These technologies also play a crucial role in the development of APS and DIY APS. However, even CGM systems which do not specifically offer that option generate data that needs to be aggregated and stored for further processing and interpretation. This is often implemented using cloud-based services [ 47 ].

In addition, the opportunity to share data with third parties like clinicians is frequently provided. Remote glucose monitoring, especially by health care professionals, may help improve therapy and patient-reported outcomes in different population groups [ 48 ], [ 49 ], [ 50 ].

Although cloud-based data storage facilitates data management and assessment, it is a meaningful privacy and security risk not only with regard to cyberattacks. Diabetic patients therefore are well advised to find out how their personal data and information are used.

Another source of risk could be the physical CGM system itself. A major issue in medical devices is the accessibility of the device by unauthorized persons.

As CGM systems rely on the wireless transfer of data, the level of encryption of transmitted data is crucial [ 47 ]. Modern CGM sensors usually allow direct data transfer to smartphones, which replace traditional CGM receivers.

Combined with data provided by other medical devices such as SMBG monitors, insulin pumps, insulin pens and also other wearable sensors not specifically developed for diabetes control, this allows the generation of large data pools [ 51 ], [ 52 ].

Other sensors are portrayed by, for example, smart clothes, smart watches or sensors for biomarkers based on tears, saliva, sweat or breath [ 53 ]. Additional data from clinical registries, electronic health records, prescription entries and quality of life and health surveys allow psychosocial and economic contextualization of CGM data [ 54 ].

Based on those data pools, software solutions could calculate prediction models which support taking proactive medical decisions.

By the implementation of pattern recognition and risk models, dashboards to identify diabetic patients with high risks of developing diabetes-related complications could be designed. However, the development of proactive medicine applications is dependent on the reliability of data sources [ 55 ], [ 56 ].

Thereby, on the one hand, patients using CGM have the ability to react quickly to their glucose levels and subsequently to avoid hypo- and hyperglycemia more reliably. Clinicians and patients can take therapy decisions on a more profound basis than enabled by SMBG.

Data sharing and cloud-based storage provide interesting new possibilities, but add a possible data security risk. However, it is important to check data validity and to interpret the large amount of data correctly.

User education in the use of CGM data is obligatory. Author contributions: GF and JM wrote the article. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission. Employment or leadership: GF is general manager of the IDT Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany , which carries out clinical studies on the evaluation of BG meters and medical devices for diabetes therapy on its own initiative and on behalf of various companies.

Competing interests: The funding organization s played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Gehr B, Holder M, Kulzer B, Lange K, Liebl A, Sahm C, et al. SPECTRUM: a training and treatment program for continuous glucose monitoring for all age groups. J Diabetes Sci Technol ;—9. Kovatchev B, Tamborlane WV, Cefalu WT, Cobelli C.

The artificial pancreas in a digital treatment ecosystem for diabetes. Diabetes Care ;—6. Zhou J, Li H, Ran X, Yang W, Li Q, Peng Y, et al. Reference values for continuous glucose monitoring in Chinese subjects. Diabetes Care ;— Matthaei S.

Assessing the value of the ambulatory glucose profile in clinical practice. J Diabetes Vasc ;— Rodbard D. Continuous glucose monitoring: a review of successes, challenges, and opportunities.

Diabetes Technol Ther ;18 Suppl. Petrie JR, Peters AL, Bergenstal RM, Holl RW, Fleming GA, Heinemann L. Improving the clinical value and utility of CGM systems: issues and recommendations: a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group.

Diabetologia ;— Basu A, Dube S, Veettil S, Slama M, Kudva YC, Peyser T, et al. Time lag of glucose from intravascular to interstitial compartment in type 1 diabetes.

J Diabetes Sci Technol ;—8. Klonoff DC, Ahn D, Drincic A. Continuous glucose monitoring: a review of the technology and clinical use. Diabetes Res Clin Pract ;— Gross TM, Bode BW, Einhorn D, Kayne DM, Reed JH, White NH, et al.

Performance evaluation of the MiniMed continuous glucose monitoring system during patient home use. Diabetes Technol Ther ;— US Food and Drug Administration. FDA expands indication for continuous glucose monitoring system, first to replace fingerstick testing for diabetes treatment decisions.

Accessed: 17 Jul Search in Google Scholar. Stellungnahme der AGDT zum Ersatz von Blutglukosemessungen durch Messungen mit Systemen zum kontinuierlichen Glukosemonitoring CGM oder Flash-Glukosemonitoring FGM.

Accessed: 08 Aug Gemeinsamer Bundesausschuss. Kontinuierliche Glukosemessung mit Real-Time-Messgeräten künftig GKV-Leistung für insulinpflichtige Diabetiker.

Accessed: 07 Aug Dungan K, Verma N. Monitoring technologies — continuous glucose monitoring, mobile technology, biomarkers of glycemic control [Updated Jan 10]. In: De Groot LJ, Chrousos G, Dungan K, Feingold KR, Grossman A, Hershman JM, et al.

Endotext [Internet]. South Dartmouth MA : MDText. com, Inc. Subramanian S, Baidal D, Skyler JS, Hirsch IB. The management of type 1 diabetes. South Dartmouth MA , Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment.

Electronics ;— Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives. Biosensors Basel ;8:E Campos-Nanez E, Breton MD. Effect of BGM Accuracy on the clinical performance of CGM: An in-silico study.

J Diabetes Sci Technol ;— Hoss U, Budiman ES. Factory-calibrated continuous glucose sensors: the science behind the technology. Diabetes Technol Ther ;S44— Kamath A, Mahalingam A, Brauker J. Analysis of time lags and other sources of error of the DexCom SEVEN continuous glucose monitor.

Wadwa RP, Laffel LM, Shah VN, Garg SK. Accuracy of a factory-calibrated, real-time continuous glucose monitoring system during 10 days of use in youth and adults with diabetes.

Freckmann G, Link M, Westhoff A, Kamecke U, Pleus S, Haug C. Prediction quality of glucose trend indicators in two continuous tissue glucose monitoring systems.

Diabetes Technol Ther ;—6. Schipfer M, Albrecht C, Ehrmann D, Haak T, Kulzer B, Hermanns N. Makes FLASH the difference between the intervention group and the treatment-as-usual group in an evaluation study of a structured education and treatment programme for flash glucose monitoring devices in people with diabetes on intensive insulin therapy: study protocol for a randomised controlled trial.

Trials ; Heinemann L, Deiss D, Siegmund T, Schlüter S, Naudorf M, von Sengbusch S, et al. Praxisempfehlung der DDG: Glukosemessung und -kontrolle bei Patienten mit Typ oder TypDiabetes. Diabetologie und Stoffwechsel ;S— Barnard KD, Ziegler R, Klonoff DC, Braune K, Petersen B, Rendschmidt T, et al.

Open source closed-loop insulin delivery systems: a clash of cultures or merging of diverse approaches? J Diabetes Sci Technol Lewis D, Leibrand S, OpenAPS Community. Real-world use of open source artificial pancreas systems.

J Diabetes Sci Technol ; Litchman ML, Lewis D, Kelly LA, Gee PM. Twitter analysis of OpenAPS DIY artificial pancreas technology use suggests improved A1C and quality of life. Omer T. BMC Med ; Lee JM, Hirschfeld E, Wedding J. A patient-designed do-it-yourself mobile technology system for diabetes: promise and challenges for a new era in medicine.

J Am Med Assoc ;—8. Farrington C. Hacking diabetes: DIY artificial pancreas systems. The Lancet Diabetes Endocrinol ; Bergenstal RM, Ahmann AJ, Bailey T, Beck RW, Bissen J, Buckingham B, et al.

Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the ambulatory glucose profile.

Pearson J, Bergenstal R. Fine-tuning control: pattern management versus supplementation. View 1: pattern management: an essential component of effective insulin management.

Diabetes Spectr ;—8. Ritholz MD, Atakov-Castillo A, Beste M, Beverly EA, Leighton A, Weinger K, et al. Psychosocial factors associated with use of continuous glucose monitoring. Diabet Med ;—5.

x Search in Google Scholar PubMed. Wong JC, Neinstein AB, Look H, Arbiter B, Chokr N, Ross C, et al. Pilot study of a novel application for data visualization in type 1 diabetes. J Diabetes Sci Technol ;—7. Kropff J, DeVries JH. Continuous glucose monitoring, future products, and update on worldwide artificial pancreas projects.

Diabetes Technol Ther ;18 Suppl 2 :S— Scheiner G. CGM retrospective data analysis. Suh S, Kim JH. Glycemic variability: how do we measure it and why is it important? Diabetes Metab J ;— Fonda SJ, Lewis DG, Vigersky RA. Minding the gaps in continuous glucose monitoring: a method to repair gaps to achieve more accurate glucometrics.

J Diabetes Sci Technol ; — Reynolds TM, Smellie WS, Twomey PJ. Glycated haemoglobin HbA1c monitoring. Br Med J ;—8. AE Search in Google Scholar PubMed PubMed Central. Nathan DM, Turgeon H, Regan S.

Relationship between glycated haemoglobin levels and mean glucose levels over time. Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE. Defining the relationship between plasma glucose and HbA 1c : analysis of glucose profiles and HbA 1c in the diabetes control and complications trial.

Diabetes Care ;—8. Kuenen JC, Borg R, Kuik DJ, Zheng H, Schoenfeld D, Diamant M, et al. Does glucose variability influence the relationship between mean plasma glucose and HbA1c levels in type 1 and type 2 diabetic patients?

Diabetes Care ;—7. Cohen RM, Holmes YR, Chenier TC, Joiner CH. Discordance between HbA1c and fructosamine: evidence for a glycosylation gap and its relation to diabetic nephropathy.

Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, et al. International consensus on use of continuous glucose monitoring. Mazze RS, Lucido D, Langer O, Hartmann K, Rodbard D.

Ambulatory glucose profile: representation of verified self-monitored blood glucose data. Kröger J, Reichel A, Siegmund T, Ziegler R. AGP-Fibel, 1st ed. Kirchheim, — Lanzola G, Losiouk E, Del Favero S, Facchinetti A, Galderisi A, Quaglini S, et al. Remote blood glucose monitoring in mHealth scenarios: a review.

Sensors Basel ; Britton KE, Britton-Colonnese JD. Privacy and security issues surrounding the protection of data generated by continuous glucose monitors. Mackillop L, Hirst JE, Bartlett KJ, Birks JS, Clifton L, Farmer AJ, et al.

Comparing the efficacy of a mobile phone-based blood glucose management system with standard clinic care in women with gestational diabetes: randomized controlled trial. JMIR Mhealth Uhealth ;6:e Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials.

BMC Health Serv Res ; Garg SK, Shah VN, Akturk HK, Beatson C, Snell-Bergeon JK. Role of mobile technology to improve diabetes care in adults with type 1 diabetes: the remote-T1D study iBGStar R in type 1 diabetes management.

Diabetes Ther ;—9. Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res ;e El-Gayar O, Timsina P, Nawar N, Eid W.

Mobile applications for diabetes self-management: status and potential. Tricoli A, Nasiri N, De S. Wearable and miniaturized sensor technologies for personalized and preventive medicine.

Adv Funct Mater ; Schatz BR. National surveys of population health: big data analytics for mobile health monitors. Big Data ;— Bellazzi R, Dagliati A, Sacchi L, Segagni D. Big data technologies: new opportunities for diabetes management.

Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. analysis, Glooko. CGDA, Continuous Glucose Data Analysis; CV, coefficient of variation; GVAP, Glycemic Variability Analyzer Program. The possible applications of GlyCulator 3.

First, investigators can use it to process data from an increasing number of clinical trials using CGM 5 and should raw files be stored cross-reference those data between trials. Secondly, the data-sharing option might promote nationwide or international analyses, which could be a critical and invaluable asset for public health policy and future guidelines development.

Finally, an application programming interface could be set up on request to enable high-throughput automated analyses for independent research networks. Overall, we believe that GlyCulator 3. Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. conceptualized the manuscript. contributed to data interpretation and wrote and edited the manuscript. and A. performed statistical analyses and wrote and edited the manuscript. collected the data used in this manuscript. contributed to data interpretation and reviewed and edited the manuscript.

is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Prior Presentation. Parts of this study were presented at the 47th Annual Conference of the International Society for Pediatric and Adolescent Diabetes, virtual, 13—15 October Sign In or Create an Account.

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Article history Received:. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Table 1 Functionality comparison of popular free CGM analysis software.

View Large. and S.

and boxplotCGM. fn to display them e. In addition, the function plotTseries. fn can be used to display the glucose levels in a time series e. When the main code in CGManalyzer is run, summaryStatistics. csv will be generated automatically to hold the summary statistics and a PDF file timeSeriesPlot.

pdf will be generated to show the glucose time series for each sensor. In Panels C — F , dI, dII, dPRE and H denote type I, type II, pre-diabetes and healthy people. fn calculates MSE. The calculation is fast because the function calls a C program i. org to obtain the major results.

The calculated MSE can be displayed by individuals Fig. When the main code is run, a PDF file MSEplot. pdf is automatically generated to show the calculated MSE by individuals and by groups. When MSE is shown by groups, the error bar can be chosen to represent standard error or SD for each group at each scale.

There are four major groups of subjects related to diabetes: type I diabetes, type II diabetes, pre-diabetes and healthy individuals. Researchers and doctors are interested in the pairwise comparison between any pair of these groups either in glucose levels or MSE. The key statistics for each comparison include mean difference, confidence interval, SSMD, P -value of t -test.

fn can calculate those statistics in addition to calculating mean, SD and number of subjects in each group. The calculated results can be displayed using a forest plot. SSMD is the mean divided by the SD of a difference between two groups. Thus, SSMD measures effective size for group comparison effectively Zhang, Based on SSMD, here we propose a new plot in which the x-axis is similar to that in a forest plot but the y-axis is SSMD.

Because the shape of this plot looks like an antenna, it is termed an antenna plot. The function antennaPlot. fn can generate antenna plots for glucose levels Fig. When the main codes is run, groupCompSSMDpvalue. csv and groupComp.

csv will be automatically generated and contain the calculated MSE results for each pairwise comparison and for each group, respectively, groupEffect. csv will contain the strength of difference, and antennaPot.

pdf will contain a series of antenna plots for glucose levels and MSE at each scale. The strengths of differences are very weak between dI and dII and between dPRE and H, fairly weak between dII and dPRE, fairly moderate between dI and dPRE, and moderate between dI and H and between dII and H, based on the SSMD criteria in Zhang Similarly, we can interpret the results in Figure 1E for average glucose levels.

Here, we develop an analytic tool CGManalyzer for CGM studies. This tool has multiple, useful features. First, it can be applied to data measured by various existing CGM devices such as FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM.

Second, it can analyze a CGM study from the beginning to the end. Third, it reads a series of data files with each representing a sensor or subject.

Fourth, it converts various formats of time stamps as long as the time stamp format is fixed in all data files. Fifth, it handles missing values. Finally, it calculates regular and nonlinear statistics.

Moreover, it has been partially applied to analyze CGM experiments successfully Zhang et al. Therefore, this package should greatly facilitate the analysis of data generated from a fast growing technology—wearable CGM device.

MSE measures the irregularity and complexity of a dynamic physiological signals and may have novel utility in diagnosis and prognosis of various diseases Chen et al. This package calculates MSE by essentially calling a c programming developed by Costa et al.

Niu et al. Furthermore, we introduce a new plot called an antenna plot for displaying the analytic results in CGM experiments, which may show the CGM changing pattern better than a forest plot He et al. This work was supported by the Start-up Research Grant SRGFHS at University of Macau.

Chen C. et al. Google Scholar. Costa M. Chaos , 24 , Elenko E. BMC Pharmacol. Toxicology , 17 , Jin Y. Chronic Obstructive Pulmonary Dis. McDonnell C. Diabetes Technol.

Therapeutics , 7 , — Bioinformatics , doi: Probstfield J. Diabetes Care , 39 , — Rodbard D. Zhang X. Genomics , 89 , — Cambridge University press , New York. Google Preview. Bioinformatics , 29 , — Plos One , 12 , E Oxford University Press is a department of the University of Oxford.

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Journal Article. CGManalyzer: an R package for analyzing continuous glucose monitoring studies. Xiaohua Douglas Zhang , Xiaohua Douglas Zhang. Faculty of Health Sciences, University of Macau, Taipa, Macau. To whom correspondence should be addressed.

Email: douglaszhang umac. Oxford Academic. Zhaozhi Zhang. Department of Statistical Sciences, Duke University, Durham, NC, USA. Dandan Wang. Revision received:. PDF Split View Views.

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Abstract Summary. Open in new tab Download slide. Google Scholar Crossref. Search ADS. Demonstrating placebo effect in clinical trials of DPP-4 inhibiters conducted in China: meta-analysis. Entropy change of biological dynamics in human chronic obstructive pulmonary disease. However, downloading those data is difficult especially for large numbers of patients or long records and researchers have to rely on manufacturer or commercial software for analysis.

An increasing number of public domain tools strive to bridge this gap, but they vary substantially in their capabilities and ease of use.

GlyCulator 2 , developed by our team, was one of the first such tools and has been updated several times along with the advancement of sensor technology and analysis guidelines. In this article, we present GlyCulator 3. This solution allows for easy integration between various CGM files and downstream analysis using more specialized tools.

pl after account creation. The current version allows users to upload CGM files downloaded from all popular CGM systems without preprocessing.

While GlyCulator 3. CGM technology and file format are automatically detected—the software is compatible with most current or past CGM sensors—or may be defined by the user on upload. The tool also provides an option to integrate multiple files from a single CGM user into a single standard file; this is especially useful for Medtronic CGM users, as those files are typically limited to storing only 90 days of data.

Following file upload, all files from one analysis are converted into a uniform format GlyQ and an analysis template is created for further editing. The GlyQ format was created to facilitate downstream analysis of raw CGM records and can be used with external software solutions with no or few modifications required.

CGM records in GlyCulator 3. After upload of full-length CGM records, the user specifies the start and end dates, thus creating the desired window of analysis for all files or selecting specific ones using a graphical interface or manual date entry.

The next step is quality check and filtration, which allows the user to review data completeness through visual representation and adjust the chosen time frame before proceeding. Afterward, the user may enable missing data imputation or skip this step and proceed directly to analysis. The analytical stage calculates glycemic variability indices GVIs compliant with the international consensus 1 for the chosen periods for all files in the analysis.

Postanalysis, the user may access separate file-specific reports including record configuration, standard ambulatory glucose profile, and GVIs , a summary detailing metadata and GVIs for all files presented visually , and raw glucose data used for calculations.

The summary report and aggregated CGM data in GlyQ format can also be downloaded for further processing with use of more specialized or custom-made analytical tools as well as external statistical software.

At each step, the users may share their results or processed files with other researchers and users of our tool. Finally, the anonymized CGM data are also securely backed up by the host institution and can potentially be used as a reference for further joint analyses or reproducibility evaluations.

The users may save, delete, access, or revisit their analyses at any time, allowing for rapid reanalysis or exploration of different analytical scenarios or variants of data imputation or filtering. We hope that by providing external data storage and analysis capabilities, we will facilitate research independent of in-house hardware limitations.

The list of features of GlyCulator 3. Our intention is not to replace the existing solutions but, rather, to allow for interoperability with outside software, which provides additional functionalities, such as nonstandard GVIs, additional data imputation algorithms, and alternative data visualizations.

GlyCulator 3. As an example, we performed such a study on 1, CGM users treated in the pediatric diabetology reference center for Lodzkie Voivodeship. Such benchmarks provide essential insight into diabetes care quality and may help with identification of areas that need improvement 3 , 4.

Xs represent functionalities implemented. Exact workings of specific functions may differ, but if a specific function was present in any extent we marked it as present. analysis, Glooko.

CGDA, Continuous Glucose Data Analysis; CV, coefficient of variation; GVAP, Glycemic Variability Analyzer Program. The possible applications of GlyCulator 3. First, investigators can use it to process data from an increasing number of clinical trials using CGM 5 and should raw files be stored cross-reference those data between trials.

Secondly, the data-sharing option might promote nationwide or international analyses, which could be a critical and invaluable asset for public health policy and future guidelines development. Finally, an application programming interface could be set up on request to enable high-throughput automated analyses for independent research networks.

Overall, we believe that GlyCulator 3. Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. conceptualized the manuscript. contributed to data interpretation and wrote and edited the manuscript. and A. performed statistical analyses and wrote and edited the manuscript. collected the data used in this manuscript. contributed to data interpretation and reviewed and edited the manuscript.

is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Prior Presentation. Parts of this study were presented at the 47th Annual Conference of the International Society for Pediatric and Adolescent Diabetes, virtual, 13—15 October Sign In or Create an Account.

Search Dropdown Menu. header search search input Search input auto suggest. filter your search All Content All Journals Diabetes Care.