Additional Signals for Exercise, Stress and Sleep and Prediction of Glucose Levels for AP Systems
Condition: Type1 Diabetes Mellitus
MeSH terms: Diabetes Mellitus, Type 1
Study type: Observational

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.
    

Detailed description: 
      The focus of the proposed work is to determine the most informative variables for meals,
      exercise, acute stress and sleep (MESS), identify select sensors that report these variables,
      and develop the algorithms to detect the occurrence of MESS, to discriminate them and to
      determine their characteristics. Research is needed to identify which wearable devices report
      the most informative and predictive variables of MESS 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 MESS factors.

      In the first year of the research, the investigators will use several devices to collect data
      and evaluate their information content and contribution to improvement of serum glucose
      prediction, best locations for collecting accurate and reliable information, and their
      acceptance by users. In the second year of the project, we will select a single wearable
      device, conduct additional clinical and free-living experiments at-home to assess the
      contributions of the information from the wearable device selected in improving the accuracy
      of glucose prediction and the performance of the multivariable AP.

      The objectives of the proposed research are to determine the most informative variables and
      sensor locations to capture reliable and accurate information that complement glucose
      concentration measurements (CGM) and to develop algorithms to determine the presence of
      exercise, stress or sleep at any given time for estimating glucose concentrations and making
      control decisions by a multivariable artificial pancreas system. The proposed research will
      be conducted by achieving the following specific aims:

      To systematically perturb the MESS factors within standardized experiments in the laboratory
      and at home for analyzing their effects and interactions on glucose concentrations; to
      identify the most informative and reliable measurement approaches (i.e. types of wearable
      sensors and locations on body) for discriminating MESS factors; to develop quantitative
      relations that identify the type and features of specific MESS activities, to develop
      algorithms to detect the presence of specific MESS activities, identify them, and predict
      glucose concentrations with recursive models in real time (for future integration with
      adaptive AP control systems); and to evaluate the performance of the multivariable models
      with data from wearable devices and CGM in closed-loop control with AP during exploratory
      clinical experiments.

      The study will take place over a period of 3-4 weeks for a total of 6 study visits. The
      subjects will be continuously monitored using CGM (DEXCOM [US G5 PLATINUM]); 3 activity of
      the following activity monitor(s); (Empatica E4, BiostampRC™, Equivital™ Life Monitor) and
      Actigraph throughout the night. In addition, the subjects will use a sleep monitor (Zmachine®
      Insight & Insight+ Model DT-200 and Zephyr Bioharness) throughout the 3-week period. Subjects
      will come in for a study visit 3 times during the first week during which the investigators
      will measure the subjects' oxygen capacity and muscular strength during a peak exercise
      stress test (peak V02) and a test of maximal muscle strength (1-RM), respectively. The
      results of this testing will establish a baseline from which we can calculate the intensity
      of submaximal aerobic and resistance exercise bouts. The next 3 study visits will occur over
      the following 3-4 weeks. During this time the subjects will perform activities that produce
      physiological and psychological stress alone, in combination or proximal to each other. Women
      will have a urine pregnancy test before each day of data collection. In addition, the
      subjects will perform a number of physical activities at home that will be scheduled with the
      research assistant. The research assistant will telephone the subjects periodically and have
      the subjects perform stressful activities, such as mental challenges.
    

Observational model: Cohort
Time perspective: Cross-Sectional
Sampling method: Non-Probability Sample
Study population: 
        Men and Women with Type 1 Diabetes
      
Keywords: Exercise; Meals; Sleep; Stress; Artificial Pancreas