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Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review

  • Mobile Systems
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Abstract

Screening for chronical diseases like type 2 diabetes can be done using different methods and various risk tests. This study present a review of type 2 diabetes risk estimation mobile applications focusing on their functionality and availability of information on the underlying risk calculators. Only 9 out of 31 reviewed mobile applications, featured in three major mobile application stores, disclosed the name of risk calculator used for assessing the risk of type 2 diabetes. Even more concerning, none of the reviewed applications mentioned that they are collecting the data from users to improve the performance of their risk estimation calculators or offer users the descriptive statistics of the results from users that already used the application. For that purpose the questionnaires used for calculation of risk should be upgraded by including the information on the most recent blood sugar level measurements from users. Although mobile applications represent a great future potential for health applications, developers still do not put enough emphasis on informing the user of the underlying methods used to estimate the risk for a specific clinical condition.

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References

  1. Shaw, J. E., Sicree, R. A., and Zimmet, P. Z., Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87:4–14, 2011.

    Article  Google Scholar 

  2. Jung, E. Y., Kim, J., Chung, K. Y., and Park, D. K., Factors influencing the acceptance of telemedicine for diabetes management. Clust Comput 17:871–880, 2014.

    Article  Google Scholar 

  3. Donsa, K., Spat, S., Beck, P., Pieber, T. R., and Holzinger A., Towards personalization of diabetes therapy using computerized decision support and machine learning: some open problems and challenges. In Smart Health. Springer International Publishing 237–260, 2015.

  4. Hanauer, D. A., Wentzell, K., Laffel, N., and Laffel, L. M., Computerized Automated Reminder Diabetes System (CARDS): E-mail and SMS cell phone text messaging reminders to support diabetes management. Diabetes Technol Ther 11:99–106, 2009.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Waki, K., Fujitaa, H., Uchimuraa, Y., Aramakia, E., Omaeb, K., Kadowakia, T., and Ohea, K., DialBetics: smartphone-based selfmanagement for type 2 diabetes patients. J Diabetes Sci Technol 6:983–985, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  6. García-Gómez, J. M., Torre-Díez, I., Vicente, J., Robles, M., López-Coronado, M., and Rodrigues, J. J., Analysis of mobile health applications for a broad spectrum of consumers: a user experience approach. Health Informatics J 20:74–84, 2014.

    Article  PubMed  Google Scholar 

  7. Collins, G. S., Mallett, S., Omar, O., and Yu, L. M., Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 9:103, 2011.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Gray, L. J., Leigh, T., Davies, M. J., Patel, N., Stone, M., Bonar, M., Badge, R., and Khunti, K., Systematic review of the development, implementation and availability of smartphone applications for assessing type 2 diabetes risk. Diabet Med 30:758–760, 2013.

    Article  CAS  PubMed  Google Scholar 

  9. Kollmann, A., Riedl, M., Kastner, P., Schreier, G., and Ludvik, B., Feasibility of a mobile phone–based data service for functional insulin treatment of type 1 diabetes mellitus patients. J Med Internet Res 9:36, 2007.

    Article  Google Scholar 

  10. Gaggioli, A., Pioggia, G., Tartarisco, G., Baldus, G., Corda, D., Cipresso, P., and Riva, G., A mobile data collection platform for mental health research. Pers Ubiquit Comput 17:241–251, 2013.

    Article  Google Scholar 

  11. Pfaeffli, L., Maddison, R., Jiang, Y., Dalleck, L., and Löf, M., Measuring physical activity in a cardiac rehabilitation population using a smartphone-based questionnaire. J Med Internet Res 15:61, 2013.

    Article  Google Scholar 

  12. Min, Y. H., Lee, J. W., Shin, Y. W., Jo, M. W., Sohn, G., Lee, J. H., Lee, G., Jung, K. H., Sung, J., Ko, B. S., Yu, J. H., Kim, H. J., Son, B. H., and Ahn, S. H., Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study. J Med Internet Res 16:135, 2014.

    Article  Google Scholar 

  13. Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., and Kessing, L. V., Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res 30:124–127, 2014.

    Article  Google Scholar 

  14. Bang, H., Edwards, A. M., and Bomback, A. S., Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med 151:775–783, 2009.

    Article  PubMed Central  PubMed  Google Scholar 

  15. United States Center for Disease Control and Prevention. National Center for Health Statistics (NCHS), National Health and Nutrition Examination Survey Laboratory Protocol. Department of Health and Human Services, Centers for Disease Control and Prevention.

  16. Hwang, S. J., Ballantyne, C. M., and Sharrett, A. R., Circulating adhesion molecules VCAM-1, ICAM-1, and E-selectin in carotid atherosclerosis and incident coronary heart disease cases the Atherosclerosis Risk In Communities (ARIC) study. Circulation 96:4219–4225, 1997.

    Article  CAS  PubMed  Google Scholar 

  17. O’Leary, D. H., Polak, J. F., and Wolfson, S. K., Use of sonography to evaluate carotid atherosclerosis in the elderly. The Cardiovascular Health Study. CHS Collaborative Research Group. Stroke 22:1155–1163, 1991.

    Article  PubMed  Google Scholar 

  18. Kaczorowski, J., Robinson, C., and Nerenberg, K., Development of the CANRISK questionnaire to screen for prediabetes and undiagnosed type 2 diabetes. CJD 33:381–385, 2009.

    Google Scholar 

  19. Robinson, C. A., Agarwal, G., and Nerenberg, K., Validating the CANRISK prognostic model for assessing diabetes risk in Canada’s multi-ethnic population. Chron Dis Inj Can 32:19–31, 2011.

    CAS  Google Scholar 

  20. Cameron, A. J., Welborn, T. A., and Zimmet, P. Z., Overweight and obesity in Australia: the 1999–2000 Australian diabetes, obesity and lifestyle study (AusDiab). MJA 178:427–432, 2003.

    PubMed  Google Scholar 

  21. Chen, L., Magliano, D. J., and Balkau, B., AUSDRISK: an Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust 192:197–202, 2010.

    PubMed  Google Scholar 

  22. Mitchell, P., Smith, W., and Attebo, K., Prevalence of age-related maculopathy in Australia: the Blue Mountains Eye Study. Ophthalmology 102:1450–1460, 1995.

    Article  CAS  PubMed  Google Scholar 

  23. Grant, J. F., Chittleborough, C. R., Taylor, A. W., Dal Grande, E., Wilson, D. H., Phillips, P. J., Adams, R. J., Cheek, J., Price, K., Gill, T., and Ruffin, R. E., The North West Adelaide Health Study: detailed methods and baseline segmentation of a cohort for selected chronic diseases. Epidemiol Perspect Innov 3:4, 2006.

    Article  PubMed Central  PubMed  Google Scholar 

  24. Lindström, J., and Tuomilehto, J., The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–31, 2003.

    Article  PubMed  Google Scholar 

  25. Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., and Brindle, P., Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 338:880, 2009.

    Article  Google Scholar 

  26. Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G., Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151:264–269, 2009.

    Article  PubMed  Google Scholar 

  27. Zapata, B. C., Niñirola, A. H., Idri, A., Fernández-Alemán, J. L., and Toval, A., Mobile PHRs compliance with android and iOS usability guidelines. J Med Syst 38:81, 2014.

    Article  Google Scholar 

  28. Spångmyr M, (2014) Development of an Open-Source Mobile Application for Emergency Data Collection. http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4252143&fileOId=4252157. Accessed 15 January 2015.

  29. Cohen, J., A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46, 1960.

    Article  Google Scholar 

  30. Holzinger, A., Treitler, P., and Slany, W., Making apps useable on multiple different mobile platforms: On interoperability for business application development on smartphones. In Multidisciplinary research and practice for information systems. Berlin: Springer Berlin Heidelberg, 176–189, 2012.

  31. Carmien, S., and Manzanares, A. G., Elders using smartphones–A set of research based heuristic guidelines for designers. In Universal Access in Human-Computer Interaction. Universal Access to Information and Knowledge. Switzerland: Springer International Publishing, 26–37, 2014.

  32. Carmien S, Garzo A (2011) Elders Using Smartphones – a Set of Research Based Heuristic Guidelines for Designers. http://www.scarmien.com/papers/Elders_Using_Smartphones_carmien.pdf. Accessed 15 January 2015.

  33. Liu, C., Zhu, Q., Holroyd, K. A., and Seng, E. K., Status and trends of mobile-health applications for iOS devices: A developer’s perspective. J Syst Softw 84:2022–2033, 2011.

    Article  Google Scholar 

  34. Peischl, B., Ferk, M., and Holzinger, A., The fine art of user-centered software development. Soft Q J 23:509–536, 2015.

    Article  Google Scholar 

  35. Latchan, Z., Seereeram, R., Kamalodeen, A., Sanchez, S., Deonarine, U., Sinanan, R., and Mungru, K., TRAQ-D (Trinidad Risk Assessment Questionnaire for Type 2 Diabetes Mellitus): a cheap, reliable, non-invasive screening tool for diabetes. Br J Diabetes Vasc Dis 10:187–192, 2010.

    Article  Google Scholar 

  36. Makrilakis, K., Liatis, S., Grammatikou, S., Perrea, D., Stathi, C., Tsiligros, P., and Katsilambros, N., Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece. Diabetes Metab 37:144–151, 2011.

    Article  CAS  PubMed  Google Scholar 

  37. Ku, G. M., and Kegels, G., The performance of the Finnish diabetes risk score, a modified Finnish diabetes risk score and a simplified Finnish diabetes risk score in community-based cross-sectional screening of undiagnosed type 2 diabetes in the Philippines. Prim Care Diabetes 7:249–259, 2013.

    Article  PubMed  Google Scholar 

  38. Holmberg, C., Harttig, U., Schulze, M. B., and Boeing, H., The potential of the Internet for health communication: the use of an interactive on-line tool for diabetes risk prediction. Patient Educ Couns 83:106–12, 2011.

    Article  PubMed  Google Scholar 

  39. Baehring, T. U., Schulze, H., Bornstein, S. R., and Scherbaum, W. A., Using the World Wide Web—a new approach to risk identification of diabetes mellitus. Int J Med Inform 46:31–39, 1997.

    Article  CAS  PubMed  Google Scholar 

  40. Thoopputra, T., Pongmesa, T., and Li, S., Feasibility of risk assessment for type 2 diabetes in community pharmacies using two different approaches: A pilot study in Thailand. Int J Med Health Pharm Biomed Eng 7:199–203, 2013.

    Google Scholar 

  41. McNeely, M. J., and Boyko, E. J., Type 2 diabetes prevalence in Asian Americans: results of a national health survey. Diabetes Care 27:66–69, 2004.

    Article  PubMed  Google Scholar 

  42. Wei, J. N., Sung, F. C., Lin, C. C., Lin, R. S., Chiang, C. C., and Chuang, L. M., National surveillance for type 2 diabetes mellitus in Taiwanese children. JAMA 290:1345–1350, 2003.

    Article  CAS  PubMed  Google Scholar 

  43. Holzinger, A., Kosec, P., Schwantzer, G., Debevc, M., Hofmann-Wellenhof, R., and Frühauf, J., Design and development of a mobile computer application to reengineer workflows in the hospital and the methodology to evaluate its effectiveness. J Biomed Inform 44:968–977, 2011.

    Article  PubMed  Google Scholar 

  44. Valdez, A. C., Ziefle, M., Alagöz, F., and Holzinger, A., Mental models of menu structures in diabetesassistants. In Computers helping people with special needs. Berlin: Springer Berlin Heidelberg, 584–591, 2010.

  45. Kalz, M., Lenssen, N., Felzen, M., Rossaint, R., Tabuenca, B., Specht, M., and Skorning, M., Smartphone apps for cardiopulmonary resuscitation training and real incident support: a mixed-methods evaluation study. J Med Internet Res 16:89, 2014.

    Article  Google Scholar 

  46. Ehrenfeld, J. M., The current and future needs of our medical systems. J Med Syst 39:1–3, 2015.

    Article  Google Scholar 

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Acknowledgments

This study was partially supported by the Swiss National Science Foundation through a SCOPES 2013 Joint Research Projects grant SNSF IZ73Z0_152415. The authors would also like to thank the anonymous reviewers for their helpful comments.

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The authors declare that they have no conflict of interest.

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Correspondence to Gregor Stiglic.

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This article is part of the Topical Collection on Mobile Systems

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Fijacko, N., Brzan, P.P. & Stiglic, G. Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review. J Med Syst 39, 124 (2015). https://doi.org/10.1007/s10916-015-0319-y

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