Carbohydrate Counting Research Articles

Artificial Pancreas Technology Offers Hope for Childhood Diabetes.

Here, we provide a review of recent advancements in closed-loop or automated insulin delivery systems commonly referred to as the "artificial pancreas" (AP) for the management of type 1 diabetes (T1D). The use of hybrid closed-loop devices in children and adolescents with T1D consistently increases time in the target glucose range (70-180mg/dL) without increasing the risk of hypoglycemia. Although hybrid closed-loop systems are able to maintain fairly tight glycemic control overnight, daytime control remains challenging, primarily due to meals and exercise, and careful carbohydrate counting and meal boluses remain essential components of optimal diabetes control. Bihormonal and fully automated AP systems remain investigational at this time. While commercially available hybrid closed-loop AP systems improve glycemic control in children with T1D, more work is needed to achieve a fully automated AP system and decrease the burden of using diabetes technology.

The impact of high-fat and high-protein meal of adolescents with type 1 diabetes mellitus receiving intensive insulin therapy on postprandial blood glucose level: a randomized, crossover, breakfast study

In addition to carbohydrate count, this study aims to investigate the impact of the determination of bolus insulin doses by fat and protein counts on postprandial blood glucose level for adolescents having type 1 diabetes mellitus. The study was crossover and randomized; and all of participants were given standard breakfast (insulin dose according to carbohydrate/insulin ratio) for 1 day, and high-fat and high-protein breakfast for 2 days. The insulin dose for one of test meal was determined based on carbohydrate/insulin ratio. In the second application, in addition to carbohydrate/insulin ratios of the adolescents, 1 unit of insulin per what was added for each 200 kcal of energy provided from fat and protein of the meal. Postprandial blood glucose was recorded for 4 h using a continuous glucose monitoring system at intervals of 30 min. The mean blood glucose level between 0 and 240 min following consumption of different meals increased significantly in second application after 150th minute comparing with first application (p 0.05).However, the glycemic excursion of 30–0 min in third application was higher than the excursion in first and second applications while the glycemic excursion in first application between 150 and 120 min was higher than in third application (p < 0.05). These evidences demonstrated that high-fat and high-protein meal increases the requirement of insulin. Eventually, this study shows that there could be alternative algorithms to carbohydrate-based approach for the determination of the bolus insulin dose.

Caregivers' Nutrition Knowledge and Dietary Intake of Type 1 Diabetic Children Aged 3-14 Years in Uganda.

PurposeThis study aimed to assess the association between caregiver’s level of type 1 diabetes (T1D) nutrition knowledge with children’s dietary diversity score (DDS), mean intake of macronutrients, nutrient adequacy ratios (NARs) and mean adequacy ratio (MAR).Research Design and MethodsA cross-sectional analytical study design was used. The study was conducted at 6 diabetes clinics in Uganda among 59 caregivers and 61 children. T1D nutrition knowledge survey (NKS) was used to assess the caregiver’s nutrition knowledge, and the 24-hour dietary recall and dietary diversity score (DDS) questionnaires were used to collect data on the child’s dietary intake.ResultsMajority (93.2%) of the caregivers had low T1D nutrition knowledge. Carbohydrate counting was the least performed nutrition knowledge domain. The children’s mean DDS, calorie intake and MAR were 5.7 ± 1.6, 666.7 ± 639.8 kcal and 0.7 ± 0.3, respectively. The mean NARs of carbohydrate, protein, and fat were 0.9 ± 0.3, 0.9 ± 0.4, 0.5 ± 0.5, respectively. There was a significant association between DDS with NARs of carbohydrate, protein, fat, vitamins A, B2, B3, B5, B12, folic acid, zinc and MAR. No formal education was significantly associated with a lower mean NKS score among caregivers (p = 0.039). Caregivers’ T1D nutrition knowledge, age and family size explained 14% of variation in the child’s dietary diversity (p = 0.041).ConclusionDespite poor nutrition knowledge among caregivers especially on carbohydrate counting, dietary diversity among children with T1D remained favorable. Excess carbohydrate intake was observed with inadequate intake of proteins, fats and micronutrients (vitamin A, B vitamins and calcium). Caregivers with low education were more likely to register poor nutrition knowledge; therefore, there is need to develop and tailor nutrition education programmes to enhance comprehensive learning among caregivers for improved outcomes.

Comparison of Insulin Glargine and Detemir Effects on Hormones of Appetite and Metabolic Control in Patients with Type 1 Diabetes: A Randomized Clinical Trial.

The aim of this study was to compare the insulin glargine and detemir effects on hormons affecting appetite and metabolic control of patients with type 1 diabetes. This single-blind randomized clinical trial was conducted on patients aged 2 to 18 years with type 1 diabetes who were referred to the endocrinology department of Ali-Asghar Children Hospital in Tehran, from April to September 2019. Patients were randomly allocated to receive insulin Glargine or insulin Detemir. Before starting treatment, blood samples were obtained for routine biochemical tests and factors affecting appetite, including Leptin, Ghrelin, Aguti-Related Peptide (AGRP), and Peptide-YY3-36 (PYY 3-36). Patients were evaluated monthly and insulin dose was adjusted based on target glucose and carbohydrate counting. At the end of three months, the anthropometric values , HbA1C and factors that influence appetite were measured again in both groups, and the results were compared. A total of 40 children with a new onset of type 1 diabetes under 18 years who were hospitalized in Ali Asghar Children Hospital were randomly assigned into two groups as Glargine (n = 20) and Detemir (n = 20). The mean age of patients in the Glargine group was 11.07 ±4.18 years and in the Detemir group was 8.06 ± 3.56. In Glargine group HbA1C, Cholesterol, LDL, AGRP significantly decreased and leptin increased after treatment., while the change of BMI Z-score was not significant. There was a significant decrease of HbA1C in the Detemir group after treatment but there was no significant change of other variables. There was no significant difference for all the variables between two groups after treatment. There was no significant difference for BMI, metabolic control and appetite hormones between Glargine and Detemir groups. BMI-z score did not change in Glargine group while leptin increased and AGRP decreased after treatment. HbA1C decreased significantly after treatment in both groups.

Behavioral characteristics of children with type-1 diabetes and the effect of family attitudes on dietary adherence problems

Objective: Assessing the behavioral characteristics and family attitudes of children and adolescents in diabetes is linked to determining the reasons for difficulties in dietary adherence. Our aim was to assess the relationship between behavioral characteristics, family attitudes in children diagnosed with type 1 diabetes mellitus (T1DM), and the dietary adherence and glycemic control. Method: Fifty-four patients T1DM patients and 47 controls aged 7-18 years were included in the study together with their parents. Among the patients diagnosed with T1DM followed in the Pediatric Endocrinology Outpatient Clinic, those with high HbA1c levels (with poor dietary compliance) and those with a HbA1c level below 7.5 (with good dietary compliance) were included in the study as the patient and control group, respectively. A psychiatric assessment interview was conducted with both groups. Sociodemographic data and information on diabetes-related variables, Strengths and Difficulties Questionnaire and Parental Attitude Scale scores were recorded. Results: We have found higher standard diet application rates in patients compared to regular carbohydrate count. The patient group had higher odds of neglecting blood glucose measurement, insulin doses, and a history of stress before decompensation. The rate of psychiatric diagnosis was 26.2%, similar to the general literature, and combined diagnoses were less frequent. Hospital admissions and hypoglycemic episodes were observed at a higher rate in the group that had problems in dietary adherence. Compared to those with good dietary compliance, patients in the dietary non-adherence group had a higher level of parental control and poorer parental perception of their children’s peer relationships. Conclusion: To reduce the risk of acute complications of the disease and to prevent long-term chronic sequelae, it is important to identify positive and some negative behavioral characteristics of child and parental attitudes. Parental role is among the key factors in supporting the autonomy of the child in ensuring dietary compliance.

Dietary Recommendation in Diabetes Care: Carbohydrate Counting and Caloric Content of Nigerian Foods

The prevalence of diabetes mellitus is rising globally and even more in low and middle-income countries such as Nigeria. Optimal management of the disease is important to improve survival and prevent or delay its complications. Lifestyle management is a standard universal approach in optimizing the care given to diabetic patients. Dietary management is the central link in lifestyle modifications of individuals living with diabetes. Medical nutrition therapy (MNT) is a systematic therapeutic approach of assessing the nutritional needs of an individual, determining nutritional goals, counselling the clients on how to achieve the goals, prescribing and monitoring meal plans to achieve the goals. It is evidence-based, effective and highly recommended. All carers of the diabetes patient, including the health workers and the family members of the patients, need to have basic understanding of medical nutrition therapy but the efforts should be coordinated by licensed dietitians. There are evidences that adherence to prescribed calories is effective in the control of cardiovascular risk factors such as blood glucose, weight, lipid profile and blood pressure. However, prescription of calories should be based on thoughtful consideration of the nutritional needs, weight goal, personal preferences and tastes and cultural practices of individuals living with diabetes mellitus. Food pyramids give a graphical illustration on the recommended classes and servings of food. Six to eleven servings per day of carbohydrates, 3-5 servings per day of vegetables, 2-4 servings per day of fruit, 2-3 servings per day of dairy products and 2-3 servings per day of fish are the recommended proportions of the different classes of foods for an adult on an average of 2000 calories. Carbohydrate counting, taken with appropriate insulin dosing and physical activity, has been demonstrated to be effective in optimizing the glycaemic control of patients on multiple daily insulin injections. There are challenges in doing this in Nigeria due to lack of food labels. The caloric contents of common Nigerian foods are highlighted so as to help in achieving dietary goals.

Tip 1 Diyabet ve Çölyak Hastalığı Birlikteliği: Olgu Sunumu

Tip 1 diyabet ve çölyak hastalığı en yaygın pediatrik otoimmün hastalıklardır. Tip 1 diyabetli çocuk ve ergenlerde çölyak hastalığı sıklığı genel popülasyondan daha yüksektir. Tip 1 diyabetli çocuklarda çölyak hastalığının genellikle asemptomatik olması nedeniyle, çoğu olgu yalnızca çölyak hastalığı taraması ile tanı alır. Glutensiz diyet tek tedavi yöntemidir. İyi metabolik kontrol ve büyümenin devamını sağlamak için aileye/bakımdan sorumlu bireylere yeterli-dengeli glutensiz diyet uygulamaları ve karbonhidrat sayımı konularında danışmanlık bu konuda deneyimli diyetisyen tarafından verilmelidir. Bu makalede, tip 1 diyabet ve çölyak hastalığı olan olgu sunulmuştur.

Development platform for artificial pancreas algorithms

Assessing algorithms of artificial pancreas systems is critical in developing automated and fault-tolerant solutions that work outside clinical settings. The development and evaluation of algorithms can be facilitated with a platform that conducts virtual clinical trials. We present in this paper a clinically validated cloud-based distributed platform that supports the development and comprehensive testing of single and dual-hormone algorithms for type 1 diabetes mellitus (T1DM). The platform is built on principles of object-oriented design and runs user algorithms in real-time virtual clinical trials utilizing a multi-threaded environment enabled by concurrent execution over a cloud infrastructure. The platform architecture isolates user algorithms located on personal machines from proprietary patient data running on the cloud. Users import a plugin into their algorithms (Matlab, Python, or Java) to connect to the platform. Once connected, users interact with a graphical interface to design experimental protocols for their trials. Protocols include trial duration in days, mealtimes and amounts, variability in mealtimes and amounts, carbohydrate counting errors, snacks, and onboard insulin levels. The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. Users can easily debug & modify code, test multiple strategies, and generate detailed clinical performance reports. We validated and integrated into the platform a glucoregulatory system of ordinary differential equations (ODEs) parameterized with clinical data to mimic the inter and intra-day variability of glucose responses of 15 T1DM patients. The platform utilizes the validated patient model to conduct virtual clinical trials for the rapid development and testing of closed-loop algorithms for T1DM.

Educating patients on carbohydrate counting: efficiency of distance support in type 1 diabetes

Due to the current historical moment, implementing technological systems has become a imperative. Niguarda Hospital in Milan dedicated an area of its official web site to a new educational course about carbohydrate counting. The project is mainly addressed to individuals with diabetes mellitus. The aim is to deliver a systematic support and a accurate educating intervention, permanently and gratuitously available. The project started in July 2020. Clinicians and healthcare professionals from Niguarda Hospital Diabetes Division recorded a number of 14 educational videos, that will be extended. The educational videos are focused on scientific subjects that are essential in daily life of people with diabetes and their caregivers, and discuss various different topics: from adequate nutrition concepts to general principles of insulin therapy, from carbohydrates classification to insulin sensitivity factor and insulin/carbohydrate ratio explanation. Outcomes related to tele-education effectiveness will be evaluated through two dietary-knowledge questionnaires and one satisfaction questionnaire, available on the same web page. Remote education allows to break down physical, economic and organizational barriers. It strengthens and supports each individual’s path, encouraging them to enhance self-management skills, and therefore their disease progression. Digital health is increasingly becoming part of our everyday life, allowing us to achieve a shared purpose of all those who deal with diabetes: improving their quality of life. KEY WORDS diabetes mellitus; CHO counting; telemedicine; remote education; nutrition.

Automatic Glucose Control During Meals and Exercise in Type 1 Diabetes: Proof-of-Concept in Silico Tests Using a Switched LPV Approach

Keeping the blood glucose levels within the safe range during meals and exercise still represents a major hurdle not only for patients with type 1 diabetes (T1D), but also for Artificial Pancreas (AP) systems. One of the reasons a fully (autonomous) closed-loop solution has not been released onto the market yet is the slow action of current insulin analogs. To partially overcome this limitation, the authors have previously designed a switched control strategy equipped with an insulin-on-board (IOB) safety loop that mitigates meal-related glucose excursions without carbohydrate counting. In this letter, a similar strategy based on a Linear Parameter-Varying (LPV) control law has been adapted to safely handle also exercise challenges with minimum user intervention. In silico results using the UVA/Padova simulator evidence that the proposed closed-loop scheme is feasible under moderate-intense exercise bouts by effectively and safely reducing the risk of hypoglycemia.

"Counting Carbs to Be in Charge": A Comparison of an Internet-Based Education Module With In-Class Education in Adolescents With Type 1 Diabetes.

Carbohydrate counting is an essential component of type 1 diabetes education but can be difficult for adolescents to learn. Because adolescents are avid users of technology, an Internet-based education module was compared with an in-class education session in terms of carbohydrate counting accuracy in adolescents with type 1 diabetes. Adolescent participants displayed increased carbohydrate counting accuracy after attending an in-class education session compared with an Internet-based education module. These results suggest that online education is best reserved as an adjunctive therapy to in-class teaching in this population.

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial.

BackgroundCarbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.ObjectiveOur objective was to test the app's usability and potential impact on carbohydrate counting accuracy.MethodsIterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed.ResultsUse of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study.ConclusionsOur results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.Trial RegistrationClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142

Dietary Association with Glycemic Status Among Adults Clinically Diagnosed with Type 2 Diabetes in Malawi

ABSTRACT We elucidated diet and its association with glycemic status among adults clinically diagnosed with type 2 diabetes mellitus (T2DM) through across-sectional study in Malawi. Glycosylated hemoglobin (A1C) and dietary intakes were assessed. Consumption of cereals, roots, and tubers and sweet, sugary food, including sweetened beverages, was significantly higher among participants with A1C≥8%. Fruit and vegetable intake were disproportionately low overall. Consuming adiet high in carbohydrates (OR: 1.167; CI = 1.107–1.231; p < .001) and having <3 meals/day (OR: 2.680; CI = 1.445–4.970; p = .002) were both associated with A1C≥8%. Therefore, dietary interventions for T2DM focusing on carbohydrate counting, total dietary quality, and meal planning are needed in Malawi.

10 - Novel Fully Automated Fiasp-Plus-Pramlintide Artificial Pancreas for Type 1 Diabetes: Randomized Controlled Trial

We aimed to assess a novel fully automated fiasp-plus-pramlintide artificial pancreas. We conducted a randomized crossover noninferiority trial comparing (i) a fully automated fiasp-and-pramlintide artificial pancreas and (ii) fiasp-alone artificial pancreas with carbohydrate counting, in 23 adults with type 1 diabetes (age 35±15 years, HbA1c 8.1±1.4 per cent [%]). Fiasp and pramlintide were delivered using a novel dosing algorithm at a fixed ratio (10 micrograms per unit [μg/u]) with a meal detection to trigger boluses. Each participant completed two 27-hour inpatient interventions. The primary outcome was time spent in target range (3.9–10.0 millimoles per liter [mmol/L]) during a 24-hour period. The fully automated system achieved similar time in target (71%) compared to fiasp-alone hybrid system (75%, p=0.47), but with less time <3.9 mmol/L (median 0%, 1.4%; p=0.039). The fully automated system achieved comparable time >10 mmol/L (27%, 22%; p=0.29), >13.9 mmol/L (6.5%, 5.7%; p=0.80) and >16.7 mmol/L (1.8%, 2.3% respectively; p=0.81). Nonmild gastrointestinal symptoms were reported by 3 participants (13%) with the fully automated system compared to 0 with the fiasp-alone system. Our novel fiasp-and-pramlintide system is fully automated and noninferior to the fiasp-alone hybrid system.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App.

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.

Efficacy of Bolus Calculation and Advanced Carbohydrate Counting in Type 2 Diabetes: A Randomized Clinical Trial.

Background: Carbohydrate counting and use of automated bolus calculators (ABCs) can help reduce HbA1c in type 1 diabetes but only limited evidence exists in type 2 diabetes. We evaluated the efficacy of advanced carbohydrate counting (ACC) and use of an ABC compared with manual insulin bolus calculation (MC) in persons with type 2 diabetes. Materials and Methods: A 24-week open-label, randomized clinical study was conducted in 79 persons with type 2 diabetes treated with basal-bolus insulin (mean age 62.5 ± 9.6 years, HbA1c 8.7% ± 1.0% [72 ± 11 mmol/mol], diabetes duration 18.7 ± 7.6 years). Participants were randomized 1:1 into two groups: ABC group received training in ACC and use of an ABC; MC group received training in ACC and manual calculation of insulin bolus. Participants wore blinded continuous glucose monitors for 6 days at baseline and at study end. Primary endpoint was change in HbA1c. Results: After 24 weeks, HbA1c decreased 0.8% (8.8 mmol/mol) in ABC group and 0.8% (9.0 mmol/mol) in MC group with no between-group difference (P = 0.96) and without increase in time in hypoglycemic range (sensor glucose <3.9 mmol/L). Glycemic variability decreased significantly in both groups, whereas the total insulin dose and body mass index (BMI) remained unchanged during the study. Treatment satisfaction increased significantly in both groups after 24 weeks. Conclusion: ACC is an effective, low-cost tool to reduce HbA1c and glycemic variability in persons with basal-bolus insulin-treated type 2 diabetes without increase in hypoglycemia or BMI. Similar effects were seen with use of an ABC and with use of manual bolus calculation. Trial registration: ClinicalTrials.gov NCT02887898.

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

Identification of Type 2 Diabetes Management Mobile App Features and Engagement Strategies: Modified Delphi Approach.

BackgroundDiabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6% of the general population, and the prevalence of T2DM is predicted to rise to 45.36% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives.ObjectiveThe main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps.MethodsIn this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care.ResultsIn total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275; P<.001). The top 5 features included insulin dose adjustment according to carbohydrate counting and blood glucose readings (5.36), alerting a caregiver of abnormal or critical readings (6.09), nutrition education (12.45), contacts for guidance if required (12.64), and offering patient-specific education tailored to the user’s goals, needs, and blood glucose readings (12.90). In total, 21 engagement strategies were generated. Overall, the experts showed a moderate degree of consensus in their strategy rankings (Kendall W=0.454; P<.001). The top 5 engagement strategies included a user-friendly design (educational and age-appropriate design; 2.82), a free app (3.73), allowing the user to communicate or send information/data to a health care provider (HCP; 5.36), HCPs prescribing the mobile app in the clinic and asking about patients’ app use compliance during clinical visits (6.91), and flexibility and customization (7.91).ConclusionsThis is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts’ opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic.

Predictive Low Glucose Suspend Algorithm in Real Life: A Five-Year Follow-Up Retrospective Analysis.

Sensor-augmented pumps with predictive low glucose suspend function (PLGS-SAP) help patients avoid hypoglycemia and improve quality of life: in this retrospective study, we investigated long-term effects of PLGS-SAP on metabolic outcomes, acute and chronic diabetic complications, in particular cardiovascular events. One hundred thirty-nine adults with type 1 diabetes (T1D) treated for more than 10 years with continuous subcutaneous insulin infusion (CSII) were followed for 5 years; 71 (Group 1) started to use PLGS-SAP, and 68 (Group 2) maintained on their non-PLGM insulin pump. Glucose control measures (hemoglobin A1c [HbA1c], acute diabetic complications), clinical outcomes (body mass index [BMI], arterial hypertension, dyslipidemia), chronic diabetes-related complications, and device utilization (continuous glucose monitoring utilization, use of temporary basal rates or special boluses, carbohydrate counting usage) were assessed. The reduction of HbA1c was significant in Group 1 (from 7.5% ± 1.1% to 7.0% ± 1.0%, P = .02), while in Group 2 it did not reach statistical significance (from 7.5% ± 1.1% to 7.4% ± 0.9%, P = .853). BMI increased significantly in Group 2 (from 25.3 ± 2.8 to 25.7 ± 3.4, P < .001), but not in Group 1 (from 25.2 ± 3.5 to 25.2 ± 2.8, P = .887). There were no statistically significant differences in occurrence of acute diabetes complications, other clinical outcomes, prevalence of diabetes-related complications, or device utilization between the groups. In our five-year follow-up experience with T1D CSII users, PLGS-SAP has resulted efficient in improving metabolic control and maintaining the body weight.

Medical nutrition therapy of pediatric type 1 diabetes mellitus in India: Unique aspects and challenges.

Medical nutrition therapy (MNT) is a vital aspect of management of type 1 diabetes mellitus (T1DM) and should be tailored to ethnic and family traditions and the socioeconomic and educational status of the patient. In this article, we discuss the unique aspects of MNT in children and adolescents with T1DM in the Indian setting, with focus on the challenges faced by patients, dieticians and physicians and how these can be overcome. The authors reviewed the available literature on MNT in T1DM from India and prepared the document based on their vast collective clinical experience in treating patients with T1DM from different regions in India. Indian diets are predominantly carbohydrate-based with high glycemic index (GI) and low protein content. Various methods are available to increase the protein and fiber content and reduce the GI of food in order to limit glycemic excursions. Insulin regimens need to be tailored to the child's school timings, meal schedule, and the availability of a responsible adult to supervise/administer insulin. All patients, irrespective of economic and education background, should be taught the broad principles of healthy eating, balanced diet and carbohydrate counting. There are various barriers to dietary compliance, including joint family system, changing lifestyles, and other factors which need to be addressed. There is a need to customize dietary management according to patient characteristics and needs and develop standardized patient educational material on principles of healthy eating in various regional languages.