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Using Digital Health Technologies to Understand the Association Between Movement Behaviors and Interstitial Glucose: Exploratory Analysis

Using Digital Health Technologies to Understand the Association Between Movement Behaviors and Interstitial Glucose: Exploratory Analysis

Assumptions of linearity and normally distributed residuals were checked visually using residual and P-P plots, and multicollinearity was assessed using variance inflation factors (VIFs). VIF values were Comparisons between movement behaviors (sedentary time, light activity, and MVPA) and glycemic variables (mean glucose, SD of glucose and MAGE) using GEE analysis for the whole sample is presented within Tables 3-5.

Andrew P Kingsnorth, Maxine E Whelan, James P Sanders, Lauren B Sherar, Dale W Esliger

JMIR Mhealth Uhealth 2018;6(5):e114

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Article has an altmetric score of 10

Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review

Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review

In reviewing 24 studies which use GPS in physical activity research [288], GPS data loss was found to be highly correlated with device wear time (r=.81, P While GPS can be used to successfully augment accelerometer measurement of physical activity, several shortcomings need to be addressed. There is currently no established approach to the analysis and interpretation of GPS data [287].

Adam Loveday, Lauren B Sherar, James P Sanders, Paul W Sanderson, Dale W Esliger

J Med Internet Res 2015;17(8):e192

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Article has an altmetric score of 15