A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps
Open accessAbstract
The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes “connected” technologies—such as smartphone apps, and wearable devices and sensors—which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in “the cloud” for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.
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.
Integrity signals
Registry
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.
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?
Passive data collection makes a more useful and less burdensome diabetes app.
“Passive data collection makes a more useful and less burdensome diabetes app [1,8].”
From One drop | Mobile on iPhone and apple watch: an evaluation of hbA1c improvement associated with tracking self-care (2017) · cites this paper as [1]
In “There’s an App for That,” the paper describes manual diabetes-data logging as burdensome and says successful apps are likely to combine passive data collection with interactive incentives and personalized feedback. This supports the less-burdensome rationale, but does not establish that passive collection alone makes an app more useful. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSuch data integration could contribute to generating a digital ecosystem of diabetes data that can be used to extract new medical knowledge that cannot be discovered by relying on a single source of information.
“Such data integration could contribute to generating a digital ecosystem of diabetes data that can be used to extract new medical knowledge that cannot be discovered by relying on a single source of information [66,67].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [66]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., et al. (2011) · Science · doi:10.1126/science.1206157 · Cited by 4,836 · see citing works
The International Telecommunication Union
Jones, R. W. (2002) · Digest - IEEE Antennas and Propagation Society. International Symposium · doi:10.1109/aps.1997.631791 · Cited by 1,903 · see citing works
Non-invasive wearable electrochemical sensors: a review
Bandodkar, A. J., & Wang, J. (2014) · Trends in Biotechnology · doi:10.1016/j.tibtech.2014.04.005 · Cited by 1,247 · see citing works
Smart fabric sensors and e-textile technologies: a review
Castano, L. M., & Flatau, A. B. (2014) · Smart Materials and Structures · doi:10.1088/0964-1726/23/5/053001 · Cited by 804 · see citing works
The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?
Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011) · Personalized Medicine · doi:10.2217/pme.11.7 · Cited by 706 · see citing works
Outpatient Glycemic Control with a Bionic Pancreas in Type 1 Diabetes
Russell, S. J., El-Khatib, F. H., Sinha, M., Magyar, K. L., McKeon, K., Goergen, L. G., BALLIRO, C. A., HILLARD, M. A., Nathan, D. M., & Damiano, E. R. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1314474 · Cited by 605 · see citing works
Barrett, P. M., Komatireddy, R., Haaser, S., Topol, S., Sheard, J., Encinas, J., Fought, A. J., & Topol, E. J. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2013.10.003 · Cited by 498 · see citing works
Arnhold, M., Quade, M., & Kirch, W. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2968 · Cited by 417 · see citing works
Mobile Applications for Diabetes Self-Management: Status and Potential
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700130 · Cited by 370 · see citing works
Recent Advances in Wearable Sensors for Health Monitoring
Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2357257 · Cited by 325 · see citing works
A practical approach for recognizing eating moments with wrist-mounted inertial sensing
Thomaz, E., Essa, I. A., & Abowd, G. D. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807545 · Cited by 300 · see citing works
Image-Assisted Dietary Assessment: A Systematic Review of the Evidence
Gemming, L., Utter, J., & Mhurchú, C. N. (2014) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.09.015 · Cited by 235 · see citing works
The Promise and Peril of Mobile Health Applications for Diabetes and Endocrinology
Eng, D. S., & Lee, J. M. (2013) · Pediatric Diabetes · doi:10.1111/pedi.12034 · Cited by 215 · see citing works
Smartphone apps for calculating insulin dose: a systematic assessment
Huckvale, K., Adomaviciute, S., Prieto, J. T., Leow, M. K., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0314-7 · Cited by 200 · see citing works
Thick-film textile-based amperometric sensors and biosensors
Yang, Y.-L., Chuang, M., Lou, S.-L., & Wang, J. (2010) · The Analyst · doi:10.1039/b926339j · Cited by 167 · see citing works
Feasibility and validity of mobile phones to assess dietary intake
Sharp, D. B., & Allman-Farinelli, M. A. (2014) · Nutrition · doi:10.1016/j.nut.2014.02.020 · Cited by 156 · see citing works
A systematic review of IT for diabetes self-management: Are we there yet?
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2013.05.006 · Cited by 148 · see citing works
mHealthApps: A Repository and Database of Mobile Health Apps
Xu, W., & Liu, Y. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4026 · Cited by 129 · see citing works
Kumar, R. B., Goren, N. D., Stark, D. E., Wall, D. P., & Longhurst, C. A. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv206 · Cited by 126 · see citing works
Mobile phone health apps for diabetes management: Current evidence and future developments
Goyal, S., & Cafazzo, J. A. (2013) · QJM · doi:10.1093/qjmed/hct203 · Cited by 107 · see citing works
Emergency ultrasound in the prehospital setting: the impact of environment on examination outcomes
Snaith, B. A., Hardy, M., & Walker, A. (2011) · Emergency Medicine Journal · doi:10.1136/emj.2010.096966 · Cited by 99 · see citing works
Computer Vision-Based Carbohydrate Estimation for Type 1 Patients With Diabetes Using Smartphones
Anthimopoulos, M., Dehais, J., Shevchik, S. A., Botwey, R. H., Duke, D. L., Diem, P., & Mougiakakou, S. G. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815580159 · Cited by 92 · see citing works
Electronics for the Human Body
Rogers, J. A. (2015) · JAMA · doi:10.1001/jama.2014.17915 · Cited by 91 · see citing works
Smart Clothing in Practice: Key Design Barriers to Commercialization
Dunne, L. E. (2010) · Fashion Practice · doi:10.2752/175693810x12640026716393 · Cited by 83 · see citing works
Cybersecurity for Connected Diabetes Devices
Klonoff, D. C. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815583334 · Cited by 79 · see citing works
The Diabetes Assistant: A Smartphone-Based System for Real-Time Control of Blood Glucose
Keith-Hynes, P., Mize, B., Robert, A., & Place, J. (2014) · Electronics · doi:10.3390/electronics3040609 · Cited by 77 · see citing works
Neinstein, A., Wong, J., Look, H., Arbiter, B., Quirk, K., McCanne, S., Sun, Y., Blum, M., & Adi, S. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv104 · Cited by 66 · see citing works
Unintrusive Eating Recognition using Google Glass
Rahman, S. A., Merck, C., Huang, Y., & Kleinberg, S. (2015) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2015.259044 · Cited by 66 · see citing works
An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results
Pesl, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N. S., Johnston, D. G., Toumazou, C., & Georgiou, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2464088 · Cited by 59 · see citing works
Place, J., Robert, A., Brahim, N. B., Keith-Hynes, P., Farret, A., Pelletier, M.-J., Buckingham, B. A., Breton, M. D., Kovatchev, B. P., & Renard, É. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700603 · Cited by 59 · see citing works
An Overview of the Technology Assisted Dietary Assessment Project at Purdue University
Khanna, N., Boushey, C. J., Kerr, D. A., Okos, M. R., Ebert, D. S., & Delp, E. J. (2010) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2010.50 · Cited by 54 · see citing works
A wearable system for detecting eating activities with proximity sensors in the outer ear
Bedri, A., Verlekar, A., Thomaz, E., Avva, V., & Starner, T. E. (2015) · Proceedings · doi:10.1145/2802083.2808411 · Cited by 45 · see citing works
Dassau, E., Jovanovic, L. G., Doyle, F. J., & Zisser, H. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300632 · Cited by 39 · see citing works
Expanding garment functionality through embedded electronic technology
Dunne, L. E., Ashdown, S. P., & Smyth, B. (2005) · Journal of textile and apparel technology and management · Cited by 39 · see citing works
Bellazzi, R., Dagliati, A., Sacchi, L., & Segagni, D. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815583505 · Cited by 29 · see citing works
A generic telemedicine infrastructure for monitoring an artificial pancreas trial
Capozzi, D., & Lanzola, G. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.01.011 · Cited by 26 · see citing works
Progress of artificial pancreas devices towards clinical use
Russell, S. J. (2015) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000142 · Cited by 23 · see citing works
Monitoring Artificial Pancreas Trials Through Agent-based Technologies
Lanzola, G., Scarpellini, S., Palma, F. D., Toffanin, C., Favero, S. D., Magni, L., & Bellazzi, R. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814522120 · Cited by 23 · see citing works
Aroda, V. R., Conway, B. N., Fernandez, S. J., Matter, N. I., Maynard, J., Orchard, T. J., & Ratner, R. E. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0211 · Cited by 22 · see citing works
Doherty, S. T., & Greaves, S. P. (2015) · Journal of Diabetes Research · doi:10.1155/2015/804341 · Cited by 20 · see citing works
Downloading Diabetes Device Data: Empowering Patients to Download at Home to Achieve Better Outcomes
Beck, R. W. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0169 · Cited by 19 · see citing works
Messori, M., Cobelli, C., & Magni, L. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.09.148 · Cited by 18 · see citing works
Armstrong, D. G., Kleidermacher, D. N., Klonoff, D. C., & Slepian, M. J. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815602100 · Cited by 17 · see citing works
Advancement of the Artificial Pancreas through the Development of Interoperability Standards
Picton, P. E., Yeung, M. S., Hamming, N., Desborough, L., Dassau, E., & Cafazzo, J. A. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700431 · Cited by 15 · see citing works
Diabetes Device Interoperability for Improved Diabetes Management
Silk, A. D. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815595051 · Cited by 15 · see citing works
Walsh, J. S. A., Roberts, R., Morris, R., & Heinemann, L. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814568806 · Cited by 14 · see citing works
Exploring Blood Glucose Variation over Geographical Space
Doherty, S. T. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0159 · Cited by 9 · see citing works
2023 IEEE International Symposium on Multimedia (ISM)
(2023) · doi:10.1109/ism59092.2023 · Cited by 5 · see citing works
Technology to Inform Insulin Dose Adjustments: Its Time Is Near
Zisser, H. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0162 · Cited by 2 · see citing works
Witte, J. C. (2015) · University of Washington Press eBooks · doi:10.1515/9780295805771-014 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Wendel J, Heintzman ND. Open-source framework for inte- grating and visualizing multimodal data from geolocation, diabetes, and exercise devices. Diabetes Technol Ther. 2013; 15:A117-A118.
- Messori M, Cobelli C, Magni L. Artificial pancreas: from in- silico to in-vivo. IFAC-PapersOnLine. 2015;48:1300-1308.
Cited by
Sorted by citations
Application of artificial intelligence in wearable devices: Opportunities and challenges
Nahavandi, D., Alizadehsani, R., Khosravi, A., & Acharya, U. (2021) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2021.106541 · Cited by 305 · see citing works
Continuous glucose monitoring: A review of the technology and clinical use
Klonoff, D. C., Ahn, D., & Drincic, A. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.08.005 · Cited by 320 · see citing works
Review of Wearable Device Technology and Its Applications to the Mining Industry
Mardonova, M., & Choi, Y. (2018) · Energies · doi:10.3390/en11030547 · Cited by 202 · see citing works
The history and future of digital health in the field of behavioral medicine
Arigo, D., Jake‐Schoffman, D. E., Wolin, K. Y., Beckjord, E. B., Hekler, E. B., & Pagoto, S. (2019) · Journal of Behavioral Medicine · doi:10.1007/s10865-018-9966-z · Cited by 189 · see citing works
Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
Kebede, M. M., & Pischke, C. R. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00135 · Cited by 136 · see citing works
Rollo, M. E., Aguiar, E. J., Williams, R. L., Wynne, K., Kriss, M., Callister, R., & Collins, C. E. (2016) · Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy · doi:10.2147/dmso.s95247 · Cited by 107 · see citing works
Ahmad, A., Rasul, T., Yousaf, A., & Zaman, U. (2020) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc6030081 · Cited by 120 · see citing works
Patient-Driven Diabetes Care of the Future in the Technology Era
Ashrafzadeh, S., & Hamdy, O. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.09.005 · Cited by 111 · see citing works
Digital health technology and diabetes management: 数字化医疗技术与糖尿病管理
Cahn, A., Akirov, A., & Raz, I. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12606 · Cited by 102 · see citing works
Continuous glucose monitoring: Current use in diabetes management and possible future applications
Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818774078 · Cited by 96 · see citing works
Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review
Holmen, H., Wahl, A. K., Småstuen, M. C., & Ribu, L. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7045 · Cited by 82 · see citing works
Towards continuous monitoring in personalized healthcare through digital twins
Rivera, L. F. Z., Jiménez, M., Angara, P. P., Villegas, N. M., Tamura, G., & Müller, H. A. (2019) · Conference of the Centre for Advanced Studies on Collaborative Research · Cited by 76 · see citing works
Digital Diabetes Management: A Literature Review of Smart Insulin Pens
Heinemann, L., Schnell, O., Gehr, B., Schloot, N. C., Görgens, S. W., & Görgen, C. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820983863 · Cited by 75 · see citing works
Kebede, M. M., Liedtke, T. P., Möllers, T., & Pischke, C. R. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7135 · Cited by 69 · see citing works
From smartphone to EHR: a case report on integrating patient-generated health data
Genes, N. G., Violante, S., Cetrangol, C., Rogers, L., Schadt, E. E., & Chan, Y. Y. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0030-8 · Cited by 67 · see citing works
Heidel, A., & Hagist, C. (2020) · JMIR mhealth and uhealth · doi:10.2196/16444 · Cited by 69 · see citing works
Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
Huang, J. K., Yeung, A. M., Armstrong, D. G., Battarbee, A. N., Cuadros, J. A., Espinoza, J., Kleinberg, S., Mathioudakis, N. N., Swerdlow, M. A., & Klonoff, D. C. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221124583 · Cited by 59 · see citing works
Kim, E. H., Jenness, J. L., Miller, A. B., Halabi, R., de Zambotti, M., Bagot, K. S., Baker, F. C., & Pratap, A. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.5681 · Cited by 53 · see citing works
Bailey, T. S., Wallace, J. F., Pardo, S. A., Warchal-Windham, M. E., Harrison, B., Morin, R., & Christiansen, M. P. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816680829 · Cited by 46 · see citing works
Six Digital Health Technologies That Will Transform Diabetes
Klonoff, A. N., Lee, W.-A. (., Xu, N. Y., Nguyen, K. T., DuBord, A., & Kerr, D. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211043498 · Cited by 44 · see citing works
Trends in Graphene-Based E-Skin and Artificial Intelligence for Biomedical Applications—A Review
Mudhulu, S., Manjunatha, C., Balaji, S., Khosla, A., & Sekhar, P. K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3294297 · Cited by 40 · see citing works
Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements
Piccinini, F., Martinelli, G., & Carbonaro, A. (2020) · Sensors · doi:10.3390/s20216293 · Cited by 37 · see citing works
Diabetes and technology in 2030: a utopian or dystopian future?
Kerr, D., Axelrod, C., Hoppe, C., & Klonoff, D. C. (2018) · Diabetic Medicine · doi:10.1111/dme.13586 · Cited by 35 · see citing works
Towards gadget-free internet services: A roadmap of the Naked world
Ahmad, I., Kumar, T., Liyanage, M., Ylianttila, M., Koskela, T., Bräysy, T., Anttonen, A., Pentikinen, V., Soininen, J.-P., & Huusko, J. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.09.020 · Cited by 35 · see citing works
Big Data and diabetes: the applications of Big Data for diabetes care now and in the future
Rumbold, J., O'Kane, M., Philip, N., & Pierscionek, B. (2019) · Diabetic Medicine · doi:10.1111/dme.14044 · Cited by 33 · see citing works
Osborn, C. Y., van Ginkel, J. R., Marrero, D. G., Rodbard, D., Huddleston, B., & Dachis, J. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8781 · Cited by 33 · see citing works
Wake, D. J., He, J., Czesak, A. M., Mughal, F., & Cunningham, S. G. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816648168 · Cited by 28 · see citing works
Christiansen, M. P., Greene, C., Pardo, S. A., Warchal-Windham, M. E., Harrison, B., Morin, R., & Bailey, T. S. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817691301 · Cited by 31 · see citing works
Farooq, M. S., Riaz, S., Tehseen, R., Farooq, U., & Saleem, K. (2023) · Digital Health · doi:10.1177/20552076231179056 · Cited by 35 · see citing works
Poudyal, A., van Heerden, A. C., Hagaman, A. K., Maharjan, S. M., Byanjankar, P., Subba, P., & Kohrt, B. A. (2019) · JMIR Research Protocols · doi:10.2196/14734 · Cited by 28 · see citing works
Lewinski, A. A., Drake, C., Shaw, R. J., Jackson, G. L., Bosworth, H. B., Oakes, M. M., Gonzales, S. E., Jelesoff, N. E., & Crowley, M. J. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz039 · Cited by 28 · see citing works
Mobile Health Technology for Pediatric Symptom Monitoring
Vaughn, J., Gollarahalli, S., Shaw, R. J., Docherty, S. L., Yang, Q., Malhotra, C., Summers-Goeckerman, E., & Shah, N. (2020) · Nursing Research · doi:10.1097/nnr.0000000000000403 · Cited by 27 · see citing works
Assessing the Feasibility of a Novel mHealth App in Hematopoietic Stem Cell Transplant Patients
Racioppi, A., Dalton, T., Ramalingam, S., Romero, K. M., Ren, Y., Bohannon, L. M., Arellano, C., Jonassaint, J., Miller, H. M., Barak, I., et al. (2020) · Transplantation and Cellular Therapy · doi:10.1016/j.jtct.2020.10.017 · Cited by 25 · see citing works
Diabetes clinic reinvented: will technology change the future of diabetes care?
Al-Badri, M. R., & Hamdy, O. (2021) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018821995368 · Cited by 27 · see citing works
Utilizing passive sensing data to provide personalized psychological care in low-resource settings
Byanjankar, P., Poudyal, A., Kohrt, B. A., Maharjan, S. M., Hagaman, A. K., & van Heerden, A. C. (2020) · Gates Open Research · doi:10.12688/gatesopenres.13117.1 · Cited by 24 · see citing works
Ghozali, M. T. (2023) · Journal of Information and Communication Technology · doi:10.32890/jict2023.22.4.1 · Cited by 23 · see citing works
Ethical Considerations and Checklist for Affective Research With Wearables
Behnke, M., Saganowski, S., Kunc, D., & Kazienko, P. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3222524 · Cited by 22 · see citing works
Older Adult Preferences of Mobile Application Functionality Supporting Medication Self-Management
Russell, A. M., Smith, S., Bailey, S., Belter, L. T., Pandit, A. U., Hedlund, L. A., Bojarski, E. A., Rush, S., & Wolf, M. (2018) · Journal of health communication · doi:10.1080/10810730.2018.1554728 · Cited by 19 · see citing works
Alam, S., Zhang, M., Harris, K. K., Fletcher, L., & Reneker, J. C. (2022) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2022.0280 · Cited by 21 · see citing works
Brew‐Sam, N., & Chib, A. I. (2019) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462319000163 · Cited by 20 · see citing works
Hoogeveen, I. J., Peeks, F., de Boer, F., Lubout, C. M. A., de Koning, T. J., Boekhorst, S. T., Zandvoort, R., Burghard, R., van Spronsen, F. J., & Derks, T. G. J. (2018) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-018-0167-2 · Cited by 17 · see citing works
One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial
Osborn, C. Y., Hirsch, A., Sears, L. E., Heyman, M., Raymond, J. K., Huddleston, B., & Dachis, J. (2020) · JMIR mhealth and uhealth · doi:10.2196/16745 · Cited by 19 · see citing works
Wang, J., Chu, C.-F., Li, C., Hayes, L., & Siminerio, L. M. (2018) · JMIR mhealth and uhealth · doi:10.2196/10206 · Cited by 17 · see citing works
New modes of engagement for big data research
Winickoff, D. E., Jamal, L., & Anderson, N. (2016) · Journal of Responsible Innovation · doi:10.1080/23299460.2016.1190443 · Cited by 17 · see citing works
Matching science to reality: how to deploy a participant-driven digital brain health platform
Anda‐Duran, I. D., Hwang, P. H., Popp, Z., Low, S., Ding, H., Rahman, S., Igwe, A., Kolachalama, V. B., Lin, H., & Au, R. (2023) · Frontiers in Dementia · doi:10.3389/frdem.2023.1135451 · Cited by 14 · see citing works
Modern noninvasive methods for monitoring glucose levels in patients: a review
Dziergowska, K., Łabowska, M. B., Gąsior‐Głogowska, M., Kmiecik, B., & Detyna, J. D. (2019) · Bio-Algorithms and Med-Systems · doi:10.1515/bams-2019-0052 · Cited by 18 · see citing works
Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring
Kesavadev, J., & Mohan, V. (2023) · Journal of the Indian Institute of Science · doi:10.1007/s41745-023-00363-y · Cited by 14 · see citing works
Digital Diabetes Communication
Klonoff, D. C., & Kerr, D. E. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816660210 · Cited by 14 · see citing works
Chakrabarty, A., Zavitsanou, S., Sowrirajan, T., Doyle, F. J., & Dassau, E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815655-1.00011-9 · Cited by 11 · see citing works
Alvarez, S. D., Fellas, A., Santos, D., Sculley, D., Wynne, K., Acharya, S., Navathe, P. D., Gironès, X., & Coda, A. (2023) · JMIR Diabetes · doi:10.2196/42389 · Cited by 10 · see citing works
Examining Theoretical Frameworks and Antecedents of Health Apps and Wearables Use: A Scoping Review
Bao, H., & Lee, E. W. J. (2023) · Health Communication · doi:10.1080/10410236.2023.2283655 · Cited by 10 · see citing works
Study on the relationship between technology, data and body isomorphism—The example of smartwatches
Lu, Y., Zhou, Z., & Li, X. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100118 · Cited by 10 · see citing works
Unpacking Happiness: Lessons from Smartphone Photography Among College Students
Chen, Y. A., Mark, G. J., Ali, S., & Ma, X. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.25 · Cited by 10 · see citing works
Wearable technologies in the fashion value ecosystem: a conceptual model
Serrano, R., FORTUNATI, L., & Lacerda, D. P. (2021) · Innovation & Management Review · doi:10.1108/inmr-02-2020-0020 · Cited by 10 · see citing works
Health Lifestyle Data-Driven Applications Using Pervasive Computing
Fernández-Luque, L., Aupetit, M. J. M., Palotti, J., Singh, M., FADLELBARI, A. M., Baggag, A., Khowaja, K., & Al‐Thani, D. (2019) · Lecture notes in bioengineering · doi:10.1007/978-3-030-06109-8_10 · Cited by 10 · see citing works
A Markov model for inferring event types on diabetes patients data
de Carvalho, D. F., Kaymak, U., Gorp, P. M. E. V., & van Riel, N. A. W. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100024 · Cited by 9 · see citing works
Kordatos, G., & Stavrakis, M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08502-3 · Cited by 6 · see citing works
Measurements of motor functional outcomes in facial transplantation: A systematic review
Boczar, D., Colon, R. R., Berman, Z. P., Diep, G. K., Chaya, B. F., Trilles, J., Gelb, B. E., Ceradini, D. J., & Rodriguez, E. D. (2022) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2022.06.050 · Cited by 7 · see citing works
A Five-Step Strategy to Combine Data Sources from Multiple Wearable Sensors
Levine, J. A. (2017) · Technology and Investment · doi:10.4236/ti.2017.81003 · Cited by 6 · see citing works
Machine Learning in Wearable Healthcare Devices
Sureja, N., Mehta, K., Shah, V., & Patel, G. M. (2023) · Springer eBooks · doi:10.1007/978-981-99-0393-1_13 · Cited by 6 · see citing works
Mumford, B., Oldham, V., Lee, D., Jones, J. P., & Das, G. C. (2021) · Endocrine and Metabolic Science · doi:10.1016/j.endmts.2021.100083 · Cited by 5 · see citing works
A Narrative Review of Artificial Intelligence in Medical Diagnostics
Hirosawa, T., & Shimizu, T. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.063803 · Cited by 5 · see citing works
Tambo, E., Madjou, G., & Ngogang, J. Y. (2016) · Journal of Health & Medical Informatics · doi:10.4172/2157-7420.1000218 · Cited by 4 · see citing works
Symptom Monitoring in Children With Life-Threatening Illness
Vaughn, J., Shah, N., Docherty, S. L., Yang, Q., & Shaw, R. J. (2021) · Advances in Nursing Science · doi:10.1097/ans.0000000000000359 · Cited by 4 · see citing works
Precision Medicine and Epigenetics
Swathi, N. L., Basavan, I., Muzzamil, M., Venkatesan, A., Rama, G. S., & Arun, A. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-1922-2.ch014 · Cited by 3 · see citing works
James, S. G., Armstrong, M. E. G., Abdallah, Z. S., Emerson, H., & O’Kane, A. A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713247 · Cited by 3 · see citing works
Distributed Microbiology Testing
Peaper, D. R., Durant, T. J. S., & Campbell, S. M. (2019) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2019.05.004 · Cited by 3 · see citing works
Apps in der digitalen Prävention und Gesundheitsförderung
Albrecht, U., & von Jan, U. (2019) · Springer Reference Pflege, Therapie, Gesundheit · doi:10.1007/978-3-662-58314-2_40 · Cited by 3 · see citing works
Amed, S., Pinkney, S., Abdulhussein, F. S., Virani, A., Zachariuk, C., Tamana, S. K., Muralidharan, S., Görges, M., Barrett, B., van Rooij, T., et al. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000861 · Cited by 1 · see citing works
Apps in der digitalen Prävention und Gesundheitsförderung
Albrecht, U., & von Jan, U. (2018) · Springer Reference Pflege, Therapie, Gesundheit · doi:10.1007/978-3-662-54179-1_40-1 · Cited by 2 · see citing works
A simple method for recommending specialized specifications for diabetes monitoring
Avila, L. O., & Errecalde, M. L. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.019 · Cited by 2 · see citing works
Kordatos, G., & Stavrakis, M. (2018) · EAI International Conference on Smart Objects and Technologies for Social Good (GOODTECHS) · doi:10.1145/3284869.3284873 · Cited by 2 · see citing works
Slugocki, M., Bialonczyk, D., & Özdener-Poyraz, A. E. (2018) · Journal of Pharmacy Technology · doi:10.1177/8755122518813889 · Cited by 2 · see citing works
Physician-Led Innovation: Digital Maturity at Johns Hopkins Aramco Healthcare
Sunbul, T. (2025) · Springer eBooks · doi:10.1007/978-3-031-80704-6_7 · Cited by 1 · see citing works
Operations-Intelligence-Strategy (OIS) Process in Healthcare
Ning, X. (2019) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-2310-0.ch004 · Cited by 1 · see citing works
Apps in der digitalen Prävention und Gesundheitsförderung
Albrecht, U., & von Jan, U. (2022) · Springer Reference Pflege, Therapie, Gesundheit · doi:10.1007/978-3-662-54179-1_40-2 · Cited by 1 · see citing works
Diabetes care: is big data the future?
Begg, A. G. (2022) · Practical Diabetes · doi:10.1002/pdi.2391 · Cited by 1 · see citing works
Apps in der digitalen Prävention und Gesundheitsförderung
Albrecht, U., & von Jan, U. (2022) · Springer Reference Pflege, Therapie, Gesundheit · doi:10.1007/978-3-662-65219-0_40 · Cited by 1 · see citing works
Supply Chain Management de la santé
Boudreault, L., & Olleros, F. X. (2019) · EMS Editions eBooks · doi:10.3917/ems.bent.2019.01.0250 · Cited by 1 · see citing works
Goyal, S. (2017) · TSpace (University of Toronto) · Cited by 1 · see citing works
Athanasiou, L. S., Fotiadis, D. I., & Michalis, L. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804734-7.00011-7
Milk, Cheese, and Other Food Intake as Measured by Mobile Apps and How That Changes Consumption
Galhotra, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809762-5.00003-6
Chew, Y. C., Swiernik, M. A., McCormick, T. A., & Stevens, A. (2021) · doi:10.2196/preprints.27851
Georga, E. I., Fotiadis, D. I., & Tigas, S. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804831-3.00010-8
Raad, H. K. (2020) · doi:10.1002/9781119617570.ch2
Rafiei, A., Hajati, F., Rezaee, A., Panahi, A., & Uddin, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4176362
Morrow, D. B., Kirk, A. F., Muirhead, F., & Lennon, M. (2023) · Connected Health And Telemedicine · doi:10.20517/chatmed.2022.022
Injection Free Diabetes (Type 1) Management Using Implantable Device
-, P. P., -, R. M., -, A. O., & -, B. R. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i02.13260
Wearable sensors for biomedical application
Singh, A., Janjua, A. N., & Arya, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24790-3.00012-0
Mannoubi, C., Vachon, B., Menezes, K. V. R. S., Desroches, S., & Kairy, D. (2025) · Digital Health · doi:10.1177/20552076251377262
AI technology and smart sensors: How wearable health devices are redefining diabetes management
Manko, B. A. (2025) · Journal of Information Technology Teaching Cases · doi:10.1177/20438869251394035
Sports and Fitness Applications of Wearable Technology
Roy, R., Padhi, A., Nambiar, A. R., & Bhamburkar, V. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4287-0.ch016
Wearables in Personalized and Precision Medicine
Almukhtar, Z., & Eappen, P. (2026) · Health informatics · doi:10.1007/978-3-032-23933-4_12
Sensors in Digital Health Solutions Empowered by Artificial Intelligence Capabilities
Hetemi, D., Pinson, J., Noël, V., Shkirskiy, V., & Hoti, K. (2026) · IEEE Sensors Reviews · doi:10.1109/sr.2026.3722596
Moghadam, M. N., Amini, S., Lotfi, M., Rad, M. H., & Nourmohammadi, J. (2026) · Intern J Endocrinol Metab · doi:10.5812/ijem-169084
- Authors
- Heintzman, N. D.
- Affiliations
- Dexcom (United States) (US)
- Corresponding author
- Heintzman, Nathaniel D.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2015-12-20
- Volume
- 10
- Issue
- 1
- Pages
- 35-41
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC4738231
- OpenAlex
- W2226201345
- Cited by
- 98
- References
- 50
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3 · 2023: 10 · 2022: 9 · 2021: 8 · 2020: 9 · 2019: 16 · 2018: 11 · 2017: 15 · 2016: 5
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Dexcom
- 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 › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 1 diabetes, digital health ecosystem, blood glucose dynamics, wearable sensors, mobile application, mobile health, telemedicine, personalized diabetes management
- MeSH terms
- Smartphone, Diabetes Mellitus, Type 1, Humans, Self Care, Telemedicine, Mobile Applications
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
