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.

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.

Artificial intelligence and diabetes technology: A review.

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

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.

Automatically accounting for physical activity in insulin dosing for type 1 diabetes.

BACKGROUND AND OBJECTIVE: Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes. METHODS: We leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to "re-simulate" the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold. RESULTS: Simulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5). CONCLUSIONS: Integrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.

Impact of Daily Physical Activity as Measured by Commonly Available Wearables on Mealtime Glucose Control in Type 1 Diabetes.

Objective: In contrast with exercise, or structured physical activity (PA), glycemic disturbances due to daily unstructured PA in patients with type 1 diabetes (T1D) is largely underresearched, with limited information on treatment recommendations. We present results from retrospective analysis of data collected under patients' free-living conditions that illuminate the association between PA, as measured by an off-the-shelf activity tracker, and postprandial blood glucose control. Research Design and Methods: Data from 37 patients with T1D during two clinical studies with identical data collection protocols were analyzed retrospectively: 4 weeks of continuous glucose monitoring, carbohydrate intake, insulin injections, and PA (assessed through wearable activity tracker) were collected in free-living conditions. Five-hour glucose area under curves (GAUCs) following the last-bolused meal of every day were computed to assess postprandial glucose excursions, and their relation with corresponding antecedent PA was analyzed using linear mixed-effects regression models, accounting for meal, insulin, and current glycemic state. Results: Datasets yielded 845 days of data from 37 subjects (22.8 ± 11.6 days/subject); postmeal GAUC was negatively associated with total daily PA measured by step count (P = 0.025), and total time spent performing higher than light-intensity PA (P = 0.042). Patients with higher median total daily PA exhibited lower average postprandial GAUC (P < 0.01). Additional analyses indicated that daily PA likely presents an immediate and delayed impact on glucose control. Conclusion: Daily PA assessed by commonly available sensors is significantly associated with glycemic exposure after an evening meal, indicating that quantitative assessment of PA may be useful in mealtime treatment decisions.

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus.

BACKGROUND: Glucose variability (GV) remains a key limiting factor in the success of diabetes management. While new technologies, for example, accurate continuous glucose monitoring (CGM) and connected insulin delivery devices, are now available, current treatment standards fail to leverage the wealth of information generated. Expert systems, from automated insulin delivery to advisory systems, are a key missing element to richer, more personalized, glucose management in diabetes. METHODS: Twenty four subjects with type 1 diabetes mellitus (T1DM), 15 women, 37 ± 11 years of age, hemoglobin A1c 7.2% ± 1%, total daily insulin (TDI) 46.7 ± 22.3 U, using either an insulin pump or multiple daily injections with carbohydrate counting, completed two randomized crossover 48-h visits at the University of Virginia, wearing Dexcom G4 CGM, and using either usual care or the UVA decision support system (DSS). DSS consisted of a combination of automated insulin titration, bolus calculation, and CHO treatment advice. During each admission, participants were exposed to a variety of meal sizes and contents and two 45-min bouts of exercise. GV and glucose control were assessed using CGM. RESULTS: The use of DSS significantly reduced GV (coefficient of variation: 0.36 ± 08. vs. 0.33 ± 0.06, P = 0.045) while maintaining glycemic control (average CGM: 155.2 ± 27.1 mg/dL vs. 155.2 ± 23.2 mg/dL), by reducing hypoglycemia exposure (%<70 mg/dL: 3.8% ± 4.6% vs. 1.8% ± 2%, P = 0.018), with nonsignificant trends toward reduction of significant hyperglycemia overnight (%>250 mg/dL: 5.3% ± 9.5% vs. 1.9% ± 4.6%) and at mealtime (11.3% ± 14.8% vs. 5.8% ± 9.1%). CONCLUSIONS: A CGM/insulin informed advisory system proved to be safe and feasible in a cohort of 24 T1DM subjects. Use of the system may result in reduced GV and improved protection against hypoglycemia.

Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.

BACKGROUND: Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting. RESEARCH DESIGN AND METHODS: The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events. RESULTS: Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria. CONCLUSIONS: MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.

Body Mass Index Effect on Differing Responses to Psychological Stress in Blood Glucose Dynamics in Patients With Type 1 Diabetes.

OBJECTIVE: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. METHODS: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based "effectiveness index" (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. RESULTS: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. CONCLUSION: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

Real-world walking in multiple sclerosis: Separating capacity from behavior.

BACKGROUND: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors. OBJECTIVE: To develop specific measures of MS-associated walking capacity through statistically sophisticated HPA analysis, thereby more precisely defining the real-world impact of disease. METHODS: Eighty-eight MS and 38 control subjects completed timed walks and patient-reported outcomes in clinic, then wore an accelerometer for 7days. HPA was analyzed with several new statistics, including the maximum step rate (MSR) and habitual walking step rate (HWSR), along with conventional methods, including average daily steps. HPA statistics were validated using clinical walking outcomes. RESULTS: The six-minute walk (6MW) step rate correlated most strongly with MSR (r=0.863, p<10-25) and HWSR (r=0.815, p<10-11) rather than average daily steps (r=0.676, p<10-11). The combination of MSR and HWSR correlated more strongly with the 6MW step rate than either measure alone (r=0.884, p<10-14). The MSR overestimated the 6MW step rate (µ=10.4, p<10-7), whereas the HWSR underestimated it (µ=-18.2, p<10-19). CONCLUSIONS: Conventional HPA statistics are poor measures of capacity due to variability in activity behaviors. The MSR and HWSR are valid, specific measures of real-world capacity which capture subjects' highest step rate and preferred step rate, respectively.

Closed-Loop Control Without Meal Announcement in Type 1 Diabetes.

OBJECTIVE: A fully closed-loop insulin-only system was developed to provide glucose control in patients with type 1 diabetes without requiring announcement of meals or activity. Our goal was to assess initial safety and efficacy of this system. RESEARCH DESIGN AND METHODS: The multiple model probabilistic controller (MMPPC) anticipates meals when the patient is awake. The controller used the subject's basal rates and total daily insulin dose for initialization. The system was tested at two sites on 10 patients in a 30-h inpatient study, followed by 15 subjects at three sites in a 54-h supervised hotel study, where the controller was challenged by exercise and unannounced meals. The system was implemented on the UVA DiAs system using a Roche Spirit Combo Insulin Pump and a Dexcom G4 Continuous Glucose Monitor. RESULTS: The mean overall (24-h basis) and nighttime (11 PM-7 AM) continuous glucose monitoring (CGM) values were 142 and 125 mg/dL during the inpatient study. The hotel study used a different daytime tuning and manual announcement, instead of automatic detection, of sleep and wake periods. This resulted in mean overall (24-h basis) and nighttime CGM values of 152 and 139 mg/dL for the hotel study and there was also a reduction in hypoglycemia events from 1.6 to 0.91 events/patient/day. CONCLUSIONS: The MMPPC system achieved a mean glucose that would be particularly helpful for people with an elevated A1c as a result of frequent missed meal boluses. Current full closed loop has a higher risk for hypoglycemia when compared with algorithms using meal announcement.

The Impact of Frequency and Tone of Parent-Youth Communication on Type 1 Diabetes Management.

BACKGROUND: The purpose of this study is to assess the impact of frequency and tone of parent-youth communication on glycemic control as measured by the Family Communication Inventory (FCI). Adolescence provides a unique set of diabetes management challenges, including suboptimal glycemic control. Continued parental involvement in diabetes management is associated with improved HbA1c outcomes; however, diabetes-related conflict within the family can have adverse effects. Although it is clear that communication plays an important role in diabetes outcomes, the specific impact of frequency and tone of such communication is largely understudied. METHODS: A total of 110 youths with type 1 diabetes and their parents completed questionnaires assessing diabetes-related adherence, family conflict, and family communication (i.e., frequency and tone) during a routine clinic visit. Routine testing of HbA1c was performed. RESULTS: Youth- and parent-reported frequency of communication were unrelated to HbA1c. Instead, greater discrepancies between parents and children on reported frequency of communication (most commonly parents reporting frequent and youth reporting less frequent communication) corresponded with poorer glycemic control and increased family conflict. More positive tone of communication as rated by youth was associated with lower HbA1c. CONCLUSIONS: Diabetes-related communication is more complex than conveyed simply by how often children and their parents communicate. Tone of communication and discrepancies in a family's perception of the frequency of communication were better than frequency as predictors of glycemic control. The FCI appears to capture the frequency and tone of diabetes-related communication, though larger-scale studies are warranted to inform future use of this scale.

Remotely engaged: Lessons from remote monitoring in multiple sclerosis.

OBJECTIVES: Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being. METHODS: Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months. RESULTS: Approximately 87% of subjects completed wbPROs without assistance, and wbPROs strongly correlated with standard PROs (r>0.91). All wbPROs were completed less frequently in the second three months (p<0.05). Frequent wbPRO completion was significantly correlated with higher step on the Expanded Disability Status Scale (EDSS) (p=0.026). Nearly 52% of subjects reported improved understanding of their disease, and approximately 16% wanted individualized wbPRO content. Over half (63.9%) of perceived well-being variance was explained by MS symptoms, notably depression (rs=-0.459), fatigue (rs=-0.390), and pain (rs=-0.389). CONCLUSIONS: wbPRO collection was feasible and reliable. More disabled subjects had higher completion rates, yet most subjects failed requirements in the second three months. Remote monitoring has potential to improve patient-centered care and communication between patient and provider, but tailored PRO content and other innovations are needed to combat declining adherence.

Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects.

BACKGROUND: The six-minute walk (6MW) is a common walking outcome in multiple sclerosis (MS) thought to measure fatigability in addition to overall walking disability. However, direct evidence of 6MW induced gait deterioration is limited by the difficulty of measuring qualitative changes in walking. OBJECTIVES: This study aims to (1) define and validate a measure of fatigue-related gait deterioration based on data from body-worn sensors; and (2) use this measure to detect gait deterioration induced by the 6MW. METHODS: Gait deterioration was assessed using the Warp Score, a measure of similarity between gait cycles based on dynamic time warping (DTW). Cycles from later minutes were compared to baseline cycles in 89 subjects with MS and 29 controls. Correlation, corrected (partial) correlation, and linear regression were used to quantify relationships to walking and fatigue outcomes. RESULTS: Warp Scores rose between minute 3 and minute 6 in subjects with mild and moderate disability (p<0.001). Statistically significant correlations (p<0.001) to the MS walking scale (MSWS-12), modified fatigue impact scale (MFIS) physical subscale, and cerebellar and pyramidal functional system scores (FSS) were observed even after controlling for walking speed. Regression of MSWS-12 scores on Warp Scores and walking speed explained 73.9% of response variance. Correlations to individual MSWS-12 and MFIS items strongly suggest a relationship to fatigability. CONCLUSION: The Warp Score has been validated in MS subjects as an objective measure of fatigue-related gait deterioration. Progressive changes to gait cycles induced by the 6MW often appeared in later minutes, supporting the importance of sustained walking in clinical assessment.

The e-MSWS-12: improving the multiple sclerosis walking scale using item response theory.

BACKGROUND: The Multiple Sclerosis Walking Scale (MSWS-12) is the predominant patient-reported measure of multiple sclerosis (MS) -elated walking ability, yet it had not been analyzed using item response theory (IRT), the emerging standard for patient-reported outcome (PRO) validation. This study aims to reduce MSWS-12 measurement error and facilitate computerized adaptive testing by creating an IRT model of the MSWS-12 and distributing it online. METHODS: MSWS-12 responses from 284 subjects with MS were collected by mail and used to fit and compare several IRT models. Following model selection and assessment, subpopulations based on age and sex were tested for differential item functioning (DIF). RESULTS: Model comparison favored a one-dimensional graded response model (GRM). This model met fit criteria and explained 87 % of response variance. The performance of each MSWS-12 item was characterized using category response curves (CRCs) and item information. IRT-based MSWS-12 scores correlated with traditional MSWS-12 scores (r = 0.99) and timed 25-foot walk (T25FW) speed (r =  -0.70). Item 2 showed DIF based on age (χ 2 = 19.02, df = 5, p < 0.01), and Item 11 showed DIF based on sex (χ 2 = 13.76, df = 5, p = 0.02). CONCLUSIONS: MSWS-12 measurement error depends on walking ability, but could be lowered by improving or replacing items with low information or DIF. The e-MSWS-12 includes IRT-based scoring, error checking, and an estimated T25FW derived from MSWS-12 responses. It is available at https://ms-irt.shinyapps.io/e-MSWS-12 .

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. METHODS: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. RESULTS: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. CONCLUSIONS: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

BACKGROUND: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series. METHODS: Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data. RESULTS: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series. CONCLUSIONS: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.

Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor.

BACKGROUND: The Predictive Hypoglycemia Minimizer System ("Hypo Minimizer"), consisting of a zone model predictive controller (the "controller") and a safety supervision module (the "safety module"), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor," a pivotal variable in the system, governs the speed and magnitude of the controller's insulin dosing characteristics in response to changes in CGM levels. METHODS: Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results. RESULTS: As aggressiveness increased from "conservative" to "medium" to "aggressive," the controller recommended less insulin (-3.3% vs -14.4% vs -19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent <70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values. CONCLUSION: The Hypo Minimizer's controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics.

Multinight "bedside" closed-loop control for patients with type 1 diabetes.

BACKGROUND: Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to "reset" the patient overnight to near-normoglycemia each morning, for several consecutive nights. SUBJECTS AND METHODS: Ten insulin pump users with type 1 diabetes (mean age, 46.4±8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h. RESULTS: Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P<0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P<0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P<0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P<0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P=0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r=0.52, P<0.01. CONCLUSIONS: Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were "reset" to near-normoglycemia each morning.

Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.