Nutritional Management and Outcomes in Malnourished Medical Inpatients: Anorexia Nervosa

BACKGROUND: Anorexia Nervosa (AN) is a psychiatric disorder characterised by a physical and psychosocial deterioration due to an altered pattern on the intake and weight control. The severity of the disease is based on the degree of malnutrition. The objective of this article is to review the scientific evidence of the refeeding process of malnourished inpatients with AN; focusing on the clinical outcome. METHODS: We conducted an extensive search in Medline and Cochrane; on April 22; 2019; using different search terms. After screening all abstracts; we identified 19 papers that corresponded to our inclusion criteria. RESULTS: The article focuses on evidence on the characteristics of malnutrition and changes in body composition; energy and protein requirements; nutritional treatment; physical activity programmes; models of organisation of the nutritional treatment and nutritional support related outcomes in AN patients. CONCLUSION: Evidence-based standards for clinical practice with clear outcomes are needed to improve the management of these patients and standardise the healthcare process

Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study

BACKGROUND: Digital technologies have evolved dramatically in the recent years finding applications in a variety of aspects of everyday life. Smartphones and mobile apps are steadily used for more and more tasks including health monitoring. A large amount of "Nutrition and Diet" apps are available with some of them being very popular in terms of user downloads highlighting a trend towards diet monitoring and assessment. OBJECTIVE: We sought to explore the perspectives of end-users on the features, current use, and acceptance of "Nutrition and Diet" mHealth apps with a survey. We expect that such a study can provide user insights, assisting researchers and developers towards innovative dietary assessment. METHODS: A multidisciplinary team designed and compiled the survey. Before its release, it has been pilot-tested by 18 end-users. A 19-question survey was finally developed which has been translated into six languages: EN, DE, FR, ES, IT, EL. The participants were mainly recruited via social media and mailing lists of universities, university hospitals and patient associations. RESULTS: Respondents (n=2382) (79.4% female, 19.9% male, 0.7% neither) with a mean age of 27.2 (SD: 8.5) completed the survey. Around half of the participants (51.5%, 1227 out of 2382) have used a "Nutrition and Diet" app. The primary criteria for selecting such an app were to be easy to use (65.9%, 1570 out of 2382), free of charge (59.3%, 1413 out of 2382) and also produce automatic readings of caloric (51.7%, 1231 out of 2382) and macronutrient content (46.9%, 1117 out of 2382) (i.e., food type and/or the portion size are estimated by the system without any contribution by the user). An app is less likely to be selected if it incorrectly estimates portion size, calories or nutrient content (33.5%, 798 out of 2382). Moreover, other important limitations include the use of a database that comprises of non-local foods (27.5%, 655 out of 2382) and which may omit major foods (41%, 977 out of 2382). CONCLUSIONS: This comprehensive study in a mostly European population assessed the preferences and perspectives of (potential) "Nutrition and Diet" app users. Understanding user needs will benefit both researchers who work on tools for innovative dietary assessment, as well as those who assist research on behavioural changes related to nutrition

A feasibility study to assess Mediterranean Diet adherence using an AI-powered system.

Mediterranean diet (MD) can play a major role in decreasing the risks of non-communicable diseases and preventing overweight and obesity. In order for a person to follow the MD and assess their adherence to it, proper dietary assessment methods are required. We have developed an Artificial Intelligence-powered system that recognizes the food and drink items from a single meal photo and estimates their respective serving size, and integrated it into a smartphone application that automatically calculates MD adherence score and outputs a weekly feedback report. We compared the MD adherence score of four users as calculated by the system versus an expert dietitian, and the mean difference was 3.5% and statistically not significant. Afterwards, we conducted a feasibility study with 24 participants, to evaluate the system's performance and to gather the users' and dietitians' feedback. The image recognition system achieved 61.8% mean Average Precision for the testing set and 57.3% for the feasibility study images (where the ground truth was taken as the participants' annotations). The feedback from the participants of the feasibility study was also very positive

The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOODTM.

A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct automatic nutritional assessment from meal images, providing a convenient, efficient, and accurate method for continuous diet evaluation. We now extend the goFOODTM automatic system to perform food segmentation, recognition, volume, as well as calorie and macro-nutrient estimation from single images that are captured by a smartphone. In order to assess our system's performance, we conducted a feasibility study with 50 participants from Switzerland. We recorded their meals for one day and then dietitians carried out a 24 h recall. We retrospectively analysed the collected images to assess the nutritional content of the meals. By comparing our results with the dietitians' estimations, we demonstrated that the newly introduced system has comparable energy and macronutrient estimation performance with the previous method; however, it only requires a single image instead of two. The system can be applied in a real-life scenarios, and it can be easily used to assess dietary intake. This system could help individuals gain a better understanding of their dietary consumption. Additionally, it could serve as a valuable resource for dietitians, and could contribute to nutritional research

MEDPass versus conventional administration of oral nutritional supplements - A randomized controlled trial comparing coverage of energy and protein requirements.

BACKGROUND & AIMS The use of oral nutritional supplements (ONS) in the hospital setting is important to reach individual protein and energy goals in patients at risk for malnutrition. Compliance with ONS can be challenging but may be improved by prescribing ONS in smaller portions with medication rounds (MEDPass). We compared the likelihood of meeting energy and protein requirements in patients receiving ONS with MEDPass versus conventional ONS administration. METHODS The MEDPass Trial is a randomized, controlled, open-label superiority trial conducted on medical and geriatric wards in a University Hospital in Switzerland. The MEDPass group was allocated to receive 50 ml of ONS four times per day with the medication rounds. The control group received ONS per conventional care between the meals. The primary outcome was the percentage of energy in relation to the individual requirement. Secondary outcomes included the coverage of protein intake in relation to the individual requirement, the amount of daily consumed ONS, the course of handgrip strength (HGS), body weight appetite and nausea. Furthermore, we compared 30-day mortality and hospital length of stay (LOS) was studied in medical patients. RESULTS From November 22nd, 2018 until November 30th, 2021, 204 patients were included in the trial (MEDPass group n = 100, control group n = 104). A total of 203 patients at nutritional risk were analyzed in the intention-to-treat analysis (ITT). Regarding the primary endpoint, there was no difference in the coverage of energy requirement between the MEDPass and control group (82 vs. 85% (Δ -3%, 95%CI -11 to 4%), p = 0.38). Similarly, no differences were found for the secondary outcomes including coverage of protein requirement (101 vs. 104% (Δ -3%, 95% CI -12 -7%), p = 0.57, average daily intake of ONS (170 vs 173 ml (Δ - 3 ml, 95% CI -14 to 8 ml), p = 0.58) and 30-day mortality (3 vs. 8 patients, OR 0.4 (95% CI 0.1-1.4), p = 0.15). The course of HGS, body weight, appetite and nausea did not differ between the groups (p = 0.29, p = 0.14, p = 0.65 and p = 0.94, respectively). The per protocol analysis including 178 patients showed similar results. CONCLUSION Within this controlled trial setting, we found a high compliance for ONS intake and high coverage of protein requirements but no further improvement when ONS was administered using MEDPass compared to conventional care. MEDPass administration may provide an alternative that is easy to integrate into nursing routines, which may lead to lower workload with cost benefits and reduction of food waste. TRIAL REGISTRATION ClinicalTrials.gov: NCT03761680

Challenges and Perspectives in Nutritional Counselling and Nursing: A Narrative Review.

Nutritional counselling has been recognised as the first line approach in the management of numerous chronic diseases. Though usually carried out by dietitians, nutritional counselling may be used by nurses, or other healthcare professionals to improve nutritional status and meet healthcare goals. Healthcare professionals require training and education to facilitate a patient centred approach to effective counselling. Advances in digital technology have the potential to improve access to nutritional counselling for some patients such as those in primary care. However, caution is required to ensure that valuable interpersonal relationships are not lost, as these form the cornerstone of effective nutritional counselling. The aim of this narrative review is to explore aspects of effective nutritional counselling, including advances in e-counselling and areas where nursing input in nutritional counselling might enhance overall nutritional care

goFOOD[TM]: An Artificial Intelligence System for Dietary Assessment

Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App

Background: Technological advancements have enabled nutrient estimation by smartphone apps such as goFOOD. This is an artificial intelligence–based smartphone system, which uses food images or video captured by the user as input and then translates these into estimates of nutrient content. The quality of the data is highly dependent on the images the user records. This can lead to a major loss of data and impaired quality. Instead of removing these data from the study, in-depth analysis is needed to explore common mistakes and to use them for further improvement of automated apps for nutrition assessment. Objective: The aim of this study is to analyze common mistakes made by participants using the goFOOD Lite app, a version of goFOOD, which was designed for food-logging, but without providing results to the users, to improve both the instructions provided and the automated functionalities of the app. Methods: The 48 study participants were given face-to-face instructions for goFOOD Lite and were asked to record 2 pictures (1 recording) before and 2 pictures (1 recording) after the daily consumption of each food or beverage, using a reference card as a fiducial marker. All pictures that were discarded for processing due to mistakes were analyzed to record the main mistakes made by users. Results: Of the 468 recordings of nonpackaged food items captured by the app, 60 (12.8%) had to be discarded due to errors in the capturing procedure. The principal problems were as follows: wrong fiducial marker or improper marker use (19 recordings), plate issues such as a noncompatible or nonvisible plate (8 recordings), a combination of various issues (17 recordings), and other reasons such as obstacles (hand) in front of the camera or matching recording pairs (16 recordings). Conclusions: No other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app’s user-friendliness and to develop automatic image checks based on participant feedback