Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-13T01:04:43.861Z Has data issue: false hasContentIssue false

Technology in dietary assessment

Published online by Cambridge University Press:  26 October 2017

Friedeburg AM Wenhold*
Affiliation:
Department of Human Nutrition Faculty of Health Sciences University of PretoriaPrivate Bag X323, Arcadia, 0007South Africa
Rights & Permissions [Opens in a new window]

Abstract

Type
Invited Commentary
Copyright
Copyright © The Author 2017 

Dietary assessment may be described as the science and art of evaluating the dietary intake of individuals or groups for research or as basis for nutrition care( Reference McCabe-Sellers 1 ). While screening and comprehensive assessments have been differentiated( Reference Correira 2 ), both are characterised by phases of varying detail. Data collection and data evaluation are respectively the beginning (input) and endpoint (output). The aim of a particular dietary evaluation will determine the most appropriate tools and techniques in each phase. Using technology should improve at least the efficiency and ideally also the quality of dietary assessments, which are known to be labour-intensive and threatened by concerns about validity( Reference Dhurandhar, Schoeller and Brown 3 ). Many reviews about the use of technology in dietary assessment have been published( Reference Arens-Volland, Spassova and Bohn 4 Reference Thompson, Subar and Loria 15 ). The current invited commentary aims to place these technologies into the context of dietary assessment viewed as a system.

Informed by the approach of Pennington et al. ( Reference Pennington, Stumbo and Murphy 16 ), Fig. 1 illustrates the phases (shaded) of dietary assessment as well as some potentially associated tools and techniques which may involve technological innovation. The ‘input’ refers to the dietary data collected by techniques such as the 24 h recall. The ‘processing’ includes the coding and analyses, while the ‘output’ is the final evaluation or judgement of the diet. Not all dietary assessments are quantitative by collecting food portion data; some assessments do not cover the whole diet, and some assessments or screeners focus on food groups, and not on nutrients. Figure 1 outlines the potential complexity of the system, even though not all phases are applicable to all dietary assessments, nor would digital technology necessarily be appropriate in each phase.

Fig. 1 Dietary assessment as a system

When dietary data are collected, technology typically searches for quicker input and standardisation of questioning and probing, all core considerations for efficiency and reliability of a research study. It is, however, important that the cognitive processes inherent to a particular dietary assessment technique are respected. For example, in the case of dietary recalls, the emphasis should be on helping participants in a non-leading way to remember all foods consumed in the reference period, e.g. through the multiple-pass technique. For FFQ, the participant should be supported in recognising relevant items in the given food list. Photographs may serve the purpose. Nesting, branching and/or filtering questions by means of technology can substantially streamline the completion of FFQ. The complexity involved in calculating and reporting frequency of intake according to the requirements of a particular FFQ is often underestimated and presents opportunities for innovation. The participant who needs to keep a diet record would benefit from technology that reduces the burden of diarising all foods consumed. Camera or voice recording and scanning using smartphones or personal digital assistants for data entry – online or offline – are examples. Portion size estimation deserves considerable attention in quantitative assessments. Food recognition, segregation and quantification of images taken by the participant may become a viable option to researchers and practitioners in the not-too-distant future.

In addition to applying the principles of science, the art of interacting with the participant in line with the principles of a particular dietary assessment technique should be kept in mind in the development and use of technology in eliciting dietary information. Participant characteristics (e.g. age, socio-economic status and culture) and preferences (e.g. interviewer v. self-administration), as well as logistical and resource-related matters, should always direct decisions. In those instances where the dietary assessment is the starting point for responsive nutrition care, it must be remembered that technology cannot show empathy or emotion; often critical for rapport between nutrition professional and client.

Dietary input data need to be analysed. When viewing dietary methodology as a system, this takes place during the ‘processing’ phase. The first step in an analysis usually requires coding of the reported dietary intake. An article in this issue of Public Health Nutrition ( Reference Harris-Fry, Beard and Harrisson 17 ) reports the use of QR (quick response) codes to link the food items reported (selected from a pre-set list) and their portion sizes (from a photographic atlas) to food composition data. This technology largely eliminated human (fieldworker) error during coding, but the compilation of a comprehensive and relevant food list and the portion sizes remain critical, and illustrate how the usefulness of technology depends on sound baseline knowledge of the target population: the integration of art and science. Using technology that is accessible to researchers in resource-limited settings and sharing this, counters costly ‘re-invention of the wheel’. This approach may also prevent the technological divide between developing and industrialised countries from deepening. The technology used by Harris-Fry et al.( Reference Harris-Fry, Beard and Harrisson 17 ) also appears to be an option for coding of FFQ that are currently not yet scannable.

The ability of technology to eliminate the repetitive tasks associated with calculating intakes of nutrients and other food components has long saved nutrition professionals much time. Increasingly, food composition databases are linked to or are incorporated into the terminals (e.g. mobile phones) which are used in the input phase of the dietary assessment. The challenges of food composition databases cannot, however, be ignored(18). Missing data, limited representation of culture-specific and branded foods, as well as changes and (regional) differences in food composition and preparation require ongoing attention. Quality control of software packages that are based on the databases needs to be rigorously applied. The impression of modernity and sophistication through the application of technology should not outweigh data quality. This also refers to the reference data included in many software packages for immediate and user-friendly (e.g. graphical) outputs, particularly for clinical settings.

Even though the comparison of calculated intake with dietary reference data can be technology-supported, the final dietary evaluation often requires the expertise of a nutrition professional. This refers particularly to the integration of findings regarding the various dietary components (nutrients) and possibly the various dietary assessment techniques that were part of a diet history, and other data (e.g. anthropometry and biochemistry) from a comprehensive nutritional assessment of an individual. Artificial intelligence in this regard is still in its infancy.

Autonomous robot-driven assessment of usual nutrient intake of individuals is not yet a reality, nor is it desirable among those who accept that art and science are integral to dietary assessment. Nevertheless, appropriate technology can successfully support, improve or take over selected tasks in some of the phases of dietary assessment, particularly of groups and in delimited assessments. In the interest of sound science, the reliability and validity of new technologies should always be established, keeping in mind that dietary intake is a complex human behaviour. Neither blind over-adoption nor avoidance serves the purpose of furthering dietary assessment for ultimately improving nutritional status and health.

Acknowledgements

Financial support: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Conflict of interest: None. Authorship: F.A.M.W. is the sole author of this invited commentary. Ethics of human subject participation: Not applicable.

References

1. McCabe-Sellers, B (2010) Advancing the art and science of dietary assessment through technology. J Am Diet Assoc 110, 5254.Google Scholar
2. Correira, MITD (2017) Nutrition screening vs nutrition assessment: what’s the difference? Nutr Clin Pract (Epublication ahead of print version).Google Scholar
3. Dhurandhar, NV, Schoeller, D, Brown, AW et al. (2015) Energy balance measurement: when something is not better than nothing. Int J Obes (Lond) 39, 11091113.Google Scholar
4. Arens-Volland, AG, Spassova, L & Bohn, T (2015) Promising approaches of computer-supported assessment and management – current research status and available applications. Int J Med Inform 84, 9971008.Google Scholar
5. Falomir, Z, Arregui, M, Madueño, F et al. (2012) Automation of food questionnaires in medical studies: a state-of-the-art review and future prospects. Comput Biol Med 31, 964974.Google Scholar
6. Sharp, DB & Allman-Farinelli, M (2014) Feasibility and validity of mobile phones to assess dietary intake. Nutrition 30, 12571266.Google Scholar
7. Gemming, L, Utter, J & Mhurchu, C (2015) Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet 115, 6477.CrossRefGoogle ScholarPubMed
8. Hongu, N, Hingle, MD, Merchant, NC et al. (2011) Dietary assessment tools using mobile technology. Top Clin Nutr 26, 300311.Google Scholar
9. Illner, AK, Freisling, H, Boeing, H et al. (2012) Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol 41, 11871203.CrossRefGoogle ScholarPubMed
10. Long, JD, Littlefield, LA, Estep, G et al. (2010) Evidence review of technology and dietary assessment. Worldviews Evid Based Nurs 7, 191204.Google Scholar
11. Ngo, J, Engelen, A, Molag, M et al. (2009) A review of the use of information and communication technologies for dietary assessment. Br J Nutr 101, Suppl. 2, S102S112.CrossRefGoogle ScholarPubMed
12. Rusin, M, Årsand, E & Hartvigsen, G (2013) Functionalities and input methods for recording food intake: a systematic review. Int J Med Inform 82, 653664.Google Scholar
13. Spook, JE, Paulussen, T, Kok, G et al. (2013) Monitoring dietary intake and physical activity electronically: feasibility, usability, and ecological validity of a mobile-based Ecological Momentary Assessment tool. J Med Internet Res 15, e214.Google Scholar
14. Stumbo, P (2013) New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc 72, 7076.CrossRefGoogle ScholarPubMed
15. Thompson, F, Subar, A, Loria, C et al. (2012) Need for technological innovation in dietary assessment. J Am Diet Assoc 110, 4851.CrossRefGoogle Scholar
16. Pennington, JAT, Stumbo, P, Murphy, SP et al. (2007) Food composition data: the foundation of dietetic practice and research. J Am Diet Assoc 107, 21052113.Google Scholar
17. Harris-Fry, H, Beard, BJ, Harrisson, T et al. (2017) Smartphone tool to collect repeated 24 h dietary recall data in Nepal. Public Health Nutr (Epublication ahead of print version).Google Scholar
18. Charrondiere, UR, Rittenschober, D, Nowak, V et al. (2016) Improving food composition data quality: three new FAO/INFOODS guidelines on conversions, data evaluation and food matching. Food Chem 193, 7581.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1 Dietary assessment as a system