Self-monitoring Of Blood Glucose Data Research Articles

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study.

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control. To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support. In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels. Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses. The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes. Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

A Minimized Measurement Scheme for Predicting HbA1c Using Discrete Self-Monitoring Blood Glucose Data Within 4 Weeks for People with Type 2 Diabetes.

Objective: To establish an accurate and robust calculation model for predicting hemoglobin A1c (HbA1c) for people with type 2 diabetes (T2D) by using the fewest discrete blood glucose values according to an irregular data set and propose an appropriate cost-effective and scientific scheme for routine blood glucose monitoring. Methods: By using two data sets obtained from 2017 to 2022, which involved 2432 people with T2D, ∼420,000 irregular blood glucose values, and 10,000 HbA1c values, multiple blood glucose monitoring schemes were designed and compared to find the optimal one. The data were structured and then fitted using a regularized extreme learning machine, and the results were evaluated on the basis of indicators such as mean absolute error (MAE), root mean square error, and the relevance analysis (R) value; the optimal scheme for routine blood glucose monitoring was determined by combining the accuracy and the cost and was compared with previous studies in terms of accuracy and stability. Results: Data fitting results for the chosen scheme: R = 0.8029 (P < 0.001), MAE = 0.3181% (95% confidence interval, 0.2666-0.3695%). Within the last 4 weeks before the prediction of HbA1c, a minimum of only seven fasting and seven postprandial blood glucose values are needed, of which are one fasting and one postprandial blood glucose values per 4 days. Compared with previous studies, the prediction model shows better accuracy and stability (P < 0.05), especially under the great glucose fluctuation group. Conclusion: A minimized calculation model for accurately and robustly predicting HbA1c using discrete self-monitoring of blood glucose data within 4 weeks for people with T2D has been established and provides a new reference for the design of a scheme for blood glucose monitoring. The diabetes care clinic of Peking University First Hospital (Registration Number: ChiCTR2300068139).

Efficacy and Safety of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Systematic Review and Meta-analysis of Interventional Evidence.

Traditional diabetes self-monitoring of blood glucose (SMBG) involves inconvenient finger pricks. Continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems offer CGM, enhancing type 2 diabetes (T2D) management with convenient, comprehensive data. To assess the benefits and potential harms of CGM and isCGM compared with usual care or SMBG in individuals with T2D. We conducted a comprehensive search of MEDLINE, Embase, the Cochrane Library, Web of Science, and bibliographies up to August 2023. We analyzed studies meeting these criteria: randomized controlled trials (RCT) with comparison of at least two interventions for ≥8 weeks in T2D patients, including CGM in real-time/retrospective mode, short-/long-term CGM, isCGM, and SMBG, reporting glycemic and relevant data. We used a standardized data collection form, extracting details including author, year, study design, baseline characteristics, intervention, and outcomes. We included 26 RCTs (17 CGM and 9 isCGM) involving 2,783 patients with T2D (CGM 632 vs. usual care/SMBG 514 and isCGM 871 vs. usual care/SMBG 766). CGM reduced HbA1c (mean difference -0.19% [95% CI -0.34, -0.04]) and glycemic medication effect score (-0.67 [-1.20 to -0.13]), reduced user satisfaction (-0.54 [-0.98, -0.11]), and increased the risk of adverse events (relative risk [RR] 1.22 [95% CI 1.01, 1.47]). isCGM reduced HbA1c by -0.31% (-0.46, -0.17), increased user satisfaction (0.44 [0.29, 0.59]), improved CGM metrics, and increased the risk of adverse events (RR 1.30 [0.05, 1.62]). Neither CGM nor isCGM had a significant impact on body composition, blood pressure, or lipid levels. Limitations include small samples, single-study outcomes, population variations, and uncertainty for younger adults. Additionally, inclusion of <10 studies for most end points restricted comprehensive analysis, and technological advancements over time need to be considered. Both CGM and isCGM demonstrated a reduction in HbA1c levels in individuals with T2D, and unlike CGM, isCGM use was associated with improved user satisfaction. The impact of these devices on body composition, blood pressure, and lipid levels remains unclear, while both CGM and isCGM use were associated with increased risk of adverse events.

982-P: Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile Application and the Cloud System on Glycemic Control and Self-Management in Outpatients with Diabetes

Objective: In this study, we evaluated the impact of an application-cloud cooperation system with feedback from the attending physician during face-to-face visits on outpatient management of diabetes. Research Design and Methods: In this multicenter, open-label, single-armed prospective study, 48 patients with diabetes treated with insulin or GLP-1 receptor agonists and performing self-monitoring of blood glucose (SMBG) at three medical centers in Japan used the Smart e-SMBG system, an application-cloud cooperation system, for 24 weeks. The SMBG data were automatically uploaded to the cloud via the application. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients’ regular visits. The primary outcome was changes in HbA1c levels. Results: While HbA1c levels did not change significantly in total patients, it significantly decreased at 12 weeks in 21 patients whose antidiabetic medication had not been adjusted during the intervention period (−0.26%, 95% confidence interval [CI], −0.47 to −0.05; p = 0.018). The frequency of SMBG was increased significantly at 12 weeks (+0.60/day, 95%CI, 0.19-1.00; p = 0.002) and 24 weeks (+0.43/day, 95%CI, 0.02-0.84; p = 0.039) compared to that before inducing the system. The Diabetes Treatment Satisfaction Questionnaire total score and “Q4; convenience” and “Q5; flexibility” scores significantly improved after using the Smart e-SMBG system, and 71.7% patients and 76.1% physicians reported that the Smart e-SMBG system helped them adjust insulin doses. Conclusions: Digitization of SMBG records and sharing of the results with attending physicians during face-to-face visits improved glycemic control and self-management in patients with diabetes. Disclosure T.Handa: None. T.Onoue: None. T.Kobayashi: None. R.Maeda: None. H.Arima: None.

Achievement of Target Glycemic Goal with Simple Basal Insulin Regimen in Women with Gestational Diabetes: A Prospective Cohort Study.

There is little evidence concerning the need to treat gestational diabetes (GDM) in the same way as pregestational diabetes. We evaluated the efficacy of the simple insulin injection (SII) regimen for achieving the target glucose goal without increasing adverse perinatal outcomes in singleton pregnant women with GDM. All subjects underwent self-monitoring of blood glucose (SMBG), and insulin therapy was indicated according to the SMBG profile. Insulin was initially started with the SII regimen, in which one daily injection of NPH insulin before breakfast was used, and another NPH injection was added at bedtime, if necessary. We used the target glucose as <95 mg/dL at fasting and <120 mg/dL postprandial and accepted <130 mg/dL for the latter. If the target glucose did not reach with the regimen, we switched to the multiple daily injection (MDI) with additional prandial insulin aspart. We compared the SMBG profile before delivery as well as the perinatal outcomes between the SII and MDI groups. Among 361 women (age 33.7 years, nullipara 41%, prepregnancy body mass index 23.2 kg/m2) with GDM, 59%, 18%, and 23% were in the diet-alone, SII, and MDI groups, respectively. Consequently, regarding women requiring insulin therapy, 43% were treated with the SII regimen throughout pregnancy. The severity of baseline hyperglycemia according to the SMBG data at baseline was the MDI>the SII>the diet group. The rate of achieving target glucose levels before delivery in the SII group at fasting, postprandial < 120 mg/dL and <130 mg/dL were 93%, 54% and 87%, respectively, which were similar to that in the MDI group (93%, 57%, and 93%, respectively), with no significant differences in perinatal outcomes. In conclusion, more than 40% of women with GDM requiring insulin therapy achieved the target glucose goal with this simple insulin regimen without any increase in adverse effects.

Using Digital Health Technology to Prevent and Treat Diabetes.

Using Digital Health Technology to Prevent and Treat Diabetes.

Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus.

For decades, self-monitoring of blood glucose (SMBG) has been considered a cornerstone of adequate diabetes management. Structured SMBG can follow different monitoring patterns, and it results in improved glycemic control, reduced hypoglycemia, and a better quality of life of people with diabetes. The technology, usability, and accuracy of SMBG systems have advanced markedly since their introduction a few decades ago. Current SMBG systems are small and easy to use, require small (capillary) blood sample volumes, and provide measurement results within seconds. In addition, devices are increasingly equipped with features such as connectivity to other devices and/or digital diaries and diabetes management tools. Although measurement quality can come close to or equal that of the glucose monitoring systems used by healthcare professionals, several available SMBG systems still do not meet internationally accepted accuracy standards, such as the International Organization for Standardization 15197 standard. Reports from China, India, and Brazil based on local experience suggest that in addition of the accuracy issues of SMBG systems, other obstacles also need to be overcome to optimize SMBG usage. Nonetheless, adequate usage of SMBG data is of high relevance for the management of people with type 2 diabetes mellitus.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-022-01254-8.

Randomized comparison of self-monitored blood glucose (BGM) versus continuous glucose monitoring (CGM) data to optimize glucose control in type 2 diabetes

AimsEvaluate whether structured BGM testing (BGM) or real-time CGM (CGM) lead to improved glucose control (A1c). Determine which approach optimized glucose control more effectively. Methods—multi-arm paralleltrial of three type 2 diabetes (T2D) therapies ± metformin: (1) sulfonylurea (SU), (2) incretin (DPP4 inhibitor or GLP-1 agonist), or (3) insulin. After a baseline CGM, 114 adult subjects were randomized to either BGM (4 times daily) or CGM (24/7) for 16 weeks with therapies adjusted every 4 weeks. ResultsA1c means decreased from 8.19 to 7.07 (1.12% difference) with CGM (n = 59) and 7.85 to 7.03 (0.82% difference) with BGM (n = 55) (p < 0.001). BGM and CGM groups showed significant improvements in time in range and glucose variability—with no significant difference between the two groups. Clinically important hypoglycemia (<50 mg/dL) was significantly reduced for the CGM group compared with BGM (p < 0.01), particularly in subjects taking insulin or therapies with higher hypoglycemic risk (SU). ConclusionIn T2D, structured, consistent use of glucose data regardless of device (structured BGM or CGM) leads to improvements in A1c control. CGM is more effective than BGM in minimizing hypoglycemia particularly for those using higher hypoglycemic risk therapies.

An Enhanced SMS Text Message-Based Support and Reminder Program for Young Adults With Type 2 Diabetes (TEXT2U): Randomized Controlled Trial.

BackgroundClinic attendance, metabolic control, engagement in self-management, and psychological health are suboptimal in young-onset (age of onset <40 years) type 2 diabetes.ObjectiveWe examined the effectiveness of an enhanced SMS text message–based support and reminder program in improving clinic attendance, metabolic control, engagement in self-management, and psychological health in young-onset type 2 diabetes.MethodsA 12-month, parallel-arm, randomized controlled trial comparing an enhanced, semipersonalized SMS text message–based intervention (incorporating 1-8 supportive and/or informative text messages per month) against standard care was conducted in a specialized clinic for young adult type 2 diabetes. The primary outcome was maintenance of 100% attendance at scheduled quarterly clinical appointments. Secondary outcomes included (1) metabolic indices, (2) pathology and self-monitored blood glucose (SMBG) data availability, and (3) psychosocial well-being.ResultsA total of 40 participants were randomized, and 32 completed their 12-month study visit. The average participant age was 32.7 (SD 5.1) years, 50% (20/40) were male, and baseline glycated hemoglobin A1c (HbA1c) was 7.3% (SD 1.9%) (56 mmol/mol, SD 20). A higher proportion of the intervention group achieved 100% attendance (12/21, 57%, vs 5/19, 26%, for the control group); Kaplan-Meier analysis demonstrated significantly greater cumulative attendance in the intervention group (P=.04). There were no between-group differences in HbA1c, BMI, lipids, or availability of pathology and SMBG data. Odds of recording an improvement in the Diabetes Empowerment Scale–Short Form score were higher in the intervention group at 6 months (odds ratio [OR] 4.3, 95% CI 1.1-17), with attenuation of this effect at study end (OR 3.1, 95% CI 0.9-11). Program acceptability was high; >90% of participants would recommend the program to new patients.ConclusionsAn enhanced SMS text message–based support and reminder program doubled scheduled clinic attendance rates for patients with young-onset type 2 diabetes. The program was highly acceptable and provided early support for patient empowerment but had no significant effect on measures of metabolic control or self-management.Trial RegistrationAustralian and New Zealand Clinical Trials Registry (ACTRN12618000479202); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373579

Continuous Glucose Monitoring (CGM) Readings During Patient-Reported Symptomatic Hypoglycemia: Assessment of the Advanced Technologies and Treatments for Diabetes Consensus Definition of CGM-Recorded Hypoglycemia.

Aim: Continuous glucose monitoring (CGM) is widely used in clinical practice and research to detect hypoglycemia. A consensus definition of CGM-recorded hypoglycemia is made by a group of international experts under the auspice of the Advanced Technologies and Treatments for Diabetes (ATTD). The purpose of this study is to compare the definition with patient-reported hypoglycemia. Methods: In a prospective, observational study of 186 patients with type 1 diabetes using blinded Medtronic iPro 2 CGM for 6 days, every patient-reported symptomatic hypoglycemic event and interstitial glucose (IG) values at the registration time were classified according to the ATTD definition of CGM-recorded hypoglycemia. For comparison between CGM and self-monitored blood glucose (SMBG) values, the International Hypoglycemia Study Group (IHSG) classification of hypoglycemia and chi-square test were used. Results: A total of 321 events of symptomatic hypoglycemia were reported by 68% of the patients, corresponding to 2.0 ± 2.3 events (mean ± standard deviation) per patient-week. A total of 206 (64%) events met the CGM consensus definition. In the remaining 115 (36%) not-confirmed events, 5 events had an IG <3.9 mmol/L, which lasted <15 min. The overall mean IG value was 3.6 ± 1.1 mmol/L (median 3.1, range 2.2-10.4). In symptomatic hypoglycemic events with both CGM and SMBG data, SMBG confirmed significantly more symptomatic hypoglycemic events than CGM (P < 0.001). Conclusion: CGM-recorded hypoglycemia according to the consensus definition is present at two thirds of all patient-reported events when recorded by the Medtronic iPro 2 system. The recommended minimum duration of a hypoglycemic event of 15 min is supported by the study. SMBG measurements detect significantly more symptomatic hypoglycemic events than Medtronic iPro 2 CGM.

107-LB: Relative Contribution of Fasting Plasma and Postprandial Glucose to HbA1c and TIR in People with T1D on Basal-Bolus Insulin Therapy

Background: A recent report was published on the contribution of fasting plasma glucose (FPG) and postprandial glucose (PPG) to HbA1c and time in range (%TIR) in people with type 2 diabetes on multiple daily injection regimens using prandial Humalog® (Lispro). We extended this analysis to type 1 diabetes (T1D) using data from people randomized to 26 weeks of mealtime ultra rapid lispro (URLi; N=451), postmeal URLi (N=329) or mealtime Humalog (N=442) in the phase 3 trial, PRONTO-T1D, and its continuous glucose monitoring (CGM) substudy. Methods: A multivariate regression model was used to quantify the contribution of FPG and PPG change to the change in HbA1c and %TIR. The contribution of PPG relative to FPG was determined. %TIR was derived from 10-point self-monitored blood glucose (SMBG) and CGM data from the CGM substudy. Only results for the mealtime groups are presented here. Results: With URLi treatment, a 1 mmol/L reduction in FPG and PPG was associated with a 0.11% ± standard error 0.02% and 0.09% ± 0.01% reduction in HbA1c, respectively, both p&amp;lt;0.0001, with a contribution of PPG relative to FPG of 82%. Similar results were obtained with Humalog: A 1 mmol/L reduction in FPG and PPG was associated with reduction in HbA1c of 0.12% ± 0.02% and 0.07% ± 0.02%, respectively, both p&amp;lt;0.0001, but the contribution of PPG relative to FPG was 58%. For SMBG-derived %TIR, with URLi treatment, a 1 mmol/L reduction in FPG and PPG was associated with an increase in %TIR of 10.0% ± 0.53% and 8.6% ± 0.47%, respectively, all p&amp;lt;0.0001. Similarly, with Humalog, a 1 mmol/L reduction in FPG and PPG was associated with an increase in %TIR of 9.8% ± 0.56% and 8.6% ± 0.52%, respectively, all p&amp;lt;0.0001 A similar trend was observed with CGM data, although the absolute values of contributions of FPG and PPG differed. Conclusions: Changes in FPG and PPG significantly impacted HbA1c and %TIR in people with T1D, reinforcing the need to manage both FPG and PPG to achieve optimal glycemic control. Disclosure C. Piras de oliveira: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. M. A. Dellva: Employee; Self; Eli Lilly and Company. J. M. Bue-valleskey: Employee; Self; Eli Lilly and Company. A. M. Chang: Employee; Self; Eli Lilly and Company. F. B. Chigutsa: None. B. Liao: Employee; Self; Eli Lilly and Company. Funding Eli Lilly and Company

615-P: Developing Self-Monitored Blood Glucose Metrics Correlated to Glycated Hemoglobin to Improve Real-World Evidence Generation

Background: Self-monitoring blood glucose (SMBG) values have been used to estimate A1c (eA1c) using the A1c-Derived Average Glucose (ADAG) equation with modifications causing limitations with the results. Our study's objective was to develop new glucose management metrics leveraging real-world SMBG values and checking patterns correlated to lab A1c values.Methods: We conducted a retrospective analysis of data from the Livongo remote management Program for Diabetes. Spearman rank was used to measure the correlation between lab A1c and mean SMBG values by relationship to meals during a 90-day time-period. Using the ADAG equation, mean SBMG metrics were used to generate eA1c values for comparison.Results: There were 815 participants with 906 lab A1c and SMBG values with mean age of 56 years, 54% male, and 10% type 1. Three metrics in Table 1 most correlated (r >= 0.70) to lab A1c had differences of eA1c from -0.5% to -0.6%. Post-meal SMBG had slightly lower correlation (0.66) but with a mean eA1c more similar to the laboratory value.Conclusions: Though real-world SMBG values are correlated to lab A1c values, checking patterns impact the ability of the ADAG equation to estimate A1c. Future work will develop a new linear transformation equation based on meal distribution checking patterns to improve A1c estimation and the ability to leverage SMBG data for real-world evidence generation.View largeDownload slideView largeDownload slide DisclosureM. Perez-nieves: Employee; Self; Eli Lilly and Company. W. Lu: Employee; Self; Teladoc Health. E. Meadows: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. F. Gelsey: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. L. Fan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health.

Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial.

BackgroundPatients with poorly controlled type 2 diabetes (T2D) experience increased morbidity, increased mortality, and higher cost of care. Self-monitoring of blood glucose (SMBG) is a critical component of diabetes self-management with established diabetes outcome benefits. Technological advancements in blood glucose meters, including cellular-connected devices that automatically upload SMBG data to secure cloud-based databases, allow for improved sharing and monitoring of SMBG data. Real-time monitoring of SMBG data presents opportunities to provide timely support to patients that is responsive to abnormal SMBG recordings. Such diabetes remote monitoring programs can provide patients with poorly controlled T2D additional support needed to improve critical outcomes.ObjectiveTo evaluate 6 months of a diabetes remote monitoring program facilitated by cellular-connected glucose meter, access to a diabetes coach, and support responsive to abnormal blood glucose recordings greater than 400 mg/dL or below 50 mg/dL in adults with poorly controlled T2D.MethodsPatients (N=119) receiving care at a diabetes center of excellence participated in a two-arm, 12-month randomized crossover study. The intervention included a cellular-connected glucose meter and phone-based diabetes coaching provided by Livongo Health. The coach answered questions, assisted in goal setting, and provided support in response to abnormal glucose levels. One group received the intervention for 6 months before returning to usual care (IV/UC). The other group received usual care before enrolling in the intervention (UC/IV) for 6 months. Change in hemoglobin A1c (HbA1c) was the primary outcome, and change in treatment satisfaction was the secondary outcome.ResultsImprovements in mean HbA1c were seen in both groups during the first 6 months (IV/UC −1.1%, SD 1.5 vs UC/IV −0.8%, SD 1.5; P<.001). After crossover, there was no significant change in HbA1c in IV/UC (mean HbA1c change +0.2, SD 1.7, P=.41); however, those in UC/IV showed further improvement (mean HbA1c change −0.4%, SD 1.0, P=.008). A mixed-effects model showed no significant treatment effect (IV vs UC) over 12 months (P=.06). However, participants with higher baseline HbA1c and those in the first time period experienced greater improvements in HbA1c. Both groups reported similar improvements in treatment satisfaction throughout the study.ConclusionsPatients enrolled in the diabetes remote monitoring program intervention experienced improvements in HbA1c and treatment satisfaction similar to usual care at a specialty diabetes center. Future studies on diabetes remote monitoring programs should incorporate scheduled coaching components and involve family members and caregivers.Trial RegistrationClinicalTrials.gov NCT03124043; https://clinicaltrials.gov/ct2/show/NCT03124043

Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It

Background: Glycated hemoglobin (HbA1c), which reflects the patient's blood sugar level, can only be measured in a hospital setting. Therefore, we developed a model predicting HbA1c using personal information and self-monitoring of blood glucose (SMBG) data solely obtained by a patient.Methods: Leave-one-out cross-validation (LOOCV) was performed at two university hospitals. After measuring the baseline HbA1c level before SMBG (Pre_HbA1c), the SMBG was recorded over a 3-month period. Based on these data, an HbA1c prediction model was developed, and the actual HbA1c value was measured after 3 months. The HbA1c values of the prediction model and actual HbA1c values were compared. Personal information was used in addition to SMBG data to develop the HbA1c predictive model.Results: Thirty model training sessions and evaluations were conducted using LOOCV. The average mean absolute error of the 30 models was 0.659 (range, 0.005–2.654). Pre_HbA1c had the greatest influence on HbA1c prediction after 3 months, followed by post-breakfast blood glucose level, oral hypoglycemic agent use, fasting glucose level, height, and weight, while insulin use had a limited effect on HbA1c values.Conclusions: The patient's SMBG data and personal information strongly influenced the HbA1c predictive model. In the future, it will be necessary to develop sophisticated predictive models using large samples for stable SMBG in patients.

Fasting and Postprandial Hyperglycemia: Their Predictors and Contributions to Overall Hyperglycemia in Korean Patients with Type 2 Diabetes.

BackgroundThis study aimed to identify factors that affect fasting hyperglycemia (FHG) and postprandial hyperglycemia (PPG) and their contributions to overall hyperglycemia in Korean patients with type 2 diabetes mellitus (T2DM).MethodsThis was a retrospective study conducted on 194 Korean T2DM patients with 7-point self-monitoring blood glucose (SMBG) profiles plotted in 4 days in 3 consecutive months. We calculated the areas corresponding to FHG and PPG (area under the curve [AUC]FHG and AUCPPG) and contributions (%) in the graph of the 7-point SMBG data. The levels of glycated hemoglobin (HbA1c) were categorized by tertiles, and the contributions of FHG and PPG were compared.ResultsThe relative contribution of FHG increased (44.7%±5.6%, 58.0%±4.4%, 66.5%±2.8%; PANOVA=0.002, PTREND <0.001), while that of PPG decreased (55.3%±5.5%, 42.0%±4.4%, 33.5%±2.8%; PANOVA=0.002, PTREND <0.001) with the elevated HbA1c. Multivariate analysis showed that HbA1c (β=0.615, P<0.001), waist circumference (β=0.216, P=0.042), and triglyceride (β=0.121, P=0.048) had a significant association with AUCFHG. Only HbA1c (β=0.231, P=0.002) and age (β=0.196, P=0.009) was significantly associated with AUCPPG.ConclusionThe data suggested that in Korean T2DM patients, FHG predominantly contributed to overall hyperglycemia at higher HbA1c levels, whereas it contributed to PPG at lower HbA1c levels. It is recommended that certain factors, namely age, degree of glycemic control, obesity, or triglyceride levels, should be considered when prescribing medications for T2DM patients.

862-P: A Machine Learning Model Predicts Engagement with a Mobile Health (mHealth)-Based Remote Patient Monitoring (RPM) Program among Persons with Diabetes (PWDs)

We developed a machine learning model that predicts risk of declining engagement (churn) with an mHealth app (Glooko) that supports diabetes self-management. The model uses self-monitored blood glucose (SMBG), demographic, and app engagement data. We selected 7134 PWD using meter and/or insulin pump devices who used the Glooko diabetes management app in a remote (outside of the clinic) setting between 1/1/2019-7/1/2019 (40,349 total weekly predictions). We trained a gradient boosting algorithm to predict risk of churn in the next 28 days. We built features using data from the prior 28 days; features included outcomes (counts of glucose checks, hypo/hyperglycemia occurrences, mean blood glucose), app engagement (counts of app clicks and meter and/or insulin pump uploads, duration of sessions), and demographics (diabetes type, gender, age). The training cohort had median age=22 years (IQR:11-49), 52% female, 60% type 1 diabetes (T1D). We performed out-of-sample validation using 4647 individuals (8646 total weekly predictions). The validation cohort had median age=20 years (IQR:11-49), 52% female, 60.5% T1D. The algorithm predicted individuals at risk of churning with a precision of 0.67 and a recall of 0.8. This correlates to sensitivity=80%, specificity=58%, and positive predictive value=67%. The present model indicates that it is feasible to predict declining engagement with an mHealth app to support diabetes self-management. The ability to predict churn may help clinics design effective mHealth-based RPM programs. These predictions can allow clinicians to proactively reach out to PWDs at risk of program dropout. Future research should examine whether dropout from RPM programs predicts worsening diabetes-related outcomes. Disclosure S. Babikian: Employee; Self; Glooko, Inc. V. Singh: Employee; Self; Glooko, Inc. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc.

A mobile app identifies momentary psychosocial and contextual factors related to mealtime self-management in adolescents with type 1 diabetes

Effective diabetes problem solving requires identification of risk factors for inadequate mealtime self-management. Ecological momentary assessment was used to enhance identification of factors hypothesized to impact self-management. Adolescents with type 1 diabetes participated in a feasibility trial for a mobile app called MyDay. Meals, mealtime insulin, self-monitored blood glucose, and psychosocial and contextual data were obtained for 30 days. Using 1472 assessments, mixed-effects between-subjects analyses showed that social context, location, and mealtime were associated with missed self-monitored blood glucose. Stress, energy, mood, and fatigue were associated with missed insulin. Within-subjects analyses indicated that all factors were associated with both self-management tasks. Intraclass correlations showed within-subjects accounted for the majority of variance. The ecological momentary assessment method provided specific targets for improving self-management problem solving, phenotyping, or integration within just-in-time adaptive interventions.

Frequency of self-monitoring of blood glucose in relation to weight loss and A1C during intensive multidisciplinary weight management in patients with type 2 diabetes and obesity

ObjectiveWe evaluated the relationship between frequency of self-monitoring of blood glucose (SMBG) and body weight, A1C, and cardiovascular risk factors in patients with type 2 diabetes (T2D) and obesity enrolled...

Long-Term Effectiveness of Continuous Subcutaneous Insulin Infusion in the Prevention of Hypoglycemia in Adults with Type 1 Diabetes

Background: Reducing hyperglycemia while avoiding hypoglycemia is the key clinical goal in managing people with type 1 diabetes. Insulin delivery techniques and regimens are constantly evolving to achieve these goals. At present, use of multiple daily injections (MDI) is the standard of care, but there is increasing interest in continuous subcutaneous insulin infusion (CSII). There is a deficit of studies comparing long-term glycemic control and hypoglycemia outcomes between these therapeutic options. Methods: This was a single-center, retrospective cohort study of adults with type 1 diabetes. Data were derived from electronic medical records and included demographic and clinical factors. Participants had all undergone intensive diabetes education, followed by CSII or continued MDI. The primary outcome was difference in hypoglycemia, defined as the percentage of self-monitoring blood glucose levels less than 3.9 mmol/L. Up to 10 years of follow-up data were available, between 2000 and 2016. Results: There were 69 participants using CSII and 78 using MDI. Self-monitoring blood glucose data showed significantly less hypoglycemia with CSII by over 30%, occurring as early as the first year and sustained throughout the follow-up period (P < 0.001). This benefit of CSII on reducing hypoglycemia was independent of more frequent hypoglycemia and higher body weight at baseline, factors that were also independently associated with reduced hypoglycemia. Conclusions: In selected adults with type 1 diabetes, long-term CSII can provide long-term clinically relevant and sustained reductions in hypoglycemia, particularly in those with greater initial risk of hypoglycemia and higher body weight, and improved glycemic control compared with MDI.

Self-Monitoring of Blood Glucose in Youth-Onset Type 2 Diabetes: Results From the TODAY Study.

To determine whether self-monitoring of blood glucose (SMBG) is associated with lower HbA1c in youth with type 2 diabetes taking oral medications only or after starting insulin for persistently elevated HbA1c. Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study participants (n = 699) taking oral medications were asked to perform SMBG twice daily. After reaching primary outcome (PO) (HbA1c ≥8% [64 mmol/mol]) over 6 months or an inability to wean from temporary insulin because of metabolic decompensation), insulin glargine was started. HbA1c and percent of SMBG (SMBG%) (percent days when the meter was used one or more times) before and after PO were analyzed. SMBG declined over time and was inversely related to HbA1c (P < 0.0001). Of 298 youth who reached PO and started insulin, 282 had SMBG data. At PO, mean ± SD age was 15.8 ± 2.3 years, BMI 35.5 ± 7.9 kg/m2, and HbA1c 9.6 ± 2.0% (81 ± 21.9 mmol/mol); 65.3% were female. Median SMBG% was 40% at PO, which increased to 49% after 6 months and fell to 41% after 1 year on insulin. At PO, 22% of youth checked ≥80% of days, which increased to 25% and fell to 19% after 6 and 12 months using insulin, respectively. At PO, compared with those who checked <80%, youth who checked ≥80% were younger and with a lower BMI, HbA1c, and blood pressure. SMBG ≥80% was associated with ≥1% reduction in HbA1c at 6 and 12 months after insulin initiation. Low SMBG adherence was common and associated with higher HbA1c. Optimal SMBG frequency in youth using or not using insulin, and whether less frequent SMBG is a marker for overall worse self-care, require further study.