Continuous glucose monitoring and closed‐loop systems

Hovorka, Roman

doi:10.1111/j.1464-5491.2005.01672.x

Cited by 379 publications

(264 citation statements)

References 83 publications

(92 reference statements)

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“…These transmission ranges represent the maximal implantation depths from the skin for the wireless sensors. Hence, applications like the insertable loop recorder (ILR) [38] and continuous glucose monitoring (CGM) systems [39] could also benefit from smaller antenna sizes by using radio interfaces within 2360-2483.5 MHz. Since these applications utilize a subcutaneous wireless sensor, i.e., a sensor implanted just under the patient's skin, the transmission range could likely be extended to IB2OFF for communication with a handheld patient assistant unit to display, analyze, and record the physiological data.…”

Section: Discussionmentioning

confidence: 99%

Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz

Chávez-Santiago

Garcia-Pardo

Fornés-Leal

et al. 2015

IEEE J. Biomed. Health Inform.

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Section: Discussionmentioning

confidence: 99%

Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz

Chávez-Santiago

Garcia-Pardo

Fornés-Leal

et al. 2015

IEEE J. Biomed. Health Inform.

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“…Several studies have documented the benefits of CGM [4][5][6][7] and charted guidelines for its clinical use 8,9 and for its future as a base for closed-loop control. 10,11 Physiology and CGM errors…”

Section: Introductionmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

179

151

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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“…The use of telecommunications and microelectronic technology can significantly contribute to this goal through the development of radio frequency (RF) wireless biomedical sensors [2]. For instance, continuous glucose monitoring (CGM) systems [3] utilizing subcutaneous RF implantable sensors can contribute greatly to the self-management of diabetes. Other in-body sensors like the wireless capsule endoscope (WCE) [4]- [6] facilitate the diagnosis of disease in the small bowel, which is difficult to visualize with conventional endoscopic techniques.…”

Section: Introductionmentioning

confidence: 99%

In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals

Floor

Chávez-Santiago

Brovoll

et al. 2015

IEEE J. Biomed. Health Inform.

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Abstract-Ultra wideband (UWB) radio technology for wireless implants has gained significant attention. UWB enables the fabrication of faster and smaller transceivers with ultra low power consumption, which may be integrated into more sophisticated implantable biomedical sensors and actuators. Nevertheless, the large path loss suffered by UWB signals propagating through inhomogeneous layers of biological tissues is a major hindering factor. For the optimal design of implantable transceivers, the accurate characterization of the UWB radio propagation in living biological tissues is indispensable. Channel measurements in phantoms and numerical simulations with digital anatomical models provide good initial insight into the expected path loss in complex propagation media like the human body, but they often fail to capture the effects of blood circulation, respiration, and temperature gradients of a living subject. Therefore, we performed UWB channel measurements within 1-6 GHz on two living porcine subjects because of the anatomical resemblance with an average human torso. We present for the first time a path loss model derived from these invivo measurements, which includes the frequency-dependent attenuation. The use of multiple on-body receiving antennas to combat the high propagation losses in implant radio channels was also investigated. Index Terms-channel model, implant, in-body, in-vivo, path lossThis work is part of the MELODY Project-Phase II (Contract no. 225885), which is financially sponsored by the Research Council of Norway.

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Continuous glucose monitoring and closed‐loop systems

Cited by 379 publications

References 83 publications

Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz

Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals

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