Macroergonomic components of the patient work system shaping dyadic care management during adolescence: a case study of type 1 diabetes.

The role of the social, physical, and organisational environments in shaping how patients and their caregivers perform work remains largely unexplored in human factors/ergonomics literature. This study recruited 19 dyads consisting of a parent and their child with type 1 diabetes to be interviewed individually and analysed using a macroergonomic framework. Our findings aligned with the macroergonomic factors as presented in previous models, while highlighting the need to expand upon certain components to gain a more comprehensive representation of the patient work system as relevant to dyadic management. Examples of design efforts that should follow from these findings include expanding existing data sharing options to include information from the external environment and capitalising on the capabilities of artificial intelligence as a decision support system. Future research should focus on longitudinally assessing patient work systems throughout transition periods in addition to more explicitly exploring the roles of social network members.

Work performed by patients and their caregivers is shaped by the social, physical, and organisational contexts they are embedded within. This paper explored how adolescents with type 1 diabetes managed their health alongside their parents in the context of these macroergonomic factors. These findings have implications for research and design.

Randomization to a Provided Higher-Complex-Carbohydrate Versus Conventional Diet in Gestational Diabetes Mellitus Results in Similar Newborn Adiposity.

OBJECTIVE: Nutrition therapy for gestational diabetes mellitus (GDM) has conventionally focused on carbohydrate restriction. In a randomized controlled trial (RCT), we tested the hypothesis that a diet (all meals provided) with liberalized complex carbohydrate (60%) and lower fat (25%) (CHOICE diet) could improve maternal insulin resistance and 24-h glycemia, resulting in reduced newborn adiposity (NB%fat; powered outcome) versus a conventional lower-carbohydrate (40%) and higher-fat (45%) (LC/CONV) diet. RESEARCH DESIGN AND METHODS: After diagnosis (at ∼28-30 weeks' gestation), 59 women with diet-controlled GDM (mean ± SEM; BMI 32 ± 1 kg/m2) were randomized to a provided LC/CONV or CHOICE diet (BMI-matched calories) through delivery. At 30-31 and 36-37 weeks of gestation, a 2-h, 75-g oral glucose tolerance test (OGTT) was performed and a continuous glucose monitor (CGM) was worn for 72 h. Cord blood samples were collected at delivery. NB%fat was measured by air displacement plethysmography (13.4 ± 0.4 days). RESULTS: There were 23 women per group (LC/CONV [214 g/day carbohydrate] and CHOICE [316 g/day carbohydrate]). For LC/CONV and CHOICE, respectively (mean ± SEM), NB%fat (10.1 ± 1 vs. 10.5 ± 1), birth weight (3,303 ± 98 vs. 3,293 ± 81 g), and cord C-peptide levels were not different. Weight gain, physical activity, and gestational age at delivery were similar. At 36-37 weeks of gestation, CGM fasting (86 ± 3 vs. 90 ± 3 mg/dL), 1-h postprandial (119 ± 3 vs. 117 ± 3 mg/dL), 2-h postprandial (106 ± 3 vs. 108 ± 3 mg/dL), percent time in range (%TIR; 92 ± 1 vs. 91 ± 1), and 24-h glucose area under the curve values were similar between diets. The %time >120 mg/dL was statistically higher (8%) in CHOICE, as was the nocturnal glucose AUC; however, nocturnal %TIR (63-100 mg/dL) was not different. There were no between-group differences in OGTT glucose and insulin levels at 36-37 weeks of gestation. CONCLUSIONS: A ∼100 g/day difference in carbohydrate intake did not result in between-group differences in NB%fat, cord C-peptide level, maternal 24-h glycemia, %TIR, or insulin resistance indices in diet-controlled GDM.

Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial.

INTRODUCTION: Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management. METHODS: We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180 mg/dl, and additional psychosocial scales. RESULTS: Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180 mg/dl and time below 70 mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups. CONCLUSIONS: While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System.

INTRODUCTION: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. METHODS: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. RESULTS: Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%). CONCLUSIONS: The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system.

BACKGROUND AND OBJECTIVE: Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals. METHODS: The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller. RESULTS: Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight). CONCLUSION: The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal.

OBJECTIVE: Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. RESEARCH DESIGN AND METHODS: Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics. RESULTS: Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01). CONCLUSIONS: A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.

Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes.

Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.

A Multiple Hypothesis Approach to Estimating Meal Times in Individuals With Type 1 Diabetes.

INTRODUCTION: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach. METHODS: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set (n = 11) and with either uniform or normally distributed (µ = 0, σ = 10 or 20 minutes) prior probabilities for the hypothesis set. RESULTS: For the simulation data set, meals were estimated with an average error of -0.77 (±7.94) minutes when uniform priors were used and -0.99 (±8.55) and -0.88 (±7.84) for normally distributed priors (σ = 10 and 20 minutes). For the clinical data set, the average estimation error was 0.02 (±30.87), 1.38 (±21.58), and 0.04 (±27.52) for the uniform priors and normal priors (σ = 10 and 20 minutes). CONCLUSION: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System.

BACKGROUND: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. METHODS: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. RESULTS: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). CONCLUSION: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control.

This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).