First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users’ View
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Background: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. Methods: To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient. Results: Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase. Conclusions: The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.
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Wearables are applied in diverse fields of health.
“Apart from these 2 examples, wearables are applied in diverse fields of health, including acoustic, gastrointestinal sensors for ileus prediction [18]; UV sun exposure [19]; heat-related illness measurements [20]; electrolyte monitoring, for example, for cystic fibrosis or training management [21,22]; early warning of AF with a wearable ring [23]; generation of electrocardiograms (ECGs) [15];”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [25]
Cites
Gait speed and survival in older adults.
Studenski, S. (2011) · JAMA · doi:10.1001/jama.2010.1923 · Cited by 4,558 · cited 1× · see citing works
“In 2011 Studenski et al then showed the link between gait speed and mortality in people above 65, concluding that faster walking leads to reduced mortality and hence an increase in median survival.(6) Taking things yet another step further and considering more complex data beyond motion, one can see that a reduction o…”
BackgroundObstructive Sleep Apnea and the Risk of Sudden Cardiac Death
Gami, A. S., Olson, E. J., Shen, W. K., Wright, R. S., Ballman, K. V., Hodge, D. O., Herges, R. M., Howard, D. E., & Somers, V. K. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.04.080 · Cited by 620 · see citing works
Effects of Walking on Mortality among Nonsmoking Retired Men
Hakim, A. A., Petrovitch, H., Burchfiel, C. M., Ross, G. W., Rodríguez, B. L., White, L. R., Yano, K., Curb, J. D., & Abbott, R. D. (1998) · New England Journal of Medicine · doi:10.1056/nejm199801083380204 · Cited by 560 · see citing works
Hansen, T. W., Thijs, L., Li, Y., Boggia, J., Kikuya, M., Björklund-Bodegård, K., Richart, T., Ohkubo, T., Jeppesen, J., Torp-Pedersen, C., et al. (2010) · Hypertension · doi:10.1161/hypertensionaha.109.140798 · Cited by 480 · see citing works
Day-by-Day Variability of Blood Pressure and Heart Rate at Home as a Novel Predictor of Prognosis
Kikuya, M., Ohkubo, T., Metoki, H., Asayama, K., Hara, A., Obara, T., Inoue, R., Hoshi, H., Hashimoto, J., Totsune, K., et al. (2008) · Hypertension · doi:10.1161/hypertensionaha.107.104620 · Cited by 400 · see citing works
Noninvasive Blood Glucose Monitoring With Optical Coherence Tomography
Larin, K. V., Eledrisi, M. S., Motamedi, M., & Esenaliev, R. O. (2002) · Diabetes Care · doi:10.2337/diacare.25.12.2263 · Cited by 293 · see citing works
Johansson, J. K., Niiranen, T. J., Puukka, P. J., & Jula, A. M. (2012) · Hypertension · doi:10.1161/hypertensionaha.111.178657 · Cited by 240 · see citing works
Jilek, C., Krenn, M., Sebah, D., Obermeier, R., Braune, A., Kehl, V., Schroll, S., Montalvan, S., Riegger, G. A. J., Pfeifer, M., et al. (2010) · European Journal of Heart Failure · doi:10.1093/eurjhf/hfq183 · Cited by 185 · see citing works
Arnold, M. A., & Small, G. W. (2005) · Analytical Chemistry · doi:10.1021/ac050429e · Cited by 155 · see citing works
Caduff, A., Talary, M. S., Mueller, M. C., Dewarrat, F., Klisic, J., Donath, M. Y., Heinemann, L., & Stahel, W. A. (2009) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2009.02.001 · Cited by 109 · see citing works
Self-citationSensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review
Ding, S., & Schumacher, M. (2016) · Sensors · doi:10.3390/s16040589 · Cited by 82 · see citing works
Speed of Heart Rate Recovery in Response to Orthostatic Challenge
McCrory, C., Berkman, L. F., Nolan, H., O’Leary, N., Foley, M. M., & Kenny, R. A. (2016) · Circulation Research · doi:10.1161/circresaha.116.308577 · Cited by 65 · see citing works
Caduff, A., Mueller, M. C., Megej, A., Dewarrat, F., Suri, R. E., Klisic, J., Donath, M. Y., Zakharov, P., Schaub, D., Stahel, W. A., et al. (2011) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2011.02.034 · Cited by 63 · see citing works
Self-citationDeBoer, M. D., Cherñavvsky, D. R., Topchyan, K. A., Kovatchev, B. P., Francis, G. L., & Breton, M. D. (2017) · Pediatric Diabetes · doi:10.1111/pedi.12454 · Cited by 63 · see citing works
Kelly, G. S. (2007) · PubMed · Cited by 61 · see citing works
Facchinetti, A., Sparacino, G., & Cobelli, C. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161083 · Cited by 60 · see citing works
Cutaneous Blood Perfusion as a Perturbing Factor for Noninvasive Glucose Monitoring
Caduff, A., Talary, M. S., & Zakharov, P. (2010) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0095 · Cited by 46 · see citing works
Self-citationZanon, M., Sparacino, G., Facchinetti, A., Riz, M., Talary, M. S., Suri, R. E., Caduff, A., & Cobelli, C. (2012) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-0932-6 · Cited by 29 · see citing works
Self-citationCaduff, A., Zanon, M., Mueller, M. C., Zakharov, P., Feldman, Y. D., Feo, O. D., Donath, M. Y., Stahel, W. A., & Talary, M. S. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815579459 · Cited by 27 · see citing works
Self-citationZüger, T., Diem, P., Mougiakakou, S. G., & Stettler, C. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0271 · Cited by 13 · see citing works
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The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379 · see citing works
Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone
Shokrekhodaei, M., & Quinones, S. (2020) · Sensors · doi:10.3390/s20051251 · Cited by 221 · see citing works
Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels
Yilmaz, T., Foster, R., & Hao, Y. (2019) · Diagnostics · doi:10.3390/diagnostics9010006 · Cited by 195 · see citing works
A review of optical methods for continuous glucose monitoring
Jernelv, I. L., Mileńko, K., Fuglerud, S. S., Hjelme, D. R., Ellingsen, R., & Aksnes, A. (2018) · Applied Spectroscopy Reviews · doi:10.1080/05704928.2018.1486324 · Cited by 136 · see citing works
Delbeck, S., Vahlsing, T., Leonhardt, S. K., Steiner, G. R., & Heise, H. M. (2018) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-018-1395-x · Cited by 95 · see citing works
Review of present method of glucose from human blood and body fluids assessment
Fiedorova, K., Augustýnek, M., Kubíček, J., Kudrna, P., & Bibbo, D. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114348 · Cited by 93 · see citing works
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Mansouri, M., Hussherr, M., Strittmatter, T., Buchmann, P., Xue, S., Camenisch, G., & Fussenegger, M. (2021) · Nature Communications · doi:10.1038/s41467-021-23572-4 · Cited by 71 · see citing works
Noninvasive blood glucose monitor via multi-sensor fusion and its clinical evaluation
Feng, R., Geng, Z., Li, A., Chu, Y., Wu, C., Zhang, N., Guo, X., Zhang, J., Wang, X., & Tang, F. (2021) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2021.129445 · Cited by 27 · see citing works
Continuous noninvasive glucose monitoring; water as a relevant marker of glucose uptake in vivo
Caduff, A., Ishai, P. B., & Feldman, Y. D. (2019) · Biophysical Reviews · doi:10.1007/s12551-019-00601-7 · Cited by 25 · see citing works
Self-citationGoncharov, A., Göröcs, Z. S., Pradhan, R., Ko, B. S., Ajmal, A., Rodriguez, A. F., Baum, D., Veszpremi, M., Yang, X., Pindrys, M., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c06527 · Cited by 25 · see citing works
Acciaroli, G., Zanon, M., Facchinetti, A., Caduff, A., & Sparacino, G. (2019) · Sensors · doi:10.3390/s19173677 · Cited by 14 · see citing works
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Bertemes-Filho, P., & Morcelles, K. F. (2021) · Lecture notes in bioengineering · doi:10.1007/978-3-030-87845-0_5 · Cited by 9 · see citing works
Sabouroux, P., Buisson, C., Mounien, L., Landrier, J., Tishkova, V., & Sabouroux, P. (2021) · Progress In Electromagnetics Research M · doi:10.2528/pierm21032803 · Cited by 7 · see citing works
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Iyawa, G. E., Velusamy, V., & Palanisamy, S. (2019) · Advances in web technologies and engineering book series · doi:10.4018/978-1-5225-9246-4.ch007 · Cited by 1 · see citing works
A review of noninvasive methods applied in diabetes management and treatment
Rydosz, A. M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99887-1.00004-2
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Crocco, L., & Kosmas, P. (2019) · MDPI eBooks · doi:10.3390/books978-3-03897-677-6
- Authors
- Caduff, A., Zanon, M., Zakharov, P., Mueller, M., Talary, M., Krebs, A., Stahel, W. A., & Donath, M.
- Affiliations
- Biovotion AG, Zurich, Switzerland
- ETH Zurich (CH)
- University Hospital of Basel (CH)
- Corresponding author
- Caduff, Andreas
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2018-01-14
- Volume
- 12
- Issue
- 3
- Pages
- 562-568
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC6154235
- OpenAlex
- W2782628637
- Cited by
- 17
- References
- 20
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 3 · 2021: 4 · 2020: 1 · 2019: 5 · 2018: 2
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- continuous glucose monitoring, type 1 diabetes, user experience, patient training, patient load, self-monitoring of blood glucose
- MeSH terms
- Wearable Electronic Devices, Adult, Algorithms, Blood Glucose, Diabetes Mellitus, Type 1, Female, Humans, Male, Middle Aged, Blood Glucose Self-Monitoring, Monitoring, Ambulatory
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