Time trend estimation of food consumption in repeated studies with different versions of food questionnaire among Brazilian schoolchildren aged 7 to 11 years

Abstract

Longitudinal study, whose objective was to evaluate of the time trend in food consumption across the 2002-2015 period in schoolchildren aged 7 to 11 years, covered five food surveys in Florianopolis, southern Brazil. Methodological differences across the surveys (typical vs. previous day food consumption, pen-and-paper versus computer screen presentation) and some known risk factors, were adjusted for statistically. Offset by maximum food/beverage consumption per day allowed comparability of a varying number of food components in a food group over survey years. Significant reduction in some healthy (fruits and animal proteins) and unhealthy diet markers (sodas, sugary drinks, sweets) was observed over the 2013-2015 period compared with the 2002 and 2007 data. Computer screen presentation of the food questionnaire systematically reduced the reporting frequency compared to the pen-and-paper presentation. Converting frequency reports into rates and using contrast analysis allowed time trend estimation based on food questionnaires with different presentation modes, varying number of items within food group over survey years, and variations in probabilistic sampling procedures.

Esquada e sua correlação com os marcadores de alimentação em inquérito de saúde: Evidências de validade

“…Food markers signal the healthiness of a population's food consumption, contributing to food and nutritional surveillance 10,21,22 and the formulation and implementation of public policies 10,22 . It is observed that population surveys have included their evaluation in their protocols, even enabling the understanding of the temporal evolution of food consumption 10,23,24,25 and different cuts in the analysis 26 . In times of health emergency, Steele and collaborators 27 analyzed changes in markers of healthy and unhealthy eating when evaluated before and during the COVID-19 pandemic.…”

Chronic Diseases: A Comprehensive Review of Applications for Prediction of Obesity

“…As we continue to amass large datasets and refine algorithmic techniques, the scope for ML to revolutionize obesity management and prevention is immense. Future avenues for research and application are likely to focus on several key areas that enhance both the precision and effectiveness of interventions [66,67].…”

“…By harnessing the power of big data analytics, ML can provide deeper insights into the multifactorial causes of obesity, predicting individual susceptibility with higher accuracy. This precision will allow for the implementation of preemptive measures tailored to individual risk profiles before obesity develops [66][67][68][69]. Secondly, personalized treatment plans based on ML predictions are set to become more nuanced.…”