scholarly journals Comparison of Physical Activity Sensors and Heart Rate Monitoring for Real-Time Activity Detection in Type 1 Diabetes and Control Subjects

2013 ◽  
Vol 15 (9) ◽  
pp. 751-757 ◽  
Author(s):  
Chinmay Manohar ◽  
Derek T. O'Keeffe ◽  
Ling Hinshaw ◽  
Ravi Lingineni ◽  
Shelly K. McCrady-Spitzer ◽  
...  
2007 ◽  
Vol 24 (6) ◽  
pp. 741-747 ◽  
Author(s):  
Su-Ru Chen ◽  
Yann-Jinn Lee ◽  
Hung-Wen Chiu ◽  
Chii Jeng

2019 ◽  
Vol 13 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Nicole Hobbs ◽  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Kamuran Turksoy ◽  
Marc Breton ◽  
...  

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.


2020 ◽  
Author(s):  
Zoë A. Marshall ◽  
Kelly A. Mackintosh ◽  
Michael J. Lewis ◽  
Elizabeth A. Ellins ◽  
Melitta A. McNarry

Author(s):  
Deborah M. Telford ◽  
Dana M. Signal ◽  
Paul L. Hofman ◽  
Silmara Gusso

Physical activity (PA) is an important part of lifestyle management for adolescents with Type 1 diabetes (T1D). Opportunities for PA were reduced by COVID-19 restrictions. Therefore, the purpose of this cross-sectional study was to compare PA among adolescents with and without T1D during the first New Zealand (NZ) COVID-19 lockdown. PA levels of adolescents aged 11–18 years with T1D (n = 33) and healthy controls (n = 34) were assessed through self-reported and parent proxy-reported questionnaires. Overall, PA levels during lockdown were below recommended levels. PA levels did not differ between T1D and control participants (p = 0.212) nor between genders (p = 0.149). Younger adolescents tended to be more active than older adolescents (p = 0.079). PA level was negatively associated with BMI z-score (r = −0.29, p = 0.026) but was not associated with socioeconomic status (SES) or T1D-related parameters. In the T1D group, higher HbA1c was associated with lower school decile (r = −0.58, p < 0.001) and higher BMI z-score (r = 0.68, p < 0.001). Overall, young people were insufficiently active during lockdown, and some sub-groups were more affected than others by the restrictions. Pandemics are likely to be part of our future, and further studies are needed to understand their impact on the health and wellbeing of adolescents.


Sign in / Sign up

Export Citation Format

Share Document