Additional Signals for Exercise, Stress and Sleep and Prediction of Glucose Levels for AP Systems

NCT04725799 | Completed DMC

• 1 other identifier: 2017-1016
• Study Type: observational
• Target: 23
• Locations: 1 country
• Sites: 1

Brief Summary

The objective of this research is to determine the most informative variables for detecting exercise, acute stress and sleep, identify select sensors that report these variables, and develop the algorithms to detect the occurrence of exercise, stress and sleep, to discriminate them and to determine their characteristics. Research is needed to identify which wearable devices report the most informative and predictive variables of exercise, acute stress and sleep with desired precision and accuracy, determine the best location to wear them for collecting reliable and informative data, and to distill accurate knowledge from data reported by wearable sensors. Data and their interpretation should be informative for various types of physical activities, stages of sleep, and types and intensities of acute stress, and concurrent occurrence of these factors. The investigators will use several devices (chest band, wristband and skin patches) to collect data and evaluate their information content and contribution to improvement of glucose concentration prediction, best locations for collecting accurate and reliable information by conducting clinical and free-living experiments at-home to assess the contributions of the wearable device in improving the accuracy of glucose concentration prediction and the performance of the multivariable artificial pancreas.

Conditions

Type1 Diabetes Mellitus

Keywords

Exercise; Meals; Sleep; Stress; Artificial Pancreas

Outcome Measures

Primary Outcomes (1)

• Measurement of Glucose Concentration

  • Glucose concentration (mg/dl) will be measured by: Continuous Glucose Monitor (DEXCOM \[US G5 PLATINUM/G6\]

  3.5 years

Study Arms (1)

MESS with and without stressful stimuli

The subjects participate in meal, exercise, sleep activities alone or in combination with stressful stimuli.

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Men and Women with Type 1 Diabetes

You may qualify if:

• Men and Women with T1DM
• years of age
• Insulin pump user

You may not qualify if:

• Metabolic instability as evidenced by hospitalizations for diabetes or other diabetes-related complications (e.g., diabetic ketoacidosis and hypoglycemic seizures) within the preceding three months;
• Severe macrovascular disease, as evidenced by severe peripheral artery disease; history of myocardial infarction, heart failure, thromboembolic disease, or unstable angina; uncontrolled hypertension; abnormal resting EKG;
• Maximal exercise stress test with significant brady/tachy arrhythmia, ectopic beats, bundle branch block, or signs of acute ischemia;
• Severe microvascular disease as evidenced by history of vision-threatening proliferative or non-proliferative retinal disease; kidney disease;
• Any uncontrolled non-musculoskeletal condition that would limit the subject's ability to participate in the exercise program (e.g., chronic obstructive airways disease);
• Musculoskeletal conditions such as neurological or orthopedic conditions affecting lower limb strength and mobility (e.g., stroke; insensitive foot);
• Pregnancy;
• Documented medical condition or physical impairment that is judged by the health care practitioner to contraindicate exercise.

Sponsors & Collaborators

• University of Illinois at Chicago: lead
• Illinois Institute of Technology: collaborator
• University of Chicago: collaborator

Study Sites (1)

University of Illinois at Chicago

Chicago, Illinois, 60612, United States

Location

Related Publications (5)

• Turksoy K, Samadi S, Feng J, Littlejohn E, Quinn L, Cinar A. Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System. IEEE J Biomed Health Inform. 2016 Jan;20(1):47-54. doi: 10.1109/JBHI.2015.2446413. Epub 2015 Jun 16.

  PMID: 26087510 BACKGROUND

• Eren-Oruklu M, Cinar A, Rollins DK, Quinn L. Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms. Automatica (Oxf). 2012 Aug;48(8):1892-1897. doi: 10.1016/j.automatica.2012.05.076. Epub 2012 Jun 22.

  PMID: 22865931 BACKGROUND

• Turksoy K, Bayrak ES, Quinn L, Littlejohn E, Rollins D, Cinar A. Hypoglycemia Early Alarm Systems Based On Multivariable Models. Ind Eng Chem Res. 2013 Sep 4;52(35):12329-36. doi: 10.1021/ie3034015.

  PMID: 24187436 BACKGROUND

• Bayrak ES, Turksoy K, Cinar A, Quinn L, Littlejohn E, Rollins D. Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models. J Diabetes Sci Technol. 2013 Jan 1;7(1):206-14. doi: 10.1177/193229681300700126.

  PMID: 23439179 BACKGROUND

• Turksoy K, Roy A, Cinar A. Real-Time Model-Based Fault Detection of Continuous Glucose Sensor Measurements. IEEE Trans Biomed Eng. 2017 Jul;64(7):1437-1445. doi: 10.1109/TBME.2016.2535412. Epub 2016 Feb 25.

  PMID: 26930674 BACKGROUND

MeSH Terms

Conditions

Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Officials

• Ali Cinar, PhD

  Illinois Institute of Technoloy

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: May 30, 2018
• First Posted: January 27, 2021
• Study Start: May 24, 2018
• Primary Completion: July 13, 2021
• Study Completion: July 13, 2021
• Last Updated: April 1, 2022

Record last verified: 2022-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)