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BACKGROUND Hypoglycemia is a serious complication in diabetes, it impairs cognitive and psychomotor function, and is linked to driving mishaps. In-vehicle voice assistants (VAs) have been designed to proactively deliver a warning of hypoglycemia while driving. However, proactive VAs can cause driving impairments through startling. Hence, we complement the voice warning from the VA with ambient light-emitting diodes (LED) and investigate the effect of this addition on emotional reaction. OBJECTIVE Designing an in-vehicle voice warning for hypoglycemia and assessing its effect on the emotional reaction and technology acceptance. METHODS We present two studies investigating the emotional reactions of drivers with diabetes to different hypoglycemia warning modalities. The same procedure was replicated in two settings: simulated and real driving. A quasi-experimental design, with two independent variables (blood glucose phase and warning modality) and one main dependent variable (emotional reaction), was implemented. The material and apparatus included intravenous catheters to manipulate blood glucose and a tablet with an app to simulate hypoglycemia warnings. The warnings had three possible modalities: Standard, Voice, and Voice + LED. Objective emotional reaction (arousal) was measured physiologically via skin conductance response (SCR). Subjective emotional reaction was measured with the Affective Slider (valence and arousal). Both emotional reaction measures were tested with a mixed-effect linear model. Secondary outcomes included self-reported measures of technology acceptance. RESULTS Our results showed that in the simulated-driving setting, the Voice + LED warning modality was preferred over other modalities. However, this advantage decreased in real-world driving because the LED was less visible outdoors. The Voice modality was more effective than the Standard warning modality in both simulated and real-world driving studies. The mixed model on self-reported emotional reaction yielded significant results. Self-reported arousal was higher during Decreasing blood glucose and Hypoglycemia for Voice and Voice + LED in the real-world driving study. In contrast, self-reported valence was influenced by blood glucose manipulation rather than the warning modality. The mixed model on SCR did not yield significant results. Participants consistently ranked the Voice and Voice + LED warning modalities superior to the Standard modality. CONCLUSIONS This paper proposes using the in-vehicle VA and ambient lighting system installed in a car to deliver a warning of hypoglycemia. It investigated how the warning modality affects emotional response and acceptance in virtual and real-world environments. The study provides insight into the potential of implementing VA-based health warnings in cars and contributes to a better understanding of the challenges and opportunities of in-vehicle VA-based health interventions.

BACKGROUND Hypoglycemia is a frequent and acute complication in type-1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring (FGM) or continuous glucose monitoring (CGM) devices. These devices, however, require manual and visual interaction, removing the focus of attention from the driving task. Hypoglycemia is known to cause a decrease in attention, challenging the safety of using such devices behind the wheel. Here, we present an investigation of hands- and distraction-free technology: an in–vehicle voice–assistant–based warning. OBJECTIVE Designing and assessing a voice–assistant–based health warning for hypoglycemia while driving and addressing the limitations of the current warning solutions. METHODS We developed and assessed the warning in three studies, where participants received the warning while driving. In all studies, we measured participants’ self-reported technology readiness, perception of the warning, and compliance behavior (whether they stopped the car and their reaction time), and assessed any room for improvement through participants’ feedback. In Study 0, 10 healthy participants drove in a simulator and assessed the feasibility of using a voice assistant to deliver a warning. In Study 1, 18 participants with T1DM drove in a simulator and assessed the revised version of the warning. In Study 2, 20 participants with T1DM undergoing hypoglycemia assessed a further revised version of the warning while driving in a real car on a test track. In all studies, we also measured self-reported technology readiness, and acceptance of the warning, and assessed compliance behavior and reaction time in response to the warning. RESULTS In all studies, 100% of participants complied with the warning. In Study 0, healthy participants perceived the warning as usable and useful, and their feedback suggested reducing speech rate and increasing driver-assistant interaction. In Study 1, participants with T1DM reported good perception and their feedback suggested the warning to be less instructive. In Study 2, we observed moderate perception (lower than in Study 1), and participants’ feedback revealed the warning was too overloading. CONCLUSIONS To the best of the authors’ knowledge, this is the first study investigating the feasibility of an in–vehicle voice–assistant–based warning for hypoglycemia while driving. Drivers find such an implementation useful and effective, although individuals with T1DM preferred a simple and direct voice warning, rather than a conversational one. This may reflect the utility and unfamiliarity of proactive behavior in voice assistants. We anticipate this research to be a starting point for the combination of driver-state warnings and for voice–assistant–based health support, and to be a guide for the design of such a combination. CLINICALTRIAL ClinicalTrials.gov NCT04035993, NCT04569630

BACKGROUND To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. OBJECTIVE In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). METHODS Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. RESULTS First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. CONCLUSIONS Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system. CLINICALTRIAL Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834

In medicine, comorbidities refer to the presence of multiple, co-occurring diseases. Due to their co-occurring nature, the course of one comorbidity is often highly dependent on the course of the other disease and, hence, treatments can have significant spill-over effects. Despite the prevalence of comorbidities among patients, a comprehensive statistical framework for modeling the longitudinal dynamics of comorbidities is missing. In this paper, we propose a probabilistic model for analyzing comorbidity dynamics over time in patients. Specifically, we develop a coupled hidden Markov model with a personalized, non-homogeneous transition mechanism, named Comorbidity-HMM. The specification of our Comorbidity-HMM is informed by clinical research: (1) It accounts for different disease states (i. e., acute, stable) in the disease progression by introducing latent states that are of clinical meaning. (2) It models a coupling among the trajectories from comorbidities to capture co-evolution dynamics. (3) It considers between-patient heterogeneity (e. g., risk factors, treatments) in the transition mechanism. Based on our model, we define a spill-over effect that measures the indirect effect of treatments on patient trajectories through coupling (i. e., through comorbidity co-evolution). We evaluated our proposed Comorbidity-HMM based on 675 health trajectories where we investigate the joint progression of diabetes mellitus and chronic liver disease. Compared to alternative models without coupling, we find that our Comorbidity-HMM achieves a superior fit. Further, we quantify the spill-over effect, that is, to what extent diabetes treatments are associated with a change in the chronic liver disease from an acute to a stable disease state. To this end, our model is of direct relevance for both treatment planning and clinical research in the context of comorbidities.

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, ISBN:978-1-4503-6819-3

Zusammenfassung. Diverse Errungenschaften führten in den letzten Dekaden zu einer laufenden Optimierung und Flexibilisierung der Diabetestherapie. Insbesondere der rasante technische Fortschritt hat völlig neue Therapieoptionen hervorgebracht, von denen sowohl Patienten mit Typ-1 als auch Typ-2 Diabetes profitieren können. Wesentliche Meilensteine waren die Einführung und Weiterentwicklung der Insulinpumpen sowie der kontinuierlichen Glucosemessung (CGM). Diese beiden Komponenten waren die Grundvoraussetzung für eine automatisierte Insulinabgabe durch Koppelung der beiden Geräte unter Zwischenschaltung von ausgeklügelten mathematischen Kontrollalgorithmen. Solche System sind heute in der Lage partiell (sog. Hybrid Closed-Loop, in gewissen Ländern bereits zugelassen) oder sogar vollautonom als geschlossener Regelkreis (Fully Closed-Loop, in Studien getestet) zu arbeiten. Dabei wird die Funktion einer intakten Betazelle nachgeahmt, weswegen diese Systeme auch als künstliches Pankreas bezeichnet werden. Nebst einer Vereinfachung des Selbstmanagements birgt diese Technologie auch das Potential, die Diabeteseinstellung längerfristig zu verbessern. Daneben hat auch die Verbreitung der Smartphones und zunehmende Konnektivität verschiedener Geräte der Diabetestechnologie zusätzlichen Schub verliehen. Dies ermöglichte unter anderem die Entwicklung von Applikationen, welche den Anwender beim Diabetes-Management, der Datenanalyse und -Dokumentation oder beim Kohlenhydratschätzen unterstützen können.

Studies on CGM assume equal glycemic control between MDI and CSII. However, observation periods are often short and the impact of hypoglycemia on glycemic control and variability is unclear. In this study, we analyzed 12846 days (35 years; CSII 2232 days [6 years]; MDI 10614 days [29 years]) of CGM readings obtained from 99 patients with T1D. In the overall analysis mean glucose was 169.6 ± 18.9mg/dl for CSII and 175.0 ± 29.5 mg/dl for MDI. Estimated A1c was 7.54 ± 0.66% (CSII) and 7.7 ± 1.0% (MDI). Percentage of readings in target range (70-180mg/dL) was 54.0 ± 15.5% for CSII and 57.4 ± 11.4% for MDI. In total, 3.1/2.2% of readings were < 54 mg/dL and 14.9/12.4% were > 250mg/dL for CSII and MDI, respectively. In total, 464 hypoglycemic events (< 54mg/dL) occurred in CSII (day: 305 [64%]; night: 170 [36%]), corresponding to 0.21 hypoglycemic events daily. In MDI, 2297 hypoglycemic events occurred (day: 1507/66%; night: 783/34%), corresponding to 0.22 hypoglycemic events daily. Mean duration of hypoglycemia was 146.5 ± 65.9 minutes (CSII) and 132.8 ± 42.8 minutes (MDI); 233 (CSII) vs. 982 (MDI) events were prolonged (>120 minutes). CV was 41.1 ± 6.7% for CSII and 40.8 ± 3.8% for MDI. In the 24 hours prior to hypoglycemia, mean glucose was higher (182.9 ± 30.5 vs. 166.9 ± 26.6mg/dl; p Disclosure A. Melmer: None. T. Züger: None. T. Pöttler: None. H. Kojzar: None. M. Cigler: None. F. Aberer: None. M. Laimer: None. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH.

Background: Thousands with type 1 diabetes are estimated to be using open source Artificial Pancreas Systems (APS) with commercially available insulin pumps, continuous glucose monitors (CGM), and an open source control algorithm to process glucose readings and adjust insulin delivery. OpenAPS and similar do-it-yourself (DIY) closed loop systems gained considerable interest in the online diabetes community. Many using DIY closed loop systems have chosen to donate their data to a shared, anonymized data repository called the “OpenAPS Data Commons.” The present study evaluated glycemic control and glycemic variability of CGM readings of 80 DIY closed loop users. Methods: We analyzed 19251 days (53 years) of CGM readings with a mean duration of 134 days per patient (min. 3 days, max. 917 days) after the patient started looping. Results: Mean glucose was 137 ± 20mg/dl and estimated glycated hemogloblin A1c (eA1c) was 6.40 ± 0.70%. Time in target range (70-180mg/dL) was 77.5 ± 10.5%, 4.3% of CGM readings were below 70mg/dL, 1.3% were below 54mg/dL, 18.2% were above 180mg/dL, and 4.1% of CGM readings were above 250mg/dL, respectively. A total of 6474 hypoglycemic events (CGM reading < 54mg/dL) was observed (daytime: 5004 [73.9%]; nighttime: 1765 [26.1%]), which corresponds to 0.34 hypoglycemic events per day. The mean duration of each hypoglycemia event was 65.4 ± 41.4 minutes, and 1484 events were prolonged (duration > 120 minutes; daytime: 1043 [76.30%]; nighttime: 324 [23.7%]). Coefficient of variation (CV) was 35.5 ± 5.9% (daytime: 35.4%; nighttime: 33.9%) and mean of daily differences (MODD) was 50.1 ± 13.5 mg/dL. Conclusion: Open source AP systems show potential to support stable glycemic control in people with T1D. This is the largest descriptive analysis of open source APS data to date. The results are promising, but open source APS should be investigated in additional detail before a conclusion about their safety and efficacy can be drawn. Disclosure A. Melmer: None. T. Züger: None. D.M. Lewis: Consultant; Self; Diabeloop SA, Roche Diabetes Care. S.M. Leibrand: Consultant; Self; Diabeloop SA, Roche Diabetes Care. M. Laimer: None.

Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9-10 mmol/L was 77.5 ± 10.5%,3.9 mmol/L was 4.3 ± 3.6%,3.0 mmol/L was 1.3 ± 1.9%,10 mmol/L was 18.2 ± 11.0% and 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor-augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (-0.6 ± 0.7; P 0.0001), lower estimated HbA1c (-0.4 ± 0.5%; P 0.0001), higher time in range 3.9-10 mmol/L (+9.3 ± 9.5%; P 0.0001), less time 3.0 mmol/L (-0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (-2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (-0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology-adept cohort, despite not being approved for the treatment of individuals with T1D.

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) autoregressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models' outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.