REVIEW PAPER
MODERN TECHNOLOGIES IN MONITORING AND TREATING TYPE 2 DIABETES IN THE ELDERLY
1
Department of Health Education, Chair of Nursing Development, Faculty of Health Sciences, Medical University of Lublin, Poland
2
Chair and Department of Environmental Medicine and Epidemiology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
Submission date: 2025-05-08
Final revision date: 2025-06-16
Acceptance date: 2025-06-18
Publication date: 2025-06-24
KEYWORDS
TOPICS
ABSTRACT
Modern technologies are increasingly being used in medicine. The following review focuses on their application in the treatment of type 2 diabetes, with a special focus on the elderly. The cornerstone of therapy is regular glucose measurement, which until now has relied mainly on self-monitoring. In contrast, continuous glycemic monitoring systems are increasingly being used. App-linked devices significantly improve a patient's quality of life, but advanced technology can be difficult for older patients. Another aspect of diabetes treatment is diet. Maintaining a diet can also be facilitated by apps and artificial intelligence (AI). The advantages of technologies such as voice-based AI and mHealth include ease of accessibility, effective therapy control, and reduced healthcare costs. This is especially important in older patients with multimorbidity and a difficult disease course. However, it is crucial to educate older patients and their caregivers to implement these solutions into daily therapy. Support from doctors, families, and interface customization will make them easier to use. AI-based systems will soon be a mainstay of diabetes therapy.
REFERENCES (47)
1.
Vermeulen R, Schymanski EL, Barabási AL, Miller GW. The exposome and health: where chemistry meets biology. Science. 2020; 367(6476): 392-396. https://doi.org/10.1126/scienc....
2.
Lu HH, Zhou Y, Chen C, Liu X, Wang Y, Zhang L, et al. Meta-analysis of the effect of exercise intervention on cognitive function in elderly patients with type 2 diabetes mellitus. BMC Geriatr. 2024; 24: 770. https://doi.org/10.1186/s12877....
3.
Huang J, Yeung AM, Armstrong DG, Sharma S, Huang Y, Zhou Y, et al. Artificial intelligence for predicting and diagnosing complications of diabetes. J Diabetes Sci Technol. 2022; 17(1): 224-238. https://doi.org/10.1177/193229....
4.
Tamura Y, Omura T, Toyoshima K, Araki A. nutrition management in older adults with diabetes: a review on the importance of shifting prevention strategies from metabolic syndrome to frailty. Nutrients. 2020; 12: 3367. https://dx.doi.org/10.3390/nu1....
5.
The Lancet. Diabetes: a defining disease of the 21st century. Lancet. 2023; 401(10394): 2087. https://doi.org/10.1016/s0140-....
6.
Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, et al. Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Reports Medicine. 2023; 4(10): 101213. https://doi.org/10.1016/j.xcrm....
7.
Zeyfang A, Wernecke J, Bahrmann A. German Diabetes Association: clinical practice guidelines diabetes mellitus at an elderly age. Exp Clin Endocrinol Diabetes. 2023; 131(01/02): 24-32. https://doi.org/10.1055/a-1946....
8.
Araszkiewicz A, Bandurska-Stankiewicz E, Borys S, Budzyński A, Cyganek K, Cypryk K, et al. 2022 Guidelines on the management of patients with diabetes. A position of Diabetes Poland. Curr Top Diabetes. 2022; 2(1): 1-130. https://doi.org/10.5114/ctd/14....
9.
Jancev M, Vissers TACM, Visseren FLJ, van Bon AC, Serné EH, DeVries JH, et al. Continuous glucose monitoring in adults with type 2 diabetes: a systematic review and meta-analysis. Diabetologia. 2024; 67(5): 798-810. https://doi.org/10.1007/s00125....
10.
Lu J, Ying Z, Wang P, Fu M, Han C, Zhang M, et al. Effects of continuous glucose monitoring on glycaemic control in type 2 diabetes: a systematic review and network meta-analysis of randomized controlled trials. Diabetes Obes Metab. 2024; 26(1): 362-372. https://doi.org/10.1111/dom.15....
11.
Tan YY, Suan E, Koh GCH, Suhairi SB, Tyagi S. Effectiveness of continuous glucose monitoring in patient management of type 2 diabetes mellitus: an umbrella review of systematic reviews from 2011 to 2024. Archives of Public Health. 2024; 82: 231. https://dx.doi.org/10.1186/s13....
12.
Kłak A, Mańczak M, Owoc J, Olszewski R. Impact of continuous glucose monitoring on improving emotional well‑being among adults with type 1 diabetes mellitus: a systematic review and meta‑analysis. Pol Arch Intern Med. 2021; 131(9): 808-818. https://doi.org/10.20452/pamw.....
13.
Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Diabetes Care. 2023; 46(10): e151-e199. https://doi.org/10.2337/dci23-....
14.
Kao K, Brandner L, Virdi N. Comparison of glucose metrics between users of CGM readers and CGM-connected apps. J Diabetes Sci Technol. 2022; 16(1): 259-260. https://doi.org/10.1177/193229....
15.
Garg SK, Kipnes M, Castorino K, Bailey TS, Akturk HK, Welsh JB, et al. Accuracy and safety of Dexcom G7 continuous glucose monitoring in adults with diabetes. Diabetes Technol Ther. 2022; 24(6): 373-380. https://doi.org/10.1089/dia.20....
16.
Ng JKC, Ling J, Luk AOY, Lau ESH, Ma RCW, Li PKT, et al. Evaluation of a fourth-generation subcutaneous real-time continuous glucose monitor (CGM) in individuals with diabetes on peritoneal dialysis. Diabetes Care. 2023; 46(6): 1191-1195. https://doi.org/10.2337/dc22-2....
17.
Zheng M, Patel A, Khoja A, Luo Y, Lin W, He Q, et al. Barriers and facilitators of diabetes management by continuous glucose monitoring systems among adults with type 2 diabetes: a protocol of qualitative systematic review. BMJ Open. 2021; 11(10): e046050. https://doi.org/10.1136/bmjope....
18.
Lin PH, Sheu SC, Chen CW, Huang SC, Li BR. Wearable hydrogel patch with noninvasive, electrochemical glucose sensor for natural sweat detection. Talanta. 2022; 241: 123187. https://doi.org/10.1016/j.tala....
19.
Karim Z, Khan MJ, Hussain A, Ahmed F, Khan ZH. Multilayer patch functionalized microfibrillated cellulosic paper sensor for sweat glucose monitoring. Sci Rep. 2024; 14(1): 23434. https://doi.org/10.1038/s41598....
20.
Xu W, Lu L, He Y, Cheng L, Liu A. Long-term detection of glycemic glucose/hypoglycemia by microfluidic sweat monitoring patch. Biosensors (Basel). 2024; 14(6): 294. https://doi.org/10.3390/bios14....
21.
Litvinova O, Eitenberger M, Bilir A, Yeung AWK, Parvanov ED, MohanaSundaram A, et al. Patent analysis of digital sensors for continuous glucose monitoring. Front Public Health. 2023; 11: 1205903. https://doi.org/10.3389/fpubh.....
22.
Park W, Seo H, Kim J, Hong YM, Song H, Joo BJ, et al. In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens. Nat Commun. 2024; 15(1): 2828. https://doi.org/10.1038/s41467....
23.
Volkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Hooper L, Kiesswetter E, et al. ESPEN practical guideline: clinical nutrition and hydration in geriatrics. Clin Nutr. 2022; 41(4): 958-989. https://doi.org/10.1016/j.clnu....
24.
Junaid OA, Ojo OA, Adejumo OA, Junaid FM, Ajiboye KJ, Ojo OE, et al. Malnutrition in elderly patients with type 2 diabetes mellitus in a Nigerian tertiary hospital: a cross-sectional study. Dialogues Health. 2022; 1: 100030. https://doi.org/10.1016/j.dial....
25.
Veronese N, Facchini S, Stubbs B, Luchini C, Solmi M, Manzato E, et al. Weight loss is associated with improvements in cognitive function among overweight and obese people: a systematic review and meta-analysis. J Nutr Health Aging. 2016; 20(8): 829-836. https://doi.org/10.1016/j.neub....
26.
Yun JS, Kim K, Ahn YB, Han K, Ko SH. Holistic and personalized strategies for managing elderly type 2 diabetes patients. Diabetes Metab J. 2024; 48(4): 531-545. https://doi.org/10.4093/dmj.20....
27.
Bonikowska I, Szwamel K, Uchmanowicz I. Adherence to medication in older adults with type 2 diabetes living in Lubuskie Voivodeship in Poland: association with frailty syndrome. J Clin Med. 2022; 11(6): 1707. https://doi.org/10.3390/jcm110....
28.
World Health Organization. Healthy diet [Internet]. Geneva: WHO [access 2023 Apr 20]. Available from: https://www.who.int/health-top....
29.
Yan Y, Li Z, Lian Y, Liu P, Zhang B, Chen J, et al. Global research dynamics in the Mediterranean diet and diabetes mellitus: a bibliometric study from 2014 to 2024. Front Nutr. 2024; 11: 1480856. https://doi.org/10.3389/fnut.2....
30.
Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. 2022; 24(7): e37430. https://doi.org/10.2196/37430.
31.
Więckowska-Rusek K, Danel J, Deja G. The usefulness of the nutrition apps in self-control of diabetes mellitus – the review of literature and own experience. Pediatr Endocrinol Diabetes Metab. 2022; 28(1): 75-80. https://doi.org/10.5114/pedm.2....
32.
Sun H, Zhang K, Lan W, Gu Q, Jiang G, Yang X, et al. An AI dietitian for type 2 diabetes mellitus management based on large language and image recognition models: preclinical concept validation study. J Med Internet Res. 2023; 25: e51300. https://doi.org/10.2196/51300.
33.
Khalifa M, Albadawy M. Artificial intelligence for diabetes: enhancing prevention, diagnosis, and effective management. Comput Biol Med. 2024; 100141. https://doi.org/10.1016/j.cmpb....
34.
Sebastian MJ, Khan SKA, Pappachan JM, Jeeyavudeen MS. Diabetes and cognitive function: an evidence-based current perspective. World J Diabetes. 2023; 14(2): 92-109. https://doi.org/10.4239/wjd.v1....
35.
Donati Zeppa S, Gervasi M, Bartolacci A, Ferrini F, Patti A, Sestili P, et al. Targeting the gut microbiota for prevention and management of type 2 diabetes. Nutrients. 2024; 16(22): 3951. https://doi.org/10.3390/nu1622....
36.
Zaid NNM, Ahmad NA, Abd Rauf MF, Zainal A, Abdul Razak FH, Tengku Shahdan TST, et al. Elderly and their barriers to accepting and learning to use technology: a scoping review. Masyarakat Kebudayaan dan Politik. 2023; 36(1): 1-17. https://doi.org/10.20473/mkp.V....
37.
Huang ES, Sinclair A, Conlin PR, Cukierman-Yaffe T, Hirsch IB, Huisingh-Scheetz M, et al. The growing role of technology in the care of older adults with diabetes. Diabetes Care. 2023; 46(8): 1455-63. https://dx.doi.org/10.2337/dci....
38.
Mallik R, Kar P, Mulder H, Krook A. The future is here: an overview of technology in diabetes. Diabetologia. 2024; 67(10): 2019-26. https://dx.doi.org/10.1007/s00....
39.
Bertolazzi A, Quaglia V, Bongelli R. Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review. BMC Public Health. 2024; 24: 506. https://doi.org/10.1186/s12889....
40.
Lee S, Ory MG, Dahlke DV, Smith ML. Technology use among older adults and their caregivers: cross-sectional survey study. JMIR Aging. 2024; 7: 50759. https://doi.org/10.2196/50759.
41.
Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med. 2020; 18: 472. https://doi.org/10.1186/s12967....
42.
Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial intelligence in current diabetes management and prediction. Curr Diab Rep. 2021; 21(12). https://doi.org/10.1007/s11892....
43.
Jia W, Fisher EB. Application and prospect of artificial intelligence in diabetes care. Med Rev. 2023; 3(1): 102-104. https://doi.org/10.1515/mr-202....
44.
Wu Y, Min H, Li M, Shi Y, Ma A, Han Y, et al. Effect of Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) for type 2 diabetes self-management: protocol for a mixed-methods study. BMC Public Health. 2023; 23: 1325. https://doi.org/10.1186/s12889....
45.
Nayak A, Vakili S, Nayak K, Nikolov M, Chiu M, Sosseinheimer P, et al. Use of voice-based conversational artificial intelligence for basal insulin prescription management among patients with type 2 diabetes: a randomized clinical trial. JAMA Netw Open. 2023; 6(12): e2340232. https://doi.org/10.1001/jamane....
46.
Owsley C, Matthies DS, McGwin G, Edberg JC, Baxter SL, Zangwill LM, et al. Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI). BMJ Open. 2024; 14(1): e097449. https://doi.org/10.1136/bmjope....
47.
Hashi R, Thamer R, Hassan A, Canna K, Ahmed M, Hassan MT, et al. A Contemporary multifaceted insight into the association between diabetes mellitus and diverticular disease: an update about geriatric syndrome. Geriatrics. 2025; 10(1): 30. https://dx.doi.org/10.3390/ger....
Share
RELATED ARTICLE
| eISSN: | 2354-0265 |
| ISSN: | 2353-6942 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
