Identification of Factors Necessary for Enabling Technology-Based Dietary Record Surveys: A Qualitative Focus Group Interview with Japanese Dietitians
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants in the Nutrition Survey
2.2. Dietary Record Survey Utilizing ICT
2.2.1. Dietary Record Survey
2.2.2. Non-Face-to-Face Interview Regarding Dietary Record Survey
2.3. FGI
2.3.1. Data Collection from FGI
2.3.2. Data Analyses
3. Results
3.1. Participating Dietitians’ Characteristics, Experiences of Interview in Nutrition Survey, and Non-Face-to-Face Meeting in Web
3.2. Analysis of FGIs
3.2.1. Positive Aspects and Negative Aspects on Non-Face-to-Face Dietary Surveys
“Participants did not have to go out and could get interviewed at home or at work, so I think it has led to saving their time.”(#2)
“I was able to check through Zoom the seasoning, foods, and/or food labels of what the participants actually ate.”(#6)
“It was great that we were able to share food photos and diet records between interviewer and participants, and it was also easy for the participants to recall what they ate.”(#5)
“Participants felt relaxed when they were being interviewed at home, so it was easy to hear various factors related to their diet.”(#3)
“It was difficult to do…, maybe it was a computer skill problem on my part, and I was unfamiliar with it.”(#1)
“I felt that smartphones are not suitable for conducting interviews of dietary records because it was a bit difficult to talk while understanding the participants’ expression”(#3)
“The participant was being interviewed while travelling in a car, so I asked her to stop the car, and then proceeded with the interview.”(#4)
“There were photos that looked like they featured just a pot with raw food ingredients, and it was a bit hard to judge how foods were prepared and how they were consumed.”(#5)
3.2.2. Factors for Widespread Use of a Dietary Record Survey Utilizing ICT
“I think that if I had more knowledge about computers, I could do more things.”(#4, 5)
“I think there are quite a few people who are not conscious of personal information”(#1)
“If there is no Wi-Fi environment, communication charges will be incurred”(#2)
“If I could imagine the meal that participants ate, and/or had the knowledge of the food products …I felt that I had very few questions for participants.”(#6)
“If as soon as I saw meals and foods, I could imagine what ingredients were used, and what the salt absorption rate of the dish would be, and what the weight conversion would be, I will be able to estimate nutritional values easy for dishes(#6)
“I spoke more clearly during the interview because it was over the screen.”(#5)
“I was careful to have the conversation calmly and with a smile.”(#1)
“It’s nice to have a photo, but sometimes I could not estimate when the meal was eaten, or the content and amount of the meal the participants ate from the meal photos...”(#5)
“Participants would like to use simpler and easy-to-use applications, such as using those assisted with artificial intelligence (AI) etc.”(#3, 5)
“For example, it would be nice if it could be as simple as just taking a photo, the smartphone application can estimate the type of meal and foods, and their weights, etc.”(#1, 6)
“I would like to have a machine that, just by taking a video of the person making the meal, could estimate what ingredients were used and how much spices were added”(#2)
“If the accuracy of weight estimation for foods is improved, there is less need for detailed interviews, so I think it’ll be okay then if you can’t see the face”(#6)
“I would like to have an application that once you launch the tool, PC or application will recognize things that move, focusing only on that part, and blurring out other parts.”(#5)
“It would be nice if there was someone in public who could teach me about PC, applications, and ICT.”(#1)
“Even if participants had a smartphone, depending on the conditions of the contract, they may not be able to respond without a Wi-Fi environment...”(#3)
“If it was possible to easily conduct dietary record surveys using tablets, I think many people would take nutrition surveys.”(#5)
“I think it would be better to do it together in a place like a public gathering place with 4 to 5 people”(#2)
“Places where personal information is protected and the network can be used in public are needed… example, it may be a railway station, etc.”(#5)
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Hoffmann, K.; Boeing, H.; Dufour, A.; Volatier, J.L.; Telman, J.; Virtanen, M.; Becker, W.; De Henauw, S.; Group, E. Estimating the distribution of usual dietary intake by short-term measurements. Eur. J. Clin. Nutr. 2002, 56 (Suppl. 2), S53–S62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Medlin, C.; Skinner, J.D. Individual dietary intake methodology: A 50-year review of progress. J. Am. Diet. Assoc. 1988, 88, 1250–1257. [Google Scholar] [CrossRef]
- Basiotis, P.P.; Welsh, S.O.; Cronin, F.J.; Kelsay, J.L.; Mertz, W. Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J. Nutr. 1987, 117, 1638–1641. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Health, Labour, and Welfare, Japan. The National Health and Nutrition Survey 2019. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/eiyou/r1-houkoku_00002.html (accessed on 14 October 2022).
- Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.; Avnit-Sagi, T.; Lotan-Pompan, M.; et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2015, 163, 1079–1094. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mortazavi, B.J.; Gutierrez-Osuna, R. A Review of Digital Innovations for Diet Monitoring and Precision Nutrition. J. Diabetes Sci. Technol. 2021, 1, 19322968211041356. [Google Scholar] [CrossRef]
- Carter, M.C.; Albar, S.A.; Morris, M.A.; Mulla, U.Z.; Hancock, N.; Evans, C.E.; Alwan, N.A.; Greenwood, D.C.; Hardie, L.J.; Frost, G.S.; et al. Development of a UK Online 24-h Dietary Assessment Tool: Myfood24. Nutrients 2015, 7, 4016–4032. [Google Scholar] [CrossRef] [Green Version]
- Albar, S.A.; Alwan, N.A.; Evans, C.E.; Greenwood, D.C.; Cade, J.E. Agreement between an online dietary assessment tool (myfood24) and an interviewer-administered 24-h dietary recall in British adolescents aged 11-18 years. Br. J. Nutr. 2016, 115, 1678–1686. [Google Scholar] [CrossRef] [Green Version]
- Bradley, J.; Simpson, E.; Poliakov, I.; Matthews, J.N.; Olivier, P.; Adamson, A.J.; Foster, E. Comparison of INTAKE24 (an Online 24-h Dietary Recall Tool) with Interviewer-Led 24-h Recall in 11–24 Year-Old. Nutrients 2016, 8, 358. [Google Scholar] [CrossRef] [Green Version]
- Labonte, M.E.; Cyr, A.; Baril-Gravel, L.; Royer, M.M.; Lamarche, B. Validity and reproducibility of a web-based, self-administered food frequency questionnaire. Eur. J. Clin. Nutr. 2012, 66, 166–173. [Google Scholar] [CrossRef] [Green Version]
- Forster, H.; Fallaize, R.; Gallagher, C.; O’Donovan, C.B.; Woolhead, C.; Walsh, M.C.; Macready, A.L.; Lovegrove, J.A.; Mathers, J.C.; Gibney, M.J.; et al. Online dietary intake estimation: The Food4Me food frequency questionnaire. J. Med. Internet Res. 2014, 16, e150. [Google Scholar] [CrossRef]
- Ministry of Internal Affairs and Communications. Results of the Reiwa 3 Communication Usage Trend Survey; Ministry of Internal Affairs and Communications: Tokyo, Japan, 2022. [Google Scholar]
- Shinozaki, N.; Murakami, K. Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan. Nutrients 2020, 12, 3327. [Google Scholar] [CrossRef] [PubMed]
- Sato, K.; Kobayashi, S.; Yamaguchi, M.; Sakata, R.; Sasaki, Y.; Murayama, C.; Kondo, N. Working from home and dietary changes during the COVID-19 pandemic: A longitudinal study of health app (CALO mama) users. Appetite 2021, 165, 105323. [Google Scholar] [CrossRef] [PubMed]
- Gill, P.; Stewart, K.; Treasure, E.; Chadwick, B. Methods of data collection in qualitative research: Interviews and focus groups. Br. Dent. J. 2008, 204, 291–295. [Google Scholar] [CrossRef] [PubMed]
- Anme, T. Group Interview Method in Human Service III/Development of Qualitative Research Methods Based on Scientific Evidence/Thesis Preparation Section; Ishiyaku Pub Inc.: Tokyo, Japan, 2020. (In Japanese) [Google Scholar]
- The Council for Science and Technology, Ministry of Education. Science, Sports and Culture. The Standard Tables of Food Composition in Japan, 7th Revised Edition Supplementary Edition 2018. Available online: https://www.mext.go.jp/a_menu/syokuhinseibun/1411578.htm (accessed on 27 September 2022).
- Fallaize, R.; Macready, A.L.; Butler, L.T.; Ellis, J.A.; Berezowska, A.; Fischer, A.R.; Walsh, M.C.; Gallagher, C.; Stewart-Knox, B.J.; Kuznesof, S.; et al. The perceived impact of the National Health Service on personalised nutrition service delivery among the UK public. Br. J. Nutr. 2015, 113, 1271–1279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rabiee, F. Focus-group interview and data analysis. Proc. Nutr. Soc. 2004, 63, 655–660. [Google Scholar] [CrossRef]
- Touvier, M.; Mejean, C.; Kesse-Guyot, E.; Pollet, C.; Malon, A.; Castetbon, K.; Hercberg, S. Comparison between web-based and paper versions of a self-administered anthropometric questionnaire. Eur. J. Epidemiol. 2010, 25, 287–296. [Google Scholar] [CrossRef] [Green Version]
- Naoko Ibe, M.S.; Okada, Y.; Kinoshita, A.; Sakafuji, Y.; Kato-Yoshinaga, Y. Availability of Remote Nutritional Education using a Web Conferencing System. J. Jpn. Soc. Shokuiku 2015, 9, 229–238. [Google Scholar] [CrossRef]
- Mikkelsen, B.E. Man or machine? Will the digital transition be able to automatize dietary intake data collection? Public Health Nutr. 2019, 22, 1149–1152. [Google Scholar] [CrossRef] [Green Version]
- Timon, C.M.; van den Barg, R.; Blain, R.J.; Kehoe, L.; Evans, K.; Walton, J.; Flynn, A.; Gibney, E.R. A review of the design and validation of web- and computer-based 24-h dietary recall tools. Nutr. Res. Rev. 2016, 29, 268–280. [Google Scholar] [CrossRef]
- Zhu, F.; Mariappan, A.; Boushey, C.J.; Kerr, D.; Lutes, K.D.; Ebert, D.S.; Delp, E.J. Technology-Assisted Dietary Assessment. Proc. SPIE Int. Soc. Opt. Eng. 2008, 6814, 681411. [Google Scholar] [CrossRef]
- Hinton, E.C.; Brunstrom, J.M.; Fay, S.H.; Wilkinson, L.L.; Ferriday, D.; Rogers, P.J.; de Wijk, R. Using photography in ’The Restaurant of the Future’. A useful way to assess portion selection and plate cleaning? Appetite 2013, 63, 31–35. [Google Scholar] [CrossRef] [PubMed]
- Sun, M.; Fernstrom, J.D.; Jia, W.; Hackworth, S.A.; Yao, N.; Li, Y.; Li, C.; Fernstrom, M.H.; Sclabassi, R.J. A wearable electronic system for objective dietary assessment. J. Am. Diet. Assoc. 2010, 110, 45–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stumbo, P.J. New technology in dietary assessment: A review of digital methods in improving food record accuracy. Proc. Nutr. Soc. 2013, 72, 70–76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xu, C.; Khanna, N.; Boushey, C.J.; Delp, E.J. Low Complexity Image Quality Measures for Dietary Assessment Using Mobile Devices. ISM 2011, 2011, 351–356. [Google Scholar] [CrossRef] [Green Version]
- Xu, C.; Zhu, F.; Khanna, N.; Boushey, C.J.; Delp, E.J. Image Enhancement and Quality Measures for Dietary Assessment Using Mobile Devices. Proc SPIE Int. Soc. Opt. Eng. 2012, 8296, 82960Q. [Google Scholar] [CrossRef] [Green Version]
- Rangan, A.M.; Tieleman, L.; Louie, J.C.; Tang, L.M.; Hebden, L.; Roy, R.; Kay, J.; Allman-Farinelli, M. Electronic Dietary Intake Assessment (e-DIA): Relative validity of a mobile phone application to measure intake of food groups. Br. J. Nutr. 2016, 115, 2219–2226. [Google Scholar] [CrossRef] [Green Version]
- Rowland, M.P.I.; Christie, S.; Simpson, E.; Foster, E. Field Testing of the Use of INTAKE24 in A Sample of Young People and Adults Living in Scotland; Newcastle University: Newcastle upon Tyne, UK, 2016. [Google Scholar]
- Klovning, A.; Sandvik, H.; Hunskaar, S. Web-based survey attracted age-biased sample with more severe illness than paper-based survey. J. Clin. Epidemiol. 2009, 62, 1068–1074. [Google Scholar] [CrossRef]
- Edwards, S.L.; Slattery, M.L.; Murtaugh, M.A.; Edwards, R.L.; Bryner, J.; Pearson, M.; Rogers, A.; Edwards, A.M.; Tom-Orme, L. Development and use of touch-screen audio computer-assisted self-interviewing in a study of American Indians. Am. J. Epidemiol. 2007, 165, 1336–1342. [Google Scholar] [CrossRef] [Green Version]
- Kirkpatrick, S.I.; Gilsing, A.M.; Hobin, E.; Solbak, N.M.; Wallace, A.; Haines, J.; Mayhew, A.J.; Orr, S.K.; Raina, P.; Robson, P.J.; et al. Lessons from Studies to Evaluate an Online 24-Hour Recall for Use with Children and Adults in Canada. Nutrients 2017, 9, 100. [Google Scholar] [CrossRef] [Green Version]
- Official Journal of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation); Official Journal of the European Union: Brussels, Belgium, 2016. [Google Scholar]
- Jia, W.; Li, Y.; Qu, R.; Baranowski, T.; Burke, L.E.; Zhang, H.; Bai, Y.; Mancino, J.M.; Xu, G.; Mao, Z.H.; et al. Automatic food detection in egocentric images using artificial intelligence technology. Public Health Nutr. 2019, 22, 1168–1179. [Google Scholar] [CrossRef]
- Mathers, J.C. Nutrigenomics in the modern era. Proc. Nutr. Soc. 2017, 76, 265–275. [Google Scholar] [CrossRef] [PubMed]
- Sikalidis, A.K. From Food for Survival to Food for Personalized Optimal Health: A Historical Perspective of How Food and Nutrition Gave Rise to Nutrigenomics. J. Am. Coll. Nutr. 2019, 38, 84–95. [Google Scholar] [CrossRef]
- Rana, S.; Kumar, S.; Rathore, N.; Padwad, Y.; Bhushana, S. Nutrigenomics and its Impact on Life Style Associated Metabolic Diseases. Curr. Genom. 2016, 17, 261–278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vereecken, C.A.; Covents, M.; Matthys, C.; Maes, L. Young adolescents’ nutrition assessment on computer (YANA-C). Eur. J. Clin. Nutr. 2005, 59, 658–667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Beasley, J.; Riley, W.T.; Jean-Mary, J. Accuracy of a PDA-based dietary assessment program. Nutrition 2005, 21, 672–677. [Google Scholar] [CrossRef]
- Williamson, D.A.; Allen, H.R.; Martin, P.D.; Alfonso, A.J.; Gerald, B.; Hunt, A. Comparison of digital photography to weighed and visual estimation of portion sizes. J. Am. Diet. Assoc. 2003, 103, 1139–1145. [Google Scholar] [CrossRef]
- Ngo, J.; Engelen, A.; Molag, M.; Roesle, J.; Garcia-Segovia, P.; Serra-Majem, L. A review of the use of information and communication technologies for dietary assessment. Br. J. Nutr. 2009, 101 (Suppl. 2), S102–S112. [Google Scholar] [CrossRef] [Green Version]
- Celis-Morales, C.; Livingstone, K.M.; Marsaux, C.F.; Forster, H.; O’Donovan, C.B.; Woolhead, C.; Macready, A.L.; Fallaize, R.; Navas-Carretero, S.; San-Cristobal, R.; et al. Design and baseline characteristics of the Food4Me study: A web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr. 2015, 10, 450. [Google Scholar] [CrossRef]
Line of Inquiry | Questions |
---|---|
The experience of an interview at Nutrition Survey | 1. The experience of a face-to-face interview at Nutrition Survey |
2. The experience of a non-face-to-face interview at Nutrition Survey | |
3. What type of the machines and tools used in the interview | |
How to handle a situation in a non-face-to-face interview at Nutrition Survey | 4. Preparations for non-face-to-face interviews and their time |
5. How were machines and ICT used for non-face-to-face interviews? | |
6. Useful devices and tools for non-face-to-face interviews | |
7. Necessary skills to use information communication devices (ICT) and tools for non-face-to-face interviews | |
8. How voice was heard and whether headsets are used in non-face-to-face interview | |
9. Tools used to confirm the weight and size of food and points to note in non-face-to-face interviews | |
10. Functions of the PC used to confirm the weight, size, and type of food in non-face-to-face interviews | |
11. How to take notes during non-face-to-face interviews | |
12. Other points to note in non-face-to-face interviews | |
Positive and negative points in non-face-to-face interview at Nutrition Survey | 13. Positive points |
14. Negative points | |
Factors for widespread use of a dietary record survey utilizing ICT | 15. Machines |
16. Knowledge and Skills | |
17. Problems that may arise |
Participating Dietitians | ||||||
---|---|---|---|---|---|---|
ID | #1 | #2 | #3 | #4 | #5 | #6 |
Sex | Female | Female | Female | Female | Female | Female |
Age (years) | 58 | 46 | 52 | 60 | 43 | 49 |
Experience of conducting face-to-face nutritional interviews | ||||||
Year(s) | 15 | 3 | 7 | 3 | 5 | 14 |
Number of persons | 7 | 15 | 140 | 70 | 20 | 100< |
Place | NHNS *1, public health center | NHNS *1, public health center | NHNS *1, research institute | Hospital, research institute | Facility for the elderly | Research institute, public health center |
Experience of conducting non-face-to-face/remote nutritional interviews | ||||||
Number of persons | 10 *2 | 8 *2 | 8 *2 | 11 *2 | 1 *2 | 6 *2 |
Place | Research institute | Research institute | Research institute | Research institute | Research institute | Research institute |
Other experiences of non-face-to-face/remote interactions | ||||||
Type | Study meetings | Study meetings, meeting friends and/or colleagues | Study meeting, workshop | Study meeting, workshop | Study meeting, workshop, meeting friends and/or colleagues | Cooking class, study meeting, workshop, meeting friends and/or colleagues |
Number of times | 30 | 20–30 | 20–30 | 5–6 | >100 | 30–40 |
Device | PC *3, smartphone | PC *3, smartphone | Smartphone | PC *3, smartphone | PC *3, smartphone | PC *3, smartphone |
Application | Zoom | Zoom, LINE | Zoom, LINE | Zoom, LINE | Zoom, WebEx, Teams, Skype, LINE | Zoom, LINE |
Themes | Summary | Examples |
---|---|---|
To what factors did you pay attention in the non-face-to-face interviews of Nutrition Survey? | Listening attentively in stipulated time | I was careful to finish the session within 30 min to a maximum of 1 h. (#2, 5)/I paid attention to ask simple questions. (#3) |
Missing entries in the dietary records | I tried not to forget to ask about the types of seasonings the participants used or what they were eating that wasn’t in the photos, to generate comprehensive dietary records. (#2) | |
Using louder voice and exaggerated gestures to make it easier to convey my message. | I spoke more clearly because the message had to be conveyed through a screen. (#5)/I tried to use gestures such as exaggerated nodding. (#5) | |
Create an atmosphere that is conducive to conversation | I was careful to be gentle in proceeding. (#6)/I chose clothes with bright colors to brighten the impression of the screen. (#1) | |
Useful tool in non-face-to-face interviews at Nutrition Survey. | Tools for information communication equipment (ICT) | I was wearing a headset, so my voice didn’t overlap, which was good. (#2)/Meal photos and dietary records can be shared through PC screen, and could be confirmed with the target person, which was very useful. (#5) |
Meal card | It was easy to imagine the size of the meal and the food portions with the meal card, which is the size of a business card, present alongside the meal in photos. (#3) The photograph of the meal card together with the meal made it easy to grasp the day on which the meal was eaten and the classification of meal as breakfast, lunch, dinner, or snacks. (#4) | |
Weighing instruments/tableware | I’ve actually used measuring spoons, plates, and ruler during interviews at Nutrition Survey. (#3) | |
Hand | I verified the size by sticking out my hand and asking, “Is it about this size?” (#1) | |
How did you take notes during the non-face-to-face interviews at Nutrition Survey. | Filled in the Nutrition dietary records (paper) with a pen | Using an erasable pen, I wrote down what I heard in the interview on the right half of the meal record sheet filled in by the participants, in a rather crude manner at that time. After the interview, I used an erasable pen to take notes so that I could rewrite them. (#2) |
Appropriate use of screenshots | Those who participated in the survey from their homes brought seasonings in front of the PC and when the participants showed me the seasonings through the camera, I took a screenshot of the same on my computer and looked at the contents later in greater detail. (#2, 6) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tousen, Y.; Shimomura, C.; Yasudomi, A.; Kaneda, Y.; Nishiwaki, N.; Fujita, M.; Oya, H.; Kobori, T.; Kobori, M.; Takimoto, H. Identification of Factors Necessary for Enabling Technology-Based Dietary Record Surveys: A Qualitative Focus Group Interview with Japanese Dietitians. Nutrients 2022, 14, 4357. https://doi.org/10.3390/nu14204357
Tousen Y, Shimomura C, Yasudomi A, Kaneda Y, Nishiwaki N, Fujita M, Oya H, Kobori T, Kobori M, Takimoto H. Identification of Factors Necessary for Enabling Technology-Based Dietary Record Surveys: A Qualitative Focus Group Interview with Japanese Dietitians. Nutrients. 2022; 14(20):4357. https://doi.org/10.3390/nu14204357
Chicago/Turabian StyleTousen, Yuko, Chifumi Shimomura, Ai Yasudomi, Yukie Kaneda, Nanako Nishiwaki, Mayumi Fujita, Hiroko Oya, Toshiro Kobori, Masuko Kobori, and Hidemi Takimoto. 2022. "Identification of Factors Necessary for Enabling Technology-Based Dietary Record Surveys: A Qualitative Focus Group Interview with Japanese Dietitians" Nutrients 14, no. 20: 4357. https://doi.org/10.3390/nu14204357
APA StyleTousen, Y., Shimomura, C., Yasudomi, A., Kaneda, Y., Nishiwaki, N., Fujita, M., Oya, H., Kobori, T., Kobori, M., & Takimoto, H. (2022). Identification of Factors Necessary for Enabling Technology-Based Dietary Record Surveys: A Qualitative Focus Group Interview with Japanese Dietitians. Nutrients, 14(20), 4357. https://doi.org/10.3390/nu14204357