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

Abstract

Background/objectivesMonitoring food intake of patients during hospitalization using simple methods and minimal training is an ongoing problem in hospitals. Therefore, there is a need to develop and validate a simple, easy to use, and quick tool that enables staff to estimate dietary intake. Thus, this study aimed to develop and validate the Pictorial Dietary Assessment Tool (PDAT).Subjects and methodsA total of 37 health care staff members consisting of dietitians, nurses, and serving assistants estimated 130 breakfast and lunch meals consumed by 67 patients using PDAT. PDAT was developed based on the hospital menu that consists of staple food (rice or porridge), animal source protein (chicken, meat, eggs, and fish), and non-animal source protein (tau fu and tempeh), with a total of six pictorials of food at each meal time. Weighed food intake was used as a gold standard to validate PDAT. Agreement between methods was analyzed using correlations, paired t-test, Bland–Altman plots, kappa statistics, and McNemar’s test. Sensitivity, specificity, and area under the curve of receiver operating characteristic were calculated to identify whether patients who had an inadequate food intake were categorized as at risk by the PDAT, based on the food weighing method. Agreement between different backgrounds of health care staff was calculated by intraclass correlation coefficient and analysis of variance test.ResultsThere was a significant correlation between the weighing food method and PDAT for energy (r=0.919, P<0.05), protein (r=0.843, P<0.05), carbohydrate (r=0.912, P<0.05), and fat (r=0.952; P<0.05). Nutrient intakes as assessed using PDAT and food weighing were rather similar (295±163 vs 292±158 kcal for energy; 13.9±7.8 vs 14.1±8.0 g for protein; 46.1±21.4 vs 46.7±22.3 g for carbohydrate; 7.4±3.1 vs 7.4±3.1 g for fat; P>0.05). The PDAT and food weighing method showed a satisfactory agreement beyond chance (k) (0.81 for staple food and animal source protein; 0.735 for non-animal source protein). Intraclass correlation coefficient ranged between 0.91 and 0.96 among respondents. There were no differences in energy, protein, carbohydrate, and fat intake estimated among health care staff (P=0.967; P=0.951; P=0.888; P=0.847, respectively).ConclusionIn conclusion, PDAT provides a valid estimation of macronutrient consumption among hospitalized adult patients.

Patient Satisfaction With Hospital Food service and its Impact on Plate Waste in Public Hospitals in East Malaysia

“…26 This scale has been adapted and modified into studies in the United States, Canada, Indonesia, and Israel. [27][28][29][30] The researcher observed the leftovers before recording the data into the scale. The scale comprises levels of all not eaten = 100%, one bite eaten = 80%, 3/4 left = 60%, 1/2 left = 40%, 1/4 left = 20%, and none left = 0%.…”

“…The results identified a weak negative correlation between the 2 variables (r = -0.16, n = 189, P = 0.03), with higher age being associated with lower satisfaction with the hospital foodservice. Subjects were divided into 3 age groups (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55), and 56-65 years). The independent sample t test results indicated no significant difference in scores for males (M = 2.62, SD = 0.74) and females (M = 2.65, SD = 0.77); t (187) = 0-.27, P = 0.79 (two-tailed).…”

Evaluation of Pictorial Dietary Assessment Tool for Hospitalized Patients with Diabetes: Cost, Accuracy, and User Satisfaction Analysis

“…In the second period, the first group used PDAT and the second group used modified Comstock for two weeks. The sample size calculation for comparing means of two groups indicated that a minimum sample size of 10 assessors was required to detect a mean difference in accuracy between the test method and reference method of 13 kcal (given a standard deviation (SD) of the difference between the two value of 10 kcal) [ 18 ] with 90% power and a type I error probability of ≤0.05. Each staff member measured three patients for two periods, so that a total of 132 patients were included.…”

“…Such overestimation can allow problems to go unrecognized by nursing staff, thus preventing follow-ups with further nutritional assessment [ 9 , 17 ]. We have successfully developed and validated a dietary assessment tool for use in a clinical setting, the Pictorial Dietary Assessment Tool (PDAT) [ 18 ]. This method is a simple, easy-to-use, quick tool that enables staff to estimate dietary intake and can easily be integrated into dietitians’ evaluations of patients during their stay in the hospital.…”

“…PDAT provides a ready reckoner of energy and protein content of meals that facilitates the nutrient intake estimation without the need for detailed mathematical computation. There were no differences in macronutrient intake estimation across different backgrounds of healthcare staff [ 18 ].…”