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DAPA Measurement Toolkit

Technology assisted dietary assessment


In the United Kingdom (UK), it is estimated that 72% of the population owns a smartphone [18], and 80% of adults used the Internet on a daily or almost daily bases in year 2017 [40]. Smartphone applications, computers and the Internet are widely used in several aspects of modern life, including health communication; widespread use of these technologies also provides opportunities for dietary assessment.

Technologies used for dietary assessment include a computer, the Internet, telecommunication and imaging technologies. In most cases, these technologies have the same aim as the paper-based subjective methods of dietary assessment, but aim to improve the accuracy of the assessment and compliance of the participants. Overall, a preference of technology-assisted approaches over traditional methods has been shown among both adolescents and adults [12, 37]. Computer-displays of portion sizes can be used for dietary interviews to aid accurate estimation of the amount of food and drink consumed. However, using these technology-based aids has limited impact on accuracy as they still rely on participants’ judgement and recall of portion sizes [48]. While this example highlights needs for careful appraisal of available technologies and their applications, modern technologies with automated data capturing and coding systems have nonetheless the potential to overcome the inherent limitations of traditional methods such as:

  1. Limited accuracy
  2. Participant’s and researcher’s burden related to dietary assessment and data entry
  3. Dependency on participant’s memory, ability, and perception of social desirability to accurately and precisely describe dietary consumption

Dietary assessment with innovative technologies is based on electronic recording of foods and drinks consumed. For example, participants can report foods from a photo list of foods in a database or they can take photographs of foods, which later can be analysed by researchers or automatic algorithms to derive dietary intakes. On the whole, technology-assisted methods can be broadly categorized based on the type of technology used, as shown in Figure 1. At the broadest level, they can be distinguished as 1) computerised/smartphone app versions of traditional subjective pen/paper methods, and 2) computerised/smartphone app which capture additional raw data over and above traditional methods. The sections below follow this structure.

Figure D.2.7 Classification of technologies used for dietary assessment.

1.         Computerised/smartphone app versions of traditional pen/paper methods

1.1       Dietary assessment with ‘static’ computerised technologies

Computerised dietary assessment technologies can be developed as self-administered dietary assessment instruments. Often with the Internet connected to a server established by the researchers, computer-based technologies collect similar data to commonly used paper-based dietary assessment tools (e.g. food frequency questionnaire and 24-hour dietary recall). Yet, computer-based assessment tools have advantages over manual tools. For example, participants can be guided to accurately report foods with a specific brand through a visual list of a large number of food items, including packages and appearances, while this is practically not possible in traditional assessment. This method is supposed to let participants recall their food consumption in different occasions in details in a standardised manner. More details regarding food photographs as aids to the portion size estimation are available here.

Interviewer-based dietary assessment can also be aided by static computerised technologies. An interview needs to be well structured to help a participant report dietary consumption including types of foods and beverages, their quantity, brands, time, and occasions. In addition to visual aids, structural guide for an interviewer to follow reduces the chance of omitting certain dietary queries, such as those about dietary supplements or snacking. In a clinical setting, which can be coupled with dietary intervention, a computer-assisted interview-based dietary assessment is of a strong option.


  1. Fewer errors from participants and researchers and better standardisation across a number of assessment, thereby improving data quality
  2. Reducing researcher and dietician burden in data collection and data entry
  3. Cost-effectiveness
  4. Suitable for studies with large sample size and geographically dispersed participants
  5. Indication of an increase in motivation among adolescents and adults and in compliance compared to the paper-based versions [4-6]


  1. Need for Internet access (for self-administered tools)
  2. Computer literacy - some population sub-groups (e.g. young children and elderly) may face difficulty to use these tools
  3. Potential non-response bias
  4. Possible design issue and technical prerequisites which could alter response behaviour
  5. Lower level of report detail due to the pre-specified food lists
  6. Cost for development, modification and maintenance – ideally, a computer-assisted tool should be modifiable for a certain setting or a target population (e.g. Asian population in a European country, which may require a substantive update of dietary database)


  • Self-administered (see Figure 2)
  1. myfood24
  2. ASA24
  3. Intake24
  4. Oxford WebQ
  • Interviewer-administered
  1. EPIC-SoftUSDA Automated Multiple-Pass Method (AMPM) [8, 47]
  2. Nutrition Data System for Research (NDSR) [28]
  3. NINA-DISH [16]
  4. UNyDIET [22]
  5. Leemoo [19]
  6. Dietary Intake Data System (DIDS) [19]
  7. FINDIET 2007 [43]
  8. LEDDAS [42]
Figure D.2.8 Examples of self-administered web-based technologies.
Sources: myfood24, ASA24, Intake24, Oxford WebQ.

1.2       Dietary assessment with mobile application technologies

These methods have similar approaches to ‘static’ computerised dietary assessment technologies listed above, with the additional feature of portability. However, the most prevalent smartphone apps are those which use digital food photography to assist the dietary assessment as advancement to the traditional methods (see section 2 below).

Some apps include a food and nutrient database, which is linked with barcodes of the foods that are purchased, so the users can scan the food labels with a barcode scanner and the nutrients can be derived [27].


  • Lose it! [2]
  • SHealth
  • MyFitnessPal
  • LifeSum

2         Computerised/smartphone app which capture additional raw data

2.1      Dietary assessment with non-automated digital food photography

These methods are based on capturing images before and after eating episodes to provide primary records of dietary intake instead of manual recording. The photograph must be taken manually. These methods require processes to digitalise photo images into dietary data. These methods typically make use of the advanced technologies of smartphones such as wireless communication, built-in cameras, global position system, portable design and external devices connectivity such as Bluetooth [46].

For better estimation of colour and portion size by experts, participants use a fiducial marker, a reference item such as pen or colour checkerboard placed within a camera frame while capturing images (see Figure 3)

Some methods using food photos have been shown to be reliable and accurate measure of food intakes, both among adults [36, 52, 53] and children [35, 38]. Previous studies also showed higher preferences (between 91 to 100%) of these methods compared to pen/paper methods among participants [50].


Procedures for data collection with these methods are as follows:

  • User training on taking photos (face-to-face or instructional videos)
  • Take a picture of any foods, snacks or beverages prior to consumption
  • Take a picture of any leftovers or an empty plate
  • Pictures should be clear, follow the instructions - some studies use a fiducial marker [20]
  • All foods and beverages should be included in images
  • Images are transmitted to a server for analysis
Figure D.2.9 Food images with the use of fiducial markers.
Source: [8].


  1. Allow for real time data collection [41]
  2. Improve reliability compared to the traditional pen/paper methods
  3. Reduce participant burden
  4. No need for participants to estimate portion sizes


  1. Participant may forget to always carry a device or smartphone
  2. Participant may forget to capture a picture of a food consumed or poor quality photos
  3. Technical problems could hamper data collections
  4. Do not allow for fully automated data analysis
  5. Need for training for back-up methods such as paper-based food records in case of facing any problems


Diet diary

◊  Remote food photography method (RFPM) [38, 46]. See Figure 4.

  • This method is the combination of images and a short description of the food plus integrated ecological momentary assessment (EMA) to remind and encourage participants to capture and send images with labelled information to research dietician.

◊  My Meal Mate (MMM) [13]. See Figure 5. 

  • UK-based smart phone electronic food diary application with imaging capability
  • Contains information regarding type and quantities of 40,000 generic and branded named food items
  • Imaging opportunity helps participant to better recall while data entry is not available at the time of consumption

◊  Pattern - Oriented Nutrition Diary (POND) [4]

◊  NutriMeter [17]

◊  Weight Management Mentor (WMM) [21]

◊  BALANCE [25]

◊  MyPlate [49]

◊  Dietary Intake Monitoring Application (DIMA) [14]

Figure D.2.10 Remote Food Photography Method (RFPM).
Source: [34].


◊  Recaller app [24] 

  • Digital imaging, time stamps, location information and note taking to create a digital food record

◊  Nutricam Dietary Assessment Method [44] 

  • The Nutricam Dietary Assessment Method (NuDAM) is a combination of voice record in addition to photographs to assess individual dietary intakes
  • Over a cellular network, dietitians receive information which will be further analysed and coded
  • Weak correlation with the doubly labelled water method (DLW); significantly underestimated energy intake compared to pen-and-paper food diaries
Figure D.2.11 Screen capture of the food diary entry page of My Meal Mate.
Source: [13].

The advanced versions of these methods are based on automated nutrient derivation. These methods are based on automated image segmentation and analyses from food images captured by participants and intended to reduce manual image analysis by dietary assessment expert (Figure 6). Based on image segmentation, colour and texture features are extracted which lead to food classification. Accuracy of segmentation depends on the number of foods sent at a time and type of food. Accuracy of food classification depends on the corresponding number of images available in the reference database.

Figure D.2.12 DietCam system architecture.
Source: [31].

Diet diary

◊  Mobile device food record (mdFR)

This method was previously based on manual image analysis by an expert. However, following further development, the method incorporates automated image analysis. Moreover, this application has an option for participants to edit any mistake related to segmentation, food labelling, and portion sizes [46].

◊  Snap-n’-Eat

Snap-n’-Eat is a mobile food recognition system, which uses machine learning algorithms to estimate nutrient intakes from photos taken by a participant [54]. The algorithm behind this application is designed to distinct foods in the photograph from its background without the user’s input, distinct foods from each other using several features such as colour, texture, and size, and estimate portion sizes based on the number of pixels. The identification of foods is based on a training food dataset which is continuously expanding along with inputs of foods in new photos. The accuracy of the algorithm was found to be above 85% for the detection of 15 different foods.

◊  DietCam

This tool combines a series of 3 images of food consumed and also a short video [31]. The combination of 3 images showed the highest accuracy of food classification and volume estimation only when one food is compared to several foods photographed at one time. The procedure behind this method is presented in Figure 7.

2.2      Dietary assessment with automated image-capture method

This method is a combination of automated image-capture with a web-based 24-hour dietary recall (e.g. Image-Diet Day [5] and SenseCam [39]). In this method a participant wears a lanyard around the neck. Connected to the mobile phone, every 10 or 20 seconds one image is captured, so it allows near-complete documentation of food and beverages consumed. Images passively taken by these methods help participants to better recall their food intake.


  1. The average daily energy intake was highly comparable to the doubly labelled water method [46]
  2. May reduce the risk of diet-behaviour change compared to intentional imaging
  3. May avoid forgetting or deciding not to include unfavourable foods
  4. Not require participants’ reactivity and portion size estimation 


  1. The applicability of this method to large population is uncertain
  2. Participants found this method cumbersome
  3. Need for positioning, technical stability, adequate phone power 

2.3      Smartphone applications for public health promotion

Apart from technologies which have been developed targeting dietary assessment alone, there are also some smartphone applications, which have been developed for public health promotion. Their main aim is to promote healthy dietary habits often in relation to weight management [15]. While there is evidence of the effectiveness of such interventions in the short-term, studies are needed to also assess long-term effectiveness and sustainability [1].


  1. Increases participants’ motivation and compliance compared to paper-based versions [26, 32]
  2. Potential to individualise dietary information by accounting for personal information, including sleeping time and sedentary time
  3. Suitable for community interventions especially when the target population is geographically dispersed [51]


  1. Same as the other smartphone applications.
  2. Not necessarily to capture individuals’ dietary habits


Three applications to promote dietary behaviour change have been developed in Australia [23]: 

  1. The fruit and vegetable smartphone app (eVIP) focuses on consumption of fruit and vegetables and includes a graphical display on associations between an amount of consumption and an amount recommended by dietary guidelines
  2. The sugar-sweetened drinks smartphone app (eSIYP) focuses on sugar-sweetened beverages
  3. The take-out (fast food) smartphone app (eTIYP) focuses on fast food and uses a traffic-light labelling to inform the user whether their intake is ideal, acceptable, or unhealthful (green, orange, and red colour respectively)

Innovative technologies for dietary assessment are used to overcome the problems of paper-based dietary assessment methods such as FFQ and 24-hour dietary recall. These limitations include: 

  • Errors in reporting and data entry
  • Efficiency for data entry

Software developed for these technologies links dietary information collected to nutrient values, using a food-composition database and accounting for the portion sizes of the foods.

There are studies showing that the acceptability of dietary assessment technologies can be varied across population groups with variable computer literacy, age, health, and sociodemographic status [30, 45]. Therefore, more and more studies are exploring the use of web- and mobile-based methods for collecting dietary intake data at the population level. 

Population groups with variable cognitive skills and computer literacy 

Young children, older adults and non-technology users are among those who may have less enthusiasm for engaging in use of a new technology. A recent field study has suggested that these population subgroups may benefit from additional interview support [45]. However, the studies using a self-administered web-based 24-hour dietary assessment tool, Web-based

Dietary Assessment Software for Children (WebDASC), in children (aged 8 to 11 years) have suggested acceptability and effectiveness [6, 7]. Using advanced features of mobile technology (e.g. receiving visual messages) may have impact on the response and accuracy among these population groups [9, 29, 30]. 


Dietary assessment among adolescents is also challenging because of irregular eating patterns and lack of personal intensives for recording dietary consumption. However this age group has reported to have higher acceptability for the technology-assisted methods compared to paper-based food records [9]. Additional features can potentially be developed for this group, including tailored support of text or visual message with entertaining properties [29]. In addition, an automated application for a mobile phone was studied to identify dietary consumption among adolescents (11-15-year olds) [55]. The method was assisted by a technology assisted dietary assessment tutorial video feature instructing how to capture images [10]. 

Populations with race/ethnic diversity 

Assessing the dietary intake in multi-ethnic populations living in the same country have challenges because of the complexity of the diet combining foods with diverse cultural background and difficulty in estimating food portion sizes as foods can be consumed in different ways such as sharing foods or eating with hands [3]. 

Dietary assessment studies targeting a population living in a low-income country remains limited due to the burden of high costs and complexity of data collection. A new project, The International Dietary Data Expansion (INDDEX), is aiming to address these issues by developing tools accounting for any culinary diversity.

If researchers intend to create their own software for the purpose of a study and not use a readily available one, there are several points to be addressed: 

  • Expertise: a good combination of software development expertise with dietetics expertise is needed (34)
  • Software intellectual property including patents, trademark and copyrights [11]
  • Secure funding for software maintenance and updates, as the aims and needs of a study might change, but also technology evolves [11]
  • Identify a food composition database to link with the dietary data 

Although there has been a lot of progress in the area of technology-assisted dietary assessment, there are still some points to be considered. Measurement errors and bias are likely to be present because of self-reporting, possible instability of compliance, and possible limitation in computer literacy in certain population subgroups (e.g. elderly people). The methods require secure infrastructure for data transfer/research system and related budget [27]. In addition, smartphone applications for public health promotion have shown promising results from short-term studies, but long-term effects remain to be demonstrated [15].


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