Combined intake of caffeine and low-dose glucose to reduce exercise-related hypoglycaemia in individuals with type 1 diabetes on ultra-long-acting insulin degludec: A randomized, controlled, double-blind, cross-over trial.

AIM: To evaluate whether caffeine combined with a moderate amount of glucose reduces the risk for exercise-related hypoglycaemia compared with glucose alone or control in adult people with type 1 diabetes using ultra-long-acting insulin degludec. MATERIALS AND METHODS: Sixteen participants conducted three aerobic exercise sessions (maximum 75 min) in a randomized, double-blind, cross-over design. Thirty minutes before exercise, participants ingested a drink containing either 250 mg of caffeine + 10 g of glucose + aspartame (CAF), 10 g of glucose + aspartame (GLU), or aspartame alone (ASP). The primary outcome was time to hypoglycaemia. RESULTS: There was a significant effect of the condition on time to hypoglycaemia (χ2 = 7.674, p = .0216). Pairwise comparisons revealed an 85.7% risk reduction of hypoglycaemia for CAF compared with ASP (p = .044). No difference was observed between GLU and ASP (p = .104) or between CAF and GLU (p = .77). While CAF increased glucose levels during exercise compared with GLU and ASP (8.3 ± 1.9 mmol/L vs. 7.7 ± 2.2 mmol/L vs. 5.8 ± 1.4 mmol/L; p < .001), peak plasma glucose levels during exercise did not differ between CAF and GLU (9.3 ± 1.4 mmol/L and 9.1 ± 1.6 mmol/L, p = .80), but were higher than in ASP (6.6 ± 1.1 mmol/L; p < .001). The difference in glucose levels between CAF and GLU was largest during the last 15 min of exercise (p = .002). Compared with GLU, CAF lowered perceived exertion (p = .023). CONCLUSIONS: Pre-exercise caffeine ingestion combined with a low dose of glucose reduced exercise-related hypoglycaemia compared with control while avoiding hyperglycaemia.

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.

Effects of home-based exergaming on cardio-metabolic and cognitive health in physically inactive individuals.

AIMS: To examine the effects of a home-based exergame training over 6 weeks on cardio-metabolic and cognitive health, as well as training adherence, in physically inactive individuals. MATERIALS AND METHODS: Twenty participants were equipped with an exergame system specifically designed for use at home. Each participant performed at least three weekly exercise sessions at ≥80% of their individual maximum heart rate, over 6 weeks. Exercise duration increased biweekly until 75 min of vigorous exercise were performed in Weeks 5 and 6. Maximum oxygen uptake (VO2max), cardio-metabolic profiling, and neuro-cognitive tests were performed at baseline and study end. Additionally, training adherence was assessed via training diaries. RESULTS: After 6 weeks of home-based exergaming, VO2max increased significantly, while there was a significant decrease in heart rate (resting and maximum), blood pressure (systolic, diastolic and mean), and low-density lipoprotein cholesterol. Dynamic balance and reaction time improved after 6 weeks of exergaming. Training adherence was 88.4%. CONCLUSIONS: Home-based exergaming induced a clinically relevant increase in VO2max, a determinant of cardiovascular health, accompanied by further improvements in cardiovascular, metabolic and neuro-cognitive parameters. Exergaming may, therefore, offer an innovative approach to increasing regular physical activity, improving metabolic risk profile, and preventing chronic diseases.

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.

Standardised Exercise Prescription for Patients with Chronic Coronary Syndrome and/or Heart Failure: A Consensus Statement from the EXPERT Working Group.

Whereas exercise training, as part of multidisciplinary rehabilitation, is a key component in the management of patients with chronic coronary syndrome (CCS) and/or congestive heart failure (CHF), physicians and exercise professionals disagree among themselves on the type and characteristics of the exercise to be prescribed to these patients, and the exercise prescriptions are not consistent with the international guidelines. This impacts the efficacy and quality of the intervention of rehabilitation. To overcome these barriers, a digital training and decision support system [i.e. EXercise Prescription in Everyday practice & Rehabilitative Training (EXPERT) tool], i.e. a stepwise aid to exercise prescription in patients with CCS and/or CHF, affected by concomitant risk factors and comorbidities, in the setting of multidisciplinary rehabilitation, was developed. The EXPERT working group members reviewed the literature and formulated exercise recommendations (exercise training intensity, frequency, volume, type, session and programme duration) and safety precautions for CCS and/or CHF (including heart transplantation). Also, highly prevalent comorbidities (e.g. peripheral arterial disease) or cardiac devices (e.g. pacemaker, implanted cardioverter defibrillator, left-ventricular assist device) were considered, as well as indications for the in-hospital phase (e.g. after coronary revascularisation or hospitalisation for CHF). The contributions of physical fitness, medications and adverse events during exercise testing were also considered. The EXPERT tool was developed on the basis of this evidence. In this paper, the exercise prescriptions for patients with CCS and/or CHF formulated for the EXPERT tool are presented. Finally, to demonstrate how the EXPERT tool proposes exercise prescriptions in patients with CCS and/or CHF with different combinations of CVD risk factors, three patient cases with solutions are presented.

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.

Clinical evidence for high-risk medical devices used to manage diabetes: protocol for a systematic review and meta-analysis.

INTRODUCTION: Medical devices, including high-risk medical devices, have greatly contributed to recent improvements in the management of diabetes. However, the clinical evidence that is submitted for regulatory approval is not transparent, and thus a comprehensive summary of the evidence for high-risk devices approved for managing diabetes in Europe is lacking. In the framework of the Coordinating Research and Evidence for Medical Devices group, we will, therefore, perform a systematic review and meta-analysis, which will evaluate the efficacy, safety and usability of high-risk medical devices for the management of diabetes. METHOD AND ANALYSIS: This study has been reported according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. We will search Embase (Elsevier), Medline All (Ovid), Cochrane Library (Wiley), Science Citation Index Expanded and Emerging Sources Citation Index (Web of Science) to identify interventional and observational studies that evaluate the efficacy and/or safety and/or usability of high-risk medical devices for the management of diabetes. No language or publication dates' limits will be applied. Animal studies will be excluded. In accordance with the Medical Device Regulation in European Union, high-risk medical devices are those in classes IIb and III. The following medical devices for diabetes management are considered as having a high risk: implantable continuous glucose monitoring systems, implantable pumps and automated insulin delivery devices. Selection of studies, data extraction and quality of evidence assessment will be performed independently by two researchers. Sensitivity analysis will be performed to identify and explain potential heterogeneity. ETHICS AND DISSEMINATION: No ethical approval is needed for this systematic review, as it is based in already published data. Our findings will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022366871.

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.

BACKGROUND: Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. OBJECTIVE: This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. METHODS: In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. RESULTS: Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. CONCLUSIONS: Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

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.

Effects of Aerobic Exercise on Systemic Insulin Degludec Concentrations in People with Type 1 Diabetes.

BACKGROUND: There is conflicting evidence on the effect of exercise on systemic insulin concentrations in adults with type 1 diabetes. METHODS: This prospective single-arm study examined the effect of exercise on systemic insulin degludec (IDeg) concentrations. The study involved 15 male adults with type 1 diabetes (age 30.7 ± 8.0 years, HbA1c 6.9 ± 0.7%) on stable IDeg regimen. Blood samples were collected every 15 minutes at rest, during 60 minutes of cycling (66% VO2max) and until 90 minutes after exercise termination. IDeg concentrations were quantified using high-resolution mass-spectrometry and analyzed applying generalized estimation equations. RESULTS: Compared to baseline, systemic IDeg increased during exercise over time (P < .001), with the highest concentrations observed toward the end of the 60-minute exercise (17.9% and 17.6% above baseline after 45 minutes and 60 minutes, respectively). IDeg levels remained elevated until the end of the experiment (14% above baseline at 90 minutes after exercise termination, P < .001). CONCLUSIONS: A single bout of aerobic exercise increases systemic IDeg exposure in adults on a stable basal IDeg regimen. This finding may have important implications for future hypoglycemia mitigation strategies around physical exercise in IDeg-treated patients.

First-trimester glycosylated hemoglobin (HbA1c) and maternal characteristics in the prediction of gestational diabetes: An observational cohort study.

INTRODUCTION: This study aimed to investigate the extent to which gestational diabetes mellitus (GDM) can be predicted in the first trimester by combining a marker of growing interest, glycosylated hemoglobin A1c (HbA1c), and maternal characteristics. MATERIAL AND METHODS: This observational study was conducted in the outpatient obstetric department of our institution. The values of HbA1c and venous random plasma glucose were prospectively assessed in the first trimester of pregnancy. We determined maternal characteristics that were independent predictors from the regression analysis and calculated areas under the receiver-operating curves by combining the maternal age, body mass index, previous history of GDM, and first-degree family history for diabetes mellitus. Moreover we investigated the predictive capability of HbA1c to exclude GDM. Patients with a first-trimester HbA1c level of 6.5% (48 mmol/mol) or more were excluded. The study was registered at ClinicalTrials.gov ID: NCT02139254. RESULTS: We included 785 cases with complete dataset. The prevalence of GDM was 14.7% (115/785). Those who developed GDM had significantly higher HbA1c and random plasma glucose values (p < 0.0001 and p = 0.0002, respectively). In addition, they had a higher body mass index, were more likely to have a history of GDM and/or a first-degree family history of diabetes. When these maternal characteristics were combined with the first-trimester HbA1c and random plasma glucose the combined area under the receiver operating characteristics curve was 0.76 (95% CI 0.70-0.81). CONCLUSIONS: Our results indicate that HbA1c and random plasma glucose values combined with age, body mass index, and personal and family history, allow the identification of women in the first trimester who are at increased risk of developing GDM.

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.

Development of a Novel Home-Based Exergame With On-Body Feedback: Usability Study.

BACKGROUND: With more than 1.4 billion adults worldwide classified as physically inactive, physical inactivity is a public health crisis leading to an increased risk of cardiometabolic diseases. Motivating and engaging training strategies are needed to tackle this public health crisis. Studies have shown that exergames, games controlled by active body movements, are potentially usable, attractive, and effective tools for home-based training. The ExerCube (by Sphery Ltd) has been developed as a physically immersive and adaptive functional fitness game. The development of a home-based version of the ExerCube could increase accessibility, reduce barriers to exercise, and provide an attractive solution to improve physical and cognitive health. OBJECTIVE: The aim was threefold: (1) to develop a usable home-based exergame system, (2) to evaluate the usability and training experience of the home-based exergame and its early-stage on-body feedback system, and (3) to identify avenues for further user-centered design iterations of the system. METHODS: A total of 15 healthy participants (mean age 25, SD 3 years) completed 2 laboratory visits consisting of four 5-minute exergame sessions. In each session, the on-body feedback system provided a different feedback modality (auditory, haptic, and visual feedback) to the participant. Following the second visit, participants completed a range of assessments, including the System Usability Scale (SUS), the Physical Activity Enjoyment Scale (PACES), the Flow Short Scale (FSS), the Immersive Experience Questionnaire (IEQ), and a rating of perceived exertions (RPEs) both physically and cognitively. Participants answered questions regarding the on-body feedback system and completed a semistructured interview. RESULTS: Usability was rated as acceptable, with a SUS score of 70.5 (SD 12). The questionnaires revealed medium-to-high values for the training experience (FSS: 5.3, SD 1; PACES: 5.3, SD 1.1; IEQ: 4.7, SD 0.9. Physical (mean 4.8, SD 1.6) and cognitive (mean 3.9, SD 1.4) RPEs were moderate. Interviews about the on-body feedback system revealed that the majority of participants liked the haptic feedback and the combination of haptic and auditory feedback the best. Participants enjoyed the distinct perceptibility, processing, and integration of the exergame and its supportive and motivating effect. The visual feedback was perceived less positively by participants but was still classified as "potentially" helpful. The auditory feedback was rated well but highlighted an area for further improvement. Participants enjoyed the training experience and described it as motivating, interactive, immersive, something new, interesting, self-explanatory, as well as physically and cognitively challenging. Moreover, 67% (n=10) of the participants could imagine exercising at home and continuing to play the exergame in the future. CONCLUSIONS: The home-based exergame and its early-stage on-body feedback system were rated as usable and an enjoyable training experience by a young healthy population. Promising avenues emerged for future design iterations.

Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes.

Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. In this study, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of 5 years, the area under the receiver operating characteristic curve was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D.

Diabetes and Myocardial Fibrosis: A Systematic Review and Meta-Analysis.

OBJECTIVES: This systematic review and meta-analysis investigated the association of diabetes and glycemic control with myocardial fibrosis (MF). BACKGROUND: MF is associated with an increased risk of heart failure, coronary artery disease, arrhythmias, and death. Diabetes may influence the development of MF, but evidence is inconsistent. METHODS: The authors searched EMBASE, Medline Ovid, Cochrane CENTRAL, Web of Science, and Google Scholar for observational and interventional studies investigating the association of diabetes, glycemic control, and antidiabetic medication with MF assessed by histology and cardiac magnetic resonance (ie, extracellular volume fraction [ECV%] and T1 time). RESULTS: A total of 32 studies (88% exclusively on type 2 diabetes) involving 5,053 participants were included in the systematic review. Meta-analyses showed that diabetes was associated with a higher degree of MF assessed by histological collagen volume fraction (n = 6 studies; mean difference: 5.80; 95% CI: 2.00-9.59) and ECV% (13 studies; mean difference: 2.09; 95% CI: 0.92-3.27), but not by native or postcontrast T1 time. Higher glycosylated hemoglobin levels were associated with higher degrees of MF. CONCLUSIONS: Diabetes is associated with higher degree of MF assessed by histology and ECV% but not by T1 time. In patients with diabetes, worse glycemic control was associated with higher MF degrees. These findings mostly apply to type 2 diabetes and warrant further investigation into whether these associations are causal and which medications could attenuate MF in patients with diabetes.

Use and Perception of Telemedicine in People with Type 1 Diabetes During the COVID-19 Pandemic: A 1-Year Follow-Up.

Background: In the spring of 2020, our research group circulated a worldwide survey with the aim of gathering information on the use and perception of telemedicine in people living with type 1 diabetes at the start of the COVID-19 pandemic. The data suggested that a large number of respondents had rapidly adopted to telemedicine, as in-person visits were not possible, and that this was perceived positively by many. In this study, we conducted a 1-year follow-up to investigate changes in opinions and experiences to telemedicine over the past year of the pandemic. Methods: An anonymous questionnaire was distributed through social media (Twitter, Facebook, and Instagram) between May 9 and May 15, 2021, using an open-access web-based platform (SurveyMonkey.com). The survey was identical to that used in the original study, covering questions relating to the use and perception of telemedicine, diabetes treatment and control, and medical supplies during the COVID-19 pandemic. The questionnaire was available in English, Spanish, German, French, and Italian. We compared the results from the two surveys descriptively and statistically, results were stratified according to age, gender, and HbA1c. Results: There were 531 survey responses from 40 countries (Europe 54%, North America 36%, South America 2%, and Africa and Asia 2%). A large percentage of respondents (67%) reported meeting with their health care provider remotely since the beginning of the pandemic, a significant increase compared with the 28% in the 2020 survey (P < 0.001). Eighty-three percent of respondents found remote appointments to be somewhat-to-extremely useful, similar to the 86% satisfaction rate in the previous survey (P = 0.061). Remote appointments were most frequently undertaken through telephone (50%) and video call (45%), which are significant changes compared with those in 2020 (72% and 28%, respectively, P < 0.001). Forty-five percent of respondents in 2021 were likely to consider remote appointments instead of in-person appointments in the future-being significantly lower than the 75% in the initial survey (P < 0.001)-whereas 37% indicated they would not. The majority of respondents (84%) reported no issues in their access to diabetes supplies and medication over the past year. Conclusions: This study showed that the use of telemedicine in the form of remote appointments increased during the COVID-19 pandemic in people living with type 1 diabetes, with high levels of satisfaction. However, a remarkable decline took place in the past year in the proportion of patients stating a willingness to continue with remote appointments beyond the pandemic. It seems that a personalized approach is needed since a substantial proportion of respondents in this follow-up still indicated a preference for in-person diabetes care, hence the use of telemedicine should be considered on an individual basis.

Association of Diabetes With Atrial Fibrillation Phenotype and Cardiac and Neurological Comorbidities: Insights From the Swiss-AF Study.

Background Diabetes is a major risk factor for atrial fibrillation (AF). However, it remains unclear whether individual AF phenotype and related comorbidities differ between patients who have AF with and without diabetes. This study investigated the association of diabetes with AF phenotype and cardiac and neurological comorbidities in patients with documented AF. Methods and Results Participants in the multicenter Swiss-AF (Swiss Atrial Fibrillation) study with data on diabetes and AF phenotype were eligible. Primary outcomes were parameters of AF phenotype, including AF type, AF symptoms, and quality of life (assessed by the European Quality of Life-5 Dimensions Questionnaire [EQ-5D]). Secondary outcomes were cardiac (ie, history of hypertension, myocardial infarction, and heart failure) and neurological (ie, history of stroke and cognitive impairment) comorbidities. The cross-sectional association of diabetes with these outcomes was assessed using logistic and linear regression, adjusted for age, sex, and cardiovascular risk factors. We included 2411 patients with AF (27.4% women; median age, 73.6 years). Diabetes was not associated with nonparoxysmal AF (odds ratio [OR], 1.01; 95% CI, 0.81-1.27). Patients with diabetes less often perceived AF symptoms (OR, 0.74; 95% CI, 0.59-0.92) but had worse quality of life (ß=-4.54; 95% CI, -6.40 to -2.68) than those without diabetes. Patients with diabetes were more likely to have cardiac (hypertension [OR, 3.04; 95% CI, 2.19-4.22], myocardial infarction [OR, 1.55; 95% CI, 1.18-2.03], heart failure [OR, 1.99; 95% CI, 1.57-2.51]) and neurological (stroke [OR, 1.39, 95% CI, 1.03-1.87], cognitive impairment [OR, 1.75, 95% CI, 1.39-2.21]) comorbidities. Conclusions Patients who have AF with diabetes less often perceive AF symptoms but have worse quality of life and more cardiac and neurological comorbidities than those without diabetes. This raises the question of whether patients with diabetes should be systematically screened for silent AF. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT02105844.