Time in range (TIR) and other continuous glucose monitoring (CGM)-derived metrics have been standardized in international consensus conferences. TIR correlates closely with A1C; a TIR of 70% correlates to an A1C of 6.7–7%. Evidence is emerging on the association of TIR with long-term diabetes complications, and each 10% increase in TIR shows a substantial decrease in risk for long-term complications. Application of TIR to clinical practice can be easily done with a stepped approach to the analysis and interpretation of CGM-derived metrics and the ambulatory glucose profile report. Clinician education and partnership with patients are crucial for successful implementation of TIR and all CGM-derived metrics in clinical practice.

A1C traditionally has been considered the gold standard for monitoring long-term glycemic status and the predominant tool to assess risk of diabetes complications (1,2). However, A1C has several limitations. It does not provide information about hypo- or hyperglycemia, glucose trends, patterns, or glycemic variability. In addition, A1C is affected by age, hemoglobinopathies, pregnancy, chronic kidney disease (CKD), and ethnic and racial differences in glycation rates (3).

The advent of continuous glucose monitoring (CGM) as a diagnostic and therapeutic tool for diabetes management has further emphasized these limitations, offering metrics in addition to the ones based solely on glucose averages, such as the detection and quantification of hypoglycemia, hyperglycemia, and glycemic variability (4). CGM has expanded substantially in the past decade thanks to increased sensor duration, improved accuracy, factory calibration, and the ability to use CGM to make therapeutic dose decisions (5).

With the increase in CGM-generated data, standardization of CGM interpretation became necessary for clinical outcomes assessment and use in clinical trials. An expert panel published a consensus report in 2017 identifying several metrics with the purpose of establishing recommendations for the interpretation of CGM data (6,7). These CGM-derived metrics were further defined in 2019, and 10 core metrics were selected as clinical targets for CGM data interpretation and use in clinical care, to assist clinicians and people with diabetes in assessing glycemic status (8). This article outlines the rationale for clinical use of CGM metrics, describes the correlation of the time in range (TIR; 70–180 mg/dL [3.9–10 mmol/L]) metric to laboratory-measured A1C, summarizes the evidence building around TIR as a predictor of long-term diabetes complications, and offers a practical approach to applying the 10 core CGM metrics and ambulatory glucose profile (AGP) in clinical practice.

CGM-derived “times in ranges” are metrics of glycemic status that provide actionable information in addition to A1C. They can be expressed as either the percentage of CGM readings in the given range, the number of hours and minutes spent in the range per day, or both (8). Three times-in-ranges metrics should be used as a starting point in the assessment of glycemic status. In addition to TIR, they include time below range (TBR; <70 mg/dL [3.9 mmol/L] and <54 mg/dL [3.0 mmol/L]) and time above range (TAR; >180 mg/dL [10 mmol/L] and >250 mg/dL [13.9 mmol/L]) (4).

Of note, these ranges are to be considered exclusively in people with diabetes, as they reflect a broader range than what is observed in individuals without diabetes. In a multicenter, prospective study of 153 individuals without diabetes who were placed on CGM, 96% of the time was spent between 70 and 140 mg/dL (3.9–7.8 mmol/L) (9). This narrower range is also accepted as the normal glucose range for individuals without diabetes (7). The lower and upper values for TIR were determined based on thresholds for hypo- and hyperglycemia. The lower limit for TIR was chosen to reflect values above the standardized threshold for hypoglycemia (<70 mg/dL [3.9 mmol/L]), at which point the release of counterregulatory hormones occurs. The upper limit, 180 mg/dL (10.0 mmol/L), was set to align with the recommended upper limit target for peak postprandial glucose levels in diabetes (10). In the 2017 consensus report (6), TBR was further refined as either level 1 (i.e., <70 mg/dL [3.9 mmol/L]), to alert a person to take action, or level 2 (i.e., <54 mg/dL [<3.0 mmol/L], requiring immediate action. Similarly, TAR was further refined as level 1 (i.e., >180 mg/dL [10.0 mmol/L], as the initial threshold defining elevated glucose, or level 2 (i.e., >250 mg/dL [13.9 mmol/L]), as indicative of increased risk of diabetic ketoacidosis and higher likelihood of complications (6,7). Tables 1 and 2 outline the 10 core CGM metrics and their recommended targets according to the 2019 International Consensus on Time in Range (8).

TIR has been also shown to correlate closely with A1C. The analysis of 1,137 patients with type 1 or type 2 diabetes from 18 articles showed a high correlation (R = −0.84, R2 = 0.71) of TIR with A1C; a TIR of 70% corresponded to an A1C of 6.7%, and each 10% change in TIR was associated with a 0.8% change in A1C (11). Similarly, Beck et al. (12) reported a high correlation between TIR and A1C in data from 545 individuals with type 1 or insulin-treated type 2 diabetes. These authors found that, on average, a TIR of 70% corresponded to an A1C of ∼7% and that an increase in TIR of 10% corresponded to a decrease in A1C of 0.6%, on average. Additional information was gathered from a retrospective analysis of 530 adults with type 1 diabetes or insulin-requiring type 2 diabetes from four randomized clinical trials. This analysis confirmed a strong inverse correlation between A1C and TIR, and every 10% change in TIR was associated with a 0.7% change in A1C. In this study, mean glucose values and glycemic variability were also evaluated. Mean glucose values were highly correlated with both TIR (r = −0.93) and percentage of glucose values >250 mg/dL (13.9 mmol/L) (r = 0.92) (13).

Wide adoption of CGM metrics in clinical practice as measures of glycemic control and predictors of long-term risk of complications requires evidence-based validation. For clinicians and patients to recognize and adopt these metrics, it is crucial to demonstrate that CGM metrics, in particular TIR, are associated with clinical benefits. Despite the increasing adoption of CGM technology by people with diabetes (14), the expansion of automated insulin delivery systems based on CGM-driven algorithms (5), and the standardization of CGM metrics (68), there have been questions regarding the validity of these metrics as surrogate outcome measures in ongoing and future clinical trials.

To assess the hypothesis that TIR could serve as an outcome measure for future trials, Beck et al. (15) evaluated the available Diabetes Control and Complications Trial (DCCT) data, calculating TIR with fingerstick glucose values captured seven times daily every 3 months for the duration of the DCCT (15,16). The analysis demonstrated that TIR, computed from quarterly seven-point blood glucose testing, had a strong association with the risk of development or progression of retinopathy and development of microalbuminuria. TIR was higher in the DCCT intensive treatment group than in the conventional therapy group (52 vs. 31%), and for every 10% lowering of TIR, the adjusted hazard rate for the retinopathy outcome in the DCCT increased by 64% (95% CI 51–78%) and the adjusted hazard rate for the microalbuminuria outcome increased by 40% (95% CI 25–56%) (15).

Before this analysis of DCCT data from Beck et al. (15), only one study had revealed an association between TIR and diabetic retinopathy (DR) (17). This study was a prospective analysis of 3,262 patients with type 2 diabetes that revealed an association between TIR and DR, with increasing prevalence and severity of DR in patients with lower TIR and higher glycemic variability (P <0.001). Furthermore, the association of TIR with the prevalence of all stages of DR was independent of A1C (17).

TIR is now emerging as a metric for assessing diabetes-related outcomes, with important implications for translating these findings into clinical practice and guiding patients’ treatment modifications. The CANDY (Continuous Glucose Monitoring to Assess Glycemia in Chronic Kidney Disease [CKD]) trial was a prospective observational study designed to examine hyperglycemia, glycemic variability, and biomarkers of glycemic control in 105 participants with type 2 diabetes, CKD, and diabetic polyneuropathy (DPN). The prevalence of DPN was inversely correlated with TIR independent of A1C; each 10% lower TIR increased the risk of DPN by 25%. In addition, the glucose management indicator (GMI), a CGM-derived estimation of A1C, was significantly associated with the prevalence of DPN (odds ratio 1.79, 95% CI 1.05–3.04) per every 1% higher GMI (18).

Studies have also focused on TIR as a predictor of risk for macrovascular complications. A cross-section analysis of 2,215 patients with type 2 diabetes investigated the relationship of TIR with carotid intima-media thickness (CIMT), a surrogate marker of cardiovascular disease (CVD). Participants with abnormal CIMT had significantly lower TIR (P <0.001), higher A1C, and higher glycemic variability metrics (P <0.05). The risk of abnormal CIMT was decreased by 6.4% for each 10% increase in TIR in the fully adjusted model (19). Similar findings were seen in 349 participants with type 2 diabetes undergoing CGM evaluation and standard cardiac autonomic reflex tests. Participants with more severe cardiac autonomic neuropathy had lower TIR (P <0.001), and the neuropathy severity declined with increasing quartiles of TIR (P <0.05) independent of A1C levels (20). Evidence is also emerging on the role of TIR as a predictor of mortality risk. A very recent prospective analysis of 6,225 patients with type 2 diabetes, followed for a median of 6.9 years, revealed that TIR is inversely correlated with the risk of all-cause mortality, with each 10% decrease in TIR having a hazard ratio of 1.08 (95% CI 1.05–1.12) for all-cause and 1.05 (95% CI 1.00–1.11) for CVD mortality (21).

It is well recognized that albuminuria is a marker for CVD and CKD and is associated with increased mortality. A randomized, controlled, open-labeled parallel trial enrolled 60 participants with type 1 diabetes and a history of albuminuria (22). Participants were randomly assigned to a sensor-augmented insulin pump (SAP) or multiple daily injection (MDI) insulin therapy regimen and were followed longitudinally for 1 year. All participants used blinded CGM at baseline and again at study end for the MDI group. The results showed an association between urinary albumin-to-creatinine ratio (UACR) and TIR. The mean change in TIR was 13.2% (95% CI 6.2–20.2%), whereas the change in A1C was −1.3% (95% CI −1.6 to −1.0%); the change in UACR was −15% (95% CI −38 to 17%) (all P <0.05). In addition, the changes in UACR from baseline to study end were inversely correlated with the changes in TIR (R = −0.03, P = 0.04), and UACR decreased by 19% for each 10% increase in TIR (P = 0.04) (22). Similar findings were reported on the relationship between TIR, TBR, and TAR and the risk of albuminuria in a retrospective review of 866 patients with type 2 diabetes. Albuminuria was found in those with lower TIR, higher TAR >180 mg/dL, and TAR >250 mg/dL (all P <0.001). Patients meeting international consensus–recommended TIR and TAR targets (8) also had a lower prevalence of albuminuria (all P <0.001) (23).

Finally, a cross-sectional, observational study of 336 patients with type 2 diabetes showed an association between TIR and lower extremities arterial disease (LEAD). Lower TIR was seen in patients with type 2 diabetes and LEAD (P <0.01), whereas the prevalence of LEAD by severity decreased with ascending quartiles of TIR (P <0.05) (24).

The results of these studies are now building a compelling case for TIR as a predictor of risks for diabetes complications and for the use of CGM metrics as acceptable end points for clinical trials and their integration into clinical practice as a complement to A1C (25).

The advent of CGM and its increased use in clinical practice has modified the approach to diabetes patients; before the development of CGM metrics, assessing the efficacy of therapies and predicting long-term risk of complications was dependent on A1C only. Today, the existence of standardized CGM metrics allows clinicians and patients the opportunity to discuss CGM data during their visits, whether as a review of personal CGM data or during a professional, diagnostic CGM session. The following sections further characterize the 10 core CGM metrics outlined in Table 1 and offer a practical approach to their interpretation, implementation in clinical practice, and use for the development of personalized therapy modifications.

Glycemic Variability

In addition to TIR, TBR, and TAR, important metrics include the coefficient of variation (CV) and standard deviation (SD), both of which are indexes of glycemic variability. Glycemic variability is a process characterized by the amplitude, frequency, and duration of glucose fluctuations that contribute to the risk for hypo- and hyperglycemia; it has been proposed to be an independent risk factor for diabetes complications (6). The CV (which is the SD divided by the mean) expresses as a percentage the degree of glycemic variability for the period of time analyzed. CV that is above the recommended standard of ≤36% reflects increased risk of hypoglycemic events (26).

GMI

The GMI is a CGM-measured mean glucose concentration calculated when adequate CGM data are available (i.e., data from active CGM use of at least ≥70% during a 14-day time period). It reflects the overall glycemic status over the period of time analyzed, and it can be a useful tool especially when A1C and glucose values are discrepant, such as in situations when A1C is unreliable because of a medical condition or racial/ethnic differences in glycation rates (3,27). In addition, the GMI can be a helpful management tool when setting individualized A1C goals or making therapy adjustments, as it does not have to reflect fully 90 days of data, but instead can be analyzed with as little as 2 weeks of CGM data. Thus, for patients who are trying to reach their glycemic goals, using the GMI every few weeks can inform their progress without having to wait for 3 months to measure A1C to see meaningful changes. Of note, it is important to consider that there may be differences between the laboratory-measured A1C and the GMI and that this difference should be considered when setting A1C goals. If the GMI is lower than the laboratory A1C, caution is recommended not to lower the glucose too aggressively, since the GMI may indicate that a person’s actual glucose levels may already be lower than one would typically associate with the laboratory A1C (28).

AGP

What brings together the wealth of CGM information for patients and clinicians is the AGP, which is recommended by the International Consensus on Time in Range as a unified report from which to analyze CGM data (8). The AGP combines inputs from multiple days, displaying CGM data over a single 24-hour time period.

Initially developed by Mazze et al. (29), this report was further developed by the International Diabetes Center and incorporates all the core CGM metrics; typically, a 14-day composite glucose profile is shown, providing fundamental information for clinical decision-making (30). The AGP has been incorporated into the data management software of all CGM systems; each brand of CGM system has specific features, but they all conform to the international consensus recommendations (8). In addition, the AGP reduces clinician and health care team burden by making glucose data easily comparable across all devices (28). The use of AGP in clinical practice, with modification of workflow to include the printing of AGP reports before putting patients in an exam room, has also been shown to reduce visit time (30). Figures 13 are brand-specific examples of AGP reports (3133).

A Streamlined Approach to CGM Data Interpretation

Despite the building evidence of CGM metrics as predictors of outcomes and the wide availability of CGM reports, hesitancy still persists in the use of CGM metrics, their significance, and their application in clinical practice to inform recommendations for personalized therapy modifications. This hesitancy is most likely indicative of a need for patient and provider education so that navigating the data does not seem like a daunting task. Simple, stepped approaches to interpreting CGM reports have been published (28,30). With the recent consensus recommendations and standardization, these approaches should be taken a step further to facilitate the application of TIR and other CGM metrics in daily clinical practice.

The dashboard showing the AGP is the recommended starting point as the place where all core metrics are reported and allows the assessment of each patient’s glycemic status (34). When reviewing the dashboard, data analysis can be easily grouped into three main “buckets.” The first bucket contains data sufficiency and percentage of CGM wear. Ideally, at least 14 days should be reviewed, as this amount of data has been shown to provide a good estimate of glycemic values for a 3-month period (30). The second bucket includes the GMI, mean glucose, SD, and CV; thus, it affords a rapid assessment of glycemic variability to determine whether a patient has increased risk of hypoglycemia (CV >36%). The third bucket includes the five times-in-ranges metrics (TIR, TBR levels 1 and 2, and TAR levels 1 and 2).

The suggested sequence of analysis is to first review the TIR to determine whether it is at the patient’s individualized target based on age and comorbidities (Table 2); a target ≥70% of CGM readings is recommended for most people with diabetes, as it correlates fairly well with an A1C of ∼6.7–7%, the A1C target recommended by professional societies (11,12,35). With recent advances in automated insulin delivery systems, it is not uncommon to see a TIR reach 80% or more, as recently reported (36).

Notably, one of the most important actions with regard to this third bucket is to analyze TBR to identify hypoglycemia, which remains the limiting step in intensification of diabetes management (37). When reviewing TBR, it is imperative not only to quantify the percentage of CGM readings at level 1 and level 2 hypoglycemia, but also to review the AGP to determine whether there are specific patterns of hypoglycemia that need to be addressed promptly. The 24-hour “modal day” presentation of the AGP enables clinicians to identify the time of the day when glucose shows specific trends or patterns and when the greatest glycemic variability is occurring.

Next, the focus should shift to the analysis of TAR to quantify level 1 and level 2 hyperglycemia. TIR is mostly an index of hyperglycemia (10); thus, TIR and TAR are closely related, especially TIR and TAR level 1, in that, frequently, a high percentage of TAR level 1 indicates postprandial hyperglycemia from miscalculation of carbohydrate counting or incorrect timing of insulin dosing for meals. If the TAR level 1 is mostly in the fasting state, adjustments in basal insulin doses or settings may be necessary.

After these initial analyses, it is recommended to turn attention to the AGP graph. Regardless of the software used, the AGP generally shows the median (50th percentile); interquartile ranges (25th to 75th percentiles), which appear as a shaded “cloud” or “ribbon”; and 10th to 90th percentiles (or 5th to 95th or 0–90th, depending on software brand), which appear as colored solid or dotted lines. The analysis of this graph should focus on how much of the data sit within the range of 70–180 mg/dL and whether they are consistent throughout the day or have specific times of day or night during which the ribbon width becomes larger, representing greater variability (Figures 13) (30).

Next, the of amount data present on the lower range should be addressed (<70 mg/dL [3.9 mmol/L]). If the data show elevated TBR (>5% of readings), it is necessary to discuss the causes of hypoglycemia and implement strategies to reduce it before addressing any other concerns (4).

Once the dashboard and AGP have been reviewed and the information shared with the patient, it is good practice to review the daily views reports. These allow for more granular information and help to determine whether the patterns of hypo- or hyperglycemia noticed on the AGP are occurring on a daily basis or are clustered on weekends or specific days and can be correlated to specific causes. If recurring patterns of hypo- or hyperglycemia are noted, focus should be directed to the duration and cause of each episode, especially hypoglycemia.

Time spent in hyperglycemia, whether fasting or postprandially, should be reviewed next, and interventions should be made to adjust therapy as needed, whether related to basal insulin doses (or basal rates for patients using insulin pump therapy), mealtime insulin doses, or the patient’s individualized insulin sensitivity factor, with which correction doses are calculated.

Patient participation is crucial in interpreting CGM data, as it helps to reveal causes of glycemic variability. In addition, when discussing data with a patient that were collected during a professional (diagnostic) CGM session, asking the patient to keep a food and activity log is extremely helpful so that specific issues can be addressed and therapeutic changes implemented at the same time (30). Similarly, when reviewing data collected from a patient’s personal CGM device (either alone or in combination with SAP therapy or a hybrid closed-loop system), it is essential to ask about mealtimes, physical activity, and weekday versus weekend changes in activities that can affect glucose levels. These analyses are particularly well suited for use in telemedicine, which has become more widely adopted during the coronavirus disease 2019 pandemic (38,39).

Application of CGM Metrics for Personalized Therapy Modifications

An in-depth review of CGM metrics allows for personalization of diabetes therapies; however, setting goals for TIR targets needs to be done in partnership with patients, considering their individual needs. To be successful, it is recommended to focus on a few specific issues at a time and proceed with small steps. Some ways in which this strategy could be used include optimizing fasting glucose levels by adjusting basal insulin doses or insulin pump basal rates adjustments, improving postprandial hyperglycemia by choosing meals with a lower glycemic index and optimizing the timing of insulin doses around meals, or reducing post-exercise hypoglycemia with appropriate modification of insulin doses before, during, and after exercise.

To make these goals easily achievable, clinicians need to encourage patients to monitor their gradual progress using CGM metrics instead of relying solely on A1C, which is measured only every 3 months. In this manner, interventions can be made within shorter time periods. Patients can easily self-monitor CGM metrics by using mobile apps that can push weekly report notifications or by regularly uploading data and reviewing improvements in TIR over the previous few weeks. It is also important to share with patients that an increase in TIR of as little as 5% can yield significant glycemic benefits (8).

When this method is applied consistently, patients can better engage in their own care and achieve their goals much faster. In view of the emerging evidence of the role of TIR as a predictor of risk for diabetes complications, engaging patients using CGM technology (with either an MDI or insulin pump therapy regimen) can promote overall long-term health for patients with diabetes. Educating patients on how to review and recognize their own glycemic trends and patterns and guiding them in making targeted therapy changes can empower them in keeping focused on reducing hyperglycemia and hypoglycemia and increasing TIR.

Application of the TIR Metric in Clinical Practice: Patients’ Perspective

When considering patients’ perspective on TIR, it is encouraging to see how people with diabetes are becoming more familiar with the CGM metrics terminology through online blogs, diabetes magazines, and social media groups, but also thanks to the tireless work of foundations such as diaTribe (https://www.diatribe.org), a patient-focused online community that seeks to empower members with useful, actionable information on many diabetes-related topics, including articles on diabetes and technology, TIR, GMI, and other CGM-related topics (40).

Evidence is also emerging on the role of TIR in patients’ perceptions of the success of their current diabetes therapies. A study involving an online survey showed that TIR was the highest-ranking outcome believed to have a “big impact” on daily life for responders with type 1 diabetes, which was on par with A1C for responders with type 2 diabetes (41). More recently, the impact or TIR on mood was investigated in 219 adults with type 1 diabetes who used CGM. Responders (36% of eligible subjects) completed baseline questionnaires and then received nightly text reminders (set at a time of their choosing) to complete a brief retrospective assessment of their mood that day for a total of 14 days. The results showed that greater TIR over the course of the day was linked to more positive mood ratings as reported that day, and the more time spent in the severe hyperglycemic range (defined as >300 mg/dL [%TAR >300]) each day was associated with more negative evening mood ratings (all P <0.05). There was no significant association between TBR and mood; however, the cohort had very low TBR values, at <3% (42).

CGM metrics have become standardized and are more widely available to patients and health care professionals (8). TIR is emerging as a predictor of risk of long-term diabetes complications (1524); it is also a very intuitive metric that correlates with perception of success and mood in people with diabetes (41,42). Finally, all CGM metrics and reports are powerful tools for clinicians, allowing rapid assessment of glycemic status and offering patients the opportunity for more timely personalized therapy modifications. Educating patients on recognizing and acting on their own glycemic patterns and trends to achieve their target TIR can empower them to focus on specific challenging areas of glycemic control and increase their sense of accomplishment with regard to diabetes self-management.

Duality of Interest

The author has served as a consultant for Dexcom and Insulet. Her institution has received research support from Dexcom, Eli Lilly, Insulet, and Novo Nordisk. No other potential conflicts of interest relevant to this article were reported.

Guarantor Statement

As the sole author, G.A. is the guarantor of this work and takes responsibility for its integrity.

1.
Nathan
DM
,
Genuth
S
,
Lachin
J
, et al.;
Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
1993
;
329
:
977
986
2.
UK Prospective Diabetes Study (UKPDS) Group
.
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)
.
Lancet
1998
;
352
:
837
853
3.
Beck
RW
,
Connor
CG
,
Mullen
DM
,
Wesley
DM
,
Bergenstal
RM
.
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
.
Diabetes Care
2017
;
40
:
994
999
4.
Galindo
RJ
,
Aleppo
G
.
Continuous glucose monitoring: the achievement of 100 years of innovation in diabetes technology
.
Diabetes Res Clin Pract
2020
;
170
:
108502
5.
Kravarusic
J
,
Aleppo
G
.
Diabetes technology use in adults with type 1 and type 2 diabetes
.
Endocrinol Metab Clin North Am
2020
;
49
:
37
55
6.
Danne
T
,
Nimri
R
,
Battelino
T
, et al
.
International consensus on use of continuous glucose monitoring
.
Diabetes Care
2017
;
40
:
1631
1640
7.
Agiostratidou
G
,
Anhalt
H
,
Ball
D
, et al
.
Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, the Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange
.
Diabetes Care
2017
;
40
:
1622
1630
8.
Battelino
T
,
Danne
T
,
Bergenstal
RM
, et al
.
Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range
.
Diabetes Care
2019
;
42
:
1593
1603
9.
Shah
VN
,
DuBose
SN
,
Li
Z
, et al
.
Continuous glucose monitoring profiles in healthy nondiabetic participants: a multicenter prospective study
.
J Clin Endocrinol Metab
2019
;
104
:
4356
4364
10.
Advani
A
.
Positioning time in range in diabetes management
.
Diabetologia
2020
;
63
:
242
252
11.
Vigersky
RA
,
McMahon
C
.
The relationship of hemoglobin A1C to time-in-range in patients with diabetes
.
Diabetes Technol Ther
2019
;
21
:
81
85
12.
Beck
RW
,
Bergenstal
RM
,
Cheng
P
, et al
.
The relationships between time in range, hyperglycemia metrics, and HbA1c
.
J Diabetes Sci Technol
2019
;
13
:
614
626
13.
Hirsch
IB
,
Welsh
JB
,
Calhoun
P
,
Puhr
S
,
Walker
TC
,
Price
DA
.
Associations between HbA1c and continuous glucose monitoring-derived glycaemic variables
.
Diabet Med
2019
;
36
:
1637
1642
14.
Foster
NC
,
Beck
RW
,
Miller
KM
, et al
.
State of type 1 diabetes management and outcomes from the T1D Exchange in 2016–2018
.
Diabetes Technol Ther
2019
;
21
:
66
72
15.
Beck
RW
,
Bergenstal
RM
,
Riddlesworth
TD
, et al
.
Validation of time in range as an outcome measure for diabetes clinical trials
.
Diabetes Care
2019
;
42
:
400
405
16.
Hirsch
IB
,
Sherr
JL
,
Hood
KK
.
Connecting the dots: validation of time in range metrics with microvascular outcomes
.
Diabetes Care
2019
;
42
:
345
348
17.
Lu
J
,
Ma
X
,
Zhou
J
, et al
.
Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes
.
Diabetes Care
2018
;
41
:
2370
2376
18.
Mayeda
L
,
Katz
R
,
Ahmad
I
, et al
.
Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease
.
BMJ Open Diabetes Res Care
2020
;
8
:
e000991
19.
Lu
J
,
Ma
X
,
Shen
Y
, et al
.
Time in range is associated with carotid intima-media thickness in type 2 diabetes
.
Diabetes Technol Ther
2020
;
22
:
72
78
20.
Guo
Q
,
Zang
P
,
Xu
S
, et al
.
Time in range, as a novel metric of glycemic control, is reversely associated with presence of diabetic cardiovascular autonomic neuropathy independent of HbA1c in Chinese type 2 diabetes
.
J Diabetes Res
2020
;
2020
:
5817074
21.
Lu
J
,
Wang
C
,
Shen
Y
, et al
.
Time in range in relation to all-cause and cardiovascular mortality in patients with type 2 diabetes: a prospective cohort study
.
Diabetes Care
.
Epub ahead of print on 23 October 2020 (doi: 10.2337/dc20-1862)
22.
Ranjan
AG
,
Rosenlund
SV
,
Hansen
TW
,
Rossing
P
,
Andersen
S
,
Nørgaard
K
.
Improved time in range over 1 year is associated with reduced albuminuria in individuals with sensor-augmented insulin pump-treated type 1 diabetes
.
Diabetes Care
2020
;
43
:
2882
2885
23.
Yoo
JH
,
Choi
MS
,
Ahn
J
, et al
.
Association between continuous glucose monitoring-derived time in range, other core metrics, and albuminuria in type 2 diabetes
.
Diabetes Technol Ther
2020
;
22
:
768
776
24.
Li
J
,
Li
Y
,
Ma
W
, et al
.
Association of time in range levels with lower extremity arterial disease in patients with type 2 diabetes
.
Diabetes Metab Syndr
2020
;
14
:
2081
2085
25.
Beyond A1C Writing Group
.
Need for regulatory change to incorporate beyond A1C glycemic metrics
.
Diabetes Care
2018
;
41
:
e92
e94
26.
Monnier
L
,
Colette
C
,
Wojtusciszyn
A
, et al
.
Toward defining the threshold between low and high glucose variability in diabetes
.
Diabetes Care
2017
;
40
:
832
838
27.
Bergenstal
RM
,
Beck
RW
,
Close
KL
, et al
.
Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring
.
Diabetes Care
2018
;
41
:
2275
2280
28.
Johnson
ML
,
Martens
TW
,
Criego
AB
,
Carlson
AL
,
Simonson
GD
,
Bergenstal
RM
.
Utilizing the ambulatory glucose profile to standardize and implement continuous glucose monitoring in clinical practice
.
Diabetes Technol Ther
2019
;
21
(
Suppl. 2
):
S217
S225
29.
Mazze
RS
,
Lucido
D
,
Langer
O
,
Hartmann
K
,
Rodbard
D
.
Ambulatory glucose profile: representation of verified self-monitored blood glucose data
.
Diabetes Care
1987
;
10
:
111
117
30.
Aleppo
G
,
Webb
K
.
Continuous glucose monitoring integration in clinical practice: a stepped guide to data review and interpretation
.
J Diabetes Sci Technol
2019
;
13
:
664
673
31.
Dexcom
.
Dexcom Clarity
.
Available from https://clarity.dexcom.com/professional. Accessed 29 November 2020
32.
Abbott Pharmaceutical
.
LibreView
.
Available from https://libreview.com. Accessed 29 November 2020
33.
Medtronic
.
Medtronic CareLink
.
Available from https://carelink.medtronic.com. Accessed 29 November 2020
34.
Bergenstal
RM
,
Ahmann
AJ
,
Bailey
T
, 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
:
198
211
35.
American Diabetes Association
.
6. Glycemic targets: Standards of Medical Care in Diabetes—2020
.
Diabetes Care
2020
;
43
(
Suppl. 1
):
S66
S76
36.
Pinsker
JE
,
Müller
L
,
Constantin
A
, et al
.
Real-world patient reported outcomes and glycemic results with initiation of Control-IQ technology
.
Diabetes Technol Ther
.
Epub ahead of print on 26 August 2020 (doi: 10.1089/dia.2020.0388)
37.
Cryer
PE
.
Hypoglycaemia: the limiting factor in the glycaemic management of type I and type II diabetes
.
Diabetologia
2002
;
45
:
937
948
38.
Peters
AL
,
Garg
SK
.
The silver lining to COVID-19: avoiding diabetic ketoacidosis admissions with telehealth
.
Diabetes Technol Ther
2020
;
22
:
449
453
39.
Garg
SK
,
Rodbard
D
,
Hirsch
IB
,
Forlenza
GP
.
Managing new-onset type 1 diabetes during the COVID-19 pandemic: challenges and opportunities
.
Diabetes Technol Ther
2020
;
22
:
431
439
40.
diaTribe Foundation
.
diaTribe home page
.
Available from https://www.diatribe.org. Accessed 28 November 2020
41.
Runge
AS
,
Kennedy
L
,
Brown
AS
, et al
.
Does time-in-range matter? Perspectives from people with diabetes on the success of current therapies and the drivers of improved outcomes
.
Clin Diabetes
2018
;
36
:
112
119
42.
Polonsky
WH
,
Fortmann
AL
.
Impact of real-time continuous glucose monitoring data sharing on quality of life and health outcomes in adults with type 1 diabetes
.
Diabetes Technol Ther
.
Epub ahead of print on 29 September 2020 (doi: 10.1089/dia.2020.0466)
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.