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| ID | Type | Description | Link |
|---|---|---|---|
| 1R01DK122583-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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Automated Insulin Delivery (AID) systems have now become an important standard-of-care for people with T1D and have demonstrated a reduction, but not elimination, of hypoglycemia during long-term studies. One limitation of current AID systems is that they have no knowledge about the context or environment that a person is currently experiencing. Contextual patterns can potentially improve the performance of an AID by recognizing environments or patterns of living that are related to changes in glucose. The team at OHSU is developing a context-aware glucose prediction algorithm that will capture context data from the patient both indoors and outdoors. This context data will be provided to the algorithm to allow for detecting contextual patterns that might relate to high or low glucose. The goal of this study will be the creation of a data set that will include contextual patterns along with glucose, insulin and physiological data.
Subjects will be on study for 28 days. Sensor glucose, activity, exercise, insulin, indoor and outdoor contextual patterns and meal data will be collected during this time. Subjects will wear the Dexcom G6 CGM system and a physical activity monitor for the entire 28 days. Subjects will continue to use their own insulin pump. Subjects will be asked to also wear a MotioWear indoor/outdoor context-aware tracking tag and to install the MotioWear beacons within their home. Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home during weeks 1 and 2 and once during weeks 3 and 4. Subjects will also ingest a self-selected meal prior to these prescribed exercise sessions. Subjects will eat a high carbohydrate dinner once each week on the same day at the same approximate time of day (but not on the exercise days).
Subjects will use the T1 DEXI mobile app created by OHSU to capture meal and exercise data along with photos of meals the day of exercise and the day after. While at home, subjects will check CBG before and after exercise, for symptoms of hypoglycemia, and for Dexcom G6 alarms for sensor <70 mg/dL and >250 mg/dL.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Aerobic exercise | Experimental |
| |
| Resistance exercise | Experimental |
| |
| High intensity interval exercise | Experimental |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Exercise | Other | Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of the Mean Absolute Relative Difference When Including Patterns in Hypoglycemia Prediction. | We used our recently published long short term memory neural network (LSTM) to predict sensor glucose 30-minutes in advance across the entire 4-week study duration when glucose was < 70 mg/dL. We then used the context-aware pattern recognition algorithm to predict when hypoglycemia would occur 30-minutes in the future, and if hypoglycemia was predicted, we included a bias correction that is specific to the hypogylcemia region of glucose measurements. The outcome measure shows the reduction of MARD when the LSTM is corrected using the pattern-based bias correction algorithm. MARD is calculated by subtracting the new sensor glucose - reference value dividing by the reference value. A negative value means that the MARD was reduced. The LSTM is being compared with Dexcom G6 CGM values to determine the MARD. Physiologically relevant thresholds are less than 55 mg/dl, less than 70 mg/dl, above 180 mg/dl and above 250 mg/dl. The Dexcom G6 target range is 70-180 mg/dl. | 28 days |
| Comparison of the Mean Relative Difference When Including Patterns in Hypoglycemia Prediction. | We used our recently published long short term memory neural network (LSTM) to predict glucose 30-minutes in advance across the entire 4-week study duration when glucose was < 70 mg/dL. We then used the context-aware pattern recognition algorithm to predict when hypoglycemia would occur 30-minutes in the future, and if hypoglycemia was predicted, we included a bias correction that is specific to the hypogylcemia region of glucose measurements. The outcome measure shows the reduction of mean relative difference (MRD) when the LSTM is corrected using the pattern-based bias correction algorithm. MARD is calculated by subtracting the new sensor glucose - reference value dividing by the reference value. A negative value means that the MARD was reduced. The LSTM is being compared with Dexcom G6 CGM values to determine the MARD. Physiologically relevant thresholds are less than 55 mg/dl, less than 70 mg/dl, above 180 mg/dl and above 250 mg/dl. The Dexcom G6 target range is 70-180 mg/dl. | 28 days |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Jessica Castle, MD | Oregon Health and Science University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Oregon Health and Science University | Portland | Oregon | 97239 | United States |
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| ID | Title | Description |
|---|---|---|
| FG000 | Aerobic Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
| FG001 | Resistance Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
| FG002 | High Intensity Interval Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Study Start-up Visit |
| |||||||||||||
| At Home Period (28 Days) |
| |||||||||||||
| Study Close-out Visit |
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| ID | Title | Description |
|---|---|---|
| BG000 | Aerobic Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
| BG001 | Resistance Exercise |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Comparison of the Mean Absolute Relative Difference When Including Patterns in Hypoglycemia Prediction. | We used our recently published long short term memory neural network (LSTM) to predict sensor glucose 30-minutes in advance across the entire 4-week study duration when glucose was < 70 mg/dL. We then used the context-aware pattern recognition algorithm to predict when hypoglycemia would occur 30-minutes in the future, and if hypoglycemia was predicted, we included a bias correction that is specific to the hypogylcemia region of glucose measurements. The outcome measure shows the reduction of MARD when the LSTM is corrected using the pattern-based bias correction algorithm. MARD is calculated by subtracting the new sensor glucose - reference value dividing by the reference value. A negative value means that the MARD was reduced. The LSTM is being compared with Dexcom G6 CGM values to determine the MARD. Physiologically relevant thresholds are less than 55 mg/dl, less than 70 mg/dl, above 180 mg/dl and above 250 mg/dl. The Dexcom G6 target range is 70-180 mg/dl. | Each arm had participants that completed the study but had incomplete insulin data which prevented inclusion in the analysis of outcome measures: 5 from aerobic arm, 4 from resistance arm, 3 from HIIT arm. | Posted | Mean | Standard Deviation | percent difference of MARD | 28 days |
28 days
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Aerobic Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
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| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| urinary tract infection | Infections and infestations | Non-systematic Assessment |
Technical problems with pump downloads after the study lead to unreliable or uninterpretable data.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Jessica Castle | Oregon Health and Science University | 1503-494-7072 | castleje@ohsu.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP_ICF | Yes | Yes | Yes | Study Protocol, Statistical Analysis Plan, and Informed Consent Form | Jun 11, 2021 | Feb 28, 2023 | Prot_SAP_ICF_000.pdf |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D015444 | Exercise |
| ID | Term |
|---|---|
| D009043 | Motor Activity |
| D009068 | Movement |
| D009142 | Musculoskeletal Physiological Phenomena |
| D055687 | Musculoskeletal and Neural Physiological Phenomena |
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| NOT COMPLETED |
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| NOT COMPLETED |
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Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home.
| BG002 | High Intensity Interval Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. |
| BG003 | Total | Total of all reporting groups |
| Participants |
|
| Age, Continuous | Mean | Standard Deviation | years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
|
|
|
| Primary | Comparison of the Mean Relative Difference When Including Patterns in Hypoglycemia Prediction. | We used our recently published long short term memory neural network (LSTM) to predict glucose 30-minutes in advance across the entire 4-week study duration when glucose was < 70 mg/dL. We then used the context-aware pattern recognition algorithm to predict when hypoglycemia would occur 30-minutes in the future, and if hypoglycemia was predicted, we included a bias correction that is specific to the hypogylcemia region of glucose measurements. The outcome measure shows the reduction of mean relative difference (MRD) when the LSTM is corrected using the pattern-based bias correction algorithm. MARD is calculated by subtracting the new sensor glucose - reference value dividing by the reference value. A negative value means that the MARD was reduced. The LSTM is being compared with Dexcom G6 CGM values to determine the MARD. Physiologically relevant thresholds are less than 55 mg/dl, less than 70 mg/dl, above 180 mg/dl and above 250 mg/dl. The Dexcom G6 target range is 70-180 mg/dl. | Posted | Mean | Standard Deviation | percent difference of MRD | 28 days |
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|
|
| 0 |
| 10 |
| 0 |
| 10 |
| 3 |
| 10 |
| EG001 | Resistance Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. | 0 | 10 | 0 | 10 | 1 | 10 |
| EG002 | High Intensity Interval Exercise | Exercise: Subjects will be randomized to complete either aerobic, high intensity interval training, or resistance exercise videos twice weekly at home. | 0 | 10 | 0 | 10 | 3 | 10 |
| upper respiratory infection | Infections and infestations | Non-systematic Assessment |
|
| rash at sensor site | Skin and subcutaneous tissue disorders | Non-systematic Assessment |
|
| Knee pain | Musculoskeletal and connective tissue disorders | Non-systematic Assessment |
|
| rash from antibiotics | Skin and subcutaneous tissue disorders | Non-systematic Assessment |
|
| Hives | Skin and subcutaneous tissue disorders | Non-systematic Assessment | unknown origin |
|
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| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |