Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.
Methods:
A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).
Results:
A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.
Conclusion:
This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.
Díez-FernándezARodríguez-HuertaMDMirón-GonzálezRLaredo-AguileraJAMartín-EspinosaNM.Flash glucose monitoring and patient satisfaction: a meta-review of systematic reviews. Int J Environ Res Public Heal. 2021;18(6):3123. doi:10.3390/ijerph18063123
2.
GalindoRJUmpierrezGERushakoffRJ, et al. Continuous glucose monitors and automated insulin dosing systems in the hospital consensus guideline. J Diabetes Sci Technol. 2020;14(6):1035-1064. doi:10.1177/1932296820954163
3.
BattelinoTAlexanderCMAmielSA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2022;11(1):42-57. doi:10.1016/S2213-8587(22)00319-9
4.
MazzeRSStrockEWesleyD, et al. Characterizing glucose exposure for individuals with normal glucose tolerance using continuous glucose monitoring and ambulatory glucose profile analysis. Diabetes Technol Ther. 2008;10(3):149-159. doi:10.1089/dia.2007.0293
5.
MazzeRStrockEMorganBWesleyDBergenstalRCuddihyR.Diurnal glucose patterns of exenatide once weekly: a 1-year study using continuous glucose monitoring with ambulatory glucose profile analysis. Endocr Pract. 2009;15(4):326-334. doi:10.4158/EP09046.ORR
6.
JohnsonMLMartensTWCriegoABCarlsonALSimonsonGDBergenstalRM.Utilizing the ambulatory glucose profile to standardize and implement continuous glucose monitoring in clinical practice. Diabetes Technol Ther. 2019;21(S2):S217-S225. doi:10.1089/dia.2019.0034
7.
OlsenMTDunguAMKlarskovCKJensenAKLindegaardBKristensenPL.Glycemic variability assessed by continuous glucose monitoring in hospitalized patients with community-acquired pneumonia. BMC Pulm Med. 2022;22(1):83. doi:10.1186/s12890-022-01874-7
8.
KlarskovCKWindumNAOlsenMT, et al. Telemetric continuous glucose monitoring during the COVID-19 pandemic in isolated hospitalized patients in Denmark: a randomized controlled exploratory trial. Diabetes Technol Ther. 2022;24(2):102-112. doi:10.1089/dia.2021.0291
9.
PiersantiAGiuratoFGöblCBurattiniLTuraAMorettiniM.Software packages and tools for the analysis of continuous glucose monitoring data. Diabetes Technol Ther. 2023;25(1):69-85. doi:10.1089/dia.2022.0237
10.
PageMJMckenzieJEBossuytPM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;29(372):n71. doi:10.1136/bmj.n71
11.
KovatchevBPClarkeWLBretonMBraymanKMcCallA.Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. Diabetes Technol Ther. 2005;7(6):849-862. doi:10.1089/dia.2005.7.849
12.
RodbardD.Display of glucose distributions by date, time of day, and day of week: new and improved methods. J Diabetes Sci Technol. 2009;3(6):1388-1394. doi:10.1177/193229680900300619
13.
RodbardD.Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose. J Diabetes Sci Technol. 2015;9(1):56-62. doi:10.1177/1932296814551046
14.
VigerskyRA.Escaping the hemoglobin A1c-centric world in evaluating diabetes mellitus interventions. J Diabetes Sci Technol. 2015;9(5):1148-1151. doi:10.1177/1932296815576363
15.
BrollSUrbanekJBuchananD, et al. Interpreting blood GLUcose data with R package iglu. PLoS One. 2021;16(4):e0248560. doi:10.1371/journal.pone.0248560
16.
CapponGSparacinoGFacchinettiA. AGATA: a toolbox for automated glucose data analysis [published online ahead of print January 5, 2023]. J Diabetes Sci Technol. doi:10.1177/19322968221147570
17.
VigersTChanCLSnell-BergeonJ, et al. Cgmanalysis: an R package for descriptive analysis of continuous glucose monitor data. PLoS One. 2019;14(10):e0216851. doi:10.1371/journal.pone.0216851
18.
KlonoffDCWangJRodbardD, et al. A glycemia risk index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings. J Diabetes Sci Technol. 2023;17(5):1226-1242. doi:10.1177/19322968221085273
19.
ChrzanowskiJGrabiaSMichalakA, et al. GlyCulator 3.0: a fast, easy-to-use analytical tool for CGM data analysis, aggregation, center benchmarking, and data sharing. Diabetes Care. 2023;46(1):e3-e5. doi:https://doi.org/10.2337/dc22-0534
20.
NeinsteinAWongJLookH, et al. A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management. J Am Med Inform Assoc. 2016;23(2):324-332. doi:10.1093/jamia/ocv104
21.
YuXLinLShenJ, et al. Calculating the mean amplitude of glycemic excursions from continuous glucose data using an open-code programmable algorithm based on the integer nonlinear method. Comput Math Methods Med. 2018;2018:6286893. doi:10.1155/2018/6286893
22.
MaricsGLendvaiZLódiC, et al. Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit. Biomed Eng Online. 2015;14:37. doi:10.1186/s12938-015-0035-3
23.
Cuesta-FrauDNovákDBurdaV, et al. Characterization of artifact influence on the classification of glucose time series using sample entropy statistics. Entropy. 2018;20(11):871. doi:10.3390/e20110871
24.
BattelinoTDanneTBergenstalRM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593-1603. doi:10.2337/dci19-0028
RodbardD.New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol Ther. 2009;11(9):551-565. doi:10.1089.dia.2009.0015
29.
BaghurstPA.Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: an automated algorithm. Diabetes Technol Ther. 2011;13(3):296-302. doi:10.1089/dia.2010.0090
30.
FritzscheGKohnertKDHeinkePVogtLSalzsiederE.The use of a computer program to calculate the mean amplitude of glycemic excursions. Diabetes Technol Ther. 2011;13(3):319-325. doi:10.1089/dia.2010.0108
31.
HillNROliverNSChoudharyPLevyJCHindmarshPMatthewsDR.Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther. 2011;13(9):921-928. doi:10.1089/dia.2010.0247
32.
RawlingsRAShiHYuanLHBrehmWPop-BusuiRNelsonPW.Translating glucose variability metrics into the clinic via continuous glucose monitoring: a graphical user interface for diabetes evaluation (CGM-GUIDE). Diabetes Technol Ther. 2011;13(12):1241-1248. doi:10.1089/dia.2011.0099
33.
MoscardóVGiménezMOliverNHillNR.Updated software for automated assessment of glucose variability and quality of glycemic control in diabetes. Diabetes Technol Ther. 2020;22(10):701-708. doi:10.1089/dia.2019.0416
34.
HallHPerelmanDBreschiA, et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018;16(7):e2005143. doi:10.1371/journal.pbio.2005143
35.
ServiceFJMolnarGDRosevearJWAckermanEGatewoodLCTaylorWF.Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970;19(9):644-655. doi:10.2337/diab.19.9.644
36.
MolnarGDTaylorWFHoMM.Day-to-day variation of continuously monitored glycaemia: a further measure of diabetic instability. Diabetologia. 1972;8(5):342-348. doi:10.1007/BF01218495
37.
WójcickiJM.“J”-index. A new proposition of the assessment of current glucose control in diabetic patients. Horm Metab Res. 1995;27(1):41-42. doi:10.1055/s-2007-979906
38.
BergenstalRMAhmannAJBaileyT, et al. Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the ambulatory glucose profile (AGP). Diabetes Technol Ther. 2013;15(3):198-211. doi:10.1089/dia.2013.0051
39.
FabrisCFacchinettiASparacinoG, et al. Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed. Diabetes Technol Ther. 2014;16(10):644-652. doi:10.1089/dia.2013.0252
40.
RodbardD.Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control. Diabetes Technol Ther. 2009;11(suppl 1):S55-S67. doi:10.1089/dia.2008.0132
41.
MonnierLColetteCOwensDR.Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? how to measure it?J Diabetes Sci Technol. 2008;2(6):1094-1100. doi:10.1177/193229680800200618
42.
SuhSKimJH.Glycemic variability: how do we measure it and why is it important?Diabetes Metab J. 2015;39(4):273-282. doi:10.4093/dmj.2015.39.4.273
43.
CerielloAMonnierLOwensD.Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol. 2019;7(3):221-230. doi:10.1016/S2213-8587(18)30136-0
44.
CandidoR.Which patients should be evaluated for blood glucose variability. Diabetes Obes Metab. 2013;15(suppl 2):9-12.
45.
RodbardD.Glucose variability: a review of clinical applications and research developments. Diabetes Technol Ther. 2018;20(S2):S25-S215. doi:10.1089/dia.2018.0092
46.
MonnierLColetteCOwensDR.The application of simple metrics in the assessment of glycaemic variability. Diabetes Metab. 2018;44(4):313-319. doi:10.1016/j.diabet.2018.02.008
47.
MonnierLColetteC.Glycemic variability. Should we and can we prevent it?Diabetes Care. 2008;31(suppl 2):150-154. doi:10.2337/dc08-s241
48.
KovatchevBBretonMClarkeW.Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes. Methods Enzymol. 2009;454:69-86. doi:10.1016/S0076-6879(08)03803-2
49.
PearsonJBergenstalR.Fine-tuning control: pattern management versus supplementation: view 1: pattern management: an essential component of effective insulin management. Diabetes Spectr. 2001;14(2):75-78.
50.
RitholzMDAtakov-CastilloABesteM, et al. Psychosocial factors associated with use of continuous glucose monitoring. Diabet Med. 2010;27(9):1060-1065. doi:10.1111/j.1464-5491.2010.03061.x
51.
ScheinerG.CGM retrospective data analysis. Diabetes Technol Ther. 2016;18(suppl 2):S214-S222. doi:10.1089/dia.2015.0281
52.
BowenMERumanaUKillgoreEAGongY.A user-centered glucose-insulin data display for the inpatient setting. Stud Heal Technol Inform. 2018;245:684-688.
RodbardD.Standardization versus customization of glucose reporting. Diabetes Technol Ther. 2013;15(5):439-443. doi:10.1089/dia.2013.0116
60.
MillardLACPatelNTillingKLewcockMFlachPALawlorDA. GLU: a software package for analysing continuously measured glucose levels in epidemiology. Int J Epidemiol. 2020;49(3):744-757. doi:10.1093/ije/dyaa004
61.
OlawskyEZhangYEberlyLEHelgesonESChowLS.A new analysis tool for continuous glucose monitor data. J Diabetes Sci Technol. 2022;16(6):1496-1504. doi:10.1177/19322968211028909
KovatchevBPCoxDJGonder-FrederickLAYoung-HymanDSchlundtDClarkeW.Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. Diabetes Care. 1998;21(11):1870-1875. doi:10.2337/diacare.21.11.1870
64.
KovatchevBPStraumeMCoxDJFarhyLS.Risk analysis of blood glucose data: a quantitative approach to optimizing the control of insulin dependent diabetes. J Theor Med. 2000;3:208936. doi:10.1080/10273660008833060
65.
ZhangXDZhangZWangD.CGManalyzer: an R package for analyzing continuous glucose monitoring studies. Bioinformatics. 2018;34(9):1609-1611. doi:10.1093/bioinformatics/btx826
66.
MatabuenaMPetersenAVidalJCGudeF.Glucodensities: a new representation of glucose profiles using distributional data analysis. Stat Methods Med Res. 2021;30(6):1445-1464. doi:10.1177/0962280221998064
67.
AttayeIvan der VossenEWJMendes BastosDNNieuwdorpMLevinE.Introducing the continuous glucose data analysis (CGDA) R package: an intuitive package to analyze continuous glucose monitoring data. J Diabetes Sci Technol. 2022;16(3):783-785. doi:10.1177/19322968211070293
68.
De LiveraAMShawJECohenNReutensASalimA.An interactive web application for the statistical analysis of continuous glucose monitoring data in epidemiological studies. J Diabetes Sci Technol. 2022;16(2):397-400. doi:10.1177/1932296820985570
69.
FitipaldiHMcCarthyMIFlorezJCFranksPW.A global overview of precision medicine in type 2 diabetes. Diabetes. 2018;67(10):1911-1922. doi:10.2337/dbi17-0045
70.
PrasadRBGroopL.Precision medicine in type 2 diabetes. J Intern Med. 2018;285(1):40-48. doi:10.1111/joim.12859
71.
MéndezDYZhouMLagerrosYT, et al. Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes. BMC Med. 2022;20(1):356. doi:10.1186/s12916-022-02551-6
72.
KhouryMJIademarcoMFRileyWT.Precision public health for the era of precision medicine. Am J Prev Med. 2016;50(3):398-401. doi:10.1016/j.amepre.2015.08.031
73.
BonnefondAFroguelP.Clustering for a better prediction of type 2 diabetes mellitus. Nat Rev Endocrinol. 2021;17(4):193-194. doi:10.1038/s41574-021-00475-4
74.
LiLChengW-YGlicksbergBS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311):311ra174. doi:10.1126/scitranslmed.aaa936
75.
SafaiNAliARossingPRidderstråleM.Stratification of type 2 diabetes based on routine clinical markers. Diabetes Res Clin Pract. 2018;141:275-283. doi:10.1016/j.diabres.2018.05.014
76.
AhlqvistEStormPKäräjämäkiA, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-369. doi:10.1016/S2213-8587(18)30051-2
77.
ContrerasIQuirósCGiménezMCongetIVehiJ.Profiling intra-patient type I diabetes behaviors. Comput Methods Programs Biomed. 2016;136:131-141. doi:10.1016/j.cmpb.2016.08.022
78.
DerozierVArnavielheSRenardEDrayGMartinS.How knowledge emerges from artificial intelligence algorithm and data visualization for diabetes management. J Diabetes Sci Technol. 2019;13(4):698-707. doi:10.1177/1932296819847739
79.
KahkoskaARAdairLAAielloAE, et al. Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c. Pediatr Diabetes. 2019;20(5):556-566. doi:10.1111/pedi.12856.Identification
80.
TaoRYuXLuJ, et al. Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes. BMJ Open Diabetes Res Care. 2021;9(1):e001869. doi:10.1136/bmjdrc-2020-001869
81.
LimMHChoYMKimS.Multi-task disentangled autoencoder for time-series data in glucose dynamics. IEEE J Biomed Health Inform. 2022;26(9):4702-4713. doi:10.1109/JBHI.2022.3175928
82.
LoboBFarhyLShafieiMKovatchevB.A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series. IEEE Trans Biomed Eng. 2022;69(2):654-665. doi:10.1109/TBME.2021.3103127
83.
Ferreira-GonzálezIBusseJWHeels-AnsdellD, et al. Problems with use of composite endpoints in cardiovascular trials: systematic review of randomised controlled trials. BMJ. 2007;334(7597):786. doi:10.1136/bmj.39136.682083.AE
84.
RodbardD. Metrics to evaluate quality of glycemic control: comparison of time in target, hypoglycemic, and hyperglycemic ranges with ‘“risk indices..”Diabetes Technol Ther. 2018;20(5):325-334. doi:10.1089/dia.2017.0416
85.
RodbardD.A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia. J Diabetes Sci Technol. 2009;3(6):1395-1401. doi:10.1177/193229680900300620
86.
KovatchevBPCoxDJGonder-FrederickLAClarkeW.Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care. 1997;20(11):1655-1658. doi:10.2337/diacare.20.11.1655
87.
BrownSABasuAKovatchevBP.Beyond HbA 1c: using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision-making. Diabet Med. 2019;36(6):679-687. doi:10.1111/dme.13944
88.
RodbardD.Quality of glycemic control: assessment using relationships between metrics for safety and efficacy. Diabetes Technol Ther. 2021;23(10):692-704. doi:10.1089/dia.2021.0115
89.
HillNRHindmarshPCStevensRJStrattonIMLevyJCMatthewsDR.A method for assessing quality of control from glucose profiles. Diabet Med. 2007;24(7):753-758. doi:10.1111/j.1464-5491.2007.02119.x
90.
ThomasASchönauerMAchermannF, et al. The “glucose pentagon”: assessing glycemic control of patients with diabetes mellitus by a model integrating different parameters from glucose profile. Diabetes Technol Ther. 2009;11(6):399-409. doi:10.1089/dia.2008.0119
91.
NguyenMHanJSpanakisEKKovatchevBPKlonoffDC.A review of continuous glucose monitoring-based composite metrics for glycemic control. Diabetes Technol Ther. 2020;22(8):613-622. doi:10.1089/dia.2019.0434.A
92.
MarlingCRShubrookJHVernierSJWileyMTSchwartzFL.Characterizing blood glucose variability using new metrics with continuous glucose monitoring data. J Diabetes Sci Technol. 2011;5(4):871-878. doi:10.1177/193229681100500408
HillNRThompsonBBruceJMatthewsDRHindmarshP.Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus. Diabet Med. 2009;26(7):740-743. doi:10.1111/j.1464-5491.2009.02763.x
95.
AugsteinPHeinkePVogtL, et al. Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies. BMC Endocr Disord. 2015;15:22. doi:10.1186/s12902-015-0019-0
96.
MoscardóVHerreroPReddyMHillNRGeorgiouPOliverN.Assessment of glucose control metrics by discriminant ratio. Diabetes Technol Ther. 2020;22(10):719-726. doi:10.1089/dia.2019.0415
BentBHenriquezMDunnJ.Cgmquantify: Python and R software packages for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data. IEEE Open J Eng Med Biol. 2021;2:263-266.
99.
KitaeAKimuraTMunekawaC, et al. Development of application to automatically calculate mean amplitude of glycaemic excursions using intermittently scanned continuous glucose monitoring data. Diabetes Obes Metab. 2021;23(9):2155-2160. doi:10.1111/dom.14457
ShaoJLiuZLiS, et al. Continuous glucose monitoring time series data analysis: a time series analysis package for continuous glucose monitoring data. J Comput Biol. 2023;30(1):112-116. doi:10.1089/cmb.2022.0100
102.
PagaczKStawiskiKSzadkowskaAMlynarskiWFendlerW.GlyCulator2: an update on a web application for calculation of glycemic variability indices. Acta Diabetol. 2018;55(8):877-880. doi:10.1007/s00592-018-1140-0
103.
CzerwoniukDFendlerWWalenciakLMlynarskiW.GlyCulator: a glycemic variability calculation tool for continuous glucose monitoring data. J Diabetes Sci Technol. 2011;5(2):447-451. doi:10.1177/193229681100500236
104.
KovatchevBPOttoECoxDGonder-FrederickLClarkeW.Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care. 2006;29(11):2433-2438. doi:10.2337/dc06-1085
105.
LeelarathnaLThabitHWilinskaME, et al. Evaluating glucose control with a novel composite continuous glucose monitoring index. J Diabetes Sci Technol. 2020;14(2):277-283. doi:10.1177/1932296819838525
106.
LeelarathnaLHoodTBallyL, et al. A novel composite glucose index (COGI) for evaluating closed-loop performance in type 1 diabetes. Diabetes. 2023;67(suppl 1):926-P. doi:10.2337/db18-926-P
MagniLRaimondoDMManCD, et al. Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diabetes Sci Technol. 2008;2(4):630-635. doi:10.1177/193229680800200414
NathanDMKuenenJBorgRZhengHSchoenfeldDHeineRJ; A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473-1478. doi:10.2337/dc08-0545
111.
PeyserTABaloAKBuckinghamBAHirschIBGarciaA.Glycemic variability percentage: a novel method for assessing glycemic variability from continuous glucose monitor data. Diabetes Technol Ther. 2018;20(1):6-16. doi:10.1089/dia.2017.0187
112.
RyanEAShandroTGreenK, et al. Assessment of the severity of hypoglycemia and glycemic lability in type 1 diabetic subjects undergoing islet transplantation. Diabetes. 2004;53(4):955-962.
113.
HermanidesJVriesendorpTMBosmanRJZandstraDFHoekstraJBDevriesJH.Glucose variability is associated with intensive care unit mortality. Crit Care Med. 2010;38(3):838-842. doi:10.1097/CCM.0B013E3181CC4BE9
SchlichtkrullJMunckOJersildM.The M-value, an index of blood-sugar control in diabetics. Acta Med Scand. 1965;177:95-102.
116.
HirschIBBaloAKSayerKGarciaABuckinghamBAPeyserTA.A simple composite metric for the assessment of glycemic status from continuous glucose monitoring data: implications for clinical practice and the artificial pancreas. Diabetes Technol Ther. 2017;19(S3):S38-S48. doi:10.1089/dia.2017.0080
117.
BatistaGEAPAKeoghEJTatawOMde SouzaVMA.CID: an efficient complexity-invariant distance for time series. Data Min Knowl Disc. 2014;28:634-669. doi:10.1007/s10618-013-0312-3
118.
KovachevBPGonder-FrederickLACoxDJClarkeWL.Evaluating the accuracy of continuous mathematical model. Diabetes Care. 2004;27(8):1922-1928. doi:10.2337/diacare.27.8.1922
119.
ClarkeWLCoxDGonder-frederickLACarterWPohlSL.Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care. 1987;10(5):622-628. doi:10.2337/diacare.10.5.622
120.
HoworkaKPumprlaJGabrielMThomaHSchabmannA.Computerized generation of circadian sensor modal days with continuous glucose monitoring for comparison of various insulin regimens based on insulin glargine in type 1 diabetes. Int J Artif Organs. 2003;26(8):728-734. doi:10.1177/039139880302600805
121.
MillerMStrangeP.Use of fourier models for analysis and interpretation of continuous glucose monitoring glucose profiles. J Diabetes Sci Technol. 2007;1(5):630-638. doi:10.1177/1932296807001005
122.
BiagiLBertachiAGiménezM, et al. Individual categorisation of glucose profiles using compositional data analysis. Stat Methods Med Res. 2019;28(12):3550-3567. doi:10.1177/0962280218808819
123.
HerreroPReddyMGeorgiouPOliverNS.Identifying continuous glucose monitoring data using machine learning. Diabetes Technol Ther. 2022;24(6):403-408. doi:10.1089/dia.2021.0498
124.
ThomasAShinJJiangBMcMahonCKolassaRVigerskyRA.The development of new composite metrics for the comprehensive analytic and visual assessment of hypoglycemia using the hypo-triad. J Diabetes Sci Technol. 2018;12(1):69-75. doi:10.1177/1932296817721242
125.
VigerskyRAShinJJiangBSiegmundTMcMahonCThomasA.The comprehensive glucose pentagon: a glucose-centric composite metric for assessing glycemic control in persons with diabetes. J Diabetes Sci Technol. 2018;12(1):114-123. doi:10.1177/1932296817718561
126.
ZouYWangWZhengDHouX.Glycemic deviation index: a novel method of integrating glycemic numerical value and variability. BMC Endocr Disord. 2021;21(1):52. doi:10.1186/s12902-021-00691-z
127.
HenriquesTMunshiMNSegalARCostaMDGoldbergerAL.“Glucose-at-a-glance”: new method to visualize the dynamics of continuous glucose monitoring data. J Diabetes Sci Technol. 2014;8(2):299-306. doi:10.1177/1932296814524095
128.
LaiYZhangZLiP, et al. Investigation of glucose fluctuations by approaches of multi-scale analysis. Med Biol Eng Comput. 2018;56(3):505-514. doi:10.1007/s11517-017-1692-0
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