Computer-Based Training in Eating and Nutrition Facilitates Person-Centered Hospital Care: A Group Concept Mapping Study.

Studies have shown that computer-based training in eating and nutrition for hospital nursing staff increased the likelihood that patients at risk of undernutrition would receive nutritional interventions. This article seeks to provide understanding from the perspective of nursing staff of conceptually important areas for computer-based nutritional training, and their relative importance to nutritional care, following completion of the training. Group concept mapping, an integrated qualitative and quantitative methodology, was used to conceptualize important factors relating to the training experiences through four focus groups (n = 43), statement sorting (n = 38), and importance rating (n = 32), followed by multidimensional scaling and cluster analysis. Sorting of 38 statements yielded four clusters. These clusters (number of statements) were as follows: personal competence and development (10), practice close care development (10), patient safety (9), and awareness about the nutrition care process (9). First and second clusters represented "the learning organization," and third and fourth represented "quality improvement." These findings provide a conceptual basis for understanding the importance of training in eating and nutrition, which contributes to a learning organization and quality improvement, and can be linked to and facilitates person-centered nutritional care and patient safety.

Long-term effects of a computer-based nutritional training program for inpatient hospital care.

RATIONALE: A previous short-term study showed that a computer-based training in eating and nutrition increased the probability for hospital inpatients at undernutrition (UN) risk to receive nutritional treatment and care without increasing overtreatment (providing nutritional treatment to those not at UN risk). The aim of this study was to investigate if a computer-based training in eating and nutrition influences the precision in nutritional treatment and care in a longer-term perspective. METHOD: A preintervention and postintervention study was conducted with a cross-sectional design at each time points (baseline and 7 months postintervention). Hospital inpatients >18 years old at baseline (2013; n = 201) and follow-up (2014; n = 209) were included. A computer-based training was implemented during a period of 3 months with 297 (84%) participating registered nurses and nurse assistants. Undernutrition risk was screened for using the minimal eating observation and nutrition form-version II. Nutritional treatment and care was recorded using a standardized protocol. RESULTS: The share of patients at UN risk that received energy-dense food (+25.2%) and dietician consultations (+22.3%) increased between baseline and follow-up, while fewer received oral nutritional supplements (-18.9%). "Overtreatment" (providing nutritional treatment to those not at UN risk) did not change between baseline and follow-up. CONCLUSION: The computer-based training increased the provision of energy-dense food and dietician consultations to patients at UN risk without increasing overtreatment of patients without UN risk.

Short-term effects of a computer-based nutritional nursing training program for inpatient hospital care.

RATIONALE: This study aimed to explore whether a computer-based training in eating and nutrition for hospital nursing staff can influence the precision in nutritional treatment and care. METHOD: A pre-intervention and post-intervention study was conducted with a cross-sectional design at each time point. The settings were one intervention (IH) and two control hospitals (CH1 and CH2). Hospital inpatients >18 years old at baseline (2012; n = 409) and follow-up (2014; n = 456) were included. The computer-based training was implemented during a period of 3 months in the IH with 297 (84%) participating registered nurses and nurse assistants. Nutritional risk was screened for using the Minimal Eating Observation and Nutrition Form. Nutritional treatment and care was recorded using a standardized protocol RESULTS: In the IH, there was an increase in the share of patients at UN risk that received energy-dense food (+16.7%) and dietician consultations (+17.3%) between baseline and follow-up, while fewer received feeding assistance (-16.2%). There was an increase in the share of patients at UN risk that received energy-dense food (+19.5%), a decrease in oral nutritional supplements (-30.5%) and food-registrations (-30.6%) in CH1, whereas there were no changes in CH2. 'Overtreatment' (providing nutritional treatment to those not at UN risk) was significantly higher in CH2 (52.7%) than in CH1 (14.3%) and in the IH (25.2%) at follow-up. CONCLUSION: The computer-based training seemed to increase the probability for patients at UN risk in the IH to receive nutritional treatment without increasing overtreatment.

Diagnostic performance of the Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) and Nutritional Risk Screening 2002 (NRS 2002) among hospital inpatients - a cross-sectional study.

BACKGROUND: The usefulness of the nutritional screening tool Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) relative to Nutritional Risk Screening 2002 (NRS 2002) remains untested. Here we attempted to fill this gap by testing the diagnostic performance and user-friendliness of the MEONF-II and the NRS 2002 in relation to the Mini Nutritional Assessment (MNA) among hospital inpatients. METHODS: Eighty seven hospital inpatients were assessed for nutritional status with the 18-item MNA (considered as the gold standard), and screened with the NRS 2002 and the MEONF-II. RESULTS: The MEONF-II sensitivity (0.61), specificity (0.79), and accuracy (0.68) were acceptable. The corresponding figures for NRS 2002 were 0.37, 0.82 and 0.55, respectively. MEONF-II and NRS 2002 took five minutes each to complete. Assessors considered MEONF-II instructions and items to be easy to understand and complete (96-99%), and the items to be relevant (87%). For NRS 2002, the corresponding figures were 75-93% and 79%, respectively. CONCLUSIONS: The MEONF-II is an easy to use, relatively quick and sensitive screening tool to assess risk of undernutrition among hospital inpatients. With respect to user-friendliness and sensitivity the MEONF-II seems to perform better than the NRS 2002, although larger studies are needed for firm conclusions. The different scoring systems for undernutrition appear to identify overlapping but not identical patient groups. A potential limitation with the study is that the MNA was used as gold standard among patients younger than 65 years.

Cut-off scores for the Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) among hospital inpatients.

BACKGROUND AND OBJECTIVE: The newly developed Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) has shown promising sensitivity and specificity in relation to the Mini Nutritional Assessment (MNA). However, the suggested MEONF-II cut-off scores for deciding low/moderate and high risk for undernutrition (UN) (>2 and >4, respectively) have not been decided based on statistical criteria but on clinical reasoning. The objective of this study was to identify the optimal cut-off scores for the MEONF-II in relation to the well-established MNA based on statistical criteria. DESIGN: Cross-sectional study. METHODS: The study included 187 patients (mean age, 77.5 years) assessed for nutritional status with the MNA (full version), and screened with the MEONF-II. The MEONF-II includes assessments of involuntary weight loss, Body Mass Index (BMI) (or calf circumference), eating difficulties, and presence of clinical signs ofUN. MEONF-II data were analysed by Receiver Operating Characteristics (ROC) curves and the area under the curve (AUC); optimal cut-offs were identified by the Youden index (J=sensitivity+specificity-1). RESULTS: According to the MEONF-II, 41% were at moderate or high UN risk and according to the MNA, 50% were at risk or already undernourished. The suggested cut-off scores were supported by the Youden indices. The lower cut-off for MEONF-II, used to identify any level of risk for UN (>2; J=0.52) gave an overall accuracy of 76% and the AUC was 80%. The higher cut-off for identifying those with high risk for UN (>4; J=0.33) had an accuracy of 63% and the AUC was 70%. CONCLUSIONS: The suggested MEONF-II cut-off scores were statistically supported. This improves the confidence of its clinical use.