ArticleJournal of diabetes science and technology2018
First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators' View.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology.Journal of diabetes science and technology · 2023Observational
- Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.Sensors (Basel, Switzerland) · 2022Review
- Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection.Frontiers in bioengineering and biotechnology · 2022Review
- Smart-watch-programmed green-light-operated percutaneous control of therapeutic transgenes.Nature communications · 2021Article
- Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone.Sensors (Basel, Switzerland) · 2020Review
- Continuous noninvasive glucose monitoring; water as a relevant marker of glucose uptake in vivo.Biophysical reviews · 2019Review
- Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device.Sensors (Basel, Switzerland) · 2019Article
- Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels.Diagnostics (Basel, Switzerland) · 2019Review
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Authors and funding
7 authors.
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Abstract
backgroundExtensive 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.
methodTo test previous findings also in uncontrolled conditions, a long term at home study has been organized 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 levels noninvasively.
resultsThe algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone A
conclusionsThe multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.
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