Objective: Food noise has received attention in the media, although no validated questionnaires exist to measure it. This study developed and tested the reliability and validity of the Food Noise Questionnaire (FNQ).
Methods: Participants (N = 400) successfully completed, the FNQ and a demographic questionnaire and self-reported weight and height. A subsample (n = 150) completed the FNQ 7 days later for test-retest reliability, and this subsample's first FNQ data were subjected to exploratory factor analysis. The remaining subsample (n = 250) completed two preoccupation with food questionnaires to test convergent validity, along with mood, anxiety, and stress questionnaires to test for discriminant validity. Confirmatory factor analysis was conducted using this subsample's FNQ data.
Results: Data from 396 participants were analyzed (4 participants did not complete all FNQ items). The FNQ had excellent internal consistency reliability (Cronbach α = 0.93) and high test-retest reliability (r = 0.79; p < 0.001; mean [SD] = 7.4 [1.0] days between administration). Factor analyses found that the five FNQ items loaded onto a single factor, with good fit indices (χ[5] = 52.87, p < 0.001; root mean square error of approximation [RMSEA] = 0.20; comparative fit index [CFI] = 0.95; standardized root mean squared residual [SRMR] = 0.03). The FNQ showed good convergent (all r > 0.78; p < 0.001) and discriminant (all r < 0.39; p < 0.001) validity.
Conclusions: The FNQ provides a psychometrically reliable and valid measure of food noise, although further research is needed to evaluate its clinical utility.
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http://dx.doi.org/10.1002/oby.24216 | DOI Listing |
ACS Omega
January 2025
Laboratory of Natural Products and Mass Spectrometry (LAPNEM), Faculty of Pharmaceutical Sciences, Food, and Nutrition (FACFAN), Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul 79070-900, Brazil.
Leishmaniases present a significant global health challenge with limited and often inadequate treatment options available. Traditional microscopic methods for detecting Leishmania amastigotes are time-consuming and error-prone, highlighting the need for automated approaches. This study aimed to implement and validate the YOLOv8 deep learning model for real-time detection, quantification, and categorization of Leishmania amastigotes to enhance drug screening assays.
View Article and Find Full Text PDFObesity (Silver Spring)
January 2025
Pennington Biomedical Research Center, Louisiana University System, Baton Rouge, Louisiana, USA.
Objective: Food noise has received attention in the media, although no validated questionnaires exist to measure it. This study developed and tested the reliability and validity of the Food Noise Questionnaire (FNQ).
Methods: Participants (N = 400) successfully completed, the FNQ and a demographic questionnaire and self-reported weight and height.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072 China. Electronic address:
The detection of pesticide residues in agricultural products is crucial for ensuring food safety. However, traditional methods are often constrained by slow processing speeds and a restricted analytical scope. This study presents a novel method that uses filter-array-based hyperspectral imaging enhanced by a dynamic filtering demosaicking algorithm, which significantly improves the speed and accuracy of detecting pesticide residues.
View Article and Find Full Text PDFBiostat Epidemiol
October 2024
Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, US.
Wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data with poorly characterized errors. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise with mean zero and constant variance. Viewing this noise as homoscedastic and independent ignores potential serial correlations.
View Article and Find Full Text PDFPLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock. However, there has been little research on using Tiny Machine Learning (Tiny ML) for continuous vocalization monitoring in poultry.
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