Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.

Background: Wearable devices can simultaneously collect data on multiple items in real time and are used for disease detection, prediction, diagnosis, and treatment decision-making. Several factors, such as diet and exercise, influence blood glucose levels; however, the relationship between blood glucose and these factors has yet to be evaluated in real practice.

Objective: This study aims to investigate the association of blood glucose data with various physiological index and nutritional values using wearable devices and dietary survey data from PhysioNet, a public database.

Methods: Three analytical methods were used. First, the correlation of each physiological index was calculated and examined to determine whether their mean values or SDs affected the mean value or SD of blood glucose. To investigate the impact of each physiological indicator on blood glucose before and after the time of collection of blood glucose data, lag data were collected, and the correlation coefficient between blood glucose and each physiological indicator was calculated for each physiological index. Second, to examine the relationship between postprandial blood glucose rise and fall and physiological and dietary nutritional assessment indices, multiple regression analysis was performed on the relationship between the slope before and after the peak in postprandial glucose over time and physiological and dietary nutritional indices. Finally, as a supplementary analysis to the multiple regression analysis, a 1-way ANOVA was performed to compare the relationship between the upward and downward slopes of blood glucose and the groups above and below the median for each indicator.

Results: The analysis revealed several indicators of interest: First, the correlation analysis of blood glucose and physiological indices indicated meaningful relationships: acceleration SD (r=-0.190 for lag data at -15-minute values), heart rate SD (r=-0.121 for lag data at -15-minute values), skin temperature SD (r=-0.121), and electrodermal activity SD (r=-0.237) for lag data at -15-minute values. Second, in multiple regression analysis, physiological indices (temperature mean: t=2.52, P=.01; acceleration SD: t=-2.06, P=.04; heart rate_30 SD: t=-2.12, P=.04; electrodermal activity_90 SD: t=1.97, P=.049) and nutritional indices (mean carbohydrate: t=6.53, P<.001; mean dietary fiber: t=-2.51, P=.01; mean sugar: t=-3.72, P<.001) were significant predictors. Finally, the results of the 1-way ANOVA corroborated the findings from the multiple regression analysis.

Conclusions: Similar results were obtained from the 3 analyses, consistent with previous findings, and the relationship between blood glucose, diet, and physiological indices in the real world was examined. Data sharing facilitates the accessibility of wearable data and enables statistical analyses from various angles. This type of research is expected to be more common in the future.