Investigating motivations to eat: Refining and validating a framework of Eating Behaviour Traits in dieters and the general population.

Due to relationships between diet and health including obesity, there is a need to examine the explanatory power of factors that motivate people to (over or under) eat. In a previous investigation, a four-factor subscale-based model of eating behaviour traits (EBTs) was developed which identified individual differences in psychological factors influencing motivations to eat and some residual uncertainties. The current study used a data-driven and theory-driven approach, including individual items to refine and extend previous EBT models. The aim was to examine and validate the domain structure of a framework for EBTs. The analysis used two samples including a representative sample of the UK population (n = 2010, 51% female, 49% male, 18 - 88 years), and members of a weight management program (n = 2317, 96.6% female, 2.8% male, 21 - 84 years), who completed 5 questionnaires including 10 EBTs. The results found some support for a 6-factor model, encompassing reactive eating, negative emotional eating, positive emotional eating, restricted eating, homeostatic eating, and body-food choice congruence (data-driven model) or eating for health (theory-driven model). There were differences between the data-driven model and the theory-driven model regarding the 6th factor. Additionally, the data-driven model did not distinguish between eating for pleasure and reactive eating. The models demonstrated that the eating behaviour factors were significantly associated with BMI category. Overall, this research contributes to a more structured understanding of the dimensions of motivation underlying EBTs, emphasising the utility of this framework for identifying at-risk individuals and tailoring interventions to meet specific individual needs.

Can eating behaviour traits be explained by underlying, latent factors? An exploratory and confirmatory factor analysis.

Eating Behaviour Traits (EBTs) are psychological constructs developed to explain patterns of eating behaviour, including factors that motivate people to (over or under) eat. There is a need to align and clarify their unique contributions and harmonise the understanding they offer for human eating behaviour. Therefore, the current study examined whether 18 commonly cited EBTs could be explained by underlying, latent factors (domains of eating behaviour). An exploratory factor analysis (EFA) was used to identify latent factors, and these factors were validated using a confirmatory factor analysis (CFA). 1279 participants including the general public and members of a weight management programme were included in the analysis (957 females, 317 males, 3 others, 2 prefer not to say), with a mean age of 54 years (median = 57 years, SD = 12.03) and a mean BMI of 31.93 kg/m2 (median = 30.86, SD = 6.00). The participants completed 8 questionnaires which included 18 commonly cited EBTs and the dataset was split at random with a 70/30 ratio to conduct the EFA (n = 893) and CFA (n = 383). The results supported a four-factor model which indicated that EBTs can be organised into four domains: reactive, restricted, emotional, and homeostatic eating. The four-factor model also significantly predicted self-reported BMI and weight change. Future research should test whether this factor structure is replicated in more diverse populations, and including other EBTs, to advance these domains of eating as a unifying framework for studying individual differences in human eating behaviour.

Diet composition and energy intake in humans.

Absolute energy from fats and carbohydrates and the proportion of carbohydrates in the food supply have increased over 50 years. Dietary energy density (ED) is primarily decreased by the water and increased by the fat content of foods. Protein, carbohydrates and fat exert different effects on satiety or energy intake (EI) in the order protein > carbohydrates > fat. When the ED of different foods is equalized the differences between fat and carbohydrates are modest. Covertly increasing dietary ED with fat, carbohydrate or mixed macronutrients elevates EI, producing weight gain and vice versa. In more naturalistic situations where learning cues are intact, there appears to be greater compensation for the different ED of foods. There is considerable individual variability in response. Macronutrient-specific negative feedback models of EI regulation have limited capacity to explain how availability of cheap, highly palatable, readily assimilated, energy-dense foods lead to obesity in modern environments. Neuropsychological constructs including food reward (liking, wanting and learning), reactive and reflective decision making, in the context of asymmetric energy balance regulation, give more comprehensive explanations of how environmental superabundance of foods containing mixtures of readily assimilated fats and carbohydrates and caloric beverages elevate EI through combined hedonic, affective, cognitive and physiological mechanisms. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Exploratory analysis of reflective, reactive, and homeostatic eating behaviour traits on weight change during the 18-month NoHoW weight maintenance trial.

Behaviour change interventions for weight management have found varied effect sizes and frequent weight re-gain after weight loss. There is interest in exploring whether differences in eating behaviour can be used to develop tailored weight management programs. This secondary analysis of an 18-month weight maintenance randomised controlled trial (RCT) aimed to investigate the association between individual variability in weight maintenance success and change in eating behaviour traits (EBT). Data was analysed from the NoHoW trial (Scott et al., 2019), which was designed to measure processes of change after weight loss of ≥5% body weight in the previous year. The sample included 1627 participants (mean age = 44.0 years, SD = 11.9, mean body mass index (BMI) = 29.7 kg/m2, SD = 5.4, gender = 68.7% women/31.3% men). Measurements of weight (kg) and 7 EBTs belonging to domains of reflective, reactive, or homeostatic eating were taken at 4 time points up to 18-months. Increases in measures of 'reactive eating' (binge eating, p < .001), decreases in 'reflective eating' (restraint, p < .001) and changes in 'homeostatic eating' (unlimited permission to eat, p < .001 and reliance on hunger and satiety cues, p < .05) were significantly and independently associated with concomitant weight change. Differences in EBT change were observed between participants who lost, maintained, or re-gained weight for all EBTs (p < .001) except for one subscale of intuitive eating (eating for physical reasons, p = .715). Participants who lost weight (n = 322) exhibited lower levels of reactive eating and higher levels of reflective eating than participants who re-gained weight (n = 668). EBT domains can identify individuals who need greater support to progress in weight management interventions. Increasing reflective eating and reducing reactive eating may enhance weight management success.

Do eating behavior traits predict energy intake and body mass index? A systematic review and meta-analysis.

At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short-term energy intake (EI) and longer-term energy balance as estimated by body mass index (BMI). This systematic review examined the impact of EBT on BMI and laboratory-based measures of EI in adults ( ≥ 18 years) in any BMI category, excluding self-report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random-effects meta-analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m2 , mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694).