scholarly journals A simple dietary assessment tool to monitor food intake of hospitalized adult patients

2016 ◽  
Vol Volume 9 ◽  
pp. 311-322 ◽  
Author(s):  
Suzana Shahar ◽  
Dwi Budiningsari ◽  
Zahara Abd Manaf ◽  
Susetyowati Susetyowati
Keyword(s):  
Food Intake ◽  
Assessment Tool ◽  
Dietary Assessment ◽  
Adult Patients

A comparison of the Remote Food Photography Method and the Automated Self-Administered 24-hour Dietary Assessment Tool for measuring full day dietary intake among school-aged children

2021 ◽  
pp. 1-26
Author(s):  
Traci A. Bekelman ◽  
Corby K. Martin ◽  
Susan L. Johnson ◽  
Deborah H. Glueck ◽  
Katherine A. Sauder ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Energy Intake ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Dietary Assessment ◽  
Ease Of Use ◽  
Self Report ◽  
Food Photography ◽  
Photography Method

Abstract The limitations of self-report measures of dietary intake are well known. Novel, technology-based measures of dietary intake may provide a more accurate, less burdensome alternative to existing tools. The first objective of this study was to compare participant burden for two technology-based measures of dietary intake among school-age children: the Automated-Self Administered 24-hour Dietary Assessment Tool-2018 (ASA24-2018) and the Remote Food Photography Method (RFPM). The second objective was to compare reported energy intake for each method to the Estimated Energy Requirement for each child, as a benchmark for actual intake. Forty parent-child dyads participated in 2, 3-day dietary assessments: a parent proxy-reported version of the ASA24 and the RFPM. A parent survey was subsequently administered to compare satisfaction, ease of use and burden with each method. A linear mixed model examined differences in total daily energy intake (TDEI) between assessments, and between each assessment method and the EER. Reported energy intake was 379 kcal higher with the ASA24 than the RFPM (p=0.0002). Reported energy intake with the ASA24 was 231 kcal higher than the EER (p = 0.008). Reported energy intake with the RFPM did not differ significantly from the EER (difference in predicted means = −148 kcal, p = 0.09). Median satisfaction and ease of use scores were 5 out of 6 for both methods. A higher proportion of parents reported that the ASA24 was more time consuming than the RFPM (74.4% vs. 25.6%, p = 0.002). Utilization of both methods is warranted given their high satisfaction among parents.

scholarly journals The Provision of Assistance Does Not Substantially Impact the Accuracy of 24-Hour Dietary Recalls Completed Using the Automated Self-Administered 24-H Dietary Assessment Tool among Women with Low Incomes

2019 ◽  
Vol 149 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Sharon I Kirkpatrick ◽  
Patricia M Guenther ◽  
Deirdre Douglass ◽  
Thea Zimmerman ◽  
Lisa L Kahle ◽  
...  
Keyword(s):  
Low Income ◽  
Assessment Tool ◽  
Dietary Assessment ◽  
Food Group ◽  
Supplemental Nutrition Assistance Program ◽  
Assistance Program ◽  
Plate Waste ◽  
Low Incomes ◽  
Portion Sizes ◽  
Nutrition Assistance

ABSTRACT Background Evidence is lacking informing the use of the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24) with populations characterized by low income. Objective This study was conducted among women with low incomes to evaluate the accuracy of ASA24 recalls completed independently and with assistance. Methods Three hundred and two women, aged ≥18 y and with incomes below the Supplemental Nutrition Assistance Program thresholds, served themselves from a buffet; amounts taken as well as plate waste were unobtrusively weighed to enable calculation of true intake for 3 meals. The following day, women completed ASA24-2016 independently (n = 148) or with assistance from a trained paraprofessional in a small group (n = 154). Regression modeling examined differences by condition in agreement between true and reported foods; energy, nutrient, and food group intakes; and portion sizes. Results Participants who completed ASA24 independently and those who received assistance reported matches for 71.9% and 73.5% (P = 0.56) of items truly consumed, respectively. Exclusions (consumed but not reported) were highest for lunch (at which participants consumed approximately 2 times the number of distinct foods and beverages compared with breakfast and dinner). Commonly excluded foods were additions to main dishes (e.g., tomatoes in salad). On average, excluded foods contributed 43.6 g (46.2 kcal) and 40.1 g (43.2 kcal) among those in the independent and assisted conditions, respectively. Gaps between true and reported intake were different between conditions for folate and iron. Within conditions, significant gaps were observed for protein, vitamin D, and meat (both conditions); vitamin A, iron, and magnesium (independent); and folate, calcium, and vegetables (assisted). For foods and beverages for which matches were reported, no difference in the gap between true and reported portion sizes was observed by condition (P = 0.22). Conclusions ASA24 performed relatively well among women with low incomes; however, accuracy was somewhat lower than previously observed among adults with a range of incomes. The provision of assistance did not significantly impact accuracy.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

scholarly journals Effect of Manual Data Cleaning on Nutrient Intakes using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24)

2021 ◽  
Author(s):  
Yasmine Y Bouzid ◽  
Joanne E Arsenault ◽  
Ellen L Bonnel ◽  
Eduardo Cervantes ◽  
Annie Kan ◽  
...  
Keyword(s):  
Energy Intake ◽  
Assessment Tool ◽  
Data Cleaning ◽  
Dietary Assessment ◽  
Assessment Tools ◽  
Energy Requirements ◽  
Nutrient Intakes ◽  
Significant Difference ◽  
Before And After ◽  
Total Fat

Abstract Background Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. Objectives We evaluated the effects of modifications made during manual data cleaning on nutrients intakes of interest: energy, carbohydrate, total fat, protein, and fiber. Methods Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. Results After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (p < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (p < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared to supervised recalls (p = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared to raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (p < 0.001). Conclusions Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.

scholarly journals A cross-sectional assessment of food- and nutrient-based standards applied to British schoolchildren’s packed lunches

2016 ◽  
Vol 20 (3) ◽  
pp. 565-570 ◽  
Author(s):  
Charlotte EL Evans ◽  
Janet E Cade
Keyword(s):  
Primary Schools ◽  
Assessment Tool ◽  
Dietary Assessment ◽  
Quality Score ◽  
Food Groups ◽  
Multilevel Regression ◽  
Optimal Model ◽  
Cross Sectional ◽  
The Uk ◽  
Dietary Data

AbstractObjectiveIn England, standards for school meals included both foods and nutrients until 2015. School policies for packed lunches are generally food based; research is needed to determine whether these are adequate or whether a small number of nutrients would potentially improve their quality.DesignFrom dietary data obtained using a weighed dietary assessment tool, a diet quality score (DQS) for packed lunches was calculated using the number of standards met out of twenty-one (eight foods and thirteen nutrients). Multilevel regression analysis determined the foods and nutrients contributing to variation in the DQS.SettingEighty-nine primary schools across the four regions of the UK (England, Wales, Scotland and Northern Ireland).SubjectsBritish schoolchildren (n 1294), aged 8–9 years, taking a packed lunch.ResultsThe optimal model included all eight foods and seven of the thirteen nutrients, explaining 72 % of the variance in DQS. Folate, Fe and vitamin C, together with the eight food groups, explained 70 % of DQS variation.ConclusionsIdeally, policies for school packed lunches should include food-based standards plus recommendations based on a small number of nutrients.

THE BALANCED NUTRITION TEST: AN ONLINE DIETARY ASSESSMENT TOOL ADAPTED FROM THE SELF CHECK PROGRAM FOR USE IN TURKEY

Nutrition ◽  
2018 ◽  
Vol 55-56 ◽  
pp. S22-S23
Author(s):  
Dr. Burcu Aksoy ◽  
Deniz Miray Arca ◽  
Prof. Halit Tanju Besler
Keyword(s):  
Assessment Tool ◽  
Dietary Assessment ◽  
The Self
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