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 (PLGS) to a ...hybrid-closed loop (HCL) and from a HCL to an advanced hybrid-closed loop (AHCL).
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.
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 (PLGS → 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).
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.
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
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.
The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes.
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.
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 (F
=4.3; P<.05 and F
=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F
=3.9; P<.05), while blood glucose phase affected it in real-world driving (F
=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F
=2.46; P=.09, blood glucose phase: F
=0.3; P=.74), nor in the real-world driving study (modality: F
=0.8; P=.47, blood glucose phase: F
=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).
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.
ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
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.
This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception.
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.
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.
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.
To develop a noninvasive hypoglycemia detection approach using smartwatch data.
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.
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.
Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.
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.
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).
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.
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.
Background: Mechanisms of postprandial hypoglycemia following gastric bypass (GB) surgery (PBH), remain incompletely understood. Thus, we aimed to assess the role of insulin secretion/beta-cell ...function (BCF), fractional hepatic insulin extraction (HE), insulin sensitivity (SI) and rate of glucose appearance (Ra) in patients with biochemically moderate and severe hypoglycemia. Methods: A total of 23 subjects with PBH (mean±SD age 41±12 years, BMI 28.1±6.1 kg/m2, 6.0±2.5 years since GB surgery) underwent an oral glucose tolerance test (75g of glucose), with frequent blood sampling for determination of glucose, insulin and C-peptide concentrations for up to 180minutes. Indices of BCF, HE, SI and Ra were calculated using the oral minimal model method and compared between subjects with nadir plasma glucose during the test below (severe hypo) and above 50mg/dL (moderate hypo). Results: Mean±SD nadir glucose was 43.3±6.0mg/dL in the severe hypo group and 60.1±9.1mg/dL in the moderate hypo group. Differences between the groups were found for indices of BCF, HE and Ra, whereas no significant differences were observed for SI (Figure 1). Conclusion: Present findings confirm three candidate mechanisms involved in PBH: increased BCF, diminished HE and faster Ra.
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