This study explored the feasibility of using continuous glucose monitoring among non-diabetes individuals to provide personalized and actionable eating-related feedback. It was presented at the 2017 Society of Behavioral Medicine Annual Meeting at San Diego, California.

1. Yue Liao, MPH, PhD; Liang Li, PhD; Mingxin Chen, MS;
Naifa Busaidy, MD; Karen Basen-Engquist, PhD, MPH;
Jimi Huh, PhD; Susan Schembre, PhD, RD
Using Mobile Glucose Monitoring
as Biological Feedback on Daily
Eating Behaviors

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Feedback in Health Behavioral Change
Goal setting, self-monitoring, and feedback are some of the popular
strategies for promoting health behavior change
• Physical activity
• Eating behavior
Advancement in wearable sensor technologies has automated the
self-monitoring and feedback process
• Provides opportunity to influence behavior change by informing decisions
at critical moments
• Relatively few work has been done in eating behaviors partly due to the
lack of objective measures

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Using Blood Glucose Dynamics to Provide
Personalized Actionable Feedback
Blood glucose as a biomarker of food intake
• Increase in blood glucose results from absorption of carbohydrates – a
major macronutrient in our diet (U.S. Department of Agriculture, 2015)
Blood glucose data to provide eating-related feedback
• Blood glucose represents short-term energy status (Campfield & Smith, 2003)
• Use pre-meal glucose value to indicate whether there is a biological need to eat
• Elevated blood glucose (hyperglycemia) is a risk factor for cardiovascular
disease, type 2 diabetes, and several cancers (Liu et al., 2000; Barclay et al., 2008)
• Use post-meal glucose data as a direct measurement of dietary glycemic quality
and personal glycemic response to foods

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Aims of Current Study
Examine the feasibility of collecting continuous glucose monitoring
(CGM) data among healthy individuals in free-living
Explore the utility of using CGM data to provide actionable eating-
related feedback

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Continuous Glucose Monitoring (CGM) Devices
CGM measures blood glucose
concentration in the interstitial fluid in
real-time
A tiny sensor is inserted under the skin
Glucose data is collected every 5
minutes for up to 7 days
Calibration (finger pricks) is needed
every 12 hours

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Project SENSE
A 7-day free-living observational study
Participants are required to
• wear the CGM and record eating events using
the receiver
• log all foods and beverages consumed using the
MyFitnessPal app
• take a time-stamped photo of all foods and
beverages using their mobile phones
• log all exercises using the receiver,
MyFitnessPal app, and wear an accelerometer
during waking hours
First 2 days are “run-in” period and data are not
intended to use for analyses

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Sample Collected Data in One Day
Sunday, June 19, 2016
10:55 AM
Oatmeal, milk, coffee (GL=39)
1:51 PM
Turkey sandwich, lima beans,
Cheetos (GL=35)
6:55 PM
Rice, chicken, squash,
zucchini, beer (GL=35)
8:22 PM
Ice cream (GL=8)
8:56 PM
Popcorn (GL=20)
9:45 PM
Beer (GL=40)
GL=Glycemic load, estimated from Nutrition Data System for Research (NDSR)
based on food record from MyFitnessPal.

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Participants
A total of 24 participants finished data collection
• Age ranged from 24 to 64 years old (M = 34.75, SD = 11.30)
• 75% female
• 33.3% normal weight, 45.8% overweight, and 20.8% obese

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Blood Glucose as Short-term Energy Status
Blood glucose < fasting level indicates a biological need to eat
Two ways to define fasting glucose level (Schembre et al., 2016)
• A universal cut-off point of 85 mg/dL
• Personalized value based on participants’ calibration value in the morning
• Mean = 93.0, SD = 11.98

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Results from CGM
Participants on average reported 29 (SD = 11.8) eating events
across the 5 analytical days
• 62.0% of those eating events occurred when glucose reading > 85 mg/dL
• 43.5% occurred when glucose reading > personalized fasting level

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Actionable Feedback for Weight Control
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent of Eating Events Occurred above 85 mg/dL by
Weight Status
Normal Weight
Overweight/Obese
t = -.17, df = 22, p = .041

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Blood Glucose as Feedback of Dietary Glycemic
Quality
Post-meal glucose level > 140 mg/dL is considered as
hyperglycemia for non-diabetics (Gerich, 2003)

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Personalized Feedback of Glycemic Response
On average, participants had 3 (SD = 4.4) hyperglycemia events
across the 5 analytical days
• Older participants tend to have more frequent hyperglycemia events (r =
.469, p = .024)
Create personalized dietary recommendation to avoid spikes in
blood glucose

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Participants’ Experience about CGM
100% of participants agreed that the CGM device “was easy to use”
87.5% of participants agreed that the CGM sensor “was
comfortable to wear”
63.6% of participants indicated that they would be willing to use a
CGM device to help them achieve their health and wellness goals
• 91% indicated in doing so if the CGM sensor becomes non-invasive

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CGM Sensor CGM Receiver MyFitnessPal Accelerometer Food Picture
“Usability: This tool is easy to use and user friendly.”
Strongly Disagree (1) Disagree (2) Neither agree nor disagree (3) Agree (4) Strongly Agree (5)

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CGM Sensor CGM Receiver MyFitnessPal Accelerometer Food Picture
“Convenience: This tool is convenient for me to use in
my everyday life.”
Strongly Disagree (1) Disagree (2) Neither agree nor disagree (3) Agree (4) Strongly Agree (5)

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Future Directions
Just-in-time/proactive adaptive interventions
• Weight control
• Personalized diet recommendation
• Diabetics decision support (diet and physical activity)
Automation of food intake
• Machine learning
• Accelerometry for eating!

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Acknowledgements
MD Anderson Cancer Center Institutional Research Grant (2015-
0219)
Center for Energy Balance in Cancer Prevention and Survivorship,
Duncan Family Institute, MD Anderson Cancer Center
Bionutrition Research Core, MD Anderson Cancer Center
Janice Davis Gordon Memorial Postdoctoral Fellowship, MD
Anderson Cancer Center
NIH P30CA016672 (PROSPR Shared Resource)
NSF Innovation Corps (I-Corps™)