Glycemic Events Research Articles

Continuous Glucose Monitoring and Reduced Diabetes-Related Hospitalizations in Patients with Type 2 Diabetes and CKD.

There is a heightened risk of glycemic variability in patients with diabetes and chronic kidney disease (CKD). This glycemic variability could lead to hypoglycemic or hyperglycemic crises. We hypothesized that initiation of continuous glucose monitoring (CGM), which provides a glucose measurement every 1-5 minutes, could reduce the incidence of hospitalizations for patients with type 2 diabetes (T2D) and CKD. A retrospective analysis of US administrative claims data from the Optum Clinformatics© Database was conducted. People with T2D, using insulin, not receiving dialysis, and living with stage 3-5 CKD who initiated CGM between 1/1/2016 and 3/31/2022 were identified. NDC and HCPCS codes were used to identify CGM device use and ICD-10 codes were used to identify CKD diagnosis and categorize healthcare encounters. Rates of diabetes-related hospitalizations were obtained, and multivariable logistic regression analyses revealed predictors of hypoglycemic and hyperglycemic encounters. A total of 8,959insulin-using patients with T2D and CKD were identified. Most were White (72.3%), had Medicare insurance coverage (82.2%), were using intensive insulin (91.3%), and had Stage 3 CKD (86.0%). After CGM initiation, rates of hospitalizations for hyperglycemia or hypoglycemia decreased by 18.2% and 17.0%, respectively (p<0.0001 for both). The proportion hospitalized with at least one hypoglycemic or hyperglycemic event also significantly decreased after CGM initiation. Significant predictors of both hypoglycemic and hyperglycemic encounters included a previous encounter of that type, age 30-59 years and depression (for hypoglycemia), and age 30-49 and neuropathy (for hyperglycemia). Use of CGM or glucagon-like peptide-1 receptor agonists was significantly protective against hypoglycemic encounters. Initiation of CGM was associated with significant reductions in diabetes-related hospitalizations among insulin-using individuals with T2D and moderate-to-severe CKD. CGM could help patients with T2D and CKD control their glucose and avoid potentially dangerous glycemic events.

Digital Interventions in Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes mellitus (T2DM) is a highly prevalent chronic disease with an increasing mortality rate over the last decade. Diabetes self-management education (DSME) programs have been reported as essential to improve survival; however, patient adherence rates are very low. Therefore, digital devices have been created to deliver DSME at a distance and enhance program attendance. This study aims to assess the effects of digitally delivered DSME programs on the glycosylated hemoglobin (HbA1C) of patients with prediabetes and T2DM. Methods: We researched PUBMED databases for randomized controlled trials (RCT) and observational studies (OS) published between 2012-2022 in English, Portuguese, or Spanish. The selected articles tested digital DSME interventions against treatment as usual (TAU) on adults (&gt;18 years) previously diagnosed with T2DM or prediabetes. The result was measured by determining the HbA1c levels. Results: Out of 261 articles, 14 RCTs were selected based on eligibility criteria. Digital DSME technologies have different objectives, including monitoring glycemic fluctuation, insulin titration, nutritional guidance, sleeping assessment, enhancement of physical activity, control of comorbidities, relevant task notifications, personalized treatment recommendations, educational content, and patient/medical staff remote interaction. Some of the technologies combined machine learning techniques for different functions, including detecting adverse glycemic events, physical activity, and blood pressure, among others. Although the level of adherence varied among the various trials, 4 of the 14 RCTs analyzed reported a significant reduction of HbA1c levels using these digital devices compared to TAU. Discussion: Programs providing digital DSME education is a potentially cost-effective tool to improve diabetes care worldwide by overcoming distance barriers, facilitating physician-patient communication, and reducing HbA1c levels. Future improvements in implementing these technologies could enhance user compliance and contribute effectively to diabetes management.

Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration.

The adverse glycemic events triggered by the inaccurate insulin infusion in Type I diabetes (T1D) can lead to fatal complications. Predicting blood glucose concentration (BGC) based on clinical health records is critical for control algorithms in the artificial pancreas (AP) and aiding in medical decision support. This paper presents a novel deep learning (DL) model incorporating multitask learning (MTL) for personalized blood glucose prediction. The network architecture consists of shared and clustered hidden layers. Two layers of stacked long short-term memory (LSTM) form the shared hidden layers that learn generalized features from all subjects. The clustered hidden layers comprise two dense layers adapting to the gender-specific variability in the data. Finally, the subject-specific dense layers offer additional fine-tuning to personalized glucose dynamics resulting in an accurate BGC prediction at the output. OhioT1DM clinical dataset is used for the training and performance evaluation of the proposed model. A detailed analytical and clinical assessment have been performed using root mean square (RMSE), mean absolute error (MAE), and Clarke error grid analysis (EGA), respectively, which demonstrates the robustness and reliability of the proposed method. Consistently leading performance has been achieved for 30- (RMSE = 16.06 ±2.74, MAE = 10.64 ±1.35), 60- (RMSE = 30.89 ±4.31, MAE = 22.07 ±2.96), 90- (RMSE = 40.51 ±5.16, MAE = 30.16 ±4.10), and 120-minute (RMSE = 47.39 ±5.62, MAE = 36.36 ±4.54) prediction horizon (PH). In addition, the EGA analysis confirms the clinical feasibility by maintaining more than 94 % BGC predictions in the clinically safe zone for up to 120-minute PH. Moreover, the improvement is established by benchmarking against the state-of-the-art statistical, machine learning (ML), and deep learning (DL) methods.

Interruption of Continuous Glucose Monitoring: Frequency and Adverse Consequences.

Removal of diabetes devices, including insulin pumps and continuous glucose monitoring (CGM), is a common practice due to hospital policies, interference with imaging studies, medications, and surgical interventions. Furthermore, these devices are inherently prone to malfunction, adhesive failure, and issues with insertion that can lead to a reduction in wear time. Prescription and dispensing practices provide an exact number of sensors per month without redundancy to account for the realities of daily CGM use. A RedCap survey was completed by adult patients with type 1 or type 2 diabetes (T1D or T2D) who utilize CGM followed in the Diabetes Center at Washington University in St Louis. Of 384 surveys sent, 99 were completed. Participants had a mean age of 54 years, T1D 69%, female 70%, White 96%, non-Hispanic 96%, and a mean duration of diabetes mellitus (DM) 28 years. Of the cohort, 100% used CGM (80.2% Dexcom, 13.5% Freestyle Libre, 6.3% Medtronic), 61% insulin pump, and 41% Hybrid closed-loop (HCL) systems. CGM-related disruption events included device malfunction (in 85.4% of participants), insertion problems (63.5%), and falling off (61.4%). Medical care-related disruption occurred most frequently in the setting of imaging (41.7%), followed by surgery/procedures (11.7%) and hospitalization (4.4%). Adverse glycemic events attributed to CGM disruption, including hyperglycemia and hypoglycemia, occurred ≥4 times in 36.5% and 12.4% of the cohort, respectively. Disruption in CGM use is common. Lack of redundancy of CGM supplies contributes to care disruption and adverse glycemic events.

Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data

IntroductionDiabetes Mellitus (DM) is characterized by impaired ability to metabolize glucose for use in cells for energy, resulting in high blood sugar (hyperglycemia). DM impacted 463 million individuals worldwide in 2019, with over four million fatalities documented. Blood glucose levels (BGL) are usually measured, as standard protocols, through invasive procedures. Recently, Artificial Intelligence (AI) based techniques have demonstrated the potential to estimate BGL using data collected by non-invasive Wearable Devices (WDs), thereby, facilitating monitoring and management of diabetics. One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. The use of such biomarkers to monitor glycemic events represents a major shift in technology for self-monitoring and developing digital biomarkers using non-invasive WDs. To do this, it is necessary to investigate the correlations between characteristics acquired from non-invasive WDs and indicators of glycemic health; furthermore, much work is needed to validate accuracy. Research Design & MethodsThe study aimed to investigate performance of AI models in estimating BGL among diabetic patients using non-invasive wearable devices data An open-source dataset was used which provided BGL readings, diabetic status (Diabetic or non-diabetic), heart rate, Blood oxygen level (SPO2), Diastolic Blood pressure, Systolic Blood Pressure, Body temperature, Sweating, and Shivering for 13 participants by age group taken from WDs. Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. ResultsWe were able to estimate with high accuracy (RMSE range: 0.099 to 0.197) the relationship between glycemic metrics and features that can be derived from non-invasive WDs when utilizing AI models. ConclusionWe provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics.

Combining rapid and sustained insulin release from conducting hydrogels for glycemic control

Innovative insulin delivery systems contemplate combining multi-pharmacokinetic profiles for glycemic control. Two device configurations have been designed for the controlled release of insulin using the same chemical compounds. The first insulin delivery system, which displays a rapid release response that, in addition, is enhanced on a short time scale by electrical stimulation, consists on an insulin layer sandwiched between a conducting poly(3,4-ethylenedioxythiophene) (PEDOT) film and a poly-γ-glutamic acid (γ-PGA) hydrogel. The second system is constituted by γ-PGA hydrogel loaded with insulin and PEDOT nanoparticles by in situ gelation. In this case, the insulin release, which only starts after the degradation of the hydrogel over time (i.e. on a long time scale), is slow and sustained. The combination of an on-demand and fast release profile with a sustained and slow profile, which act on different time scales, would result in a very efficient regulation of diabetes therapy in comparison to current systems, allowing to control both fast and sustained glycemic events. Considering that the two systems developed in this work are based on the same chemical components, future work will be focused on the combination of the two kinetic profiles by re-engineering a unique insulin release device using γ-PGA, PEDOT and insulin.

Classification and prediction of hypoglycemia in diabetic patients using machine learning techniques

Type 1 diabetics can lower their risk of microvascular and macrovascular problems by carefully regulating their blood glucose levels. The downside is that these measures are incredibly challenging because of the wide diversity across individuals, as well as other factors that affect glycemic management. Keeping glucose levels under control is difficult due to the possibility of severe hypoglycemia in patients receiving intensive insulin therapy. In people with diabetes, hypoglycemia is a common complication, which has a negative impact on overall health and well-being. Improving patient safety by anticipating unfavourable glycemic events has become a practical approach to enhanced patient safety using machine learning decision assistance. This paper suggests the use of three machine learning techniques to solve the problem of diabetic safety: (1) Random Forest for continuous glucose predictions, (2) support vector machines for postprandial period predictions, and (3)artificial neural networks for overnight hypoglycemic predictions. It has the two different categorization and prediction capabilities already established. A major system feature is the overall reduction in bouts of hypoglycemia, which results in an increase in patient safety and provides better confidence in treatment decisions.

Investigating Temporal Patterns of Glycemic Control around Holidays.

Maintaining good glycemic control is a central part of diabetes care. However, it can be a tedious task because many factors in daily living can affect glycemic control. To support management, a growing number of people living with diabetes are now being prescribed continuous glucose monitors (CGMs) for real-time tracking of their blood glucose levels. However, routine use of CGMs is also an invaluable source of patient-generated data for individual and population-level studies. Prior research has shown that festive periods such as holidays can be a notable contributor to overeating and weight gain. Thus, in this work, we sought to investigate patterns of glycemic control around the holidays, particularly Thanksgiving, Christmas, and New Year, by using 3-months of CGM data from 14 patients with Type 1 Diabetes. We leveraged clinically validated metrics for quantifying glycemic control from CGM data and well-established statistical tests to compare diabetes management on holiday weeks versus non-holiday weeks. Based on our analysis, we found that 86% of subjects (12 out of 14) had worse glycemic control (i.e., more ad-verse glycemic events) during holiday weeks compared to non-holiday weeks. This general trend was prevalent amongst most subjects, however, we also observed unique individual patterns of glycemic control. Our findings provide a basis for further research on temporal patterns in diabetes management and data-driven interventions to support patients and caregivers with maintaining good glycemic control all year round.

Enhancing self-management in type 1 diabetes with wearables and deep learning

People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D.

706-P: CGM Disruption: A Common Phenomenon

Background: Advances in diabetes technology have led to reliance on continuous glucose monitors (CGM) for glycemic management. Insufficient wear time due to device malfunction, insertion problems, displacement or removal for imaging, medical procedures or hospitalization is common. Prescription practices that provide an exact number of sensors without redundancy fail to account for the realities of CGM use. The aim of this study is to characterize CGM disruption rates and consequences. Methods: A RedCap survey was completed by adults from a university based diabetes center with T1D or T2D who utilize CGM. Results: Of 262 surveys sent, 76 were completed. Participants had mean age 54, T1D 69%, female 72%, White 97%, and mean duration of diabetes 28 years. 100% used CGM (85.1% Dexcom, 9.5% Freestyle Libre, 5.4% Medtronic) , 61% insulin pump, and 41% HCL systems. The percent of participants reporting ≥1 CGM disruption events in 1 year: device malfunction (85.1%) , insertion problems (63.0%) , displacement (56.8%) ; removal for imaging (44.6%) , surgery/procedures (13.7%) and hospitalization (4.1%) . Adverse glycemic events attributed to disruption of CGM including hyperglycemia and hypoglycemia occurred ≥1 time in 55.4% and 45.9% of the participants respectively (Figure) . Conclusion: Loss of CGM use prior to the anticipated change date is common. Lack of redundancy of CGM supplies contributes to disruptions in care and adverse glycemic events. Disclosure A.Cedeno: None. P.Krutilova: None. A.Markov: None. J.B.Mcgill: Advisory Panel; Gilead Sciences, Inc., Lilly Diabetes, MannKind Corporation, Novo Nordisk A/S, Provention Bio, Inc., Salix Pharmaceuticals, Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Research Support; Dexcom, Inc., Novo Nordisk. A.M.Mckee: Consultant; Medtronic.

Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models

Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models

A Study on Identifying the Causes of Hypoglycemia and to Implement a Process to Prevent Hypoglycemia Events in Diabetic Patients in a Tertiary Care Hospital, Hyderabad

Background: Low blood glucose is common among people with type 1 diabetes and among people with type 2 diabetes who take insulin or oral Hyperglycemic agents. In a large global study of people with diabetes who take insulin, 4 in 5 people with type 1 diabetes and nearly half of those with type 2 diabetes reported a low blood sugar event at least once over a 4-week period. Therefore, this study helps in identifying the causes of hypoglycemia and to implement a process to prevent hypoglycemia events in diabetic patients. Before taking up the study we have noticed an increased number in Hypoglycemia events for 2 months Period. For which the Retrospective data was collected and analyzed to understand the most common factors causing Hypoglycemia Aim: To identify the causes of Hypoglycemia and to implement a process to prevent Hypoglycemia events Design: Initial retrospective analysis was done to understand the most common factors causing Hypoglycemia. Post Retrospective analysis we have initiated a structured training programme to all health care staff who are involved in the care of diabetic patients. Training was done over a period of 2 weeks, following an observation study done for duration of 6 months to analyze the occurrence of hypoglycemia events in details and implemented a process to identify and treat hypoglycemia in time. Result: Mean Hypoglycemia was similar across age, gender, based on diet intake and also based on insulin duration of action. Mean Hypoglycemia was significantly (P&lt;0.05) higher in evening (12 pm to 12 am) 58.8, then the morning (1 am to 12 pm). Asymptomatic glycemic events were significantly (P&lt;0.05) higher by mean of symptomatic hypoglycemia. Conclusion: Implementation of structured training program about the most common causes leading to hypoglycemia and the process of correcting event on time reduce morbidity related to hypoglycemia. Recommendations: Training programme should be carried out for nurses related to process of preventing hypoglycemia and process in correcting hypoglycemia in time Keywords: Hypoglycemia, Diabetes, Insulin, OHA.

Use of Retrospective Continuous Glucose Monitoring Data Is Underrated and Underused.

Access to real-time glucose data in real time allows users to make informed decisions about their insulin dosages, appropriate carbohydrate intake, exercise, and other health behaviors. Programmable alarms and alerts warn users about current and/or impending acute glycemic events. However, the value and utility of retrospective data analysis for clinical decision-making have gone mostly unrecognized. This article presents a series of patient case examples that illustrate how our use of retrospective data enables us to identify and effectively address patterns of problematic glycemia.

The role of the emergency department in the management of diabetes and complications: a systematic review

Diabetes mellitus (DM) is a chronic metabolic disease; its prevalence is increasing globally. DM has several complications such as hypoglycemia and hyperglycemia that require admission to hospital emergency services to be managed, especially in developing regions. There is no focus on the role of the emergency department (ED) regarding DM and its complications. The present study to identify the ED&amp;apos;s role in managing diabetes and its associated complications by reviewing the previous studies concerned with this subject. PubMed, Science direct, and Google scholar databases were explored to obtain articles related to our current subject from 2017 to 2022. The keywords included were &amp;quot;Emergent, Emergency Department, Diabetes, Complications, Management, Hyperglycemia, Hypoglycaemia, Glycemic events, and Diabetic Ketoacidosis.&amp;quot; The inclusion criteria were original studies that reported diabetic patients and patients with diabetic complications admitted to the ED. A total of 2,643 articles were obtained; only nine articles were eligible for our inclusion criteria. The nine studies included a total number of 176,160 patients. The problems reported included diabetes, glycemic events, diabetic ketoacidosis (DKA), and diabetic coma. The management of diabetic coma, glycemic events, diabetes, and DKA in the ED was reported. The ED has a significant role in managing diabetes and its complications with successful management.

Automated Adaptation of Insulin Treatment in Type 1 Diabetes.

Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.

Pros and cons of continuous glucose monitoring in the intensive care unit.

Diabetes mellitus affects people worldwide, and management of its acute complications or treatment-related adverse events is particularly important in critically ill patients. Previous reports have confirmed that hyperglycemia can increase the risk of mortality in patients cared in the intensive care unit (ICU). In addition, severe and multiple hypoglycemia increases the risk of mortality when using insulin or intensive antidiabetic therapy. The innovation of continuous glucose monitoring (CGM) may help to alert medical caregivers with regard to the development of hyperglycemia and hypoglycemia, which may decrease the potential complications in patients in the ICU. The major limitation of CGM is the measurement of interstitial glucose levels rather than real-time blood glucose levels; thus, there will be a delay in the treatment of hyperglycemia and hypoglycemia in patients. Recently, the European Union approved a state-of-art artificial intelligence directed loop system coordinated by CGM and a continuous insulin pump for diabetes control, which may provide a practical way to prevent acute adverse glycemic events related to antidiabetic therapy in critically ill patients. In this mini-review paper, we describe the application of CGM to patients in the ICU and summarize the pros and cons of CGM.

How Safe are Fluoroquinolones for Diabetic Patients? A Systematic Review of Dysglycemic and Neuropathic Effects of Fluoroquinolones.

IntroductionThe US Food and Drug Administration issued safety warnings about neuropathy in 2013 and dysglycemia in 2018 caused by fluoroquinolone use, mainly based on case reports and case series. We conducted this systematic review to evaluate the safety of fluoroquinolones in diabetic patients by investigating their dysglycemic and neuropathic effects.MethodsPubMed, Scopus, and Google Scholar were searched for randomized controlled trials and observational studies published from inception till September 2019 evaluating the safety of fluoroquinolones. Efficacy studies of fluoroquinolones reporting these adverse effects were also included. Primary outcomes were hypoglycemia, hyperglycemia, and neuropathy among patients with or without diabetes and treated with fluoroquinolones compared with placebo or other antibiotics. The Cochrane Collaboration tool for randomized controlled trials and modified Newcastle–Ottawa quality-assessment scale were used for assessment of the included studies.Results and DiscussionA total of 725 studies were identified in the initial search. After screening of titles and abstracts and full-text review, 16 articles fulfilled the inclusion criteria. The sampled patients were aged 30–78 years. Hyperglycemia was reported in 1,588 patients that received fluoroquinolone among eight studies with 4,663 patients, and hypoglycemia was reported in 2,179 patients that received fluoroquinolones among eleven studies with 6,208 patients. Dysglycemia was not generally associated with diabetes mellitus per se. Nevertheless, patients with more comorbidities, especially those with chronic kidney disease, receiving antidiabetics and/or steroids had more glycemic events when treated with fluoroquinolones.ConclusionMoxifloxacin was found to be associated the most and ciprofloxacin the least with dysglycemia. fluoroquinolones must be used with great caution among diabetic patients who have comorbidities and are receiving antidiabetics and/or steroids. Further evidence is required from studies on neuropathy caused by fluoroquinolones.

Digital intervention increases influenza vaccination rates for people with diabetes in a decentralized randomized trial

People with diabetes (PWD) have an increased risk of developing influenza-related complications, including pneumonia, abnormal glycemic events, and hospitalization. Annual influenza vaccination is recommended for PWD, but vaccination rates are suboptimal. The study aimed to increase influenza vaccination rate in people with self-reported diabetes. This study was a prospective, 1:1 randomized controlled trial of a 6-month Digital Diabetes Intervention in U.S. adults with diabetes. The intervention group received monthly messages through an online health platform. The control group received no intervention. Difference in self-reported vaccination rates was tested using multivariable logistic regression controlling for demographics and comorbidities. The study was registered at clinicaltrials.gov: NCT03870997. A total of 10,429 participants reported influenza vaccination status (5158 intervention, mean age (±SD) = 46.8 (11.1), 78.5% female; 5271 control, Mean age (±SD) = 46.7 (11.2), 79.4% female). After a 6-month intervention, 64.2% of the intervention arm reported influenza vaccination, vers us 61.1% in the control arm (diff = 3.1, RR = 1.05, 95% CI [1.02, 1.08], p = 0.0013, number needed to treat = 33 to obtain 1 additional vaccination). Completion of one or more intervention messages was associated with up to an 8% increase in vaccination rate (OR 1.27, 95% CI [1.17, 1.38], p < 0.0001). The intervention improved influenza vaccination rates in PWD, suggesting that leveraging new technology to deliver knowledge and information can improve influenza vaccination rates in high-risk populations to reduce public health burden of influenza. Rapid cycle innovation could maximize the effects of these digital interventions in the future with other populations and vaccines.

GlucoMine

The growing popularity of wearable devices for continuous sensing has made personal health data increasingly available, yet methods for data interpretation are still a work in progress. This paper investigates potential under-utilization of wearable device data in diabetes management and develops an analytic approach - GlucoMine - to uncover individualized patterns in extended periods of such data to support and improve care. In addition, we conduct a user study with clinicians to assess and compare conventional tools used for reviewing wearable device data in diabetes management with the proposed solution. Using 3-6 months of continuous glucose monitor (CGM) data from 54 patients with type 1 diabetes, we found that: 1) the recommended practice of reviewing only short periods (e.g., the most recent 2-weeks) of CGM data based on correlation analysis is not sufficient for finding hidden patterns of poor management; 2) majority of subjects (96% in this study) had clinically-recognized episodes of recurrent adverse glycemic events observable from analysis of extended periods of their CGM data; 3) majority of clinicians (89% in this study) believe there is benefit to be gained in having an algorithm for extracting patterns of adverse glycemic events from longer periods of wearable device data. Findings from our user study also provides insights, including strengths and weakness of various data presentation tools, to guide development of better solutions that improve the use of wearable device data for patient care.

Evaluation of ultrasound examination combined with intensive injection technique education on insulin-induced lipohypertrophy (LH) management: a prospective cohort study in China

This study was aimed to assess the impact of ultrasound examination combined with intensive injection technique education for LH management. A total of 120 patients with diabetes who were identified as existing clinical LH were enrolled into the study. All patients underwent ultrasound examination and received intensive injection technique education based on ultrasound detection results and Chinese guideline for diabetic injection technology in a prospective single study in China with a follow-up of 3 months. Injection Technique Questionnaire (ITQ) and assessment of glycemic control were used to assess the impact of ultrasound examination combined with intensive injection technique education for LH management. As expected, after 3 months of intensive injection technique education based on ultrasound examination, the proportion of patients who had mastered insulin injection technique was significantly increased (p<0.05). The mean HbA1c was decreased by 0.60 (0.28)% from 7.61 (0.34)% to 7.01 (0.23)%, mean FBG was decreased by 1.20 (1.05) mmol/L from 8.10 (1.02) mmol/L to 6.90 (0.71) mmol/L, mean 2hPG was decreased by 1.70 (1.31) mmol/L from 11.49 (1.25) mmol/L to 9.79 (0.87) mmol/L without increasing the insulin dosage. The glycemic variability (GV) indicators (LAGE, MBG, SDBG, and PPGE), hypoglycemic, hyperglycemic, and IM injection events were markedly decreased (p<0.05). Ultrasound examination combined with intensive injection technique education markedly improved glycemic control in diabetes with LH without increasing the insulin dosage, as well as reducing the occurrence of adverse blood glucose events. It can be a new effective approach for LH management.