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This is a longitudinal study involving use of the January App which collects multiple data streams and employs machine learning techniques to offer personalized lifestyle recommendations and structured food and activity challenges.
The Sugar Challenge Study is a real-world, longitudinal study aimed at understanding the impact of lifestyle (i.e. food choices, physical activity and sleep patterns), genetics and personalized lifestyle recommendations on blood glucose levels, blood pressure, immune system status, stress hormone levels and microbiome composition. The purpose of this 10-day observational study was to primarily elucidate the impact of food choices, physical activity, and sleep patterns on an individual's blood glucose. In addition, we probed if continuous and personalized feedback to participants would improve glycemic control acutely through better decision making. Multiple investigations may be performed over an indefinite period of time to improve the understanding of these interconnected relationships. For example, we will perform studies to understand an individual's blood glucose response to an oral glucose load or mixed meal. We will also perform studies to determine the phenotype (clinical, metabolic, or immunologic) associated with a particular genotype. The data collected will be analyzed using proprietary machine learning methods.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Type 2 Diabetes | Individuals that have been previously diagnosed with Type 2 Diabetes. Those with Fasting Blood Glucose levels 126 mg/dL in two separate tests and/or HbA1C values greater than 6.5%. Individuals may take Metformin, SGLT2 inhibitors, of GLP-1 therapeutics. Not including those using Insulin therapeutics. |
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| Pre-Diabetes | Individuals that have been previously diagnosed with Pre-Diabetes. This group may include individuals with pre-diabetes that may be unaware of their condition. HbA1C values between 5.7% and 6.4%. |
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| Healthy | Individuals that have not been previously diagnosed with Metabolic Syndromes including Type 2 Diabetes, obesity, or increased levels of blood sugar. HbA1C values below 5.7%. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| January Mobile Application (Sugar AI) | Combination Product | Sugar AI is a mobile health and fitness app the allows participants to log and record health-related data through Continuous Glucose Monitors, Heart Rate Monitors, and manual food, water, and activity logging. Using machine learning data science techniques, the Sugar AI App integrates the data collected to personalize lifestyle recommendations and help individuals optimize their health and wellness. |
| Measure | Description | Time Frame |
|---|---|---|
| Assessment of percent of total time monitored that blood glucose is in range 70-180 mg/dL | The percentage of time an individual remains within a target blood glucose range through the measure of Continuous Glucose Monitor. Ranges defined by the American Diabetes Association (ADA). Current guidelines in range are to 70% of time within 70-180 mg/dL range. | 10 Continuous Days |
| Measure | Description | Time Frame |
|---|---|---|
| Assessment of change in the mean daily fasting glucose | 10 days | |
| Assessment of change in the mean daily postprandial glucose levels | 10 days | |
| Assessment of change in the mean daily total caloric intake |
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Inclusion Criteria:
Exclusion Criteria:
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Individuals that volunteer for study through web-based advertisement. Includes predefined sub-populations of healthy individuals, pre-diabetes, and Type 2 Diabetes.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| January, Inc. | Menlo Park | California | 94025 | United States |
To inform participant and primary care physician of abnormal CGM levels.
30 Days following 10 day participation
Reporting a notification of abnormal CGM data to participant. Primary care physician has access to CGM glucose values on request.
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D018149 | Glucose Intolerance |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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Stool samples optimized for Microbiome and Functional Microbial studies. Blood, urine, saliva and/or buccal swabs for potential laboratory testing.
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| 10 days |
| Assessment of change in the daily number of meals consumed | 10 days |
| Assessment of micro and macronutrients of meals consumed through manual food logging. | Participants will log foods using a proprietary food database consisting of 18M+ items | 10 days |
| Assessment of change in the daily caloric composition of meals consumed through manual food logging. | Composition will be calculated based on a cross reference of the logged meal with a proprietary food database to yield caloric contributions of each food item. | 10 days |
| Assessment of change in physical activity as measured by heart rate monitor coupled to participant logs. | 10 days |
| Assessment of change in sleep quality and pattern as measured by heart rate monitor coupled to participant logs. | 10 days |
| D004700 | Endocrine System Diseases |
| D006943 | Hyperglycemia |