Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.

BACKGROUND: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. OBJECTIVE: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. METHODS: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. RESULTS: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84). CONCLUSIONS: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. TRIAL REGISTRATION: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial.

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 or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology-a voice warning that can potentially be delivered via an in-vehicle voice assistant. OBJECTIVE: This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. METHODS: We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants' self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants' verbal feedback. RESULTS: Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. CONCLUSIONS: This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.

Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season.

AIMS: To analyse glycaemic patterns of professional athletes with type 1 diabetes during a competitive season. MATERIALS AND METHODS: We analysed continuous glucose monitoring data of 12 professional male cyclists with type 1 diabetes during exercise, recovery and sleep on days with competitive exercise (CE) and non-competitive exercise (NCE). We assessed whether differences exist between CE and NCE days and analysed associations between exercise and dysglycaemia. RESULTS: The mean glycated haemoglobin was 50 ± 5 mmol/mol (6.7 ± 0.5%). The athletes cycled on 280.8 ± 28.1 days (entire season 332.6 ± 18.8 days). Overall, time in range (3.9-10 mmol/L) was 70.0 ± 13.7%, time in hypoglycaemia (<3.9 mmol/L) was 6.4 ± 4.7% and time in hyperglycaemia (>10 mmol/L) was 23.6 ± 12.5%. During the nights of NCE days, athletes spent 10.1 ± 7.4% of time in hypoglycaemia, particularly after exercise in the endurance zones. The CE days were characterized by a higher time in hyperglycaemia compared with NCE days (25.2 ± 12.5% vs. 22.2 ± 12.1%, p = .012). This was driven by the CE phase, where time in range dropped to 60.4 ± 13.0% and time in hyperglycaemia was elevated (38.5 ± 12.9%). Mean glucose was higher during CE compared with NCE sessions (9.6 ± 0.9 mmol/L vs. 7.8 ± 1.1 mmol/L, p < .001). The probability of hyperglycaemia during exercise was particularly increased with longer duration, higher intensity and higher variability of exercise. CONCLUSIONS: The analysis of glycaemic patterns of professional endurance athletes revealed that overall glycaemia was generally within targets. For further improvement, athletes, team staff and caregivers may focus on hyperglycaemia during competitions and nocturnal hypoglycaemia after NCE.

Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

AIM: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ). RESULTS: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). CONCLUSIONS: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS: Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS: Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.

Unraveling, contributing factors to the severity of postprandial hypoglycemia after gastric bypass surgery.

BACKGROUND: Despite the increasing prevalence of postbariatric hypoglycemia (PBH), a late metabolic complication of bariatric surgery, our understanding of its diverse manifestations remains incomplete. OBJECTIVES: To contrast parameters of glucose-insulin homeostasis in 2 distinct phenotypes of PBH (mild versus moderate hypoglycemia) based on nadir plasma glucose. SETTING: University Hospital (Bern, Switzerland). METHODS: Twenty-five subjects with PBH following gastric bypass surgery (age, 41 ± 12 years; body mass index, 28.1 ± 6.1kg/m2) received 75g of glucose with frequent blood sampling for glucose, insulin, C-peptide, and glucagon-like peptide 1 (GLP)-1. Based on nadir plasma glucose (

Glycaemic outcomes in adults with type 1 diabetes transitioning towards advanced automated insulin delivery systems - a real-world analysis at a Swiss tertiary centre.

AIMS OF THE STUDY: To assess glucose levels in adults with diabetes at a Swiss tertiary hospital when transitioning from insulin delivery with a sensor-augmented pump with (predictive) low-glucose suspend ([P]LGS) to a hybrid-closed loop (HCL) and from a HCL to an advanced hybrid-closed loop (AHCL). METHODS: Continuous glucose monitoring data for 44 adults with type 1 diabetes transitioning from (P)LGS to hybrid-closed loop and from hybrid-closed loop to advanced hybrid-closed loop were analysed, including the percentage of time spent within, below, and above glucose ranges. In addition, a subgroup analysis (n = 14) of individuals undergoing both transitions was performed. RESULTS: The transition from a (P)LGS to a hybrid-closed loop was associated with increased time in range (6.6% [2.6%-12.7%], p <0.001) and decreased time above range (5.6% [2.3%-12.7%], p <0.001). The transition from a hybrid-closed loop to an advanced hybrid-closed loop was associated with increased time in range (1.6% [-0.5%-4.5%], p = 0.046) and decreased time above range (1.5% [-1.8%-5.6%], p = 0.050). Both transitions did not change the time below range. In the subgroup analysis ([P]LGS → HCL → AHCL), the time in range increased from 69.4% (50.3%-79.2%) to 76.5% (65.3%-81.3%) and 78.7% (69.7%-85.8%), respectively (p <0.001). CONCLUSIONS: Glucose levels significantly improved when transitioning from a (P)LGS to a hybrid-closed loop. Glucose levels improved further when switching from a hybrid-closed loop to an advanced hybrid-closed loop. However, the added benefit of an advanced hybrid-closed loop was comparably smaller. This pattern was also reflected in the subgroup analysis.

A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development.

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. TRIAL REGISTRATION: Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834.

The impact of postbariatric hypoglycaemia on driving performance: A randomized, single-blind, two-period, crossover study in a driving simulator.

Postbariatric hypoglycaemia (PBH) is an increasingly recognized complication of bariatric surgery, but its effect on daily functioning remains unclear. In this randomized, single-blind, crossover trial we assessed driving performance in patients with PBH. Ten active drivers with PBH (eight females, age 38.2 ± 14.7 years, body mass index 27.2 ± 4.6 kg/m2 ) received 75 g glucose to induce PBH in the late postprandial period and aspartame to leave glycaemia unchanged, on two different occasions. A simulator was driven during 10 minutes before (D0) and 20 (D1), 80 (D2), 125 (D3) and 140 minutes (D4) after the glucose/aspartame ingestion, reflecting the expected blood glucose (BG) increase (D1), decrease (D2) and hypoglycaemia (D3, D4). Seven driving features indicating impaired driving were integrated in a Bayesian hierarchical regression model to assess the difference in driving performance after glucose/aspartame ingestion. Mean ± standard deviation peak and nadir BG after glucose were 182 ± 24 and 47 ± 14 mg/dL, while BG was stable after aspartame (85 ± 4 mg/dL). Despite the lack of a difference in symptom perception, driving performance was significantly impaired after glucose versus aspartame during D4 (posterior probability 98.2%). Our findings suggest that PBH negatively affects driving performance.

Lower Daily Carbohydrate Intake Is Associated With Improved Glycemic Control in Adults With Type 1 Diabetes Using a Hybrid Closed-Loop System.

OBJECTIVE: To assess the association between daily carbohydrate (CHO) intake and glycemic control in adult hybrid closed-loop (HCL) users with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Mean individual daily CHO intake (MIDC) and relative deviation from MIDC (≤80% low, 81-120% medium, >120% high CHO consumption) were compared with parameters of glycemic control assessed by continuous glucose monitoring. RESULTS: Records from 36 patients (26 male, 10 female; age 36.9 ± 13.5 years; HbA1c 7.1 ± 0.9% [54 ± 10 mmol/mol]) provided 810 days of data (22.5 ± 6.7 days per patient). Time in range (70-180 mg/dL) for low, medium, and high CHO consumption was 77.4 ± 15.4%, 75.2 ± 16.7%, and 70.4 ± 17.8%, respectively (P < 0.001). Time above range (>180 mg/dL) was 20.1 ± 14.7%, 22.0 ± 16.9%, and 27.2 ± 18.4%, respectively (P < 0.001). There was no between-group difference for time in hypoglycemia (<70 mg/dL; P = 0.50). CONCLUSIONS: Daily CHO intake was inversely associated with glycemic control in adults with T1D using an HCL system. Lower CHO intake may be a strategy to optimize glucose control in HCL users.

White coat adherence effect on glucose control in adult individuals with diabetes.

BACKGROUND: White coat adherence (WCA) is defined as an increased adherence to treatment regimens directly before a visit with a healthcare provider. Little is known on the effect of WCA on glucose control in adult patients with diabetes mellitus. METHODS: The present study is based on 618 CGM-observations of 276 patients with diabetes treated between January 2013 and July 2018. The analysis compares data from the 3 days prior to a visit (p1) with the preceding 25 days (p2). RESULTS: Sensor use was higher during p1 than p2 (92.8 ± 7.3% vs 88.8 ± 7.5%; p < 0.001). Mean glucose [MG] and coefficient of variation [CV] were lower in p1 compared to p2 (MG 163.9 ± 39.2 mg/dL vs 166.9 ± 35.7 mg/dL, p = 0.001; CV 33.5 ± 8.4% vs 36.0 ± 7.0%, p < 0.001; respectively). Time in range (70-180 mg/dL) was higher in p1 than p2 (61.4 ± 21.2% vs 60.0 ± 18.4%, p = 0.002). Sensitivity-analysis showed that WCA effect was mainly detected in patients with HbA1c > 7% [53 mmol/mol]. CONCLUSION: This study reveals a WCA effect on pre-visit glucose control in adult patients with diabetes. The effect was most pronounced in patients with moderate to poor glycemic control. In these patients, analysis of CGM data should encompass a minimum of 1 to 2 weeks prior to a consultation.

Findings in susceptibility weighted imaging in pediatric patients with migraine with aura.

BACKGROUND: Migraine with aura (MwA) in pediatric patients is clinically frequent. Clinically complex symptoms need to be differentiated to exclude mimicking conditions. PURPOSE: We hypothesize that MwA in children induces abnormalities readily visible in perfusion time to peak (TTP) maps as well as non-enhanced susceptibility weighted magnetic resonance imaging (SWI). MATERIALS AND METHODS: Between 2010 and 2018, we retrospectively evaluated symptoms and imaging of consecutive pediatric patients <18 years with MwA. We visually scored abnormalities on SWI and TTP maps in 12 regions of interest on both hemispheres on three axial slices, as normal, slightly, distinctly or severely abnormal. RESULTS: 99 patients (69.7% female), mean age 14.07 y (±2.8) were included. Focally increased deoxygenation (FID) in SWI was present in 61.6%. FID on SWI was dominant for the left hemisphere (60.7% vs. 31.1%, (p < .001)), and in 8.2% symmetric. Side of aura symptoms and contralateral hemispheric imaging alterations in patients with FID correlated significantly (p = .002.). 61 of 99 patients had perfusion MR and 59% of these patients showed focal increase of TTP. Age correlated significantly with FID in SWI (r = -.248, p = .013) and increase of TTP in perfusion (r = -.252, p = .05). Focal abnormalities correlated significantly between SWI and TTP maps. Brain regions most often abnormal were the temporal superior, occipital and fronto-parietal regions. CONCLUSIONS: This study provides confidence in recognizing FID, and linking FID in SWI to acute MwA in pediatric patients. FID phenomenon had a left hemispheric significant dominance, and can be found bilaterally.

Comparison of perioperative automated versus manual two-dimensional tumor analysis in glioblastoma patients.

OBJECTIVES: Current recommendations for the measurement of tumor size in glioblastoma continue to employ manually measured 2D product diameters of enhancing tumor. To overcome the rater dependent variability, this study aimed to evaluate the potential of automated 2D tumor analysis (ATA) compared to highly experienced rater teams in the workup of pre- and postoperative image interpretation in a routine clinical setting. MATERIALS AND METHODS: From 92 patients with newly diagnosed GB and performed surgery, manual rating of the sum product diameter (SPD) of enhancing tumor on magnetic resonance imaging (MRI) contrast enhanced T1w was compared to automated machine learning-based tumor analysis using FLAIR, T1w, T2w and contrast enhanced T1w. RESULTS: Preoperative correlation of SPD between two rater teams (1 and 2) was r=0.921 (p<0.0001). Difference among the rater teams and ATA (p=0.567) was not statistically significant. Correlation between team 1 vs. automated tumor analysis and team 2 vs. automated tumor analysis was r=0.922 and r=0.897, respectively (p<0.0001 for both). For postoperative evaluation interrater agreement between team 1 and 2 was moderate (Kappa 0.53). Manual consensus classified 46 patients as completely resected enhancing tumor. Automated tumor analysis agreed in 13/46 (28%) due to overestimation caused by hemorrhage and choroid plexus enhancement. CONCLUSIONS: Automated 2D measurements can be promisingly translated into clinical trials in the preoperative evaluation. Immediate postoperative SPD evaluation for extent of resection is mainly influenced by postoperative blood depositions and poses challenges for human raters and ATA alike.