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With rapid growth in unhealthy diet behaviors, implementing strategies that improve healthy eating is becoming increasingly important. One approach to improving diet behavior is to continuously monitor dietary intake (e.g., calorie intake) and provide educational, motivational, and dietary recommendation feedback. Although technologies based on wearable sensors, mobile applications, and light-weight cameras exist to gather diet-related information such as food type and eating time, there remains a gap in research on how to use such information to close the loop and provide feedback to the user to improve healthy diet. We address this knowledge gap by introducing a diet behavior change framework that generates real-time diet recommendations based on a user’s food intake and considering user’s deviation from the suggested diet routine. We formulate the problem of optimal diet recommendation as a sequential decision making problem and design a greedy algorithm that provides diet recommendations such that the amount of change in user’s dietary habits is minimized while ensuring that the user’s diet goal is achieved within a given time-frame. This novel approach is inspired by the Social Cognitive Theory, which emphasizes behavioral monitoring and small incremental goals as being important to behavior change. Our optimization algorithm integrates data from a user’s past dietary intake as well as the USDA nutrition dataset to identify optimal diet changes. We demonstrate the feasibility of our optimization algorithms for diet behavior change using real-data collected in two study cohorts with a combined N=10 healthy participants who recorded their diet for up to 21 days. 

This article provides an up-to-date review of technological advances in 3 key areas related to diet monitoring and precision nutrition. First, we review developments in mobile applications, with a focus on food photography and artificial intelligence to facilitate the process of diet monitoring. Second, we review advances in 2 types of wearable and handheld sensors that can potentially be used to fully automate certain aspects of diet logging: physical sensors to detect moments of dietary intake, and chemical sensors to estimate the composition of diets and meals. Finally, we review new programs that can generate personalized/precision nutrition recommendations based on measurements of gut microbiota and continuous glucose monitors with artificial intelligence. The article concludes with a discussion of potential pitfalls of some of these technologies. 

Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal’s PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary’s meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions.