bluuebunny/crossref_metadata_2025_split huggingface dataset:
1907-LB: Estimating Optimal CGM Wear Time for Individuals with Type 2 Diabetes Not on Insulin
Authors: BARUA SOUPTIK , UPADHYAY DHAIRYA A. , CURRAN MARGARET , BERUBE LAUREN , POPP COLLIN , LOU POMPEII MARY , WANG JOSEPHINE C. , VANEGAS SALLY M. , DORCELY BRENDA , HU LU , ST-JULES DAVID , BERGMAN MICHAEL , SEGAL ERAN , ANN SEVICK MARY 
Published: June 2024

Objective: Continuous glucose monitoring (CGM) consensus guidelines, developed using large CGM datasets in type 1 and insulin-treated type 2 diabetes, recommend 14 days of wear time with at least 70% (~10 days) complete data. We sought to examine whether fewer days could reliably capture glycemic variability measures such as time in range (TIR) in individuals with type 2 diabetes (T2D) not on insulin.
Methods: The NIH-funded DiaTeleMed study is a 6-month randomized controlled trial comparing three different dietary strategies in adults diagnosed with T2D managed using diet alone or diet and metformin. We analyzed blinded Freestyle Libre Pro CGM data collected for up to 14 days before the intervention started. For participants with at least 10 complete days of data, we computed their time in 70-140 mg/dL (TIR140) and 70-180 mg/dL (TIR180) range using a cumulative approach starting from the first day and successively adding each day of data until their final day. We then identified the minimum number of days it took for the cumulative TIR metrics to stabilize within a clinically meaningful ±2% of their final values.
Results: We analyzed 80 participants (age 60±12 years, 63% female, 47% non-White, 14% Hispanic/Latino, BMI 30±6 kg/m2, HbA1c 6.7±0.6%, 66% on metformin) who had at least 10 days of complete CGM data. Participants’ cumulative TIR stabilized within ±2% of final values in 70±20% of days worn for TIR140 and in 50±23% of days worn for TIR180. Extrapolating these percentages to 14 days of wear, effectively 10±3 days and 7±3 days would be sufficient to estimate TIR140 and TIR180 respectively in this population.
Conclusion: Fewer than the 14 recommended days may be sufficient to estimate important CGM-derived metrics such as the TIR in individuals with T2D managed with diet alone or diet plus metformin. CGMs worn for less than the mandated 14-day period due to issues such as device fall-off, discomfort, or malfunction may still provide reliable glycemic variability information.

Disclosure
S. Barua: None. M. Curran: None. L. Berube: None. C. Popp: None. M. Pompeii: None. J.C. Wang: None. S.M. Vanegas: None. B. Dorcely: None. L. Hu: None. D. St-Jules: None. M. Bergman: None. E. Segal: None. M. Sevick: None.

Funding
National Institutes of Health (R01-NR018916 )
1911-LB: Developing CGM Knowledge Survey for Non-endocrinologists
Authors: FANCHIANG SHANPIN , BOUCHONVILLE MATTHEW F. , SHAHINIAN SUNSHINE S. , GARG RAJESH K. , LO YU-CHIEH , PETERS ANNE L. 
Published: June 2024

Introduction: Optimal use of continuous glucose monitoring (CGM) is essential for healthcare professionals (HCPs), including primary care practitioners and allied health professionals, to enhance diabetes care. We designed the CGM Knowledge Survey (CGM KS) as an assessment and learning tool to support non-endocrinologists using CGM as an improvement project. We hypothesized that HCPs (P) participating in either of the two scheduled CGM education activities (I), the CGM seminar and the Diabetes Technology Workshop, would lead to a significant improvement (C) in CGM KS scores (O) among HCPs.
Methods: A multidisciplinary diabetes care team developed a 13-item CGM KS. The HCPs completed the CGM KS anonymously both before and after participating in one of two educational activities. A group of CGM ECs, mostly certified diabetes educators with experience in helping patients using CGM, were asked to fill out the CGM KS to provide a benchmark. Non-parametric statistical methods were used to analyze the data.
Results: The CGM KS was completed by HCPs pre-education (n=16), post-education (n=12), and CGM ECs (n=18). No significant difference was found between the scores of the CGM KS pre-education among the HCPs attending the two education activities. The CGM KS scores showed a significant improvement from 4.69 (± 1.92) to 7.00 (± 2.17) (P=0.008) pre- and post-education. Prior to education, the scores of CGM KS were significantly higher among CGM ECs compared to HCPs, with scores of 8.56 (± 2.23) versus 4.69 (± 1.92) (P=0.002). However, post-education, the CGM KS scores among CGM ECs were not found to be significantly different from those among the HCPs, with scores of 8.56 (± 2.23) versus 7.00 (± 2.17) (P=0.053).
Conclusion: The CGM KS has good potential to evaluate the impact of educational interventions aimed at building expertise in the use of CGM in diabetes care.

Disclosure
S. Fanchiang: None. M.F. Bouchonville: None. R.K. Garg: None. Y. Lo: None. A.L. Peters: Advisory Panel; Lilly Diabetes, Vertex Pharmaceuticals Incorporated, Medscape. Research Support; Abbott, Insulet Corporation.
1920-LB: Strong Correlation between More Readings in Tight Range and Greater Engagement with the OneTouch Reveal Diabetes App in a Real-World Dataset of 23,147 People with Type 1 Diabetes
Authors: HOLT ELIZABETH H. , CAMERON HILARY , GRADY MIKE 
Published: June 2024

Background: Time in tight range (TITR) is a new metric applied to continuous glucose monitoring (CGM) data. We investigated the relationship between readings in tight range (RITR) and readings in range (RIR) using blood glucose meter (BGM) data in people with type 1 diabetes (PwT1D) using the OneTouch Reveal® diabetes app.
Methods: Anonymized glucose and app analytics were extracted from our server for 23,147 PwT1D. Glycemic data from their first 14 days using the app and meter were compared to the 14 days prior to a 180-day timepoint using paired within-subject differences.
Results: Glucose RITR (70-140 mg/dl) improved by +5.3 percentage points (%pts) (from 36.5 to 41.8%) in PwT1D performing 1 to 2 app sessions per week and this increased to +11.2 %pts (from 41.9 to 53.0%) in those performing &gt;4 to 10 app sessions. Those performing 1-2 app sessions spent 9 minutes per week on the app compared to 38 minutes in those performing &gt;4-10 sessions. Trends in % RITR with increasing app engagement were mirrored by improving % RIR, lower mean glucose, and reductions in % hyperglycemic readings (see Figure 1, P&lt;0.001 for all).
Conclusion: Real-world data from 23,147 people with type 1 diabetes found improved percent readings in tight range with increasing engagement with the OneTouch Reveal® diabetes app.

Disclosure
E.H. Holt: Employee; LifeScan Inc. H. Cameron: Other Relationship; Lifescan Scotland. M. Grady: Employee; Lifescan Scotland.
1929-LB: Calculating Postmeal Interstitial Fluid Glucose (IFG) Appearance Using FreeStyle Libre CGM
Authors: MELISH JOHN S. 
Published: June 2024

Introduction: CGM and carbohydrate counting (CC) have been proven to improve glucose control in type 1 and 2 diabetic patients. Those who continuously use CGM in diabetes management review the data frequently, make changes based on that data, and improve insulin related glucose control. Current CGM data summaries are considered useful to both patients and providers who adjust therapy to reduce glucose (G) variability and hypoglycemia with resulting improvements in A1c. The following describes the immediate use of CGM data for a pharmacokinetic analysis of meal related interstitial fluid glucose changes determined by the amount and timing of insulin administration.
Method: It requires the use of industry developed mobile phone software (LibreLinkUp) to immediately and quantitatively evaluate the meal related glucose excursion. A specific meal related glucose/insulin ratio results which confirms (and can replace) carbohydrate counting when carb counting is difficult or impossible to assess. Interstitial fluid glucose is evaluated from the time of pre-meal insulin injection to the return to baseline. IFG is adjusted to plasma glucose values using an adjusted Volume of Distribution (Vd) based on Kg body weight and hematocrit. The glucose concentration peak is deconvoluted and the lowest post meal baseline change is subtracted. The difference is multiplied by the adjusted volume of distribution to obtain an estimate of the ingested carbohydrate presenting as interstitial fluid glucose.
Results: Examples of identical meals and timed insulin dosing are evaluated and compared. Reference: breakfast bagel 56 g CHO. Variables: Vd=157 dL; Peak Post Meal G = 128 mg/dL; Calculation: 128*e*157 = 54,627 mg rounded to 55 g. I = 16 unit. CHO/I ratio =3.4 g/unit. Same breakfast another day: Calculated: 53 g; third breakfast: 54 g.
Conclusion: Meal related CC compares well with the calculated ISF glucose appearance using real-time CGM data.

Disclosure
J.S. Melish: None.
1930-LB: A Randomized Trial of Real-Time Continuous Glucose Monitoring (RT-CGM) with Culturally Tailored Education for Latino Patients with Type 2 Diabetes (DM)
Authors: EHRHARDT NICOLE M. , WRIGHT LORENA , MONTOUR LAURA , BERBERIAN PETER , JONES EVELIN , GIL MENCHACA DARINKA D. , GABRIELA VASCONCELOS ANA , COMSTOCK BRYAN A. 
Published: June 2024

Background: Culturally tailored diabetes education and RT-CGM both support patients living with DM. However, data on education delivered virtually is limited in the Latino population and few studies have assessed the effect of RT-CGM combined with DM education.
Methods: We conducted a study of Latino patients diagnosed with Type 2 DM who were not on intensive insulin therapy. Patients were randomized (1:1) to virtually delivered sessions (12) of education only or education with cyclic RT-CGM over 12 weeks. The primary outcome was A1c at 12 weeks.
Results: 120 participants were randomized to education (59) or education + RT-CGM (61) with 33 in each arm completing 12 weeks of intervention. The mean age was 46 years, 44% were female, 98% preferred Spanish language, 30% had income &lt;$25,000, and 30% had health insurance. Most participants (70%) attended &gt;50% of 12 educational sessions. Mean A1c at baseline was 10.5 with 74% having A1c &gt;9.0. A1c improved at 12 weeks by 1.3mg/dL with education and by 2.5mg/dL with education + RT CGM, with a difference of 1.3 (p=0.012) between groups.
Conclusions: Virtually delivered culturally tailored education improved A1c at 12 weeks in both groups, with RT-CGM conferring even greater improvement in A1c over education alone. The use of RT-CGM should be considered as an adjunctive therapy to education early in diabetes.

Disclosure
N.M. Ehrhardt: Advisory Panel; Bayer Inc., Dexcom, Inc. Research Support; Dexcom, Inc., Novo Nordisk. Advisory Panel; Novo Nordisk. Research Support; Merck & Co., Inc. L. Wright: None. P. Berberian: None. E. Jones: None. A. Vasconcelos: None. B.A. Comstock: None.

Funding
American Diabetes Association (11-21-ICTSHD-51)
1935-LB: Integrated Model of Digital Health and Clinic Care Pathway—A Prospective Observational Study to Manage HbA1c Levels and Time in Range among Indian Type 2 Diabetes Mellitus
Authors: JOSHI ARATI K. , SABOO BANSHI D. , SINGHAL TAVISHI , CHAKRABARTY SUJIT , GATTANI MANSI , NERKAR PRAJWAL , BANERJEE TRIJA , GODSE ANAND S. 
Published: June 2024

Aim: To assess the impact of Smit.fit’s CGM-powered Diabetes Care program on reducing HbA1c levels and increasing Time in Range (TIR) for T2DM patients at health clinics.
Method: A single group design study of repeated measures evaluated Smit.fit’s 4-month Diabetes Care program deployed at partner health clinics across 50 cities in India. The intervention included glucose monitoring by CGM and HbA1c tests with data-driven behavioural changes facilitated by Smit.fit’s certified health experts tracked on Smit.fit’s app.
Results: Of 808 adults, at baseline, 35.1% had severely uncontrolled T2DM (A1c &gt; 9%), 41.5% had uncontrolled T2DM (7% &lt; A1c ≤ 9%) and 23.4% had controlled T2DM (A1c ≤ 7%). The mean age of subjects was 52.34 ± 11.99 yrs, BMI 26.53 ± 4.68, and duration of DM 7.99 ± 7.53 yrs. With Smit.fit’s integrated intervention, a significant difference was observed from baseline HbA1c (8.62 ± 1.95%) to post-program (7.55 ± 1.51%)(p&lt;0.001), CGM Est. A1c (week 1: 8.12 ± 2.54%, week 2: 7.47 ± 2.24%)(p&lt;0.001), and TIR (week 1: 40.32 ± 30.95%, week 2: 47.48 ± 30.79%)(p&lt;0.001). In baseline and Est. A1c, reductions of 1.07, 0.65 respectively, and a 7.16 increase in TIR were observed. Baselining HbA1c levels and HbA1c impact showed a significant positive correlation (r=0.647, p&lt;0.001). In participants with severely uncontrolled T2DM (A1c &gt; 9%), HbA1c reduction of 2.18 ± 1.72% was seen with 72.2% users showing more than 1.0% HbA1c reduction and 19.7% users showing 0.1-1.0% HbA1c reduction. Additional factors like duration of DM, complications and comorbidities lacked significant correlation with HbA1c impact.
Conclusion: Smit.fit’s comprehensive approach of data-driven monitoring and coaching led to significant reductions in HbA1c levels and improved glucose control, especially with uncontrolled baseline HbA1c (A1c&gt;7%) where 71.92% users showed &gt;0.5% HbA1c reductions.

Disclosure
A.K. Joshi: None. B.D. Saboo: None. T. Singhal: None. S. Chakrabarty: None. M. Gattani: None. P. Nerkar: None. T. Banerjee: None.
1938-LB: Improving Screening for Retinopathy in Youth with Diabetes Mellitus
Authors: RHODES ERINN , STERNS OLIVIA R. , MOYNIHAN JENNIFER L. , HIRSCH REBECCA , NANAVATI SONAL , GONZALEZ EFREN , GARVEY KATHARINE 
Published: June 2024

Introduction & Objective: Despite the rare occurrence of retinopathy in youth with diabetes (DM), routine screening is required. For type 1 diabetes (T1DM), screening starts at age 10 yrs. and DM duration of 5 yrs.; for type 2 diabetes (T2DM), at diagnosis.
Methods: Retinopathy screening with non-mydriatic fundus photography with Welch Allyn RetinaVue 700 Imager (RV) was offered to eligible DM patients at a pediatric, tertiary care center. Completed scans were reviewed by an ophthalmologist at the same center. Completion and reasons for incomplete testing were tracked. Control charts analyzed monthly completion. Refining implementation and uptake occurred through iterative PDSA cycles.
Results: From 10/20 to 12/23, 449 RV scans were completed (71% T1DM), and 4 (&lt;1%) patients had retinopathy. There was a significant shift in completion in 1/23 from 49.9% to 73.5% among eligible patients, who attended a visit with a RV order. In 2023, the most common reason for incomplete testing among eligible patients was a scheduled ophthalmology appointment (32%). Observation showed RV added ~5 minutes to clinic time. Overall, biannual retinopathy screening between CY19 and CY23 increased from 33 to 37% among eligible T1DM patients and from 25 to 55% among T2DM patients.
Conclusion: Data suggest that non-mydriatic fundus photography with RV is a feasible method for increasing screening for retinopathy especially among pediatric patients with T2DM.

Disclosure
E. Rhodes: None. O.R. Sterns: None. R. Hirsch: None. S. Nanavati: None. E. Gonzalez: None. K. Garvey: Advisory Panel; Sanofi.
1939-LB: Improving Capacity for Quality Improvement in Type 2 Diabetes Primary Care Centers
Authors: RIOLES NICOLE , MUNGMODE ANN , ODUGBESAN ORI , WRIGHT TREVON , PASUPNETI TEJASVI , GARG RAJESH K. , HAFT HOWARD M. , GANGUPANTULA GOPIKA , EBEKOZIEN OSAGIE 
Published: June 2024

Background: Primary healthcare centers (PHCs) caring for people with diabetes are often overwhelmed and burdened by operational challenges and low resources. The aim of this study was to increase the capacity for PHCs to implement evidence-based quality improvement (QI) projects.
Methods: American Diabetes Association (ADA) recruited four PHCs into this project. T1D Exchange coached PHC from August 2023 - March 2024 on QI and change management. The PHCs selected QI metrics for the pilot phase; increasing HbA1c testing, kidney health screening, and statin prescribing for people with type 2 diabetes (PwT2D). PHCs identified barriers and tested improvement changes through Plan-Do-Study-Act cycles; PCPs reported aggregate monthly data for almost 8,000 people with type 2 diabetes.
Results: Three of the four participating centers completed the pilot phase. Demonstrating overall improvement in QI capacity, all centers improved in at least one clinical outcome measure. One of the centers improved statin prescribing by 7% (Fig.1). A1c screenings increased by 14%.
Conclusion: Common challenges observed across PHCs include fewer staff with dedicated time for QI, data reporting challenges, limited QI resources, constraints implementing workflow change, and lack of dedicated QI leadership. Providing external assistance through QI coaching and support can improve capacity to make robust practice transformation. Fig. 1

Disclosure
N. Rioles: None. A. Mungmode: None. O. Odugbesan: None. T. Wright: None. T. Pasupneti: None. R.K. Garg: None. H.M. Haft: None. G. Gangupantula: None. O. Ebekozien: Advisory Panel; Sanofi, Medtronic. Research Support; Vertex Pharmaceuticals Incorporated. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; Medtronic, Lilly Diabetes, Abbott, Dexcom, Inc.

Funding
Statement of Work, executed with ADA for the Know Diabetes By Heart program (2023)
194-OR: Improved Linkage to the Health System and Better Diabetes Control Due to Community-Based m-Health–Enabled Screening and Follow-up in India
Authors: MOHAN SAILESH , JARHYAN PRASHANT , DUBEY MANISHA , SRINIVASAPURA VENKATESHMURTHY NIKHIL , PRABHAKARAN DORAIRAJ 
Published: June 2024

Introduction: India faces a huge burden of undiagnosed and uncontrolled diabetes, resulting in a significant challenge to the healthcare system.
Methods: To improve linkage to the primary healthcare system and blood glucose control among diabetics, we conducted a large community-based screening and follow-up study of individuals’ ≥ 30 years led by trained non-physician health workers (NPHWs) enabled with an m-health enabled screening and follow-up system on a tablet device, in rural and urban areas of North and South India. NPHWs collected data using an interviewer-administered questionnaire, measured random capillary blood glucose (RBG) during the screening and follow-up and collected data on health facility visits during subsequent follow-up visits. Diabetes was defined according to standard definitions. NPHWs also provided tailored lifestyle modification education.
Results: 147828 individuals were screened at their homes, of which 53.1% were from rural areas. The mean age of participants was 48.1 (SD ±13.8) years and prevalence of self-reported diabetes was 7.9% (95% CI: 7.8 % - 8.1%). The proportion of diabetics visiting any health facility increased significantly [visit 1: 32.1% (31.2%-32.9% %), visit 4: 52.2% (50.1%-54.3%] between the first and the fourth follow-up visits. There was an increase in proportion of those who had their blood glucose measured at the health facility between the two visits. Overall, there was a decrease in the mean RBG [visit 1: 215.4 (SD:86.6) mg/dl Vs visit 4: 191.5 (SD 63.9) mg/dl. There was a significant increase in proportion of diabetics with controlled blood glucose in the fourth follow-up visit, 59.2% (56.9%-61.5%) as compared to the first visit, 45.2% (43.4%-47.0%).
Conclusion: Our tailored screening and follow-up program indicates that non-physician health worker led m-health enabled screening significantly improved the health service utilization and blood glucose control among diabetics.

Disclosure
S. Mohan: None. P. Jarhyan: None. M. Dubey: None. N. Srinivasapura Venkateshmurthy: None. D. Prabhakaran: None.

Funding
An unrestricted grant under the Lilly NCD Partnership programme.
1944-LB: Control of Impatient Hyperglycemia Using a Decision Support System
Authors: CARDIM THALITA M. , FERNANDES ADRIANA M. , INAOKA RIGUEL J. , RIZZO ARIEL , MUNHOZ ANA PAULA A. , SREDOJA THAIS L. , FIEDLER DANIELA B. , OLIVEIRA ANDREA , ROZZINO TATIANNA P. 
Published: June 2024

Introduction: Hyperglycemia is an independent marker of hospital mortality, regardless of the previous diagnosis of diabetes. insulin is the clear choice for the hospitalized patient. Insulin should be administered using validated protocols that allow predefined adjustments in insulin doses based on glycemic fluctuations.
Objective: P = High rate of hyperglycemia in hospitalized patients; I = Use of a decision support tool; C = Patients routinely treated without using the tool; O = Better control of hyperglycemia rates and outcomes.
Methods: After a broad discussion on the relevant aspects of hyperglycemia, the construction of Carepathway began in the electronic medical record (Cerner Millennium) with 13 different scenarios that guide the physicians towards best practice. Each scenario triggers a warning with guidelines and the possibility of prescribing insulin doses according to the protocol.
Results: After the implementation of the Pathway, February 2022, we could notice a sustained drop in the rates of hyperglycemia and severe hyperglycemia and better adherence to institutional protocols.
Conclusion: The decision support tool is providing excellent care to patients with glycemic disorders, in addition to facilitating and standardizing the conduct of non-specialist doctors.

Disclosure
T.M. Cardim: None. A. Rizzo: None.
1945-LB: Evaluation of Proactive Targeting Models Used to Identify People with Diabetes at Risk of Being Uncontrolled during Participation in a Digital Diabetes Management Program (DDMP)
Authors: KHALILNEJAD ARASH , KOMPALA TEJASWI , JAMES ROBERTA , PAINTER STEFANIE , WANG YAJUAN 
Published: June 2024

Introduction & Objective: Predictive machine learning models were developed for each month during DDMP to predict individual risk of having an estimated A1c (eA1c) ≥7.5% at 12 months for personalized Health Nudges to be deployed for improved fingerstick blood glucose monitoring (FBGM) and program engagement associated with improved glycemic control. This study evaluated short-term impact on FBGM and reduction in eA1c after 9 months on program.
Methods: Deployment of models and personalized Health Nudges launched in May 2023 to a population allowing for comparison to two control groups of DDMP participants without (C1) and randomly assigned Health Nudges (C2). Participants were selected for treatment group if they had a self-reported A1c at program initiation and adequate BG checks to eA1c at 9 months using a modified Nathan Method developed by Teladoc Health. Repeated measures ANOVA analysis was used to evaluate differences between treatment and control groups adjusting for diabetes type.
Results: Treatment (n=526) and control groups (C1 n=73, C2 n=55) were similar in age [years: 55 (11.7), 56 (8.8) and 56 (12.5)], gender [female: 48%, 44%, 51%], and diabetes type [type 2: 91.2%, 86%, 91.8%], respectively. At Month-9, average 30-day FBGM checks were 32, 30, and 28, for treatment, C1, and C2. Treatment group’s eA1c at Month 9 was 7.8% while C1 and C2 were 8.2%. Statistical analysis showed personalized Health Nudges led to significant reduction in eA1C levels from baseline (P &lt; 0.001). C2 group showed reduction as well, although not as pronounced as the treatment group (P = 0.038). Conversely, the C1 group did not demonstrate a significant reduction in eA1C levels (P = 0.070).
Conclusion: Preliminary evaluation of machine learning models supports the ability to target DDMP users in early months on program with Health Nudges aimed at increasing program engagement that was associated with improved clinical outcomes.

Disclosure
A. Khalilnejad: Employee; Teladoc Health. Stock/Shareholder; Teladoc Health. T. Kompala: None. R. James: Employee; Teladoc Health. Stock/Shareholder; Teladoc Health. S. Painter: Employee; Teladoc Health. Y. Wang: Employee; Teladoc Health. Stock/Shareholder; Teladoc Health.