BackgroundWrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise.ObjectiveThe objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise.MethodsWe assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias.ResultsFitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures.ConclusionsTwo common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

A recent development in portable biosensors allows rapid, accurate, and on-site detection of biomarkers, which helps to prevent disease spread by the control of sources. Less invasive sample collection is necessary to use portable biosensors in remote environments for accurate on-site diagnostics and testing. For non- or minimally invasive sampling, easily accessible body fluids, such as saliva, sweat, blood, or urine, have been utilized. It is also imperative to find accurate biomarkers to provide better clinical intervention and treatment at the onset of disease. At the same time, these reliable biomarkers can be utilized to monitor the progress of the disease. In this review, we summarize the most recent development of portable biosensors to detect various biomarkers accurately. In addition, we discuss ongoing issues and limitations of the existing systems and methods. Lastly, we present the key requirements of portable biosensors and discuss ideas for functional enhancements.

OBJECTIVE To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia (&lt;70 mg/dL) from the start of in-clinic aerobic exercise (45 min at 60% VO2max) to 4 h after. RESULTS DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0–4.2], SH 8.3% [0.0–12.5], P = 0.025). There was an increased time in hyperglycemia (&gt;180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70–180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study. CONCLUSIONS The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

Introduction: Glycemic control during exercise is challenging for people with type 1 diabetes. Better quantification of insulin dynamics at different exercise intensities and across insulin loading conditions is necessary for accurate insulin dosing during exercise. Methods: Twenty seven participants with type 1 diabetes were evaluated during 3 separate 10-hour fasting aerobic exercise studies with low, medium, and high levels of insulin infusion (100%, 150%, and 300% of basal rate) across two exercise arms: moderate (40-45% of VO2 max), and intense (60-65% of VO2 max) exercise. Glucose levels were clamped during a 3-hour run-in period, followed by 45 minutes of exercise on a treadmill. 6,6-2H2-dideuterated glucose was infused to match endogenous glucose production during the entire trial. Glucose tracer data were fitted to a two-compartment ODE model, solved via Bayesian inference. We intend to quantify insulin and non-insulin mediated disposal of glucose by observing the rate of disposal (Rd) across different rates of insulin infusion in each exercise arm. Results: Area under the curve (AUC) of Rd during 45 minutes of moderate exercise increased by 0.084 mmol/L for every percent increase in baseline insulin infusion rate (95% CI=0.064-0.104, p&lt;0.001). AUC during intense exercise increased by 0.109 mmol/L (95% CI= 0.079-0.139, p&lt;0.001). Mixed-effects model analysis showed no difference between exercise arms. Conclusion: Results indicate Rd is substantial during exercise but that the difference in glucose uptake during aerobic exercise at 40-45% VO2 max may be comparable with 60-65% VO2 max. Disclosure T.P. Nguyen: None. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. L.M. Wilson: None. D. Branigan: None. V. Gabo: None. B. Senf: None. F.H. Guillot: None. J. El Youssef: None. Funding National Institutes of Health