Carbohydrate Counting Research Articles

Level 3 Carbohydrate counting at diagnosis of Type I Diabetes Mellitus in children: How does it affect HbA1c?

Level 3 Carbohydrate counting at diagnosis of Type I Diabetes Mellitus in children: How does it affect HbA1c?

Clinical characteristics and management of type 1 diabetes in Spain. The SED1 study

ObjectivesTo determine the sociodemographic and clinical profile of a representative sample of people with type 1 diabetes mellitus (DM1) in Spain and identify factors associated with glycemic control. Material and methodsA cross-sectional observational study was carried out in adults and children with DM1 treated in 75 Spanish public hospitals, geographically distributed in order to be representative of the Spanish population. Within each center, the patients were included on a consecutive basis as they visited the clinic. They were interviewed, and their clinical histories were reviewed. A descriptive statistical analysis was made, and factors associated with HbA1c were analysed using multivariate linear regression analysis. ResultsA total of 647 patients were included: 55.3% females, aged 36.6 ± 14.4 years, 97.2% Caucasians, BMI 24.7 ± 4.4 kg/m2 (12.1% ≥ 30 kg/m2), and 74.0% had secondary / university education. A total of 20.2% were active smokers. The mean time from the diagnosis of DM1 was 17.9 ± 12.0 years. A total of 48.7% presented comorbidities: 19.3% retinopathy and 16.4% hypothyroidism. As regards treatment for DM1, 76.5% received basal-bolus insulin therapy and 20.7% continuous subcutaneous insulin infusion (CSII); 51.0% of the patients used an insulin/carbohydrate ratio (ICR), with 4.6 ± 1.6 self-monitored capillary blood glucose (SMCBG) measurements a day, and 24.8% used continuous glucose monitoring (CGM). The mean HbA1c value was 7.6 ± 1.1% (30% below 7%). Metabolic control improved (lower HbA1c) with more daily SMCBG (B = −0.053; p = 0.009), a higher educational level (B = 0.461; P < 0.001), greater number of hypoglycemia episodes (B = −0.253; P = 0.018) and carbohydrate counting (B = −0.190; P = 0.048), and worsened the longer the duration of the disease (B = 0.010; P = 0.010), higher total dose of insulin (B = 0.010; P < 0.0001), poorer adherence to diet (B = 0.650; P < 0.0001) and a family history of DM (B = −0.233; P = 0.007). ConclusionsThe management of patients with DM1 in Spain, as well as the treatment they receive, is similar to that seen in other Western countries. Blood glucose control is associated with educational level, disease duration, and the characteristics of treatment and self-care.

Alleviating Carbohydrate Counting With a FiASP-plus-Pramlintide Closed-Loop Delivery System (Artificial Pancreas): Feasibility and Pilot Studies

We report two studies assessing whether a FiASP-and-pramlintide closed-loop system can replace carbohydrate counting with a simple meal announcement strategy, without degrading glycemic control compared to a FiASP closed-loop system.

Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients

Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.

Feasibility Study of an Automated Carbohydrate Estimation System Using Thai Food Images in Comparison With Estimation by Dietitians.

Carbohydrate counting is essential for well-controlled blood glucose in people with type 1 diabetes, but to perform it precisely is challenging, especially for Thai foods. Consequently, we developed a deep learning-based system for automatic carbohydrate counting using Thai food images taken from smartphones. The newly constructed Thai food image dataset contained 256,178 ingredient objects with measured weight for 175 food categories among 75,232 images. These were used to train object detector and weight estimator algorithms. After training, the system had a Top-1 accuracy of 80.9% and a root mean square error (RMSE) for carbohydrate estimation of <10 g in the test dataset. Another set of 20 images, which contained 48 food items in total, was used to compare the accuracy of carbohydrate estimations between measured weight, system estimation, and eight experienced registered dietitians (RDs). System estimation error was 4%, while estimation errors from nearest, lowest, and highest carbohydrate among RDs were 0.7, 25.5, and 7.6%, respectively. The RMSE for carbohydrate estimations of the system and the lowest RD were 9.4 and 10.2, respectively. The system could perform with an estimation error of <10 g for 13/20 images, which placed it third behind only two of the best performing RDs: RD1 (15/20 images) and RD5 (14/20 images). Hence, the system was satisfactory in terms of accurately estimating carbohydrate content, with results being comparable with those of experienced dietitians.

The Role of Protein and Fat Intake on Insulin Therapy in Glycaemic Control of Paediatric Type 1 Diabetes: A Systematic Review and Research Gaps.

Carbohydrate counting (CHC) is the established form of calculating bolus insulin for meals in children with type 1 diabetes (T1DM). With the widespread use of continuous glucose monitoring (CGM) observation time has become gapless. Recently, the impact of fat, protein and not only carbohydrates on prolonged postprandial hyperglycaemia have become more evident to patients and health-care professionals alike. However, there is no unified recommendation on how to calculate and best administer additional bolus insulin for these two macronutrients. The aim of this review is to investigate: the scientific evidence of how dietary fat and protein influence postprandial glucose levels; current recommendations on the adjustment of bolus insulin; and algorithms for insulin application in children with T1DM. A PubMed search for all articles addressing the role of fat and protein in paediatric (sub-)populations (<18 years old) and a mixed age population (paediatric and adult) with T1DM published in the last 10 years was performed. Conclusion: Only a small number of studies with a very low number of participants and high degree of heterogeneity was identified. While all studies concluded that additional bolus insulin for (high) fat and (high) protein is necessary, no consensus on when dietary fat and/or protein should be taken into calculation and no unified algorithm for insulin therapy in this context exists. A prolonged postprandial observation time is necessary to improve individual metabolic control. Further studies focusing on a stratified paediatric population to create a safe and effective algorithm, taking fat and protein into account, are necessary.

Survey of Australian general paediatricians regarding insulin initiation practices in children with new onset of type 1 diabetes.

Australian and New Zealand (NZ) paediatric endocrinologists' and NZ general paediatricians' insulin initiation strategies for children with type 1 diabetes (T1D) was recently described. The aim of this study was to document the insulin initiation practices of Australian general paediatricians in newly diagnosed children with T1D. An online survey was sent to Australian general paediatricians identified through the Australian Paediatric Society diabetes database. Twenty four general paediatricians participated on behalf of their Australian regional and metropolitan diabetes units managing 2059 patients. The diabetes units averaged 86 patients and all practices were multidisciplinary models of care. Intensive insulin therapy regimens were initiated at diagnosis for children age 2-10 years by 93% respondents compared with 73% Australian endocrinologists, 17% NZ endocrinologists and 36% NZ general paediatricians. Carbohydrate counting as part of flexible bolus dosing was usual practice for 83% of respondents, which was substantially more than Australian endocrinologists (63%), NZ endocrinologists (64%) and NZ general paediatricians (33%). Almost all Australian general paediatricians who completed the survey initiate intensive insulin therapy regimes with carbohydrate counting in newly diagnosed children with T1D, consistent with the 2018 evidence-based recommendations of the International Society of Pediatric and Adolescent Diabetes. A substantial proportion of children with T1D within Australia are managed by general paediatricians who tend to align with international peak body guidelines.

Carbohydrate counting in type 1 diabetes mellitus: dietitians’ perceptions, training and barriers to use

Objectives A study was undertaken to determine the perceptions, training and barriers regarding the use of carbohydrate counting in the dietary management of type 1 diabetes mellitus (T1DM) among dietitians in KwaZulu-Natal (KZN). Design A cross-sectional, descriptive study was conducted. Setting Dietitians who were registered with the Health Professions Council of South Africa (HPCSA), and working in the province of KZN at the time of the study, were invited to participate. Methods Data were collected using a self-administered electronic questionnaire distributed through SurveyMonkey, an Internet-based survey programme. Results Dietitians agreed that carbohydrate counting was a useful dietary management approach for diabetes (p < 0.05) and that it was essential to manage T1DM (p < 0.05). However, they felt it was a difficult concept for patients with T1DM to understand (p = 0.001) and teaching it to patients was time consuming (p < 0.05). Although dietitians believed that there was a strong evidence base for teaching carbohydrate counting to patients with T1DM (p < 0.05), they indicated a need for further training or education in it (p < 0.05). Barriers to using carbohydrate counting included a lack of training, confidence and experience, financial resources, time, blood glucose records and poor patient motivation and patient illiteracy (p < 0.05). Conclusions Overall, dietitians who participated in the study had a positive perception towards the use of carbohydrate counting in the management of T1DM. However, further training needs to be addressed for carbohydrate counting to be used with confidence by dietitians in KZN to optimize their management of T1DM.

Fully closed-loop insulin delivery—are we nearly there yet?

Fully closed-loop insulin delivery—are we nearly there yet?

A fully artificial pancreas versus a hybrid artificial pancreas for type 1 diabetes: a single-centre, open-label, randomised controlled, crossover, non-inferiority trial

For people with type 1 diabetes, there is currently no automated insulin delivery system that does not require meal input. We aimed to assess the efficacy of a novel faster-acting insulin aspart (Fiasp) plus pramlintide fully closed-loop system that does not require meal input. In this open-label, randomised controlled, crossover, non-inferiority trial we compared the Fiasp (Novo Nordisk, Bagsværd, Denmark) plus pramlintide closed-loop system with no meal input (fully artificial pancreas) and the Fiasp-alone closed-loop system with precise carbohydrate counting (hybrid artificial pancreas). Adults (≥18 years) who had a clinical diagnosis of type 1 diabetes for at least 12 months, had glycated haemoglobin 12% or lower, and had been on insulin pump therapy for at least 6 months were enrolled at McGill University Health Centre, Montreal, QC, Canada. The Fiasp plus pramlintide fully closed-loop system delivered pramlintide in a basal-bolus manner with a fixed ratio of 10 μg:U relative to insulin. A research staff member counted the carbohydrate content of meals to input in the hybrid closed-loop system. Participants completed the two full-day crossover interventions in a random order allocated by a computer-generated code implementing a blocked randomisation (block size of four). The primary outcome was the percentage of time spent within the glucose target range (3·9-10·0 mmol/L), with a 6% non-inferiority margin, assessed in all participants who completed both interventions. This trial is registered with ClinicalTrials.gov, NCT03800875. Between Feb 8, 2019, and Sept 19, 2020, we enrolled 28 adults, of whom 24 completed both interventions and were included in analyses. The percentage of time spent in the target range was 74·3% (IQR 61·5-82·8) with the fully closed-loop system versus 78·1% (66·3-87·5) with the hybrid Fiasp-alone closed-loop system (paired difference 2·6%, 95% CI -2·4 to 12·2; non-inferiority p=0·28). Eight (33%) participants had at least one hypoglycaemia event (<3·3 mmol/L) with the fully closed-loop system compared with 14 (58%) participants with the hybrid closed-loop system (2200-2200 h). Non-mild nausea was reported by three (13%) participants and non-mild bloating by one (4%) participant with the fully closed-loop system compared with zero participants with the hybrid closed-loop system. The Fiasp plus pramlintide fully closed-loop system was not non-inferior to the Fiasp-alone hybrid closed-loop system for the overall percentage of time in the glucose target range. However, participants still spent a high percentage of time within the target range with the fully-closed loop system. Outpatient studies comparing the fully closed-loop hybrid systems with patient-estimated, rather than precise, carbohydrate counting are warranted. Diabetes Canada.

Strategically Playing with Fire: SGLT Inhibitors as Possible Adjunct to Closed-Loop Insulin Therapy.

As closed-loop insulin therapies emerge into clinical practice and evolve in medical research for type 1 diabetes (T1D) treatment, the limitations in these therapies become more evident. These gaps include unachieved target levels of glycated hemoglobin in some patients, postprandial hyperglycemia, the ongoing need for carbohydrate counting, and the lack of non-glycemic benefits (such as prevention of metabolic syndrome and complications). Multiple adjunct therapies have been examined to improve closed-loop systems, yet none have become a staple. Sodium-glucose-linked cotransporter inhibitors (SGLTi's) have been extensively researched in T1D, with average reductions in placebo-adjusted HbA1c by 0.39%, and total daily dose by approximately 10%. Unfortunately, many trials revealed an increased risk of diabetic ketoacidosis, as high as 5 times the relative risk compared to placebo. This narrative review discusses the proven benefits and risks of SGLTi in patients with T1D with routine therapy, what has been studied thus far in closed-loop therapy in combination with SGLTi, the potential benefits of SGLTi use to closed-loop systems, and what is required going forward to improve the benefit to risk ratio in these insulin systems.

Evaluation of patient experience post structured education for diabetes self management (Dose Adjustment For Normal Eating-OzDAFNE)

AimsTo examine OzDAFNE participant feedback to determine if OzDAFNE results in positive participant reported outcomes and experiences, improves quality of life; and to identify areas for improvement. MethodsQuantitative and qualitative evaluations of participants’ experience were undertaken prior to, and at the end of, every OzDAFNE program from 2010 to 2019. Evaluations included Likert scale and open-ended questions. Responses were analysed descriptively, for response rates and to identify themes. Mean difference in Problem Area in Diabetes (PAID) score was calculated from pre-course to 12 months. Results189 participants attended OzDAFNE. 93% rated the overall quality of OzDAFNE as “Excellent”. Confidence in managing diabetes increased from 25% pre-OzDAFNE to 96% at completion. Major themes identified as most useful and relevant were carbohydrate counting (89/189), insulin adjustment (87/189) and exercise (46/189). At 12 months (n = 44), 97% were “mostly”/ “always” using OzDAFNE principles; 72% reported their diabetes control was “a lot better” than pre-OzDAFNE due to increased knowledge and implementation of principles. The value of the shared patient experience was reported at all time points. By 12 months, mean PAID score decreased significantly (p < 0.001). ConclusionsThe OzDAFNE patient experience was very positive, with high satisfaction reported. Increased confidence and knowledge and ongoing implementation of principles resulted in improved diabetes management. OzDAFNE offers a patient-centred approach that is valued by participants.

Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26week randomised, controlled, multicentre trial.

We aimed to compare the effects of intermittently scanned continuous glucose monitoring (isCGM) and carbohydrate counting with automated bolus calculation (ABC) with usual care. In a randomised, controlled, open-label trial carried out at five diabetes clinics in the Capital Region of Denmark, 170 adults with type 1 diabetes for ≥1year, multiple daily insulin injections and HbA1c > 53mmol/mol (7.0%) were randomly assigned 1:1:1:1 with centrally prepared envelopes to usual care (n = 42), ABC (n = 41), isCGM (n = 48) or ABC+isCGM (n = 39). Blinded continuous glucose monitoring data, HbA1c and patient-reported outcomes were recorded at baseline and after 26weeks. The primary outcome was change in time in range using isCGM vs usual care. Baseline characteristics were comparable across arms: mean age 47 (SD 13.7) years, median (IQR) diabetes duration 18 (10-28) years and HbA1c 65 (61-72) mmol/mol (8.1% [7.7-8.7%]). Change in time in range using isCGM was comparable to usual care (% difference of 3.9 [-12-23], p = 0.660). The same was true for the ABC and ABC+isCGM arms and for hypo- and hyperglycaemia. Also compared with usual care, using ABC+isCGM reduced HbA1c (4 [95% CI 1, 8] mmol/mol) (0.4 [0.1, 0.7] %-point) and glucose CV (11% [4%, 17%]) and improved treatment satisfaction, psychosocial self-efficacy and present life quality. Treatment satisfaction also improved by using isCGM alone vs usual care. Statistical significance was maintained after multiple testing adjustment concerning glucose CV and treatment satisfaction with ABC+isCGM, and treatment satisfaction with isCGM. Discontinuation was most common among ABC only users, and among completers the ABC was used 4 (2-5) times/day and the number of daily isCGM scans was 5 (1-7) at study end. isCGM alone did not improve time in range, but treatment satisfaction increased in technology-naive people with type 1 diabetes and suboptimal HbA1c. The combination of ABC+isCGM appears advantageous regarding glycaemic variables and patient-reported outcomes, but many showed resistance towards ABC. ClinicalTrials.gov NCT03682237. The study is investigator initiated and financed by the Capital Region of Denmark.

Application of Machine Learning to Assess Interindividual Variability in Rapid-Acting Insulin Responses After Subcutaneous Injection in People With Type 1 Diabetes

ObjectivesCirculating insulin concentrations mediate vascular-inflammatory and prothrombotic factors. However, it is unknown whether interindividual differences in circulating insulin levels are associated with different inflammatory and prothrombotic profiles in type 1 diabetes (T1D). We applied an unsupervised machine-learning approach to determine whether interindividual differences in rapid-acting insulin levels associate with parameters of vascular health in patients with T1D. MethodsWe re-analyzed baseline pretreatment meal-tolerance test data from 2 randomized controlled trials in which 32 patients consumed a mixed-macronutrient meal and self-administered a single dose of rapid-acting insulin individualized by carbohydrate counting. Postprandial serum insulin, tumour necrosis factor (TNF)-alpha, plasma fibrinogen, human tissue factor (HTF) activity and plasminogen activator inhibitor-1 (PAI-1) were measured. Two-step clustering categorized individuals based on shared clinical characteristics. For analyses, insulin pharmacokinetic summary statistics were normalized, allowing standardized intraindividual comparisons. ResultsDespite standardization of insulin dose, individuals exhibited marked interpersonal variability in peak insulin concentrations (48.63%), time to peak (64.95%) and insulin incremental area under the curve (60.34%). Two clusters were computed: cluster 1 (n=14), representing increased serum insulin concentrations; and cluster 2 (n=18), representing reduced serum insulin concentrations (cluster 1: 389.50±177.10 pmol/L/IU h−1; cluster 2: 164.29±41.91 pmol/L/IU h−1; p<0.001). Cluster 2 was characterized by increased levels of fibrinogen, PAI-1, TNF-alpha and HTF activity; higher glycated hemoglobin; increased body mass index; lower estimated glucose disposal rate (increased insulin resistance); older age; and longer diabetes duration (p<0.05 for all analyses). ConclusionsReduced serum insulin concentrations are associated with insulin resistance and a prothrombotic milieu in individuals with T1D, and therefore may be a marker of adverse vascular outcome.

Health promotion challenges for young adults living with intellectual disability and type 1 diabetes.

Self-management of type 1 diabetes mellitus (T1DM) can be challenging for people with intellectual disability. Often, parents provide health support due to lack of appropriate services outside the home. The study aim was to identify barriers and facilitators to T1DM self-management for young adults with intellectual disability and the implications for health promotion. Five male participants with intellectual disability, aged 17-26 years, and seven parents were interviewed between October 2017 and February 2019. Interview data were descriptively analysed. Two categories for barriers and facilitators were identified: 1) Diabetes self-management is complex (carbohydrate counting, blood glucose level monitoring, insulin therapy); 2) support for diabetes care (reliance on parents and carers, the National Disability Insurance Scheme, mainstream diabetes service support). Parents are critical for the support of people with intellectual disability and T1DM in the absence of disability staff with appropriate health skills.

Continuous Glucose Monitoring and Glycemic Control in Young Adults with Type 1 Diabetes: Benefit for Even the Simplest Insulin Administration Methods.

The impact of continuous glucose monitoring (CGM) on glycemic control in young adults with type 1 diabetes (T1D) is controversial. Data from 888 young adults with T1D were reviewed (ages 18-30 years, 52% female, glycated hemoglobin [HbA1c] 8.1% ± 1.5%). Prescription of CGM was recorded for 54% of young adults; 66% were pump users, 46% on insulin injections and carbohydrate (carb) counting, and 32% on insulin doses without carb counting (P ≤ 0.001). HbA1c was lower in young adults with CGM versus no CGM (7.7% ± 1.3% vs. 8.2% ± 1.7%, P ≤ 0.001). Difference in HbA1c between CGM versus no CGM was greater in young adults noncarb counting (7.9% ± 1.4% vs. 8.9% ± 2.3%, P = 0.002) than carb counting (7.7% ± 1.5% vs. 8.2% ± 1.7%, P = 0.0008), or pump users (7.6 ± 1.2 vs. 7.9 ± 1.1, P = 0.01). Prescription of CGM was higher with increasing complexity of insulin regimens; however, impact of CGM was greatest with simpler insulin administration methods. Further studies are needed to clarify this association.

Exploring the attitudes and experiences of adolescents with type 1 diabetes towards transition of care

IntroductionTransition from adolescence to adult care is very challenging for most patients. Without appropriate appointments and education, adolescents can get lost to follow up within one-year of transitioning to adult care (Mistry et al. Diabet Med 32(7):881–885, 2015). Loss to follow-up can increase risks of adverse short and long term diabetes-related complications, with healthcare contacts mainly limited to crisis-based management (Iversen et al. Scand J Caring Sci 33(3):723–730, 2019).AimsThe purpose of this study was to evaluate the patient’s perspective of the process of transition from paediatric to adult-based diabetes services in the Mid-West Region of Ireland.MethodsWe implemented a new transition clinic at University Hospital Limerick with the collaboration of paediatric and adult endocrinology teams. Eighteen patients opted to attend the clinic, but only 17 patients consented to participate in a qualitative assessment study and completed questionnaires before and after the transition clinic.Results and conclusionIn terms of medical management, patients had a good understanding of hypoglycaemia and insulin dose adjustment principles, but were least comfortable with carbohydrate counting. Patients self-ranked their knowledge on driving and sexual health with a diagnosis of diabetes as poor, in comparison to understanding effects of alcohol and smoking on diabetes. Overall, a majority of the respondents felt more confident in moving to adult-care after attending the transition clinic.

Bayesian optimization assisted meal bolus decision based on Gaussian processes learning and risk-sensitive control

Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific glucose management parameters. The postprandial glucose dynamics is learnt using Gaussian process regression. Considering the asymmetric risks of hyper- and hypoglycemia and the uncertainties in the predicted glucose trajectories, an asymmetric risk-sensitive cost function is designed. Bayesian optimization is utilized to solve the optimization problem, since the gradient of the cost function is unavailable. The proposed approach is evaluated using the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the standard insulin bolus calculator. For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia. Similar results are observed for the case without the meal information (assuming that the patient follows a consistent diet) and the case of basal rate mismatches. In addition, a comparison with a run-to-run based method for the scenario of potentially incorrect meal carbohydrate counts is also performed, and the results show that the proposed method is more robust to the carbohydrate counting disturbances. At last, advisory-mode analysis is performed based on clinical data, which indicates that the method can determine safe and reasonable meal boluses in real clinical settings. The results verify the effectiveness and robustness of the proposed method and indicate the feasibility of achieving improved postprandial glucose regulation through a data-driven optimal control method.

Type 1 diabetes: dietary modification over 100 years since insulin

AbstractDiet has been central for the management of type 1 diabetes for over three thousand years, and the discovery of insulin in 1921 was associated with the introduction of formal dietary education. Carbohydrate was soon identified as the macronutrient responsible for postprandial glucose excursions and over the past one hundred years, dietary interventions have alternated between severe carbohydrate restriction and so‐called free diets. The very low carbohydrate diets of the 1920s gave way to the free diets of the 1930s, and carbohydrate restriction was then reintroduced in the 1940s to the 1960s. In the 1970s, carbohydrate intake was increased with an emphasis on dietary fibre and low fat, healthy eating was recommended in the 1980s. The past few decades have reflected the growing evidence following the DCCT and DAFNE trials and dietary interventions are now individualised and emphasise carbohydrate counting and insulin adjustment and quality of life. Copyright © 2021 John Wiley &amp; Sons.

Alleviating carbohydrate counting with a FiASP-plus-pramlintide closed-loop delivery system (artificial pancreas): Feasibility and pilot studies.

To assess whether a FiASP-and-pramlintide closed-loop system has the potential to replace carbohydrate counting with a simple meal announcement (SMA) strategy (meal priming bolus without carbohydrate counting) without degrading glycaemic control compared with a FiASP closed-loop system. We conducted a 24-hour feasibility study comparing a FiASP system with full carbohydrate counting (FCC) with a FiASP-and-pramlintide system with SMA. We conducted a subsequent 12-day outpatient pilot study comparing a FiASP-and-placebo system with FCC, a FiASP-and-pramlintide system with SMA, and a FiASP-and-placebo system with SMA. Basal-bolus FiASP-and-pramlintide were delivered at a fixed ratio (1 U:10 μg). Glycaemic outcomes were measured, surveys evaluated gastrointestinal symptoms and diabetes distress, and participant interviews helped establish a preliminary coding framework to assess user experience. Seven participants were included in the feasibility analysis. Time spent in 3.9-10 mmol/L was similar between both interventions (81%-84%). Four participants were included in the pilot analysis. Time spent in 3.9-10 mmol/L was similar between the FiASP-and-placebo with FCC and FiASP-and-pramlintide with SMA interventions (70%), but was lower in the FiASP-and-placebo with SMA intervention (60%). Time less than 3.9 mmol/L and gastrointestinal symptoms were similar across all interventions. Emotional distress was moderate at baseline, after the FiASP-and-placebo with FCC and SMA interventions, and fell after the FiASP-and-pramlintide with SMA intervention. SMA reportedly afforded participants flexibility and reduced mealtime concerns. The FiASP-and-pramlintide system has the potential to substitute carbohydrate counting with SMA without degrading glucose control.