# Associations between external eating cues, diet history and weight history in French subjects with obesity

Emilie Ducy, Monique Romon, Philippe Amouyel, Aline Meirhaeghe

<figure><img src="image_1.png" /></figure>

## To cite this version:

Emilie Ducy, Monique Romon, Philippe Amouyel, Aline Meirhaeghe. Associations between external eating cues, diet history and weight history in French subjects with obesity. Appetite, 2025, 207, pp.107868. 10.1016/j.appet.2025.107868. hal-05420960

HAL Id: hal-05420960

https://hal.science/hal-05420960v1

Submitted on 13 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_3.png" /></figure>

# Associations between external eating cues, diet history and weight history in French subjects with obesity

Emilie Ducy<sup>a, b</sup>, Monique Romon<sup>c</sup>, Philippe Amouyel<sup>b</sup>, Aline Meirhaeghe<sup>b, * ID</sup>

## ARTICLE INFO

### Keywords:

- Obesity

- Eating behavior

- Hierarchical ascending classification

- External cues

- Diet

## ABSTRACT

Obesity remains a serious public health concern. Historically, restrictive diets were the first line of treatment for obesity and are still frequently followed by subjects with obesity. However, some studies showed that dieting may contribute to long-term weight gain. External eating cues might be implicated in this deleterious effect. We then wanted to determine whether the history of dieting was associated with higher reliance on external eating cues (such as emotional eating, uncontrolled eating, or ending a food intake because of the stomach overfullness or the plate emptiness) in individuals with obesity, and to examine whether these associations differed between subjects with different weight history. We included 1093 subjects with obesity from the GECCOS study and examined the association between external eating cues and dieting history (if < 3 diets or ≥ 3 diets) in the sample and in clusters (defined by hierarchical ascending classification on their weight history (based on current BMI, BMI at 20 years old and age of 1st diet)). Three clusters were obtained: a “early-onset obesity” group, a “late-onset obesity” group and an “intermediate” group. Having followed at least 3 diets was associated with higher BMI, higher emotional eating and with eating until stomach overfullness in the global population, but it was not associated with uncontrolled eating or for ending the meal when the plate is empty. These associations were not always detected in each cluster, showing that weight history modulated these associations. To conclude, dieting history is associated with certain external eating cues such as emotional eating and stopping the meal at stomach overfullness in subjects with obesity, even when their weight history is taken into account.

## 1. Introduction

Obesity is an ongoing public health concern defined as an excess of body fat mass leading to medical complications according to the World Health Organization. The mechanism responsible for a gain in weight relies on energy balance, which is mainly regulated by eating behavior. Indeed, altered eating behaviors are known to be implicated in obesity (Rohrer et al., 2009; Vasileiou & Abbott, 2023).

On the other hand, diets have been a historical treatment for obesity for many years and continue to be followed by some subjects with obesity. However, diets are also implicated in long-term weight gain (Pietiläinen et al., 2012; Sares-Jäske et al., 2019). Indeed, the relationship between diets and eating behavior is complex, as diets tend to be involved in eating disorders (Tuschl, 1990) and changes in eating behaviors, some of which are associated with weight gain (Bénard et al.,

2018; Rohrer et al., 2009).

Studies indicate that uncontrolled eating and emotional eating are more prevalent in individuals with obesity (Vasileiou & Abbott, 2023). Undergoing multiple restrictive diets can lead individuals to rely solely on external eating cues regarding what and when to eat in order to achieve weight loss (Bilman et al., 2017). As a result, subjects may lose the ability to perceive internal signals (hunger or satiety) without significant effort and concentration, and are more often led by more objective signals such as ending a meal when the plate is empty or because of stomach overfullness. Then, energy intakes are no longer in equilibrium with energy expenditure.

Obesity is a multifactorial disease that evolves over time (Green et al., 2016; Jimenez et al., 2018). It limits the ability to engage in physical activity, increases stigmatization and psychological issues, which contributes to further weight gain, which can also be exacerbated

<footnote><sup>a</sup> Diabetology & Nutrition, GHICL, Hopital Saint Philibert, Lomme, France</footnote>

<footnote><sup>b</sup> University of Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE, Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France</footnote>

<footnote><sup>c</sup> University of Lille, ULR, 2694, METRICS: Évaluation des technologies de santé et des pratiques médicales, Lille, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: aline.meirhaeghe@inserm.fr (A. Meirhaeghe).</footnote>
by diets. Therefore, there is a need to study different aspects of eating behaviors to better understand and address this condition.

Based on these observations, we hypothesized that having followed multiple diets can contribute to a lower sensitivity to internal cues (like satiety) and a higher reliance on external cues to initiate a meal (i.e. emotional eating or uncontrolled eating) or to stop a meal (i.e. stomach overfullness or an empty plate). This leads to a mismatch between energy intake and expenditure and, ultimately, a higher BMI. However, the impact of this mismatch may vary between groups of individuals with different weight history. To our knowledge, the effect of multiple dieting history on these eating behaviors in individuals with obesity is not well described in the literature, nor are the differences in dieting history according to weight history. Yet, these considerations are essential to provide appropriate care for subjects with obesity, excluding dieting.

To investigate this hypothesis, the first objective of this study was to determine, in a sample of individuals with obesity, whether dieting history was associated with variations in the reliance in external eating cues (emotional eating, uncontrolled eating; and external reasons to stop food intake such as stomach overfullness or an empty plate). Secondly, with a cluster analysis, we created groups of individuals according to their weight history and we assessed whether these groups differed in terms of external eating cues. Thirdly we evaluated if the history of dieting was associated with variations in the reliance in external eating cues and BMI in each cluster separately.

## 2. Material and methods

### 2.1. Data source

We used data from the GECCOS (Gene Environment Case Control Study of Obesity), a case-control study of obesity conducted between 2006 and 2016, which enrolled 2237 individuals with obesity who consulted for obesity at Lille University hospital (Pigeyre et al., 2016). The main inclusion criteria were as follows: age 18 or over and BMI >30 kg/m².

The protocol was approved by the local investigational review board (CPP Nord Ouest IV; reference: CP05/63). All participants provided their written, informed consent.

### 2.2. Data collected

Anthropometric measurements such as weight, height and abdominal circumference measured by physicians during the examination. Weight history including age of onset of obesity, history of dieting, age

of first diet, and weight at age 20 as well as medical history including depression, smoking status and the family history of obesity were recorded.

Socio Economic Status (SES) including occupational social class, educational level, personal and household monthly incomes, was aggregated into a score ranging from 0 to 40. “High SES” corresponds to scores between 0 and 20, “Intermediate SES” to scores between 21 and 30 and “Low SES” to scores between 31 and 40 (Pigeyre et al., 2016). The presence of a governmental financial help was also recorded as well as the single occupancy status.

Physical activity level with the Ricci and Gagnon questionnaire which measures leisure physical activity, daily physical activity and sedentary behavior. Subjects were classified as non-active if the score was under 17, active if between 17 and 32, and very active above 32.

Dieting history (the physician asked whether there had been 0, between 1 and 3, or more than 3 episodes of dieting in the past). We created two groups: “Many diets” if individuals followed 3 diets or more, and “Few diets” otherwise (Fig. 1).

External eating cues assessed with the Three Factor Eating Questionnaire (TFEQ) for emotional eating and uncontrolled eating (Cappelleri et al., 2009) (scores ranged from 0 to 100 for each eating factor). Questions regarding eating attitudes, including a question on the main reason for stopping a meal, distinguishing between internal eating cues (e.g. satiety (not feeling hungry anymore)) and external eating cues (e.g. stomach overfullness or plate emptiness) were also asked to participants.

Quality of life evaluated through the 3-level version of the EuroQol five-dimensional questionnaire (EQ 5D3L) (Chevalier & De Pouvourville, 2013), encompassing five dimensions: current mobility, autonomy, impact of pain, anxiety or depression status and daily activities. Scores ranged from 1 to -0.53, with 1 indicating the better quality of life possible.

### 2.3. Study participants

Subjects with missing values for the variables “BMI”, “BMI at 20 years old” and “age of the first diet” were excluded (n = 1144) as these 3 variables were used to create clusters. It resulted in 1093 subjects remaining for our analysis. Missing values for the other variables were imputed using the MissMDA package (iterates Principal Component Analysis (PCA) until convergence with a terminal correction).

<figure><img src="image_4.png" /><figcaption>Fig. 1. Flowchart.</figcaption></figure>
## 2.4. Statistical analyses

The SAS 9.4 and R Studio version 4.3.0 softwares were used for the analyses. After visually confirming the normality of quantitative variables, an inverse transformation was applied to achieve a normal distribution for BMI, BMI at 20 years old, and the score of quality of life. Categorical, binary, or discrete variables with few modalities were expressed as numbers and percentages. Quantitative variables were expressed as means and standard deviations (SD) if the histogram showed a symmetric distribution, and median first and third quartile (Q1; Q3) otherwise.

A PCA was first performed using the FactoShiny and FactoMineR packages. This permitted to pre-treat the variables (center and reduce the data and optimize the axes) but also to verify the absence of outliers, check collinearity, and have a good visualization of the population in two dimensions according to the selected axes. We used 3 quantitative variables (BMI, BMI at 20 years old, and the age of the first diet) and kept the 3 first axes to explain 100% of the variance. We then performed a Hierarchical Ascendant Classification to create a dendrogram. The division into three groups was chosen based on the significant reduction in inertia gain between the first and second divisions, but no further reduction after these, to achieve a good compromise between sufficiently high intergroup variability and low intragroup variability. To homogenize the groups created, a consolidation with a k-means method was performed.

The main outcome variables were external eating cues, internal eating cues and BMI. The main exposure variable was the number of previous dieting episodes. The adjustment variables were sex, age, physical activity, SES group ± BMI (when BMI was not an outcome).

Chi² test/Fisher's exact test and logistic regression tests were used for categorical variables and Student test, ANOVA and linear regression tests for continuous variables. P ≤ 0.05 was the threshold for statistical significance.

The data used in the study are available on request and the lead author has full access to the data reported in the manuscript.

# 3. Results

## 3.1. Description of the population

Our sample included 1093 individuals with obesity. The descriptive characteristics of the individuals are shown in Table 1. We observed that 85.3% of the participants were women, with an average age of 43.3 ± 13.2 years. The median BMI was 39.8 interquartile range [35.9; 45.1] kg/m². Almost all subjects had at least one first-degree relative with a history of obesity. More than one third of individuals reported having gained weight before the age of 7. The age at which participants initiated their first diet was on average at 23.7 ± 11.1 years. The median BMI at 20 years old was 26.1 interquartile range [22.5; 31.3] kg/m². Additionally, a large majority (69.5%) of the sample had experienced at least 3 diets in their life.

External eating cues played a predominant role, with 60.2% of participants stopping their meals when their plate was empty and a quarter because of stomach overfullness. The average scores for emotional eating and uncontrolled eating were elevated (55.0 ± 29.2 and 39.7 ± 23.0, respectively).

## 3.2. Association between external eating cues/BMI and history of diets

We compared the external/internal cues and BMI according to dieting history. As shown in Table 2, BMI was higher in subjects who had followed many diets compared to those who had followed few diets (42.0 ± 7.8 vs 39.2 ± 6.0, p < 0.0001). Emotional eating was also higher in the "many diets" group (56.8 ± 29.1 vs 50.9 ± 29.0, p = 0.03), as was eating until stomach overfullness (17.8% vs 9.3%, p = 0.002). In contrast, satiety (i.e. stopping the meal when feeling no longer hungry)

<table><caption>**Table 1**

Description of the population (N max = 1093).</caption><thead><tr><th>Variables</th><th>N with missing data</th><th>After imputation of missing data</th></tr></thead><tbody><tr><td>Anthropometry</td><td></td><td></td></tr><tr><td>Current BMI (kg/m2) <sup>a</sup></td><td>/</td><td>39.8 [35.9; 45.1]</td></tr><tr><td>BMI at 20 years old (kg/m2) <sup>a</sup></td><td>/</td><td>26.1 [22.5; 31.3]</td></tr><tr><td>Women</td><td>/</td><td>85.3 %</td></tr><tr><td>Age (y)</td><td>1</td><td>43.4 ± 13.2</td></tr><tr><td>Abdominal circumference (cm)</td><td>549</td><td>118.9 ± 13.4</td></tr><tr><td>Weight history</td><td></td><td></td></tr><tr><td>Weight gain: &lt;7 y</td><td>47</td><td>31.7 %</td></tr><tr><td>Weight gain: 7 y-18y</td><td>47</td><td>29.7 %</td></tr><tr><td>Weight gain: ≥18y</td><td>47</td><td>38.5 %</td></tr><tr><td>Number of previous diets: 0</td><td>72</td><td>/</td></tr><tr><td>Number of previous diets: 1-2</td><td>72</td><td>30.5 %</td></tr><tr><td>Number of previous diets: ≥3</td><td>72</td><td>69.5 %</td></tr><tr><td>Age of the first diet (y) <sup>a</sup></td><td>/</td><td>23.7 ± 11.1</td></tr><tr><td>Maximum weight loss during a diet (kg)</td><td>266</td><td>17.9 ± 10.9</td></tr><tr><td>Medical history/rowhead</td><td></td><td></td></tr><tr><td>History of depression</td><td>29</td><td>19.8 %</td></tr><tr><td>Obese first-degree relatives</td><td>748</td><td>99.8 %</td></tr><tr><td>Current smoking</td><td>180</td><td>14.3 %</td></tr><tr><td>Socio economic indicators</td><td></td><td></td></tr><tr><td>Socio-economic category: high</td><td>1</td><td>25.1 %</td></tr><tr><td>Socio-economic category: intermediate</td><td>1</td><td>41.8 %</td></tr><tr><td>Socio-economic category: low</td><td>1</td><td>33.1 %</td></tr><tr><td>Single occupancy</td><td>26</td><td>17.9 %</td></tr><tr><td>Government financial aids</td><td>/</td><td>19.2 %</td></tr><tr><td>Physical activity</td><td></td><td></td></tr><tr><td>Non active</td><td>15</td><td>57.7 %</td></tr><tr><td>Active</td><td>15</td><td>42.0 %</td></tr><tr><td>Very active</td><td>15</td><td>0.3 %</td></tr><tr><td>Quality of life score (EQ5D5L)</td><td>256</td><td>0.64 [0.49; 0.80]</td></tr><tr><td>Mobility: normal</td><td>233</td><td>58.9 %</td></tr><tr><td>Mobility: altered</td><td>233</td><td>40.9 %</td></tr><tr><td>Mobility: much limited</td><td>233</td><td>0.2 %</td></tr><tr><td>Autonomy: normal</td><td>235</td><td>88.7 %</td></tr><tr><td>Autonomy: altered</td><td>235</td><td>10.5 %</td></tr><tr><td>Autonomy: much limited</td><td>235</td><td>0.8 %</td></tr><tr><td>Pain: no consequences on daily life</td><td>239</td><td>11.5 %</td></tr><tr><td>Pain: low consequences on daily life</td><td>239</td><td>74.2 %</td></tr><tr><td>Pain: much consequences on daily life</td><td>239</td><td>14.3 %</td></tr><tr><td>Anxiety/Depression: no consequences on daily life</td><td>235</td><td>25.7 %</td></tr><tr><td>Anxiety/Depression: low consequences on daily life</td><td>235</td><td>61.4 %</td></tr><tr><td>Anxiety/Depression: much consequences on daily life</td><td>235</td><td>12.9 %</td></tr><tr><td>Daily activities: normal</td><td>237</td><td>60.2 %</td></tr><tr><td>Daily activities: altered</td><td>237</td><td>38.5 %</td></tr><tr><td>Daily activities: much limited</td><td>237</td><td>1.3 %</td></tr><tr><td>Eating attitudes</td><td></td><td></td></tr><tr><td>Reason to stop the meal: full stomach</td><td>550</td><td>15.2 %</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>550</td><td>62.0 %</td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>550</td><td>22.8 %</td></tr><tr><td>Eating behavior scores</td><td></td><td></td></tr><tr><td>Uncontrolled eating</td><td>21</td><td>39.7 ± 23.0</td></tr><tr><td>Cognitive restraint</td><td>22</td><td>40.3 ± 18.9</td></tr><tr><td>Emotional eating</td><td>21</td><td>55.0 ± 29.2</td></tr></tbody></table>

Data are mean ± SD or median [Q1; Q3] or percentage.

<sup>a</sup> variables used for the clustering analysis.

was less prevalent in subjects who followed many diets (20.4% vs 28.2%, p = 0.006).

## 3.3. Description of the clusters

We then classified the individuals according to weight history (with the variables BMI, BMI at 20 years old and age at first diet) using cluster analysis (Fig. 2). This resulted in the formation of 3 distinct groups (Table 3).
<table><caption>**Table 2**

Association between BMI, external and internal cues according to the number of diets followed in the past.</caption><thead><tr><th>Variables</th><th>1 or 2 diets followed (n = 333)</th><th>≥3 diets followed (n = 760)</th><th>crude p value</th><th>p value <sup>a</sup></th><th>p value <sup>b</sup></th></tr></thead><tbody><tr><td>External cues</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncontrolled Eating</td><td>39.2 ± 23.6</td><td>39.9 ± 22.7</td><td>0.63</td><td>0.68</td><td>0.40</td></tr><tr><td>Emotional Eating</td><td>50.9 ± 29.0</td><td>56.8 ± 29.1</td><td>0.002</td><td>0.06</td><td>0.03</td></tr><tr><td>Reason to stop the meal: full stomach</td><td>9.3 %</td><td>17.8 %</td><td>0.0004</td><td>0.0005</td><td>0.002</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>62.5 %</td><td>61.8 %</td><td>0.85</td><td>0.51</td><td>0.88</td></tr><tr><td>Internal cue</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>28.2 %</td><td>20.4 %</td><td>0.005</td><td>0.03</td><td>0.006</td></tr><tr><td>Body Mass Index (kg/m2)</td><td>39.3 ± 6.0</td><td>42.0 ± 7.8</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td></td></tr></tbody></table>

Data are mean ± SD or percentage. Significant p values are indicated in bold.

<sup>a</sup> adjusted for age, sex, physical activity and SES group.

<sup>b</sup> adjusted for age, sex, physical activity, BMI and SES group.

Cluster 1 (n = 378) was characterized by subjects who developed obesity at a younger age compared to the other clusters, with 88.3% gaining weight before adulthood (46.8% before 7 years old). They had the highest mean BMI (46.4 [43.3; 50.6] kg/m²), with a BMI already elevated at 20 years old (32.9 [28.8; 39.1] kg/m²). The mean age of individuals in the group was significantly younger than the others (38.7 ± 12.2 y). Most individuals in this cluster (80.7%) had undergone multiple diets, started dieting at an earlier age than the other groups (17.6 ± 6.7 years) so we labelled this group “early-onset obesity”. In addition, ending the meal at stomach overfullness was significantly more frequent in this group (19.3%, p < 0.05). They had the lowest cognitive restraint score (37.9 ± 17.7) and seemed to experience greater socio economic precarity, based on the SES group and the financial aids they received.

Cluster 2 (n = 317) included older subjects (53.5 ± 9.7y) with the latest onset of obesity (78.5% after 18 years old), a normal BMI (21.8 [19.9; 23.9] kg/m²) at 20 years old and a current grade 2 obesity (BMI 38.1 [34.4; 42.4] kg/m²). Around half of individuals had undergone 3 diets or more (51.1%), with the first diet occurring late in life (36.9 ± 9.3 years). This cluster was labelled as “late-onset obesity”. Eating until stomach overfullness was not frequent in this cluster (8.5%, p < 0.05).

Cluster 3 (n = 398), labelled as “intermediate obesity”, was characterized by individuals who had undergone multiple diets (73.6%), and had the highest scores of cognitive restraint, of emotional eating and of

uncontrolled eating. Subjects belonging to this cluster had the lowest BMI (36.7 [34.1; 38.8] kg/m², p < 0.05) and the highest level of physical activity (45% were active). This cluster had the highest prevalence of subjects with a high socio-economic status (30.7%) and the lowest prevalence of subjects with a low socio-economic status (26.4%).

### 3.4. Association between external eating cues/BMI and the number of diets followed in each cluster

We then assessed the association between the number of diets and external eating cues/BMI in each cluster (Fig. 2). These results are shown in Tables 4–6 for clusters 1, 2 and 3, respectively.

In the “early-onset obesity” cluster (Table 4), compared to subjects in the “few diets” group, subjects in the “many diets” group displayed higher cognitive restraint (31.7 ± 16.3 vs 39.4 ± 17.7, respectively, p = 0.01), higher BMI (45.2 [42.2; 48.8] vs 46.7 [43.8; 51.1] kg/m², respectively, p = 0.01) and a higher prevalence of eating until stomach overfullness (11.0% vs 21.3%, respectively, p = 0.03). On the contrary, satiety was less prevalent in the “many diet” group (22.6% vs 37.0%, p = 0.004).

In the “late-onset obesity” cluster (Table 5), individuals in the “many diets” group had higher BMI (39.2 [34.7; 42.9] vs 37.1 [34.3; 41.9], p = 0.05), higher emotional eating (55.9 ± 31.6 vs 48.2 ± 31.4, p = 0.05), and ate more frequently until stomach overfullness (11.7% vs 5.2% p = 0.05, only without adjustment for BMI) compared to the “few diets” group.

In the “intermediate obesity” cluster (Table 6), only BMI was higher in the “many diets” group compared to the “few diets” group (36.9 [34.3; 38.9] vs 36.3 [33.3; 38.5], respectively, p = 0.04).

## 4. Discussion

We showed that our sample of 1093 subjects with obesity could be clustered into three groups on the basis of BMI, BMI at 20 and the age of first diet, which represents a brief summary of their weight history. We also showed that a history of at least 3 diets were associated with higher BMI, higher emotional eating scores and stopping meals because of a feeling of stomach overfullness.

### 4.1. Clustering data

To our knowledge, there has been no previous clustering study specifically on weight history in subjects with obesity. Existing studies reporting clusters frequently focused on somatic complications of obesity (like depression, diabetes, hypertension, etc ...) or on general behavioral characteristics and included all the available variables.

For example, a clustering study (Romain et al., 2022) identified groups according to eating behavior (assessed by the TFEQ-21R and the DEBQ) and other variables such as BMI, depressive symptoms, comfort

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Clustering process and creation of dieting history groups.</figcaption></figure>
<table><caption>**Table 3**
Description of the clusters.</caption><thead><tr><th rowspan="2">Variables</th><th>Early-onset obesity</th><th>Late-onset obesity</th><th>Intermediate obesity</th><th colspan="2">crude p value</th><th>adjusted p value <sup>b</sup></th></tr><tr><th>(Cluster 1, n = 378)</th><th>(Cluster 2, n = 317)</th><th>(Cluster 3, n = 398)</th><th>global effect</th><th>modality effect</th><th></th></tr></thead><tbody><tr><td>Anthropometry</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current BMI (kg/m2) <sup>a</sup></td><td>46.4 [43.3; 50.6]</td><td>38.1 [34.4; 42.4]</td><td>36.7 [34.1; 38.8]</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>BMI at 20 years old (kg/m2) <sup>a</sup></td><td>32.9 [28.8; 39.1]</td><td>21.8 [19.9; 23.9]</td><td>25.8 [23.4; 28.8]</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Women <sup>c</sup></td><td>81.0 %</td><td>83.9 %</td><td>90.5 %</td><td>0.0007</td><td></td><td>0.0005</td></tr><tr><td>Age (y) <sup>d</sup></td><td>38.7 &plusmn; 12.2</td><td>53.5 &plusmn; 9.7</td><td>39.7 &plusmn; 12.2</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Abdominal circumference (cm)</td><td>128.6 &plusmn; 12.7</td><td>117.5 &plusmn; 11.0</td><td>110.9 &plusmn; 9.4</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Weight history</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Weight gain: &lt;7 y</td><td>46.8 %</td><td>11.7 %</td><td>33.4 %</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Weight gain: 7 y-18 y</td><td>41.5 %</td><td>9.8 %</td><td>34.4 %</td><td></td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Weight gain: &ge;18 y</td><td>11.6 %</td><td>78.5 %</td><td>32.2 %</td><td></td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Number of previous diets: &ge;3</td><td>80.7 %</td><td>51.1 %</td><td>73.6 %</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Age of the first diet (y) <sup>a</sup></td><td>17.6 &plusmn; 6.7</td><td>36.9 &plusmn; 9.3</td><td>19.0 &plusmn; 5.6</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Maximum weight loss/diet (kg)</td><td>21.3 &plusmn; 12.6</td><td>15.1 &plusmn; 8.0</td><td>16.7 &plusmn; 10.3</td><td>&lt;0.0001</td><td></td><td>&lt;0.0001</td></tr><tr><td>Medical history</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current smoking</td><td>18.8 %</td><td>7.9 %</td><td>15.1 %</td><td>0.0002</td><td></td><td>0.40</td></tr><tr><td>History of depression</td><td>18.8 %</td><td>19.2 %</td><td>21.1 %</td><td>0.69</td><td></td><td>0.20</td></tr><tr><td>Socio economic indicators</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High SES <sup>e</sup></td><td>22.8 %</td><td>20.8 %</td><td>30.7 %</td><td>0.0018</td><td>0.0049</td><td>0.0091</td></tr><tr><td>Intermediate SES <sup>e</sup></td><td>39.9 %</td><td>42.6 %</td><td>43.0 %</td><td></td><td>0.66</td><td>0.38</td></tr><tr><td>Low SES <sup>e</sup></td><td>37.3 %</td><td>36.6 %</td><td>26.4 %</td><td></td><td>0.0017</td><td>0.0028</td></tr><tr><td>Single occupancy</td><td>16.4 %</td><td>22.1 %</td><td>16.1 %</td><td>0.07</td><td>&lt;0.0001</td><td>0.80</td></tr><tr><td>Government financial aids</td><td>26.5 %</td><td>11.7 %</td><td>18.3 %</td><td>&lt;0.0001</td><td>0.07</td><td>0.06</td></tr><tr><td>Physical activity (Ricci and Gagnon Questionnaire)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Active/Very active <sup>f</sup></td><td>40.7 %</td><td>40.7 %</td><td>45.0 %</td><td>0.39</td><td></td><td>0.39</td></tr><tr><td>Quality of life score (EQ 5D5L)</td><td>0.64 [0.49; 0.80]</td><td>0.58 [0.36; 0.80]</td><td>0.80 [0.49; 0.80]</td><td>0.80</td><td></td><td>0.62</td></tr><tr><td>Mobility: normal</td><td>51.6 %</td><td>53.3 %</td><td>70.4 %</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Mobility: altered</td><td>47.9 %</td><td>46.7 %</td><td>29.6 %</td><td></td><td>&lt;0.0001</td><td>0.002</td></tr><tr><td>Mobility: much limited</td><td>0.5 %</td><td>0%</td><td>0%</td><td>NA</td><td></td><td>NA</td></tr><tr><td>Autonomy: normal</td><td>84.9 %</td><td>85.8 %</td><td>94.5 %</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.0003</td></tr><tr><td>Autonomy: altered</td><td>14.3 %</td><td>12.3 %</td><td>5.5 %</td><td></td><td>0.0003</td><td>0.0007</td></tr><tr><td>Autonomy: much limited</td><td>0.8 %</td><td>1.9 %</td><td>0%</td><td>NA</td><td></td><td>NA</td></tr><tr><td>Pain: no consequences on daily life</td><td>14.3 %</td><td>7.3 %</td><td>12.3 %</td><td>0.0111</td><td>0.0145</td><td>0.78</td></tr><tr><td>Pain: low consequences on daily life</td><td>72.8 %</td><td>74.4 %</td><td>75.4 %</td><td></td><td>0.70</td><td>0.70</td></tr><tr><td>Pain: much consequences on daily life</td><td>13.0 %</td><td>18.3 %</td><td>12.3 %</td><td></td><td>0.05</td><td>0.87</td></tr><tr><td>Anxiety/Depression: no consequences on daily life</td><td>31.7 %</td><td>21.5 %</td><td>23.4 %</td><td>0.0002</td><td>0.0036</td><td>0.23</td></tr><tr><td>Anxiety/Depression: low consequences on daily life</td><td>57.7 %</td><td>59.9 %</td><td>66.1 %</td><td></td><td>0.0459</td><td>0.10</td></tr><tr><td>Anxiety/Depression: much consequences on daily life</td><td>10.6 %</td><td>18.6 %</td><td>10.6 %</td><td></td><td>0.0018</td><td>0.12</td></tr><tr><td>Daily activities: normal</td><td>55.0 %</td><td>54.6 %</td><td>69.6 %</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.0005</td></tr><tr><td>Daily activities: altered</td><td>43.7 %</td><td>43.5 %</td><td>29.6 %</td><td></td><td>&lt;0.0001</td><td>0.0009</td></tr><tr><td>Daily activities: much limited</td><td>1.3 %</td><td>1.9 %</td><td>0.8 %</td><td></td><td>0.42</td><td>0.60</td></tr><tr><td>Eating attitudes/rowhead</td><td></td><td></td><td></td><td>0.002</td><td></td><td></td></tr><tr><td>Reason to stop the meal: full stomach</td><td>19.3 %</td><td>8.5 %</td><td>16.6 %</td><td></td><td>0.0004</td><td>0.041</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>55.3 %</td><td>68.1 %</td><td>63.6 %</td><td></td><td>0.0018</td><td>0.11</td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>25.4 %</td><td>23.3 %</td><td>19.8 %</td><td></td><td>0.18</td><td>0.67</td></tr><tr><td>Eating behavior scores</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncontrolled eating</td><td>40.1 &plusmn; 22.6</td><td>35.7 &plusmn; 23.7</td><td>42.5 &plusmn; 22.3</td><td>0.0004</td><td></td><td>0.33</td></tr><tr><td>Cognitive restraint</td><td>37.9 &plusmn; 17.7</td><td>41.2 &plusmn; 19.2</td><td>41.9 &plusmn; 19.5</td><td>0.0074</td><td></td><td>0.16</td></tr><tr><td>Emotional eating</td><td>53.8 &plusmn; 28.5</td><td>52.1 &plusmn; 31.7</td><td>58.5 &plusmn; 27.6</td><td>0.0098</td><td></td><td>0.26</td></tr></tbody></table>

Data are mean ± SD or median [Q1; Q3] or percentage. Significant p values are indicated in bold. NA: not available.

<sup>a</sup> variables used for the clustering analysis.

<sup>b</sup> adjusted for BMI, age, sex, physical activity and SES group.

<sup>c</sup> adjusted for BMI, age, physical activity and SES group.

<sup>d</sup> adjusted for BMI, sex, physical activity and SES group.

<sup>e</sup> adjusted for BMI, age, sex and physical activity.

<sup>f</sup> adjusted for BMI, age, sex and SES group.

with food, etc ...). They studied 126 subjects with 81 % of women, a mean age of 47.2 ± 13.9 years old and a mean BMI 41.0 ± 5.7 kg/m². They detected three clusters in their sample, the first one with the highest scores of emotional eating (77 in average)/uncontrolled eating (61 in average) and a low score of comfort with food; the second one with the lowest scores of emotional eating (43 in average)/uncontrolled eating (30 in average) and the highest quality of life and the third cluster with a high comfort with food, the highest physical activity levels but also depressive symptoms. Profiles are not exactly the same, and emotional and uncontrolled eating scores are higher than in our study. Eating behavior scores did not differ between clusters in our study, probably because eating behavior was not considered an input variable
<caption>**Table 4**</caption>

Association between the number of diets experienced, BMI and external/internal eating cues in the “early-onset obesity” group (cluster 1, n = 378).

<table><thead><tr><th>Number of previous diets</th><th>1-2 (n = 73)</th><th>≥3 (n = 305)</th><th>crude p value</th><th>adjusted p value <sup>a</sup></th><th>adjusted p value <sup>b</sup></th></tr></thead><tbody><tr><td>External cues</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncontrolled Eating</td><td>44.7 ± 24.4</td><td>39.0 ± 22.0</td><td>0.05</td><td>0.23</td><td>0.32</td></tr><tr><td>Emotional Eating</td><td>52.1 ± -25.5</td><td>54.2 ± 29.1</td><td>0.56</td><td>1.00</td><td>0.96</td></tr><tr><td>Cognitive Restraint</td><td>31.7 ± 16.3</td><td>39.4 ± 17.7</td><td>0.0009</td><td>0.009</td><td>0.01</td></tr><tr><td>Reason to stop the meal: full stomach</td><td>11.0 %</td><td>21.3 %</td><td>0.05</td><td>0.03</td><td>0.03</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>52.1 %</td><td>56.1 %</td><td>0.54</td><td>0.65</td><td>0.45</td></tr><tr><td>Internal cue</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>37.0 %</td><td>22.6 %</td><td>0.01</td><td>0.009</td><td>0.004</td></tr><tr><td>BMI (kg)</td><td>45.2 [42.2; 48.8]</td><td>46.7 [43.8; 51.1]</td><td>0.01</td><td>0.01</td><td></td></tr></tbody></table>

Data are mean ± SD or median [Q1; Q3] or percentage. Significant p values are indicated in bold.

<sup>a</sup> adjusted for age, sex, physical activity and SES group.

<sup>b</sup> adjusted for age, sex, physical activity, BMI and SES group.

<caption>**Table 5**</caption>

Association between the number of diets experienced, BMI and external/internal eating cues in the “late-onset obesity” group (cluster 2, n = 317).

<table><thead><tr><th>Number of previous diets</th><th>1-2 (n = 155)</th><th>≥3 (n = 162)</th><th>crude p value</th><th>adjusted p value <sup>a</sup></th><th>adjusted p value <sup>b</sup></th></tr></thead><tbody><tr><td>External cues</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncontrolled Eating</td><td>35.0 ± 23.2</td><td>36.4 ± 24.2</td><td>0.59</td><td>0.48</td><td>0.41</td></tr><tr><td>Emotional Eating</td><td>48.2 ± 31.4</td><td>55.9 ± 31.6</td><td>0.03</td><td>0.08</td><td>0.05</td></tr><tr><td>Cognitive Restraint</td><td>38.7 ± 18.5</td><td>43.6 ± 19.6</td><td>0.02</td><td>0.03</td><td>0.04</td></tr><tr><td>Reason to stop the meal: full stomach</td><td>5.2 %</td><td>11.7 %</td><td>0.04</td><td>0.05</td><td>0.06</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>69.0 %</td><td>67.3 %</td><td>0.74</td><td>0.71</td><td>0.93</td></tr><tr><td>Internal cue</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>25.8 %</td><td>21.0 %</td><td>0.31</td><td>0.34</td><td>0.24</td></tr><tr><td>BMI (kg)</td><td>37.1 [34.3; 41.9]</td><td>39.2 [34.7; 42.9]</td><td>0.08</td><td>0.05</td><td></td></tr></tbody></table>

Data are mean ± SD or median [Q1; Q3] or percentage. Significant p values are indicated in bold.

<sup>a</sup> adjusted for age, sex, physical activity and SES group.

<sup>b</sup> adjusted for age, sex, physical activity, BMI and SES group.

in the cluster analysis.

In our study, the “early-onset obesity” cluster likely represents individuals with a significant genetic predisposition to obesity. Indeed, obesity and diet initiation occurred at a very young age in this group, and individuals who underwent many diets tend to have a higher BMI than those who experienced fewer diets despite they have lost the most weight in the past. Surprisingly, they exhibited the lowest cognitive restraint scores. The “late-onset obesity” cluster, characterized by older individuals with more recent obesity, less exposed to diets, may reflect environmental influences on obesity, like excess pregnancy weight gain (Rooney & Schauberger, 2002), sociocultural factors, build environment

<caption>**Table 6**</caption>

Association between the number of diets experienced, BMI and external/internal eating cues in the “intermediate obesity” group (cluster 3, n = 398).

<table><thead><tr><th>Number of previous diets</th><th>1-2 (n = 105)</th><th>≥3 (n = 293)</th><th>crude p value</th><th>adjusted p value <sup>a</sup></th><th>adjusted p value <sup>b</sup></th></tr></thead><tbody><tr><td>External cues</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncontrolled Eating</td><td>41.6 ± 22.5</td><td>42.8 ± 22.3</td><td>0.62</td><td>0.42</td><td>0.44</td></tr><tr><td>Emotional Eating</td><td>54.1 ± 27.5</td><td>60.0 ± 27.5</td><td>0.06</td><td>0.42</td><td>0.39</td></tr><tr><td>Cognitive Restraint</td><td>39.0 ± 19.0</td><td>43.0 ± 19.6</td><td>0.07</td><td>0.19</td><td>0.13</td></tr><tr><td>Reason to stop the meal: full stomach</td><td>14.3 %</td><td>17.4 %</td><td>0.46</td><td>0.23</td><td>0.27</td></tr><tr><td>Reason to stop the meal: empty plate</td><td>60.0 %</td><td>64.8 %</td><td>0.38</td><td>0.97</td><td>0.96</td></tr><tr><td>Internal cue</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reason to stop the meal: not hungry anymore</td><td>25.7 %</td><td>17.7 %</td><td>0.08</td><td>0.31</td><td>0.31</td></tr><tr><td>BMI (kg)</td><td>36.3 [33.3; 38.5]</td><td>36.9 [34.3; 38.9]</td><td>0.05</td><td>0.04</td><td></td></tr></tbody></table>

Data are mean ± SD or median [Q1; Q3] or percentage. Significant p values are indicated in bold.

<sup>a</sup> adjusted for age, sex, physical activity and SES group.

<sup>b</sup> adjusted for age, sex, physical activity, BMI and SES group.

and screen related immobility (Nicolaidis, 2019). These individuals reported the poorest quality of life. The “intermediate” cluster included subjects who gained weight later in life and were only overweight at the age of 20. They might represent subjects subjected to external pressures and stigmatization in early adulthood, who have tended to follow a diet to achieve an ideal weight even if they were not in a situation of obesity. This cluster included significantly more women, and it is known that altered eating behaviors are more frequent in women (Sze et al., 2021); who are also more victims of stigmatization (Giel et al., 2012). Moreover, there were more individuals with a high socio-economic score in this group. It has been shown that women from higher SES wish to lose weight at a lower BMI and have higher restrained eating scores (Pigeyre et al., 2012).

## 4.2. External eating cues and diets

External eating cues encompass a wide range of behaviors as they are represented by situations in which internal cues are not respected. This could be eating without hunger, under the influence of emotion or a loss of control in the presence of food, for example; or also ending a meal for a reason other than satiety, like when the plate is empty or when feeling a stomach overfullness.

Emotional eating was significantly higher in individuals who followed at least 3 diets than in individuals who followed few diets in the whole sample. Emotional eating was also different between clusters when the analysis was not adjusted. Sex and SES appear to be the main confounding variables in this association.

In contrast, uncontrolled eating was not different when comparing the groups of history of diet. Uncontrolled eating differed between clusters in the crude model, but age abolished this association.

Cognitive restraint, the third trait evaluated by the TFEQ, was not exploited like the other two traits of the TFEQ in our study. Indeed, this eating behavior is defined by the voluntary control of the timing and content of food intake to achieve a goal (rather than following internal cues) and is associated with repeated history of diets according to (Polivy, Herman, & Mills, 2020).

We also assessed the feeling of stomach overfullness or plate emptiness as an end-of meal signal to evaluate the reliance on external eating cues to stop food intake. Stomach overfullness was significantly
more frequent in the “many diets” group than in the “few diets” group in the global sample, and in the “early-onset obesity” and “late-onset obesity” clusters. In contrast, the tendency to eat until the plate was empty did not differ significantly between groups. Although the study did not assess how full the plate was or how many times it was filled, the key information was whether the visual cue of an empty plate could signal the end of a meal. The lack of significant difference might be due to a lack of statistical power. Otherwise, satiety, which is an internal cue, was higher in the “few diets” group in the global sample and in the “early-onset obesity” cluster.

These results are consistent with our hypothesis that there is a greater proportion of subjects following external eating cues in the “many diets” group, and in some subgroups depending on the history of weight (“early-onset” and “late-onset” obesity groups). In opposite ways, we also showed that a bigger proportion of the subjects followed one available internal eating cue (no longer being hungry as a reason for ending the meal) in the “few diets” group of the whole sample and in the “early-onset obesity” group.

In the literature, very few articles evaluated the impact of the number of diets followed. In a study on bariatric candidates (Roehrig et al., 2013), only 1.4% of the sample never followed any diet and 27.4% of the population spent nearly all their adulthood following diets. These subjects were more likely to be on a diet (between 81 and 91% according to the type of diets) than in our study, and the mean BMI was 51.4 kg/m². We could not include subjects with no dieting history in our analyses because these subjects had missing data on the clustering variables and were therefore excluded. However, these individuals are in the minority among subjects with obesity. Yet, counting diets as we did is somewhat original. It would have been interesting to get more details on the episodes of dieting that were followed (type of diets, length, reasons for discontinuation etc ...). Of note, in the present study, none of the patients were taking weight-loss drugs; 58 patients had undergone bariatric surgery in the past before the consultation but were still obese at recruitment and started a new medical care. We performed the same statistical analyses after removing these patients and no difference in the results could be detected.

### 4.3. Strengths, weaknesses and opportunities

Strengths of our study included a robust methodology, a large sample size, and a comprehensive analysis of obesity-related phenotypic traits. To our knowledge there is no clustering study on weight history exploring eating behavior and dieting history at the same time in the literature. Data on the quantitative history of diets are also scarce. Moreover, our PCA imputation process allowed us to increase statistical power. This process is more accurate than imputing missing variables with means, as it takes into account the link between each variable, and the final correction avoids being overly influenced by outliers.

Our study also included weaknesses. As already mentioned, we excluded subjects who had never followed a diet when selecting subjects without missing data for the clustering analyses. Also, the imputation process for variables with too many missing data (such as the reason to stop eating) may have limitations. However, before imputation, the variable eating until stomach overfullness, but not satiety, was already more prevalent in the “many diet” group than in the “few diet” group ($p = 0.004$) so the imputation process did not alter the proportions. We might not have enough power to explore the association between some external eating cues and the dieting history. Also, we chose to create groups based on weight history without taking into account the number of diets followed, given that this was one of the main independent variables studied. Finally, as our study is cross-sectional, no assumption on the causal effect of the variables can be made. Nevertheless, we could imagine that subjects with early-onset obesity would be more likely to have a longer story of dieting and therefore to have followed more dieting episodes in an attempt to lose weight.

These findings, combined with the existing literature, underline the

inappropriateness of the use of restrictive diets in obesity, given their uncertain efficacy and the association with a more frequent reliance on external eating cues/less frequent reliance on satiety, added to the risk of eating disorders, weight cycling, deficiencies and sarcopenia. The management of obesity is now focusing more on the development of a multifactorial approach to lifestyle modification (Wadden et al., 2020), aimed at improving health rather than just reducing weight. This approach embraces body diversity, respects intuitive eating (listening to internal hunger cues and food desires with mindful-eating programs), promotes enjoyable physical activity, and has been an important part of the update of the French “patient care” in 2024. To go further, Health At Every Size (HAES) is a movement that advocates for not focusing on whatever the weight is (Dimitrov et al., 2018, 2018).

In the future, longitudinal human studies should be set up at an early stage of weight gain, considering all the factors that might be linked to weight gain (nutrition, diet, physical activity, psychological factors, environmental factors, and genetic background) evaluated several times along the follow-up, as there is a need to characterize the different phenotypes of this disease before they become complicated with other factors that also promote weight gain.

### 5. Conclusion

Our sample of subjects with obesity could be clustered into groups according to their weight history. It led to significant different clusters (a “early-onset obesity” cluster, a “late-onset obesity” cluster and an “intermediate” cluster). These clusters differed in the number of diets followed by their members. Among external eating cues, we showed that emotional eating and BMI were higher in subjects who had followed many diets. Also, stopping the meal at stomach overfullness was associated with dieting history in the total population and in some of our clusters, indicating also a link for this variable with weight history. This variation in external eating cues across different obesity phenotypes based on weight history highlights the need for longitudinal studies with repeated assessment of behavioral variables to gain deeper insights into these associations.

#### CRediT authorship contribution statement

**Emilie Ducy:** Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization.
**Monique Romon:** Writing – review & editing, Validation, Resources, Project administration, Methodology, Investigation, Funding acquisition.
**Philippe Amouyel:** Writing – review & editing, Resources, Project administration, Methodology, Investigation, Funding acquisition.
**Aline Meirhaeghe:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

#### Ethical statement

The work described has not been published previously except in the form of an abstract.

The article is not under consideration for publication elsewhere.

The article’s publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out if accepted, the article will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.

#### Declaration of competing interest

The authors have nothing to disclose.
## Acknowledgments

This research was funded by the Fondation pour la Recherche Médicale (grant to A. Meirhaeghe), Institut Pasteur de Lille, INSERM, Lille University, Centre Hospitalier Universitaire de Lille, Nord-Pas de Calais Regional Council, and the European Regional Development Fund.

## Data availability

Data will be made available on request.

## References

Bénard, M., Bellisle, F., Etilé, F., Reach, G., Kesse-Guyot, E., Hercberg, S., & Péneau, S. (2018). Impulsivity and consideration of future consequences as moderators of the association between emotional eating and body weight status. *International Journal of Behavioral Nutrition and Physical Activity, 15*(1), 84. https://doi.org/10.1186/s12966-018-0721-1

Bilman, E., Van Kleef, E., & Van Trijp, H. (2017). External cues challenging the internal appetite control system—Overview and practical implications. *Critical Reviews in Food Science and Nutrition, 57*(13), 2825. https://doi.org/10.1080/10408398.2015.1073140

Cappelleri, J. C., Bushmakin, A. G., Gerber, R. A., Leidy, N. K., Sexton, C. C., Lowe, M. R., & Karlsson, J. (2009). Psychometric analysis of the Three-Factor Eating Questionnaire-R21: Results from a large diverse sample of obese and non-obese participants. *International Journal of Obesity, 33*(6), 611. https://doi.org/10.1038/ijo.2009.74

Chevalier, J., & De Pouvourville, G. (2013). Valuing EQ-5D using time Trade-Off in France. *The European Journal of Health Economics, 14*(1), 57. https://doi.org/10.1007/s10198-011-0351-x

Dimitrov, U. M., Pinto, AJ., De Morais Sato, P., B Benatti, F., Lopes de Campos-Ferraz, P., Coelho, D., Roble, OJ., Sabatini, F., Perez, I., Aburad, L., Vessoni, A., Fernadez Unsain, R., Macedo Rogero, M., Toporcov, TN., de Sa-Pinto, AL., Gualano, B., & B Scagliusi, F. (2018). Effects of a new intervention based on the Health at Every Size approach for the management of obesity: The “Health and Wellness in Obesity” study. *PLoS One, 13*(7). https://doi.org/10.1371/journal.pone.0198401. e0198401.

Giel, K. E., Zipfel, S., Alizadeh, M., Schäffeler, N., Zahn, C., Wessel, D., Hesse, F. W., Thiel, S., & Thiel, A. (2012). Stigmatization of obese individuals by human resource professionals: An experimental study. *BMC Public Health, 12*(1), 525. https://doi.org/10.1186/1471-2458-12-525

Green, M. A., Strong, M., Razak, F., Subramanian, S. V., Relton, C., & Bissell, P. (2016). Who are the obese? A cluster analysis exploring subgroups of the obese. *Journal of Public Health, 38*(2), 258. https://doi.org/10.1093/pubmed/fdv040

Jimenez, M. P., Green, M. A., Subramanian, S. V., & Razak, F. (2018). A demographic, clinical, and behavioral typology of obesity in the United States: An analysis of National Health and Nutrition Examination Survey 2011–2012. *Annals of Epidemiology, 28*(3), 175. https://doi.org/10.1016/j.annepidem.2018.01.001

Nicolaidis, S. (2019). Environment and obesity. *Metabolism Clinical and Experimental, 100*(S), Article 153942. https://doi.org/10.1016/j.metabol.2019.07.006

Pietiläinen, K. H., Saarni, S. E., Kaprio, J., & Rissanen, A. (2012). Does dieting make you fat? A twin study. *International Journal of Obesity, 36*(3), 456. https://doi.org/10.1038/ijo.2011.160

Pigeyre, M., Duhamel, A., Poulain, J.-P., Rousseaux, J., Barbe, P., Jeanneau, S., Tibère, L., & Romon, M. (2012). Influence of social factors on weight-related behaviors according to gender in the French adult population. *Appetite, 58*(2), 703. https://doi.org/10.1016/j.appet.2011.11.031

Pigeyre, M., Rousseaux, J., Trouiller, P., Dumont, J., Goumidi, L., Bonte, D., ... Meirhaeghe, A. (2016). How obesity relates to socio-economic status: Identification of eating behavior mediators. *International Journal of Obesity, 40*(11), 1794. https://doi.org/10.1038/ijo.2016.109

Polivy, J., Herman, CP., & Mills, JS. (2020). What is restrained eating and how do we identify it? *Appetite, 155*, Article 104820. https://doi.org/10.1016/j.appet.2020.104820

Roehrig, M., Masheb, R. M., White, M. A., Rothschild, B. S., Burke-Martindale, C. H., & Grilo, C. M. (2013). Chronic dieting among Extremely obese bariatric surgery candidates. *Obesity Surgery, 19*(8), 1116. https://doi.org/10.1007/s11695-009-9865-z

Rohrer, J. E., Vickers-Douglas, K. S., & Stroebel, R. J. (2009). Uncontrolled eating and obesity in adult primary care patients. *Obesity Research & Clinical Practice, 3*(2), 115. https://doi.org/10.1016/j.orcp.2009.01.004

Romain, A. J., Avignon, A., Macioce, V., Boegner, C., & Attalin, A. A. S. (2022). Patterns of eating behavior in people with severe obesity seeking weight loss treatment : An exploratory study. *Appetite, 1* (169), Article 105797. https://doi.org/10.1016/j.appet.2021.105797

Rooney, B. L., & Schaubberger, C. W. (2002). Excess pregnancy weight gain and long-term obesity: One decade later. *Obstetrics & Gynecology, 100*(2), 245.

Sares-Jäske, L., Knekt, P., Männistö, S., Lindfors, O., & Heliövaara, M. (2019). Self-report dieting and long-term changes in body mass index and waist circumference. *Obesity Science & Practice, 5*(4), 291. https://doi.org/10.1002/osp4.336

Sze, K. Y. P., Lee, E. K. P., Chan, R. H. W., & Kim, J. H. (2021). Prevalence of negative emotional eating and its associated psychosocial factors among urban Chinese undergraduates in Hong Kong: A cross-sectional study. *BMC Public Health, 21*(1), 583. https://doi.org/10.1186/s12889-021-10531-3

Tuschl, R. J. (1990). From dietary restraint to binge eating: Some theoretical considerations. *Appetite, 14*(2), 105. https://doi.org/10.1016/0195-6663(90)90004-R

Vasileiou, V., & Abbott, S. (2023). Emotional eating among adults with healthy weight, overweight and obesity : A systematic review and meta-analysis. *Journal of Human Nutrition and Dietetics, 36*(5), 1922. https://doi.org/10.1111/jhn.13176

Wadden, T. A., Tronieri, J. S., & Butryn, M. L. (2020). Lifestyle modification approaches for the treatment of obesity in adults. *American Psychologist, 75*(2), 235. https://doi.org/10.1037/amp0000517

## Further reading

Herman, C. P., & Mills, J. S. (2020). What is restrained eating and how do we identify it? *Appetite*, Article 104820. https://doi.org/10.1016/j.appet.2020.104820, 1, 155.

Lluch, A., Kahn, J., Stricker-Krongrad, A., Ziegler, O., Drouin, P., & Méjean, L. (1996). Internal validation of a French version of the Dutch eating behaviour questionnaire. *European Psychiatry, 11*(4), 198. https://doi.org/10.1016/0924-9338(96)88391-X