Diabetes Metab J.  2020 Dec;44(6):819-827. 10.4093/dmj.2020.0088.

Present and Future of Digital Health in Diabetes and Metabolic Disease

Affiliations
  • 1Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea
  • 2Department of Digital Health, Scripps Research Translational Institute, La Jolla, CA, USA
  • 3Department of Internal Medicine, Seoul Wise Hospital, Uiwang, Korea
  • 4Department of Family Medicine/Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  • 5Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea

Abstract

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Keyword

Diabetes mellitus; Education; Mobile applications; Nutritional sciences; Obesity; Prediabetic state; Prevention and control; Self care; Smartphone; Wearable electronic devices

Figure

  • Fig. 1 Various tools for digital health. Adapted from IQVIA by the policies for data download and sharing [6].


Reference

1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018; 138:271–81.
Article
2. Won JC, Lee JH, Kim JH, Kang ES, Won KC, Kim DJ, Lee MK. Diabetes fact sheet in Korea, 2016: an appraisal of current status. Diabetes Metab J. 2018; 42:415–24.
Article
3. Steinhubl SR, Topol EJ. Digital medicine, on its way to being just plain medicine. NPJ Digit Med. 2018; 1:20175.
Article
4. Sharma A, Harrington RA, McClellan MB, Turakhia MP, Eapen ZJ, Steinhubl S, Mault JR, Majmudar MD, Roessig L, Chandross KJ, Green EM, Patel B, Hamer A, Olgin J, Rumsfeld JS, Roe MT, Peterson ED. Using digital health technology to better generate evidence and deliver evidence-based care. J Am Coll Cardiol. 2018; 71:2680–90.
5. Ricciardi W, Pita Barros P, Bourek A, Brouwer W, Kelsey T, Lehtonen L. Expert Panel on Effective Ways of Investing in Health (EXPH). How to govern the digital transformation of health services. Eur J Public Health. 2019; 29:7–12.
Article
6. IQVIA. Digital health tools. Available from: https://www.iqvia.com/insights/the-iqvia-institute/reports/the-growing-value-of-digital-health(cited 2020 Oct 20).
7. Neborachko M, Pkhakadze A, Vlasenko I. Current trends of digital solutions for diabetes management. Diabetes Metab Syndr. 2019; 13:2997–3003.
Article
8. Chin SO, Keum C, Woo J, Park J, Choi HJ, Woo JT, Rhee SY. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity. Sci Rep. 2016; 6:34563.
Article
9. Tremmel M, Gerdtham UG, Nilsson PM, Saha S. Economic burden of obesity: a systematic literature review. Int J Environ Res Public Health. 2017; 14:435.
Article
10. Arigo D, Jake-Schoffman DE, Wolin K, Beckjord E, Hekler EB, Pagoto SL. The history and future of digital health in the field of behavioral medicine. J Behav Med. 2019; 42:67–83.
Article
11. McConnell MV, Shcherbina A, Pavlovic A, Homburger JR, Goldfeder RL, Waggot D, Cho MK, Rosenberger ME, Haskell WL, Myers J, Champagne MA, Mignot E, Landray M, Tarassenko L, Harrington RA, Yeung AC, Ashley EA. Feasibility of obtaining measures of lifestyle from a smartphone app: the myheart counts cardiovascular health study. JAMA Cardiol. 2017; 2:67–76.
12. Nelson BW, Allen NB. Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: intraindividual validation study. JMIR Mhealth Uhealth. 2019; 7:e10828.
Article
13. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, Hung G, Lee J, Kowey P, Talati N, Nag D, Gummidipundi SE, Beatty A, Hills MT, Desai S, Granger CB, Desai M, Turakhia MP. Apple Heart Study Investigators. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019; 381:1909–17.
Article
14. Liang Z, Chapa-Martell MA. Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors. JMIR Mhealth Uhealth. 2019; 7:e13384.
Article
15. Davison BK, Quigg R, Skidmore PML. Pilot testing a photo-based food diary in nine-to twelve-year old-children from Dunedin, New Zealand. Nutrients. 2018; 10:240.
16. Fuller NR, Fong M, Gerofi J, Ferkh F, Leung C, Leung L, Zhang S, Skilton M, Caterson ID. Comparison of an electronic versus traditional food diary for assessing dietary intake: a validation study. Obes Res Clin Pract. 2017; 11:647–54.
17. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163:1079–94.
Article
18. Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018; 361:k2179.
Article
19. Boerger NL, Barleen NA, Marzec ML, Moloney DP, Dobro J. The impact of specialized telephonic guides on employee engagement in corporate well-being programs. Popul Health Manag. 2018; 21:32–9.
Article
20. Levinson CA, Fewell L, Brosof LC. My fitness pal calorie tracker usage in the eating disorders. Eat Behav. 2017; 27:14–6.
Article
21. Shin DW, Yun JM, Shin JH, Kwon H, Min HY, Joh HK, Chung WJ, Park JH, Jung KT, Cho B. Enhancing physical activity and reducing obesity through smartcare and financial incentives: a pilot randomized trial. Obesity (Silver Spring). 2017; 25:302–10.
Article
22. Mahmood A, Kedia S, Wyant DK, Ahn S, Bhuyan SS. Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions. Digit Health. 2019; 5:2055207619882181.
Article
23. Moin T, Damschroder LJ, AuYoung M, Maciejewski ML, Havens K, Ertl K, Vasti E, Weinreb JE, Steinle NI, Billington CJ, Hughes M, Makki F, Youles B, Holleman RG, Kim HM, Kinsinger LS, Richardson CR. Results from a trial of an online diabetes prevention program intervention. Am J Prev Med. 2018; 55:583–91.
Article
24. Jasik CB, Joy E, Brunisholz KD, Kirley K. Practical tips for implementing the diabetes prevention program in clinical practice. Curr Diab Rep. 2018; 18:70.
Article
25. Mosst JT, DeFosset A, Sivashanmugam M, Kuo T. Exploring reimbursement options for the national diabetes prevention program: lessons learned from a pilot project in Los Angeles, 2014–2018. J Public Health Manag Pract. 2020. Jan. 30. [Epub]. https://doi.org/10.1097/PHH.0000000000001136 .
Article
26. Cha SA, Lim SY, Kim KR, Lee EY, Kang B, Choi YH, Yoon KH, Ahn YB, Lee JH, Ko SH. Community-based randomized controlled trial of diabetes prevention study for high-risk individuals of type 2 diabetes: lifestyle intervention using web-based system. BMC Public Health. 2017; 17:387.
Article
27. Norris SL, Engelgau MM, Narayan KM. Effectiveness of self-management training in type 2 diabetes: a systematic review of randomized controlled trials. Diabetes Care. 2001; 24:561–87.
Article
28. Greenwood DA, Gee PM, Fatkin KJ, Peeples M. A systematic review of reviews evaluating technology-enabled diabetes self-management education and support. J Diabetes Sci Technol. 2017; 11:1015–27.
Article
29. Mierzwa S, Souidi S, Conroy T, Abusyed M, Watarai H, Allen T. On the potential, feasibility, and effectiveness of chat bots in public health research going forward. Online J Public Health Inform. 2019; 11:e4.
Article
30. Rhee SY, Han SW, Woo JT. Artificial pancreas: a concise review. J Korean Diabetes. 2017; 18:141–9.
Article
31. Voelker R. “Artificial pancreas” is approved. JAMA. 2016; 316:1957.
Article
32. DeVries JH. The artificial pancreas-ready for prime time? Lancet Diabetes Endocrinol. 2017; 5:238–9.
Article
33. Kim J, Sempionatto JR, Imani S, Hartel MC, Barfidokht A, Tang G, Campbell AS, Mercier PP, Wang J. Simultaneous monitoring of sweat and interstitial fluid using a single wearable biosensor platform. Adv Sci (Weinh). 2018; 5:1800880.
Article
34. Park J, Kim J, Kim SY, Cheong WH, Jang J, Park YG, Na K, Kim YT, Heo JH, Lee CY, Lee JH, Bien F, Park JU. Soft, smart contact lenses with integrations of wireless circuits, glucose sensors, and displays. Sci Adv. 2018; 4:eaap9841.
Article
35. Huang Z, Tan E, Lum E, Sloot P, Boehm BO, Car J. A smartphone app to improve medication adherence in patients with type 2 diabetes in Asia: feasibility randomized controlled trial. JMIR Mhealth Uhealth. 2019; 7:e14914.
Article
36. Peters-Strickland T, Pestreich L, Hatch A, Rohatagi S, Baker RA, Docherty JP, Markovtsova L, Raja P, Weiden PJ, Walling DP. Usability of a novel digital medicine system in adults with schizophrenia treated with sensor-embedded tablets of aripiprazole. Neuropsychiatr Dis Treat. 2016; 12:2587–94.
Article
37. Eiland L, McLarney M, Thangavelu T, Drincic A. App-based insulin calculators: current and future state. Curr Diab Rep. 2018; 18:123.
Article
38. Klonoff DC, Kerr D. Smart pens will improve insulin therapy. J Diabetes Sci Technol. 2018; 12:551–3.
Article
39. Shanmugam MP, Mishra DK, Madhukumar R, Ramanjulu R, Reddy SY, Rodrigues G. Fundus imaging with a mobile phone: a review of techniques. Indian J Ophthalmol. 2014; 62:960–2.
Article
40. Rajalakshmi R, Arulmalar S, Usha M, Prathiba V, Kareemuddin KS, Anjana RM, Mohan V. Validation of smartphone based retinal photography for diabetic retinopathy screening. PLoS One. 2015; 10:e0138285.
Article
41. Basatneh R, Najafi B, Armstrong DG. Health sensors, smart home devices, and the internet of medical things: an opportunity for dramatic improvement in care for the lower extremity complications of diabetes. J Diabetes Sci Technol. 2018; 12:577–86.
Article
42. Habib MA, Mohktar MS, Kamaruzzaman SB, Lim KS, Pin TM, Ibrahim F. Smartphone-based solutions for fall detection and prevention: challenges and open issues. Sensors (Basel). 2014; 14:7181–208.
Article
43. Rushakoff RJ, Sullivan MM, MacMaster HW, Shah AD, Rajkomar A, Glidden DV, Kohn MA. Association between a virtual glucose management service and glycemic control in hospitalized adult patients: an observational study. Ann Intern Med. 2017; 166:621–7.
44. Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019; 62:877–87.
Article
45. Kumar RB, Goren ND, Stark DE, Wall DP, Longhurst CA. Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. J Am Med Inform Assoc. 2016; 23:532–7.
Article
46. Castro Sweet CM, Chiguluri V, Gumpina R, Abbott P, Madero EN, Payne M, Happe L, Matanich R, Renda A, Prewitt T. Outcomes of a digital health program with human coaching for diabetes risk reduction in a medicare population. J Aging Health. 2018; 30:692–710.
Article
47. Michaelides A, Raby C, Wood M, Farr K, Toro-Ramos T. Weight loss efficacy of a novel mobile diabetes prevention program delivery platform with human coaching. BMJ Open Diabetes Res Care. 2016; 4:e000264.
Article
48. Jardine J, Fisher J, Carrick B. Apple’s ResearchKit: smart data collection for the smartphone era? J R Soc Med. 2015; 108:294–6.
Article
49. Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey ER, Friend SH, Trister AD. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data. 2016; 3:160011.
Article
50. Egger HL, Dawson G, Hashemi J, Carpenter KLH, Espinosa S, Campbell K, Brotkin S, Schaich-Borg J, Qiu Q, Tepper M, Baker JP, Bloomfield RA Jr, Sapiro G. Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study. NPJ Digit Med. 2018; 1:20.
Article
51. Yamaguchi S, Waki K, Nannya Y, Nangaku M, Kadowaki T, Ohe K. Usage patterns of gluconote, a self-management smartphone app, based on ResearchKit for patients with type 2 diabetes and prediabetes. JMIR Mhealth Uhealth. 2019; 7:e13204.
Article
52. Baca-Motes K, Edwards AM, Waalen J, Edmonds S, Mehta RR, Ariniello L, Ebner GS, Talantov D, Fastenau JM, Carter CT, Sarich TC, Felicione E, Topol EJ, Steinhubl SR. Digital recruitment and enrollment in a remote nationwide trial of screening for undiagnosed atrial fibrillation: lessons from the randomized, controlled mSToPS trial. Contemp Clin Trials Commun. 2019; 14:100318.
Article
53. Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, Carter C, Baca-Motes K, Felicione E, Sarich T, Topol EJ. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. JAMA. 2018; 320:146–55.
54. Izmailova ES, Wagner JA, Perakslis ED. Wearable devices in clinical trials: hype and hypothesis. Clin Pharmacol Ther. 2018; 104:42–52.
Article
55. Lee TT, Kesselheim AS. U.S. Food and drug administration precertification pilot program for digital health software: weighing the benefits and risks. Ann Intern Med. 2018; 168:730–2.
Article
56. U.S. Food Drug Administration. Digital Health Software Precertification (Pre-Cert) Program. 2020. Available from: https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-program(updated 2020 Sep 14).
57. Frieden TR. Evidence for health decision making: beyond randomized, controlled trials. N Engl J Med. 2017; 377:465–75.
58. Kim JA, Yoon S, Kim LY, Kim DS. Towards actualizing the value potential of Korea Health Insurance Review and Assessment (HIRA) data as a resource for health research: strengths, limitations, applications, and strategies for optimal use of HIRA data. J Korean Med Sci. 2017; 32:718–28.
Article
59. Park SY, Jeong SJ, Ustulin M, Chon S, Woo JT, Lim JE, Oh B, Rhee SY. Incidence of diabetes mellitus in male moderate alcohol drinkers: a community-based prospective cohort study. Arch Med Res. 2019; 50:315–23.
60. Keesara S, Jonas A, Schulman K. Covid-19 and health care’s digital revolution. N Engl J Med. 2020; 382:e82.
Article
61. Bradley WG, Golding SG, Herold CJ, Hricak H, Krestin GP, Lewin JS, Miller JC, Ringertz HG, Thrall JH. Globalization of P4 medicine: predictive, personalized, preemptive, and participatory: summary of the proceedings of the Eighth International Symposium of the International Society for Strategic Studies in Radiology, August 27–29, 2009. Radiology. 2011; 258:571–82.
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