An LSTM-based Neural Network Wearable System for Blood Glucose Prediction in People with Diabetes.

This article proposes the first hardware implemen-tation of a low-power LSTM neural network targeting a wearable medical device designed to predict blood glucose at a 30-minute horizon. This work aims to reduce energy consumption by propos-ing new activation functions that target hardware implementation. On top of this proposal, we also prove there is room for improve-ment in energy consumption by applying neural network optimiza-tions at the algorithmic, such as quantization, and architecture level, LSTM hyperparameters, that consider the target hardware.

A telehealth nursing intervention to improve the transition from the neonatal intensive care unit to home for infants & caregivers: Preliminary evaluation.

Purpose: The purpose of this nurse-led telehealth intervention was to support caregivers and infants during the difficult period of transition from the neonatal intensive care unit (NICU) to home.

Methods: The Baby Steps project was designed using quality improvement methodology, and was implemented in April 2020 at a stand-alone pediatric institution in South Florida. Using a nurse-led telehealth model, follow-up nursing care was provided in the home setting for two weeks after discharge.

Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes.

This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons-30, 60, and 120 min-and compares their performance with ten recently proposed neural networks. The twelve models' performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model's error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.

Public spaces and physical activity in adults: insights from a mixed-methods study.

Public spaces could promote health equity by providing a place for people to engage in physical activity. Whereas most studies show a positive association between public spaces and physical activity, there are still mixed results, especially in low- and middle-income countries. The objectives of this mixed-methods study were: (i) to assess the quantitative association between access to public spaces and physical activity; (ii) to assess the modification of the association by public spaces quality, area-level walkability, and social stratifiers; (iii) to explain the quantitative results using qualitative data.

[Psychological alterations in patients with adult celiac disease].

Unlabelled: Patients with recently-diagnosed adult celiac disease were evaluated with the Gastrointestinal Symptom rating Scale (GSRS) and Psychological General Well-Being Index (PGWBI) to evaluate their psychological alterations, the association between any alterations and gastrointestinal symptoms, and their outcome after starting a gluten-free diet. The patients underwent nutritional assessment and then started a gluten-free diet; they were reassessed 6 months later. Quantitative variables are expressed as the median and 25th-75th percentiles.