Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes

Cutruzzolà, Antonio; Irace, Concetta; Parise, Martina; Fiorentino, Raffaella; Tripodi, P.; Ungaro, Serena; Babinsky, Valerie; Gnasso, Agostino

doi:10.1016/j.numecd.2020.06.009

Cited by 23 publications

(39 citation statements)

References 23 publications

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“…However, we have recently demonstrated that a strict correlation between TIR calculated by specific software, after downloading SMBG values, is significantly correlated with glycated hemoglobin levels. The strength of this association in our study was comparable to that between TIR calculated from CGM and glycated hemoglobin levels reported in other studies [13]. Patients using the meter reported a significant increase in CV compared to the other groups.…”

Section: Principal Findingssupporting

confidence: 90%

Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study

Parise¹,

Tartaglione²,

Cutruzzolà³

et al. 2021

J Med Internet Res

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Background Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. Objective During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. Methods This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. Results Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; P<.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (P=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; P=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; P<.001) than in those with a GMI of <7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. Conclusions These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.

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Section: Principal Findingssupporting

confidence: 90%

Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study

Parise¹,

Tartaglione²,

Cutruzzolà³

et al. 2021

J Med Internet Res

Self Cite

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“…For most patients with type 2 diabetes mellitus, capillary blood glucose is still considered the most valuable tool for monitoring blood glucose and guiding the adjustment of hypoglycemic drugs 24,25 . A recent study suggested that TIR calculated by SMBG was also negatively correlated with HbA1c 26 . Moreover, the mean TIR values were similar when measured using eight‐point glucose testing (overnight monitoring was added based on the seven‐point glucose profile) and measured with CGM (50% and 49%, respectively) in a study conducted by the Diabetes Research in Children Network 27 .…”

Section: Discussionmentioning

confidence: 99%

Time in range assessed by capillary blood glucose in relation to insulin sensitivity and β‐cell function in patients with type 2 diabetes mellitus: A cross‐sectional study in China

Ye¹,

Deng

Liang

et al. 2022

J of Diabetes Invest

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Aims: This study investigated the association of capillary blood glucose (CBG)-assessed time in range (TIR) (3.9-10.0 mmol/L) with insulin sensitivity and islet b-cell function. Materials and Methods: We recruited 455 patients with type 2 diabetes mellitus. Seven-point glucose-profile data (pre-and 120 min post-main meals, bedtime) were collected over three consecutive days. Plasma glucose and serum insulin concentrations were measured at 0, 60, and 120 min after a 100 g standard steamed bread meal test. The homeostasis model assessment of insulin resistance (HOMA-IR) and Matsuda index were computed to evaluate insulin resistance. The HOMA of b-cell function (HOMA-b) and the area under the curve between insulin and blood glucose (IAUC 0-120 /GAUC 0-120 ) were used to estimate b-cell function. Results: TIR was positively correlated with the 60 and 120 min insulin values, IAUC 0-120 , the Matsuda index, HOMA-b, and IAUC 0-120 /GAUC 0-120 (r s : 0.154, 0.129, 0.137, 0.194, 0.341, and 0.334, respectively; P < 0.05) but inversely correlated with HOMA-IR (r s : -0.239, P < 0.001). After adjusting for confounders, multinomial multiple logistic regression analysis revealed that the odds ratios (ORs) of achieving the target time in range (>70%) increased by 12% (95% confidence interval [CI]: 3-21%), 7% (95% CI: 1-14%), 10% (95% CI: 5-16%), and 45% (95% CI: 25-68%) for each 10 mIU/L increase in the 60 and 120 min insulin values, 10 unit increase in HOMA-b, and unit increase in IAUC 0-120 /GAUC 0-120 , respectively (P < 0.05). Nevertheless, the OR decreased by 10% (95% CI: 1-18%) for each unit increase in HOMA-IR (P < 0.05). Conclusions: Insulin resistance and islet b-cell function are related to capillary blood glucose-assessed TIR.Jingwen Ye and Jiajin Deng contributed equally to this work.

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“…Among those with low baseline %PIR, exposure to forecasts was associated with a 45% increase in %PIR. This increase in %PIR is associated with an eHbA 1c reduction of 1.8%, representing a potential US $2477.72 cost savings [ 31 , 36 ]. These cost savings are incremental increases for those receiving forecasts compared to those not receiving forecasts.…”

Section: Discussionmentioning

confidence: 99%

“…Percentage of blood glucose points in range (%PIR) is a metric adapted from the CGM-specific metric time in range, applied to measurements obtained from manual blood glucose meters. A 10% change in %PIR has been associated with a change in HbA 1c of 0.4% [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

Imrisek¹,

Lee²,

Goldner³

et al. 2022

JMIR Diabetes

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Background Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop’s digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date. Objective This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range. Methods This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses. Results Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose. Conclusions Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.

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Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes

Cited by 23 publications

References 23 publications

Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study

Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study

Time in range assessed by capillary blood glucose in relation to insulin sensitivity and β‐cell function in patients with type 2 diabetes mellitus: A cross‐sectional study in China

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study

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