Glucose levels measured with continuous glucose monitoring in uncomplicated pregnancies.

INTRODUCTION: To characterize glucose levels during uncomplicated pregnancies, defined as pregnancy with a hemoglobin A1c <5.7% (<39 mmol/mol) in early pregnancy, and without a large-for-gestational-age birth, hypertensive disorders of pregnancy, or gestational diabetes mellitus (ie, abnormal oral glucose tolerance test). RESEARCH DESIGN AND METHODS: Two sites enrolled 937 pregnant individuals aged 18 years and older prior to reaching 17 gestational weeks; 413 had an uncomplicated pregnancy (mean±SD body mass index (BMI) of 25.3±5.0 kg/m2) and wore Dexcom G6 continuous glucose monitoring (CGM) devices throughout the observed gestational period. Mealtimes were voluntarily recorded. Glycemic levels during gestation were characterized using CGM-measured glycemic metrics. RESULTS: Participants wore CGM for a median of 123 days each. Glucose levels were nearly stable throughout all three trimesters in uncomplicated pregnancies. Overall mean±SD glucose during gestation was 98±7 mg/dL (5.4±0.4 mmol/L), median per cent time 63-120 mg/dL (3.5-6.7 mmol/L) was 86% (IQR: 82-89%), median per cent time <63 mg/dL (3.5 mmol/L) was 1.8%, median per cent time >120 mg/dL (6.7 mmol/L) was 11%, and median per cent time >140 mg/dL (7.8 mmol/L) was 2.5%. Mean post-prandial peak glucose was 126±22 mg/dL (7.0±1.2 mmol/L), and mean post-prandial glycemic excursion was 36±22 mg/dL (2.0±1.2 mmol/L). Higher mean glucose levels were low to moderately associated with pregnant individuals with higher BMIs (103±6 mg/dL (5.7±0.3 mmol/L) for BMI ≥30.0 kg/m2 vs 96±7 mg/dL (5.3±0.4 mmol/L) for BMI 18.5-<25 kg/m2, r=0.35). CONCLUSIONS: Mean glucose levels and time 63-120 mg/dL (3.5-6.7 mmol/L) remained nearly stable throughout pregnancy and values above 140 mg/dL (7.8 mmol/L) were rare. Mean glucose levels in pregnancy trend higher as BMI increases into the overweight/obesity range. The glycemic metrics reported during uncomplicated pregnancies represent treatment targets for pregnant individuals.

[Erratum in "Point-of-Care Blood Glucose Testing: Post-Market Performance Assessment of the Accu-Chek Inform II Hospital-Use Glucose Meter" (DOI: 10.26442/00403660.2023.12.202522)].

In the article "Point-of-care blood glucose testing: post-market performance assessment of the Accu-Chek Inform II hospital-use glucose meter," published in the Terapevticheskii Arkhiv journal, Vol. 95, No.12, 2023 (DOI: 10.26442/00403660.2023.12.202522), errors were made: the term "measurements at the place of treatment" was changed, as well as the section "Conflict of interest." At the request of the authors' team, errors in the conflict of interest and the wording of the term have been corrected, and the section "Information about the authors" has been updated. The publisher replaced the original version of the published article with the corrected one; the information on the website was also corrected. Correct text of the section "Conflict of interest": Conflict of interest. All authors are not employees or consultants of Roche Diagnostics and have not received any compensation from Roche Diagnostics. Correct wording of the term in Russian: "измерения по месту лечения". Changes were made to the title of the article in Russian: "Измерения глюкозы по месту лечения: пострегистрационное испытание госпитального глюкометра Акку-Чек Информ II", the text of the abstract, keywords, citation, in the text of the article, and abbreviations. Information of the place of work has been updated: Center for Laboratory Diagnostics of the Russian Children Clinical Hospital, a Branch of the Pirogov Russian National Research Medical University. The publisher apologizes to readers and authors for the errors and is confident that the correction of errors will ensure the correct perception and interpretation of the results of the study described in the text.

Feasibility of continuous glucose monitoring in patients with type 1 diabetes at two district hospitals in Neno, Malawi: a randomised controlled trial.

OBJECTIVES: To assess the feasibility and change in clinical outcomes associated with continuous glucose monitoring (CGM) use among a rural population in Malawi living with type 1 diabetes. DESIGN: A 2:1 open randomised controlled feasibility trial. SETTING: Two Partners In Health-supported Ministry of Health-run first-level district hospitals in Neno, Malawi. PARTICIPANTS: 45 people living with type 1 diabetes (PLWT1D). INTERVENTIONS: Participants were randomly assigned to Dexcom G6 CGM (n=30) use or usual care (UC) (n=15) consisting of Safe-Accu glucose monitors and strips. Both arms received diabetes education. OUTCOMES: Primary outcomes included fidelity, appropriateness and severe adverse events. Secondary outcomes included change in haemoglobin A1c (HbA1c), acceptability, time in range (CGM arm only) SD of HbA1c and quality of life. RESULTS: Participants tolerated CGM well but were unable to change their own sensors which resulted in increased clinic visits in the CGM arm. Despite the hot climate, skin rashes were uncommon but cut-out tape overpatches were needed to secure the sensors in place. Participants in the CGM arm had greater numbers of dose adjustments and lifestyle change suggestions than those in the UC arm. Participants in the CGM arm wore their CGM on average 63.8% of the time. Participants in the UC arm brought logbooks to clinic 75% of the time. There were three hospitalisations all in the CGM arm, but none were related to the intervention. CONCLUSIONS: This is the first randomised controlled trial conducted on CGM in a rural region of a low-income country. CGM was feasible and appropriate among PLWT1D and providers, but inability of participants to change their own sensors is a challenge. TRIAL REGISTRATION NUMBER: PACTR202102832069874.

Summary of clinical investigation plan for The DIATEC trial: in-hospital diabetes management by a diabetes team and continuous glucose monitoring or point of care glucose testing - a randomised controlled trial.

BACKGROUND: Worldwide, up to 20 % of hospitalised patients have diabetes mellitus. In-hospital dysglycaemia increases patient mortality, morbidity, and length of hospital stay. Improved in-hospital diabetes management strategies are needed. The DIATEC trial investigates the effects of an in-hospital diabetes team and operational insulin titration algorithms based on either continuous glucose monitoring (CGM) data or standard point-of-care (POC) glucose testing. METHODS: This is a two-armed, two-site, prospective randomised open-label blinded endpoint (PROBE) trial. We recruit non-critically ill hospitalised general medical and orthopaedic patients with type 2 diabetes treated with basal, prandial, and correctional insulin (N = 166). In both arms, patients are monitored by POC glucose testing and diabetes management is done by ward nurses guided by in-hospital diabetes teams. In one of the arms, patients are monitored in addition to POC glucose testing by telemetric CGM viewed by the in-hospital diabetes teams only. The in-hospital diabetes teams have operational algorithms to titrate insulin in both arms. Outcomes are in-hospital glycaemic and clinical outcomes. DISCUSSION: The DIATEC trial will show the glycaemic and clinical effects of in-hospital CGM handled by in-hospital diabetes teams with access to operational insulin titration algorithms in non-critically ill patients with type 2 diabetes. The DIATEC trial seeks to identify which hospitalised patients will benefit from CGM and in-hospital diabetes teams compared to POC glucose testing. This is essential information to optimise the use of healthcare resources before broadly implementing in-hospital CGM and diabetes teams. TRIAL REGISTRATION: Prospectively registered at ClinicalTrials.gov with identification number NCT05803473 on March 27th 2023.

Impact on diabetes control and patient-reported outcomes of a newer implantable continuous glucose monitoring system (Eversense® CGM System): a single-centre retro- and prospective observational study.

AIMS OF THE STUDY: The Eversense® CGM System is the first and only continuous glucose monitoring system (CGMS) that uses a fully subcutaneous implanted sensor. This study aimed to evaluate effectiveness, safety and patient-reported outcomes in patients using the Eversense® CGM System in a realistic clinical setting, assessed at a single Swiss diabetes centre (Luzerner Kantonsspital) with prolonged follow-up. METHODS: This was a prospective and retrospective observational study that included patients with type 1 diabetes mellitus in whom at least one Eversense® glucose sensor was implanted between 2017 and 2022. The primary endpoint was the change in HbA1c levels from the baseline (before implantation of the sensor) to 6 ± 2 and 12 ± 2 months and the last follow-up (newest available value) after implantation. The secondary outcome measures were the number of premature sensor breakdowns, adverse events related to the implantation procedure (infection, bleeding, difficulties with implantation or explantation) and patient-related outcomes (assessed with a questionnaire). RESULTS: A total of 33 patients participated in this study. The median follow-up time was 50 (IQR 22.3-58.5) months. In total, 178 sensor implantations were performed. Valid HbA1c results were available for 26 participants. Compared to the baseline values, HbA1c levels at 6 and 12 months and the last follow-up changed by -0.25%, -0.45 and -0.2 (p = 0.278, 0.308 and 0.296, respectively). We recorded 16 (9%) premature sensor breakdowns, all occurring between 2019 and 2020. Apart from one late-onset infection and four complicated sensor removals, no major complications were assessed. The results of the questionnaire showed a subjective improvement in hypoglycaemia rates, a better perception of hypoglycaemia and the impression of better diabetes management. Common issues with the device reported by the patients were technical errors (connection problems) and problems with the removal procedure. CONCLUSIONS: The use of the Eversense® CGM System resulted in changes in HbA1c of between -0.2% and -0.45%. The rate of premature sensor breakdown was low. Major complications following sensor implantation or removal were absent, apart from one case of infection and four cases of complicated removal. Patient-reported outcomes with the Eversense® CGM System showed a subjective positive impact on hypoglycaemia rates, greater confidence in managing hypoglycaemia and diabetes in general, and easy handling of the transmitter and mobile app. Technical issues must be considered but are nowadays, with the use of the newest sensor generation, very rare.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Intensive management from diagnosis improves HbA1c at 12 months post-diagnosis: results from a prospective cohort study in children with newly diagnosed type 1 diabetes.

AIMS: To examine the impact of intensive management of type 1 diabetes (T1D) from diagnosis on HbA1c 12 months from diagnosis. METHODS: HbA1c measured 12 months after diagnosis for 70 consecutively newly diagnosed children with T1D following implementation of an intensive management protocol was compared with 70 children consecutively diagnosed immediately pre-implementation. Intensive management involved carbohydrate counting and flexible insulin dosing from first meal with subcutaneous insulin, targeted blood glucose levels from 4-8mmol/L irrespective of time of day, avoidance of twice daily insulin regimen and promotion of continuous glucose monitoring (CGM). HbA1c, diabetes technology use and insulin regimen at 12 months post-diagnosis were compared. RESULTS: The post-intensive management implementation cohort had an improved mean HbA1c of 58.2±15.3mmol/mol vs 63.7±10.7mmol/mol at 12 months (p=0.014). The proportion of young people with diabetes meeting a target HbA1c of <53mmol/mol at 12 months improved from 11% to 40% (p=<0.001). There was a reduction of twice daily insulin regimen from 66% to 11% (p=<0.001), and increased CGM use from 57% to 76% (p=0.02). CONCLUSION: Intensive management when implemented with consistent messaging from the multi-disciplinary team resulted in clinic-wide improvements in HbA1c and the proportion meeting HbA1c targets.

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes.

Background: Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Materials and methods: Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Results: Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Conclusion: Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction.

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

Intermittently scanned continuous glucose monitoring provides no benefit over structured self-monitoring of blood glucose in type 2 diabetes not on prandial insulin, in the context of diabetes self-management education: GLucose monitoring programme SingaporE (GLiMPSE).

OBJECTIVE: We evaluated the impact of intermittently scanned continuous glucose monitoring(is-CGM)over self-monitoring of blood glucose(SMBG) in the context of diabetes self-management education (DSME) in sub-optimally controlled type 2 diabetes(T2D) in a multi-ethnicsetting. RESEARCH DESIGN AND METHOD: Randomized-controlled, open-label trial (NCT04564911), of T2D with HbA1c ≥ 7.5-≤10 %, on oral agents with/without basal insulin was carried out. Intervention arm received 6 weeks(w) continuous is-CGM, followed by one is-CGM/month till 24w. Control arm was advised to perform 4 SMBG/day. Educationwas delivered at weeks 0, 2, 8, 16. PRIMARY OUTCOME: Change in HbA1c from baseline at 24w. Modified intention-to-treat (mITT) analysis with linear mixed-effect model for repeated measurementswas performed. RESULTS: 176 subjects, age 55 ± 10.7 years(y), DM duration 11 ± 7.3y, BMI 27.8 ± 5.9 kg/m2, 58 % Male, 29.5 % basal insulin users were analysed. Within each arm,from baseline to 24w, mean HbA1c decreasedby -0.6 % (-6.6.mmol/mol, p-value < 0.01)and weight decreased(isCGM: -1.44 kg; SMBG: -1.25 kg, both p < 0.01). These changes were sustained to one year. However, there wasno significant difference in these parameters between arms (p-value > 0.05). CONCLUSION: In the context of DSME, use of either SMBG or is-CGM led to improved glycaemia and reduced weight over a period of 24 weeks, sustained to one year.

Changes in glucometric parameters in people living with diabetes users of the free-style libre 2 system before and after the update possibility to real-time glucose readings in real world practice.

In Spain, from October 10th, 2023, the FreeStyle Libre 2 system offers the possibility to automatically changed from isCGM to rtCGM with a system update. Our study aimed to evaluate the glucometric before and after that date. We didn't find significant changes in TIR, however time of use increased and TBR decreased.

Long-term assessment of the NHS hybrid closed-loop real-world study on glycaemic outcomes, time-in-range, and quality of life in children and young people with type 1 diabetes.

Hybrid closed-loop (HCL) systems seamlessly interface continuous glucose monitoring (CGM) with insulin pumps, employing specialised algorithms and user-initiated automated insulin delivery. This study aimed to assess the efficacy of HCLs at 12 months post-initiation on glycated haemoglobin (HbA1c), time-in-range (TIR), hypoglycaemia frequency, and quality of life measures among children and young people (CYP) with type 1 diabetes mellitus (T1DM) and their caregivers in a real-world setting. Conducted between August 1, 2021, and December 10, 2022, the prospective recruitment took place in eight paediatric diabetes centres across England under the National Health Service England's (NHSE) HCL pilot real-world study. A cohort of 251 CYP (58% males, mean age 12.3 years) with T1DM participated (89% white, 3% Asian, 4% black, 3% mixed ethnicity, and 1% other). The study utilised three HCL systems: (1) Tandem Control-IQ AP system, which uses the Tandem t:slim X2 insulin pump (Tandem Diabetes Care, San Diego, CA, USA) with the Dexcom G6® CGM (Dexcom, San Diego, CA, USA) sensor; (2) Medtronic MiniMed™ 780G with the Guardian 4 sensor (Medtronic, Northridge, CA, USA); and (3) the CamAPS FX (CamDiab, Cambridge, UK) with the Ypsomed insulin pump (Ypsomed Ltd, Escrick, UK) and Dexcom G6® CGM.All systems were fully funded by the NHS. Results demonstrated significant improvements in HbA1c (average reduction at 12 months 7 mmol/mol; P < 0.001), time-in-range (TIR) (average increase 13.4%; P < 0.001), hypoglycaemia frequency (50% reduction), hypoglycaemia fear, and quality of sleep (P < 0.001) among CYP over a 12-month period of HCL usage. Additionally, parents and carers experienced improvements in hypoglycaemia fear and quality of sleep after 6 and 12 months of use. In addition to the improvements in glycaemic management, these findings underscore the positive impact of HCL systems on both the well-being of CYP with T1DM and the individuals caring for them.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Glucometrics and device satisfaction in children and adolescents with type 1 diabetes using different treatment modalities: A multicenter real-world observational study.

AIMS: To analyze metabolic outcomes, diabetes impact and device satisfaction in children and adolescents with type 1 diabetes in Italy who used different treatment modalities for diabetes care in a real-life context. METHODS: In this multicenter, nationwide, cross-sectional study, 1464 participants were enrolled at a routine visit. The following treatment modalities were considered MDI + SMBG; MDI + CGM; Sensor Augmented Pump Therapy; predictive management of low glucose; Hybrid Closed Loop (HCL); Advanced Hybrid Closed Loop (AHCL). Health related quality of life was evaluated by the Italian version of the Diabetes Impact and Device Satisfaction Scale (DIDS) questionnaire. RESULTS: Patients treated with AID systems were more likely to have HbA1c ≤ 6.5 %, higher percentage of time with glucose levels between 70 and 180 mg/dL, lower percentage of time with glucose levels above 180 mg/dL, higher device satisfaction, and reduced impact of diabetes. All the therapeutic modalities with respect to MDI + CGM, except for MDI + SMBG, contributed to increase the device satisfaction. HCL and AHCL respect to MDI + CGM were associated with lower diabetes impact. CONCLUSION: Real-life use of automated insulin delivery systems is associated with reduced type 1 diabetes impact, increased device satisfaction, and achievement of glycemic goals.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort.

AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Evaluation of a community-based pharmacy resident-led continuous glucose monitoring program within a family medicine clinic.

BACKGROUND: Pharmacist-driven continuous glucose monitoring (CGM) is associated with reduced hemoglobin A1c (HbA1c) and achievement of daily glycemic goals. Community-based pharmacists are well-positioned to improve CGM uptake among patients with diabetes due to their accessibility and expertise. However, little data exists evaluating the outcomes of CGM services led by a community-based pharmacist. OBJECTIVE: To evaluate the impact of a community-based pharmacy resident-driven CGM service on HbA1c, revenue, and patient satisfaction. PRACTICE DESCRIPTION: Independent community pharmacy sharing a clinical services agreement with a primary care clinic for Postgraduate Year One (PGY1) Community-based Pharmacy Residents to provide patient care under general supervision of the physician. PRACTICE INNOVATION: Patients were offered CGM services if they were 18+ years with an HbA1c > 7.0% and had insurance coverage for CGM. Enrolled patients engaged in three months of pharmacist-led appointments for CGM application, data interpretation, diabetes education, and lifestyle management. Current Procedural Terminology (CPT) codes 99211, 95250, or 95251 were billed based on each encounter. HbA1c values were collected at program enrollment and conclusion. Patients completed a satisfaction survey at program conclusion. EVALUATION METHODS: Demographics and billed CPT codes were collected from the electronic health record. Descriptive statistics were used to analyze data. RESULTS: Eighteen patients were included. A mean reduction of 1.2% occurred in HbA1c (n = 12; 9.7%-8.5%). Forty CPT codes were billed, generating $3671.40 of revenue. Satisfaction surveys were collected for 50% of participants (n = 9). Most were satisfied with the CGM service and its individual components (n = 8, 89%). Most were willing to continue using CGM devices and receive diabetes education from a pharmacist (n = 8, 89%). CONCLUSION: A community-based pharmacist-led CGM service demonstrated a reduction in HbA1c and generated revenue for the clinic. Patients reported satisfaction and willingness to continue the service.