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

Mobile Health (mhealth) (2939) Research Instruments, Questionnaires, and Tools (576) Human Factors and Usability Case Studies (787) New Methods (944) Formative Studies and eHealth/mHealth Development (524) Usability of Apps and User Perceptions of mHealth (974) mHealth for Wellness, Behavior Change and Prevention (2871) Instruments and Questionnaires for Nutrition and Food Intake (166)

Published on in Vol 14, No 2 (2012): Mar-Apr

Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents

Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents

Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents

Authors of this article:

Bethany L Daugherty1 ;   TusaRebecca E Schap1 ;   Reynolette Ettienne-Gittens2 ;   Fengqing M Zhu3 ;   Marc Bosch3 ;   Edward J Delp3 Author Orcid Image ;   David S Ebert3 ;   Deborah A Kerr4 Author Orcid Image ;   Carol J Boushey1, 2 Author Orcid Image
Article Authors Cited by (117) Tweetations (18) Metrics

Journals

  1. Gilmore L, Duhé A, Frost E, Redman L. The Technology Boom. Journal of Diabetes Science and Technology 2014;8(3):596 View
  2. Helander E, Kaipainen K, Korhonen I, Wansink B. Factors Related to Sustained Use of a Free Mobile App for Dietary Self-Monitoring With Photography and Peer Feedback: Retrospective Cohort Study. Journal of Medical Internet Research 2014;16(4):e109 View
  3. Panizza C, Boushey C, Delp E, Kerr D, Lim E, Gandhi K, Banna J. Characterizing Early Adolescent Plate Waste Using the Mobile Food Record. Nutrients 2017;9(2):93 View
  4. 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
  5. Ashman A, Collins C, Brown L, Rae K, Rollo M. A Brief Tool to Assess Image-Based Dietary Records and Guide Nutrition Counselling Among Pregnant Women: An Evaluation. JMIR mHealth and uHealth 2016;4(4):e123 View
  6. Peterson C, Apolzan J, Wright C, Martin C. Video chat technology to remotely quantify dietary, supplement and medication adherence in clinical trials. British Journal of Nutrition 2016;116(9):1646 View
  7. Banna J, Panizza C, Boushey C, Delp E, Lim E. Association between Cognitive Restraint, Uncontrolled Eating, Emotional Eating and BMI and the Amount of Food Wasted in Early Adolescent Girls. Nutrients 2018;10(9):1279 View
  8. Harray A, Boushey C, Pollard C, Delp E, Ahmad Z, Dhaliwal S, Mukhtar S, Kerr D. A Novel Dietary Assessment Method to Measure a Healthy and Sustainable Diet Using the Mobile Food Record: Protocol and Methodology. Nutrients 2015;7(7):5375 View
  9. Ainaa Fatehah A, Poh B, Nik Shanita S, Wong J. Feasibility of Reviewing Digital Food Images for Dietary Assessment among Nutrition Professionals. Nutrients 2018;10(8):984 View
  10. Stanhope K. Sugar consumption, metabolic disease and obesity: The state of the controversy. Critical Reviews in Clinical Laboratory Sciences 2016;53(1):52 View
  11. Gemming L, Utter J, Ni Mhurchu C. Image-Assisted Dietary Assessment: A Systematic Review of the Evidence. Journal of the Academy of Nutrition and Dietetics 2015;115(1):64 View
  12. Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults. JMIR mHealth and uHealth 2015;3(2):e42 View
  13. Christoph M, Loman B, Ellison B. Developing a digital photography-based method for dietary analysis in self-serve dining settings. Appetite 2017;114:217 View
  14. 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
  15. Ji Y, Plourde H, Bouzo V, Kilgour R, Cohen T. Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial. JMIR mHealth and uHealth 2020;8(9):e16953 View
  16. Kerr D, Pollard C, Howat P, Delp E, Pickering M, Kerr K, Dhaliwal S, Pratt I, Wright J, Boushey C. Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC Public Health 2012;12(1) View
  17. Lee J, Song S, Ahn J, Kim Y, Lee J. Use of a Mobile Application for Self-Monitoring Dietary Intake: Feasibility Test and an Intervention Study. Nutrients 2017;9(7):748 View
  18. Zapata-Lamana R, Lalanza J, Losilla J, Parrado E, Capdevila L. mHealth technology for ecological momentary assessment in physical activity research: a systematic review. PeerJ 2020;8:e8848 View
  19. Boeing H. Nutritional epidemiology: New perspectives for understanding the diet-disease relationship?. European Journal of Clinical Nutrition 2013;67(5):424 View
  20. Bathgate K, Sherriff J, Leonard H, Dhaliwal S, Delp E, Boushey C, Kerr D. Feasibility of Assessing Diet with a Mobile Food  Record for Adolescents and Young Adults with  Down Syndrome. Nutrients 2017;9(3):273 View
  21. Chung L, Law Q, Fong S, Chung J, Yuen P. A cost-effectiveness analysis of teledietetics in short-, intermediate-, and long-term weight reduction. Journal of Telemedicine and Telecare 2015;21(5):268 View
  22. Derbyshire E, Dancey D. Smartphone Medical Applications for Women’s Health: What Is the Evidence-Base and Feedback?. International Journal of Telemedicine and Applications 2013;2013:1 View
  23. Sanghvi A, Redman L, Martin C, Ravussin E, Hall K. Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake. The American Journal of Clinical Nutrition 2015;102(2):353 View
  24. 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
  25. 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
  26. Kim S, Chung S. Development and User Satisfaction of a Mobile Phone Application for Image-based Dietary Assessment. Korean Journal of Community Nutrition 2017;22(6):485 View
  27. Gibney M, Walsh M. The future direction of personalised nutrition: my diet, my phenotype, my genes. Proceedings of the Nutrition Society 2013;72(2):219 View
  28. 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
  29. Sun M, Burke L, Baranowski T, Fernstrom J, Zhang H, Chen H, Bai Y, Li Y, Li C, Yue Y, Li Z, Nie J, Sclabassi R, Mao Z, Jia W. An Exploratory Study on a Chest‐Worn Computer for Evaluation of Diet, Physical Activity and Lifestyle. Journal of Healthcare Engineering 2015;6(1):1 View
  30. Boushey C, Harray A, Kerr D, Schap T, Paterson S, Aflague T, Bosch Ruiz M, Ahmad Z, Delp E. How Willing Are Adolescents to Record Their Dietary Intake? The Mobile Food Record. JMIR mHealth and uHealth 2015;3(2):e47 View
  31. 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
  32. Arens-Volland A, Spassova L, Bohn T. Promising approaches of computer-supported dietary assessment and management—Current research status and available applications. International Journal of Medical Informatics 2015;84(12):997 View
  33. Rollo M, Williams R, Burrows T, Kirkpatrick S, Bucher T, Collins C. What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation. Current Nutrition Reports 2016;5(4):307 View
  34. Casperson S, Sieling J, Moon J, Johnson L, Roemmich J, Whigham L. A Mobile Phone Food Record App to Digitally Capture Dietary Intake for Adolescents in a Free-Living Environment: Usability Study. JMIR mHealth and uHealth 2015;3(1):e30 View
  35. Aflague T, Leon Guerrero R, Delormier T, Novotny R, Wilkens L, Boushey C. Examining the Influence of Cultural Immersion on Willingness to Try Fruits and Vegetables among Children in Guam: The Traditions Pilot Study. Nutrients 2019;12(1):18 View
  36. da Costa F, Schmoelz C, Davies V, Di Pietro P, Kupek E, de Assis M. Assessment of Diet and Physical Activity of Brazilian Schoolchildren: Usability Testing of a Web-Based Questionnaire. JMIR Research Protocols 2013;2(2):e31 View
  37. Bruening M, van Woerden I, Todd M, Brennhofer S, Laska M, Dunton G. A Mobile Ecological Momentary Assessment Tool (devilSPARC) for Nutrition and Physical Activity Behaviors in College Students: A Validation Study. Journal of Medical Internet Research 2016;18(7):e209 View
  38. Eslick S, Jensen M, Collins C, Gibson P, Hilton J, Wood L. Characterising a Weight Loss Intervention in Obese Asthmatic Children. Nutrients 2020;12(2):507 View
  39. Chow C, Hall K. Short and long-term energy intake patterns and their implications for human body weight regulation. Physiology & Behavior 2014;134:60 View
  40. Fitt E, Cole D, Ziauddeen N, Pell D, Stickley E, Harvey A, Stephen A. DINO (Diet In Nutrients Out) – an integrated dietary assessment system. Public Health Nutrition 2015;18(2):234 View
  41. Ashman A, Collins C, Brown L, Rae K, Rollo M. Validation of a Smartphone Image-Based Dietary Assessment Method for Pregnant Women. Nutrients 2017;9(1):73 View
  42. Gemming L, Doherty A, Kelly P, Utter J, Ni Mhurchu C. Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake. European Journal of Clinical Nutrition 2013;67(10):1095 View
  43. Gibney M, McNulty B, Ryan M, Walsh M. Nutritional Phenotype Databases and Integrated Nutrition: From Molecules to Populations. Advances in Nutrition 2014;5(3):352S View
  44. Wang J, Hsieh R, Tung Y, Chen Y, Yang C, Chen Y. Evaluation of a Technological Image-Based Dietary Assessment Tool for Children during Pubertal Growth: A Pilot Study. Nutrients 2019;11(10):2527 View
  45. Hongu N, Pope B, Bilgiç P, Orr B, Suzuki A, Kim A, Merchant N, Roe D. Usability of a smartphone food picture app for assisting 24-hour dietary recall: a pilot study. Nutrition Research and Practice 2015;9(2):207 View
  46. Segovia-Siapco G, Sabaté J. Using Personal Mobile Phones to Assess Dietary Intake in Free-Living Adolescents: Comparison of Face-to-Face Versus Telephone Training. JMIR mHealth and uHealth 2016;4(3):e91 View
  47. Burrows T, Golley R, Khambalia A, McNaughton S, Magarey A, Rosenkranz R, Alllman‐Farinelli M, Rangan A, Truby H, Collins C. The quality of dietary intake methodology and reporting in child and adolescent obesity intervention trials: a systematic review. Obesity Reviews 2012;13(12):1125 View
  48. Lytle L, Nicastro H, Roberts S, Evans M, Jakicic J, Laposky A, Loria C. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Behavioral Domain. Obesity 2018;26(S2) View
  49. Panizza C, Lim U, Yonemori K, Cassel K, Wilkens L, Harvie M, Maskarinec G, Delp E, Lampe J, Shepherd J, Le Marchand L, Boushey C. Effects of Intermittent Energy Restriction Combined with a Mediterranean Diet on Reducing Visceral Adiposity: A Randomized Active Comparator Pilot Study. Nutrients 2019;11(6):1386 View
  50. Turner T, Spruijt‐Metz D, Wen C, Hingle M. Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatric Obesity 2015;10(6):403 View
  51. Boushey C, Spoden M, Delp E, Zhu F, Bosch M, Ahmad Z, Shvetsov Y, DeLany J, Kerr D. Reported Energy Intake Accuracy Compared to Doubly Labeled Water and Usability of the Mobile Food Record among Community Dwelling Adults. Nutrients 2017;9(3):312 View
  52. Ho T, Lee C, Wong S, Lau Y. Internet-based self-monitoring interventions for overweight and obese adolescents: A systematic review and meta-analysis. International Journal of Medical Informatics 2018;120:20 View
  53. Kerr D, Dhaliwal S, Pollard C, Norman R, Wright J, Harray A, Shoneye C, Solah V, Hunt W, Zhu F, Delp E, Boushey C. BMI is Associated with the Willingness to Record Diet  with  a  Mobile  Food  Record  among  Adults  Participating in Dietary Interventions. Nutrients 2017;9(3):244 View
  54. Spook J, Paulussen T, Kok G, Van Empelen P. Monitoring Dietary Intake and Physical Activity Electronically: Feasibility, Usability, and Ecological Validity of a Mobile-Based Ecological Momentary Assessment Tool. Journal of Medical Internet Research 2013;15(9):e214 View
  55. Raatz S, Scheett A, Johnson L, Jahns L. Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial. Journal of Medical Internet Research 2015;17(1):e21 View
  56. Aflague T, Boushey C, Guerrero R, Ahmad Z, Kerr D, Delp E. Feasibility and Use of the Mobile Food Record for Capturing Eating Occasions among Children Ages 3–10 Years in Guam. Nutrients 2015;7(6):4403 View
  57. Storey K. A changing landscape. Current Opinion in Clinical Nutrition and Metabolic Care 2015;18(5):437 View
  58. Banna J, Buchthal O, Delormier T, Creed-Kanashiro H, Penny M. Influences on eating: a qualitative study of adolescents in a periurban area in Lima, Peru. BMC Public Health 2015;16(1) View
  59. Probst Y, Zammit G. Predictors for Reporting of Dietary Assessment Methods in Food-based Randomized Controlled Trials over a Ten-year Period. Critical Reviews in Food Science and Nutrition 2016;56(12):2069 View
  60. Banna J. Considerations for Evaluation of Diabetes Prevention Programs in Hispanic Adults in the United States. American Journal of Lifestyle Medicine 2018;12(1):21 View
  61. Boushey C, Delp E, Ahmad Z, Wang Y, Roberts S, Grattan L. Dietary assessment of domoic acid exposure: What can be learned from traditional methods and new applications for a technology assisted device. Harmful Algae 2016;57:51 View
  62. Taylor J, Johnson R. Farm to School as a strategy to increase children's fruit and vegetable consumption in the United States: Research and recommendations. Nutrition Bulletin 2013;38(1):70 View
  63. Schembre S, Liao Y, O'Connor S, Hingle M, Shen S, Hamoy K, Huh J, Dunton G, Weiss R, Thomson C, Boushey C. Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review. JMIR mHealth and uHealth 2018;6(11):e11170 View
  64. Comulada W, Swendeman D, Koussa M, Mindry D, Medich M, Estrin D, Mercer N, Ramanathan N. Adherence to self-monitoring healthy lifestyle behaviours through mobile phone-based ecological momentary assessments and photographic food records over 6 months in mostly ethnic minority mothers. Public Health Nutrition 2018;21(4):679 View
  65. Park S, Palvanov A, Lee C, Jeong N, Cho Y, Lee H. The development of food image detection and recognition model of Korean food for mobile dietary management. Nutrition Research and Practice 2019;13(6):521 View
  66. Rangan A, O'Connor S, Giannelli V, Yap M, Tang L, Roy R, Louie J, Hebden L, Kay J, Allman-Farinelli M. Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls. JMIR mHealth and uHealth 2015;3(4):e98 View
  67. Chen Y, Wong J, Ayob A, Othman N, Poh B. Can Malaysian Young Adults Report Dietary Intake Using a Food Diary Mobile Application? A Pilot Study on Acceptability and Compliance. Nutrients 2017;9(1):62 View
  68. Rivera J, McPherson A, Hamilton J, Birken C, Coons M, Peters M, Iyer S, George T, Nguyen C, Stinson J. User-Centered Design of a Mobile App for Weight and Health Management in Adolescents With Complex Health Needs: Qualitative Study. JMIR Formative Research 2018;2(1):e7 View
  69. Sharp D, Allman-Farinelli M. Feasibility and validity of mobile phones to assess dietary intake. Nutrition 2014;30(11-12):1257 View
  70. Ahn Y, Bae J, Kim H. The development of a mobile u-Health program and evaluation for self-diet management for diabetic patients. Nutrition Research and Practice 2016;10(3):342 View
  71. Stephen A, Mak T, Fitt E, Nicholson S, Roberts C, Sommerville J. Innovations in national nutrition surveys. Proceedings of the Nutrition Society 2013;72(1):77 View
  72. Rollo M, Ash S, Lyons-Wall P, Russell A. Evaluation of a Mobile Phone Image-Based Dietary Assessment Method in Adults with Type 2 Diabetes. Nutrients 2015;7(6):4897 View
  73. Zhao X, Xu X, Li X, He X, Yang Y, Zhu S. Emerging trends of technology-based dietary assessment: a perspective study. European Journal of Clinical Nutrition 2021;75(4):582 View
  74. Vasiloglou M, van der Horst K, Stathopoulou T, Jaeggi M, Tedde G, Lu Y, Mougiakakou S. The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App. JMIR mHealth and uHealth 2021;9(1):e24467 View
  75. 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
  76. 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
  77. Papadimitriou N, Markozannes G, Kanellopoulou A, Critselis E, Alhardan S, Karafousia V, Kasimis J, Katsaraki C, Papadopoulou A, Zografou M, Lopez D, Chan D, Kyrgiou M, Ntzani E, Cross A, Marrone M, Platz E, Gunter M, Tsilidis K. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. Nature Communications 2021;12(1) View
  78. Banna J, Danible K, Panizza C, Boushey C, Kerr D, Zhu F. A Novel to Method to Measure Food Waste: The Mobile Food Record. Journal of Extension 2021;59(Summer 2021) View
  79. Zuppinger C, Taffé P, Burger G, Badran-Amstutz W, Niemi T, Cornuz C, Belle F, Chatelan A, Paclet Lafaille M, Bochud M, Gonseth Nusslé S. Performance of the Digital Dietary Assessment Tool MyFoodRepo. Nutrients 2022;14(3):635 View
  80. Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Yunsheng M, Chen S, Hou P. A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure. IEEE Transactions on Services Computing 2018;11(2):249 View
  81. Ding Y, Lu X, Xie Z, Jiang T, Song C, Wang Z. Evaluation of a Novel WeChat Applet for Image-Based Dietary Assessment among Pregnant Women in China. Nutrients 2021;13(9):3158 View
  82. Caon M, Prinelli F, Angelini L, Carrino S, Mugellini E, Orte S, Serrano J, Atkinson S, Martin A, Adorni F. PEGASO e-Diary: User Engagement and Dietary Behavior Change of a Mobile Food Record for Adolescents. Frontiers in Nutrition 2022;9 View
  83. Karamnova N, Izmailova O, Shvabskaia O. Nutrition research methods: usage cases, possibilities, and limitations. Profilakticheskaya meditsina 2021;24(8):109 View
  84. 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
  85. Tahir G, Loo C. A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment. Healthcare 2021;9(12):1676 View
  86. Fialkowski M, Kai J, Young C, Langfelder G, Ng-Osorio J, Shao Z, Zhu F, Kerr D, Boushey C. An Active Image-Based Mobile Food Record Is Feasible for Capturing Eating Occasions among Infants Ages 3–12 Months Old in Hawai‘i. Nutrients 2022;14(5):1075 View
  87. Ploderer B, Rezaei Aghdam A, Burns K. Patient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review. Journal of Medical Internet Research 2022;24(4):e28867 View
  88. Tanweer A, Zia M, Riaz K, Mushtaq H, Siddique M, Imran S, Humayun A, Hussain Z. Comparing the web-based and traditional self-reported 24-hour dietary recall data in the PakNutriStudy. Computer Methods and Programs in Biomedicine 2023;240:107682 View
  89. Fan R, Chen Q, Song L, Wang S, You M, Cai M, Wang X, Li Y, Xu M. The Validity and Feasibility of Utilizing the Photo-Assisted Dietary Intake Assessment among College Students and Elderly Individuals in China. Nutrients 2024;16(2):211 View
  90. Nabitchita B, Gonçalves N, Coelho P, Pimenta L, Zdravevski E, Lameski P, Costa M, Neves P, Pires I. Methods for volume inference of non-medical objects from images: A short review. Journal of Ambient Intelligence and Smart Environments 2024:1 View
  91. van der Heijden Z, de Gooijer F, Camps G, Lucassen D, Feskens E, Lasschuijt M, Brouwer-Brolsma E. User Requirements in Developing a Novel Dietary Assessment Tool for Children: Mixed Methods Study. JMIR Formative Research 2024;8:e47850 View
  92. Wang B, Bu T, Hu Z, Yang L, Zhao Y, Li X. Coarse-to-Fine Nutrition Prediction. IEEE Transactions on Multimedia 2024;26:3651 View
  93. Oei K, Choi E, Bar-Dayan A, Stinson J, Palmert M, Alfonsi J, Hamilton J. An Image-Recognition Dietary Assessment App for Adolescents with Obesity: Usability and Feasibility Testing (Pilot Randomized Controlled Trial) (Preprint). JMIR Formative Research 2024 View
  94. Foster-Nicholas L, Shavlik D, Heskey C, Dyett P, Segovia-Siapco G. Validity and Reproducibility of a Culture-Specific Electronic Food Frequency Questionnaire: A Trinidad and Tobago Diet Assessment Study. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2024;61 View
  95. Tagi M, Hamada Y, Shan X, Ozaki K, Kubota M, Amano S, Sakaue H, Suzuki Y, Konishi T, Hirose J. A Food Intake Estimation System Using an Artificial Intelligence–Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study. JMIR Formative Research 2024;8:e55218 View
  96. Yu Y, Zhang Z, Gao X, Hu S, Speakman J. Dietary Patterns of Healthy Underweight Individuals Compared to Normal-BMI Individuals Using Photographic Food Diaries. Nutrients 2024;16(21):3637 View

Books/Policy Documents

  1. Cui Y, Balshaw D. Unraveling the Exposome. View
  2. Fang S, Liu C, Zhu F, Boushey C, Delp E. New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. View
  3. Boeing H, Margetts B. Handbook of Epidemiology. View
  4. . Childhood Obesity. View
  5. Braconi D, Cicaloni V, Spiga O, Santucci A. Trends in Personalized Nutrition. View
  6. Xu X, Hou L, Guo Z, Wang J, Li J. Big Data – BigData 2018. View
  7. Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Ma Y. Inclusive Smart Cities and Digital Health. View
  8. Saraf S, Bagaria R, Kuresan H, Dhanalakshmi S. Smart Trends in Computing and Communications. View
  9. Bagaria R, Krithiga , Tripathi A, Ayush K. Human-Centric Smart Computing. View