Skip to main content
NCBI home page
Clinical Neuropsychiatry logoLink to Clinical Neuropsychiatry
. 2023 Dec;20(6):486–494. doi: 10.36131/cnfioritieditore20230603

Is Food Addiction a Specific Feature of Individuals Seeking Dietary Treatment from Nutritionists?

Armando Piccinni 1,2, Claudio Cargioli 3, Annalisa Oppo 4, Federica Vanelli 5, Mauro Mauri 6, Valentina Formica 1, Alessandro Arone 7, Tiziana Stallone 8, Stefania Palermo 7, Donatella Marazziti 1,2,7,✉
PMCID: PMC10852409  PMID: 38344459

Abstract

Objective

Food addiction (FA) is a condition characterized by excessive and dysregulated consumption of high-energy food, and impulsivity. The diagnostic and nosological framework of FA is still controversial. Therefore, this study aimed at exploring the prevalence of FA in patients seeking help from nutritionists for weight loss, along with its relationship with eating habits, in a pool of 842 participants of both sexes.

Method

Eating habits and FA were assessed by, respectively, a self-administered questionnaire and the Yale Food Addiction Scale (YFAS). Statistical analysis included Chi-square for categorical variables, independent t tests to investigate continuous variables and an univariate logistic regression analysis to determine potential risk factors for FA. The relationship between FA diagnosis and potential risk factors was assessed through a stepwise logistic regression model, controlling for age, sex, and body mass index (BMI) classes.

Results

Our results indicate that a prevalence of FA in our sample was 15.3%, with no difference between women and men. A higher prevalence was recorded in overweight subjects or obese. According to the YFAS criteria, women were more likely to report a persistent desire and withdrawal than men. Patients with FA compared with those without it, reported a greater number of attempts to lose weight, to self-dieting, a different mealtime repertoire, and to nibble continuously throughout the day. Moreover, the amount of carbohydrates ingested in the same meal seems to represent an eating habit significantly associated with FA.

Conclusions

Taken together, our findings show how patients seeking help from nutritionists may display some peculiar features of FA. In spite of its diagnostic controversies, it is evident that FA may play a role in obesity and may also be a feature of some psychopathological conditions. Therefore, it should be more deeply investigated and possibly specifically targeted with tailored therapeutic interventions.

Keywords: food addiction, psychopathological dimensions, obesity, eating habits, nutritionists

Introduction

The concept of addiction, in spite of the impressive amount of research and the growing interest of the scientific community, still remains elusive. Not surprisingly, the nosological and diagnostic framework of addictions, due to the heterogeneity of symptoms, has undergone several revisions up to the current systematization within the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5, American Psychiatric Association, 2013) and the International Classification of Disease, 11th Edition (ICD-11) (Grant & Chamberlain, 2016; World Health Organization, 2019). To complicate this issue, a novel class of conditions have been grouped in the so-called “behavioral addictions'' or “non-pharmacological addictions'', to label certain behaviors, attitudes or feelings with features resembling those typical of substance use disorder (SUD) (Alavi et al., 2012; Marazziti et al., 2014). Behavioral addictions encompass excessive Internet use, now called Problematic Internet Use (PIU), videogame addiction, compulsive shopping, gambling, sex addiction, exercise addiction, but even that condition known as food addiction (FA) (Hartston, 2012; Weinstein & Lejoyeux, 2010; Karila et al., 2014; Weinstein & Weinstein, 2014; Yau & Potenza, 2015; Marazziti et al., 2023; Mohammad et al., 2023). The first historical and scientific notes on FA date back to 1890 in one of the first journals focusing on addiction, the Journal of Inebriety, with reference to one of the most addictive foods that is chocolate (Weiner & White, 2007). However, only 60 years later, in 1956, the term FA entered the scientific literature as being mentioned in a paper of Theron Randolph, an American physician and researcher (Randolph, 1956). Since then, it has been subjected to continuous changes of both its definition and conceptualizations. Currently, FA can be defined as an excessive and dysregulated consumption of high-energy foods through several pathological behaviors (Pursey et al., 2014; Gearhardt et al., 2016; Hauck et al., 2020; Ahmadkaraji et al., 2023). Specifically, loss of control and excessive consumption of highly palatable foods may constitute some of its peculiar features (Davis, 2014). Furthermore, FA seems to share characteristics with substance abuse including craving (i.e. intense or eager desire for the substance) and several pathological behaviors, such as continuous use despite the awareness of the negative effects on own health, and decreased self-control over consumption (Gearhardt et al., 2011a). Indeed, it seems heavily characterized by impulsiveness, a dimension that has long been associated with the overconsumption of food with loss of control (Espel et al., 2017). Similarly to SUD, in FA there is evidence of alterations of the dopamine system regulating reward, motivation and impulse control (Davis & Carter, 2009; Gearhardt et al., 2011b; Schulte et al., 2015; Fletcher et al., 2018; Dong et al., 2020).

Food addiction is frequently reported even in some eating disorders, such as bulimia nervosa and binge eating disorder that show common symptoms, such as excessive food consumption in a short time and lack of control (Gearhardt et al., 2012; American Psychiatric Association, 2013; Meule et al., 2014). In any case, it is still unclear whether it is a symptom of other conditions, or if it might be considered an autonomous pathological condition (Gearhardt et al., 2011; Piccinni et al., 2021).

Furthermore, FA is attracting an increasing interest due to its potential role as a driver to obesity (Meseri & Akanalci, 2020). Obesity is a severe and growing public health problem. Despite efforts to promote healthy eating behaviors and physical activity, and the attempts of the scientific community to better understand the pathophysiology of food intake, the prevalence of obesity and related metabolic disorders continue to increase all over the world, along with its several associated health complications (Cornier et al., 2011; Boutari & Mantzouros, 2022; Marazziti et al., 2023). According to the World Health Organization (WHO) reports, more than 1 billion people worldwide are affected by obesity, specifically 650 million adults, 340 million adolescents and 39 million children (WHO, 2022). Its worldwide rate has also almost doubled in the last four decades. In Italy, according to the 4th Italian Obesity Barometer Report, the prevalence is around 46% amongst adults and around 26% amongst children (Corsaro et al., 2022). Despite the well-known relationship between eating habits and health, many people fail to maintain a healthy diet. Dieting is usually initiated to self-improve, to achieve a better health status, a better body image/ appearance and wellbeing. Diet could represent an attempt to regain control over nutrition and weight that is usually completely lost, while overeating or practicing unhealthy eating habits. Furthermore, as reported more than three decades ago, the tendency to overeat may be a consequence of periods of prolonged dietary restriction (Garner & Wooley, 1991). To complicate the matter, people who try to lose weight have to face a multitude of challenges, such as the abundance of junk foods full of sugars and fats, a sedentary lifestyle, as well as hereditary metabolic defects (e.g. genes that affect fat cell growth, high birth weight, etc.) (Burmeister et al., 2013). When it is necessary to follow a restrictive diet regimen, several problems can be encountered. A first-phase challenge is to be compliant with the treatment to lose weight. Weight regain is generally the rule, as between one-third and two-thirds of the weight lost is being recuperated within 1 year and almost completely within 5 years (MacLean at al., 2015).

While the role of the physiologic regulation of energy intake is mostly drawn, it should be highlighted that the fine regulation of food intake is a complex process, possibly not merely due to hypothalamic regulation. Psychosocial and cognitive factors, such as motivation, reward, learned behaviors, habits and social context may interfere (Cornier, 2011). Moreover, the reinforcement and hedonic properties of food are also powerful modulators of feeding behaviors (Lattemann, 2008). Lack of time, reluctance to give up favorite foods and lack of motivation and willpower are also some of the most common factors in diet failure (Pänkäläinen et al., 2018). The influence of other variables, such as FA, may also be hypothesized, as it may pose a hindering factor towards weight loss in overweight individuals or with obesity, thus representing a potential target in the treatment of these conditions. Indeed, in a large cohort of adults affected by obesity, FA was associated with both a higher BMI and a larger waist circumference (Camacho-Barcia et al., 2021). The association with obesity was also demonstrated in a few studies in younger individuals (Mies et al., 2017; Borisenkov et al., 2018). The prevalence of FA was found to be around 14%-78% in a sample of subjects affected by obesity undergoing bariatric surgery (BS) treatment (Penzenstadler et al., 2019), and about 26.4% in a pool of 110 patients seeking BS (Guérrero Perez et al., 2018). However, the correlation between FA and overweight conditions has not been demonstrated subsequently (Kiyici et al., 2020), thus making the issue even more elusive. If a bridge between the two is conceivable, it may also be considered that FA may not directly cause obesity but, instead, it may influence a broad range of factors that may hinder weight loss treatment. These factors may include psychopathological disorders, such as depression and eating disorders, but also impaired control with a consequent craving towards specific foods (De Almeida et al., 2022). It is, thus, possible that an adequate assessment and a tailored treatment of FA might be important to assist weight loss and to prevent obesity.

Since FA is still a controversial topic and the information available is quite meager in Italy, the aim of the present study was to examine the prevalence and characteristics of FA in a sample of patients of both sexes and from all over the country, who were consulting nutritionists. The relationship with eating habits was also investigated.

Methods

A sample of 842 participants coming from all over the country were recruited by 82 Italian nutritionists and included in this cross-sectional study. All participants voluntarily agreed to take part in the research that was approved by the Ethics Committee after signing an informed written consent. The large majority of participants were women (N = 657; 78.0%), with the age ranging between 18 and 64 years (mean + SD: 39.93 + 12.82 years).

Data on socio demographic information, eating habits, and anthropometric measurements were collected and recorded in the medical chart (table 1).

Table 1.

Demographic characteristics of participants

Total sample (N = 842) Men (N = 185) Women (N = 657)
Age (Mean+SD years) 39.93 (12.82) 40.79 (12.94) 39.70 (12.78)
BMI (Mean+SD kg/m 2) 27.32 (5.57) 28.47 (5.28) 27 (5.60)
Sex
 Female N (%) 657 (78.0) - -
 Male N (%) 185 (22.0) - -
Marital status
 Single 376 (44.7) 88 (47.6) 288 (43.8)
 Married 409 (49.7) 86 (44.3) 323 (46.1)
 Separated/ Divorced 42 (5.0) 8 (4.3) 34 (5.2)
 Widower/Widow 15 (1.8) - 15 (2.3)
Employment status
 Student 90 (10.7) 15 (8.1) 75 (11.4)
 Unemployed 63 (7.5) 9 (4.9) 54 (8.2)
 Housewife 103 (12.2) - 103 (15.7)
 Worker/ farmer/ home worker 85 (10.1) 26 (14.1) 59 (9.0)
 Dealer/ artisan 60 (7.1) 20 (10.8) 40 (6.1)
 Employed/ teacher/ military 284 (33.7) 58 (31.4) 226 (34.4)
 Manager/freelance 107 (12.7) 42 (22.7) 65 (9.9)
 Retired 37 (4.4) 15 (8.1) 22 (3.3)
 Missing 13 (1.5) - 13 (2.0)
Education
 Specialization 2 (0.2) - 2 (0.3)
 Degree 10 (1.2) 2 (1.1) 8 (1.2)
 High school degree 111 (13.2) 23 (12.4) 88 (13.4)
 Middle school certificate 431 (51.2) 97 (52.4) 334 (50.8)
 Primary school 221 (26.2) 46 (24.9) 175 (26.6)
 Primary school not completed 58 (6.9) 15 (8.1) 43 (6.5)
 Missing data 9 (1.1) 2 (1.1) 7 (1.1)
BMI classes
 Underweight 12 (1.4) 1 (0.5) 11 (1.7)
 Normal weight 286 (34.0) 48 (25.9) 238 (36.2)
 Overweight 282 (33.5) 70 (37.8) 212 (32.3)
 Obesity Class I 133 (15.8) 38 (20.5) 95 (14.5)
 Obesity Class II 59 (7.0) 16 (8.6) 43 (6.5)
 Obesity Class III 20 (2.4) 4 (2.2) 16 (2.4)
 Missing data 50 (5.9) 8 (4.3) 42 (6.4)
Open in a new tab

Significant associations are shown in in bold characters

Height and weight values of participants were collected during their visits. The Body Mass Index (BMI: weight/height2, kg/m2) was calculated from height and weight. This parameter was used to categorize study participants into different weight classes, as defined by the WHO (Weir & Jan, 2022): underweight: BMI under 18.5 kg/m2; normal weight: BMI greater than or equal to 18.5 – 24.9 kg/m2; overweight: BMI greater than or equal to 25 – 29.9 kg/ m2; obesity: BMI greater than or equal to 30 kg/m2; obesity class I: BMI 30 – 34.9 kg/m2; obesity class II: BMI 35 – 39.9 kg/m2; obesity class III: BMI greater than or equal to 40 kg/m2. According to this classification, 282 subjects (33.5%) were overweight, 133 (15.8%) belonged to the obesity class I, 59 (7.0 %) to class II, and 20 (2.4%) to class III (table 1).

The patients were assessed by the Yale Food Addiction Scale (YFAS, Gearhardt et al., 2009; 2012; 2016), a questionary specifically designed to evaluate addiction toward foods according to the DSM-IV-TR criteria for a SUD. The YFAS is a 25-questions survey that measures several aspects of FA behavior, including food dependence, withdrawal, tolerance, continued use despite problems, time spent eating, loss of control, inability to cut down and clinically significant impairment. The FA diagnosis is made when 3 or more criteria are met, and clinically significant impairment or distress is present. The YFAS is worldwide used in the clinic and research field, it is easy to use and was shown to have reliable psychometric properties.

The subjects completed also a self-report questionnaire developed by us to assess their eating habits. This questionnaire examines individual’s eating behaviors, their tastes, diet-related history, lifestyle and habits in relation to main meal and snacks (mealtime repertoire).

Statistical Analyses

Categorical data are shown as N (%), and continuous data as means (± SD). The Chi-square was used to evaluate differences between categorical variables, whereas unpaired, two-tailed t-tests were used when appropriate to investigate differences between continuous variables. To determine potential risk factors for FA, univariate logistic regression analysis was performed, and the associations between risk factors and outcomes are presented as odds ratios (ORs) and 95% CIs. Finally, the relationship between the risk factors and a FA diagnosis was analyzed using a stepwise logistic regression model, controlling for age, sex, and BMI Classes. SPSS software version 24.0 (IBM Corp) was used for statistical analyses, and the significance level was set at α = .05.

Results

One hundred and twenty-nine patients, out of a total of 842, met criteria for FA diagnosis (15.3%), with an equal distribution between men (14.6%) and women (15.5%) (table 2).

Table 2.

YFAS score in the total sample and according to sex

Total sample N (%) Male N (%) Female N (%) Chi-2; p OR (95% CI)
Diagnosis of FA 129 (15.3%) 27 (14.6%) 102 (15.5%) =.096; p =.756 1.07 (0.68-1.70)
Criterion 1 “Substance” 91 (10.8%) 17 (9.2%) 74 (11.3%) 0.644; p=0.422 1.25 (0.72-2.18)
Criterion 2 “Persistent desire” 687 (81.6%) 134 (72.4%) 553 (84.2%) 13.24; p= <0.001 2.02 (1.38-2.97)
Criterion 3 “Much Time” 201 (23.9%) 44 (23.8%) 157 (23.9%) 0.001; p=0.975 1.01 (0.69-1.48)
Criterion 4 “Social reduced” 67 (8.0%) 16 (8.6%) 51 (7.8%) 0.155; p=0.694 0.89 (0.49-1.60)
Criterion 5 “Use continues” 216 (25.7%) 40 (21.6%) 176 (26.8%) 2.021; p=0.155 1.33 (0.90-1.96)
Criterion 6 “Tolerance” 243 (28.9%) 48 (25.9%) 195 (29.7%) 0.981; p=0.322 1.20 (0.83-1.74)
Criterion 7 “Withdrawal” 238 (28.3%) 40 (21.6%) 198 (30.1%) 5.162; p=0.023 1.56 (1.06-2.30)
Open in a new tab

Significant associations are shown in in bold characters

Some gender-related differences were detected in some YFAS dimension, as women were more likely to report persistent desire (Criterion 2) (84.2% in women vs. 72.4% in men) and withdrawal (Criterion 7) (26.8% in women vs. 21.6% in men).

The features of FA as explored with YFAS were also evaluated by dividing the subjects according to their BMI. The distribution of the FA diagnosis, according to BMI classes was the following: 38 (13.3%) in normal weight, 34 (12.1%) in overweight, and 45 (21.2%) in individuals with obesity. Subjects affected by obesity were overall more likely to meet criteria for FA than normal-weight patients (OR = 1.76, 95% CI: 1.09-2.83), while no differences emerged between overweight and normal-weight patients. Again, as compared with normal weight subjects, those with obesity were more likely to present the following YFAS criterions: substance use (Criterion 1), persistent desire (Criterion 2), continued use (Criterion 5), tolerance (Criterion 6) and withdrawal (Criterion 7). The only difference between normal-weight and overweight individuals was in the Criterion 6 (Tolerance) (table 3).

Table 3.

YFAS score in the total sample according to BMI classes

Normal weight N = 286 N (%) Overweight N = 282 N (%) Obese N = 212 N (%) Risk in Overweight OR (95% CI) Risk in Obese OR (95% CI)
Diagnosis 38 (13.3%) 34 (12.1%) 45 (21.2%) 0.90 (0.54-1.46) 1.76 (1.09-2.83)
Criterion 1 “Substance” 23 (8.0%) 29 (10.3%) 30 (14.2%) 1.31 (0.74-2.33) 1.88 (1.06-3.35)
Criterion 2 “Persistent desire” 214 (74.8%) 228 (80.9%) 195 (92.0%) 1.42 (0.95-2.12) 3.85 (2.20-6.78)
Criterion 3 “Much Time” 65 (22.7%) 65 (23.0%) 53 (25.0%) 1.01 (0.69-1.51) 1.13 (0.75-1.71)
Criterion 4 “Social reduced” 23 (8.0%) 22 (7.8%) 18 (8.5%) 0.97 (0.53-1.78) 1.06 (0.56- 2.02)
Criterion 5 “Use continues” 52 (18.2%) 68 (24.1%) 79 (37.3%) 1.43 (0.95-2.15) 2.67 (1.77-4.02)
Criterion 6 "Tolerance" 62 (21.7%) 82 (29.1%) 79 (37.3%) 1.48 (1.01-2.17) 2.15 (1.44-3.19)
Criterion 7 “Withdrawal” 75 (26.2%) 70 (24.8%) 75 (35.4%) 0.93 (0.64-1.35) 1.54 (1.05-2.26)
Open in a new tab

Significant associations are shown in in bold characters

The relationship between the eating repertoire and FA was assessed by the means of an univariate analysis. The eating repertoire was grouped into the following four macro areas: #1. diet-related repertoire, #2. mealtime repertoire, #3. repertoire related to carbohydrates consumption, and the referred #4. addiction for specific foods. As far the macro area # 1, subjects with FA reported a greater number of attempts to lose weight (t = 3.89; p <.001; d = 0.37), more attempts to lose weight with a do-it-yourself diet (OR = 1.89; 95% CI: 1.29-2.77), and through Internet diets (OR = 2.87; 95% CI: 1.82-4.53), as compared to individuals without FA. They were also noted to having breakfast less frequently than patients with no FA (OR=0.47; 95% CI: 0.29-0.80), to having a bedtime snack (OR=1.96; 95% CI: 1.24-3.10), and to nibbling continuously throughout the day about three times more than patients without FA (OR = 3.29; 95% CI: 2.22-4.88) (macro area # 2). The evaluation or carbohydrates consumption showed how FA subjects reported to eat bread, potatoes and pasta in the same meal more frequently than those without FA (OR = 1.98; 95% CI: 1.36-2.89), with no significant differences between the common consumption of bread alternatives (macro area # 3. Finally, in the macro area #4, subjects with FA stated that they had both a more generalized addiction (i.e., sweet and savory) (OR = 8.08; 95% CI: 4.30-15.18) and a more specific addiction (sweet or savory) (OR = 5.01; 95% CI: 3.17-7.91) than patients without FA. The preference towards sweet foods (46.9%; N = 145) and savory foods (53.1%; N = 164) was equally distributed in 390 subjects with a specific addiction of whom 77 with and 232 without FA (chi-square = 0.013; p = 0.909) (table 4).

Table 4.

Behavioral repertoire and food addiction: univariate analysis

No Food Addiction N (%) Food addiction N (%) Risk in food addiction OR (95% CI)
1) Diet-related repertoire
 Do-it-yourself diet 296 (41.5%) 74 (57.4%) 1.89 (1.29-2.77)
 Internet Diet 79 (11.1%) 34 (26.4%) 2.87 (1.82-4.53)
 Professionally provided diet 583 (81.9%%) 107 (82.9%) 1.08 (0.66-1.77)
 Number of attempts 1.92 (1.64) 2.55 (1.92) t=3.89; p<.001; d=0.37
2) Mealtime repertoire
 Breakfast 646 (90.6%) 102 (82.2%) 0.47 (0.29-0.80)
 Midmorning Snack 505 (70.8%) 96 (74.4%) 1.20 (0.79-1.84)
 Bedtime snack 99 (13.3%) 31 (24.0%) 1.96 (1.24-3.10)
 Nibble 258 (36.2%) 84 (65.1%) 3.29 (2.22-4.88)
3) Carbs Repertoire
 Carbs Amount (N° of different carb X meal) 297 (37.4%) 70 (54.3%) 1.98 (1.36-2.89)
 Bread Alternatives 243 (34.1%) 45 (34.9%) 1.04 (.70-1.54)
4) Referred Addiction
 No Addiction 438 (61.4%) 29 (22.5%)
 Specific Addiction 232 (32.5%) 77 (59.7%) 5.01 (3.17-7.91)
 Generalized Addiction 43 (6.0%) 23 (17.8%) 8.08 (4.30-15.18)
Open in a new tab

Significant associations are shown in in bold characters

In order to identify the specific eating habits and features eventually associated with a diagnosis of FA, a stepwise logistic regression model was shaped controlling for age, sex, and BMI classes. As shown in Table 5, where the general model accounting for about 13% of the variance (0.133 Cox–Snell R-square) is reported, an association was found between a YFAS diagnosis of FA and age (adjOR = .965; 95% CI .947.984), Internet diet (adjOR = 2.090; 95% CI: 1.2363.536), number of attempts (adjOR = 1.303; 95% CI 1.088-1.560), nibble (adjOR = 1.674; 95% CI 1.0532.66), carbohydrate amount (adjOR = 1.57; 95% CI 1.00-2.466), specific addiction (adjOR = 3.652; 95% CI: 2.203-6.053), and non-specific addiction (adjOR = 4.308; 95% CI: 2.064-8.993).

Table 5.

Stepwise logistic regression model to assess any specific eating habits and features eventually associated with a diagnosis of FA

Risk Factor Β S.E. Wald d.f. p OR 95%CI
Age -.035 .010 13.28 1 .000 .965 .947-.984
Sex -.270 .291 .856 1 .355 .764 .431- 1.352
 BMI 4.264 2 .119
 Overweight vs. Normal -.354 .279 1.612 1 .204 .702 .406-1.212
 Obesity vs. Normal .207 .290 .509 1 .475 1.230 .697-2.169
Internet Diet .737 .268 7.557 1 .006 2.090 1.236-3.536
Number of atempts .265 .092 8.284 1 .004 1.303 1.088-1.560
Nibble .515 .236 4.745 1 .029 1.674 1.053-2.660
Carbs Amount .451 .230 3.840 1 .050 1.570 1.000-2.466
Referred addiction 27.89 2 .000
 Specific vs. no-Addiction 1.295 .258 25.24 1 .000 3.652 2.203-6.053
 Aspecific vs. no-Addiction 1.460 .375 15.12 1 .000 4.308 2.064-8.993
Constant -2.042 .481 18.02 1 .000 .130
Open in a new tab

Significant associations are shown in in bold characters

Discussion

The concept of different FA is still a controversial topic, as it is also strictly intertwined with other psychopathological dimensions and/or disorders. That would be the case of emotional dysregulation, a transnosographic dimension characterized by difficulties in managing the emotions to stimuli, thus impairing goal-oriented activities (Thompson, 2019), that was found to be associated with SUD, but also with FA (Carlson et al., 2018). Specifically, individuals with FA were suggested to be highly aware of emotions, but they may lack the needed skills to cope with negative emotions (Bunio et al., 2020). On the other hand, some individuals may use highly processed foods with the aim of regulating their emotions, in a similar way of other addictive substances (Carlson et al., 2018). A recent review argued that FA may represent a subtype of

SUD or a regulatory mechanism in affective disorders, or a distinct pattern of eating disorders (Piccinni et al., 2021). Indeed, to understand the relationship between FA and other psychopathological dimensions could be a major goal, as it might possibly pave the way to more appropriate and tailored treatment of common psychiatric disorders and obesity (Piccinni et al., 2021). Given the controversies around the issue and the paucity of data from our country, we aimed to assess the prevalence and features of FA in a sample of individuals consulting nutritionists, as well as the possible relationship with their eating habits. This study was the first of an ongoing Italian collaboration network between the “Ente Nazionale di Previdenza e Assistenza a favore dei Biologi” (ENPAB) and the “Brain Research Foundation” (BRF Onlus, Lucca, Italy). The YFAS, the most widely-employed scale of FA, was used to effectively assess its presence and the features in our sample.

The ensuing results indicate that the prevalence of FA amongst the subjects recruited by nutritionists was 15.3%. These data are consistent with a previous meta-analysis including 20 studies and reporting a prevalence of 19.9% (Pursey et al., 2014). Similarly, a subsequent meta-analysis including 272 studies highlighted a prevalence of FA of 20% (Praxedes et al., 2022).

In our sample, although gender seems not to be associated with FA, we observed a significant gender difference in two YFAS criteria, as women were more likely to report a persistent desire and withdrawal than their male counterparts, as already noted (Pedram et al., 2013; Pursey et al., 2014; Imperatori et al., 2016). However, at variance with the literature, we did not detect a higher prevalence of FA in younger individuals.

The assessment of FA through YFAS according to BMI classes, confirmed that individuals with obesity were more likely to meet criteria for FA, as compared with normal weight patients. This is in agreement with previous studies, as individuals with FA have been shown to have a higher BMI (Pursey et al., 2015; Ayaz et al., 2018; Penzenstadler et al., 2019). In any case, a non-linear relationship between FA and BMI was also described (Hauck et al., 2017). Our results also demonstrated how the amount of carbohydrates (pasta and bread) ingested is an eating habit significantly associated with a FA diagnosis. A recent systematic review that included studies assessing FA by means of YFAS, concluded that the most common associations between addictive eating and foods were those combining fat and refined carbohydrates (Pursey et al., 2021). Indeed, highly-processed and high glycemic index foods (that contain a large amount of added fats and sugar) are those most frequently associated with addictive-like symptoms (Pursey et al., 2015). That seems to be in line with the assumption that perhaps FA would better fit criteria for a SUD, due to the different effects of certain types of foods on eating behavior (Schulte et al., 2017). In our sample, subjects with FA reported to nibble continuously throughout the day about three times more than those without FA. This is in line with some reports that food craving is a crucial component of FA, as it is associated with several eating disorders and, more generally, with overweight and obesity (Joyner et al., 2015; Chao et al., 2016; Imperatori et al. 2016; Reents & Pedersen, 2021).

We also explored behaviors associated with the risk for FA by analyzing the results of their eating repertoire. Our data suggest that patients with FA reported more frequently some features of diet-related repertoire. To the best of our knowledge, only three studies had previously investigated foods and dietary profiles associated with FA. One study noted that subjects with FA reported significantly more problems with foods with high amounts of fat and sugar, as compared with participants without FA (Ayaz et al., 2018). Another study showed that high YFAS scores were associated with a larger percentage of energy intake from energy-dense, nutrient-poor foods (i.e. candy, take out and baked sweet products) (Pursey et al., 2015). Finally, higher levels of soft drink, confectionery consumption and anxiety sensitivity (as well as severe depressive symptoms), could predict severe FA, and that only vegetable intake was the variable that might decrease the odds of it (Burrows et al., 2017).

The focus on eating behavioral habits, but not on daily dietary energy, represents one of the key features of the present research. According to our data, participants with FA, compared to those without FA, reported a greater number of attempts to lose weight by a do-it-yourself diet and through searches on the Internet, while there were no significant differences in professionally provided diets. That is consistent with the literature, as FA has been associated with less weight loss in individuals with obesity who resort to dieting and lifestyle-related interventions (De Almeida et al., 2022). Notwithstanding, another study examining a sample of 178 subjects following a 1000-1200 kcal/day portion-controlled diet along with weekly group lifestyle modification sessions showed that percent weight loss did not differ between subjects meeting or not the YFAS-criteria for FA (Chao et al., 2019). These findings further strengthen the hypothesis that FA may hinder weight loss. Our patients also reported a different mealtime repertoire, having breakfast less frequently than patients without FA, but also referring to bedtime snack, and nibbling continuously throughout the day, while there were no significant differences in midmorning snack. They also referred a specific repertoire related to the consumption of carbohydrates, as they eat bread, potatoes and pasta in the same meal more frequently than patients without FA, while there were no significant differences regarding habitual consumption of bread alternatives. More interestingly, patients with FA had a generalized addiction (i.e., sweet and savory) and specific addiction (sweet or savory), respectively, five and eight fold more than patients without FA. Furthermore, amongst people reporting a specific addiction, the preference toward sweet foods and savory foods was equally distributed both in participants with FA and without FA. Taken together, these findings seem to define a specific eating habit indicating that FA behaviors, also present in other eating disorders and could be considered similar to an addictive behavioral pattern (Fletcher & Kenny, 2018).

Limitations of the study

Our research did not evaluate the psychopathology of our participants, especially of mood disorders, and the eventual correlation with FA, as previously explored by other authors. Indeed, FA was found to be associated with depressive symptoms, negative affect, emotion dysregulation and lower self-esteem in a sample of patients with obesity and affected by binge-eating disorder, thus leading to the conclusion that, in these patients, the presence of FA may present a more severe clinical picture than those without it (Davis et al., 2011; Gearhardt et al., 2012). Similarly, there are reports of highest odds of severe FA in individuals with severe or extremely severe depressive symptoms (Burrows et al., 2017). Overall, a positive and moderate association seems to exist between FA and binge-eating disorder, depression and anxiety symptoms as well, that seems to indicate a certain overlapping between these various mental disorders (Burrows et al., 2018). Although this was not not the aim of our present research, it is a goal of our ongoing studies on FA.

Conclusions

Our study would indicate that FA is a common characteristic of subjects seeking help from a nutritionist that may be associated with specific eating profiles and repertoires. Although up to now it seems premature to draw firm conclusions, nevertheless it seems interesting to deepen the mechanisms underlying FA and its complex relationship with unhealthy dietary habits. Indeed, FA may be associated with obesity and poor eating habits, so that possibly its targeting might be helpful in the prevention of the related conditions. Moreover, the complex relationship and clustering between FA and other psychopathological symptoms or disorders, particularly affective or eating disorders, represents another interesting topic to be clarified.

References

  1. Ahmadkaraji, S., Farahani, H., Orfi, K., & Fathali Lavasani, F. (2023). Food addiction and binge eating disorder are linked to shared and unique deficits in emotion regulation among female seeking bariatric surgery. Journal of eating disorders, 11(1), 97. 10.1186/s40337-023-00815-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Aloi M, Rania M, Rodríguez Muñoz RC, Jiménez Murcia S, Fernández-Aranda F, De Fazio P, Segura-Garcia C. Validation of the Italian version of the Yale Food Addiction Scale 2.0 (I-YFAS 2.0) in a sample of undergraduate students. Eat Weight Disord. 2017 Sep;22(3): 527–533. doi: 10.1007/s40519-017-0421-x. Epub 2017 Aug 5. PMID: 28780748. [DOI] [PubMed] [Google Scholar]
  3. Alavi, S. S., Ferdosi, M., Jannatifard, F., Eslami, M., Alaghemandan, H., & Setare, M. (2012). Behavioral addiction versus substance addiction: correspondence of psychiatric and psychological views. International journal of preventive medicine, 3(4), 290–294. [PMC free article] [PubMed] [Google Scholar]
  4. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders, (5th ed.). 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
  5. Ayaz, A., Nergiz-Unal, R., Dedebayraktar, D., Akyol, A., Pekcan, A. G., Besler, H. T., & Buyuktuncer, Z. (2018). How does food addiction influence dietary intake profile? PloS one, 13(4), e0195541. 10.1371/journal.pone.0195541 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Borisenkov, M. F., Tserne, T. A., & Bakutova, L. A. (2018). Food addiction in Russian adolescents: associations with age, sex, weight, and depression. European eating disorders review: the journal of the Eating Disorders Association, 26(6), 671–676. 10.1002/erv.2644 [DOI] [PubMed] [Google Scholar]
  7. Boutari, C., & Mantzoros, C. S. (2022). A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism: clinical and experimental, 133, 155217. 10.1016/j.metabol.2022.155217 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bunio, L. K., Battles, J. A., & Loverich, T. M. (2021). The nuances of emotion regulation difficulties and mindfulness in food addiction. Addiction Research & Theory, 29(1), 11–17. 10.1080/16066359.2020.1714038 [DOI] [Google Scholar]
  9. Burmeister, J. M., Hinman, N., Koball, A., Hoffmann, D. A., & Carels, R. A. (2013). Food addiction in adults seeking weight loss treatment. Implications for psychosocial health and weight loss. Appetite, 60(1), 103–110. 10.1016/j.appet.2012.09.013 [DOI] [PubMed] [Google Scholar]
  10. Burrows, T., Hides, L., Brown, R., Dayas, C. V., & Kay-Lambkin, F. (2017). Differences in dietary preferences, personality and mental health in Australian adults with and without food addiction. Nutrients, 9(3), 285. 10.3390/nu9030285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Burrows, T., Kay-Lambkin, F., Pursey, K., Skinner, J., & Dayas, C. (2018). Food addiction and associations with mental health symptoms: a systematic review with meta-analysis. Journal of human nutrition and dietetics: the official journal of the British Dietetic Association, 31(4), 544–572. 10.1111/jhn.12532 [DOI] [PubMed] [Google Scholar]
  12. Camacho-Barcia, L., Munguía, L., Lucas, I., de la Torre, R., Salas-Salvadó, J., Pintó, X., Corella, D., Granero, R., Jiménez-Murcia, S., González-Monje, I., Esteve-Luque, V., Cuenca-Royo, A., Gómez-Martínez, C., Paz-Graniel, I., Forcano, L., & Fernández-Aranda, F. (2021). Metabolic, affective and neurocognitive characterization of metabolic syndrome patients with and without food addiction. Implications for weight progression. Nutrients, 13(8), 2779. 10.3390/nu13082779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carlson, L., Steward, T., Agüera, Z., Mestre-Bach, G., Magaña, P., Granero, R., Jiménez-Murcia, S., Claes, L., Gearhardt, A. N., Menchón, J. M., & Fernández-Aranda, F. (2018). Associations of food addiction and nonsuicidal self-injury among women with an eating disorder: a common strategy for regulating emotions? European eating disorders review: the journal of the Eating Disorders Association, 26(6), 629–637. 10.1002/erv.2646 [DOI] [PubMed] [Google Scholar]
  14. Chao, A. M., Grilo, C. M., & Sinha, R. (2016). Food cravings, binge eating, and eating disorder psychopathology: exploring the moderating roles of gender and race. Eating behaviors, 21, 41–47. 10.1016/j.eatbeh.2015.12.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chao, A. M., Wadden, T. A., Tronieri, J. S., Pearl, R. L., Alamuddin, N., Bakizada, Z. M., Pinkasavage, E., Leonard, S. M., Alfaris, N., & Berkowitz, R. I. (2019). Efects of addictive-like eating behaviors on weight loss with behavioral obesity treatment. Journal of behavioral medicine, 42(2), 246–255. 10.1007/s10865-018-9958-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cornier, M. A. (2011). Is your brain to blame for weight regain? Physiology & behavior, 104(4), 608–612. 10.1016/j.physbeh.2011.04.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Corsaro L., Trivellato L., & Vaccaro, G. (2022). 4th Italian Obesity Barometer Report 2022, L’obesità in Italia: è tempo di agire. IBDO Foundation, Istat, CoResearch, BHave. [(Accessed on 18 June 2023)]. Available online: https://www.researchgate.net/publication/368825113_ITALIAN_OBESITY_BAROMETER_REPORT_2022 [Google Scholar]
  18. Davis, C., & Carter, J. C. (2009). Compulsive overeating as an addiction disorder. A review of theory and evidence. Appetite, 53(1), 1–8. 10.1016/j.appet.2009.05.018 [DOI] [PubMed] [Google Scholar]
  19. Davis, C., Curtis, C., Levitan, R. D., Carter, J. C., Kaplan, A. S., & Kennedy, J. L. (2011). Evidence that 'food addiction' is a valid phenotype of obesity. Appetite, 57(3), 711–717. 10.1016/j.appet.2011.08.017 [DOI] [PubMed] [Google Scholar]
  20. Davis, C. (2014). Evolutionary and neuropsychological perspectives on addictive behaviors and addictive substances: relevance to the "food addiction" construct. Substance abuse and rehabilitation, 5, 129–137. 10.2147/SAR.S56835 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. De Almeida, R., Kamath, G., & Cabandugama, P. (2022). Food addiction in application to obesity management. Missouri medicine, 119(4), 372–378. [PMC free article] [PubMed] [Google Scholar]
  22. Dong, T. S., Mayer, E. A., Osadchiy, V., Chang, C., Katzka, W., Lagishetty, V., Gonzalez, K., Kalani, A., Stains, J., Jacobs, J. P., Longo, V. D., & Gupta, A. (2020). A distinct brain-gut-microbiome profile exists for females with obesity and food addiction. Obesity (Silver Spring, Md.), 28(8), 1477–1486. 10.1002/oby.22870 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Espel, H. M., Muratore, A. F., & Lowe, M. R. (2017). An investigation of two dimensions of impulsivity as predictors of loss-of-control eating severity and frequency. Appetite, 117, 9–16. 10.1016/j.appet.2017.06.004 [DOI] [PubMed] [Google Scholar]
  24. Fletcher, P. C., & Kenny, P. J. (2018). Food addiction: a valid concept? Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology, 43(13), 2506–2513. 10.1038/s41386-018-0203-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Garner, D. M., & Wooley, S. C. (1991). Confronting the failure of behavioral and dietary treatments for obesity. Clinical psychology review, 11(6), 729–780. 10.1016/0272-7358(91)90128-H [DOI] [Google Scholar]
  26. Gearhardt, A. N., Corbin, W. R., & Brownell, K. D. (2009). Preliminary validation of the Yale Food Addiction Scale. Appetite, 52(2), 430–436. 10.1016/j.appet.2008.12.003 [DOI] [PubMed] [Google Scholar]
  27. Gearhardt, A. N., Grilo, C. M., DiLeone, R. J., Brownell, K. D., & Potenza, M. N. (2011a). Can food be addictive? Public health and policy implications. Addiction (Abingdon, England), 106(7), 1208–1212. 10.1111/j.1360-0443.2010.03301.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Gearhardt, A. N., Yokum, S., Orr, P. T., Stice, E., Corbin, W. R., & Brownell, K. D. (2011b). Neural correlates of food addiction. Archives of general psychiatry, 68(8), 808–816. 10.1001/archgenpsychiatry.2011.32 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gearhardt, A. N., White, M. A., Masheb, R. M., Morgan, P. T., Crosby, R. D., & Grilo, C. M. (2012). An examination of the food addiction construct in obese patients with binge eating disorder. The International journal of eating disorders, 45(5), 657–663. 10.1002/eat.20957 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gearhardt, A. N., Corbin, W. R., & Brownell, K. D. (2016). Development of the Yale Food Addiction Scale Version 2.0. Psychology of addictive behaviors: journal of the society of psychologists in addictive behaviors, 30(1), 113–121. 10.1037/adb0000136 [DOI] [PubMed] [Google Scholar]
  31. Grant, J. E., & Chamberlain, S. R. (2016). Expanding the definition of addiction: DSM-5 vs. ICD-11. CNS spectrums, 21(4), 300–303. 10.1017/S1092852916000183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Guerrero Pérez, F., Sánchez-González, J., Sánchez, I., Jiménez-Murcia, S., Granero, R., Simó-Servat, A., Ruiz, A., Virgili, N., López-Urdiales, R., Montserrat-Gilde Bernabe, M., Garrido, P., Monseny, R., García-Ruiz-de-Gordejuela, A., Pujol-Gebelli, J., Monasterio, C., Salord, N., Gearhardt, A. N., Carlson, L., Menchón, J. M., Vilarrasa, N., … & Fernández-Aranda, F. (2018). Food addiction and preoperative weight loss achievement in patients seeking bariatric surgery. European eating disorders review: the journal of the Eating Disorders Association, 26(6), 645–656. 10.1002/erv.2649 [DOI] [PubMed] [Google Scholar]
  33. Hartston, H. (2012). The case for compulsive shopping as an addiction. Journal of psychoactive drugs, 44(1), 64–67. 10.1080/02791072.2012.660110 [DOI] [PubMed] [Google Scholar]
  34. Hauck, C., Weiß, A., Schulte, E. M., Meule, A., & Ellrott, T. (2017). Prevalence of 'food addiction' as measured with the Yale Food Addiction Scale 2.0 in a representative German sample and its association with sex, age and weight categories. Obesity facts, 10(1), 12–24. 10.1159/000456013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hauck, C., Cook, B., & Ellrott, T. (2020). Food addiction, eating addiction and eating disorders. The proceedings of the Nutrition Society, 79(1), 103–112. 10.1017/S0029665119001162 [DOI] [PubMed] [Google Scholar]
  36. Imperatori, C., Fabbricatore, M., Vumbaca, V., Innamorati, M., Contardi, A., & Farina, B. (2016). Food addiction: definition, measurement and prevalence in healthy subjects and in patients with eating disorders. Rivista di psichiatria, 51(2), 60–65. 10.1708/2246.24196 [DOI] [PubMed] [Google Scholar]
  37. Joyner, M. A., Gearhardt, A. N., & White, M. A. (2015). Food craving as a mediator between addictive-like eating and problematic eating outcomes. Eating behaviors, 19, 98–101. 10.1016/j.eatbeh.2015.07.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Karila, L., Wéry, A., Weinstein, A., Cottencin, O., Petit, A., Reynaud, M., & Billieux, J. (2014). Sexual addiction or hypersexual disorder: diferent terms for the same problem? A review of the literature. Current pharmaceutical design, 20(25), 4012–4020. 10.2174/13816128113199990619 [DOI] [PubMed] [Google Scholar]
  39. Kiyici, S., Koca, N., Sigirli, D., Aslan, B. B., Guclu, M., & Kisakol, G. (2020). Food addiction correlates with psychosocial functioning more than metabolic parameters in patients with obesity. Metabolic syndrome and related disorders, 18(3), 161–167. 10.1089/met.2019.0108 [DOI] [PubMed] [Google Scholar]
  40. Lattemann, D. F. (2008). Endocrine links between food reward and caloric homeostasis. Appetite, 51(3), 452–455. 10.1016/j.appet.2008.06.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. MacLean, P. S., Higgins, J. A., Giles, E. D., Sherk, V. D., & Jackman, M. R. (2015). The role for adipose tissue in weight regain after weight loss. Obesity reviews: an official journal of the International Association for the Study of Obesity, 16(Suppl 1), 45–54. 10.1111/obr.12255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Marazziti, D., Presta, S., Baroni, S., Silvestri, S., & Dell'Osso, L. (2014). Behavioral addictions: a novel challenge for psychopharmacology. CNS spectrums, 19(6), 486–495. 10.1017/S1092852913001041 [DOI] [PubMed] [Google Scholar]
  43. Marazziti, D., Parra, E., Arone, A., Carbone, M. G., Del Prete, L., Fantasia, S., Palermo, S., & Dell'Osso, L. (2023). Internet use amongst professional tennis players. Clinical neuropsychiatry, 20(1), 9–16. 10.36131/cnfioritieditore20230102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Marazziti, D., Arone, A., Palermo, S., Annuzzi, E., Cappellato, G., Chiarantini, I., Prete, L. D., & Dell'Osso, L. (2023). The wicked relationship between depression and metabolic syndrome. Clinical neuropsychiatry, 20(2), 100–108. 10.36131/cnfioritieditore20230202 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Meseri, R., & Akanalci, C. (2020). Food addiction: a key factor contributing to obesity? Journal of research in medical sciences: the official journal of Isfahan University of Medical Sciences, 25, 71. 10.4103/jrms.JRMS_971_19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Meule, A., von Rezori, V., & Blechert, J. (2014). Food addiction and bulimia nervosa. European eating disorders review: the journal of the Eating Disorders Association, 22(5), 331–337. 10.1002/erv.2306 [DOI] [PubMed] [Google Scholar]
  47. Mies, G. W., Treur, J. L., Larsen, J. K., Halberstadt, J., Pasman, J. A., & Vink, J. M. (2017). The prevalence of food addiction in a large sample of adolescents and its association with addictive substances. Appetite, 118, 97–105. 10.1016/j.appet.2017.08.002 [DOI] [PubMed] [Google Scholar]
  48. Mohammad, S., Jan, R. A., & Alsaedi, S. L. (2023). Symptoms, mechanisms, and treatments of video game addiction. Cureus, 15(3), e36957. 10.7759/cureus.36957 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pänkäläinen, M., Fogelholm, M., Valve, R., Kampman, O., Kauppi, M., Lappalainen, E., & Hintikka, J. (2018). Pessimism, diet, and the ability to improve dietary habits: a three-year follow-up study among middle-aged and older Finnish men and women. Nutrition journal, 17(92). 10.1186/s12937-018-0400-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Pedram, P., Wadden, D., Amini, P., Gulliver, W., Randell, E., Cahill, F., Vasdev, S., Goodridge, A., Carter, J. C., Zhai, G., Ji, Y., & Sun, G. (2013). Food addiction: its prevalence and significant association with obesity in the general population. PloS one, 8(9), e74832. 10.1371/journal.pone.0074832 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Penzenstadler, L., Soares, C., Karila, L., & Khazaal, Y. (2019). Systematic review of food addiction as measured with the Yale Food Addiction Scale: implications for the food addiction construct. Current neuropharmacology, 17(6), 526–538. 10.2174/1570159X16666181108093520 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Piccinni, A., Bucchi, R., Fini, C., Vanelli, F., Mauri, M., Stallone, T., Cavallo, E. D., & Claudio, C. (2021). Food addiction and psychiatric comorbidities: a review of current evidence. Eating and weight disorders: EWD, 26(4), 1049–1056. 10.1007/s40519-020-01021-3 [DOI] [PubMed] [Google Scholar]
  53. Praxedes, D. R. S., Silva-Júnior, A. E., Macena, M. L., Oliveira, A. D., Cardoso, K. S., Nunes, L. O., Monteiro, M. B., Melo, I. S. V., Gearhardt, A. N., & Bueno, N. B. (2022). Prevalence of food addiction determined by the Yale Food Addiction Scale and associated factors: a systematic review with meta-analysis. European eating disorders review: the journal of the Eating Disorders Association, 30(2), 85–95. 10.1002/erv.2878 [DOI] [PubMed] [Google Scholar]
  54. Pursey, K. M., Stanwell, P., Gearhardt, A. N., Collins, C. E., & Burrows, T. L. (2014). The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review. Nutrients, 6(10), 4552–4590. 10.3390/nu6104552 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Pursey, K. M., Collins, C. E., Stanwell, P., & Burrows, T. L. (2015). Foods and dietary profiles associated with 'food addiction' in young adults. Addictive behaviors reports, 2, 41–48. 10.1016/j.abrep.2015.05.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Pursey, K. M., Skinner, J., Leary, M., & Burrows, T. (2021). The relationship between addictive eating and dietary intake: a systematic review. Nutrients, 14(1), 164. 10.3390/nu14010164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Randolph, T. G. (1956). The descriptive features of food addiction; addictive eating and drinking. Quarterly journal of studies on alcohol, 17(2), 198–224. [PubMed] [Google Scholar]
  58. Reents, J., & Pedersen, A. (2021). Differences in food craving in individuals with obesity with and without binge eating disorder. Frontiers in psychology, 12, 660880. 10.3389/fpsyg.2021.660880 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schulte, E. M., Avena, N. M., & Gearhardt, A. N. (2015). Which foods may be addictive? The roles of processing, fat content, and glycemic load. PloS one, 10(2), e0117959. 10.1371/journal.pone.0117959 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Schulte, E. M., Potenza, M. N., & Gearhardt, A. N. (2017). A commentary on the "eating addiction" versus "food addiction" perspectives on addictive-like food consumption. Appetite, 115, 9–15. 10.1016/j.appet.2016.10.033 [DOI] [PubMed] [Google Scholar]
  61. Thompson, R. A. (2019). Emotion dysregulation: a theme in search of definition. Development and psychopathology, 31(3), 805–815. 10.1017/S0954579419000282 [DOI] [PubMed] [Google Scholar]
  62. Weinstein, A., & Lejoyeux, M. (2010). Internet addiction or excessive internet use. The American Journal of drug and alcohol abuse, 36(5), 277–283. 10.3109/00952990.2010.491880 [DOI] [PubMed] [Google Scholar]
  63. Weinstein, A., & Weinstein, Y. (2014). Exercise addiction-diagnosis, bio-psychological mechanisms and treatment issues. Current pharmaceutical design, 20(25), 4062–4069. 10.2174/13816128113199990614 [DOI] [PubMed] [Google Scholar]
  64. Weir, C. B., & Jan, A. (2022). BMI classification percentile and cut off points. In StatPearls. StatPearls Publishing. [PubMed] [Google Scholar]
  65. Weiner, B., & White, W. (2007). The Journal of inebriety (1876–1914): history, topical analysis, and photographic images. Addiction, 102, 15–23. 10.1111/j.1360-0443.2006.01680.x [DOI] [PubMed] [Google Scholar]
  66. Westphal, M., Aldao, A., & Jackson, C. (2017). Emotion dysregulation in comorbid posttraumatic stress disorder and substance use disorders: a narrative review. Military Psychology, 29(3), 216–233. 10.1037/mil0000157 [DOI] [Google Scholar]
  67. World Health Organization. (2019). International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/ [Google Scholar]
  68. World Health Organization. (n.d.). World Obesity Day 2022 – accelerating action to stop obesity. World Health Organization. https://www.who.int/news/item/04-03–2022-world-obesity-day-2022-accelerating-action-to-stop-obesity [Google Scholar]
  69. Yau, Y. H., & Potenza, M. N. (2015). Gambling disorder and other behavioral addictions: recognition and treatment. Harvard review of psychiatry, 23(2), 134–146. 10.1097/HRP.0000000000000051 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Clinical Neuropsychiatry are provided here courtesy of Giovanni Fioriti Editore

RESOURCES