Dataset Information

Measuring Motivations to Eat Palatable Foods: Adaptation and Psychometric Properties of the Italian Version of the Palatable Eating Motives Scale (PEMS-IT).

ABSTRACT:

Background

Gaining knowledge of the various reasons behind people's consumption of highly processed foods has the potential to enhance obesity prevention initiatives and open avenues to tailor treatment approaches for obesity and binge eating at a more personalized level. This contribution aimed to test the psychometric properties and factor structure of the Palatable Eating Motives Scale (PEMS-IT) in a community sample of Italian adults.

Methods

A confirmatory factor analysis was performed to test the factor structure of the Italian version of the PEMS (PEMS-IT) on a total of 616 respondents. Furthermore, the reliability and convergent validity analysis of the tool were evaluated.

Results

The analysis confirmed the four-factor structure of PEMS-IT [(YBχ² (164) = 537.901; p < 0.001, the CFI = 0.918, RMSEA = 0.072; 90%CI [0.065-0.078]; p(RMSEA < 0.05) < 0.001, and SRMR = 0.080] and satisfactory reliability on its subscales (Cronbach's α: 0.745-0.917). Positive correlations were also found with food addiction and binge-eating symptoms, compulsive eating behavior, and uncontrolled and emotional eating.

Conclusions

The PEMS-IT appears to be an instrument with promising psychometric properties and potential applications in clinical settings. However, it also has some limitations, and future studies could focus on improving the semantic content of the elements to increase the overall utility and precision of the instrument.

SUBMITTER: Pietrabissa G

PROVIDER: S-EPMC10930930 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Measuring Motivations to Eat Palatable Foods: Adaptation and Psychometric Properties of the Italian Version of the Palatable Eating Motives Scale (PEMS-IT).

Pietrabissa Giada G Castelnuovo Gianluca G Semonella Michelle M Mannarini Stefania S Rossi Alessandro Alberto AA

Healthcare (Basel, Switzerland) 20240229 5

<h4>Background</h4>Gaining knowledge of the various reasons behind people's consumption of highly processed foods has the potential to enhance obesity prevention initiatives and open avenues to tailor treatment approaches for obesity and binge eating at a more personalized level. This contribution aimed to test the psychometric properties and factor structure of the Palatable Eating Motives Scale (PEMS-IT) in a community sample of Italian adults.<h4>Methods</h4>A confirmatory factor analysis was ...[more]

PMID: 38470685

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Project description:BackgroundWhy do we eat? Our motives for eating are diverse, ranging from hunger and liking to social norms and affect regulation. Although eating motives can vary from eating event to eating event, which implies substantial moment-to-moment differences, current ways of measuring eating motives rely on single timepoint questionnaires that assess eating motives as situation-stable dispositions (traits). However, mobile technologies including smartphones allow eating events and motives to be captured in real time and real life, thus capturing experienced eating motives in-the-moment (states).ObjectiveThis study aimed to examine differences between why people think they eat (trait motives) and why they eat in the moment of consumption (state motives) by comparing a dispositional (trait) and an in-the-moment (state) assessment of eating motives.MethodsA total of 15 basic eating motives included in The Eating Motivation Survey (ie, liking, habit, need and hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image) were assessed in 35 participants using 2 methodological approaches: (1) a single timepoint dispositional assessment and (2) a smartphone-based ecological momentary assessment (EMA) across 8 days (N=888 meals) capturing eating motives in the moment of eating. Similarities between dispositional and in-the-moment eating motive profiles were assessed according to 4 different indices of profile similarity, that is, overall fit, shape, scatter, and elevation. Moreover, a visualized person × motive data matrix was created to visualize and analyze between- and within-person differences in trait and state eating motives.ResultsSimilarity analyses yielded a good overall fit between the trait and state eating motive profiles across participants, indicated by a double-entry intraclass correlation of 0.52 (P<.001). However, although trait and state motives revealed a comparable rank order (r=0.65; P<.001), trait motives overestimated 12 of 15 state motives (P<.001; d=1.97). Specifically, the participants assumed that 6 motives (need and hunger, price, habit, sociability, traditional eating, and natural concerns) are more essential for eating than they actually were in the moment (d>0.8). Furthermore, the visualized person × motive data matrix revealed substantial interindividual differences in intraindividual motive profiles.ConclusionsFor a comprehensive understanding of why we eat what we eat, dispositional assessments need to be extended by in-the-moment assessments of eating motives. Smartphone-based EMAs reveal considerable intra- and interindividual differences in eating motives, which are not captured by single timepoint dispositional assessments. Targeting these differences between why people think they eat what they eat and why they actually eat in the moment may hold great promise for tailored mobile health interventions facilitating behavior changes.

Project description:BackgroundThe 26-item Eating Attitudes Test (EAT-26) is a commonly used tool to assess eating disorder risk. The purpose of this study was to examine the psychometric properties of the EAT-26 with a combined sample: (1) of adults with overweight and obesity enrolled in a behavioral weight loss program and (2) general adult sample (n = 469; age = 36.17 ± 17.83 years; female = 72.5%; white = 66.3%; obese BMI category = 58%).MethodsRasch modeling was used to assess model-data fit, create an item-person map to evaluate relative distribution items and persons, item difficulty, and person's eating disorder (ED) risk level of the EAT-26. Differential item functioning (DIF) and rating scale functioning of the EAT-26 were also evaluated using Rasch analysis.ResultsA total of 7 misfit items were removed from the final analysis due to unacceptable Infit and Outfit mean square residual values. The item-person map showed that the items were biased toward participants with moderate to high levels of ED risk and did not cover those who had low risk for having an ED (< - 1 logits). The DIF analyses results showed that none of the items functioned differently across sex, but 5 items were flagged based on obesity status. The six-category Likert-type rating scale did not function well indicating a different response format may be needed.ConclusionSeveral concerns were identified with the psychometric evaluation of the EAT-26 that may question its utility in assessing ED risk in individuals at low risk for ED, within samples of people who have overweight and obesity seeking weight loss treatment. The 26-item Eating Attitudes Test is a self-rated measure of eating attitudes that measures symptoms and concerns of eating disorders (ED). Very little is known about how this instrument performs differently based on individual factors like body mass index (BMI) and sex (male/female). We used an advanced measurement theory (i.e., Rasch analysis) to determine if the EAT-26 is an adequate measure to detect disordered eating in men and women of different BMIs. Results indicated that the EAT-26 was biased toward participants with moderate to high levels of disordered eating risk and did not adequately detect individuals at low risk for disordered eating. The EAT-26 did not function differently based on sex (male/female). However, five questions did function differently based on obesity status (those without obesity/ those with obesity). Finally, we observed the six-category rating scale did not function appropriately and that a new response format may be warranted. In sum, there were several issues (e.g., poor rating scale and different item functioning) with the EAT-26 and future work should develop screening tools that detect low risk of disordered eating as well as function well in adults with overweight and obesity.

Dataset Information

Measuring Motivations to Eat Palatable Foods: Adaptation and Psychometric Properties of the Italian Version of the Palatable Eating Motives Scale (PEMS-IT).

Background

Methods

Results

Conclusions

Publications

Measuring Motivations to Eat Palatable Foods: Adaptation and Psychometric Properties of the Italian Version of the Palatable Eating Motives Scale (PEMS-IT).

Similar Datasets

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