3.2: Technical improvements in food con­sump­tion mea­sure­ments

The increasing evidence of the relationship between diet and chronic disease has led to a number of technical advances in mea­sure­ments of food con­sump­tion for indi­viduals. They include the use of the telephones in surveys, digital photo­graphs of food portions displayed on a computer/tablet or captured with a cell-phone camera, as well as web-based tools, mobile apps, and image-based assess­ment. These innovative technologies aim to improve the speed, accuracy, and partic­ipation, and reduce reporting burden, bias, and the cost of collecting and analyzing dietary intake data during large scale epidemio­logical studies as well as national surveys. A summary of the strengths and limitations of these innovative technologies designed to improve dietary assess­ment methods is shown in Table 3.5. A robust assess­ment of the perform­ance, cost, and response rates should be undertaken, however, prior to the adoption of any new-technology-based method.

Table 3.5 Strengths and limitations of innovative technologies to improve dietary assess­ment methods. From: Dietary Assessment: A resource guide. FAO (2018).

All innovative technologies used in dietary assess­ment
Strengths	Limitations
Costs for data collection can be lower (less need for person-to-person interaction)	Larger up-front investments (i.e. purchase of mobile phones, cameras, computers, software development, etc.)
Convenient for users, good acceptability, may improve compliance	Risk of losing devices
Do not rely on respon­dent's memory	Risk of technical problems (i.e. low battery, loss of Internet connection) could impede data collection
Record of qualitative information (e.g. date and time of recording)	A backup method is required to collect information, if technical problems occur
Significantly cuts down data processing time
Personal Digital Assistant (PDA)
Strengths	Limitations
Is portable and can be easily carried by study participants	Face-to-face training of the participants is required
Facilitates real-time data collection, entry and coding	Low level of dietary data details because of pre-coded food listings
It is possible to set an alarm within the PDA to alert participants to record their food intake	Increases the respon­dent burden compared with pen and paper records, due to the extensive list of foods (depending on each PDA)
Can be programmed to allow partic- ipants strict access to the dietary intake software only	Reports of difficulty using the search function and inability to find certain foods
Image-assisted methods i.e. digital photographs
Strengths	Limitations
Easy to use	Participants may forget to take some images
Suitable for low literacy popu­lations (if the technologies are easy-to-use, e.g. digital cameras)	Not all information can be captured with a single photo­graph/image
Quality of digital cameras keeps improving and pictures with higher resolutions can help improve the accuracy of analysis	Difficulties in estimating portion size of food con­sumed from common mixed dishes
Suitable for subjects with memory impairments and for children	Lack of details about cooking methods
Lower under-reporting compared with some tradi­tional methods	Probably more limited accuracy for coun­tries with a wide range of mixed dishes (e.g. Asia) Still needs a written record of foods obscured in photos, and when details of ingredients are required
Image-assisted methods i.e. mobile-based technologies
Strengths	Limitations
Possible higher quality control of data because of shorter delays and real-time responses	It is costly and time-consuming to develop the application as an interface, and the software for automated portion size estimation
Possibility of sending reminders	Certain types of foods, such as mixed dishes (soups, stews, casseroles, etc.) can be difficult to analyse with automated image analysis
Internet access would allow respon­dents to send instant photos, thus minimizing system­atic mistakes	Requires certain level of literacy Requires network/Internet access (for real-time data collection)
Interactive computer and web-based technologies
Strengths	Limitations
Efficiency in terms of recording information and data processing (i.e. reduced costs and interviewer workload)	Some imaging algorithms presently fail to identify foods correctly and to accurately estimate the quantity of food in the computerized images
Increased levels of quality control	Need adaptation of the software to local settings
Include interactive visual and audible aids	Require high levels of literacy and computer skills from the participants
Suitable for large, geo­graph­ically dispersed samples; can include different coun­tries/languages (web-based)	Possibility of collecting less food details (i.e. ingredients, methods of preparation, etc.)
Data processing can be finalized at any time and location (web-based) Can provide personalized dietary feedback (web-based) Possibility of sending interactive reminders (web-based)	Require Internet access (web-based)

3.2.1 Telephone

A telephone survey that is well-designed and carefully-admin­istered appears to be a promising method of obtaining dietary inform­ation. In partic­ular, telephoned 24h recalls are being increasingly used. The USDA has conducted several studies to examine the feasibility of using telephone follow-up surveys instead of mail follow-up for 24h recalls. Results have been promising: response rates for telephone follow-up were much greater than those for mail follow-up. Casey et al. (1999) carried out a validation study in which the results of 24h recalls conducted over the telephone were compared with in-person recalls collected in the 1994-1996 USDA Continuing Survey of Food Intakes by Individuals; strong corre­la­tions were reported. As a result, since 2002 NHANES have used the telephone to admin­ister the second recall day 3–10d later using the AMPR method (Hennigar et al., 2018).

Several other large-scale telephone dietary surveys have been used in the United States, some of which have used a food fre­quency instrument rather than a 24h recall. Lyu et al.(1998) successfully showed that agreement between telephone and face-to-face inter­views of a semi-quanti­tative food fre­quency question­naire in Hawaii made up of 115 food items was good and unaffected by age, gender, ethnicity, or education of the respon­dents. These inves­tigators did recommend mailing photo­graphs of foods in three portion sizes in advance, to help respon­dents estimate amounts eaten more accurately.

Many of the smaller telephone dietary studies have been conducted on adult women (Casey et al. 1999; Tran et al., 2000; Yanek et al., 2000). Very few have been carried out on adolescents and adult men (Bogle et al., 2001). In college students, food intakes by telephoned recalls have been compared against actual intakes determined surrep­titiously in the college cafeteria (Krantzler et al., 1982). More studies are needed, however, among certain life-stage groups, to establish the validity of 24h recalls or food fre­quency question­naires admin­istered over the telephone. Under-reporting of self-reported food intakes may still occur in telephone recalls, as they do with in-person recalls; this was observed, for example, when total energy intakes derived from telephone-admin­istered 24h recalls were compared with total energy expenditure measured by doubly labeled water (Tran et al., 2000) (see Chapter 7).

Telephone surveys do have several advantages. These include their ability to reach a large number of persons at perhaps less than half the cost of face-to-face surveys. As well, with the advent of computer-assisted telephone inter­viewing, the inter­views can be readily stan­dardized, queries can be clarified, and responses can be coded immediately, during an inter­viewing time that is much shorter than a face-to-face inter­view. As a result, the response rate is enhanced.

Potential inter­viewer biases can be eliminated by using computer-assisted telephone inter­viewing, an approach used with the AMPR in the NHANES surveys. However, other sources of bias may occur with telephone surveys; these may arise from non­coverage and non­response. In the United States, although over 87% of the popu­lation owns a telephone, subgroups such as the poor, certain minorities, and the` elderly still have fewer telephones than the general popu­lation. Such dif­fer­ential coverage can introduce bias in national surveys unless alternative compensating strategies are employed. Strategies may include using supplementary face-to-face or mail inter­views for persons without telephones; statis­tical adjustments employing weighting for sex, age, race, and income; selective over-sampling of the non-telephone users; and random-digit dialing to contact those with unlisted numbers. Nonresponse is also a source of potential bias that influences telephone surveys. This is espe­cially a prob­lem among certain subgroups such as the ethnic minorities for whom language barriers may be consid­erable, the elderly who may have hearing difficulties, and persons with less education.

Despite these limitations, telephone dietary surveys are a practical, economical, and valid alternative to the conventional face-to-face methods for large-scale epidemio­logical studies and nutrition surveys and for developing and evaluating community-based nutrition inter­ventions in higher income coun­tries.

3.2.2 Portion size estimation aids

Errors in quantifying the portions of food con­sumed are often the largest error in most food con­sump­tion surveys, unless weighed food records are used. As a result, several types of portion-size measurement aids (PSMAs) have been devel­oped in an effort to enhance the accuracy of portion size esti­mates (see Chapter 5 for more details). PSMAs can be divided into two categories: (1) non-photo­graphic aids and (2) photo­graphic aids. Canada was one of the first coun­tries to use non-photo­graphic aids in its National Nutrition Survey in 1973. The aids consisted of a collection of three-dimensional graduated food models of various volumes and surface areas prepared from paper-mache, wood, or hardboard. A range of graduated food models was used to prevent the tendency to generate a “direct” response. The latter phenomenon is observed when plastic food models representing only “average” portion sizes are used (Samuelson, 1970). The Canadian surface-area models were accom­panied by stan­dard thickness indi­cators made of hardboard squares. These were used during the 24h recalls to assist in assessing the overall size and thickness of foods such as cheese, cold meats, cakes and cookies. Use of thickness indi­cators is critical for assessing portion sizes of intact cuts of meat, espe­cially when irregular in shape.

Since 1973, graduated food models (Figure 3.2) have been used with 24h recalls in many national food con­sump­tion surveys, including those in the United States (NHANES, 2017) and New Zealand (MOH, 1997). In the dietary component of the NHANES nationwide surveys, the tool devel­oped to measure portion sizes in the 24h recalls consists of a food model booklet of 32 life-size two-dimensional drawings of house­hold vessels (glasses, mugs, bowls), abstract shapes (mounds and spreads), and geometrical models (circles, a grid, wedges, and thickness bars), together with a set of measuring cups, spoons, and rulers (USDA 2002+); More details are given in Chapter 5. In low-income coun­tries, PSMAs for 24h recalls may be salted replicas of staple foods, real foods, and local house­hold utensils (e.g., bowls, plates, spoons). These are often used in combination with items to simulate liquid and solid foods (e.g., water or raw rice), manip­ulative models (modelling clay or play dough), tape measures or rulers and graduated food models (Gibson and Ferguson 2008). Guidelines on PSMAs for use with 24h recall methods in low-income coun­tries are available from www.intake.org. The use of less than five dif­fer­ent PSMAs are recommended for surveys in low-income coun­tries. (Vossenaar et al., 2020).

Figure 3.2. USDA Food Model Booklet

More recently, photo­graphs are being increasingly used as PSMAs. They may include pictures of indi­vidual foods or meals which have been drawn, printed, or displayed digitally, often in color and sometimes shown next to a stan­dard sized object (e.g., a coin) or grid­lines to aid the respon­dent's perception of scale. For surveys using 24h recalls, a series of graduated portion-size photo­graphs (i.e., amount con­sumed on any one occasion) for each food item are commonly displayed during the inter­view, whereas for surveys employing food fre­quency question­naires, either an average serving size for each food (i.e., average amount served in one helping) or a series of serving sizes (small, average, large) are often used. The graduated portion-size photo­graphs are often bound together in a photo­graphic atlas.

Practical guidelines on how to develop a photo­graphic atlas are given by Nelson and Haraldsdottir (1998) and Vossenaar et al. (2020). Factors that must be consid­ered in relation to the format of the photo­graphs include size of the image, number and range of portions sizes depicted, and the inter­val between portion sizes. In the EPIC study, for example, there was a 25% difference between portion sizes to allow a real visual perception of differences in size (Vilela et al., 2018). Nelson et al. (1994) used portion weights from the British Adult Dietary Survey ranging from the 5th to the 95th percentile for a series of eight photo­graphs for each food. Other important factors that should be stan­dardized include the order of presentation of the photo­graphs, labels used, angle at which the photo­graph was taken, background and use of reference objects for scale, color versus black and white, and use of one versus several foods on a plate.

Increasingly digital photos of food portions with multiple images displayed on a computer or a tablet are replacing printed photos to reduce the cost of printing atlases and their transportation to dif­fer­ent inter­view sites. Nichelle et al. (2019) compared the accuracy of three portion sizes (small, medium, large) from printed photo­graphs in the Brazilian GloboDiet atlas with digital photos displayed on a computer screen for 20 foods selected from the Brazilian GloboDiet atlas. The mean error (difference between the estimated and true portions) was not significantly dif­fer­ent between the printed and digital photos, with agreement on using the printed and digital photos being 91% and 90%, respe­ctively.

During the development of the Internet-based, Automated, Self-Admin­istered 24h recall (ASA24), Subar et al. (2012) investigated the best way to provide digital images as portion-size estimation aides. They compared the photo­graphs said to represent portion sizes of foods con­sumed by the respon­dents on the previous day with the actual weights obtained by surrep­titious weighing. The images were also varied by the angle in which the photo­graphs were taken, the type of image, and the number and size of digital images presented in an effort to determine how best to present the digital images to facilitate accuracy. None of the photo­graphs were labeled with portion-size inform­ation. Results indi­cated that accuracy was not affected by the type of image, whether the images were presented simul­taneously vs. sequen­tially, or the size and number of images presented, although there was a tendency for eight rather than four images to be more accurate. Based on these results, aerial photo­graphs are used for portion size estimation in the ASA24.

Alter­natively, real-time images of foods and bever­ages con­sumed by the respon­dent, and captured passively, or actively with a cell-phone camera (Arab et al., 2011), digital camera (Lazarte et al., 2012), or wearable camera (Gemming et al., 2013), can be used as PSMAs during a 24h recall the following day. The active methods require the respon­dents to take pictures in the appropriate way (e.g., 45°angle, arms distance from the plate, with a size reference included for scale) before and after eating all meals, and to add accompanying notes. However, all of these tasks necessitate training the respon­dents. As a result, for low-literacy popu­lations, the passive tech­niques are more suitable, where an indi­vidual wears a camera to capture photo­graphs, thus requiring less involvement by the respon­dent.

In many food con­sump­tion surveys in both lower and higher income coun­tries, a mixture of non-photo­graphic and photo­graphic aids have been used as PSMAs. A system­atic review that included both non-photo­graphic and photo­graphic PSMAs concluded that culturally appropriate digital or hard-copy photo­graphic PSMAs were more accurate than non-photo­graphic food models and house­hold utensils (Amoutzopoulos et al., 2020). The inves­tigators also emphasized when selecting an appropriate tool for estimating portion sizes, both the setting and popu­lation under study must be consid­ered. Most of the studies included in this review were from high income coun­tries. In a study in rural Bolivia, however, the mean energy and nutrient intakes of 45 women (20–52y) obtained using an inter­viewer-admin­istered atlas of graduated photos of local foods combined with digital food photo­graphs taken by the respon­dents on the day prior to the 24h recall, were compared with those derived from a weighed record. No significant mean differences were reported in estimated and actual food amounts between the 24h recall using the food photo album and the digital photo­graphs and weighed food records, except for a few foods (rice, potatoes, eggs, veg­etables). For the latter foods, differences ranged from −5.4% for veg­etables to −6.8% for rice. However, there was a slight under­estimation in values between the two methods for some nutrients (Lazarte et al., 2012).

3.2.3 Web-based systems for dietary assess­ment

Table 3.6 Some of the web-based 24-h recall tools for dietary assess­ment

Web-based 24-h dietary recall tools for dietary assess­ment
Author	Tool (Country)	Notes (All tools use photographs to aid portion size estimation)
(Cade, 2017),	(myfood24), (UK)	Large data­base with generic and branded food items; meal-based approach; simplified AMPM approach,
(Bradley et al., 2016),	(INTAKE24), (UK)	Meal-based approach; based on AMPM approach; Multipass recall for subjects 11–24y.
(Lassale et al., 2015),	(France)	Web-based 24h diet record; Food portion pictures for 250 foods
(Thompson et al., 2015),	( ASA24), (USA)	Meal-based approach; uses the five steps of the AMPM; questions about food preparation; available in English and Spanish; optional supplement module
(Arab et al., 2015),	DietDay (USA)	Multiple steps similar to the AMPM approach; questions about food preparation; recording of supplements; provides dietary feedback
(Biltoft-Jensen et al., 2014),	WebDASC (Denmark)	Self-administered tool for children 8–11y; animated; guides response to six eating occasions; 1300 foods selected via category browse or search.
(Vereecken et al., 2014).	CANAA-W (Belgium)	A feasibility study on 131 children and 53 parents; to refine the lay out and structure of the tool; and the list of food items included.

On-line dietary assessment tools based on 24h recalls, food fre­quency question­naires, and dietary records are becoming increasingly available. They can be inter­viewer- or self-admin­istered so data can be collected at a time and location that is convenient to the respon­dent. Examples of web-based 24h dietary recall tools are shown in Table 3.6. These include the US National Cancer Institute ASA24 and DietDay (Arab et al., 2011), the UK INTAKE24 (Bradley et al., 2016), and UK myfood24 (Cade, 2017), the French NutriNet Sante (Touvier et al., 2010), WebDAS in Denmark (Biltoft-Jensen, 2014), and the CANAA-W in Belgium (Vereecken et al., 2014). Some of these have been modified for use in other high-income coun­tries such as Canada, Australia and Germany.

The use of automated 24h recall inter­views in web-based systems stan­dardizes the inter­viewing process so there is consistency in the format in relation to the questioning and sequence, food descriptors, and methods used to estimate food portion sizes. Some (e.g., ASA24; INTAKE24; myfood24) use a meal-based approach. Figure 3.3 shows an example of a screenshot from ASA24 for the recall of breakfast. Most are based on a version of the AMPM inter­view format devel­oped by USDA (Moshfegh et al., 2008).

Figure 3.3. A sample screen-view from a web-based assessment tool (ASA24), showing the user selecting, in an unstructured manner, recalled food items for breakfast. Note the user-prompted drop-down list.

Groupings of foods and recipes, as well as the level of detail used to describe foods varies across systems. In most of the applications, ingredients of mixed dishes are entered separately in either the raw or cooked form, with an option to select a generic mixed dish if the ingredients of the recipe are unknown. Some of these on-line systems use only food portion photo­graphs to help respon­dents report the amount of each food con­sumed (e.g., ASA24; DietDay), whereas others (e.g., INTAKE24) use multiple options for portion size selection. These may include graduated photo­graphs or images of actual foods, guide photo­graphs for selected prepackaged items (e.g., snacks), with or without additional options such as photo­graphs/pictures of house­hold measures (glasses, cups, bowls, teaspoons etc.) Generally, additional automated prompts for potentially forgotten items are programmed and quality controls are implemented at each step of the inter­view procedure, with checks made on outlier values. Standardized algorithms are often used to calculate automatically grams of the food con­sumed, and the amounts of ingredients (either raw or cooked) con­sumed from a mixed dish. Some of the applications, espe­cially for children, record the amounts served and leftovers rather than the amounts con­sumed. Most applications have a separate screen for describing and quantifying dietary supplements. Usually, at the end of the inter­view, a summary is displayed with the opportunity for the respon­dent to correct or edit inform­ation at each stage of the inter­view. Each web-based system is linked to an in-country food composition data­base, some of which include branded food items (e.g., ASA24, myfood24), with automated coding for those reported foods and bever­ages that are in the food composition data­base. Items not in the data­base are coded manually.

In low-income coun­tries where reliable inter­net access may be lacking, espe­cially in rural areas, web-based methods are not feasible for assessing dietary intakes, espe­cially when self-admin­istered, due to the low literacy rates that often exist. To address these concerns in low-income coun­tries, the International Dietary Data Expansion (INDDEX) Project has devel­oped a dietary assess­ment platform called INDDEX24 (Coates et al., 2017). The platform consists of a mobile application for the collection of off-line indi­vidual-level dietary data using the 24h recall method with the AMPM, linked to a web data­base application so that the users can download the required country-specific food composition data. The dietary recall mobile application uses an open source mobile data collection platform (Commcare) so that it is accessible to researchers at no or low cost. The application is designed so that it is straightforward to use and contains a number of quality control features such as controls to prevent entry of implausible values, pre-defined portion-size estimation methods depending on the type of food reported, and real time calcu­lation of the respon­dent's reported energy intake in order to identify possible under- and over-reported intakes (Burrows et al., 2014). The mobile app converts food portions reported in non-stan­dard units into gram-weight equiv­alents by linking to context-specific portion size conversion factors housed within the web data­base (Coates et al., 2017). Both the perfor­mance and cost of a 24h recall using the INDDEX24 mobile app compared with a pen-and-paper 24h recall have been explored. See Rogers et al. (2022) and Adams et al. (2023) for further details.

3.2.4 Image-based methods of dietary assess­ment

These are defined as any method in which images are used for identifying foods, or for portion size estimation. Personal digital assistants (i.e., handheld computers) were used initially for these image-based methods, but have now been replaced by camera-enabled cellular phones or smartphones, digital, disposable, or 35mm cameras, wearable cameras, or digital video cameras. Many of these image-enabled devices rely on users manually taking photo­graphs of foods (i.e., active image capture). Alter­natively, for those methods termed “passive image capture”, pictures are automatically taken, usually at timed inter­vals (e.g., every 10 seconds) by the devices (predominantly wearable digital cameras), so that no inter­vention is required on the part of the user. The majority of tools that use active image capture have been designed for research purposes to enhance the accuracy of self-reported intakes. Fewer passive image capture methods exist and none is fully automated, most relying on specialists to manually estimate portion size (Gemming et al., 2015a).

A few image-based methods are based on a combination of tools. Examples include: SmartPlate (SRI International, 2015), which consists of a camera and sensor-enabled dinner plate, the Diet Data Recorder System (DDSR), a three-part system comprised of a camera-enabled smartphone, laser module, and circuit module (Bell et al., 2017), and the Dietary Intake Monitoring System (DIMS). The latter is a five-part system with a digital camera, weighing scale, infrared thermometer, radio-fre­quency identification reader (RFID), and a user RFID transponder card (Ofei et al., 2014). Most of the image-based methods are portable. Exceptions are the DIMS which is mounted on a small cart and the SmartPlate which is dinner-plate size and hence less convenient to carry compared to the other smaller devices.

Image-based methods can be further subdivided into three groups: image-based food records, image-assisted food records, and image-assisted 24h recalls. Image-based food records are defined as methods in which images of foods captured during eating episodes serve as the entire basis for the dietary assess­ment, whereas in image-assisted food records, the images captured are used to enhance or supplement a tradi­tional text-based food record (written or electronic). In image-assisted 24h recalls, the food images captured during eating episodes are used to aid self-reporting during the 24h recall. For more details of image-based methods, the reader is referred to the following reviews (Illner et al., 2012; Gemming et al., 2015a; Boushey et al., 2017).

Several studies have confirmed that image-based methods of dietary assess­ment are preferred over tradi­tional dietary records, and can enhance self-reported dietary intake by revealing unreported foods and misreporting errors. However, study costs will be increased when respon­dents have to be provided with a suitable device (Gemming et al., 2015a). With the additional dietary inform­ation from the images, there is likely to be an increase in reported energy intake and a decrease in reporting errors, except when the methods are followed incorrectly so the images are of poor quality, or the user forgets to capture images before eating episodes. In addition, details of mixed dishes are difficult to extract from the images, some ingredients may be hidden, and cooking methods omitted. To avoid these prob­lems, respon­dents should be trained to review the images after taking them, to take a second image if necessary, and to record descrip­tions of the mixed dishes, including lists of ingredients and quantities. Coding errors associated with image analysis, however, are likely to be less random and less prob­lematic compared to the system­atic bias observed when food type and portion size are self-reported.

Figure 3.4 depicts the entire system — the Technology Assisted Dietary Assessment (TADA) system — that commences with the respon­dent capturing an image with the mobile food record. In this method, respon­dents use the camera of a mobile devise to capture images of their intake of foods and bever­ages which they send to a secure server, where trained analysts estimate the reported intakes of energy and nutrients.

Figure 3.4 Diagram of the Technology Assisted Dietary Assessment (TADA) system. This shows (1) a user capturing an image of an eating occasion with the mobile food record; the image is sent to a server. (2) The image is analysed to identify the foods and drinks. (3) The labelled image is returned to the user for the review process; (4) The user confirms the automatic labels or corrects the labels. (5) The image is returned to the server for final identification and volume estimation. (6) Identified foods and amounts are matched for nutrient analysis to the Food and Nutrient Database for Dietary Studies (FNDDS). (7) Images and data are stored in a server for use by researchers or clinicians. Source: Dietary Assessment: A resource guide FAO, 2018.

Figure 3.5 compares the mean total energy expenditure (TEE based on doubly labeled water (DLW), with the reported energy intake (rEI) using images from a mobile food record (mFR) recorded over 7.5d by 45 community dwelling men and women, 21–65y (Boushey et al., 2017). In this study, mean energy intake for TEE and rEI were 2932 vs. 2353kcal/day with a significant mean difference of 580kcal/d, p < 0.0001). A moderate statis­tically significant corre­la­tion was found between rEI and TEE (Spearman corre­la­tion coefficients 0.58; p < 0.0001), with reporting accuracy that was consistent across all energy intake levels. The mFR was well received by the respon­dent and usability was rated as easy by Boushey et al.(2017, For details of the development of the mobile food record (mFR), see Six et al. (2010).

Figure 3.5. A compar­ison of mean total energy expenditure (TEE) based on doubly labeled water (DLW), reported energy intake (rEI) using images from a mobile food record, and presumed energy intake (pEI) based on returned preweighed servings of food over 7.5 days by total sample and by sex (men = 15 and women = 30). Modified from Boushey et al. (2017).

Unfor­tu­nately, there are few validation studies of image-based methods among the elderly, children, and adolescents, and the feasibility of their use in large studies (n > 100) has not yet been demonstrated. Burrows et al. (2019) provide a review of validation studies based on image-based methods using doubly labeled water.

3.2.5 Wearable devices

Under development are wearable devices worn around the neck, wrist, ear, or attached to clothing and designed to objectively measure diet without relying on user-reported food intake (Fontana et al., 2015). Some (e.g., WearSens, worn around the neck) lack the ability to detect specific types of foods, and thus quantify nutrient intakes, while others (e.g., GoBe, worn as a wristband) only measure calories, fat, protein, and carbohydrate. Several chewing and bite-related wearable sensors are also being devel­oped which aim to dif­fer­entiate between food intake and non-food intake, with some aiming to estimate energy intake (e.g., BitBite) (Fontana et al., 2015). Discomfort has been reported by some users of these wearable devices which suggests that their format needs to be modified (Boushey et al., 2017). Privacy issues also need to be addressed when using wearable devices (Gemming et al., 2015a).