Purpose of review: Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition. Recent findings: Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The combination of these tools has also translated into practical clinical applications, such as decision support tools, risk prediction, and diet optimization. Summary: The state of digital technology for clinical nutrition is still young, although there is much promise for growth and disruption in the future.

Recently released recommendations for detection and documentation of malnutrition in adults in clinical practice define 3 types of malnutrition: starvation related, acute disease or injury related, and chronic disease related. The first 2 are more easily recognized, but the third may be more often unnoticed, particularly in obese patients. Critical care patients tend to be at high risk for malnutrition and thus require a thorough nutritional assessment. Compared with patients of earlier times, intensive care unit patients today tend to be older, have more complex medical and comorbid conditions, and often are obese. Missed or delayed detection of malnutrition in these patients may contribute to increases in hospital morbidity and longer hospital stays. Critical care nurses are in a prime position to screen patients at risk for malnutrition and to work with members of the interprofessional team in implementing nutritional intervention plans.

This cross-sectional study examined correlations between physical activity and anthropometrics as well as sleep quality and anthropometrics.College students (n = 85) with average age 25.6 ± 5.4 yea...