Innovative technologies for the assessment of usual intake

Technological progress and a significant increase in internet usage in the past years has resulted in the development of a number of innovative technologies for dietary assessment. Different technological strategies are followed to address the challenges in dietary assessment including web-based 24-h recalls, mobile food records or simple closed-ended online questionnaires that assess the food intake of the previous 24 h. To date, a number of literature reviews, each focussing on different new technologies, have been published⁽ Reference Illner, Freisling and Boeing ^{15 ,} Reference Lieffers and Hanning ^{20 –} Reference Franco, Fallaize and Lovegrove ^{28 )}. The most comprehensive systematic literature review was conducted by Illner et al. in 2012⁽ Reference Illner, Freisling and Boeing ^{15 )}. They classified available tools into six categories: mobile phone-based technologies; personal digital-assistant technologies; interactive computer-based technologies; web-based technologies; camera- and tape-recorder-based technologies; scan- and sensor-based technologies. In the present review, the focus is on web-based instruments and mobile technologies as promising assessment tools in large-scale study settings.

A number of self-administered, web-based 24-h recalls have been developed as illustrated in Table 1. The instruments differ with respect to the number of foods available in the database and the way of collecting information on dietary intake. The myfood24 is an online 24-h dietary assessment tool developed for the application among British adults and adolescents⁽ Reference Carter, Albar and Morris ^{18 )}. So far, it is available for application in the UK with respective national databases. An Australian and a German version are under development⁽ Reference Carter, Hancock and Albar ^{29 )}. The tool can be used for multiple recalls or as a food record. To reduce completion time the myfood24 does not follow the detailed Automated Multiple-Pass Method; however, some aspects are included such as an optional quicklist function, a detailed food search, prompts for commonly forgotten foods and a final review before submission. The UK version of the tool is linked to an extensive database that contains about 40 000 generic and branded food items⁽ Reference Carter, Hancock and Albar ^{29 )}. Food portion images help in choosing the appropriate portion size. The relative validity of the myfood24 against a traditional interviewer-administered recall was tested among British adolescents with strong correlations for energy and most nutrients⁽ Reference Albar, Alwan and Evans ^{30 )}. The automated self-administered 24-h recall, developed by the US National Cancer Institute (NCI), represents a detailed 24-h recall for use in adults and children. It collects and automatically codes dietary intake data, and includes detailed questions about portion sizes and food preparation methods based on the five steps of the state-of-the-art Automated Multiple-Pass Method. The database includes approximately 8000 food items⁽ Reference Subar, Kirkpatrick and Mittl ^{19 ,} Reference Zimmerman, Hull and McNutt ^{31 )}. The automated self-administered 24-h recall was compared with traditional interviewer-administered 24-h recalls in a diverse sample of adults aged between 20 and 70 years from three different geographical areas. Equivalent energy intake estimates between the two recall methods were found for men and women⁽ Reference Thompson, Dixit-Joshi and Potischman ^{32 )}. The web-based recall DietDay, which contains 9349 food items assesses information on portion sizes and preparation methods, and was designed for repeated administration⁽ Reference Arab, Wesseling-Perry and Jardack ^{33 )}. The DietDay also applies multiple steps similar to the Automated Multiple-Pass Method approach. The validity of six administrations of DietDay was tested using the doubly labelled water method. The rate of underreporting for energy was on average about 30 %, which is comparable with conventional 24-h recalls⁽ Reference Arab, Tseng and Ang ^{34 )}.

Table 1. Web-based 24-h dietary recall tools for dietary assessment

AMPM, Automated Multiple-Pass Method; ASA24, automated self-administered 24-h recall.

To further reduce demands on time for dietary assessment, the development of abbreviated, web-based, self-administered instruments has been initiated that recall the diet of the previous 24 h, but with a finite list of food items⁽ Reference Liu, Young and Crowe ^{35 ,} Reference Freese, Feller and Harttig ^{36 )}. The Oxford WebQ, for instance, has been especially designed for the use in several large-scale prospective studies in the UK⁽ Reference Liu, Young and Crowe ^{35 ,} Reference Galante, Adamska and Young ^{37 )}. The instrument is closed-ended like an FFQ, but is intended to be administered at multiple time points in a study similar to a 24-h recall. It obtains information on consumption amounts of twenty-one food groups. Median time for self-completion is 12·5 min. Nutrient intakes are calculated automatically and stored in a secure database. Compared with an interviewer-administered 24-h recall, the Oxford WebQ provided similar mean estimates of energy and nutrient intakes and study participants were reasonably well ranked⁽ Reference Liu, Young and Crowe ^{35 )}. Recently, it was shown that 66 % of UK Biobank participants completed the questionnaire more than once⁽ Reference Galante, Adamska and Young ^{37 )}. The 24-h food list has been developed for use in the German National Cohort⁽ Reference Freese, Feller and Harttig ^{36 , 38 )}. It is by definition intended to be used in a combined approach with an FFQ and not as stand-alone instrument. The tool includes a total of 246 food items. Consumption of food items during the previous day is assessed dichotomously (yes/no). In a feasibility study with 505 participants, median completion time was 9 min and the majority of study participants completed the tool three times.

Mobile phones have a variety of technological features that are promising to facilitate dietary assessment⁽ Reference Sharp and Allman-Farinelli ^{22 )}. This technology is mainly used for real-time recording of food intake due to the advantage of portability⁽ Reference Illner, Freisling and Boeing ^{15 ,} Reference Carter, Burley and Nykjaer ^{39 )}. Smartphone applications (app) have been developed allowing self-monitoring of food and beverage intake⁽ Reference Franco, Fallaize and Lovegrove ^{28 ,} Reference Carter, Burley and Nykjaer ^{39 ,} Reference Rangan, Tieleman and Louie ^{40 )}. Intake data can be directly transferred to nutrient output for subsequent analysis. The electronic Dietary Intake Assessment app was developed for use in Australia as a weighed or estimated food record⁽ Reference Rangan, Tieleman and Louie ^{40 ,} Reference Rangan, O'Connor and Giannelli ^{41 )}. Its relative validity to measure nutrient and food group intakes was tested against repeated 24-h recalls. While a good agreement was found on the group level, large variability of reported intakes at the individual level was observed. Similar results have been observed for the My Meal Mate app, an electronic food record app that was developed to facilitate weight loss⁽ Reference Carter, Burley and Nykjaer ^{39 )}.

Another promising feature of smartphone-based dietary assessment is the possibility to take pictures of food and beverages⁽ Reference Gemming, Utter and Ni Mhurchu ^{25 )}. Here, collected data can either be analysed afterwards by trained dietitians or automatically⁽ Reference Sharp and Allman-Farinelli ^{22 )}. Using for example the remote food photography method, study participants sent images to a server, which were then analysed to estimate food intake⁽ Reference Martin, Nicklas and Gunturk ^{42 )}. Further features of this technology include a semi-automated procedure to estimate portion sizes and an automatic identification of foods via bar code scanning. Compared with doubly labelled water, the remote food photography method did not significantly over- or underestimate energy intake⁽ Reference Martin, Correa and Han ^{43 )}. The mobile device food record is a fully automated food photograph analysis tool that analyses type and amount of foods⁽ Reference Daugherty, Schap and Ettienne-Gittens ^{44 )}. Users capture images of their foods and beverages before and after eating. A fiducial marker has to be included in the picture to estimate the amount consumed. However, the method overestimated energy intake when compared with laboratory weighed foods in adolescents⁽ Reference Lee, Chae and Schap ^{45 )}.

Innovation in statistical methods for the estimation of usual intake

Various statistical methods for the estimation of usual dietary intake with focus on intake distributions have been proposed⁽ Reference Slob ^{46 –} Reference Dekkers, Verkaik-Kloosterman and van Rossum ^{58 )}. The majority of these methods have been developed for the use in dietary surveys or risk analysis. Following a similar general approach, the methods use data that assess dietary intake on at least two independent days for each subject (e.g. repeated 24-h recalls). Statistical modelling considers the naturally occurring day-to-day variability by removing the so called within-person variation from the total variation⁽ Reference Hoffmann, Boeing and Dufour ^{5 ,} Reference Laureano, Torman and Crispim ^{59 )}.

To consider a statistical method suitable for the estimation of diet–health relationships, it must enable the estimation of individual usual dietary intake and not only intake distributions. Moreover, the method has to be able to estimate individual intake from both daily and episodically or rarely consumed foods. In this regard, two more recently developed methods are of particular interest, also with respect to large-scale prospective studies: the NCI Method⁽ Reference Tooze, Midthune and Dodd ^{54 –} Reference Tooze, Kipnis and Buckman ^{56 )}, and the Multiple Source Method (MSM)⁽ Reference Haubrock, Nothlings and Volatier ^{57 ,} Reference Harttig, Haubrock and Knuppel ^{60 )}. The NCI Method has been implemented with SAS macros (SAS Institute, Inc., Cary, NC, USA). The MSM was developed for use in Europe and is available through an online interface.

Both methods follow a two-step approach⁽ Reference Tooze, Midthune and Dodd ^{54 ,} Reference Haubrock, Nothlings and Volatier ^{57 ,} Reference Laureano, Torman and Crispim ^{59 ,} Reference Souverein, Dekkers and Geelen ^{61 )}. In the first part, the probability of consumption is estimated using a logistic regression model. The second part includes an estimation of the amount consumed and is restricted to observed positive intakes on the 24-h recalls. Firstly, a transformation step is used to obtain normally distributed data. Next, mean usual intake and between- and within-person variance on the transformed scale are estimated. The last step eliminates the within-person variance and the results are back-transformed to the original scale. Finally, the two model parts are combined to obtain the individual usual intake by multiplying the probability of consumption and the average consumption-day amount. For daily consumed foods, only the second part of the model is of relevance.

The statistical methods allow the inclusion of covariates such as age, sex or BMI in both parts of the model to represent the effect of personal characteristics. This is important as studies showed that sociodemographic factors such as education⁽ Reference Worsley, Blasche and Ball ^{62 )}, family status⁽ Reference Billson, Pryer and Nichols ^{63 )} and income⁽ Reference Worsley, Blasche and Ball ^{64 )} are associated with food consumption. More recently, the combined impact of eight different determinants of the consumption-day amount was analysed using state-of-the-art variable selection procedures. It was shown that sex, age and smoking status were the most relevant determinants of food intake in a representative German population⁽ Reference Freese, Pricop-Jeckstadt and Heuer ^{65 )}.

The 24-h dietary recall is limited in adequately measuring usual intake of foods or nutrients that are not consumed daily⁽ Reference Subar, Dodd and Guenther ^{66 )}. Even with two administrations of 24-h recalls, the probability of consumption for most foods and nutrients is poorly captured at the individual level. This has led to the extension of the statistical procedures by implementing a combined use of both repeated 24-h recalls and FFQ⁽ Reference Tooze, Midthune and Dodd ^{54 ,} Reference Haubrock, Nothlings and Volatier ^{57 ,} Reference Subar, Dodd and Guenther ^{66 )}. The FFQ assesses the probability of consumption, queried as frequency of usual intake over a specified period of time, and thus, levels out the weakness of the 24-h recall method. These reported FFQ frequencies can be used as a covariate in both parts of the statistical model to enhance the estimation of usual intakes from 24-h recall data. For the MSM, FFQ information can further be used to identify true non-consumers. In this approach, study participants who reported non-consumption of a certain food item or food group within the FFQ and did not report consumption of this food in the 24-h recall are defined as true non-consumers. Here, the probability of consumption as well as the consumption-day amount is set to zero. It has been suggested that this approach of combining instruments may provide high-quality dietary information, especially for the assessment of foods that are not consumed every day⁽ Reference Carroll, Midthune and Subar ^{16 ,} Reference Kipnis, Midthune and Buckman ^{55 ,} Reference Haubrock, Nothlings and Volatier ^{57 ,} Reference Subar, Dodd and Guenther ^{66 )}.

Simulation studies were conducted to compare the performance of different statistical methods, including the MSM and NCI Method⁽ Reference Laureano, Torman and Crispim ^{59 ,} Reference Souverein, Dekkers and Geelen ^{61 )}. These studies concluded that the overall performance of methods was similar. However, a small sample size or large within- and between-person variances might lead to inaccurate estimates. Ultimately, practical reasons such as availability of statistical programs or user-friendliness play a major role in choosing one method over the other.

For Germany, it was recently proposed to use a short 24-h food list to assess the probability of consumption complemented by person-specific standard consumption-day amounts derived from national nutrition survey data instead of individual amounts⁽ Reference Freese, Feller and Harttig ^{36 ,} Reference Freese, Pricop-Jeckstadt and Heuer ^{65 )}. Thus, the two parts of the statistical model (i.e. (1) estimation of consumption probability and (2) consumption-day amount) are separated as illustrated in Fig. 1. This approach is backed by the insight that the consumption frequency contributes more to the between-person variation than does variation in portion size⁽ Reference Noethlings, Hoffmann and Bergmann ^{67 )}. Information from an FFQ is added to provide information on true non-consumption and on frequency of consumption of rarely consumed foods. The 24-h food list was designed to have a simple structure and a rapid completion time to facilitate multiple administrations in large-scale settings.

Fig. 1. Proposed dietary assessment and statistical method to derive individual usual dietary intake in the German National Cohort⁽ Reference Freese, Feller and Harttig ^{36 ,} Reference Freese, Pricop-Jeckstadt and Heuer ^{65 )}. 24-h DR, 24-h dietary recall; 24-h FL, 24-h food list.