Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review

Rodriguez-León, Ciro; Villalonga, Claudia; Munoz-Torres, Manuel; Ruiz, Jonatan R; Banos, Oresti

doi:10.2196/25138

Cited by 132Mobile Health and mHealth ApplicationsContext-Aware Activity Recognition SystemsDiabetes Management and Research

Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review

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Rodriguez-León, Ciro, Villalonga, Claudia, Munoz-Torres, Manuel, Ruiz, Jonatan R, Banos, Oresti·2021·JMIR mHealth and uHealth

DOI 10.2196/25138PMC PMC8212630

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Abstract

Background: Diabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences. Objective: This study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus. Methods: A systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. Results: The search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level. Conclusions: The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.

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Compared to traditional treatments, digital health interventions offer greater portability, real-time feedback, and continuous objective data recording.
“Compared to traditional treatments, DHIs offer greater portability, real-time feedback (29, 30), and continuous objective data recording (31).”
From The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis (2025) · cites this paper as [31]

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Untitled work
Alberti (1998) · doi:10.1002/(sici)1096-9136(199807)15:7

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Ghosh, P. K., Chakraborty, A., Hasan, M., Rashid, K., & Siddique, A. H. (2023) · Systems · doi:10.3390/systems11010038 · Cited by 204 · see citing works
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
Makroum, M. A., Adda, M., Bouzouane, A., & Ibrahim, H. (2022) · Sensors · doi:10.3390/s22051843 · Cited by 145 · see citing works
“Mobile and wearable technology for the monitoring of diabetes-related parameters: Systematic review [19] 2021 - Focused on the devices rather than machine learning”
Background
The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review
Jafleh, E. A., Alnaqbi, F. A., Almaeeni, H. A., Faqeeh, S., Alzaabi, M. A., & Al Zaman, K. (2024) · Cureus · doi:10.7759/cureus.68921 · Cited by 125 · see citing works
“User satisfaction with these devices is high due to the convenience of CGM without needing needle sticks [117-119].”
Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications
Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68 · see citing works
“Other studies have shown promising benefits of wearables diabetes [21], health behaviour change intervention [20, 22] and cancer [23].”
Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review
Ahmed, A., Aziz, S., Abd-alrazaq, A., Farooq, F., & Sheikh, J. (2022) · Journal of Medical Internet Research · doi:10.2196/36010 · Cited by 57 · see citing works
“We encourage researchers to perform systematic reviews to assess the efficacy of AI-based and non–AI-based WDs compared with traditional medical devices.”
BackgroundInfluential
Automating epistemology: how AI reconfigures truth, authority, and verification
Shin, D. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02560-y · Cited by 57 · see citing works
The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities
Gao, Z., Liu, W.-X., McDonough, D., Zeng, N., & Lee, J. E. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10245951 · Cited by 55 · see citing works
“In particular, a number of empirical studies and reviews have identified currently available evidence concerning the use of accelerometers for PA measurement among various clinical populations, including patients with diabetes [10], knee osteoarthritis [11], chronic health conditions [12], and overweight and obesity […”
BackgroundMethod
Efficacy of Mobile Health Applications to Improve Physical Activity and Sedentary Behavior: A Systematic Review and Meta-Analysis for Physically Inactive Individuals
Zhang, M., Wang, W., Li, M., Sheng, H., & Zhai, Y. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19084905 · Cited by 63 · see citing works
“Results could therefore be generalized to clinical populations such as diabetes mellitus or osteoarthritis patients [46,47], and more research would be required in developing countries.”
Background
Updates on Methods for Body Composition Analysis: Implications for Clinical Practice
Thomas, D. M., Crofford, I., Scudder, J., Oletti, B., Deb, A., & Heymsfield, S. (2025) · Current Obesity Reports · doi:10.1007/s13679-024-00593-w · Cited by 52 · see citing works
“Studies have cautioned that Smartwatch BIA measurements do not have the same accuracy as gold standard clinical methods, however, are accurate enough for settings where clinical methods are unavailable [112].”
Smartwatch interventions in healthcare: A systematic review of the literature
Triantafyllidis, A. K., Kondylakis, H., Katehakis, D. G., Kouroubali, A., Alexiadis, A., Segkouli, S., Votis, K., & Tzovaras, D. K. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105560 · Cited by 57 · see citing works
Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
Wang, Y.-C., Xu, X., Hajra, A., Apple, S. J., Kharawala, A., Duarte, G., Liaqat, W., Fu, Y., Li, W., Chen, Y., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030689 · Cited by 55 · see citing works
“Approximately 20% of US residents currently own a smart wearable device, and the global market of these devices is expected to reach USD 70 billion by 2025 [49].”
BackgroundMethodInfluential
Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration
Taherdoost, H. (2024) · Computers, Materials & Continua · doi:10.32604/cmc.2024.054378 · Cited by 47 · see citing works
The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review
Ahmed, A., Aziz, S., Abd-alrazaq, A. A., Farooq, F., Househ, M., & Sheikh, J. (2023) · Journal of Medical Internet Research · doi:10.2196/40259 · Cited by 45 · see citing works
“A recent systematic review looked at mobile and wearable technology for monitoring parameters related to diabetes mellitus; although the authors report some ML features of each review, it does not include in-depth extraction of features (ie, the focus is not on AI) [10].”
Diagnostic accuracy of ECG smart chest patches versus PPG smartwatches for atrial fibrillation detection: a systematic review and meta-analysis
Sibomana, O., Hakayuwa, C. M., Obianke, A., Gahire, H., Munyantore, J., & Chilala, M. M. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-04582-2 · Cited by 44 · see citing works
“Given the expanding market for wearable health devices, which is expected to reach USD 70 billion by 2025 [24], these tools are poised to play a crucial role in the future of cardiovascular diagnostics.”
Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems
Cimolin, V., Vismara, L., Ferraris, C., Amprimo, G., Pettiti, G., Lopez, R., Galli, M., Cremascoli, R., Sinagra, S., Mauro, A., et al. (2022) · Sensors · doi:10.3390/s22030824 · Cited by 45 · see citing works
“Several systems are now available for the remote monitoring of subjects with specific pathologies in the real world, including diabetes [91], osteoarthritis [92], respiratory dysfunctions [93], amyotrophic lateral sclerosis [94], and cognitive rehabilitation [95].”
Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes
Fraser, R., Walker, R. J., Campbell, J., Ekwunife, O. I., & Egede, L. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02036-9 · Cited by 40 · see citing works
“Wearable Devices: Terms related to wearable technology, including “ wearable devices, ” “ smartwatches, ” “ continuous glucose monitors, ” and “ ﬁ tness trackers ” were used 17,55,56 .”
Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review
Gagnon, M., Ouellet, S., Attisso, E. A., Supper, W., Amil, S., Rhéaume, C., Paquette, J., Chabot, C., Laferrière, M.-C., & Sasseville, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/55925 · Cited by 42 · see citing works
T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus
Rodriguez-León, C., Avilés-Pérez, M. D., Baños, O., Quesada‐Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Muñoz‐Torres, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02737-4 · Cited by 35 · see citing works
“In addition, mobile or wearable devices, such as wristbands or chest straps, are also used to measure other important variables for the disease like physical activity 5 .”
BackgroundSelf-citation
Deciphering the Intricacies of Immune System Dysfunction and its Impact on Diabetes Mellitus: Revisiting the Communication Strategies to Manage Diabetes Mellitus
Vaibhav, Nishad, S., Dongare, D., Tripathi, A. C. P., Tripathi, T., & Tripathi, P. (2024) · Health Sciences Review · doi:10.1016/j.hsr.2024.100201 · Cited by 33 · see citing works
Wearable broadband MoS2 photodetector for dual heart rate and UV detection powered by PDMS-MXene TENG
Mirsepah, A., Shooshtari, L., Mohammadpour, R., Esfandiar, A., & Irajizad, A. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.155953 · Cited by 29 · see citing works
The Role of Natural Antioxidant Products That Optimize Redox Status in the Prevention and Management of Type 2 Diabetes
Tuell, D. S., LOS, E. A., Ford, G. A., & Stone, W. L. (2023) · Antioxidants · doi:10.3390/antiox12061139 · Cited by 34 · see citing works
Personalized and predictive strategies for diabetic foot ulcer prevention and therapeutic management: potential improvements through introducing Artificial Intelligence and wearable technology
Ardelean, A., Balta, D.-F., Neamțu, C., Neamtu, A., Roșu, M., & Totolici, B. (2024) · Medicine and Pharmacy Reports · doi:10.15386/mpr-2818 · Cited by 25
Equity in Digital Health: Assessing Access and Utilization of Remote Patient Monitoring, Medical Apps, and Wearables in Underserved Communities
Adepoju, O., Dang, P., Nguyen, H., & Mertz, J. (2024) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580241271137 · Cited by 25 · see citing works
“29,30 However, work by Venn et al 31 suggests that commercial wearable devices are still largely underutilized.”
Background
Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study
Patil, V., Singhal, D., Naik, N., Hameed, B., Shah, M., Ibrahim, S., Smriti, K., Chatterjee, G., Kale, A., Sharma, A., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11237019 · Cited by 20 · see citing works
“This is a questionnaire survey, hence cannot elicit development, use, or loss to attrition of wearable healthcare devices use, which has been elucidated in multiple previous studies [23,24].”
Method
Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives
Francisco, A., Pascoal, C., Lamborne, P., Morais, H., & Gonçalves, M. A. D. A. (2025) · Cureus · doi:10.7759/cureus.77404 · Cited by 12 · see citing works
Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance
Shahsavari, Y., Baseri, Y., Hafid, A. S., Dambri, O. A., & Makrakis, D. (2026) · Journal of Medical Internet Research · doi:10.2196/80178 · Cited by 23 · see citing works
Artificial intelligence in clinical data analysis: A review of large language models, foundation models, digital twins, and allergy applications
Fuse, Y., Murphy, S. N., Ikari, H., Takahashi, A., Fuse, K., & Kawakami, E. (2025) · Allergology International · doi:10.1016/j.alit.2025.06.005 · Cited by 22 · see citing works
Big Data and AI‐Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition
Zhang, S., Lai, C., Zhao, J.-B., & Wang, J.-J. (2025) · Advancement of science · doi:10.1002/advs.202507564 · Cited by 20 · see citing works
Rethinking the Diabetes–Cardiovascular Disease Continuum: Toward Integrated Care
Caturano, A., Morciano, C., Zielińska, K., Russo, V., Perrone, M., Berra, C., & Conte, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14186678 · Cited by 20 · see citing works
“…devices are enabling continuous assessment of key parameters, such as heart rate variability, glucose trends, and physical activity, offering the potential for early alerts, which might also be useful in case more aggressive therapies are needed, and real-time behavioral feedback [104–107].”
Advances and challenges in sensor-based research in mobility, health, and place
Kim, E., Conrow, L., Röcke, C., Chaix, B., Weibel, R., & Perchoux, C. (2023) · Health & Place · doi:10.1016/j.healthplace.2023.102972 · Cited by 20 · see citing works
Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature
Seng, J. J. B., Gwee, M. F. R., Yong, M. H. A., Yu, H.-K., Thumboo, J., & Low, L. (2023) · Journal of Medical Internet Research · doi:10.2196/46988 · Cited by 16 · see citing works
“…good diabetes control and regular self-monitoring of T2DM are paramount to slowing T2DM progression and reducing one’s risk of developing T2DM-related complications, it is unsurprising that research and development in diabetes-related remote monitoring technologies has been ramping up [9].”
Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies
Jaiswal, S. J., Pawelek, J., Warshawsky, S., Quer, G., Trieu, M., Pandit, J. A., & Owens, R. L. (2024) · Current Sleep Medicine Reports · doi:10.1007/s40675-023-00272-7 · Cited by 16 · see citing works
“…sensors for the monitoring of diabetes-related parameters in studies published between 2010 and 2020 and found that out of 26 studies, acceler-ometer-based devices with PPG were the most common with the intent of measuring activity and heart rate, with no specific validation data used [47].”
Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review
Piet, A., Jablonski, L., Onwuchekwa, J. I. D., Unkel, S., Weber, C., Grzegorzek, M., Ehlers, J. P., Gaus, O., & Neumann, T. (2023) · Bioengineering · doi:10.3390/bioengineering10111321 · Cited by 13 · see citing works
“As highlighted in the systematic review by Rodriguez-León et al. (2021), heart rate sensors were employed in 27% of the studies in predicting exercise intensity, calorie consumption, and activity recognition.”
BackgroundMethodInfluential
Effectiveness of wearable technology‐based physical activity interventions for adults with type 2 diabetes mellitus: A systematic review and meta‐regression
Chua, R. E. C., Lau, Y., Ang, W. W., Boey, A. A. Y. F., & Lau, S. T. (2024) · Journal of Diabetes · doi:10.1111/1753-0407.70002 · Cited by 14 · see citing works
“26 Rodriguez-Leon et al. 23 and Wang et al. 24 did not search Embase, and all nine identified SRs did not search in technology-specific databases, which could result in unidentified studies since Embase is considered an important database in biomedical research, 27 and WT-BPAI are based in the…”
BackgroundMethodInfluential
Effectiveness of Wearable Activity Monitors on Metabolic Outcomes in Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis
Peng, P., Zhang, N., Huang, J., Jiao, X., & Shen, Y. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.02.004 · Cited by 12 · see citing works
Organic Bioelectronics: Diversity of Electronics Along with Biosciences
Moiz, S. A., Alshaikh, M. S., & Alahmadi, A. N. M. (2025) · Biosensors · doi:10.3390/bios15090587 · Cited by 13 · see citing works
The dual challenge of diabesity: pathophysiology, management, and future directions
Sindhwani, R., Bora, K., & Hazra, S. (2024) · Naunyn-Schmiedeberg's Archives of Pharmacology · doi:10.1007/s00210-024-03713-4 · Cited by 12 · see citing works
“Technology is revolutionizing lifestyle and behavioral interventions, with mobile apps, wearables, continuous glucose monitors (CGMs), and smart insulin pens providing real-time data and personalized coaching (Rodriguez-León et al. 2021).”
The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis
Xue, H., Zhang, L., Shi, Y., Zhang, H., Zhang, C., Liu, Y., Tan, W., & Liu, Y. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1630588 · Cited by 15 · see citing works
Smartphone-assisted biosensors in point-of-care diagnostics: integration, applications, and future challenges
Çelik, H., Caf, B. B., & Çebi, G. (2025) · Chemical Papers · doi:10.1007/s11696-025-04319-7 · Cited by 12 · see citing works
“This real-time tracking greatly improves glycemic control and helps prevent complications related to diabetes (Georga et al. 2014; Rodriguez-León et al. 2021; Anikwe et al. 2022).”
Wearable Devices in Veterinary Health Care.
Mitek, A. E., Jones, D., Newell, A. J., & Vitale, S. (2022) · The Veterinary clinics of North America. Small animal practice · doi:10.1016/j.cvsm.2022.05.001 · Cited by 11
Patient Adherence for Oral Combination Therapies in Diabetes Management: A Scoping Review
Zarrinkamar, M., Geran, M., & Geran, M. (2025) · Health Science Reports · doi:10.1002/hsr2.70780 · Cited by 10 · see citing works
Advanced Wearable Sensors Technologies for Healthcare Monitoring
Tamura, T. (2025) · Italian National Conference on Sensors · doi:10.3390/s25020322 · Cited by 10 · see citing works
A scoping review of wrist-worn wearables in education
Schroeder, N. L., Romine, W. L., & Kemp, S. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100154 · Cited by 12 · see citing works
A Multi-Layered Hybrid Machine Learning Algorithm (MLHA) for Type II Diabetes Classification
Jannoud, I., Masoud, M. Z., Jaradat, Y., Manaserah, A., & Zaidan, D. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.05.126 · Cited by 10
Conversational agent interventions in diabetes care: a systematic review
Shegal, M., Hu, L. T., Friesen, E. L., Minian, N., Maslej, M. M., Rodak, T., Whitmore, C., Sherifali, D., Selby, P., & Melamed, O. C. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112429 · Cited by 11 · see citing works
A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems
Talha, M., Kyrarini, M., & Ali, E. (2023) · Technologies · doi:10.3390/technologies11060163 · Cited by 11 · see citing works
“Listen to Your Immune System When It’s Calling for You”: Monitoring Autoimmune Diseases Using the iShU App
Ortet, C. P., & Costa, L. V. (2022) · Sensors · doi:10.3390/s22103834 · Cited by 11 · see citing works
Next-Day Prediction of Hypoglycaemic Episodes Based on the Use of a Mobile App for Diabetes Self-Management
Alexiadis, A., Tsanas, A., Shtika, L., Efopoulos, V., Votis, K., Tzovaras, D. K., & Triantafyllidis, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3350201 · Cited by 9 · see citing works
“Mobile health (mHealth) has recently shown benefits [10] of efficient medical care for chronic diseases anytime-anywhere [11], [12].”
Deep Learning for Diabetic Retinopathy Prediction
Rodriguez-León, C., Arévalo, W., Baños, O., & Villalonga, C. (2021) · International Work-Conference on Artificial and Natural Neural Networks · doi:10.1007/978-3-030-85030-2_44 · Cited by 9 · see citing works
“Especially chronic hyperglycemia can cause vascular damage and lead to developing diabetic retinopathy (DR) [21].”
BackgroundSelf-citation
Smart feature extraction using deep learning for early diagnosis of chronic diseases in next-generation medical decision support systems
Ahmed, Y. K., & Naji, A. N. A. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00641-y · Cited by 5 · see citing works
Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring
Hasasneh, A., Hijazi, H., Talib, M. W. A., Afadar, Y., Nassif, A. B., & Nasir, Q. (2023) · Diagnostics · doi:10.3390/diagnostics13193071 · Cited by 12 · see citing works
“Millions of people worldwide use affordable wearable devices, mainly smartwatches [10], to track their physiological and physical activities, including heart rate, sleeping periods, and stress [11].”
Background
Application of Wearable Devices in Diabetes Management
Du, Z., Zhang, F.-F., Ge, Y.-F., Liu, Y.-J., Yu, H.-H., Wang, Y., Dalan, R., & Shen, X. (2025) · Health and Metabolism · doi:10.53941/hm.2025.100007 · Cited by 6 · see citing works
Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review
Mir, R. R., Haq, N. U., Ishaq, K., Safie, N., & Dogar, A. B. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2568 · Cited by 6 · see citing works
The role of circadian rhythm disruptions in the diabetes pathogenesis
Karadeniz, A. (2025) · Endocrine Regulations · doi:10.2478/enr-2025-0024 · Cited by 5 · see citing works
“These technologies have shown efficacy in diabetes control (Rodriguez-Leon et al. 2021).”
Developments in the design and delivery of self‐management support for children and young people with diabetes: A narrative synthesis of systematic reviews
Litchfield, I. J., Barrett, T. G., Hamilton-Shield, J. P., Moore, T. H., Narendran, P., Redwood, S., Searle, A., Uday, S., Wheeler, J., & Greenfield, S. (2022) · Diabetic Medicine · doi:10.1111/dme.15035 · Cited by 6 · see citing works
Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques
Gerlein, E. A., Calderon, F., Diaz, M. L. Z., & Naemi, R. (2024) · Algorithms · doi:10.3390/a17110519 · Cited by 5 · see citing works
Identifying and mapping measures of medication safety during transfer of care in a digital era: a scoping literature review
Leon, C., Hogan, H., & Jani, Y. H. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2022-015859 · Cited by 5 · see citing works
“37 38 45 The focus of this scoping review was to identify the range of measures that are currently being used to evaluate the safety of insulin, anticoagulants and other HRMs during ToC.”
Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging
Martínez-Lozano, A., Bernal, A. B., Roy, L., Vicente-Samper, J. M., & Juan, C. G. (2026) · Biosensors · doi:10.3390/bios16010067 · Cited by 4 · see citing works
Cultural Tailoring and Implementation Science for Cardiometabolic Interventions in Asian Americans: A Systematic Review
Nguyen, A., Tadano, M., Im, B., Kim, M. T., & Nguyen, T. H. (2024) · Nursing Open · doi:10.1002/nop2.70083 · Cited by 4 · see citing works
Use of wearables among Multiple Sclerosis patients and healthcare Professionals: A scoping review
Alsulami, S., Konstantinidis, S. T., & Wharrad, H. J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105376 · Cited by 8 · see citing works
Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study
Agarwal, A., Graft, J., Schroeder, N. L., & Romine, W. L. (2021) · Signals · doi:10.3390/signals2040051 · Cited by 7 · see citing works
“For some time, researchers have investigated how to leverage wearables to facilitate a number of outcomes in varied domains, such as physical activity [1,2], disease management or monitoring [3,4], and even education [5,6].”
Background
Artificial intelligence for diabetes management – a review
Maheshwari, S., Kalia, A., Tewari, J., Tewari, A., Srivastava, A., Dantu, R., Sachan, A. K., Verma, N., & Maheshwari, A. (2025) · Journal of Diabetes Metabolic Disorders & Control · doi:10.15406/jdmdc.2025.12.00292 · Cited by 3 · see citing works
Advances in Health Monitoring Technologies: A Systematic Review of Diagnostic Precision, Patient Empowerment, and Integration Challenges
Kariasa, I. M., Koestoer, R. A., & Juanamasta, I. G. (2025) · The Open Biomarkers Journal · doi:10.2174/0118753183373795250212104110 · Cited by 2 · see citing works
Toward an Interpretable Continuous Glucose Monitoring Data Modeling
Guerrero, J., Ruiz, J. L. L., Espinilla, M., & Martínez-Cruz, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3419260 · Cited by 8 · see citing works
“In the context of glucose monitoring in diabetic patients, the literature provides a large number of studies related to this type of system [15].”
BackgroundSelf-citation
Assessment and Analysis of Wearables and Companion Mobile (Health) Applications: A Usability Evaluation Framework
Moorthy, P. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610767 · Cited by 2 · see citing works
“and calories burned but covering a wider range of uses like selfmanagement of long-term conditions, preventive care, aftercare like diabetic monitoring [18, 64], rehabilitation [13, 20, 57], fall detection [8, 67], wound healing [26], and monitoring long-term illness symptoms [5, 22, 72].”
Background
A Review on Next-Generation Wireless Health Monitoring System: Privacy and Protection
Bhattacharyya, A., Basumatary, H., & Barma, M. K. D. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.638 · Cited by 3 · see citing works
The transformative role of artificial intelligence in gestational diabetes mellitus: advancements in screening, management, and long-term outcome prediction
Taheri, Z., Tajdari, M., Ghadikolaei, Y. H., Eftekhari, H., Dodangeh, S., Jamshidi, P., Andarzbakhsh, K., & Rezazadeh, A. (2026) · Journal of Diabetes and Metabolic Disorders · doi:10.1007/s40200-025-01817-z · Cited by 2 · see citing works
“Technology has been particularly valuable in resource-constrained settings, where regular clinical consultations may be out of reach [131].”
Probabilistic Meal Detection and Estimation in Type 1 Diabetes With Extreme Shape Variability
Sontakke, M., Cameron, F. M., & Bequette, B. W. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251363234 · Cited by 1 · see citing works
“17,18 Since the current methodology focuses on the use of PFs, a summary of the available literature is presented below.”
Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions
Prioleau, T., Lu, B., & Cui, Y.-J. (2025) · arXiv.org · doi:10.48550/arxiv.2507.14077 · Cited by 2
“…data include insulin pumps used for administering insulin (the hormone needed to metabolize glucose), and activity trackers used for monitoring relevant behavioral and physiological factors, such as physical activity, sedentary behavior, heart rate, body temperature, and sleep metrics [36, 97].”
Influence of personality on wearable activity trackers use among Chinese patients over 50 with type 2 diabetes mellitus: A technology acceptance model perspective.
Li, Z., Chen, P., & Sun, X.-Y. (2025) · Experimental Gerontology · doi:10.1016/j.exger.2025.112813 · Cited by 2 · see citing works
Progress in the management of type 2 diabetes mellitus: a narrative review of telerehabilitation and wearable devices
Khan, H., Ali, K., Aslam, A., Singla, D., & Aman, I. (2025) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2448162.081 · Cited by 3 · see citing works
“There is evidence that going above these recommendations has a significant positive impact on blood sugar regulation and the prevention of cardiovascular illnesses [10].”
Wearables and Smartphones for Tracking Modifiable Risk Factors in Metabolic Health: Protocol for a Scoping Review
Brügger, V., Kowatsch, T., & Jovanova, M. (2024) · JMIR Research Protocols · doi:10.2196/59539 · Cited by 4 · see citing works
Wearables and smartphones for modifiable risk factors in metabolic health: a scoping review protocolPreprint
Brügger, V., Kowatsch, T., & Jovanova, M. (2024) · medRxiv · doi:10.1101/2024.04.15.24305819 · Cited by 2 · see citing works
“Second, alongside the proliferation of studies using wearables and smartphones, there are concerns regarding the comparability of data even when measuring the same risk factors [21].”
Interventions utilizing smartwatches in healthcare: A comprehensive literature review
Alabeidi, F., Aljawad, ‏. M., Alswaied, ‏. A., Alanazi, ‏. N., Aljabri, ‏. S., Jaafari, ‏. A., Alanazi, ‏. J., Alhomod, ‏. A., Alhamed, ‏. A., AlHizan, K., et al. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15262 · Cited by 3 · see citing works
“Previous reviews have primarily addressed wearable technology as a whole [16], [17], as well as their application in specific health conditions like cardiovascular disease [18], diabetes [19], and depression [20], or general physical activity trackers [21], [22].”
Background
Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas
Wiebe, M., MacKay, M., Krishnan, R., Tian, J., Larsson, J., Modanloo, S., McIntosh, C. J., Sztym, M., Elton-Smith, G., Rose, A., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000571 · Cited by 6 · see citing works
“In short, 18 total symptom scales (6-point scale, ‘absent’ [1] to ‘very severe’ [6]) were grouped by body system (constitutional, gastrointestinal, neurologic, respiratory, cardiovascular).”
Background
Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection
Bagheri, R., Alikhani, S., & Miri-Moghaddam, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-48322-y · Cited by 2 · see citing works
Modern technologies in dieting and diabetic management
Barma, R., & Hossen, M. S. (2023) · Global Journal of Engineering and Technology Advances · doi:10.30574/gjeta.2023.16.2.0162 · Cited by 2 · see citing works
“Patients with diabetes, medical professionals, and researchers can all profit from its progress (40).”
Background
Knowledge, practice, and challenges of diabetes foot care among patients at the University of Benin Teaching Hospital, Benin City: A cross-sectional study
Osunde, R. N., & Olorunfemi, O. (2023) · Ayu · doi:10.4103/ayu.ayu_282_22 · Cited by 2
Effects of Incontro, Alleanza, Responsabilita, Autonomia Intervention Model Combined with Orem Self-Care Model and the Use of Smart Wearable Devices on Perceived Stress and Self-Efficacy in Patients after Total Hip Arthroplasty
Cui, M., Zhao, D., Wang, H., Zhu, Y.-Q., & Wang, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5780084 · Cited by 2 · see citing works
“Our results are consistent with the previous report [15].”
Result
Retracted
Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor
Chow, D. H., Iqbal, Z., Tremblay, L., Lam, C., & Zhao, R. (2022) · Symmetry · doi:10.3390/sym14061092 · Cited by 2
The Use of Wearable Devices as a Self-Help Approach to Wellness Promotion, Overcoming Health Disparities and Reducing Medical Distrust Among African Americans
Hines, A., Jones, L. M., Pizzetta, C., Berhie, G., Cecchetti, A. A., Ikem, F., Azevedo, M., Davidson, E., White, M., & Malone, S. (2023) · Journal of Public Health Issues and Practices · doi:10.33790/jphip1100220 · Cited by 2 · see citing works
LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
Gao, Y., Gong, Y.-M., Shi, Y., & Guo, Y.-X. (2026) · IEEE International Conference on Healthcare Informatics · doi:10.1109/ichi69079.2026.00060 · Cited by 1
Industry perspective: Wearable electronic devices for remote patient monitoring
Nightingale, A., & Bird, G. (2026) · APL Electronic Devices · doi:10.1063/5.0317022 · Cited by 1 · see citing works
The Role of Information Management-Based Blood Glucose Management Pathways in Improving the Diagnostic Rate of Newly Diagnosed Diabetes Patients
Yang, L., Du, L., Jiang, L., Zhang, Y., & Fan, Q. (2026) · British Journal of Hospital Medicine · doi:10.31083/bjhm50385 · Cited by 2 · see citing works
Wearable device adoption and its role in healthcare management: A cross-sectional study
Mohammed, R., Khan, J., & Khan, S.-U.-D. (2025) · Journal of Health Science Research · doi:10.25259/jhsr_47_2025 · Cited by 1 · see citing works
Treatment of Type 2 Diabetes: A Comprehensive Review of Recent Improvements, Therapeutic Strategies, Challenges, and Future Perspectives
Islam, T., & Uddin, N. (2025) · EMJ Diabetes · doi:10.33590/emjdiabet/rkyo7834 · Cited by 1 · see citing works
Service Design Strategies to Enhance Exercise Adherence in Extended Reality Interventions for Older Adults: Systematic Review
Niu, J., Yin, Y., & Wang, S. (2026) · JMIR Aging · doi:10.2196/86595
SELF-MANAGEMENT: A NEW APPROACH FOR HEALTH MANAGEMENT IN MASS GATHERINGS
Moradiani, K., Babaee, T., Kolivand, P., Khalili, F., Heidari, Z., & Moradi, V. (2025) · Mass Gathering Medicine · doi:10.1016/j.mgmed.2025.100032 · Cited by 1 · see citing works
Digital gait measures, but not the 400-meter walk time, detect abnormal gait characteristics in people with Prediabetes
Shah, V. V., Carlson‐Kuhta, P., Mancini, M., Sowalsky, K. L., & Horak, F. B. (2024) · Gait & Posture · doi:10.1016/j.gaitpost.2024.01.030 · Cited by 2 · see citing works
DiaSense: A Multisensing and Adaptive AI-Driven Persuasive System for Type 2 Diabetes Prevention and Management
Adekanbi, J., Folaji, J., Adeleke, O. T., Okunade, I., Orji, R., & Oyebode, O. (2026) · IEEE International Conference on Intelligent Systems · doi:10.1109/is71416.2026.11727268
“Rodriguez-León et al. [26] reported that integrating wearables with mHealth applications strengthened engagement and improved clinical outcomes over time.”
Impact of smartwatch mobile application on the risk treatment of type 2 diabetes mellitus (iSMART-DM).
Wisana, I. D. G., Luthfiyah, S., Triwiyanto, T., & Ismath, M. (2026) · Primary Care Diabetes · doi:10.1016/j.pcd.2026.08.004
Interpretable non-invasive glucose monitoring: an attention-based deep learning framework for visualizing hemodynamic correlates in PPG signals
Syamsul, R., & Kim, D.-S. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1843361
Living alone with diabetes: a phenomenological study
Susianty, S., Mailani, F., Sari, I., Lagadec, D. L., & Johnston‐Devin, C. (2026) · British Journal of Community Nursing · doi:10.12968/bjcn.2025.0230
Role of Wearable Devices in the Current Era of Healthcare
Starlet, A. R., Darbari, A., Rawat, S., & Jhalani, I. (2026) · Smart Wearable Technology · doi:10.47852/bonviewswt62029375
“Wearable devices can also measure the step count and motivate the patient in reaching target step counts and hence in early mobilization and early recovery [50].”
Sugar slay: a gamified decision support ecosystem for type 1 diabetes
Rengarajan, S., Abrams, N., Tabar, A., Sundaram, H., Singh, K. P., & Chukoskie, L. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1779790
Digital Health Interventions for Type 2 Diabetes: A Narrative Review of Mobile Technologies and Their Impact on Patients' Outcomes
Gohardehi, F., Ghanami, B., Silakhor, M., & Sobhanian, P. (2026) · Health Science Reports · doi:10.1002/hsr2.72116
Wearable health devices: The role of perceived complexity and effectiveness in shaping positive Word-of-Mouth (WOM). A study on Automated Insulin Delivery (AID) systems
Schillaci, C., Mertoli, F., Benvenuto, M., Pipoli, M., Virgillito, D., & Piper, L. (2026) · MECOSAN · doi:10.3280/mesa2025-134oa22030
Feasibility of Activity Fingerprints for Glucose Prediction
Bogina, V., Yahalom-Peri, T., Kuflik, T., Kodesh, E., & Cukierman-Yaffe, T. (2026) · IUI Companion · doi:10.1145/3742414.3794724
“More recent work has begun to acknowledge the temporal nature of diabetes management, with applications such as Accu-Chek SmartGuide Predict 1 predicting glucose dips and issuing alerts [10, 14].”

Authors: Rodriguez-León, C., Villalonga, C., Munoz-Torres, M., Ruiz, J. R., & Banos, O.
Affiliations: University of Cienfuegos (CU)
Universidad de Granada (ES)
ORCID: Rodriguez-León, Ciro
Villalonga, Claudia
Munoz-Torres, Manuel
Ruiz, Jonatan R
Banos, Oresti
Venue: JMIR mHealth and uHealth
Publisher: JMIR Publications Inc.
Published: 2021-06-03
Volume: 9
Issue: 6
Pages: e25138
ISSN: 2291-5222 (ISSN-L)
DOI: 10.2196/25138
PMC: PMC8212630
OpenAlex: W3138313270
Cited by: 132
References: 56
Citations by year: 2026: 22 · 2025: 36 · 2024: 36 · 2023: 21 · 2022: 13 · 2021: 4
FWCI: 3.82
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: JMIR mhealth and uhealth (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RISalud Institutional Health Repository of Andalucía (submittedVersion, cc-by)
Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords: diabetes mellitus, wearable devices, mobile health, diabetes monitoring, blood glucose monitoring, physical activity monitoring, accelerometer, type 2 diabetes, activity recognition, machine learning, data privacy and security, diabetes complications
MeSH terms: Wearable Electronic Devices, Blood Glucose, Diabetes Mellitus, Type 2, Humans, Monitoring, Physiologic, Exercise

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