Introduction

Importance of Assessing Diet

Poor diet quality, characterised by low intakes of nutrient-dense foods (e.g. fruits and vegetables) and excessive intakes of energy-dense, nutrient-poor foods (e.g. fast food and sugar-sweetened beverages), increases the risk of type 2 diabetes, cardiovascular disease (CVD), arthritis, depression and colorectal cancer [1], and all-cause mortality [2]. Dietary patterns that meet guidelines for a healthy diet have the potential to manage sub-optimal lipid profiles, elevated blood pressure and high blood glucose, each of which contribute independently to chronic disease risk [3].

Assessing food and nutrient intakes and their relationship with disease outcomes [4] is critical to the refinement of national nutrition guidelines, but assessing dietary intake accurately is challenging. Dietary assessment methods have limitations, including inaccuracies inherent in self-report data, including those derived from both traditional and newer technology-based methods. Additionally, as new research techniques emerge, nutrient databases are expanded and analytic techniques refined, there is a need to continually assess evidence of relationships between dietary intake and disease outcomes. This can challenge previous research. For example, the role of saturated and unsaturated fats with CVD risk has long been studied [5, 6], but more recent evidence has fuelled a debate about the relative contribution of macronutrients, specific fatty acids and CVD risk [7, 8]. For other potential diet-disease relationships, the evidence is only beginning to emerge. Examples include the link between sugar-sweetened beverages and chronic disease risk [9] and the relationship between phytonutrients and reduced disease risk [10]. The complexity in understanding how diet influences health and disease is compounded by shifts in population intakes, for example, due to the introduction of new products (e.g. highly fortified foods and drinks), food and diet trends that influence consumer purchases (e.g. gluten-free, Paleo diet and other diets) [11], and changes to food labelling and packaging [12]. To inform nutrition guidelines, policies and food manufacturing, it is important to assess the impact of changes in dietary intake on disease risk and outcomes.

Continuing to build our understanding of how diet influences health and disease highlights the need for the ongoing refinement of existing dietary assessment tools. There is also a need to develop and evaluate the capacity of new methods for capturing intakes in different population subgroups as accurately as possible, while minimising burden for researchers and study participants. Guidance to inform the appropriate use of new tools and the interpretation of the resulting data is also needed to support a stronger evidence base.

There is a range of self-reported dietary assessment methods, including food frequency questionnaires (FFQs), 24 h recalls, weighed food records (WFRs) and short assessment screeners, each of which has strengths and limitations [13, 14]. Advances in methods of collecting dietary intake data have been achieved in recent years through technological innovations. In particular, the use of the Internet and smartphones for dietary assessment has resulted in the extension of traditional methods, allowing the collection of detailed dietary intake with lower costs and burden, and facilitating timely approaches to data analysis. Further, image-based methods and wearable devices have been developed and may eventually provide an alternative to self-report methods. However, continued refinement and evaluation of these methods are needed to expand the scope of their use and to optimise their validity and reliability. From the perspective of evaluation, recovery biomarkers have been identified as the optimal approach given their capacity to provide estimates of true intake. However, these have been identified for only a few dietary components to date, limiting the scope of their use [15•]. In addition, there is relatively low usage of biomarkers due to barriers of cost, access to specific analytic expertise and facilities, and the need for invasive measures such as blood samples. However, the use of biomarkers to evaluate diet assessment tools and to improve estimates of intake is an active area of discovery.

Given this context, the aims of this narrative review are to summarise the latest advances in dietary assessment, including technological innovations and methods for evaluation and validation, and to provide directions for future research in this area. Keeping abreast of developments in dietary assessment can help researchers take advantage of methodological advances, appropriately address challenges in the measurement of diet, and improve the quality of assessment and reporting of research related to diet, changes in population intakes, and relationships with health outcomes [16, 17].

Trends and Developments in Dietary Assessment Methods and Tools

Self-reported dietary assessment has recently been criticised due to the inherent measurement error [18–22]. Nutrition researchers have long acknowledged the limitations of self-reported dietary intake data but have reaffirmed their value for informing research and hence, refining nutrition guidelines [23•]. Recent efforts have been directed towards enhancing existing self-report methods (e.g. recalls, FFQs, and records) and developing novel instruments (e.g. wearable devices) to capture intake with greater accuracy while also reducing burden [24–26], as well as improving study design and statistical methods to mitigate error [14, 27–30].

Like any type of health assessment, there is a need for balance between the collection of accurate and reliable data versus burden for participants and researchers [23•]. The use of technology in dietary intake assessment aligns with these goals, allowing the development of instruments that facilitate real-time collection of detailed dietary intake data [31]. The Internet has allowed great enhancements of traditional approaches, such as shifts from a reliance on interviewer-administration of recalls to self-administration. Further, since the introduction of smartphones in the early 2000s, the development and use of technology-based applications have increased dramatically [32••, 33]. The development of research-grade tools, such as web-based 24-h recalls, has been accompanied by the availability and popularity of self-monitoring products, such as smartphone applications and devices (e.g. FitBit® and MyFitness Pal®) that can be used by consumers to track dietary intakes. Besides increasing exposure to the task of monitoring intake, mobile coverage is increasing worldwide [34], meaning access to these products and other technology-based applications will likely increase and in turn alter expectations around existing methods for assessing dietary intake. Therefore, researchers need to progress the area of technology use for dietary assessment to keep up with the industry. However, in order to be confident in the data collected and inferences made, one must ensure that any methods to be used for research purposes are valid and reliable [35].

Adaption of Traditional Dietary Assessment Methods

Web-Based Methods

In addition to new methods of dietary assessment, there is also potential for self-administered paper-based tools or interviewer-administered dietary assessment methods, including both brief and more comprehensive tools, to be modified so that they are accessible in an online format. For example, the advent of web-based 24-h recall systems means that it is now possible to collect 24-h recalls in large-scale undertakings, for example, using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24), which previously would not have been possible [36]. Web-based tools have also been extended to allow collection of food frequency data online. From the perspective of brief tools, a novel, web-based dietary assessment method has been developed to rapidly collect food intake data, automatically analyse nutrients, compare intake patterns to national recommendations, and generate personalised feedback reports in real-time (within 15 min of completing). This online quiz, the healthy eating quiz (HEQ), is based on the Australian Recommended Food Score (ARFS), a brief diet quality index [37, 38] which has been previously evaluated and found to have reasonable validity. However, it is important to note that, although the conversion of paper-based methods into web-based methods may have benefits, including faster completion, greater reach in terms of population groups from whom dietary intake data can be collected, and the ability to maximise the collection of complete data, it does not necessarily address limitations in terms of misreporting. Thus, there is a need for continued development and evaluation of methods, as well as the continued evolution of statistical methods to mitigate error.

Image-Based Methods

Use of digital photographs or images to measure diet has increased significantly in the last 15 years, due the availability and uptake of digital cameras either as standalone devices or embedded in personal digital assistants, mobile phones and smartphones or as wearable devices [32••]. As a prospective method for assessing dietary intake and an extension of the food record method, image-based methods require the individual to capture digital photographs of food and drink items prior to and following consumption. To assist with the quantification of food items contained in the images, individuals may be instructed to place a fiducial marker (a reference object of known dimensions) next to the food items during image capture. Individuals may capture the image via active methods, such as the use of a smartphone with camera or a standalone digital camera, or by passive means such as the use of a wearable camera that continuously collects images regardless of whether eating is occurring. Examples of active capture image-based methods include mobile food records used in projects such as the Technology Assisted Dietary Assessment Project [39–42] and the associated health and technology projects [43, 44], and in the Nutricam Dietary Assessment Method, in which images are supplemented with voice recordings describing the content [45]. Passive methods such as the eButton [46, 47] have been used either exclusively to assess intake or to complement traditional methods, such as a 24-h recall [48, 49].

Approaches to the analysis of image-based records may be automated, semi-automated or manual. For automated or semi-automated analysis, the images are analysed using computer vision techniques, which automatically identify, segment and quantify foods contained in the images exclusively [41, 50] or assisted with manual user input [51]; these are in various stages of development and evaluation. In comparison, manual analysis involves a trained analyst identifying and quantifying food and drink items contained within the image-based record. The strategies used for manual analysis remain unclear, with only a few papers providing detail on the training provided and/or whether aids are used to assist the analyst in the quantification of food items contained in the images [44, 45, 52].

Due to the accessibility of mobile devices and the high rates of use in both developed and developing countries [53], the use of image-based methods for dietary assessment has the potential to reach large populations, benefiting both the consumer or user, who previously may not have had access to health services, and the researcher. Image-based applications on mobile phones have previously been found to be easier and more convenient to use than manually weighing and measuring foods every day [45]. Further, when combined with existing methods, image-based approaches that use passive capture methods may reduce misreporting, particularly under-reporting, as images capture foods that may be commonly under-reported due to social desirability biases and other factors [49]. Compared to traditional paper-based methods, the wide accessibility to technologies such as smartphones offers researchers the potential to undertake research with large samples. Despite this promise, large-scale use of image-based methods is currently somewhat limited by the analysis methods used to derive estimations of nutrient intake. In particular, the intra- and inter-individual variability with which food is prepared, served, and consumed adds levels of complexity that are challenging to program into computer vision systems to provide fully automated and accurate analysis of day-to-day dietary intake data collected in free-living situations. Studies are needed to evaluate the accuracy and reliability of image-based dietary assessment methods. However, progress in this area in gaining momentum and image-based methods hold great promise for the future of dietary intake assessment.

Wearable Devices

The use of wearable devices to monitor and assess intake is an emerging area of dietary assessment methodology. These devices aim to collect objective data unobtrusively through the use of sensors to detect hand-to-mouth actions and biting, chewing, and swallowing of food. Examples include devices placed on the wrist to detect the action of moving food to the mouth [54, 55] and sensors placed on the neck and jaw to detect sounds and movements associated with eating [56–58]. Dong et al. describe a wrist worn ‘bite counter’ that uses a gyroscope to track the unique wrist action attributed to eating, which then allows estimation of energy intake. This counter has been shown to capture 86 % and 94 % of bites in free-living and laboratory-based situations, respectively [54]. In another study using a similar wrist-worn bite counter, Salley and colleagues found the device to be more accurate at estimating energy intake compared to estimations made by the individuals, both when individuals did and did not have access to the calorie content of the foods [55].

Another approach, the automatic ingestion monitor, combines sensors for monitoring jaw, hand-to-mouth, and body motion with a smartphone application and has been tested in adults [59] and infants [60]. Recent iterations have resulted in a smaller data collection device that can be attached to the arm of eyeglasses in addition to the small sensor used for the detection of chewing [61]. These studies have demonstrated the feasibility of these novel multi-sensor wearable devices method for measuring energy intake; however, most evaluations have been conducted in small samples and often in controlled environments (e.g. cafeterias) using a limited variety of foods. Therefore, to determine the accuracy of wearables devices and their utility as a method to objectively assess dietary intake, further evaluation among larger samples of free-living individuals that also examine changes to eating behaviours as a result of wearing these devices (i.e. reactivity) is needed.

Trends and Developments in Evaluation of Dietary Methods and Instruments

For the purposes of evaluating dietary methods and instruments, often intake obtained via new tools is compared to a measure of self-reported dietary intake to establish relative validity [13]. In addition, the tool of interest may be compared to an objective reference, such as a biomarker that is considered to provide a measure of intake that is closer to truth, for the purpose of establishing the criterion validity of the new method [13].

Indeed, data from biomarkers cannot be translated into what people actually eat and drink, but can provide information on the validity of different dietary assessment methods [62]. Recovery biomarkers, such as those that appear in urine, show a direct relationship with dietary intake and can be used as an objective measure of intake [14, 63, 64]. Recovery biomarkers assess the balance between a measured or known intake and the amount excreted [65] and include doubly labelled water to estimate total energy intake [66, 67] and urine markers of sodium and potassium intake [68], along with urinary nitrogen, which is a marker of protein intake [69]. However, recovery biomarkers are available for few dietary components, [70] and thus, their use is limited. Concentration biomarkers are another class of biomarkers and are measured in biological materials, such as plasma, but are not directly related to intake due to the effects of metabolism and personal characteristics such as obesity [14]. Concentration biomarkers can be useful in assessing relationships between tissue concentrations and health outcomes and can be measured in many samples including plasma, red blood cell membranes, adipose tissue and hair. Examples include plasma carotenoids [71, 72] and red blood cell membrane fatty acids [73]. These biomarkers have been well researched and have been used to evaluate a number of dietary assessment methods. However, there are numerous considerations that are biomarker-specific that can influence results from dietary studies when used for evaluation purposes [15•].

There is a need for identification of new biomarkers that can assess other dietary components or broader dietary patterns. There are a number of emerging biomarkers, which may make an important contribution to the area of dietary assessment and validation.

Biomarkers: New Directions

Despite the need for additional dietary biomarkers, measurement has come a long way from processing venous blood samples in the laboratory [74] to taking a finger prick blood samples [75] to measuring dietary biomarkers. However, identification of new dietary biomarkers is complex, requiring information on the bioavailability and metabolism of the food components of interest [76]. Analytical methods for identifying biomarker concentrations or recovery of metabolites need to be identified followed by feeding studies targeting the food component of interest with concurrent measurement of the biomarker or metabolite [76].

A number of predictive biomarkers receiving attention in recent years include markers for sugar and wholegrain intakes, as well as polyphenols, isoflavones and phytosterols/stanols [15•]. Biomarkers for sugars include 24-h urinary excretion of sucrose and fructose [77] and also the carbon stable isotope ratio of 13C and 12C [78, 79]. Alkylresorcinols (ARs), which are phenolic lipids, are a proposed biomarker for wholegrain intake [80]. The bran fraction of wheat and rye contain the largest amount of ARs, whereas barley, rice and oats contain lesser amounts. Skin reflectance spectroscopy is another new method, which can be used to objectively evaluate measures of fruit and vegetable intake. Carotenoid pigments from fruits and vegetables accumulate in the skin and contribute to the normal yellowness of the skin [81, 82]. The benefit of measuring skin reflectance over plasma carotenoids is that it is a non-invasive method, although initially expensive.

In recent years, metabolomics has emerged, with metabolites measured in saliva, blood, urine, and biopsy samples by mass spectrometry (MS) or nuclear magnetic resonance (NMR) [25, 83, 84]. Metabolomics can be applied to different areas of nutrition research, including informing hypotheses regarding new diet-disease relationships [85]. Recently, the relationship between branched chain amino acids (BCAAs) and type 2 diabetes risk was identified using metabolomics [86]. Other applications include the identification of new dietary biomarkers and investigating the mechanism of action of nutritional interventions [87•]. Dietary biomarkers for broccoli, tea, coffee and citrus fruits have already been identified using metabolomics [87•]. Due to the rapidly growing interest in metabolomics, large international databases are being developed, which allow for sharing of data on the metabolome. One of the most comprehensive databases of food constituents is the Human Metabolome Database (HMDB) with over 40,000 metabolites registered. Development and sharing of databases such as this will facilitate development of this area. However, few new biomarkers have been evaluated for validity nor quantified against dietary intake in population groups, limiting their use. The area of metabolomics is still developing but shows potential for multiple applications in various areas of nutrition and health research.

Assessment of dietary biomarkers, namely concentration and predictive, does have confounding factors that need to be considered to minimise errors and inaccuracies in the resulting data [65, 76]. Factors such as sex, body mass index, and both the bioavailability and the bioaccessibility all influence how closely the dietary biomarker reflects dietary intake [65] and should be considered within analyses. Measurement of dietary biomarkers is expensive and impractical for most studies but can be used to evaluate self-reported dietary assessment methods and to establish the performance of novel methods. Researchers are likely to increasingly look to use biomarkers to measure the performance of self-report methods and their accuracy in varied population groups as the field continues to evolve. Further, as new biomarkers emerge, access is likely to improve and expanded use will add to the evidence base, helping to contribute to a better understanding of the validity of existing and new dietary intake assessment methods.

Implications for Research and Practice

Technology is now ubiquitous in daily life. Traditional dietary assessment methods, such as 24-h recalls and FFQs previously administered via an interviewer and/or collected by paper-based methods, are now available as web-based tools, facilitating self-administration and automated analysis. An important development in moving FFQs such as the Australian Eating Survey online [88] (http://www.australianeatingsurvey.com.au/) is the ability to develop algorithms to provide real-time nutrient assessment and comparison to nutrient recommendations, with personalised real-time feedback. Further, the adaptation of brief screening tools, for example, the healthy eating quiz [38] (http://healthyeatingquiz.com.au/), into online versions can result in extensive reach and provide a means of rapid online assessment of diet quality, with the opportunity for providing real-time feedback. Similarly, food records can now be collected via an application on a mobile or smartphone, extending the accessibility of these methods and hence the opportunity to measure dietary intake in new contexts or population groups. As a relatively more recent method, image-based approaches further reduce participant burden. Wearable devices are another innovation in modern dietary assessment and aim to provide an objective measure of intake through the use of unobtrusive body sensors. All of these methods mean that researchers, clinicians and individuals potentially have access to more information on dietary intake than ever before. However, their feasibility outside of controlled settings needs further evaluation, and the impact of wearing these devices on typical eating behaviours is not yet established. Evaluation of all tools must account for the evidence that certain groups may be more prone to misreporting. For example, comparing self-report data to that from recovery biomarkers suggests that under-reporting of energy, protein, and sodium intakes is more common among individuals affected by obesity [68, 89]. Whether dietary misreporting occurs consciously or unconsciously remains to be established. A limited body of evidence currently exists on the characteristics of individuals who indicate intentions to misreport intake [90]; however, this has not been confirmed by comparison to measured intake.

Despite advances in dietary intake methods, all self-report assessment methods contain inherent error [23•]. While our understanding of the error associated with use of self-report methods is more established for traditional methods compared to image-based approaches and wearable devices, all methods should be rigorously evaluated (ideally with recovery biomarkers) and particularly when used in new settings or population groups. An awareness of the limitations of these methods is essential for the appropriate analysis and interpretation of the data collected. The use of checklists, including the recently-released STROBE-nut guidelines, may improve reporting of epidemiological and validation studies involving dietary assessment methods and enhance the quality of the published evidence [91].

The selection of dietary assessment methods should be based on the appropriateness of the method to sufficiently measure the dietary variable of interest in the given setting and population. Given the widespread use of mobile and smartphones and access to the Internet, there may be a tendency to favour the use of technology-assisted methods to assess dietary intake. However, sometimes these may not be appropriate, and researchers should consider the characteristics of the population to be studied and likely compliance with the data collection method when deciding which assessment methods to use.

Conclusions

Technology is streamlining dietary data collection, analysis, and interpretation. However, challenges and questions remain related to data capture and the automation of the analysis of image-based records, while the feasibility of wearable devices in uncontrolled settings needs to be established. In addition to developing new methods, novel technologies can aid in refining established dietary assessment methods. When combined with advances in dietary biomarkers, particularly metabolomics, opportunities present to further advance the field of dietary assessment methods. The ability to more accurately quantify dietary intake is essential to enhancing our understanding of diet-disease relationships, which in turn will contribute to refining dietary guidelines and optimising diet-related health internationally.