2134/33352
Andrew Kingsnorth
Andrew
Kingsnorth
Maxine E. Whelan
Maxine E.
Whelan
James Sanders
James
Sanders
Lauren Sherar
Lauren
Sherar
Dale Esliger
Dale
Esliger
Using digital health technologies to understand the association between movement behaviors and interstitial glucose: Exploratory analysis
Loughborough University
2018
Accelerometry
Glucose
Physical activity
Physiological monitoring
Sedentary time
Medical and Health Sciences not elsewhere classified
2018-06-08 12:36:48
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Using_digital_health_technologies_to_understand_the_association_between_movement_behaviors_and_interstitial_glucose_Exploratory_analysis/9618746
© Andrew P Kingsnorth, Maxine E Whelan, James P Sanders, Lauren B Sherar, Dale W Esliger. Background: Acute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings. Objective: The objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults. Methods: This study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1] ) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations. Results: After adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P < .001), SD of glucose (mmol/L; beta=0.0006; 95% CI 0.00037-0.00081; P < .001), and MAGE (mmol/L; beta=0.002; 95% CI 0.00131-0.00273; P < .001) for those of a lower fitness. Additionally, light activity was inversely associated with mean glucose (mmol/L; beta=−0.0004; 95% CI −0.00078 to −0.00006; P=.02), SD of glucose (mmol/L; beta=−0.0006; 95% CI −0.00085 to −0.00039; P < .001), and MAGE (mmol/L; beta=−0.002; 95% CI −0.00285 to −0.00146; P < .001) for those of a lower fitness. Moderate to vigorous physical activity was only inversely associated with mean glucose (mmol/L; beta=−0.002; 95% CI −0.00250 to −0.00058; P=.002). Conclusions: Evidence of an acute stimulus-response model was observed between sedentary time, physical activity, and glucose variability in low fitness individuals, with sedentary time and light activity conferring the most consistent changes in glucose variability. Further work is required to investigate the coupling of movement behaviors and glucose responses in larger samples and whether providing these rich data sources as feedback could induce lifestyle behavior change.