Journal of Nutrition and Health

  • 제53권3호
  • /
  • Pages.319-328
  • /
  • 2020
  • /
  • 2288-3886(pISSN)
  • /
  • 2288-3959(eISSN)
한국영양학회 (The Korean Nutrition Society)
권호 표지
DOI QR코드

DOI QR Code

식사 전후의 사진 비교를 통한 스마트폰 앱의 영양소섭취량 타당도 평가

Validation of nutrient intake of smartphone application through comparison of photographs before and after meals

  • Lee, Hyejin (Department of Clinical Nutrition, Graduate School of Public Health, Dongduk Women's University) ;
  • Kim, Eunbin (Department of Clinical Nutrition, Graduate School of Public Health, Dongduk Women's University) ;
  • Kim, Su Hyeon (Department of Clinical Nutrition, Graduate School of Public Health, Dongduk Women's University) ;
  • Lim, Haeun (Department of Food and Nutrition, School of Natural Science, Dongduk Women's University) ;
  • Park, Yeong Mi (Department of Food and Nutrition, School of Natural Science, Dongduk Women's University) ;
  • Kang, Joon Ho (Samsung Genome Institute, Samsung Medical Center) ;
  • Kim, Heewon (Samsung Genome Institute, Samsung Medical Center) ;
  • Kim, Jinho (Samsung Genome Institute, Samsung Medical Center) ;
  • Park, Woong-Yang (Samsung Genome Institute, Samsung Medical Center) ;
  • Park, Seongjin (Machine Learning Lab, Samsung Advanced Institute of Technology) ;
  • Kim, Jinki (Machine Learning Lab, Samsung Advanced Institute of Technology) ;
  • Yang, Yoon Jung (Department of Food and Nutrition, School of Natural Science, Dongduk Women's University)
  • 투고 : 2019.08.12
  • 심사 : 2020.05.22
  • 발행 : 2020.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 만 19세 이상 60세 미만 성인남녀 98명을 대상으로 스마트폰 앱인 Gene-Health 이용하여 식사 기록을 통해 분석된 영양소섭취량과 동일한 날의 식사 섭취 전과 후의 사진비교를 통해 섭취량을 추정하여 분석된 영양소섭취량을 비교함으로 Gene-Health의 타당도를 조사하기 위해 수행되었다. 주요 결과는 다음과 같다. 첫째, Gene-Health의 영양소섭취량과 사진을 통해 추정한 영양소섭취량을 비교한 결과 에너지, 탄수화물, 지방, 지방으로부터의 에너지 섭취비율은 통계적으로 유의한 차이가 없었으나 단백질 섭취량과 단백질로부터의 에너지 섭취 비율은 Gene-Health가 높았고, 탄수화물로부터의 에너지 섭취비율은 사진추정군이 높았다. 둘째, Gene-Health와 사진을 통한 영양소섭취량의 상관성은 에너지, 탄수화물, 단백질, 지방섭취량과 탄수화물 비율, 단백질 비율, 지질 비율은 모두 상관계수 0.382-0.708로 유의적인 양의 상관관계를 보였다. 셋째, Gene-Health와 사진을 통한 에너지, 탄수화물, 단백질, 지방섭취량과 탄수화물 비율, 단백질 비율, 지질 비율의 가중 카파 계수는 0.588-0.662로 상당히 일치하는 경향을 보였다. 에너지와 다량영양소, 다량영양소 섭취 비율의 same agreement는 41.8%-48.0%이며 adjacent agreement는 75.5%-88.8%였다. 본 연구를 통하여 Gene-Health는 에너지와 다량영양소 섭취량을 추정하기 위한 타당한 도구라고 사료된다. 추후 연구에서는 다양한 연령과 여성 참가자를 확대하여 성별과 연령에 따른 Gene-Health의 타당도를 연구할 필요가 있다.

Purpose: This study was conducted to evaluate the validity of the Gene-Health application in terms of estimating energy and macronutrients. Methods: The subjects were 98 health adults participating in a weight-control intervention study. They recorded their diets in the Gene-Health application, took photographs before and after every meal on the same day, and uploaded them to the Gene-Health application. The amounts of foods and drinks consumed were estimated based on the photographs by trained experts, and the nutrient intakes were calculated using the CAN-Pro 5.0 program, which was named 'Photo Estimation'. The energy and macronutrients estimated from the Gene-Health application were compared with those from a Photo Estimation. The mean differences in energy and macronutrient intakes between the two methods were compared using paired t-test. Results: The mean energy intakes of Gene-Health and Photo Estimation were 1,937.0 kcal and 1,928.3 kcal, respectively. There were no significant differences in intakes of energy, carbohydrate, fat, and energy from fat (%) between two methods. The protein intake and energy from protein (%) of the Gene-Health were higher than those from the Photo Estimation. The energy from carbohydrate (%) for the Photo Estimation was higher than that of the Gene-Health. The Pearson correlation coefficients, weighted Kappa coefficients, and adjacent agreements for energy and macronutrient intakes between the two methods ranged from 0.382 to 0.607, 0.588 to 0.649, and 79.6% to 86.7%, respectively. Conclusion: The Gene-Health application shows acceptable validity as a dietary intake assessment tool for energy and macronutrients. Further studies with female subjects and various age groups will be needed.

키워드

참고문헌

  1. Park JS, Heo NR, Beon YH, Hwang MH, Kim SH. The relation analysis of obesity and blood lipid on total physical activity level in short sleeping adults. J Sport Leis Stud 2014; 57: 879-892. https://doi.org/10.51979/KSSLS.2014.08.57.879
  2. Ministry of Health and Welfare (KR). Walking: ${\geq}$ 19 years, by sex [Internet]. Sejong: Ministry of Health and Welfare; 2018 [cited 2019 Feb 20]. Available from: http://kosis.kr/statHtml/statHtml.do?orgId=117&tblId=DT_11702_N054&conn_path=I2.
  3. Ministry of Health and Welfare (KR). Obesity: ${\geq}$ 19 years, by sex [Internet]. Sejong: Ministry of Health and Welfare; 2018 [cited 2019 Feb 20]. Available from: http://kosis.kr/statHtml/statHtml.do?orgId=117&tblId=DT_11702_N101&conn_path=I2.
  4. Korea Institute for Health and Social Affairs. Chronic disease of the house members [Internet]. Sejong: Korea Institute for Health and Social Affairs; 2016 [cited 2018 Sep 12]. Available from: http://kosis.kr/statHtml/statHtml.do?orgId=331&tblId=DT_33109_N022&conn_path=I2.
  5. Cho CM. Trend analysis associated dietary habit factors on obesity in Korean adolescents. J Korean Soc Living Environ Syst 2014; 21(1): 97-107. https://doi.org/10.21086/ksles.2014.02.21.1.97
  6. Kim HK, Kim MJ. Effects of weight control program on dietary habits and blood composition in obese middle-aged women. Korean J Nutr 2010; 43(3): 273-284. https://doi.org/10.4163/kjn.2010.43.3.273
  7. Jang HM, Kim SK, Kim NY, Yoon HJ, Cho HY, Ha KS, et al. Association between personality and eating style in Korean obese adults. Korean J Obes 2013; 22(2): 100-106. https://doi.org/10.7570/kjo.2013.22.2.100
  8. Kim ES, Jung BM, Chun HJ. The survey of meal habits for the urban salaried workers. Korean J Soc Food Cookery Sci 2001; 17(2): 91-104.
  9. Capling L, Beck KL, Gifford JA, Slater G, Flood VM, O'Connor H. Validity of dietary assessment in atheltes: a systematic review. Nutrients 2017; 9(12): 1313. https://doi.org/10.3390/nu9121313
  10. Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst 2011; 103(14): 1086-1092. https://doi.org/10.1093/jnci/djr189
  11. Souverein OW, Dekkers AL, Geelen A, Haubrock J, de Vries JH, Ocke MC, et al. Comparing four methods to estimate usual intake distributions. Eur J Clin Nutr 2011; 65 Suppl 1: S92-S101. https://doi.org/10.1038/ejcn.2011.93
  12. Zhang S, Midthune D, Guenther PM, Krebs-Smith SM, Kipnis V, Dodd KW, et al. A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Ann Appl Stat 2011; 5(2B): 1456-1487.
  13. Shin JH, Kim YH, Oh YS. Report No.17-11-02. 2017 Korea media panel survey. Jincheon: Korea Information Society Development Institute; 2017.
  14. Ministry of Science and ICT, National Information Society Agency. The survey on smart phone overdependence. Gwacheon: Ministry of Science and ICT, National Information Society Agency; 2017.
  15. Jeon E, Park HA, Min YH, Kim HY. Analysis of the information quality of korean obesity-management smartphone applications. Healthc Inform Res 2014; 20(1): 23-29. https://doi.org/10.4258/hir.2014.20.1.23
  16. Kim JW, Lee EJ. Current status of dietary education applications (app) as a smart educational material. J Korean Pract Arts Educ 2013; 26(4): 81-110.
  17. Carter MC, Burley VJ, Nykjaer C, Cade JE. 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr 2013; 109(3): 539-546. https://doi.org/10.1017/s0007114512001353
  18. Ahmed M, Mandic I, Lou W, Goodman L, Jacobs I, L'Abbe MR. Validation of a tablet application for assessing dietary intakes compared with the measured food intake/food waste method in military personnel consuming field rations. Nutrients 2017; 9(3): 200. https://doi.org/10.3390/nu9030200
  19. FatSecret. The FatSecret Platform API [Internet]. c2020. Available from: https://platform.fatsecret.com/api/.
  20. United States Department of Agriculture, Agricultural Research Service. Food composition database. Food and Nutrient Database for Dietary Studies (FNDDS) 2015-2016 [Internet]. Washington, D.C.: Agricultural Research Service; c2020. Available from: https//fdc.nal.usda.gov/.
  21. Park IN, Kang SS, Noh EH, Kim MY, Seong TJ. Automatic scoring of Korean short answers by answer template description. J KIISE Comput Pract Lett 2013; 19(12): 630-636.
  22. Kim HS, Lee SH, Kim HS, Kwon OR. Validation of initial nutrition screening tool for hospitalized patients. J Nutr Health 2019; 52(4): 332-341. https://doi.org/10.4163/jnh.2019.52.4.332
  23. Ministry of Health and Welfare (KR). Nutrient intakes per capita per day (standardization): ${\geq}$ 1 year, total [Internet]. Sejong: Ministry of Health and Welfare; 2018 [cited 2020 May 20]. Available from: http://kosis.kr/statHtml/statHtml.do?orgId=117&tblId=DT_11702_N225&conn_path=I2.
  24. Ashman AM, Collins CE, Brown LJ, Rae KM, Rollo ME. Validation of a smartphone image-based dietary assessment method for pregnant women. Nutrients 2017; 9(1): 73. https://doi.org/10.3390/nu9010073
  25. Rangan AM, O'Connor S, Giannelli V, Yap ML, Tang LM, Roy R, et al. Electronic dietary intake assessment (e-DIA): comparison of a mobile phone digital entry App for dietary data collection with 24-hour dietary recalls. JMIR Mhealth Uhealth 2015; 3(4): e98. https://doi.org/10.2196/mhealth.4613
  26. Russell JC, Flood VM, Sadeghpour A, Gopinath B, Mitchell P. Total Diet Score as a valid method of measuring diet quality among older adults. Asia Pac J Clin Nutr 2017;26(2): 212-219.
  27. Jacques S, Lemieux S, Lamarche B, Laramee C, Corneau L, Lapointe A, et al. Development of a web-based 24-h dietary recall a French-Canadian population. Nutrients 2016; 8(11): 724. https://doi.org/10.3390/nu8110724
  28. Savard C, Lemieux S, Lafreniere J, Laramee C, Robitaille J, Morisset AS. Validation of a self-administered web-based 24-hour dietary recall among pregnant women. BMC Pregnancy Childbirth 2018; 18(1): 112. https://doi.org/10.1186/s12884-018-1741-1
  29. Timon CM, Blain RJ, McNulty B, Kehoe L, Evans K, Walton J, et al. The development, validation, and user evaluation of Foodbook24: a web-based dietary assessment tool developed for the Irish adult population. J Med Internet Res 2017; 19(5): e158. https://doi.org/10.2196/jmir.6407

피인용 문헌

  1. Interaction of genetic and environmental factors for body fat mass control: observational study for lifestyle modification and genotyping vol.11, pp.1, 2020, https://doi.org/10.1038/s41598-021-92229-5