kangwon. p. 677–679.
34. Pouladzadeh P, Shirmohammadi S, Almaghrabi R. Measuring calorie and nutrition from food image. IEEE Trans Instrum Meas. 2014; 63(8):1947–1956.
35. Noom, Inc. Noom [internet]. Noom, Inc;2016. cited 2016 Dec 1. Available from: https://www.noom.com/.
36. JUVIS co., Ltd. JUVIS [internet]. JUVIS co., Ltd;2016. cited 2016 Dec 1. Available from: http://www.juviscorp.co.kr/.
TOOLS
ORCID iDs

Sang-Jin Chung
https://orcid.org/0000-0003-4804-7206

Similar articles