Technology-assisted dietary assessment

In the United Kingdom (UK), a national survey gave the estimates that 72% of the population owned a smartphone [1], and 80% of adults used the Internet on a daily or almost daily basis in the year 2017 [37]. 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 for technology-assisted approaches over traditional methods has been shown among both adolescents and adults [48, 28]. 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 a limited impact on accuracy as they still rely on participants’ judgement and recall of portion sizes [5]. While this example highlights the need 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 burdens 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 an 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 categorised, based on the type of technology used, as shown in Figure D.13.1. At the broadest level, they can be distinguished as 1) computerised/smartphone app versions of traditional subjective pen-and-paper methods, and 2) computerised/smartphone app which captures additional raw data over and above traditional methods. The sections below follow this structure.

Figure D.13.1 Classification of technologies used for dietary assessment.

1. Computerised/smartphone app versions of traditional pen-and-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 detail, and 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, a 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 interviewer-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's and dietician/nutritionist's 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 [7, 44].


  1. Need for Internet access (for self-administered tools).
  2. Computer literacy - some population subgroups (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 the dietary database).


  1. Self-administered (see Figure D.13.2):
    1. myfood24
    2. ASA24
    3. Intake24
    4. Oxford WebQ
  1. Interviewer-administered:
    1. EPIC-SoftUSDA Automated Multiple-Pass Method (AMPM) [14, 18]
    2. Nutrition Data System for Research (NDSR) [10]
    3. NINA-DISH [32]
    4. UNyDIET [34]
    5. Leemoo [38]
    6. Dietary Intake Data System (DIDS) [38]
    7. FINDIET 2007 [23]
    8. LEDDAS [20]

Figure D.13.2 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 [29].


  1. Lose it! [36]
  2. SHealth
  3. MyFitnessPal
  4. 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 [22].

For better estimation of colour and portion size by experts, participants use a fiducial marker, a reference item such as a pen or colour checkerboard placed within a camera frame while capturing images (Figure D.13.3).

Some methods using food photos have been shown to be reliable and accurate measures of food intakes, both among adults [17, 12, 43] and children [33, 30]. Previous studies also showed higher preferences (between 91 to 100%) for these methods compared to pen-and-paper methods among participants [50].


Procedures for data collection with these methods are as follows:

  1. User training on taking photos (face-to-face or instructional videos).
  2. Take a picture of any foods, snacks, or beverages prior to consumption.
  3. Take a picture of any leftovers or an empty plate.
  4. Pictures should be clear, follow the instructions - some studies use a fiducial marker [54].
  5. All foods and beverages should be included in the images.
  6. Images are transmitted to a server for analysis.

Figure D.13.3 Food images with the use of fiducial markers.
Source: [14].


  1. Allows for real-time data collection [11].
  2. Improves reliability compared to the traditional pen-and-paper methods.
  3. Reduces participant's burden.
  4. Does not require 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 food consumed or poor quality photos.
  3. Technical problems could hamper data collection.
  4. Does not allow for fully automated data analysis.
  5. Need for training for backup methods such as paper-based food records in case of facing any problems.


Diet diary

Remote food photography method (RFPM) (Figure D.13.4) [30, 22].

  1. 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 researchers.

My Meal Mate (MMM) (Figure D.2.11) [27]. 

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

Pattern - Oriented Nutrition Diary (POND) [7]

NutriMeter [8]

Weight Management Mentor (WMM) [9]


MyPlate [6]

Dietary Intake Monitoring Application (DIMA) [25]

Figure D.13.4 Remote Food Photography Method (RFPM).
Source: [15].


Recaller app [42]

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

Nutricam Dietary Assessment Method [4];

  1. The Nutricam Dietary Assessment Method (NuDAM) is a combination of voice record in addition to photographs to assess individual dietary intakes.
  2. Over a cellular network, researchers receive information for further coding and analysis.
  3. Weak correlation with the doubly labelled water method (DLW); significantly underestimated energy intake compared to pen-and-paper food diaries.

Figure D.13.5 Screen capture of the food diary entry page of My Meal Mate.

Source: [27].

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 D.13.6). 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.13.6 DietCam system architecture.

Source: [2].

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 labeling, and portion size [22].


Snap-n-Eat is a mobile food recognition system that uses machine learning algorithms to estimate nutrient intakes from photos taken by a participant [41]. The algorithm behind this application is designed to distinguish foods in the photograph from its background without the user’s input, distinguish 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.


This tool combines a series of 3 images of food consumed and also a short video [2]. 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. 

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 [21] and SenseCam [3]). 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 [22].
  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. Does not require participants’ reactivity and portion size estimation.


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

2.3      Smartphone applications for public health promotion

Apart from technologies developed solely for dietary assessment, there are also smartphone applications developed for public health promotion. Their main aim is to promote healthy dietary habits often in relation to weight management [45]. 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 [47].


  1. Increases participants’ motivation and compliance compared to pen-and-paper based versions [40, 53].
  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 [13].


  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 [31]: 

  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: 

  1. Errors in reporting and data entry.
  2. 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 [49, 39]. 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 the technology-assisted dietary assessment. A recent field study has suggested that these population subgroups may benefit from additional interview support [39]. However, 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 [44, 35]. Using advanced features of mobile technology (e.g. receiving visual messages) may have an impact on the response and accuracy among these population groups [16, 46, 49]. 


Dietary assessment among adolescents is also challenging because of irregular eating patterns and lack of personal incentives for recording dietary consumption. However this age group has been reported to have higher acceptability for the technology-assisted methods compared to paper-based food records [16]. Additional features can potentially be developed for this group, including tailored support of text or visual message with entertaining properties [46]. In addition, an automated application for a mobile phone was studied to identify dietary consumption among adolescents (11-15-year olds). The method was assisted by a technology assisted dietary assessment tutorial video feature instructing how to capture images [51]. 

Populations with race/ethnic diversity

Dietary assessment in multi-ethnic population subgroups in a single study is challenging because of the complexity of their diets linked to diverse cultural background. The reason is that, for example, foods can be consumed in different ways such as sharing foods or eating with hands [52]. 

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: 

  1. Expertise from both a programmer and a dietary researcher [15].
  2. Software intellectual property including patents, trademark, and copyrights [26].
  3. Secure funding for software maintenance and updates, as the aims and needs of a study might change, but also technology evolves [26].
  4. 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, other required systems, and related budget [29]. In addition, smartphone applications for public health promotion have shown promising results from short-term studies, but long-term effects remain to be demonstrated [45].

  1. Deloitte. Beyond the hype: The true potential of mobile: Deloitte; 2013 [cited 2017 28 September]. Available from:
  2. Kong F, Tan J. Dietcam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing. 2012;8:147-63.
  3. Office for National Statistics. Internet access – households and individuals: 2017: Office for National Statistics; 2017 [cited 2017 28 September]. Available from:
  4. Rowland M, Poliakov I, Christie S, Simpson E, Foster E. Field testing of the use of intake24 in a sample of young people and adults living in scotland Food Standards Scotland; 2016 [cited 2017 28 September]. Available from:
  5. Teo YM, Pimentel T, Wong SS, Cluskey M, Dorbolo J, Dinsmore M, et al. My plate visual mobile application device for college students’ food intake tracking (1022.10). The FASEB Journal. 2014;28(1 Supplement).
  6. Timmins KA, Vowden K, Hussein F, Burley V. Making the best use of new technologies in the national diet and nutrition survey: A review: University of Leeds; 2014 [cited 2017 28 September]. Available from:
  7. Andrew AH, Borriello G, Fogarty J. Simplifying mobile phone food diaries: Design and evaluation of a food index-based nutrition diary. Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare; Venice, Italy. 2534560: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering); 2013. p. 260-3.
  8. Das S, Robinson LM, Valko LL, Roberts SB, Gilhooly CH. Remote monitoring of body weight and food intake in free-living humans. The FASEB Journal. 2011;25(1 Supplement):341.4.
  9. Freyne J, Bhandari D, Berkovsky S, Borlyse L, Campbell C, Chau S. Mobile mentor: Weight management platform. Proceedings of the 15th international conference on Intelligent user interfaces; Hong Kong, China. 1720046: ACM; 2010. p. 409-10.
  10. Jonnalagadda SS, Mitchell DC, Smiciklas-Wright H, Meaker KB, Van Heel N, Karmally W, Ershow AG, Kris-Etherton PM, Accuracy of energy intake data estimated by a multiple-pass, 24-hour dietary recall technique. Journal of the American Dietetic Association.2000;100:303-8; quiz 309-11
  11. Parnell WR, Wilson NC, Russell DG, Methodology of the 1997 New Zealand National Nutrition Survey. The New Zealand medical journal.2001;114:123-6
  12. Williamson DA, Allen HR, Martin PD, Alfonso AJ, Gerald B, Hunt A, Comparison of digital photography to weighed and visual estimation of portion sizes. Journal of the American Dietetic Association.2003;103:1139-45
  13. Williamson DA, Allen HR, Martin PD, Alfonso A, Gerald B, Hunt A, Digital photography: a new method for estimating food intake in cafeteria settings. Eating and weight disorders : EWD.2004;9:24-8
  14. Blanton CA, Moshfegh AJ, Baer DJ, Kretsch MJ, The USDA Automated Multiple-Pass Method accurately estimates group total energy and nutrient intake. The Journal of nutrition.2006;136:2594-9
  15. Martin CK, Newton RL, Anton SD, Allen HR, Alfonso A, Han H, Stewart T, Sothern M, Williamson DA, Measurement of children's food intake with digital photography and the effects of second servings upon food intake. Eating behaviors.2005;8:148-56
  16. Boushey CJ, Kerr DA, Wright J, Lutes KD, Ebert DS, Delp EJ, Use of technology in children's dietary assessment. European journal of clinical nutrition.2009;63 Suppl 1:S50-7
  17. Ngo J, Engelen A, Molag M, Roesle J, García-Segovia P, Serra-Majem L, A review of the use of information and communication technologies for dietary assessment. The British journal of nutrition.2009;101 Suppl 2:S102-12
  18. Subar AF, Crafts J, Zimmerman TP, Wilson M, Mittl B, Islam NG, McNutt S, Potischman N, Buday R, Hull SG, et al. Assessment of the accuracy of portion size reports using computer-based food photographs aids in the development of an automated self-administered 24-hour recall. Journal of the American Dietetic Association.2009;110:55-64
  19. Hughes DC, Andrew A, Denning T, Hurvitz P, Lester J, Beresford S, Borriello G, Bruemmer B, Moudon AV, Duncan GE, et al. BALANCE (Bioengineering Approaches for Lifestyle Activity and Nutrition Continuous Engagement): developing new technology for monitoring energy balance in real time. Journal of diabetes science and technology.2010;4:429-34
  20. Reinivuo H, Hirvonen T, Ovaskainen ML, Korhonen T, Valsta LM, Dietary survey methodology of FINDIET 2007 with a risk assessment perspective. Public health nutrition.2010;13:915-9
  21. Arab L, Estrin D, Kim DH, Burke J, Goldman J, Feasibility testing of an automated image-capture method to aid dietary recall. European journal of clinical nutrition.2011;65:1156-62
  22. Slimani N, Casagrande C, Nicolas G, Freisling H, Huybrechts I, Ocké MC, Niekerk EM, van Rossum C, Bellemans M, De Maeyer M, et al. The standardized computerized 24-h dietary recall method EPIC-Soft adapted for pan-European dietary monitoring. European journal of clinical nutrition.2011;65 Suppl 1:S5-15
  23. Rollo ME, Ash S, Lyons-Wall P, Russell A, Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future applications. Journal of telemedicine and telecare.2011;17:318-23
  24. Martin CK, Correa JB, Han H, Allen HR, Rood JC, Champagne CM, Gunturk BK, Bray GA, Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity (Silver Spring, Md.).2011;20:891-9
  25. Connelly K, Siek KA, Chaudry B, Jones J, Astroth K, Welch JL, An offline mobile nutrition monitoring intervention for varying-literacy patients receiving hemodialysis: a pilot study examining usage and usability. Journal of the American Medical Informatics Association : JAMIA.2012;19:705-12
  26. Buday R, Tapia R, Maze GR, Technology-driven dietary assessment: a software developer's perspective. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.2012;27 Suppl 1:10-7
  27. Carter MC, Burley VJ, Nykjaer C, Cade JE, 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. The British journal of nutrition.2012;109:539-46
  28. Nicklas TA, O'Neil CE, Stuff J, Goodell LS, Liu Y, Martin CK, Validity and feasibility of a digital diet estimation method for use with preschool children: a pilot study. Journal of nutrition education and behavior.2009;44:618-23
  29. Illner AK, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani N, Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. International journal of epidemiology.2012;41:1187-203
  30. O'Loughlin G, Cullen SJ, McGoldrick A, O'Connor S, Blain R, O'Malley S, Warrington GD, Using a wearable camera to increase the accuracy of dietary analysis. American journal of preventive medicine.2012;44:297-301
  31. Hebden L, Cook A, van der Ploeg HP, Allman-Farinelli M, Development of smartphone applications for nutrition and physical activity behavior change. JMIR research protocols.2012;1:e9
  32. Daniel CR, Kapur K, McAdams MJ, Dixit-Joshi S, Devasenapathy N, Shetty H, Hariharan S, George PS, Mathew A, Sinha R, et al. Development of a field-friendly automated dietary assessment tool and nutrient database for India. The British journal of nutrition.2013;111:160-71
  33. Martin CK, Nicklas T, Gunturk B, Correa JB, Allen HR, Champagne C, Measuring food intake with digital photography. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.2013;27 Suppl 1:72-81
  34. García de Diego L, Cuervo M, Martínez JA, [Software for performing a global phenotypic and genotypic nutritional assessment]. Nutricion hospitalaria.2013;28:1622-32
  35. Biltoft-Jensen A, Hjorth MF, Trolle E, Christensen T, Brockhoff PB, Andersen LF, Tetens I, Matthiessen J, Comparison of estimated energy intake using Web-based Dietary Assessment Software with accelerometer-determined energy expenditure in children. Food & nutrition research.2013;57:
  36. Allen JK, Stephens J, Dennison Himmelfarb CR, Stewart KJ, Hauck S, Randomized controlled pilot study testing use of smartphone technology for obesity treatment. Journal of obesity.2013;2013:151597
  37. Oliver E, Baños RM, Cebolla A, Lurbe E, Alvarez-Pitti J, Botella C, An electronic system (PDA) to record dietary and physical activity in obese adolescents; data about efficiency and feasibility. Nutricion hospitalaria.2014;28:1860-6
  38. Ejtahed HS, Sarsharzadeh MM, Mirmiran P, Asghari G, Yuzbashian E, Azizi F, Leemoo, a dietary assessment and nutritional planning software, using fuzzy logic. International journal of endocrinology and metabolism.2012;11:e10169
  39. Sharp DB, Allman-Farinelli M, Feasibility and validity of mobile phones to assess dietary intake. Nutrition (Burbank, Los Angeles County, Calif.).2013;30:1257-66
  40. Hutchesson MJ, Rollo ME, Callister R, Collins CE, Self-monitoring of dietary intake by young women: online food records completed on computer or smartphone are as accurate as paper-based food records but more acceptable. Journal of the Academy of Nutrition and Dietetics.2014;115:87-94
  41. Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ, Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE journal of biomedical and health informatics.2015;19:377-88
  42. Hongu N, Pope BT, Bilgiç P, Orr BJ, Suzuki A, Kim AS, Merchant NC, Roe DJ, Usability of a smartphone food picture app for assisting 24-hour dietary recall: a pilot study. Nutrition research and practice.2014;9:207-12
  43. Zhang W, Yu Q, Siddiquie B, Divakaran A, Sawhney H, "Snap-n-Eat": Food Recognition and Nutrition Estimation on a Smartphone. Journal of diabetes science and technology.2015;9:525-33
  44. Biltoft-Jensen A, Damsgaard CT, Andersen EW, Ygil KH, Andersen R, Ege M, Christensen T, Thorsen AV, Tetens I, Wu H, et al. Validation of Reported Whole-Grain Intake from a Web-Based Dietary Record against Plasma Alkylresorcinol Concentrations in 8- to 11-Year-Olds Participating in a Randomized Controlled Trial. The Journal of nutrition.2015;146:377-83
  45. Coughlin SS, Whitehead M, Sheats JQ, Mastromonico J, Hardy D, Smith SA, Smartphone Applications for Promoting Healthy Diet and Nutrition: A Literature Review. Jacobs journal of food and nutrition.2016;2:021
  46. Kerr DA, Harray AJ, Pollard CM, Dhaliwal SS, Delp EJ, Howat PA, Pickering MR, Ahmad Z, Meng X, Pratt IS, et al. The connecting health and technology study: a 6-month randomized controlled trial to improve nutrition behaviours using a mobile food record and text messaging support in young adults. The international journal of behavioral nutrition and physical activity.2015;13:52
  47. Afshin A, Babalola D, Mclean M, Yu Z, Ma W, Chen CY, Arabi M, Mozaffarian D, Information Technology and Lifestyle: A Systematic Evaluation of Internet and Mobile Interventions for Improving Diet, Physical Activity, Obesity, Tobacco, and Alcohol Use. Journal of the American Heart Association.2016;5
  48. Cade JE, Measuring diet in the 21st century: use of new technologies. The Proceedings of the Nutrition Society.2016;76:276-282
  49. Kirkpatrick SI, Gilsing AM, Hobin E, Solbak NM, Wallace A, Haines J, Mayhew AJ, Orr SK, Raina P, Robson PJ, et al. Lessons from Studies to Evaluate an Online 24-Hour Recall for Use with Children and Adults in Canada. Nutrients.2016;9:
  50. Tonkin E, Brimblecombe J, Wycherley TP, Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review. Advances in nutrition (Bethesda, Md.).2017;8:308-322
  51. Boushey CJ, Spoden M, Delp EJ, Zhu F, Bosch M, Ahmad Z, Shvetsov YB, DeLany JP, Kerr DA, Reported Energy Intake Accuracy Compared to Doubly Labeled Water and Usability of the Mobile Food Record among Community Dwelling Adults. Nutrients.2017;9:
  52. Almiron-Roig E, Aitken A, Galloway C, Ellahi B, Dietary assessment in minority ethnic groups: a systematic review of instruments for portion-size estimation in the United Kingdom. Nutrition reviews.2017;75:188-213
  53. Kong K, Zhang L, Huang L, Tao Y, Validity and practicability of smartphone-based photographic food records for estimating energy and nutrient intake. Asia Pacific journal of clinical nutrition.2017;26:396-401
  54. Fang S, Liu C, Zhu F, Boushey C, Delp E, A Printer Indexing System for Color Calibration with Applications in Dietary Assessment. New trends in image analysis and processing -- ICIAP 2015 Workshops : ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM, Genoa, Italy, September 7-8, 2015, Proceedings.2017;9281:358-365