CoChat
Sign Up
Cited by 145Diabetes Management and ResearchMobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare

Machine Learning and Smart Devices for Diabetes Management: Systematic Review

Open access
Makroum, Mohammed Amine, Adda, Mehdi, Bouzouane, Abdenour, Ibrahim, Hussein·2022·Sensors
DOI 10.3390/s22051843PMC PMC8915068
Open this paper in CoChat — free accountOpen PDF

Abstract

(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as "Diabetes", "Technology", "Self-management", "Artificial Intelligence", etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s22051843

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 84 · Cited by 145

Research works printed but not linked: 69

  1. [2]Sharma R. Dave V. Sharma S. Jain P. Yadav S. Experimental models on diabetes: A comprehensive review Int. J. Adv. Pharm. Sci. 2013 4 01 08
  2. [3]IDF Diabetes Atlas Key Figures from the IDF Diabetes Atlas 9th Edition 2019 Available online: https://web.archive.org/web/20211208190021/https://diabetesatlas.org/ (accessed on 24 March 2021)
  3. [7]Marks J.B. Perioperative management of diabetes Am. Fam. Physician 2003 67 93 100 12537172
  4. [8]Deshpande A.D. Harris-Hayes M. Schootman M. Epidemiology of diabetes and diabetes-related complications Phys. Ther. 2008 88 1254 1264 10.2522/ptj.20080020 18801858 PMC3870323 · DOI · Paper page
  5. [9]Maric-Bilkan C. Sex differences in micro-and macro-vascular complications of diabetes mellitus Clin. Sci. 2017 131 833 846 10.1042/CS20160998 28424377 · DOI · Paper page
  6. [10]Ameen N. Tarhini A. Reppel A. Anand A. Customer experiences in the age of artificial intelligence Comput. Hum. Behav. 2021 114 106548 10.1016/j.chb.2020.106548 32905175 PMC7463275 · DOI · Paper page
  7. [11]Wright R. Keith L. Wearable technology: If the tech fits, wear it J. Electron. Resour. Med. Libr. 2014 11 204 216 10.1080/15424065.2014.969051 · DOI
  8. [12]Srizongkhram S. Shirahada K. Chiadamrong N. Critical factors for adoption of wearable technology for the elderly: Case study of Thailand Proceedings of the 2018 Portland International Conference on Management of Engineering and Technology (PICMET) Honolulu, HI, USA 19–23 August 2018 1 9
  9. [13]Kalyankar G.D. Poojara S.R. Dharwadkar N.V. Predictive analysis of diabetic patient data using machine learning and Hadoop Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Palladam, Tamil Nadu, India 10–11 February 2017 619 624
  10. [14]Contreras I. Vehi J. Artificial intelligence for diabetes management and decision support: Literature review J. Med. Int. Res. 2018 20 e10775 10.2196/10775 29848472 PMC6000484 · DOI · Paper page
  11. [17]Dike F.O. Mutabazi J.C. Ubani B.C. Isa A.S. Ezeude C. Musa E. Iheonye H. Ainavi I.I. Implementation and impact of mobile health (mHealth) in the management of diabetes mellitus in Africa: A systematic review protocol BMJ Open 2021 11 e047556 10.1136/bmjopen-2020-047556 34921072 PMC8685937 · DOI
  12. [18]Represas-Carrera F.J. Martínez-Ques Á.A. Clavería A. Effectiveness of mobile applications in diabetic patients’ healthy lifestyles: A review of systematic reviews Prim. Care Diabetes 2021 15 751 760 10.1016/j.pcd.2021.07.004 34275771 · DOI
  13. [20]de Araújo W.R. Martins L.E.G. Zorzal E.R. Mobile apps for the treatment of diabetes patients: A systematic review Res. Biomed. Eng. 2021 37 273 288 10.1007/s42600-021-00150-7 · DOI
  14. [21]Horstink K.A. van der Woude L.H.V. Hijmans J.M. Effects of offloading devices on static and dynamic balance in patients with diabetic peripheral neuropathy: A systematic review Rev. Endocr. Metab. Disord. 2021 22 325 335 10.1007/s11154-020-09619-9 33452959 PMC8087551 · DOI
  15. [24]Stefana E. Marciano F. Rossi D. Cocca P. Tomasoni G. Wearable Devices for Ergonomics: A Systematic Literature Review Sensors 2021 21 777 10.3390/s21030777 33498904 PMC7865302 · DOI
  16. [26]Stefana E. Marciano F. Cocca P. Alberti M. Predictive models to assess Oxygen Deficiency Hazard (ODH): A systematic review Saf. Sci. 2015 75 1 14 10.1016/j.ssci.2015.01.008 · DOI
  17. [29]Fagherazzi G. Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research Diabetes Metab. 2019 45 322 329 10.1016/j.diabet.2018.08.012 30243616 · DOI
  18. [31]Yu K.H. Beam A.L. Kohane I.S. Artificial intelligence in healthcare Nat. Biomed. Eng. 2018 2 719 731 10.1038/s41551-018-0305-z 31015651 · DOI · Paper page
  19. [32]Kavakiotis I. Tsave O. Salifoglou A. Maglaveras N. Vlahavas I. Chouvarda I. Machine learning and data mining methods in diabetes research Comput. Struct. Biotechnol. J. 2017 15 104 116 10.1016/j.csbj.2016.12.005 28138367 PMC5257026 · DOI · Paper page
  20. [34]Kumar D.P. Amgoth T. Annavarapu C.S.R. Machine learning algorithms for wireless sensor networks: A survey Inf. Fusion 2019 49 1 25 10.1016/j.inffus.2018.09.013 · DOI
  21. [36]Koteluk O. Wartecki A. Mazurek S. Kołodziejczak I. Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine J. Pers. Med. 2021 11 32 10.3390/jpm11010032 33430240 PMC7825660 · DOI
  22. [37]Han J. Kamber M. Pei J. Data mining concepts and techniques third edition Morgan Kaufmann Ser. Data Manag. Syst. 2011 5 83 124
  23. [38]Bishop C.M. Pattern Recognition and Machine Learning Springer Berlin/Heidelberg, Germany 2006
  24. [39]Shankar K. Zhang Y. Liu Y. Wu L. Chen C.H. Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification IEEE Access 2020 8 118164 118173 10.1109/ACCESS.2020.3005152 · DOI
  25. [41]Janiesch C. Zschech P. Heinrich K. Machine learning and deep learning Electron. Mark. 2021 31 685 695 10.1007/s12525-021-00475-2 · DOI · Paper page
  26. [42]Lloyd S. Mohseni M. Rebentrost P. Quantum algorithms for supervised and unsupervised machine learning arXiv 2013 1307.0411
  27. [43]Bifet A. Gavaldà R. Holmes G. Pfahringer B. Machine Learning for Data Streams: With Practical Examples in MOA MIT Press Cambridge, MA, USA 2018
  28. [44]Ogbuabor G. Ugwoke F. Clustering algorithm for a healthcare dataset using silhouette score value Int. J. Comput. Sci. Inf. Technol. 2018 10 27 37 10.5121/ijcsit.2018.10203 · DOI
  29. [45]Zhu X. Goldberg A.B. Introduction to semi-supervised learning Synth. Lect. Artif. Intell. Mach. Learn. 2009 3 1 130 10.2200/S00196ED1V01Y200906AIM006 · DOI
  30. [46]Chen Y.W. Jain L.C. Deep Learning in Healthcare Springer Berlin/Heidelberg, Germany 2020
  31. [47]Wu J. Diao Y.B. Li M.L. Fang Y.P. Ma D.C. A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis Interdiscip. Sci. Comput. Life Sci. 2009 1 151 155 10.1007/s12539-009-0016-2 20640829 · DOI
  32. [48]Choy G. Khalilzadeh O. Michalski M. Do S. Samir A.E. Pianykh O.S. Geis J.R. Pandharipande P.V. Brink J.A. Dreyer K.J. Current applications and future impact of machine learning in radiology Radiology 2018 288 318 328 10.1148/radiol.2018171820 29944078 PMC6542626 · DOI · Paper page
  33. [50]Yu C. Liu J. Nemati S. Reinforcement learning in healthcare: A survey arXiv 2019 1908.08796 10.1145/3477600 · DOI
  34. [51]Bothe M.K. Dickens L. Reichel K. Tellmann A. Ellger B. Westphal M. Faisal A.A. The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas Expert Rev. Med. Devices 2013 10 661 673 10.1586/17434440.2013.827515 23972072 · DOI
  35. [53]Murphy K.P. Machine Learning: A Probabilistic Perspective MIT Press Cambridge, MA, USA 2012
  36. [54]Hazra A. Mandal S.K. Gupta A. Study and analysis of breast cancer cell detection using Naïve Bayes, SVM and Ensemble Algorithms Int. J. Comput. Appl. 2016 145 39 45 10.5120/ijca2016910595 · DOI
  37. [57]Le Cessie S. Van Houwelingen J.C. Ridge estimators in logistic regression J. R. Stat. Soc. Ser. (Appl. Stat.) 1992 41 191 201 10.2307/2347628 · DOI
  38. [59]Dada E.G. Bassi J.S. Chiroma H. Adetunmbi A.O. Ajibuwa O.E. Machine learning for email spam filtering: Review, approaches and open research problems Heliyon 2019 5 e01802 10.1016/j.heliyon.2019.e01802 31211254 PMC6562150 · DOI
  39. [61]Wei J. He J. Chen K. Zhou Y. Tang Z. Collaborative filtering and deep learning based recommendation system for cold start items Expert Syst. Appl. 2017 69 29 39 10.1016/j.eswa.2016.09.040 · DOI
  40. [62]Ellahham S. Artificial Intelligence: The Future for Diabetes Care Am. J. Med. 2020 10.1016/j.amjmed.2020.03.033 32325045 · DOI
  41. [63]Buch V. Varughese G. Maruthappu M. Artificial intelligence in diabetes care Diabet. Med. 2018 35 495 497 10.1111/dme.13587 29368355 · DOI
  42. [64]Pérez-Gandía C. Facchinetti A. Sparacino G. Cobelli C. Gómez E. Rigla M. de Leiva A. Hernando M. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring Diabetes Technol. Ther. 2010 12 81 88 10.1089/dia.2009.0076 20082589 · DOI
  43. [65]Wang Y. Wu X. Mo X. A novel adaptive-weighted-average framework for blood glucose prediction Diabetes Technol. Ther. 2013 15 792 801 10.1089/dia.2013.0104 23883406 PMC3781119 · DOI
  44. [66]Georga E.I. Protopappas V.C. Ardigo D. Marina M. Zavaroni I. Polyzos D. Fotiadis D.I. Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression IEEE J. Biomed. Health Informatics 2012 17 71 81 10.1109/TITB.2012.2219876 23008265 · DOI
  45. [67]Hidalgo J.I. Colmenar J.M. Kronberger G. Winkler S.M. Garnica O. Lanchares J. Data based prediction of blood glucose concentrations using evolutionary methods J. Med. Syst. 2017 41 1 20 10.1007/s10916-017-0788-2 28791547 · DOI
  46. [69]San P.P. Ling S.H. Nguyen H. Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system IEEE Trans. Cybern. 2013 44 1338 1349 10.1109/TCYB.2013.2283296 24122616 · DOI
  47. [70]Jensen M.H. Christensen T.F. Tarnow L. Seto E. Dencker Johansen M. Hejlesen O.K. Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes Diabetes Technol. Ther. 2013 15 538 543 10.1089/dia.2013.0069 23631608 · DOI
  48. [71]Sudharsan B. Peeples M. Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes J. Diabetes Sci. Technol. 2014 9 86 90 10.1177/1932296814554260 25316712 PMC4495530 · DOI
  49. [72]San P.P. Ling S.H. Nguyen H.T. Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Orlando, FL, USA 16–20 August 2016 3503 3506 28269053
  50. [73]Deschamps K. Matricali G.A. Roosen P. Desloovere K. Bruyninckx H. Spaepen P. Nobels F. Tits J. Flour M. Staes F. Classification of forefoot plantar pressure distribution in persons with diabetes: A novel perspective for the mechanical management of diabetic foot? PLoS ONE 2013 8 e79924 10.1371/journal.pone.0079924 24278219 PMC3838415 · DOI
  51. [74]Migliore C.L. Vorderstrasse A. Pan W. Melkus G.D. Renal disease risk factors among risk groups comprised of African American women with type 2 diabetes: A secondary analysis Diabetes Educ. 2015 41 569 581 10.1177/0145721715593814 26202051 · DOI
  52. [76]Turksoy K. Paulino T.M.L. Zaharieva D.P. Yavelberg L. Jamnik V. Riddell M.C. Cinar A. Classification of physical activity: Information to artificial pancreas control systems in real time J. Diabetes Sci. Technol. 2015 9 1200 1207 10.1177/1932296815609369 26443291 PMC4667299 · DOI
  53. [77]Jacobs P.G. Resalat N. El Youssef J. Reddy R. Branigan D. Preiser N. Condon J. Castle J. Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate J. Diabetes Sci. Technol. 2015 9 1175 1184 10.1177/1932296815609371 26438720 PMC4667295 · DOI · Paper page
  54. [78]Anthimopoulos M. Dehais J. Shevchik S. Ransford B.H. Duke D. Diem P. Mougiakakou S. Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones J. Diabetes Sci. Technol. 2015 9 507 515 10.1177/1932296815580159 25883163 PMC4604531 · DOI
  55. [79]Allam F. Nossai Z. Gomma H. Ibrahim I. Abdelsalam M. A recurrent neural network approach for predicting glucose concentration in type-1 diabetic patients Engineering Applications of Neural Networks Springer Berlin/Heidelberg, Germany 2011 254 259
  56. [80]Nuryani N. Ling S.S. Nguyen H. Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection Ann. Biomed. Eng. 2012 40 934 945 10.1007/s10439-011-0446-7 22012087 · DOI
  57. [81]Bunescu R. Struble N. Marling C. Shubrook J. Schwartz F. Blood glucose level prediction using physiological models and support vector regression Proceedings of the 2013 12th International Conference on Machine Learning and Applications Miami, FL, USA 4–7 December 2013 Volume 1 135 140
  58. [82]Zecchin C. Facchinetti A. Sparacino G. Cobelli C. Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information Comput. Methods Programs Biomed. 2014 113 144 152 10.1016/j.cmpb.2013.09.016 24192453 · DOI
  59. [83]Zhang Y. Zhu J.m. Liang Y.b. Chen H.b. Yin S.m. Chen Z.c. Non-invasive blood glucose detection system based on conservation of energy method Physiol. Meas. 2017 38 325 10.1088/1361-6579/aa50cf 28107204 · DOI
  60. [84]Pustozerov E. Popova P. Tkachuk A. Bolotko Y. Yuldashev Z. Grineva E. Development and evaluation of a mobile personalized blood glucose prediction system for patients with gestational diabetes mellitus JMIR mHealth uHealth 2018 6 e6 10.2196/mhealth.9236 29317385 PMC5780619 · DOI
  61. [85]Chen M. Yang J. Zhou J. Hao Y. Zhang J. Youn C.H. 5G-smart diabetes: Toward personalized diabetes diagnosis with healthcare big data clouds IEEE Commun. Mag. 2018 56 16 23 10.1109/MCOM.2018.1700788 · DOI
  62. [86]Cappon G. Facchinetti A. Sparacino G. Georgiou P. Herrero P. Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes—An in silico proof-of-concept Sensors 2019 19 3168 10.3390/s19143168 31323886 PMC6679291 · DOI
  63. [87]Tsai C.W. Li C.H. Lam R.W.K. Li C.K. Ho S. Diabetes care in motion: Blood glucose estimation using wearable devices IEEE Consum. Electron. Mag. 2019 9 30 34 10.1109/MCE.2019.2941461 · DOI
  64. [88]Aljihmani L. Kerdjidj O. Zhu Y. Mehta R.K. Erraguntla M. Sasangohar F. Qaraqe K. Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor Sensors 2020 20 6897 10.3390/s20236897 33287112 PMC7729463 · DOI
  65. [90]Dave D. DeSalvo D.J. Haridas B. McKay S. Shenoy A. Koh C.J. Lawley M. Erraguntla M. Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction J. Diabetes Sci. Technol. 2020 10.1177/1932296820922622 PMC8258517 32476492 · DOI
  66. [91]Stolfi P. Valentini I. Palumbo M.C. Tieri P. Grignolio A. Castiglione F. Potential predictors of type-2 diabetes risk: Machine learning, synthetic data and wearable health devices BMC Bioinform. 2020 21 1 19 10.1186/s12859-020-03763-4 PMC7733701 33308172 · DOI
  67. [92]Rghioui A. Lloret J. Harane M. Oumnad A. A Smart Glucose Monitoring System for Diabetic Patient Electronics 2020 9 678 10.3390/electronics9040678 · DOI
  68. [93]Sosale B. Aravind S.R. Murthy H. Narayana S. Sharma U. Gowda S.G. Naveenam M. Simple, Mobile-based artificial intelligence algorithm in the detection of diabetic retinopathy (SMART) study BMJ Open Diabetes Res. Care 2020 8 10.1136/bmjdrc-2019-000892 32049632 PMC7039584 · DOI
  69. [95]Wright L.A.C. Hirsch I.B. Metrics beyond hemoglobin A1C in diabetes management: Time in range, hypoglycemia, and other parameters Diabetes Technol. Ther. 2017 19 S-16 S-26 10.1089/dia.2017.0029 28541136 PMC5444503 · DOI

Authors
Makroum, M. A., Adda, M., Bouzouane, A., & Ibrahim, H.
Affiliations
  • Université du Québec à Rimouski (CA)
  • Université du Québec à Chicoutimi (CA)
  • Institut Technologique de Maintenance Industrielle, 75 Rue de la Vérendrye, Sept-Iles, QC G4R 5B7, Canada
Corresponding author
  • Makroum, Mohammed Amine
  • Adda, Mehdi
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-02-25
Volume
22
Issue
5
Pages
1843
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22051843
PMC
PMC8915068
OpenAlex
W4214595570
Cited by
145
References
84
Citations by year
2026: 23 · 2025: 40 · 2024: 32 · 2023: 37 · 2022: 13
FWCI
4.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Natural Sciences and Engineering Research Council of Canada (06351)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
diabetes management, smart devices, wearable devices, artificial intelligence, blood glucose prediction, hypoglycemia detection, hyperglycemia detection, insulin dose adjustment, diabetes self-management, chronic disease management, quality of life
MeSH terms
Machine Learning, Self-Management, Artificial Intelligence, Diabetes Mellitus, Humans, Quality of Life

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review