Impact on glycaemic outcomes of transitioning from in-person to virtual FreeStyle Libre onboarding during the Covid-19 pandemic

Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.

Managing diabetes remotely: Evaluation of the uptake and efficacy of the DBm app

Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.

Optimal continuous glucose monitoring metrics for identification of women with gestational diabetes

Aims: Continuous glucose monitoring (CGM) is widely used in pregnant women with pre-gestational diabetes, but optimal targets have not been defined in gestational diabetes. Previous work identified mild hyperglycaemia in pregnant women without gestational diabetes, but with risk factors such as obesity. We aimed to examine CGM metrics and patterns of glycaemia in women with gestational diabetes compared to healthy pregnant women with comparable risk factors. Method(s): We recruited 73 healthy women with >1 risk factor (gestational diabetes excluded using Covid-19 criteria, OGTT) and 200 women with gestational diabetes (NICE and interim-Covid- 19 criteria) from antenatal clinics at 28 weeks' gestation. A Dexcom G6 CGM device was cited on the non-dominant upper arm. Result(s): Women with gestational diabetes had significantly higher weight (mean +/- SEM 95.7 kg +/- 1.3 Vs 85.4 kg +/- 2.2) and BMI (36.0 +/- 0.5 Vs 31.3 +/- 0.7) compared to healthy pregnant women (p < 0.01). Women with gestational diabetes had significantly higher mean CGM-glucose (mean +/- SEM 5.6 +/- 0.01 Vs 5.4 +/- 0.01mmol/l;p < 0.01), significantly altered time-below- range (median(IQR);1.0% (0.2-2.9) vs 2.5% (0.7-5.5);p < 0.05) and time-in- range (95.0% (91.1-97.9) vs 94.5% (87.9-96.2);p < 0.05) but comparable time-above- range to healthy women with risk factors. Diurnal glucose profiles in women with gestational diabetes were comparable to healthy women between 14:00 and 18:00, but demonstrated significant increases in glucose at all other time points during the 24-h cycle (p < 0.01). Conclusion(s): Mean CGM glucose is the most reliable CGM metric to distinguish women with gestational diabetes from healthy pregnant women with risk factors.

Change in glycaemic control immediately after Covid-19 vaccination in people with type 1 diabetes: What happens after the most recent vaccination?

Aims: We have shown previously in 93 individuals with type 1 diabetes using the FreeStyle Libre flash glucose monitor that the week after their first Covid-19 vaccination, the percent 'time in target range 3.9-10mmol/ l' (%TTR) average went from 55.2%-> 52.4% (effect size -5.1%) with 58% of people recording a fall. 47 (50%) people with HbA1c < 56mmol/mol %TTR went from 69.3-> 63.5 (-8.3%) and 24 (25%) people using insulin+oral treatment 56.7%-> 50.7% (-10.1%). We have now repeated the exercise after the most recent Covid-19 vaccination. Method(s): FreeStyle Libre data and medical records of the same patients from the previous study were examined for the week before and week after their most recent Covid-19 vaccination. () in the results section show change in %TTR as % of the prior value to show effect size. TTR% results from 2 weeks before and after were also considered. Result(s): Median time between vaccines was 38 weeks IQR (37-40). After the latest vaccination average %TTR average went from 51.1%-> 49.8% (-2.5%) with a reduction found in 54% of patients. Impact on the 39 patients with HbA1c < 56mmol/mol -% TTR from 66.2%-> 61.8% (-6.5%) and the 20 (25%) patients using insulin+oral %TTR from 48.2%-> 47.1% (-2.2%). 65% of the patients whose %TTR fell previously, fell again after this vaccination. Fortnight average %TTR 53.5%-> 52.1% (-2.7%) whereas in the previous study across fortnight %TTR 55.4%-> 54.0% (-2.4%). Conclusion(s): The perturbation effect on blood glucose with 1st Covid-19 vaccination was seen again in the latest vaccination but reduced in magnitude, confirming that a significant group of type 1 diabetes individuals' glycaemic control is still being impacted by the Covid-19 vaccination.

My diabetes My Way: Intelligent delivery of diabetes education and support for self-management

Background: My Diabetes My Way (MDMW) is NHS Scotland's interactive website, offering education, structured eLearning and online records access for people living with diabetes. We aimed to analyse user activity during the last 12 months. Method(s): Data were collected during the period from November 2021 to October 2022. Registration and user audit logs were analysed, observing activity across all website content and features. Result(s): An average of 62,853 pages were accessed on the public website each month. Significant activity increases were observed in December 2021 (n = 81,237). There were increased views in September 2022 (n = 76,502) and October (n = 73,039) The top five pages accessed were;Coronavirus: advice for people living with diabetes (n = 12,478), FreeStyle Libre (n = 4325), Emergency advice (n = 1576), Blood pressure-reducing your risks of complications (n = 1559) and Blood glucose monitoring and HbA1c targets (n = 1485). eLearning: During this period, 382 individuals completed one of 11 QISMET-accredited structured eLearning courses. eLearning course usage increased in relation to patient awareness activity. Social Media: There are currently 3919 Facebook and 3600 Twitter followers. Records Access: 67,655 patients had registered to access their data and 35,157 had actively accessed their records by the end of October 2022. Patient feedback remains highly positive. Conclusion(s): MDMW is a consistent and reliable resource for people with diabetes and their families to access at any time online. User statistics continue to rise, while latest development plans include the addition of new Patient Reported Outcome Measures, risk prediction features, and enhanced sharing of data with the healthcare team.

Comparing Glucose Outcomes Following Face-to-Face and Remote Initiation of Flash Glucose Monitoring in People Living With Diabetes.

BACKGROUND: When launched, FreeStyle Libre (FSL; a flash glucose monitor) onboarding was mainly conducted face-to-face. The COVID-19 pandemic accelerated a change to online starts with patients directed to online videos such as Diabetes Technology Network UK for education. We conducted an audit to evaluate glycemic outcomes in people who were onboarded face-to-face versus those who were onboarded remotely and to determine the impact of ethnicity and deprivation on those outcomes. METHODS: People living with diabetes who started using FSL between January 2019 and April 2022, had their mode of onboarding recorded and had at least 90 days of data in LibreView with >70% data completion were included in the audit. Glucose metrics (percent time in ranges) and engagement statistics (previous 90-day averages) were obtained from LibreView. Differences between glucose variables and onboarding methods were compared using linear models, adjusting for ethnicity, deprivation, sex, age, percent active (where appropriate), and duration of FSL use. RESULTS: In total, 935 participants (face-to-face 44% [n = 413]; online 56% [n = 522]) were included. There were no significant differences in glycemic or engagement indices between onboarding methods and ethnicities, but the most deprived quintile had significantly lower percent active time (b = -9.20, P = .002) than the least deprived quintile. CONCLUSIONS: Online videos as an onboarding method can be used without significant differences in glucose and engagement metrics. The most deprived group within the audit population had lower engagement metrics, but this did not translate into differences in glucose metrics.

Hyperglycemia Associated With Raynaud Phenomenon and Liver Dysfunction After COVID-19 Vaccination in Type 1 Diabetes Mellitus.

Background/Objective: The association of COVID-19 vaccinations and the changes in glycemic control remains debatable. We report a case of a patient with type 1 diabetes mellitus (DM) with previously well-controlled glucose on a hybrid closed-loop insulin pump who developed significant glucose variation, new onset Raynaud phenomenon, and liver dysfunction after the vaccination. Case Report: A 33-year-old man with type 1 DM since the age of 5 years was on an insulin pump for 17 years. He had a reasonable controlled glucose level with a hemoglobin A1c level of 6.8% (51 mmol/mol). Three days after he received the COVID-19 vaccination, his glucose level started to fluctuate in the range of 46 to 378 mg/dL with 3.5 times higher total daily insulin requirement. The patient developed white-pale cold hands, weight gain, fatigue, and liver dysfunction. Computed tomography of the abdomen revealed mild hepatomegaly, and laboratory workup was negative for hepatitis. One month later, his glucose level became better controlled, and his liver function improved. Continuous glucose monitoring revealed that his glucose profile returned to baseline after 6 weeks. Discussion: COVID-19 vaccination resulted in significant glucose variation and fluctuations in this patient. It could be explained by the vaccine-induced immune response causing an increase in insulin resistance, such as in adipose tissue and muscle cells. Immune stimulation could have also caused the abnormal liver function and explain his new onset Raynaud phenomenon. Conclusion: We described, for the first time, the long-term continuous glucose monitoring glucose profile with a hybrid closed-loop system in type 1 DM after COVID-19 vaccination. Clinicians need to keep alert to glycemic excursion and side effects after immunization in type 1 DM.

The Optimal Method to Predict Hemoglobin A1c from Flash Glucose Monitor Data

In situations where it is difficult for patients to visit hospitals, such as the coronavirus disease pandemic, it is important to more detailly predict hemoglobin A1C (HbA1c) from flash glucose monitor (FGM) data. CGM data over 14 days can be obtained from a FGM sensor;therefore, there are many options for extracting the duration from which glucose levels are derived. Thus, the extracted durations were closely studied to determine which mean glucose levels can predict HbA1c more accurately. Seventy-three outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a FGM (FreeStyle Libre Pro), and did not change diabetic treatments, on a hospital visit. FGM data over 24 h 13 days (from 00:00 on day 2 to 24:00 on day 14 [FGM attachment: day 1]) were analyzed. The mean glucose levels were calculated corresponding to the following durations: 1 day: day 2 ~ day 14 (n=13), 2 days: days 2-3 ~ days 13-14 (n=12) 12 days: days 2-13 ~ days 3-14 (n=2), 13 days: days 2-14 (n=1) [total 91 durations] (extracted mean glucose levels). Data were analyzed in all patients (n=73), in patients with hypoglycemia in the 13 days (Hypo) group (n=40), and in patients without hypoglycemia in the 13 days (Nonhypo) group (n=33). In all patients, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.76-0.86, p<0.001). HbA1c was the most significantly correlated to the mean glucose levels over 13 days (days 2-14). "Correlation coefficients between HbA1c and extracted mean glucose levels" ("r, HbA1c, EMGL") were also correlated to number of extracted days for the extracted mean glucose levels (r=0.80, p<0.001 [n=91]). In the Hypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.55-0.73, p<0.001). The mean glucose levels over 13 days (days 2-14) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.68, p<0.001;Fig. 2). In the Nonhypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.73-0.87, p<0.001). The mean glucose levels over 12 days (days 2-13) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.61, p<0.001). The results of the present study are consistent with that of a previous study reporting that the minimum duration needed to estimate time in range over 90 days is 14 days. In the prediction of HbA1c using data from one FGM sensor, prolonged measurement can make the glucose management indicator more accurate. Especially for patients with hypoglycemia, the importance of prolonged measurement may be applicable.

Clinical Experience of Using Telemedicine for the Management of Patients Using Continuous Subcutaneous Insulin Infusion in a Highly Complex Latin American Hospital.

INTRODUCTION: Continuous subcutaneous insulin infusion (CSII) has emerged as a potential solution for diabetes management during the pandemic, as it reduces the need for in-person visits and allows for remote monitoring of patients. Telemedicine has also become increasingly important in the management of diabetes during the pandemic, as it allows healthcare providers to provide remote consultations and support. Here, we discuss the implications of this approach for diabetes management beyond the pandemic, including the potential for increased access to care and improved patient outcomes. METHODS: We performed a longitudinal observational study between 1 March 2020 and 31 December 2020 to evaluate glycemic parameters in diabetic patients with CSII in a telehealth service. Glycemic parameters were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability control. RESULTS: A total of 36 patients were included in the study, with 29 having type 1 diabetes and 6 having type 2 diabetes. The study found that the proportion of patients achieving target glucose variability and GMI remained unchanged during follow-up. However, in patients with type 2 diabetes, the time in target range increased from 70% to 80%, and the time in hyperglycemia decreased from 2% to 0%. CONCLUSIONS: The results of this study suggest that telemedicine is a strategy for maintaining glycemic control in patients using CSII. However, the lack of access to the internet and adequate telemonitoring devices make it difficult to use on a large scale in emerging countries like ours.

Accuracy and Glycemic Efficacy of Continuous Glucose Monitors in Critically Ill COVID-19 Patients: A Retrospective Study.

BACKGROUND: Continuous glucose monitoring (CGM) is approved for insulin dosing decisions in the ambulatory setting, but not currently for inpatients. CGM has the capacity to reduce patient-provider contact in inpatients with coronavirus disease 2019 (COVID-19), thus potentially reducing in hospital virus transmission. However, there are sparse data on the accuracy and efficacy of CGM to titrate insulin doses in inpatients. METHODS: Under an emergency use protocol, CGM (Dexcom G6) was used alongside standard point-of-care (POC) glucose measurements in patients critically ill from complications of COVID-19 requiring intravenous (IV) insulin. Glycemic control during IV insulin therapy was retrospectively assessed comparing periods with and without adjunctive CGM use. Accuracy metrics were computed and Clarke Error Grid analysis performed comparing CGM glucose values with POC measurements. RESULTS: Twenty-four critically ill patients who met criteria for emergency use of CGM resulted in 47 333 CGM and 5677 POC glucose values. During IV insulin therapy, individuals' glycemic control improved when CGM was used (mean difference -30.7 mg/dL). Among 2194 matched CGM: POC glucose pairs, a high degree of concordance was observed with a mean absolute relative difference of 14.8% and 99.5% of CGM: POC pairs falling in Zones A and B of the Clarke Error Grid. CONCLUSIONS: Continuous glucose monitoring use in critically ill COVID-19 patients improved glycemic control during IV insulin therapy. Continuous glucose monitoring glucose data were highly concordant with POC glucose during IV insulin therapy in critically ill patients suggesting that CGM could substitute for POC measurements in inpatients thus reducing patient-provider contact and mitigating infection transmission.

Comparisons of School-Day Glycemia in Different Settings for Children with Type 1 Diabetes Using Continuous Glucose Monitoring

Objective. Using continuous glucose monitoring (CGM), we examined patterns in glycemia during school hours for children with type 1 diabetes, exploring diferences between school and nonschool time. Methods. We conducted a retrospective analysis of CGM metrics in children 7-12 years (n = 217, diabetes duration 3.5 +/- 2.5 years, hemoglobin A1c 7.5 +/- 0.8%). Metrics were obtained for weekday school hours (8 AM to 3 PM) during four weeks in fall 2019. Two comparison settings included weekend (fall 2019) and weekday (spring 2020) data when children had transitioned to virtual school due to COVID-19. We used multilevel mixed models to examine factors associated with time in range (TIR) and compare glycemia between in-school, weekends, and virtual school. Results. Tough CGM metrics were clinically similar across settings, TIR was statistically higher, and time above range (TAR), mean glucose, and standard deviation (SD) were lower, for weekends and virtual school (p < 0.001). Hour and setting exhibited a signifcant interaction for several metrics (p < 0.001). TIR in-school improved from a mean of 40.9% at the start of the school day to 58.0% later in school, with a corresponding decrease in TAR. TIR decreased on weekends (60.8 to 50.7%) and virtual school (62.2 to 47.8%) during the same interval. Mean glucose exhibited a similar pattern, though there was little change in SD. Younger age (p = 0.006), lower hemoglobin A1c (p < 0.001), and insulin pump use (p = 0.02) were associated with higher TIR in-school. Conclusion. Although TIR was higher for weekends and virtual school, glycemic metrics improve while in-school, possibly related to benefcial school day routines.

Continuous glucose monitoring empowers adolescents to take responsibility of diabetes management.

BACKGROUND: Managing diabetes is especially challenging for adolescents, and they often struggle to believe they can manage the condition. Illness perception has been widely associated with better diabetes management outcomes, but the influence of continuous glucose monitoring (CGM) on adolescents has been largely neglected. AIM: The study aimed to explore the illness perception of a group of adolescents living with type 1 diabetes (T1D) using CGM. SETTING: The study was conducted at a medical centre that provides diabetes care services to youth living with T1D in Parktown, South Africa. METHODS: A qualitative research approach using semi-structured online interviews was used to gather data that was thematically analysed. RESULTS: Themes emerging from the data confirmed that CGM creates a sense of control over diabetes management as blood glucose measures were more visible. A sense of normalcy was established as CGM influences a new routine and a way of life, integrating diabetes into a young person's identity. Despite the users' awareness of being different due to diabetes management, CGM assisted in creating a sense of belonging, contributing to developing a better quality of life. CONCLUSION: Findings of this study support the use of CGM as a means of empowering adolescents struggling with diabetes management to achieve better treatment outcomes. The important role of illness perception in facilitating this change was also evident.Contribution: By listening to the adolescent's voice, CGM was identified as a possible intervention to empower adolescents to improve diabetes management.

Real-Time Continuous Glucose Monitoring in the Hospital: A Real-World Experience.

BACKGROUND: Glycemic control in the hospital setting is imperative for improving outcomes among patients with diabetes. Bedside point-of-care (POC) glucose monitoring has remained the gold standard for decades, while only providing momentary glimpses into a patient's glycemic control. Continuous glucose monitoring (CGM) has been shown to improve glycemic control in the ambulatory setting. However, a paucity of inpatient experience and data remains a barrier to US Food and Drug Administration (FDA) approval and expanded/non-research use in the hospital setting. METHOD: Amid the COVID-19 pandemic, the FDA exercised its enforcement discretion to not object to the use of CGM systems for the treatment of patients in hospital settings to support COVID-19 health care-related efforts to reduce viral exposure of health care workers. Following this announcement, Scripps Health, a large not-for-profit health care system in San Diego, California, implemented CGM as the new "standard of care" (CGM as SOC) for glucose monitoring and management in the hospital. RESULTS: The present report serves to (1) detail the implementation procedures for employing this new SOC; (2) describe the patients receiving CGM as SOC, their glycemic control, and hospital outcomes; and (3) share lessons learned over two years and nearly 900 hospital encounters involving CGM. CONCLUSIONS: Here, we conclude that CGM is feasible in the hospital setting by using a dedicated diabetes care team and the CGM technology with remote monitoring.

Continuous Glucose Monitoring in the Intensive Care Unit.

Traditionally, the care of critically ill patients with diabetes or stress hyperglycemia in the intensive care unit (ICU) demands the use of continuous intravenous insulin (CII) therapy to achieve narrow glycemic targets. To reduce the risk of iatrogenic hypoglycemia and to achieve glycemic targets during CII, healthcare providers (HCP) rely on hourly point-of-care (POC) arterial or capillary glucose tests obtained with glucose monitors. The burden of this approach, however, was evident during the beginning of the pandemic when the immediate reduction in close contact interactions between HCP and patients with COVID-19 was necessary to avoid potentially life-threatening exposures. Taking advantage of the advancements in current diabetes technologies, including continuous glucose monitoring (CGM) devices integrated with digital health tools for remote monitoring, HCP implemented novel protocols in the ICU to care for patients with COVID-19 and hyperglycemia. We provide an overview of research conducted in the ICU setting with the use of initial CGM technology to current devices and summarize our recent experience in the ICU.

Nursing Perspectives on the Use of Continuous Glucose Monitoring in the Intensive Care Unit.

BACKGROUND: The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting. METHODS: A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload. RESULTS: Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use. CONCLUSION: These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.

Indications for Inpatient Glucose Telemetry

Objective: The primary objective was to explore indications for inpatient glucose telemetry. Method(s): The inpatient glucose telemetry (IGT) has been instituted at the peak of COVID cases at an urban academic medical center. Besides remote glucose monitoring due to infection isolation, feasibility, reliability and indications for IGT were investigated in hospitalized patients. Result(s): IGT was used in n=75 patients in critical and non-critical care inpatient settings. In addition to remote glucose monitoring due to infection isolation, feasibility, reliability and indications for IGT were investigated in cases requiring hypoglycemia prevention, multimorbidity, fingertip bruising, transplant, cancer, intensive insulin management, brain/psychiatric disease/injury, and inpatient rehabilitation.IGT was used in patients hospitalized with personal home continuous glucose monitoring system and patients with recurrent diabetes hospitalizations. Conclusion(s): Inpatient glucose telemetry indications must be expanded beyond hypoglycemia prevention.

Piloting a new compensation model for endocrinologists to improve diabetes care

There is a consensus that fee-for-service reimbursement does too little to encourage the provision of high-value care. Our Enterprise, an integrated payer-provider based in Pittsburgh, created an alternative compensation model for endocrinologists. Our plan introduces a gradual shift in the role of endocrinologists from clinical duties to a more collaborative role with their primary care colleagues. Considering that most patients with diabetes are managed under primary care, this shift allows endocrinologists to support primary care physicians (PCPs) in managing patients with diabetes and other endocrine-related illnesses while decreasing the number of traditional in-office referrals to endocrinology. Despite the unexpected changes brought on by COVID, in first 9 months of the compensation model, we observed its impact on care delivery as well as the relationship between participating specialists and PCPs. Practice- and provider-level quality data has shown improvement in diabetes-specific quality metrics. In one year, 16 out of 54 target practices earned NCQA recognition for diabetes management. A total of 88% of participating PCPs reported a satisfaction score > 90% with the new plan. Ultimately, our model shows promise as a replacement for fee-for-service compensation, with a likelihood of lowering costs and improved quality of care.Copyright © 2022 The Author(s)

Managing Diabetes in Pregnancy Before, During, and After COVID-19.

Background: Pregnant women with diabetes are identified as being more vulnerable to the severe effects of COVID-19 and advised to stringently follow social distancing measures. Here, we review the management of diabetes in pregnancy before and during the lockdown. Methods: Majority of antenatal diabetes and obstetric visits are provided remotely, with pregnant women attending hospital clinics only for essential ultrasound scans and labor and delivery. Online resources for supporting women planning pregnancy and for self-management of pregnant women with type 1 diabetes (T1D) using intermittent or continuous glucose monitoring are provided. Retinal screening procedures, intrapartum care, and the varying impact of lockdown on maternal glycemic control are considered. Alternative screening procedures for diagnosing hyperglycemia during pregnancy and gestational diabetes mellitus (GDM) are discussed. Case histories describe the remote initiation of insulin pump therapy and automated insulin delivery in T1D pregnancy. Results: Initial feedback suggests that video consultations are well received and that the patient experiences for women requiring face-to-face visits are greatly improved. As the pandemic eases, formal evaluation of remote models of diabetes education and technology implementation, including women's views, will be important. Conclusions: Research and audit activities will resume and we will find new ways for supporting pregnant women with diabetes to choose their preferred glucose monitoring and insulin delivery.

Relationship Between Moderate-to-Vigorous Physical Activity and Glycemia Among Young Adults with Type 1 Diabetes and Overweight or Obesity: Results from the Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON) Study.

Aims: Using data from the ACT1ON study, we conducted secondary analyses to assess the relationship between minutes of moderate-to-vigorous physical activity (MVPA) and glycemia in adults with type 1 diabetes (T1D) and overweight or obesity. Materials and Methods: Participants (n = 66) with T1D provided measures of glycemia (hemoglobin A1c [HbA1c], percent of time below range <70 mg/dL, time-in-range [TIR 70-180 mg/dL], and time above range [TAR >180 mg/dL]) and self-reported physical activity (Global Physical Activity Questionnaire [GPAQ] and Previous Day Physical Activity Recalls [PDPAR]) at baseline, 3, 6, and 9 months postintervention. Wearable activity data were available for a subset of participants (n = 27). Associations were estimated using mixed effects regression models adjusted for design, demographic, clinical, and dietary covariates. Results: Among young adults 19-30 years of age with a baseline HbA1c of 7.9% ± 1.4% and body mass index of 30.3 (interquartile range 27.9, 33.8), greater habitual weekly MVPA minutes were associated with higher HbA1c through the GPAQ (P < 0.01) and wearable activity data (P = 0.01). We did not observe a significant association between habitual MVPA and any continuous glucose monitoring metrics. Using PDPAR data, however, we observed that greater daily MVPA minutes were associated with more TAR (P < 0.01) and reduced TIR (P < 0.01) on the day following reported physical activity. Conclusions: Among young adults with T1D and overweight or obesity, increased MVPA was associated with worsened glycemia. As physical activity is vital to cardiovascular health and weight management, additional research is needed to determine how to best support young adults with T1D and overweight or obesity in their efforts to increase physical activity. Clinical Trial Registration number: NCT03651622.