What are some example uses of Continuous Glucose Monitors in research studies?
During the 2023 MeTRIC Symposium, some presenters and attendees discussed examples of how they are using CGMs in studies that involve other mobile data and/or mental health measures. One such example was using CGM data to validate food diary entries - for example, if participants reported eating 15g of carbohydrates but the CGM showed a huge spike in glucose, indicating consumption of far more than 15g. What other novel things are researchers doing with CGMs (and even insulin pumps) in their studies? How is CGM data being integrated with other mobile data such as HR or sleep data?
And related to this - is there a list of U-M studies that are focused on mental health and are utilizing CGMs?
Answers (4)
Not a particularly novel use of the CGM technology as a whole, but a population that's not typically involved in research like this. I worked on a pilot using both Dexcom and Libres with individuals with spinal cord injuries. Data analysis is ongoing.
Other examples from ongoing research at University of Michigan include:
- Determining the validity of a food logs based on glucose values following the reported food diary entries. For example, does the CGM graph show a much higher raise in glucose than expected for the amount and type of carbs entered in the food diary?
- Glucose variability and basal temperature changes as predictors for episodes of depression.
- Relationship between stress, heart rate variation, glucose variation, and inflammation.
- Exercise effects on glucose TIR in pediatric populations.
- Relationship between playing video games, glucose control, HRV, and mood.
Areas of research that might benefit from CGMs:
- Effectiveness of different treatment modalities, AIDs (pumps), etc. on rare types of diabetes, such as monogenic types (including MODY) and rare types identified by the RADIANT study.
- Relationship between glucose variability and mood.
- Relationship between stress, glucose and inflammation.
- Drug-free diabetes interventions (e.g. lifestyle medicine).
- Relationship between sleep and glucose.
- Long-term effects of hypoglycemia on different organs and anatomical systems.
- Lifestyle interventions adherence.
- Relationship between manic/depressive episodes and glucose variability, heart rate variability, and stress.
- AI-generated exercise plans and their effect on glucose.
- Relationship between glucose control and genetic mutations that affect metabolism (e.g. small chain fatty acid dysfunction, ACADS mutations, PMMA mutations).
- Validation of MARD values for different brands of CGMs, and in different combinations with AID systems.
- Glucose estimation from off-the-shelf, consumer-grade wearables, or the potential for wearable sensors to approximate EGVs from CGMs.
(The above are just ideas... there may be ongoing research on these areas elsewhere).
Another one:
Continuous Glucose Monitoring With Low-Carbohydrate Diet Coaching in Adults With Prediabetes: Mixed Methods Pilot Study
https://diabetes.jmir.org/2020/4/e21551/
"Type 2 diabetes mellitus (T2DM) is preventable; however, few patients with prediabetes participate in prevention programs. The use of user-friendly continuous glucose monitors (CGMs) with low-carbohydrate diet coaching is a novel strategy to prevent T2DM."
I found another interesting use case in Psychology:
DIGIT-MI: Continuous Measures of Diet, Sleep & Physical Activity in Disease-Specific Research
https://depressioncenter.org/news-events/events/digit-mi-continuous-measures-diet-sleep-physical-activity-disease-specific
Continuous Measures of Diet, Sleep & Physical Activity in Disease-Specific Research
In this DIGIT-MI session, Ashley Gearhardt, Ph.D., will discuss the integration of continuous measures of blood glucose alongside Fitbit measures of sleep and physical activity in a study on depression. She will discuss the challenges and benefits of integrating these measures to inform disease-specific research, such as the clinical care of depression.