Statistical Evaluation of Total Energy Intake Based on the Number of Food Portions and Body Weight

Research Article

Int J Nutr Sci. 2021; 6(2): 1054.

Statistical Evaluation of Total Energy Intake Based on the Number of Food Portions and Body Weight

Rousset S1*, Médard S2, Fleury G2, Fardet A1, Goutet O3 and Lacomme P2

1University Clermont Auvergne, UNH, UMR1019, INRAE, Clermont Ferrand, France

2University Clermont Auvergne, LIMOS UMR CNRS 6158, Clermont Ferrand, France

3Openium, 15 rue Jean Claret Bâtiment le XV, La Pardieu, Clermont-Ferrand, France

*Corresponding author: Rousset S, Human Nutrition Unit, INRAE, 63000 Clermont-Ferrand, France

Received: May 14, 2021; Accepted: June 11, 2021; Published: June 18, 2021


The evaluation of food intake based on various assessment methods is critical and underreporting is frequent. The aim of the study was to develop an indirect statistical method of the total energy intake estimation based on gender, weight and the number of portions. Energy intake prediction was developed and evaluated for validity using energy expenditure measurements given by the WellBeNet app. A total of 190 volunteers with various BMIs were recruited and assigned either in the train or the test sample. The mean energy provided by a portion was evaluated by linear regression models from the train sample. The absolute values of the error between the energy intake estimation and the energy expenditure measurement were calculated for each volunteer, by subgroup and for the whole group. The performance of the models was determined using the validation dataset. As the number of portions is the only variable used in the model, the error was 30.7% and 26.5% in the train and test sample. After adding body weight in the model, the error in absolute value decreased to 8.8% and 10.8% for the normal-weight women and men, and 11.7% and 12.8% for the overweight female and male volunteers, respectively. The findings of this study indicate that a statistical approach and knowledge of the usual number of portions and body weight is effective and sufficient to obtain a precise evaluation of energy intake (about 10% of error) after a simple and brief enquiry.

Keywords: Prediction of energy intake; Total number of food portions; Body mass index; Energy expenditure; Dietary apps


DLW: Doubly Labelled Water; TEE: Total Energy Expenditure; BMI: Body Mass Index; ICT: Information and Communication Technology; NW: Normal Weight; OW: Overweight; EI: Energy Intake; GLM: General Linear Model; LSMeans: Least Squares Means; M: Men; W: Women; SD: Standard Deviation; Pi: Number of Portion for an Individual i; Wi: Body Weight for an Individual i; Ei: Error for an Individual i


The evaluation of dietary intake is commonly performed using the 24-hour dietary recall or frequency questionnaire, or 3- to 7-day reported food intake [1,2]. Doubly Labelled Water (DLW) is used as a reference method to measure Total Energy Intake (TEE) in free-living conditions and to validate reported energy intake in many studies [3,4]. This reference methodology is based on the fundamental principle of the energy balance, meaning that Total Energy Expenditure (TEE) is equal to energy intake when the body weight is stable (in the absence of a significant weight change) [5]. Many authors found a positive correlation between TEE measured by DLW and body weight, but a flat slope between TEE and reported energy intake [6-8]. According to these authors, the underestimation of energy intake concurrent with increasing weight may be due to the imitation error of the food reported by the general population. That means that food intake is reported in the same way, regardless of the body weight range. This is confirmed by Novotny et al. (2003), who found an overall underreporting of 294kcal/d energy intake [9].

This underestimation of energy intake was higher in women than in men: 85% of women underreported their food intake by 621kcal/d, whereas 61% of men underreported theirs by 581kcal/d. In contrast, 15% of women over reported their energy intake by 304kcal/d and 39% of men by 683kcal/d. The poor food intake estimation was mainly related to body fat mass and body dissatisfaction. The higher the body fat percentage was, the higher the underreporting of energy intake was [9]. Gender also played an important part in the correct estimation of food intake, and men were better estimators than women. Many other studies have proved that both underreporting and overreporting occur, regardless of the methods used for food intake assessments [3,10].

Since the cost of the DLW method is a liming factor for largescale studies such as epidemiological ones, it would be advantageous to replace DLW by another less costly technique or procedure able to estimate energy intake with a high level of accuracy. The development of the new information and communication technologies and the widespread use of smartphones open new application prospects in nutrition and dietary assessment. For example, the use of dietary mobile applications led to a decrease in weight, waist circumference and energy intake compared to control in adults with chronic diseases [11]. Researchers also expect that technology could improve diary reporting by reducing memory and representation bias and errors from data processing [12]. In several studies, volunteers were told to take photographs with their smartphones in order to improve reporting and avoid food omission. However, there were many problems with the quality, the angle and the lack of pictures or descriptive comments associated with the picture [13]. Pendergast et al. (2017) used the smartphone meal diary app (FoodNow) to measure food intake and compare the energy intake estimation with the total energy expenditure provided by an accurate physical activity research monitor (SenseWear Armband) in a population of young people with a healthy BMI range [14]. The authors demonstrated that there is a high correlation coefficient between the estimated energy intake and the measured energy expenditure. The mean difference between the estimation and the measurement was 197 kcal/d for a mean energy expenditure of 2395kcal/d, i.e., an underestimation of energy intake by about 8%. However, they showed wide levels of agreement between the two methods (Armband and FoodNow app) at the individual level (-886kcal to +491kcal). The authors concluded that the app is a more suitable tool for estimating the mean energy intake of a group rather than that of an individual. A recent review of food evaluation provided by smartphone showed that smartphone applications provided similar but not better validity or reliability when their results were compared with classical dietary assessments [14].

Further work is necessary to improve Information and Communication Technology (ICT) tools used for in-depth evaluation. Improvement of food intake evaluation should focus on data collection but, instead, on data treatment. In this study, we propose a simple model of energy intake estimation with a satisfactory level of accuracy.



This observational study was conducted on 190 volunteers. They were recruited anonymously for the open-door event of an INRAE center and through social networks. The volunteers must be adult (older than 18 years), have an Android smartphone and consent free to participate to the study during four days. Moreover we asked them to fill in personal and diet information honestly in the App.

116 women and 74 men, either normal weight (NW, n = 123), or overweight (OW, n = 67), were studied in free-living conditions (Table 1). A total of 131 were used for model development (train sample) and 59 volunteers (test sample) were used to evaluate the validity of energy intake estimation.