Search

Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.

The gradual decline in routine patterns is a major symptom of early-stage dementia, therefore an unobtrusive real-life assessment of the elder's routine can potentially be of significant clinical importance. This article focuses on the assessment of changes in a person's daily routine using longitudinal data recorded from a network of nonintrusive motion sensors in a smart home environment. In this article, we propose to identify repeating patterns in a person's daily routine over the span of multiple days using hierarchical clustering algorithms, which provide an effective way to mitigate noise artifacts and confounding factors that contribute to the momentary variability of the sensor data. We have evaluated our proposed algorithm on both synthetic and real-world data recorded in the span of 50-100 days from four elderly adults. Our results indicate that the proposed hierarchical clustering approach can more reliably capture the gradual change in the degree of routineness compared to baseline approaches that measure the similarity between two consecutive days or capture variations in the occurrence of recognized activities.