Publications by authors named "Guido Freckmann"

Background: The CE-marked CareSens Air continuous glucose monitoring (CGM) system (CSAir) features a 15-day sensor lifetime, a 2-hour warm-up period and mandatory manual calibrations. During subsequent product development, the algorithm was updated to reduce the warm-up period to 30 minutes and make user-entered calibrations optional. This study compared the CSAir's performance between the manual and updated algorithms.

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This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM's diagnostic accuracy and screening protocols.

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While current systems for continuous glucose monitoring (CGM) are safe and effective, there is a high degree of variability between readings within and across CGM systems. In current CGM performance studies, device readings are compared with glucose concentrations obtained with a comparator ("reference") measurement procedure (usually capillary or venous glucose). However, glucose concentrations from capillary and venous samples can systematically differ, often by as much as 5 to 10%.

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Objective: This study analyzed the differences in continuous glucose monitoring (CGM)-derived metrics among three current-generation systems and evaluated their impact on therapeutic decision-making.

Research Design And Methods: Twenty-three participants wore the FreeStyle Libre 3, Dexcom G7, and Medtronic Simplera CGM systems for 14 days in parallel. CGM metrics were calculated for each participant and CGM system separately.

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Raman spectroscopy has been demonstrated as a viable technique for non-invasive glucose monitoring (NIGM). However, its clinical utility is limited by an extended calibration period lasting several weeks. In this study, we address this limitation by employing a pre-trained calibration model, which is individualized through a brief calibration phase consisting of 10 measurements.

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Background: In continuous glucose monitoring (CGM) system performance studies, it is common to implement specific procedures for manipulating the participants' blood glucose (BG) levels during the collection of comparator BG measurements. Recently, such a procedure was proposed by a group of experts, and this study assessed its ability to produce combinations of BG levels and rates of change (RoCs) with certain characteristics.

Methods: During three separate in-clinic sessions conducted over 15 days, capillary BG measurements were carried out every 15 minutes for 7 hours.

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Background: Clinical interpretation of continuous glucose monitoring (CGM) data for people without diabetes has not been well established. This study aimed to investigate concordance among CGM experts in recommending clinical follow-up for individuals without diabetes, based upon their independent review of CGM data.

Methods: We sent a survey out to expert clinicians ( = 18) and asked them to evaluate 20 potentially challenging Dexcom G6 Pro CGM reports (and hemoglobin A1c [HbA1c] and fasting venous blood glucose levels) from individuals without diabetes.

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Background: The performance of continuous glucose monitoring (CGM) systems is difficult to compare due to different study designs and a lack of head-to-head studies. This study evaluated the performance of FreeStyle Libre 3 (FL3), Dexcom G7 (DG7), and Medtronic Simplera (MSP) against different comparator methods and during clinically relevant glycemic scenarios.

Method: Twenty-four adult participants with type 1 diabetes mellitus wore one sensor of each CGM system in parallel for up to 15 days.

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Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems.

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Continuous glucose monitoring (CGM) systems provide frequent glucose measurements in interstitial fluid and have been used widely in ambulatory settings for diabetes management. During the coronavirus disease 2019 (COVID-19) pandemic, regulators in the U.S.

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Article Synopsis
  • An error grid is a tool that helps compare glucose levels measured by devices to see if they are correct and to identify any risks.
  • Experts created a new error grid called the DTS Error Grid that works for both blood glucose monitors (BGMs) and continuous glucose monitors (CGMs), organizing accuracy into five risk zones.
  • The results showed that the DTS Error Grid provides a clearer picture of how accurate these devices are and includes a separate matrix to evaluate how well CGMs track glucose trends over time.
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Introduction: The shortage of general practitioners (GPs) and the increasing prevalence of type 2 diabetes create significant pressure on primary healthcare services. To enable that medical services are available to all that need it, innovative solutions are needed. One of those, a Short Message Service (SMS)-supported basal insulin titration service is investigated in this study.

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Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article.

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Background: In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated.

Methods: Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days.

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Background: Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature.

Method: Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks.

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Background: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D).

Method: A study of 118 participants with non-insulin-treated T2D (HbA ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA of 7.

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Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer to the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, and reduction of certain pre-analytical errors, especially those related to specimen transportation. On the other hand, POCT also poses some limitations and risks, namely the risk of lower accuracy and reliability compared to traditional laboratory tests, quality control and connectivity issues, high dependence on operators (with varying levels of expertise or training), challenges related to patient data management, higher costs per individual test, regulatory and compliance issues such as the need for appropriate validation prior to clinical use (especially for rapid diagnostic tests; RDTs), as well as additional preanalytical sources of error that may remain undetected in this type of testing, which is usually based on whole blood samples (i.

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Background: During the last decade, Germany has seen an increased prevalence and a redistribution from undetected to diagnosed diabetes mellitus. Due to this substantial epidemiological development, the number of people with documented type 2 diabetes was 8.7 million in 2022.

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Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations.

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Background: Usability engineering analyzes the interaction between the intended users and a device. Its implementation is mandatory for manufacturers to obtain regulatory approval for the European market. The aim of this evaluation was assessing the role of usability testing in the development process.

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The assessment and characterization of trend accuracy, that is, the ability of a continuous glucose monitoring (CGM) system to correctly indicate the direction and rate of change (RoC) of glucose levels, has received comparatively little attention in the overall evaluation of CGM performance. As such, only few approaches that examine the trend accuracy have been put forward. In this article, we review existing approaches and propose the clinical trend concurrence analysis (CTCA) which is an adaptation of the conventional trend concurrence analysis.

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