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Citing this Article

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Published on 01.08.16 in Vol 4, No 3 (2016): Jul-Sept

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

Works citing "Popular Nutrition-Related Mobile Apps: A Feature Assessment"

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.5846):

(note that this is only a small subset of citations)

  1. Zarnowiecki D, Mauch CE, Middleton G, Matwiejczyk L, Watson WL, Dibbs J, Dessaix A, Golley RK. A systematic evaluation of digital nutrition promotion websites and apps for supporting parents to influence children’s nutrition. International Journal of Behavioral Nutrition and Physical Activity 2020;17(1)
    CrossRef
  2. Rozga M, Latulippe ME, Steiber A. Advancements in Personalized Nutrition Technologies: Guiding Principles for Registered Dietitian Nutritionists. Journal of the Academy of Nutrition and Dietetics 2020;120(6):1074
    CrossRef
  3. Segredo E, Miranda G, Ramos JM, Leon C, Rodriguez-Leon C. SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals. IEEE Access 2020;:1
    CrossRef
  4. Flaherty SJ, McCarthy MB, Collins AM, McCafferty C, McAuliffe FM. A phenomenological exploration of change towards healthier food purchasing behaviour in women from a lower socioeconomic background using a health app. Appetite 2020;147:104566
    CrossRef
  5. Gadenz SD, Harzheim E, Amaral HG, Drehmer M. Development and Assessment of a Mobile Nutritional Counseling Tool for Primary Care Physicians. Telemedicine and e-Health 2020;26(6):805
    CrossRef
  6. Tejpal S, Sanghera N, Manoharan V, Planas-Iglesias J, Bastie CC, Klein-Seetharaman J. Angiotensin Converting Enzyme (ACE): A Marker for Personalized Feedback on Dieting. Nutrients 2020;12(3):660
    CrossRef
  7. Ahmed M, Oh A, Vanderlee L, Franco-Arellano B, Schermel A, Lou W, L’Abbé MR. A randomized controlled trial examining consumers’ perceptions and opinions on using different versions of a FoodFlip© smartphone application for delivery of nutrition information. International Journal of Behavioral Nutrition and Physical Activity 2020;17(1)
    CrossRef
  8. Tavares BF, Pires IM, Marques G, Garcia NM, Zdravevski E, Lameski P, Trajkovik V, Jevremovic A. Mobile Applications for Training Plan Using Android Devices: A Systematic Review and a Taxonomy Proposal. Information 2020;11(7):343
    CrossRef
  9. Bland C, Dalrymple KV, White SL, Moore A, Poston L, Flynn AC. Smartphone applications available to pregnant women in the United Kingdom: An assessment of nutritional information. Maternal & Child Nutrition 2020;16(2)
    CrossRef
  10. Sustamy RP, Widyawati MN, Suryono S. Information system implementation for the management of malnutrition in pregnant women: a systematic literature review. Journal of Physics: Conference Series 2020;1524:012115
    CrossRef
  11. Samoggia A, Riedel B. Assessment of nutrition-focused mobile apps' influence on consumers' healthy food behaviour and nutrition knowledge. Food Research International 2020;128:108766
    CrossRef
  12. Osadchiy T, Poliakov I, Olivier P, Rowland M, Foster E. Progressive 24-Hour Recall: Usability Study of Short Retention Intervals in Web-Based Dietary Assessment Surveys. Journal of Medical Internet Research 2020;22(2):e13266
    CrossRef
  13. Minnens F, Marques A, Domingo JL, Verbeke W. Consumers’ acceptance of an online tool with personalized health risk-benefit communication about seafood consumption. Food and Chemical Toxicology 2020;144:111573
    CrossRef
  14. Katz D, Rhee L, Katz C, Aronson D, Frank G, Gardner C, Willett W, Dansinger M. Dietary assessment can be based on pattern recognition rather than recall. Medical Hypotheses 2020;140:109644
    CrossRef
  15. Zhang L, Nawijn E, Boshuizen H, Ocké M. Evaluation of the Recipe Function in Popular Dietary Smartphone Applications, with Emphasize on Features Relevant for Nutrition Assessment in Large-Scale Studies. Nutrients 2019;11(1):200
    CrossRef
  16. Demiris G, Iribarren SJ, Sward K, Lee S, Yang R. Patient generated health data use in clinical practice: A systematic review. Nursing Outlook 2019;67(4):311
    CrossRef
  17. Minnens F, Marques A, Domingo JL, Verbeke W. Consumers’ acceptance of an online tool with personalized health risk-benefit communication about seafood consumption (Preprint). JMIR Formative Research 2019;
    CrossRef
  18. Potemkina NS, Bolshakov AM, Krutko VN, Mamikonova OA. INFORMATION AND COMPUTER SUPPORT OF HEALTHY NUTRITION AS A CURRENT METHOD OF HEALTH SAVING AND FOOD HYGIENE IN MODERN ENVIRONMENTAL CONDITIONS. Hygiene and sanitation 2019;96(11):1078
    CrossRef
  19. Fallaize R, Zenun Franco R, Pasang J, Hwang F, Lovegrove JA. Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method. JMIR mHealth and uHealth 2019;7(2):e9838
    CrossRef
  20. Fallaize R, Franco RZ, Hwang F, Lovegrove JA, Portero-Otin M. Evaluation of the eNutri automated personalised nutrition advice by users and nutrition professionals in the UK. PLOS ONE 2019;14(4):e0214931
    CrossRef
  21. Pentikäinen S, Tanner H, Karhunen L, Kolehmainen M, Poutanen K, Pennanen K. Mobile Phone App for Self-Monitoring of Eating Rhythm: Field Experiment. JMIR mHealth and uHealth 2019;7(3):e11490
    CrossRef
  22. Holmes WS, Moorhead SA, Coates VE, Bond RR, Zheng H. Impact of digital technologies for communicating messages on weight loss maintenance: a systematic literature review. European Journal of Public Health 2019;29(2):320
    CrossRef
  23. Villasana MV, Pires IM, Sá J, Garcia NM, Zdravevski E, Chorbev I, Lameski P, Flórez-Revuelta F. Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy Proposal. The Open Bioinformatics Journal 2019;13(1):50
    CrossRef
  24. Flaherty SJ, McCarthy MB, Collins AM, McAuliffe FM. A different perspective on consumer engagement: exploring the experience of using health apps to support healthier food purchasing. Journal of Marketing Management 2019;35(3-4):310
    CrossRef
  25. Li Y, Ding J, Wang Y, Tang C, Zhang P. Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality. JMIR mHealth and uHealth 2019;7(7):e13261
    CrossRef
  26. Kosa SD, Monize J, D'Souza M, Joshi A, Philip K, Reza S, Samra S, Serrago B, Thabane L, Gafni A, Lok CE. Nutritional Mobile Applications for CKD Patients: Systematic Review. Kidney International Reports 2019;4(3):399
    CrossRef
  27. Tejpal S, Sanghera N, Manoharan V, Planas-Iglesias J, Myler K, Klein-Seetharaman J. Towards personalised molecular feedback for weight loss. BMC Obesity 2019;6(1)
    CrossRef
  28. Franco RZ, Fallaize R, Hwang F, Lovegrove JA. Strategies for online personalised nutrition advice employed in the development of the eNutri web app. Proceedings of the Nutrition Society 2019;78(3):407
    CrossRef
  29. Honary M, Bell BT, Clinch S, Wild SE, McNaney R. Understanding the Role of Healthy Eating and Fitness Mobile Apps in the Formation of Maladaptive Eating and Exercise Behaviors in Young People. JMIR mHealth and uHealth 2019;7(6):e14239
    CrossRef
  30. Villasana MV, Pires IM, Sá J, Garcia NM, Zdravevski E, Chorbev I, Lameski P, Flórez-Revuelta F. Mobile Applications for the Promotion and Support of Healthy Nutrition and Physical Activity Habits: A Systematic Review, Extraction of Features and Taxonomy Proposal. The Open Bioinformatics Journal 2019;12(1):50
    CrossRef
  31. Maringer M, Wisse-Voorwinden N, Veer PV, Geelen A. Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications. Public Health Nutrition 2018;:1
    CrossRef
  32. Agapito G, Simeoni M, Calabrese B, Caré I, Lamprinoudi T, Guzzi PH, Pujia A, Fuiano G, Cannataro M. DIETOS: A dietary recommender system for chronic diseases monitoring and management. Computer Methods and Programs in Biomedicine 2018;153:93
    CrossRef
  33. Calegari L, Barbosa J, Marodin G, Fettermann D. A conjoint analysis to consumer choice in Brazil: Defining device attributes for recognizing customized foods characteristics. Food Research International 2018;109:1
    CrossRef
  34. Biel J, Martin N, Labbe D, Gatica-Perez D. Bites‘n’Bits. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;1(4):1
    CrossRef
  35. Zhang P, Dong L, Chen H, Chai Y, Liu J. The Rise and Need for Mobile Apps for Maternal and Child Health Care in China: Survey Based on App Markets. JMIR mHealth and uHealth 2018;6(6):e140
    CrossRef
  36. Mauch CE, Wycherley TP, Laws RA, Johnson BJ, Bell LK, Golley RK. Mobile Apps to Support Healthy Family Food Provision: Systematic Assessment of Popular, Commercially Available Apps. JMIR mHealth and uHealth 2018;6(12):e11867
    CrossRef
  37. Maringer M, van’t Veer P, Klepacz N, Verain MCD, Normann A, Ekman S, Timotijevic L, Raats MM, Geelen A. User-documented food consumption data from publicly available apps: an analysis of opportunities and challenges for nutrition research. Nutrition Journal 2018;17(1)
    CrossRef
  38. 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
    CrossRef
  39. Flaherty S, McCarthy M, Collins A, McAuliffe F. Can existing mobile apps support healthier food purchasing behaviour? Content analysis of nutrition content, behaviour change theory and user quality integration. Public Health Nutrition 2018;21(2):288
    CrossRef
  40. Teasdale N, Elhussein A, Butcher F, Piernas C, Cowburn G, Hartmann-Boyce J, Saksena R, Scarborough P. Systematic review and meta-analysis of remotely delivered interventions using self-monitoring or tailored feedback to change dietary behavior. The American Journal of Clinical Nutrition 2018;107(2):247
    CrossRef
  41. Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eating Behaviors 2017;26:89
    CrossRef
  42. 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
    CrossRef
  43. 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
    CrossRef
  44. Pendergast FJ, Leech RM, McNaughton SA. Novel Online or Mobile Methods to Assess Eating Patterns. Current Nutrition Reports 2017;6(3):212
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.5846)

:
  1. de Moraes Lopes MHB, Ferreira DD, Ferreira ACBH, da Silva GR, Caetano AS, Braz VN. Artificial Intelligence in Precision Health. 2020. :465
    CrossRef
  2. Valentim Pereira GF, Pires IM, Marques G, Garcia NM, Zdravevski E, Lameski P, Flórez-Revuelta F, Spinsante S. Internet of Things and Big Data Applications. 2020. Chapter 7:107
    CrossRef
  3. Kwong K, Collins A. Enhancing Student-Centred Teaching in Higher Education. 2020. Chapter 13:201
    CrossRef
  4. Giazitzi K, Karathanos VT, Boskou G. Quality Assurance in the Era of Individualized Medicine. 2020. chapter 6:147
    CrossRef
  5. Al-Sayed L. Food Tech Transitions. 2019. Chapter 8:129
    CrossRef