Skip to main content

Advertisement

SpringerLink
Log in

Technology Interventions to Manage Food Intake: Where Are We Now?

  • Obesity (J McCaffery, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

This review describes the state-of-the-art for dietary assessment using smartphone apps and digital technology and provides an update on the efficacy of technology-mediated interventions for dietary change.

Recent Findings

Technology has progressed from apps requiring entry of foods consumed, to digital imaging to provide food intake data. However, these methods rely on patients being active in data collection. The automated estimation of the volume and composition of every meal consumed globally is years away. The use of text messaging, apps, social media, and combinations of these for interventions is growing and proving effective for type 2 diabetes mellitus (T2DM). Effectiveness of text messaging for obesity management is improving and multicomponent interventions show promise. A stand-alone app is less likely to produce positive outcomes and social media is relatively unexplored.

Summary

A concentrated effort will be needed to progress digital dietary assessment. Researcher-designed technology programs are producing positive outcomes for T2DM but further research is needed in the area of weight management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.

    Article  CAS  PubMed  Google Scholar 

  2. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014;37(Suppl 1):S120–43.

    Article  PubMed  Google Scholar 

  3. Illner AK, Freisling H, Boeing H, Huybrechts I, Crispim SP, Slimani N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol. 2012;41(4):1187–203.

    Article  PubMed  Google Scholar 

  4. Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. 2015;16(5):376–92.

    Article  CAS  PubMed  Google Scholar 

  5. Sharp DB, Allman-Farinelli M. Feasibility and validity of mobile phones to assess dietary intake. Nutrition. 2014;30(11–12):1257–66.

    Article  PubMed  Google Scholar 

  6. • Gemming L, Utter J, Ni MC. Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet. 2015;115(1):64–77. State-of-the-art review.

    Article  PubMed  Google Scholar 

  7. • Boushey CJ, Spoden M, Zhu FM, Delp EJ, Kerr DA. New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. Proc Nutr Soc. 2017;76(3):283–94. State-of-the-art review.

  8. Steele R. An overview of the state of the art of automated capture of dietary intake information. Crit Rev Food Sci Nutr. 2015;55(13):1929–38.

    Article  CAS  PubMed  Google Scholar 

  9. Rollo ME, WIlliams RL, Burrows T, Kirkpatrick SI, Bucher T, Collins CE. What are they really eating? A review on new approaches to dietary intake assessment and validation. Curr Nutr Rep. 2016;5(4):307–14.

    Article  Google Scholar 

  10. Rusin M, Arsand E, Hartvigsen G. Functionalities and input methods for recording food intake: a systematic review. Int J Med Inform. 2013;82(8):653–64.

    Article  PubMed  Google Scholar 

  11. Rangan AM, O'Connor S, Giannelli V, Yap ML, Tang LM, Roy R, et al. 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 Uhealth. 2015;3(4):e98.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Rangan AM, Tieleman L, Louie JC, Tang LM, Hebden L, Roy R, et al. Electronic dietary intake assessment (e-DIA): relative validity of a mobile phone application to measure intake of food groups. Br J Nutr. 2016;115(12):2219–26.

    Article  CAS  PubMed  Google Scholar 

  13. Bucher Della Torre S, Carrard I, Farina E, Danuser B, Kruseman M. Development and evaluation of e-CA, an electronic mobile-based food record. Nutrients. 2017;9(1):E76.

  14. Mescoloto SB, Caivano S, Domene SMÁ. Evaluation of a mobile application for estimation of food intake. Rev Nutr. 2017;30:91–8.

    Article  Google Scholar 

  15. Martin CK, Correa JB, Han H, Allen HR, Rood JC, Champagne CM, et al. Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity. 2012;20(4):891–9.

    Article  CAS  PubMed  Google Scholar 

  16. Lieffers JR, Haresign H, Mehling C, Hanning RM. A retrospective analysis of real-world use of the eaTracker(R) My Goals website by adults from Ontario and Alberta, Canada. BMC Public Health. 2016;16:978.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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. Br J Nutr. 2013;109(3):539–46.

    Article  CAS  PubMed  Google Scholar 

  18. McClung HL, Sigrist LD, Smith TJ, Karl JP, Rood JC, Young AJ, et al. Monitoring energy intake: a hand-held personal digital assistant provides accuracy comparable to written records. J Am Diet Assoc. 2009;109(7):1241–5.

    Article  PubMed  Google Scholar 

  19. Yon BA, Johnson RK, Harvey-Berino J, Gold BC. The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. J Am Diet Assoc. 2006;106(8):1256–9.

    Article  PubMed  Google Scholar 

  20. Beasley J, Riley WT, Jean-Mary J. Accuracy of a PDA-based dietary assessment program. Nutrition. 2005;21(6):672–7.

    Article  PubMed  Google Scholar 

  21. Beasley JM, Riley WT, Davis A, Singh J. Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial. J Am Coll Nutr. 2008;27(2):280–6.

    Article  PubMed  Google Scholar 

  22. Livingstone MB, Black AE. Markers of the validity of reported energy intake. J Nutr. 2003;133(Suppl 3):895s–920s.

    CAS  PubMed  Google Scholar 

  23. Chen J, Cade JE, Allman-Farinelli M. The most popular smartphone apps for weight loss: a quality assessment. JMIR Mhealth Uhealth. 2015;3(4):e104.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hales S, Dunn C, Wilcox S, Turner-McGrievy GM. Is a picture worth a thousand words? Few evidence-based features of dietary interventions included in photo diet tracking mobile apps for weight loss. J Diabetes Sci Technol. 2016;10(6):1399–405.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Patrick K, Raab F, Adams MA, Dillon L, Zabinski M, Rock CL, et al. A text message-based intervention for weight loss: randomized controlled trial. J Med Internet Res. 2009;11(1):e1.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Farmer AJ, McSharry J, Rowbotham S, McGowan L, Ricci-Cabello I, French DP. Effects of interventions promoting monitoring of medication use and brief messaging on medication adherence for people with type 2 diabetes: a systematic review of randomized trials. Diabet Med. 2016;33(5):565–79.

    Article  CAS  PubMed  Google Scholar 

  27. Siopis G, Chey T, Allman-Farinelli M. A systematic review and meta-analysis of interventions for weight management using text messaging. J Hum Nutr Diet. 2015;28(Suppl 2):1–15.

    Article  PubMed  Google Scholar 

  28. Ahn A, Choi J. A one-way text messaging intervention for obesity. J Telemed Telecare. 2016;22(3):148–52.

    Article  PubMed  Google Scholar 

  29. Stephens JD, Yager AM, Allen J. Smartphone technology and text messaging for weight loss in young adults: a randomized controlled trial. J Cardiovasc Nurs. 2017;32(1):39–46.

    Article  PubMed  Google Scholar 

  30. Lee S, Schorr E, Chi CL, Treat-Jacobson D, Mathiason MA, Lindquist R. Peer group and text message-based weight-loss and management intervention for African American women. West J Nurs Res. 2017. https://doi.org/10.1177/0193945917697225.

  31. Kulendran M, King D, Schmidtke KA, Curtis C, Gately P, Darzi A, et al. The use of commitment techniques to support weight loss maintenance in obese adolescents. Psychol Health. 2016;31(11):1332–41.

    Article  PubMed  Google Scholar 

  32. Lombard C, Harrison C, Kozica S, Zoungas S, Ranasinha S, Teede H. Preventing weight gain in women in rural communities: a cluster randomised controlled trial. PLoS Med. 2016;13(1):e1001941.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zwickert K, Rieger E, Swinbourne J, Manns C, McAulay C, Gibson AA, et al. High or low intensity text-messaging combined with group treatment equally promote weight loss maintenance in obese adults. Obes Res Clin Pract. 2016;10(6):680–91.

    Article  PubMed  Google Scholar 

  34. Muller AM, Alley S, Schoeppe S, Vandelanotte C. The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: a systematic review. Int J Behav Nutr Phys Act. 2016;13(1):109.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wickham CA, Carbone ET. Who’s calling for weight loss? A systematic review of mobile phone weight loss programs for adolescents. Nutr Rev. 2015;73(6):386–98.

    Article  PubMed  Google Scholar 

  36. Lee J, Piao M, Byun A, Kim J. A systematic review and meta-analysis of intervention for pediatric obesity using mobile technology. Stud Health Technol Inform. 2016;225:491–4.

    PubMed  Google Scholar 

  37. Smith AJ, Skow A, Bodurtha J, Kinra S. Health information technology in screening and treatment of child obesity: a systematic review. Pediatrics. 2013;131(3):e894–902.

    Article  PubMed  Google Scholar 

  38. Carfora V, Caso D, Conner M. Randomized controlled trial of a text messaging intervention for reducing processed meat consumption: the mediating roles of anticipated regret and intention. Appetite. 2017;117:152–60.

  39. Carfora V, Caso D, Conner M. Randomized controlled trial of a messaging intervention to increase fruit and vegetable intake in adolescents: affective versus instrumental messages. Br J Health Psychol. 2016;21(4):937–55.

    Article  PubMed  Google Scholar 

  40. • Arambepola C, Ricci-Cabello I, Manikavasagam P, Roberts N, French DP, Farmer A. The impact of automated brief messages promoting lifestyle changes delivered via mobile devices to people with type 2 diabetes: a systematic literature review and meta-analysis of controlled trials. J Med Internet Res. 2016;18(4):e86. Comprehensive review with meta-analysis.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sahu M, Grover A, Joshi A. Role of mobile phone technology in health education in Asian and African countries: a systematic review. Int J Electron Healthc. 2014;7(4):269–86.

    Article  PubMed  Google Scholar 

  42. Saffari M, Ghanizadeh G, Koenig HG. Health education via mobile text messaging for glycemic control in adults with type 2 diabetes: a systematic review and meta-analysis. Prim Care Diabetes. 2014;8(4):275–85.

    Article  PubMed  Google Scholar 

  43. Buhi ER, Trudnak TE, Martinasek MP, Oberne AB, Fuhrmann HJ, McDermott RJ. Mobile phone-based behavioural interventions for health: a systematic review. Health Educ J. 2013;72(5):564–83.

    Article  Google Scholar 

  44. Faruque LI, Wiebe N, Ehteshami-Afshar A, Liu Y, Dianati-Maleki N, Hemmelgarn BR, et al. Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials. CMAJ. 2017;189(9):E341–64.

  45. Su D, McBride C, Zhou J, Kelley MS. Does nutritional counseling in telemedicine improve treatment outcomes for diabetes? A systematic review and meta-analysis of results from 92 studies. J Telemed Telecare. 2016;22(6):333–47.

    Article  PubMed  Google Scholar 

  46. Van Olmen J, Kegels G, Korachais C, de Man J, Van Acker K, Kalobu JC, et al. The effect of text message support on diabetes self-management in developing countries—a randomised trial. J Clin Transl Endocrinol. 2017;7:33–41.

    Article  PubMed Central  Google Scholar 

  47. Holcomb LS. A taxonomic integrative review of short message service (SMS) methodology: a framework for improved diabetic outcomes. J Diabetes Sci Technol. 2015;9(6):1321–6.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Fortmann AL, Gallo LC, Garcia MI, Taleb M, Euyoque JA, Clark T, et al. Dulce Digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. 2017. https://doi.org/10.2337/dc17-0230

  49. Pfammatter A, Spring B, Saligram N, Dave R, Gowda A, Blais L, et al. mHealth intervention to improve diabetes risk behaviors in India: a prospective, parallel group cohort study. J Med Internet Res. 2016;18(8):e207.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Peimani M, Rambod C, Omidvar M, Larijani B, Ghodssi-Ghassemabadi R, Tootee A, et al. Effectiveness of short message service-based intervention (SMS) on self-care in type 2 diabetes: a feasibility study. Prim Care Diabetes. 2016;10(4):251–8.

    Article  PubMed  Google Scholar 

  51. Fischer HH, Fischer IP, Pereira RI, Furniss AL, Rozwadowski JM, Moore SL, et al. Text message support for weight loss in patients with prediabetes: a randomized clinical trial. Diabetes Care. 2016;39(8):1364–70.

    Article  CAS  PubMed  Google Scholar 

  52. Nikolaou CK, Lean ME. Mobile applications for obesity and weight management: current market characteristics. Int J Obes. 2017;41(1):200–2.

    Article  CAS  Google Scholar 

  53. Gan KO, Allman-Farinelli M. A scientific audit of smartphone applications for the management of obesity. Aust N Z J Public Health. 2011;35(3):293–4.

    Article  PubMed  Google Scholar 

  54. Bardus M, van Beurden SB, Smith JR, Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act. 2016;13:35.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med. 2013;45(5):576–82.

    Article  PubMed  Google Scholar 

  56. DiFilippo KN, Huang WH, Andrade JE, Chapman-Novakofski KM. The use of mobile apps to improve nutrition outcomes: a systematic literature review. J Telemed Telecare. 2015;21(5):243–53.

    Article  PubMed  Google Scholar 

  57. Turner-McGrievy GM, Campbell MK, Tate DF, Truesdale KP, Bowling JM, Crosby L. Pounds off digitally study: a randomized podcasting weight-loss intervention. Am J Prev Med. 2009;37(4):263–9.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Aguilar-Martinez A, Sole-Sedeno JM, Mancebo-Moreno G, Medina FX, Carreras-Collado R, Saigi-Rubio F. Use of mobile phones as a tool for weight loss: a systematic review. J Telemed Telecare. 2014;20(6):339–49.

    Article  PubMed  Google Scholar 

  59. Ross KM, Wing RR. Impact of newer self-monitoring technology and brief phone-based intervention on weight loss: a randomized pilot study. Obes. 2016;24(8):1653–9.

    Article  Google Scholar 

  60. • Chin SO, Keum C, Woo J, Park J, Choi HJ, Woo JT, et al. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci Rep. 2016;6:34563. One of the first analyses of commercial data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Serrano KJ, Coa KI, Yu M, Wolff-Hughes DL, Atienza AA. Characterizing user engagement with health app data: a data mining approach. Transl Behav Med. 2017;7(2):277–85.

  62. Cui M, Wu X, Mao J, Wang X, Nie M. T2DM self-management via smartphone applications: a systematic review and meta-analysis. PLoS One. 2016;11(11):e0166718.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do mobile phone applications improve glycemic control (HbA1c) in the self-management of diabetes? A systematic review, meta-analysis, and GRADE of 14 randomized trials. Diabetes Care. 2016;39(11):2089–95.

    Article  PubMed  Google Scholar 

  64. David SK, Rafiullah MR. Innovative health informatics as an effective modern strategy in diabetes management: a critical review. Int J Clin Pract. 2016;70(6):434–49.

    Article  CAS  PubMed  Google Scholar 

  65. Wang Y, Xue H, Huang Y, Huang L, Zhang D. A systematic review of application and effectiveness of mHealth interventions for obesity and diabetes treatment and self-management. Adv Nutr. 2017;8(3):449–62.

    Article  PubMed  Google Scholar 

  66. Bonoto BC, de Araujo VE, Godoi IP, de Lemos LL, Godman B, Bennie M, et al. Efficacy of mobile apps to support the care of patients with diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth. 2017;5(3):e4.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Whitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Med Internet Res. 2016;18(5):e97.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wu Y, Yao X, Vespasiani G, Nicolucci A, Dong Y, Kwong J, et al. Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR Mhealth Uhealth. 2017;5(3):e35.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Maher CA, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. Are health behavior change interventions that use online social networks effective? A systematic review. J Med Internet Res. 2014;16(2):e40.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Wang Y, Willis E. Supporting self-efficacy through interactive discussion in online communities of weight loss. J Health Psychol. 2016. https://doi.org/10.1177/1359105316653264

  71. Ashrafian H, Toma T, Harling L, Kerr K, Athanasiou T, Darzi A. Social networking strategies that aim to reduce obesity have achieved significant although modest results. Health Aff. 2014;33(9):1641–7.

    Article  Google Scholar 

  72. Willis EA, Szabo-Reed AN, Ptomey LT, Steger FL, Honas JJ, Washburn RA, et al. Do weight management interventions delivered by online social networks effectively improve body weight, body composition, and chronic disease risk factors? A systematic review. J Telemed Telecare. 2017;23(2):263–72.

    Article  PubMed  Google Scholar 

  73. Yang Q. Are social networking sites making health behavior change interventions more effective? A meta-analytic review. J Health Commun. 2017;22(3):223–33.

    Article  PubMed  Google Scholar 

  74. Mita G, Ni Mhurchu C, Jull A. Effectiveness of social media in reducing risk factors for noncommunicable diseases: a systematic review and meta-analysis of randomized controlled trials. Nutr Rev. 2016;74(4):237–47.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Laranjo L, Arguel A, Neves AL, Gallagher AM, Kaplan R, Mortimer N, et al. The influence of social networking sites on health behavior change: a systematic review and meta-analysis. J Am Med Inf Assoc. 2015;22(1):243–56.

    Article  Google Scholar 

  76. Williams G, Hamm MP, Shulhan J, Vandermeer B, Hartling L. Social media interventions for diet and exercise behaviours: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2014;4(2):e003926.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Chang T, Chopra V, Zhang C, Woolford SJ. The role of social media in online weight management: systematic review. J Med Internet Res. 2013;15(11):e262.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Pappa GL, Cunha TOL, Bicalho PV, Ribeiro A, Couto Silva AP, Meira W Jr, et al. Factors associated with weight change in online weight management communities: a case study in the LoseIt Reddit community. J Med Internet Res. 2017;19(1):e17.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Evans M, Faghri PD, Pagoto SL, Schneider KL, Waring ME, Whited MC, et al. The weight loss blogosphere: an online survey of weight loss bloggers. Transl Behav Med. 2016;6(3):403–9.

    Article  PubMed  Google Scholar 

  80. Patel R, Chang T, Greysen SR, Chopra V. Social media use in chronic disease: a systematic review and novel taxonomy. Am J Med. 2015;128(12):1335–50.

    Article  PubMed  Google Scholar 

  81. Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016;13(1):127.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17(2):e52.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Allman-Farinelli M, Partridge SR, McGeechan K, Balestracci K, Hebden L, Wong A, et al. A mobile health lifestyle program for prevention of weight gain in young adults (TXT2BFiT): nine-month outcomes of a randomized controlled trial. JMIR Mhealth Uhealth. 2016;4(2):e78.

    Article  PubMed  PubMed Central  Google Scholar 

  84. • Godino JG, Merchant G, Norman GJ, Donohue MC, Marshall SJ, Fowler JH, et al. Using social and mobile tools for weight loss in overweight and obese young adults (project SMART): a 2 year, parallel-group, randomised, controlled trial. Lancet Diabetes Endocrinol. 2016;4(9):747–55. State-of-the-art RCT of mHealth.

    Article  PubMed  PubMed Central  Google Scholar 

  85. • Nystrom CD, Sandin S, Henriksson P, Henriksson H, Trolle-Lagerros Y, Larsson C, et al. Mobile-based intervention intended to stop obesity in preschool-aged children: the MINISTOP randomized controlled trial. Am J Clin Nutr. 2017;105(6):1327–35. Excellent example of the comprehensive use of mHealth.

    PubMed  Google Scholar 

  86. • Lim S, Kang SM, Kim KM, Moon JH, Choi SH, Hwang H, et al. Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes. Acta Diabetol. 2016;53(2):189–98. Excellent example of the comprehensive use of mHealth.

    Article  CAS  PubMed  Google Scholar 

  87. • Riley WT, Serrano KJ, Nilsen W, Atienza AA. Mobile and wireless technologies in health behavior and the potential for intensively adaptive interventions. Curr Opin Psychol. 2015;5:67–71. The future of mHealth.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Brooke MJ, Thompson BM. Food and Drug Administration regulation of diabetes-related mHealth technologies. J Diabetes Sci Technol. 2013;7(2):296–301.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Grundy Q, Held FP, Bero LA. Tracing the potential flow of consumer data: a network analysis of prominent health and fitness apps. J Med Internet Res. 2017;19(6):e233.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Margaret Allman-Farinelli reports grants from Australian Research Council, Cancer Council NSW, Australian Meat and Livestock, NSW Health, and HCF Medical Foundation.

Author information

Authors and Affiliations

  1. University of Sydney, School of Life and Environmental Science, Level E 4 East, Charles Perkins Centre D17, University of Sydney, Sydney, NSW, 2006, Australia

    Margaret Allman-Farinelli & Luke Gemming

Authors
  1. Margaret Allman-Farinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luke Gemming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Margaret Allman-Farinelli.

Ethics declarations

Conflict of Interest

Margaret Allman-Farinelli reports personal fees from NMHRC, and non-financial support from Qantas. Luke Gemming declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors. The articles included in this review that were conducted by the authors ensured all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

This article is part of the Topical Collection on Obesity

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Allman-Farinelli, M., Gemming, L. Technology Interventions to Manage Food Intake: Where Are We Now?. Curr Diab Rep 17, 103 (2017). https://doi.org/10.1007/s11892-017-0937-5

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11892-017-0937-5

Keywords

Search

Navigation