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

Comparative effect of physical health training and psychological training of the theory of reasoned action (TRA) model on the life quality of patients with diabetes in Tehran, Iran: utilization of message texting

BACKGROUND AND PURPOSE

Providing physical health and mental health training promotion is necessary for a sustainable change in attitude and lifestyle of diabetic patients. The present study was conducted with the aim of comparing the effect of physical health training and psychological training of the theory of reasoned action (TRA) model on the life quality of patients with type 2 diabetes.

METHODS

This experimental study was conducted in 2022 with two intervention groups and one control group consisting of 129 patients with type 2 diabetes who were referred to Imam Khomeini Hospital in Tehran. Over the course of one month, each individual in intervention group 1 received 15 text messages focusing on physical health, while intervention group 2 received 15 psychological text messages related to the TRA. The control group did not receive any text messages during this period. The data collection tool used was the "Audit of Diabetes-Dependent Quality of Life (ADDQoL)" questionnaire, which was completed by the participants before and after the intervention. The data were analyzed using SPSS version 16 software at a statistical significance level of 0.05.

RESULTS

In the intervention-1 group, the average life quality score was 8.51 units (P < 0.001), while in the intervention-2 group, it was 19.25 units (P < 0.001) higher than the control group. The psychological training group had a 17.62 units (P < 0.05) lower average fasting blood sugar (FBS) and a 10.74 units (P < 0.001) higher average quality of life compared to the physical training group.

CONCLUSION

The results of this study showed that the effectiveness of psychological training of the TRA model in improving life quality and reducing FBS in patients with diabetes is greater than physical health training. It is suggested that policy makers and health managers base future plans on physical health promotion training along with TRA model mental health training for the development of education in patients with diabetes. Specialists and healthcare workers can also act to improve personal health characteristics, especially those related to reducing FBS and increasing the quality of life of patients with diabetes, by using training through mobile phone text messages, particularly with psychological content TRA based.

Higher Derived Time in Range With IDegLira Versus Insulin Glargine U100 in People With Type 2 Diabetes

BACKGROUND

Derived time in range (dTIR), calculated from self-monitored blood glucose (SMBG-dTIR) profiles, has demonstrated correlation with risk of cardiovascular and microvascular complications. This post hoc analysis of the DUAL V and DUAL VIII trials aimed to compare dTIR with an insulin degludec/liraglutide fixed-ratio combination (IDegLira) versus insulin glargine 100 units/mL (glargine U100) in people with type 2 diabetes (T2D).

MATERIALS AND METHODS

Nine-point SMBG profiles were taken more than 24 hours at baseline and end of trial (EOT: 26 weeks [DUAL V] and 104 weeks [DUAL VIII]) and used to derive the percentage of readings within target range (70-180 mg/dL). Estimated treatment differences (ETDs, IDegLira-glargine U100) were analyzed using analysis of covariance, with treatment as fixed effects and baseline response as a covariate.

RESULTS

ETDs for change from baseline to EOT in dTIR were significantly greater with IDegLira versus glargine U100 in DUAL V (4.18%, P = .027) and DUAL VIII (5.17%, P = .001). The proportions of people achieving ≥70% dTIR at EOT with IDegLira and glargine U100, respectively, were 62% and 60% in DUAL V (P = .7541), and 50% and 26% in DUAL VIII (P < .0001). The proportion achieving a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 was 63% in both groups in DUAL V (P = .9043), and 44% and 25%, respectively, in DUAL VIII (P < .0001).

CONCLUSIONS

IDegLira was associated with significantly greater increases in dTIR versus basal insulin alone in people with T2D.

TRIAL ID(S)

ClinicalTrials.gov, NCT01952145 (DUAL V); ClinicalTrials.gov, NCT02501161 (DUAL VIII).

Correlation between time on target and glycated hemoglobin in people with diabetes mellitus: systematic review

to analyze the correlation between time on target and glycated hemoglobin in people living with diabetes mellitus and carrying out continuous blood glucose monitoring or self-monitoring of capillary blood glucose.

systematic review of etiology and risk based on JBI guidelines and reported according to Preferred Reporting Items for Systematic Reviews and Meta- Analyses, covering six databases and grey literature. The sample included 16 studies and methodological quality was assessed using JBI tools. Protocol registered in the Open Science Framework, available at https://doi.org/10.17605/OSF.IO/NKMZB.

time on target (70-180 mg/dl) showed a negative correlation with glycated hemoglobin, while time above target (>180 mg/dl) showed a positive correlation. Correlation coefficients ranged between -0.310 and -0.869 for time on target, and between 0.66 and 0.934 for time above target. A study was carried out on a population that performed self-monitoring.

there is a statistically significant correlation between time on target and time above target with glycated hemoglobin. The higher the proportion in the adequate glycemic range, the closer to or less than 7% the glycated hemoglobin will be. More studies are needed to evaluate this metric with data from self-monitoring of blood glucose.

Evaluating the role of time in range as a glycemic target during short-term intensive insulin therapy in patients with newly diagnosed type 2 diabetes

BACKGROUND

Tight glycemic control during short-term intensive insulin therapy (SIIT) is critical for inducing diabetes remission in patients with newly diagnosed type 2 diabetes (T2D). This work aimed to investigate the role of time in range (TIR) during SIIT as a novel glycemic target by predicting clinical outcomes.

METHODS

SIIT was given to 116 patients with newly diagnosed T2D, with daily eight-point capillary glucose monitored. Glycemic targets (fasting/premeal glucose, 3.9-6.0 mmol/L; 2 h postprandial blood glucose, 3.9-7.8 mmol/L) were achieved and maintained for 2 weeks. TIR_PIR was calculated as the percentage of glucose points within these glycemic targets during the maintenance period and was compared to TIR_{3.9-7.8mmol/L} and TIR_{3.9-10.0mmol/L} . Acute insulin response (AIR), HOMA-IR, HOMA-B, and disposition index (DI) were measured. Patients were followed up for 1 year to observe clinical outcomes.

RESULTS

TIR_PIR , TIR_{3.9-7.8mmol/L} , and TIR_{3.9-10.0mmol/L} were 67.2 ± 11.2%, 80.8 ± 9.2%, and 90.1 ± 6.2%, respectively. After SIIT, β-cell function and insulin sensitivity improved remarkably, and the 1-year remission rate was 55.2%. △AIR and △DI were positively correlated with all the TIR values, whereas only TIR_PIR was correlated with △HOMA-IR (r = -0.22, p = 0.03). Higher TIR_PIR but not TIR_{3.9-7.8mmol/L} or TIR_{3.9-10.0mmol/L} was robustly associated with diabetes remission; patients in the lower TIR_PIR tertile had an elevated risk of hyperglycemia relapse (hazard ratio 3.4, 95% confidence interval 1.6-7.2， p = .001). Only those with TIR_PIR  ≥ 65% had a one-year remission rate of over 60%.

CONCLUSIONS

These findings advocate TIR_PIR  ≥ 65% as a novel glycemic target during SIIT for clinical decision-making.

Improved Glycemic Control Using a Bluetooth®-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes

BACKGROUND

The OneTouch Verio Flex® (OTVF) blood glucose (BG) meter features a ColorSure® Range Indicator. Diabetes management is enhanced by connecting the meter to the OneTouch Reveal® (OTR) mobile app. We sought to provide real-world evidence (RWE) that combining both devices improves glycemic control.

METHODS

Anonymized glucose and app analytics were extracted from a server from over 144 000 people with diabetes (PWDs). Data from their first 14 days using OTVF and OTR were compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.

RESULTS

In people with type 1 diabetes (PwT1D) or people with type 2 diabetes (PwT2D), readings in-range (RIR) improved by +6.1 (54.5% to 60.6%) and +11.9 percentage points (68.2% to 80.1%), respectively, over 180 days, and hyperglycemia was reduced by -6.6 (40.5% to 33.9%) and -12.0 (30.3% to 18.3%). In total, 35% of PwT1D and 40% of PwT2D improved RIR by >10 percentage points. People with type 1 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +5.1 and 7.0, respectively. People with type 2 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +11.6 and 12.0, respectively. In PwT1D or PwT2D, mean BG reduced by -11.4 and -19.5 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings. All glycemic changes were statistically significant (P < .0005 level).

CONCLUSION

Real-world data from over 144 000 PWDs demonstrated improved percentage readings in-range and reduced hyperglycemia in PWDs using the OneTouch Verio Flex blood glucose meter and OneTouch Reveal app.

Application of the New "Points in Range" Metrics in the Assessment of In-Hospital Glycemic Control

Introduction Capillary blood glucose (CBG) monitoring remains the most used testing form in hospitals and allows for "points in range (PIR)" metric calculation. This study was conceived to evaluate the metabolic control in patients with diabetes mellitus (DM) at a hospital through PIR metrics. Methods This was an observational cross-sectional study conducted on October 9, 2020, that included non-critical adults admitted to Centro Hospitalar Universitário do Porto (except pregnant/postpartum women) with DM under CBG monitoring and a minimum of 24 hours of hospitalization. Glycemic control was evaluated by previous day CBG monitoring. Results The study sample consisted of 110 patients with DM (93.6% type 2) with a median number of CBG tests of 4.00 (1.00) and a median CBG of 166.20 (69.41) mg/dL, SD 41.93 ± 27.20 mg/dL, and variation coefficient of 22.56 ± 12.51%. Points below range were 0.5%, with 0% below 54 mg/dL. The points in ranges 70-140 mg/dL and 140-180 mg/dL were 32.8% and 22.0%, respectively, and the total number of patients with all points in range 70-180 mg/dL was 19 (17.3%), with only 3 (2.7%) having all points in range 140-180 mg/dL and 10 (9.1%) in range 70-140 mg/dL. Regarding points above range (PAR), 29.9% and 14.8% points were at levels 1 and 2 hyperglycemia, respectively, and 15 (13.6%) patients had all points above 180 mg/dL. Correlations were identified between PAR and the total number of CBG assessments (ρ = 0.689, p < 0.001). Conclusion We conclude that in-hospital glycemic control remains suboptimal: only few have adequate control according to the PIR metrics despite low glycemic variability. PIR metrics are a new, valuable, simple and valid way to take better advantage of CBG monitoring at no added cost.

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

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 β-cell function (BCF).

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 (IR). HOMA of β-cell function (HOMA-β) and the area under the curve between insulin and blood glucose (IAUC_0-120 /GAUC_0-120 ) were used to estimate BCF.

RESULTS

TIR was positively correlated with 60- and 120-min insulin values, IAUC_0-120 , the Matsuda index, HOMA-β, 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 TIR (>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 60- and 120-min insulin value, 10-unit increase in HOMA-β, 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

IR and BCF are related to CBG-assessed TIR.

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

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.

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

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.