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Functional Near Infrared Spectroscopy (fNIRS) Assessment of State Vs. Trait Eating Behavior

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Problem: Disordered Eating Behaviors (DEBs) are state-dependent (short-term) behaviors that can lead to trait-dependent (long-term) characteristics over time. The presence of DEBs for those seeking weight loss presents a risk for development of Eating Disorders (EDs); however, using the Eating Disorder Examination-Questionnaire (EDE-Q) Global Score (GS) to verify non-ED status does not allow for identification of DEBs. Objectives: The overarching objective of this work is to use a state-dependent neuroimaging method, functional Near Infrared Spectroscopy (fNIRS), to assess a modified EDE-Q GS for identifying DEBs in a non-ED population. Specifically, this work aims to (1) explore relationships between fNIRS variables, the original EDE-Q GS, and waist circumference, (2) statistically assess the construction of the original EDE-Q GS, and (3) modify the original EDE-Q GS to embed the Behavioral Features (BF) subscale into it. Methods: Secondary data analysis of fNIRS variables and self-reported eating behaviors (GS+BF) was conducted on data from a study examining relationships between regional prefrontal cortex activation and amount of food eaten in a test meal. A modified GS, the Disordered Eating Behavior Screener and Questionnaire (DEBSQ), was developed to include and address BF. GS and DEBSQ were statistically assessed using multiple regression. Results: Data from 97 participants (M=45, W=52) were analyzed. HbR added significantly to the model predicting GS, F(3, 76) = 3.107, p < 0.05, adj. R2 = 0.109. WC predicted GS in models with Oxy and HbO, F (3, 78) = 1.895, p > 0.05, adj. R2 = 0.068 and F(3, 78) = 1.565, p > 0.05, adj. R2 = 0.057, respectively. WC significantly predict DEBSQ in all the multiple regression models and identified 18 participants who endorsed DEBs (as per the BF subscale). Conclusions: Use of fNIRS revealed that using both state- and trait-dependent questions may provide a more comprehensive assessment of eating behavior for individuals not diagnosed with ED. DEBSQ has potential to incite a contemporary approach to potentially identifying DEBs sooner, given that all antecedent EDE constructs (including the BF subscale) are embedded within it. As such, it may also encourage extension of its use in a non-ED population. Additional studies in non-clinical and clinical samples are warranted.
Title: Functional Near Infrared Spectroscopy (fNIRS) Assessment of State Vs. Trait Eating Behavior
Description:
Problem: Disordered Eating Behaviors (DEBs) are state-dependent (short-term) behaviors that can lead to trait-dependent (long-term) characteristics over time.
The presence of DEBs for those seeking weight loss presents a risk for development of Eating Disorders (EDs); however, using the Eating Disorder Examination-Questionnaire (EDE-Q) Global Score (GS) to verify non-ED status does not allow for identification of DEBs.
Objectives: The overarching objective of this work is to use a state-dependent neuroimaging method, functional Near Infrared Spectroscopy (fNIRS), to assess a modified EDE-Q GS for identifying DEBs in a non-ED population.
Specifically, this work aims to (1) explore relationships between fNIRS variables, the original EDE-Q GS, and waist circumference, (2) statistically assess the construction of the original EDE-Q GS, and (3) modify the original EDE-Q GS to embed the Behavioral Features (BF) subscale into it.
Methods: Secondary data analysis of fNIRS variables and self-reported eating behaviors (GS+BF) was conducted on data from a study examining relationships between regional prefrontal cortex activation and amount of food eaten in a test meal.
A modified GS, the Disordered Eating Behavior Screener and Questionnaire (DEBSQ), was developed to include and address BF.
GS and DEBSQ were statistically assessed using multiple regression.
Results: Data from 97 participants (M=45, W=52) were analyzed.
HbR added significantly to the model predicting GS, F(3, 76) = 3.
107, p < 0.
05, adj.
R2 = 0.
109.
WC predicted GS in models with Oxy and HbO, F (3, 78) = 1.
895, p > 0.
05, adj.
R2 = 0.
068 and F(3, 78) = 1.
565, p > 0.
05, adj.
R2 = 0.
057, respectively.
WC significantly predict DEBSQ in all the multiple regression models and identified 18 participants who endorsed DEBs (as per the BF subscale).
Conclusions: Use of fNIRS revealed that using both state- and trait-dependent questions may provide a more comprehensive assessment of eating behavior for individuals not diagnosed with ED.
DEBSQ has potential to incite a contemporary approach to potentially identifying DEBs sooner, given that all antecedent EDE constructs (including the BF subscale) are embedded within it.
As such, it may also encourage extension of its use in a non-ED population.
Additional studies in non-clinical and clinical samples are warranted.

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