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We present a multitask learning approach to the problem of hypoglycemia (HG) prediction in diabetes. The approach is based on a state-of-the-art time series forecasting model, N-BEATS, and extends it by adding a classification task so that the model performs both glucose forecasting (i.e., predicting future glucose values) and HG prediction (i.e., probability of future HG events sometime within the prediction horizon). We also propose an alternative loss function that penalizes forecasting errors in the HG range. We evaluate the approach on a dataset containing over 1.6M recordings from 112 patients with type 1 diabetes who wore a continuous glucose monitor (CGM) for 90 days. Our results show that the classification branch significantly outperforms the forecasting branch on the problem of HG prediction, and that the new loss function is more effective at reducing forecasting errors in the HG range than multi-task learning. 

Background: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. Methods: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine-learning approaches to predict glycemic excursions: a classification model and a regression model. Results: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. Conclusions: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction. 

Working in a fast-paced environment can lead to shallow breathing, which can exacerbate stress and anxiety. To address this issue, this study aimed to develop micro-interventions that can promote deep breathing in the presence of stressors. First, we examined two types of breathing guides to help individuals learn deep breathing: providing their breathing rate as a biofeedback signal, and providing a pacing signal to which they can synchronize their breathing. Second, we examined the extent to which these two breathing guides can be integrated into a casual game, to increase enjoyment and skill transfer. We used a 2 × 2 factorial design, with breathing guide (biofeedback vs. pacing) and gaming (game vs. no game) as independent factors. This led to four experimental groups: biofeedback alone, biofeedback integrated into a game, pacing alone, and pacing integrated into a game. In a first experiment, we evaluated the four experimental treatments in a laboratory setting, where 30 healthy participants completed a stressful task before and after performing one of the four treatments (or a control condition) while wearing a chest strap that measured their breathing rate. Two-way ANOVA of breathing rates, with treatment (5 groups) and time (pre-test, post-test) as independent factors shows a significant effect for time [ F (4, 50) = 18.49, p < 0.001, η t i m e 2 = 0 . 27 ] and treatment [ F (4, 50) = 2.54, p = 0.05, η 2 = 0.17], but no interaction effects. Post-hoc t-tests between pre and post-test breathing rates shows statistical significance for the game with biofeedback group [ t (5) = 5.94, p = 0.001, d = 2.68], but not for the other four groups, indicating that only game with biofeedback led to skill transfer at post-test. Further, two-way ANOVA of self-reported enjoyment scores on the four experimental treatments, with breathing guide and game as independent factors, found a main effect for game [ F ( 1 , 20 ) = 24 . 49 , p < 0 . 001 ,   η g a m e 2 = 0 . 55 ], indicating that the game-based interventions were more enjoyable than the non-game interventions. In a second experiment, conducted in an ambulatory setting, 36 healthy participants practiced one of the four experimental treatments as they saw fit over the course of a day. We found that the game-based interventions were practiced more often than the non-game interventions [ t (34) = 1.99, p = 0.027, d = 0.67]. However, we also found that participants in the game-based interventions could only achieve deep breathing 50% of the times, whereas participants in the non-game groups succeeded 85% of the times, which indicated that the former need adequate training time to be effective. Finally, participant feedback indicated that the non-game interventions were better at promoting in-the-moment relaxation, whereas the game-based interventions were more successful at promoting deep breathing during stressful tasks. 

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.