Dataset Information

Using Continuous Glucose Monitoring to Motivate Physical Activity in Overweight and Obese Adults: A Pilot Study.

ABSTRACT:

Background

Regular physical activity (PA) is associated with a lower risk of several types of cancers. However, two-thirds of overweight/obese adults are not sufficiently active; this, in combination with the unfavorable effect of excess body weight, puts them at a greater risk for cancer. One reason that these individuals do not engage in enough PA may be their lack of motivation to change their current behavior due to the perception of putting in effort for possible future gain without obvious short-term benefits. There is a need for innovative ways to help individuals recognize the immediate health benefits of PA and thus increase their motivation.

Methods

This pilot intervention tested a PA education module that included a one-on-one counseling session highlighting the acute effects of PA on glucose patterns, followed by a 10-day self-monitoring period with a continuous glucose monitor (CGM) and a Fitbit tracker. Participants rated the acceptability of the education module on a 5-point Likert scale and completed surveys assessing stages of change for motivational readiness.

Results

Nineteen overweight/obese adults (84% female) completed the study. Participants gave high ratings to the counseling session for improving their PA-related knowledge (mean = 4.22), increasing motivation (mean = 4.29), and providing personally relevant information (mean = 4.35). The summary acceptability scores for the self-monitoring period were 4.46 for CGM and 4.51 for Fitbit. Participants reported a significant decrease in the precontemplation stage and an increase in the action stage (P < 0.05).

Conclusions

CGM is a feasible tool for PA interventions.

Impact

Information from CGM could be used as biological-based feedback to motivate PA.See all articles in this CEBP Focus section, "Modernizing Population Science."

SUBMITTER: Liao Y

PROVIDER: S-EPMC7958438 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundClosed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.MethodsWe used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).ResultsModels trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).ConclusionsMachine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.