Impact of Daily Physical Activity as Measured by Commonly Available Wearables on Mealtime Glucose Control in Type 1 Diabetes

Abstract

Objective: In contrast with exercise, or structured physical activity (PA), glycemic disturbances due to daily unstructured PA in patients with type 1 diabetes (T1D) is largely underresearched, with limited information on treatment recommendations. We present results from retrospective analysis of data collected under patients' free-living conditions that illuminate the association between PA, as measured by an off-the-shelf activity tracker, and postprandial blood glucose control. Research Design and Methods: Data from 37 patients with T1D during two clinical studies with identical data collection protocols were analyzed retrospectively: 4 weeks of continuous glucose monitoring, carbohydrate intake, insulin injections, and PA (assessed through wearable activity tracker) were collected in free-living conditions. Five-hour glucose area under curves (GAUCs) following the last-bolused meal of every day were computed to assess postprandial glucose excursions, and their relation with corresponding antecedent PA was analyzed using linear mixed-effects regression models, accounting for meal, insulin, and current glycemic state. Results: Datasets yielded 845 days of data from 37 subjects (22.8 -11.6 days/subject); postmeal GAUC was negatively associated with total daily PA measured by step count (P = 0.025), and total time spent performing higher than light-intensity PA (P = 0.042). Patients with higher median total daily PA exhibited lower average postprandial GAUC (P < 0.01). Additional analyses indicated that daily PA likely presents an immediate and delayed impact on glucose control. Conclusion: Daily PA assessed by commonly available sensors is significantly associated with glycemic exposure after an evening meal, indicating that quantitative assessment of PA may be useful in mealtime treatment decisions.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

“…It was able to detect COVID-19 in the pre-symptomatic period as well as the symptomatic phase of the patients, with a precision score of 0.91 (CI: 0.854–0.967) [ 10 ]. Cho et al proposed a one-class SVM method that can detect COVID-19 23.5–40% earlier compared to the method of Mishra et al [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ,…”

“…Further, assessing PA quantitatively may show to be useful in making mealtime treatment decisions. It was also observed that participating in PA every day demonstrated an immediate or later impact on glucose control [ 30 ]. Akyol et al reported a novel consumer-wearable device called Diafit that works as a customizable glucose monitor for diabetes patients [ 40 ].…”

Diabetes Technology Meeting 2020

“…182 Commercially available wearable trackers have been utilized as surrogates for exercise in some AID devices with some success. 183 Extreme exercise or exercise with inadequate insulin levels can cause ketosis. 184 A continuous ketone sensor is being developed by PercuSense 185 with clinical studies likely beginning in 2021.…”

Enhancing self-management in type 1 diabetes with wearables and deep learning

“…Smartphone apps to log daily events 10 , 11 and calculate bolus insulin are increasingly being adopted to successfully reduce the daily burden associated with T1D self-management. Other wearables, such as wristbands, have been used in recent literature to estimate physical activity for T1D subjects 12 , 13 . Nonetheless, the clinical efficacy of apps and sensor wristbands remains unproven 14 , and there is a lack of an integrated platform that synchronizes the real-time physiological measurements of sensor wristbands and other wearable devices to improve decision support 14 , 15 .…”