This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (955) Mobile Health (mhealth) (2941) mHealth for Wellness, Behavior Change and Prevention (2872) Instruments and Questionnaires for Nutrition and Food Intake (166) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (93)

Published on in Vol 3, No 4 (2015): Oct-Dec

Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls

Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls

Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls

Authors of this article:

Anna M Rangan1 Author Orcid Image ;   Sarah O'Connor1 Author Orcid Image ;   Valentina Giannelli1 Author Orcid Image ;   Megan LH Yap1 Author Orcid Image ;   Lie Ming Tang2 Author Orcid Image ;   Rajshri Roy1 Author Orcid Image ;   Jimmy Chun Yu Louie1 Author Orcid Image ;   Lana Hebden1 Author Orcid Image ;   Judy Kay2 Author Orcid Image ;   Margaret Allman-Farinelli1 Author Orcid Image
Article Authors Cited by (76) Tweetations (3) Metrics

Journals

  1. Chen J, Allman-Farinelli M. Impact of Training and Integration of Apps Into Dietetic Practice on Dietitians’ Self-Efficacy With Using Mobile Health Apps and Patient Satisfaction. JMIR mHealth and uHealth 2019;7(3):e12349 View
  2. Jung J, Wellard-Cole L, Cai C, Koprinska I, Yacef K, Allman-Farinelli M, Kay J. Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(2):1 View
  3. Teixeira V, Voci S, Mendes‐Netto R, da Silva D. The relative validity of a food record using the smartphone application MyFitnessPal. Nutrition & Dietetics 2018;75(2):219 View
  4. Chen J, Lieffers J, Bauman A, Hanning R, Allman-Farinelli M. Designing Health Apps to Support Dietetic Professional Practice and Their Patients: Qualitative Results From an International Survey. JMIR mHealth and uHealth 2017;5(3):e40 View
  5. Chen J, Berkman W, Bardouh M, Ng C, Allman-Farinelli M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 2019;57:208 View
  6. Wellard-Cole L, Potter M, Jung J, Chen J, Kay J, Allman-Farinelli M. A Tool to Measure Young Adults’ Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App. JMIR mHealth and uHealth 2018;6(11):e12136 View
  7. Lee H, Kim E, Kim S, Lim H, Park Y, Kang J, Kim H, Kim J, Park W, Park S, Kim J, Yang Y. Validation of nutrient intake of smartphone application through comparison of photographs before and after meals. Journal of Nutrition and Health 2020;53(3):319 View
  8. Hurt R, McClave S. Nutritional Assessment in Primary Care. Medical Clinics of North America 2016;100(6):1169 View
  9. Pendergast F, Ridgers N, Worsley A, McNaughton S. Evaluation of a smartphone food diary application using objectively measured energy expenditure. International Journal of Behavioral Nutrition and Physical Activity 2017;14(1) View
  10. Ferreira L, Smolarek A, Chilibeck P, Barros M, McAnulty S, Schoenfeld B, Zandona B, Souza-Junior T. High doses of sodium bicarbonate increase lactate levels and delay exhaustion in a cycling performance test. Nutrition 2019;60:94 View
  11. Ambrosini G, Hurworth M, Giglia R, Trapp G, Strauss P. Feasibility of a commercial smartphone application for dietary assessment in epidemiological research and comparison with 24-h dietary recalls. Nutrition Journal 2018;17(1) View
  12. Béjar L. First evaluation steps of a new method for dietary intake estimation regarding a list of key food groups in adults and in different sociodemographic and health-related behaviour strata. Public Health Nutrition 2017;20(15):2660 View
  13. Htet M, Fahmida U, Do T, Dibley M, Ferguson E. The Use of Tablet-Based Multiple-Pass 24-Hour Dietary Recall Application (MP24Diet) to Collect Dietary Intake of Children under Two Years Old in the Prospective Cohort Study in Indonesia. Nutrients 2019;11(12):2889 View
  14. Parr E, Devlin B, Callahan M, Radford B, Blankenship J, Dunstan D, Hawley J. Effects of Providing High-Fat versus High-Carbohydrate Meals on Daily and Postprandial Physical Activity and Glucose Patterns: a Randomised Controlled Trial. Nutrients 2018;10(5):557 View
  15. Fallaize R, Zenun Franco R, Pasang J, Hwang F, Lovegrove J. Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method. JMIR mHealth and uHealth 2019;7(2):e9838 View
  16. Allman-Farinelli M, Gemming L. Technology Interventions to Manage Food Intake: Where Are We Now?. Current Diabetes Reports 2017;17(11) View
  17. Chen J, Gemming L, Hanning R, Allman-Farinelli M. Smartphone apps and the nutrition care process: Current perspectives and future considerations. Patient Education and Counseling 2018;101(4):750 View
  18. Khazen W, Jeanne J, Demaretz L, Schäfer F, Fagherazzi G. Rethinking the Use of Mobile Apps for Dietary Assessment in Medical Research. Journal of Medical Internet Research 2020;22(6):e15619 View
  19. Rangan A, Tieleman L, Louie J, Tang L, Hebden L, Roy R, Kay J, Allman-Farinelli M. Electronic Dietary Intake Assessment (e-DIA): relative validity of a mobile phone application to measure intake of food groups. British Journal of Nutrition 2016;115(12):2219 View
  20. Chmurzynska A, Mlodzik-Czyzewska M, Malinowska A, Czarnocinska J, Wiebe D. Use of a Smartphone Application Can Improve Assessment of High-Fat Food Consumption in Overweight Individuals. Nutrients 2018;10(11):1692 View
  21. Regan M, Chung S, Sofoluke T, Rahmaty Z, Zhang G, Zvenyach T, Ryan A, Storr C, Johantgen M, Hunter C. FoodFoto. CIN: Computers, Informatics, Nursing 2020;38(6):265 View
  22. Conrad J, Nöthlings U. Innovative approaches to estimate individual usual dietary intake in large-scale epidemiological studies. Proceedings of the Nutrition Society 2017;76(3):213 View
  23. Eldridge A, Piernas C, Illner A, Gibney M, Gurinović M, De Vries J, Cade J. Evaluation of New Technology-Based Tools for Dietary Intake Assessment—An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients 2018;11(1):55 View
  24. Wellard-Cole L, Chen J, Davies A, Wong A, Huynh S, Rangan A, Allman-Farinelli M. Relative Validity of the Eat and Track (EaT) Smartphone App for Collection of Dietary Intake Data in 18-to-30-Year Olds. Nutrients 2019;11(3):621 View
  25. Ahmed M, Mandic I, Lou W, Goodman L, Jacobs I, L’Abbé M. Validation of a Tablet Application for Assessing Dietary Intakes Compared with the Measured Food Intake/Food Waste Method in Military Personnel Consuming Field Rations. Nutrients 2017;9(3):200 View
  26. Jenner S, Trakman G, Coutts A, Kempton T, Ryan S, Forsyth A, Belski R. Dietary intake of professional Australian football athletes surrounding body composition assessment. Journal of the International Society of Sports Nutrition 2018;15(1) View
  27. Béjar L, Reyes Ó, García-Perea M. Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records. JMIR mHealth and uHealth 2018;6(6):e10409 View
  28. Lancaster R, Radd‐Vagenas S, Fiatarone Singh M, Noble Y, Daniel K, Mavros Y, Sachdev P, Lautenschlager N, Cox K, Brodaty H, O'Leary F, Flood V. Electronic food records among middle‐aged and older people: A comparison of self‐reported and dietitian‐assisted information. Nutrition & Dietetics 2021;78(2):145 View
  29. Griffiths C, Harnack L, Pereira M. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public Health Nutrition 2018;21(8):1495 View
  30. Kim S, Chung S. Validity of Estimating Sodium Intake using a Mobile Phone Application of 24-hour Dietary Recall with Meal Photos. Korean Journal of Community Nutrition 2020;25(4):317 View
  31. Bejar L, Sharp B, García-Perea M. The e-EPIDEMIOLOGY Mobile Phone App for Dietary Intake Assessment: Comparison with a Food Frequency Questionnaire. JMIR Research Protocols 2016;5(4):e208 View
  32. Fowler L, Yingling L, Brooks A, Wallen G, Peters-Lawrence M, McClurkin M, Wiley Jr K, Mitchell V, Johnson T, Curry K, Johnson A, Graham A, Graham L, Powell-Wiley T. Digital Food Records in Community-Based Interventions: Mixed-Methods Pilot Study. JMIR mHealth and uHealth 2018;6(7):e160 View
  33. Wellard-Cole L, Jung J, Kay J, Rangan A, Chapman K, Watson W, Hughes C, Ni Mhurchu C, Bauman A, Gemming L, Yacef K, Koprinska I, Allman-Farinelli M. Examining the Frequency and Contribution of Foods Eaten Away From Home in the Diets of 18- to 30-Year-Old Australians Using Smartphone Dietary Assessment (MYMeals): Protocol for a Cross-Sectional Study. JMIR Research Protocols 2018;7(1):e24 View
  34. Slavin M, Polasky A, Vieyra K, Best A, Durant L, Frankenfeld C. Single-Meal Nutrient Assessment by a Self-Administered, Electronic Exit Survey Compared with a Multipass Dietary Interview in University Undergraduates in an All-You-Care-to-Eat Campus Dining Hall. Journal of the Academy of Nutrition and Dietetics 2019;119(5):739 View
  35. Pendergast F, Leech R, McNaughton S. Novel Online or Mobile Methods to Assess Eating Patterns. Current Nutrition Reports 2017;6(3):212 View
  36. Roy R, Rangan A, Hebden L, Yu Louie J, Tang L, Kay J, Allman-Farinelli M. Dietary contribution of foods and beverages sold within a university campus and its effect on diet quality of young adults. Nutrition 2017;34:118 View
  37. Béjar L, García-Perea M, Reyes Ó, Vázquez-Limón E. Relative Validity of a Method Based on a Smartphone App (Electronic 12-Hour Dietary Recall) to Estimate Habitual Dietary Intake in Adults. JMIR mHealth and uHealth 2019;7(4):e11531 View
  38. Evenepoel C, Clevers E, Deroover L, Van Loo W, Matthys C, Verbeke K. Accuracy of Nutrient Calculations Using the Consumer-Focused Online App MyFitnessPal: Validation Study. Journal of Medical Internet Research 2020;22(10):e18237 View
  39. Malaeb S, Harindhanavudhi T, Dietsche K, Esch N, Manoogian E, Panda S, Mashek D, Wang Q, Chow L. Time-Restricted Eating Alters Food Intake Patterns, as Prospectively Documented by a Smartphone Application. Nutrients 2020;12(11):3396 View
  40. Shinozaki N, Murakami K. Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan. Nutrients 2020;12(11):3327 View
  41. Wada S, Yamamoto E, Kobayashi Y, Otsuki M, Takada C, Aoi W, Okagaki M, Neriya H, Hamaguchi M, Tanaka M, Fukui M, Higashi A. Validation of computer software to estimate dietary intake among patients with type 2 diabetes. Journal of Clinical Biochemistry and Nutrition 2021;68(1):105 View
  42. Ocké M, Dinnissen C, Stafleu A, de Vries J, van Rossum C. Relative Validity of MijnEetmeter: A Food Diary App for Self-Monitoring of Dietary Intake. Nutrients 2021;13(4):1135 View
  43. Ruf A, Koch E, Ebner-Priemer U, Knopf M, Reif A, Matura S. Studying Microtemporal, Within-Person Processes of Diet, Physical Activity, and Related Factors Using the APPetite-Mobile-App: Feasibility, Usability, and Validation Study. Journal of Medical Internet Research 2021;23(7):e25850 View
  44. Chen J, Bertrand S, Galy O, Raubenheimer D, Allman-Farinelli M, Caillaud C. The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families. Nutrients 2021;13(5):1668 View
  45. Zhang L, Misir A, Boshuizen H, Ocké M. A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps. Advances in Nutrition 2021;12(6):2321 View
  46. Davies A, Shi Y, Bauman A, Allman-Farinelli M. Validity of New Technologies That Measure Bone-Related Dietary and Physical Activity Risk Factors in Adolescents and Young Adults: A Scoping Review. International Journal of Environmental Research and Public Health 2021;18(11):5688 View
  47. Natalucci V, Marmondi F, Biraghi M, Bonato M. The Effectiveness of Wearable Devices in Non-Communicable Diseases to Manage Physical Activity and Nutrition: Where We Are?. Nutrients 2023;15(4):913 View
  48. Bessell E, Meroni A, Jualim N, Fuller N. Comparison of an Online Dietary Assessment Tool (the “Boden Food Plate”) With 24-Hour Dietary Recalls. Topics in Clinical Nutrition 2022;37(3):242 View
  49. Lucassen D, Brouwer-Brolsma E, Slotegraaf A, Kok E, Feskens E. DIetary ASSessment (DIASS) Study: Design of an Evaluation Study to Assess Validity, Usability and Perceived Burden of an Innovative Dietary Assessment Methodology. Nutrients 2022;14(6):1156 View
  50. Nassif E, Davies A, Bente K, Wellard-Cole L, Jung J, Kay J, Hughes C, Koprinska I, Watson W, Yacef K, Chapman K, Rangan A, Bauman A, Ni Mhurchu C, Allman-Farinelli M. The Contribution of Nutrients of Concern to the Diets of 18-to-30-Year-Old Australians from Food Prepared Outside Home Differs by Food Outlet Types: The MYMeals Cross-Sectional Study. Nutrients 2022;14(18):3751 View
  51. Hattab S, Badrasawi M, Anabtawi O, Zidan S. Development and validation of a smartphone image-based app for dietary intake assessment among Palestinian undergraduates. Scientific Reports 2022;12(1) View
  52. König L, Van Emmenis M, Nurmi J, Kassavou A, Sutton S. Characteristics of smartphone-based dietary assessment tools: a systematic review. Health Psychology Review 2022;16(4):526 View
  53. Long Z, Huang S, Zhang J, Zhang D, Yin J, He C, Zhang Q, Xu H, He H, Sun H, Xie K. A Digital Smartphone-Based Self-administered Tool (R+ Dietitian) for Nutritional Risk Screening and Dietary Assessment in Hospitalized Patients With Cancer: Evaluation and Diagnostic Accuracy Study. JMIR Formative Research 2022;6(10):e40316 View
  54. Tanweer A, Khan S, Mustafa F, Imran S, Humayun A, Hussain Z. Improving dietary data collection tools for better nutritional assessment – A systematic review. Computer Methods and Programs in Biomedicine Update 2022;2:100067 View
  55. Thornton L, Osman B, Champion K, Green O, Wescott A, Gardner L, Stewart C, Visontay R, Whife J, Parmenter B, Birrell L, Bryant Z, Chapman C, Lubans D, Slade T, Torous J, Teesson M, Van de Ven P. Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review. JMIR mHealth and uHealth 2022;10(2):e27337 View
  56. Carpenter C, Ugwoaba U, Cardel M, Ross K. Using self-monitoring technology for nutritional counseling and weight management. DIGITAL HEALTH 2022;8:205520762211027 View
  57. Bzikowska-Jura A, Sobieraj P, Raciborski F. Low Comparability of Nutrition-Related Mobile Apps against the Polish Reference Method—A Validity Study. Nutrients 2021;13(8):2868 View
  58. Baum Martinez I, Peters B, Schwarz J, Schuppelius B, Steckhan N, Koppold-Liebscher D, Michalsen A, Pivovarova-Ramich O. Validation of a Smartphone Application for the Assessment of Dietary Compliance in an Intermittent Fasting Trial. Nutrients 2022;14(18):3697 View
  59. Simmons L, Phipps J, Whipps M, Smith P, Carbajal K, Overstreet C, McLaughlin J, De Lombaert K, Noonan D. From hybrid to fully remote clinical trial amidst the COVID-19 pandemic: Strategies to promote recruitment, retention, and engagement in a randomized mHealth trial. DIGITAL HEALTH 2022;8:205520762211290 View
  60. Moyen A, Rappaport A, Fleurent-Grégoire C, Tessier A, Brazeau A, Chevalier S. Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study. Journal of Medical Internet Research 2022;24(11):e40449 View
  61. Lucassen D, Brouwer-Brolsma E, Boshuizen H, Mars M, de Vogel-Van den Bosch J, Feskens E. Validation of the smartphone-based dietary assessment tool “Traqq” for assessing actual dietary intake by repeated 2-h recalls in adults: comparison with 24-h recalls and urinary biomarkers. The American Journal of Clinical Nutrition 2023;117(6):1278 View
  62. Halle-Smith J, Powell-Brett S, Hall L, Duggan S, Griffin O, Phillips M, Roberts K. Recent Advances in Pancreatic Ductal Adenocarcinoma: Strategies to Optimise the Perioperative Nutritional Status in Pancreatoduodenectomy Patients. Cancers 2023;15(9):2466 View
  63. Murai U, Tajima R, Matsumoto M, Sato Y, Horie S, Fujiwara A, Koshida E, Okada E, Sumikura T, Yokoyama T, Ishikawa M, Kurotani K, Takimoto H. Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review. Nutrients 2023;15(8):1816 View
  64. Khor B, Sumida K, Scholes-Robertson N, Chan M, Lambert K, Kramer H, Lui S, Wang A. Nutrition Education Models for Patients With Chronic Kidney Disease. Seminars in Nephrology 2023;43(2):151404 View
  65. Topan M, Sporea I, Dănilă M, Popescu A, Ghiuchici A, Lupușoru R, Șirli R. Association between Dietary Habits and Sarcopenia in Patients with Liver Cirrhosis. Journal of Clinical Medicine 2023;12(14):4693 View
  66. Ho D, Chiu W, Kao J, Tseng H, Yao C, Su H, Wei P, Le N, Nguyen H, Chang J. Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app. Nutrition 2023;116:112212 View
  67. Mahal S, Kucha C, Kwofie E, Ngadi M. Design and Development of ‘Diet DQ Tracker’: A Smartphone Application for Augmenting Dietary Assessment. Nutrients 2023;15(13):2901 View
  68. Larke J, Chin E, Bouzid Y, Nguyen T, Vainberg Y, Lee D, Pirsiavash H, Smilowitz J, Lemay D. Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment. Nutrients 2023;15(23):4972 View
  69. Braga B, Nguyen P, Tran L, Hoang N, Bannerman B, Doyle F, Folson G, Gangupantulu R, Karachiwalla N, Kolt B, McCloskey P, Palloni G, Thi Tran T, Thuy Thi Trơưng D, Hughes D, Gelli A. Feasibility of Using an Artificial Intelligence-based Telephone Application for Dietary Assessment and Nudging to Improve the Quality of Food Choices of Female Adolescents in Vietnam: Evidence from a Randomized Pilot Study. Current Developments in Nutrition 2024;8(6):102063 View
  70. Aydınlar A, Mavi A, Kütükçü E, Kırımlı E, Alış D, Akın A, Altıntaş L. Awareness and level of digital literacy among students receiving health-based education. BMC Medical Education 2024;24(1) View
  71. Mahal S, Kucha C, Kwofie E, Ngadi M. A systematic review of dietary data collection methodologies for diet diversity indicators. Frontiers in Nutrition 2024;11 View
  72. Ho D, Chiu W, Kao J, Tseng H, Lin C, Huang P, Fang Y, Chen K, Su T, Yang C, Yao C, Su H, Wei P, Chang J. Reliability Issues of Mobile Nutrition Apps for Cardiovascular Disease Prevention: Comparative Study. JMIR mHealth and uHealth 2024;12:e54509 View

Books/Policy Documents

  1. Rajasekera J, Mishal A, Mori Y. Big Data Analytics in Healthcare. View
  2. Fontana J, Farooq M, Sazonov E. Wearable Sensors. View
  3. Karahanoğlu A, Ludden G. Advances in Longitudinal HCI Research. View