[1]许 君,贾慧敏,齐硕樑,等.基于2型糖尿病的柔性智能医疗服饰及可穿戴设备的研究进展[J].服装学报,2026,11(01):36-44.
 XU Jun,JIA Huimin,QI Shuoliang,et al.Research Progress on Flexible Smart Medical Textiles and Wearable Monitoring Devices for Type 2 Diabetes[J].Journal of Clothing Research,2026,11(01):36-44.
点击复制

基于2型糖尿病的柔性智能医疗服饰及可穿戴设备的研究进展()

《服装学报》[ISSN:2096-1928/CN:32-1864/TS]

卷:
第11卷
期数:
2026年01期
页码:
36-44
栏目:
功能服装
出版日期:
2026-03-13

文章信息/Info

Title:
Research Progress on Flexible Smart Medical Textiles and Wearable Monitoring Devices for Type 2 Diabetes
作者:
许 君1; 2; 3;  贾慧敏1;  齐硕樑4;  何天虹1;  蒋 蕾1; 2
1. 天津工业大学 纺织科学与工程学院,天津 300387; 2. 天津工业大学 先进纺织复合材料教育部重点实验室电 天津 300387; 3 大津工业大学 天津市光电检测技术与系统重点实验室,天津 300387; 4. 天津工业大学 国际交流与合作处,天津 30038
Author(s):
XU Jun1; 2; 3;  JIA Huimin1;  QI Shuoliang4;  HE Tianhong1;  JIANG Lei1; 2
( 1. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China; 2. Key Laboratory of Advanced Textile Composite Materials, Tiangong University, Tianjin 300387; 3.Tianjin Key Laboratory of Optoelectronic Detection and System, Tiangong University,Tianjin 300387; 4. Office of International Exchange and Cooperation, Tiangong University, Tianjin 300387, China)
分类号:
TS 941.2; TP 212
文献标志码:
A
摘要:
智能医疗服饰与可穿戴监测设备对提升2型糖尿病治疗的精准性、预防并发症及长期预后具有重要潜力。基于2型糖尿病的生理、心理特征及其并发症的多维度监测需求,以柔性智能医疗服饰为载体,系统综述2型糖尿病在糖尿病慢性病管理中的可穿戴监测技术进展。概述2型糖尿病的发病机制与患者的长期管理需求; 分析基于生理参数(如血糖、心率、血压等)的智能服饰及可穿戴设备传感技术的研究现状、性能优势与应用前景; 阐述基于心理参数的智能劝导与情绪支持的可穿戴监测设备的发展现状; 探讨针对糖尿病足部、眼部等并发症监测需求设计的柔性智能医疗服饰及可穿戴设备; 展望面向多参数、专用型糖尿病管理的智能医疗服饰与监测设备的未来发展趋势与挑战。研究认为,柔性智能医疗服饰及可穿戴监测设备在实现2型糖尿病连续、无感、多维度监测方面具有显著优势,正逐步向临床实用化方向推进。
Abstract:
Abstract:Smart medical clothing and wearable monitoring devices hold significant potential for enhancing the precision of type 2 diabetes treatment, preventing complications, and improving long-term prognosis. Given the multi-dimensional monitoring requirements arising from the physiological and psychological characteristics of type 2 diabetes and its associated complications, this review, systematically examined the advancements in wearable monitoring technologies for chronic disease management, using flexible smart medical textiles as a platform. Firstly, the pathogenesis of type 2 diabetes and the long-term management needs of patients were elucidated. Next, the research status, performance advantages, and application prospects of sensing technologies integrated into clothing or wearable for monitoring physiological parameters such as blood glucose, heart rate, and blood pressure, were analyzed. The development status of wearable devices designed for intelligent prompting and emotional support based on psychological parameters were then described. Furthermore, we discussed flexible smart medical textiles and wearable devices specifically developed to monitor complications like diabetic foot and retinopathy. Finally, this paper discussed future development trends and challenges for multi-parameter, dedicated smart clothing and monitoring systems in diabetes management. The review concludes that flexible smart medical textiles and wearable monitoring devices offer distinct advantages in enabling continuous, unobtrusive, and multi-dimensional monitoring of type 2 diabetes and are progressively advancing toward clinical application.

参考文献/References:

[1] Miller V, Jenkins D A, Dehghan M, et al. Associations of the glycaemic index and the glycaemic load with risk of type 2 diabetes in 127 594 people from 20 countries(PURE): a prospective cohort study[J]. The Lancet Diabetes and Endocrinology, 2024, 12(5): 330-338.
[2] Kubicek J, Fiedorova K, Vilimek D, et al. Recent trends, construction, and applications of smart textiles and clothing for monitoring of health activity: a comprehensive multidisciplinary review[J]. IEEE Reviews in Biomedical Engineering, 2020, 15: 36- 60.
[3] 中华医学会糖尿病学分会. 中国糖尿病防治指南(2024版)[J]. 中华糖尿病杂志, 2025, 17(1): 16-139.
Chinese Diabetes Prevention Guide.Guideline for the prevention and treatment of diabetes mellitus in China(2024 edition)[J]. Chinese Journal of Diabetes Mellitus, 2025, 17(1): 16-139.(in Chinese)
[4] Metwally A A, Perelman D, Park H, et al. Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning[J]. Nature Biomedical Enginee-ring, 2025, 9(8): 1222-1239.
[5] Uhl, S, Choure A, Rouse B, et al. Effectiveness of continuous glucose monitoring on metrics of glycemic control in type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials[J]. The Journal of Clinical Endocrinology and Metabolism, 2024, 109(4): 1119-1131.
[6] Bayoumy K, Gaber M, Elshaffeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward[J]. Nature Reviews Cardiology, 2021, 18(8): 581-599.
[7] Gao X, Chen X, Hu H, et al. A photoacoustic patch for three-dimensional imaging of hemoglobin and core temperature[J]. Nature Communications, 2022, 13(1): 7757.
[8] Hung C L, Lin Y L, Chou C M, et al. Efficacy of aromatherapy at relieving the work-related stress of nursing staff from various hospital departments during COVID-19[J]. Healthcare, 2023, 11(2): 157.
[9] Choi J Y, Jeon S, Kim H, et al. Health-related indicators measured using earable devices: systematic review[J]. JMIR MHealth and UHealth, 2022, 10(11): e36696.
[10] Karmen C L, Reisfeld M A, Mclntyre M K, et al. The clinical value of heart rate monitoring using an apple watch[J]. Cardiology in Review, 2019, 27(2): 60- 62.
[11] Jayasekera S, Hensel E, Robinson R, et al. Feasibility of using the Hexoskin smart garment for natural environment observation of respiration topography[J]. International Journal of Environmental Research and Public Health, 2021, 18(13): 7012.
[12] Miao F, Wu D, Liu Z d, et al. Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective[J]. Chinese Medical Journal, 2023, 136(9): 1015-1025.
[13] Xie J, Wen D, Liang L, et al. Evaluating the validity of current mainstream wearable devices in fitness tracking under various physical activities: comparative study[J]. JMIR MHealth and UHealth, 2018, 6(4): 9754.
[14] 刘津池,于淼,程文杰,等.运动文胸设计研究现状及趋势[J].服装学报,2019,4(5):388-397.
Liu Jinchi, Yu Miao, Cheng Wenjie, et al. Current situations and trends of sports bra design [J]. Journal of Clothing Research, 2019,4(5): 388-397.(in Chinese)
[15] Yeung J, Catolico D, Fullmer N, et al. Evaluating the sensoria smart socks gait monitoring system for rehabilitation outcomes[J]. PM and R, 2019, 11(5): 512-521.
[16] Reyelman A M, Koelewyn K, Murphy M, et al. Continuous temperature-monitoring socks for home use in patients with diabetes: observational study[J]. Journal of Medical Internet Research, 2018, 20(12): e12460.
[17] Park C, Mishra R, Vigano D, et al. Smart offloading boot system for remote patient monitoring: toward adherence reinforcement and proper physical activity prescription for diabetic foot ulcer patients[J]. Journal of Diabetes Science and Technology, 2023, 17(1): 42-51.
[18] Zhu J X, Zhou X W, Kim H J, et al. Gelatin methacryloyl microneedle patches for minimally invasive extraction of skin interstitial fluid[J]. Small, 2020, 16(16): e1905910.
[19] Lee H, Bonfante G, Sasaki Y, et al. Porous microneedles on a paper for screening test of prediabetes [J]. Medical Devices and Sensors, 2020, 3(4): e10109.
[20] Chinnadayyala S R, Park J, Satti A T, et al. Minimally invasive and continuous glucose monitoring sensor based on non-enzymatic porous platinum black-coated gold microneedles [J]. Electrochimica Acta, 2021, 369: 137691.
[21] Wang Y, Wu Y, Lei Y. Microneedle-based glucose monitoring: a review from sampling methods to wearable biosensors [J]. Biomater Science, 2023, 11(17): 5727-5757.
[22] Ju J, Hsieh C M, Tian Y, et al. Surface enhanced raman spectroscopy based biosensor with a microneedle array for minimally invasive in vivo glucose measurements [J]. ACS Sensors, 2020, 5(6): 1777-1785.
[23] 李晓燕, 王振飞, 陈煜. 基于可穿戴微针贴片的血糖监测器研究进展[J]. 自动化与仪器仪表, 2024(7): 1- 4, 15.
Li Xiaoyan, Wang Zhenfei, Chen Yu. Research advances of blood glucose monitoring devices based on wearable microneedle patches[J]. Automation and Instrumentation, 2024(7): 1- 4, 15.(in Chinese)
[24] Cheng Y X, Gong X, Yang J, et al. A touch-actuated glucose sensor fully integrated with microneedle array and reverse iontophoresis for diabetes monitoring [J]. Biosens and Bioelectron, 2022, 203: 114026.
[25] Yang J, Gong X, Chen S, et al. Development of smartphone-controlled and microneedle-based wearable continuous glucose monitoring system for home-care diabetes management [J]. ACS Sensors, 2023, 8(3): 1241-1251
[26] Toi P T, Trung T Q, Dang T M L, et al. Highly electroca-talytic, durable, and stretchable nanohybrid fiber for on-body sweat glucose detection[J].ACS Applied Materials and Interfaces,2019,11(11):10707-10717.
[27] Sempionatto J R, Brazaca L C, García-carmona L, et al. Eyeglasses-based tear biosensing system: non-invasive detection of alcohol, vitamins and glucose[J]. Biosensors and Bioelec-tronics,2019,137:161-170.
[28] Arakawa T, Tomoto K, Nitta H, et al. A wearable cellulose acetate-coated mouthguard biosensor for in vivo salivary glucose measurement[J]. Analytical Chemistry,2020,92(18):12201-12207.
[29] 张玉娟,孙仁华.2型糖尿病患者心率变异性降低的影响因素[J].实用心电学杂志,2024,33(5):468- 474.
Zhang Yujuan, Sun Renhua. Influencing factors of decreased heart rate variability in patients with type 2 diabetes mellitus [J]. Journal of Practical Electrocardiology, 2024,33(5): 468- 474.(in Chinese)
[30] Jacobs P G, Herrero P, Facchinetti A, et al. Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls and opportunities[J]. IEEE Reviews in Biomedical Engineering, 2024, 17: 19-41.
[31] Polar.com. product_variant_data_title:POLAR_VERITY_SENSE_BLACK.[2025-03-01]. https://www.polar.com/zh-hans/products/accessories/polar-verity-sense.
[32] Garmin China. HRM-Pro Plus 心率传感器. Garmin 心率带. 运动休闲.[2025-03-01]. https://www.garmin.com.cn/products/sports-recreation/hrm-pro-plus/.
[33] Apple. Apple Watch Series[2025-03-01]. https://www.apple.com.cn/apple-watch-series-11/.
[34] Downey R J, Ferris D P,Downey R J, et al. ICanClean removes motion, muscle, eye, and line-noise artifacts from phantom EEG[J]. Sensors, 2023, 23(19): 8214.
[35] Islam M S, Shifat-e-rabbi M, Ali Dobaie A M A, et al. Preheat: Precision heart rate monitoring from intense motion artifact corrupted PPG signals using constrained RLS and wavelets[J]. Biomedical Signal Processing andControl, 2017, 38(1): 212-223.
[36] Li J, Huang J, Zheng L B, et al. Application of artificial intelligence in diabetes education and management: present status and promising prospect[J]. Frontiers in Public Health, 2020, 8: 173.
[37] Lee S Y, Hung Y W, Su P H, et al. Biosignal monitoring clothing system for the acquisition of ECG and respiratory signals[J]. IEEE Access, 2022, 10: 66083- 66097.
[38] 翟红艺,王春民,张晶,等.基于织物电极的心电监测系统[J].吉林大学学报(信息科学版),2012,30(02):185-191.
Zhai Hongyi, Wang Chunmin, Zhang Jing, et al. ECG signal monitoring system based on textile electrodes [J]. Journal of Jilin University(Information Science Edition), 2012,30(2): 185-191.(in Chinese)
[39] Salehi S, Olyaeemanesh A, Mobinizadeh M, et al. Assessment of remote patient monitoring(RPM)systems for patients with type 2 diabetes: a systematic review and meta-analysis[J]. Journal of Diabetes and Metabolic Disorders, 2020, 19(1): 115-127.
[40] Kireev D, Sel K, Ibrahim B, et al. Continuous cuffless monitoring of arterial blood pressure via graphene bioimpedance tattoos[J].Nature Nanotechnology,2022,17(8):864-870.
[41] B?hm M, de la Sierra A, MAHFOUD F, et al. Office measurement vs. ambulatory blood pressure monitoring: associations with mortality in patients with or without diabetes[J]. European Heart Journal, 2024, 45(31): 2851-2861.
[42] Kim J, Chou E F, Le J, et al. Soft wearable pressure sensors for beat-to -beat blood pressure monitoring[J].Advanced Healthcare Materials,2019,8(13):1900109.
[43] Abbasianjahromi H, Sohrab E. Developing a wearable device based on IoT to monitor the use of personal protective equipment in construction projects[J]. Iranian Journal of Science and Techno-logy, Transactions of Civil Engineering, 2022, 46(3): 2561-2573.
[44] Bari D S, Rammoo M N S, Aldosky H Y Y, et al. The five basic human senses evoke electrodermal activity[J]. Sensors, 2023, 23(19): 8181.
[45] Shu L, Yu Y, Chen W Z, et al. Wearable emotion recognition using heart rate data from a smart bracelet[J]. Sensors, 2020, 20(3): 718.
[46] Sun J Z, Yang K, Wozniak M. Research on hybrid data clustering algorithm for wireless communication intelligent bracelets[J]. Mobile Networks and Applications, 2023: 28(5): 1762-1771.
[47] Rashtian H, Torbaghan S S, Rahili S, et al. Heart rate and CGM feature representation diabetes detection from heart rate: learning joint features of heart rate and continuous glucose monitors yields better representations[J]. IEEE Access, 2021, 9: 83234-83240.
[48] Ahmed A, Aziz S, Abd-alrazaq A, et al. The effectiveness of wearable devices using artificial intelligence for blood glucose level forecasting or prediction: systematic review[J]. Journal of Medical Internet Research, 2023, 25: e40259.
[49] 陈晓君, 刘金, 梁晓玲, 等. 定量CT评估2型糖尿病并发症[J]. 中国介入影像与治疗学, 2024, 21(11): 702-707.
Chen Xiaojun, Liu Jin, Liang Xiaoling, et al. Quantitative CT for assessing complications of type 2 diabetes mellitus[J]. Chinese Journal of Interventional Imaging and Therapy, 2024, 21(11): 702-707.(in Chinese)
[50] Issabek M, Oralkhan S, Anash A, et al. AI-enhanced gait analysis insole with self-powered triboelectric sensors for flatfoot condition detection[J]. Advanced Materials Technologies, 2025, 10(6): 2401282.
[51] Kasai T, Orito E, Furukawa A, et al. Smart insole-based analysis of gait biomechanics for insoles in patients with flatfoot[J]. Gait and Posture, 2024, 114: 42- 47.
[52] Yavuz M, Ersen A, Monga A, et al. Temperature-and pressure-regulating insoles for prevention of diabetic foot ulcers[J]. The Journal of Foot and Ankle Surgery, 2020, 59(4): 685- 688.
[53] Aerts W, Scarton A, De G F, et al. Validation of plantar pressure simulations using finite and discrete element modelling in healthy and diabetic subjects[J]. Computer Methods in Biomechanics and Biomedical Engineering.2017,20(13):1442-1452.
[54] Elendu C, David J A, Udoyen A O, et al. Comprehensive review of diabetic ketoacidosis: an update[J]. Annals of Medicine and Surgery, 2023, 85(6): 2802-2807.
[55] Su Y J, Chen G R, Chen C X, et al. Self-powered respiration monitoring enabled by a triboelectric nanogenerator[J]. Advanced Materials, 2021, 33(35): 2101262.
[56] 贺军, 考希宾, 万红, 等. 穿戴式生理监测装置设计研究[J]. 针织工业, 2023(9): 69-73.
He Jun, Kao Xibin, Wan Hong, et al. Design and research of wearable physiological monitoring device[J]. Knitting Industries, 2023(9): 69-73.(in Chinese)
[57] Liu J, Xie F, Zhou Y Q, et al. A wearable health monitoring system with multi-parameters[C]//2013 6th International Conference on Biomedical Engineering and Informatics.Hangzhou: IEEE, 2014: 332-336.
[58] 汤晓军, 刘平. 一种基于ZigBee的智能可穿戴生理参数监测系统[J]. 现代电子技术, 2023, 46(8): 176-180.
Tang Xiaojun, Liu Ping. A ZigBee-based intelligent wearable physiological parameter monitoring system[J]. Modern Electronics Technique, 2023, 46(8): 176-180.(in Chinese)
[59] 胡卓瑜,陈向东,胡齐,等.基于文献的糖尿病视网膜病变模型应用分析[J].国际眼科杂志,2024,24(12):1900-1907.
Hu Zhuoyu, Chen Xiangdong, Hu Qi, et al. Bibliometrics analysis of the application of diabetic retinopathy model [J]. International Eye Science, 2024,24(12): 1900-1907.(in Chinese)
[60] Ma X, Ahadian S, Liu S, et al. Smart contact lenses for biosensing applications[J]. Advanced Intelligent Systems, 2021, 3(5): 2000263.
[61] Keum D H, Kim S K, Koo J, et al. Wireless smart contact lens for diabetic diagnosis and therapy[J]. Science Advances, 2020, 6(17): 3252.
[62] Lee G H, Jeon C, Mok J W, et al. Smart wireless near-infrared light emitting contact lens for the treatment of diabetic retinopathy[J]. Advanced Science, 2022, 9(9): 2103254.
(责任编辑:张 雪)

相似文献/References:

[1]王 军,赵梓同.医疗健康类智能可穿戴设备的情感化设计研究进展[J].服装学报,2026,11(01):53.
 WANG Jun,ZHAO Zitong.Research Progress on Emotional Design in Intelligent Wearable Devices for Healthcare[J].Journal of Clothing Research,2026,11(01):53.

更新日期/Last Update: 2026-02-28