The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. Graphical abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Wrist-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. Objective: The objective of this study was to validate the accuracy of both HR and EE for two common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. Methods: We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2TM, Garmin vívosmart® HR+) during dynamic activities. Over a two-day period, 20 healthy adults (age: 27.5 ± 6.0 yrs; BMI: 22.5 ± 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 a 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. Results: Fitbit 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% ± 16.7 while Fitbit estimated HR with a %RE of -4.7% ± 19.6 across all activities. The highest error was observed during high intensity intervals on bike (Fitbit: -11.4% ± 35.7; Garmin: -14.3% ± 20.5) and lowest error during high intensity intervals on treadmill (Fitbit: -1.7% ± 11.5; Garmin: -0.5% ± 9.4). Fitbit and Garmin EE estimates differed significantly with Garmin having less negative bias (Fitbit: -19.3% ± 29.9, Garmin: -1.6% ± 30.6, P<.001) across all activities, with both correlating poorly with indirect calorimetry measures. Conclusions: Two common wrist-worn devices 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.

OBJECTIVE: Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear.RESEARCH DESIGN AND METHODS: The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4 weeks. Participants self-reported study and nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data.RESULTS: A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 6.6 ± 0.8% [49 ± 8.7 mmol/mol]) assigned to structured aerobic (n = 162), interval (n = 165), or resistance (n = 170) exercise were analyzed. The mean (± SD) change in glucose during assigned exercise was −18 ± 39, −14 ± 32, and −9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70–180 mg/dL (3.9–10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001).CONCLUSIONS: Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time in range but may have slightly increased time below range.