Evaluation of a mobile application for estimation of food intake

Abstract

A B S T R A C T ObjectiveEvaluate the use of the Nutrabem (São Paulo, Brasil) mobile application as a tool for measurement of food intake among university students. MethodsCross-sectional study of a random sample of 40 undergraduate students at the Universidade Federal de São Paulo, Campus Baixada Santista. Food intake data were estimated using the Nutrabem app and the 24-hour dietary recall. Intakes of energy, carbohydrates, proteins, lipids, calcium, iron, and vitamin C were calculated. The intake of food groups and diet quality were evaluated by the Diet Quality Index associated with the Digital Food Guide. The agreement between the methods was assessed using the Pearson's correlation coefficient and the Student' t-test. ResultsStrong correlations were observed between energy (0.77), carbohydrates (0.82) and protein (0.83). The groups: poultry, fish, and eggs; beef and pork; refined grains and breads; and fruits and legumes showed strong correlations (between 0.76 and 0.85). There were moderate correlations (0.59 and 0.71) between the groups sugars and sweets; whole grains, tubers and roots, milk and dairy products, animal fats, and the Diet Quality Index associated with the Digital Food Guide scores. Vegetables and leafy greens, nuts, and vegetable oils showed weak correlations (0.31 and 0.43). Homogeneity assessment revealed similarity between the results obtained by both methods (p>0.05). ConclusionThe Nutrabem app can be used as a tool to assess dietary intake among university students since it produces results similar to those obtained by the 24-hour dietary recall method.

A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps

“…The period of app use ranged mostly from 2 to 7 d and contained at least 1 weekend day for most studies, while 2 studies asked participants to record every day for 3 mo ( 28 , 29 ). The app use was on nonconsecutive days for 3 of the studies ( 12 , 30 , 36 ). Ten studies used 24HRs as the only reference method for 2 d ( n = 6) ( 16 , 20 , 28 , 34 , 37 , 38 ) or 3 d ( n = 4) ( 32 , 33 , 35 , 36 ).…”

“…The app use was on nonconsecutive days for 3 of the studies ( 12 , 30 , 36 ). Ten studies used 24HRs as the only reference method for 2 d ( n = 6) ( 16 , 20 , 28 , 34 , 37 , 38 ) or 3 d ( n = 4) ( 32 , 33 , 35 , 36 ). One study used an FFQ ( 29 ), 1 study used dietary records ( 30 ), 2 studies used an accelerometer (to measure energy expenditure) ( 12 , 31 ), and 1 study used a combination of accelerometer, 24HRs, and dietary records ( 34 ).…”

“…Table 2 shows the included nutrients and statistical tests in each study. Ten studies investigated the energy and macronutrient intake, while 6 of them also compared micronutrient intake ( 28 , 29 , 32 , 35 , 36 , 38 ). Four studies looked at food group intakes ( 33 , 34 , 36 , 37 ).…”

“…Ten studies investigated the energy and macronutrient intake, while 6 of them also compared micronutrient intake ( 28 , 29 , 32 , 35 , 36 , 38 ). Four studies looked at food group intakes ( 33 , 34 , 36 , 37 ). In terms of statistical parameters and tests, the frequency of using a paired t test was the highest ( n = 12), followed by correlation coefficients ( n = 11) and Bland-Altman LOAs ( n = 11).…”

Characteristics of smartphone-based dietary assessment tools: A systematic review

“…In Ali et al (143), participants were able to choose from predetermined portion sizes if they indicated that they consumed foods from certain restaurants that were included in the app. In some studies, providing portion sizes was not mandatory, but only asked for if no other recording (e.g., taking a food picture) was performed (138). To increase accuracy, some studies trained participants in portion size estimation (70,79,107), while others provided a scale and measurement cups and spoons (94) or asked participants to weigh the foods before consumption (110).…”