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Eating Disorders: Current Knowledge and Treatment Update

• B. Timothy Walsh , M.D.

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Although relatively uncommon, eating disorders remain an important concern for clinicians and researchers as well as the general public, as highlighted by the recent depiction of Princess Diana’s struggles with bulimia in “The Crown.” This brief review will examine recent findings regarding the diagnosis, epidemiology, neurobiology, and treatment of eating disorders.

Eight years ago, DSM-5 made major changes to the diagnostic criteria for eating disorders. A major problem in DSM-IV ’s criteria was that only two eating disorders, anorexia nervosa and bulimia nervosa, were officially recognized. Therefore, many patients presenting for treatment received the nonspecific diagnostic label of eating disorder not otherwise specified (EDNOS), which provided little information about the nature of the patient’s difficulties. This problem was addressed in several ways in DSM-5 (see DSM-5 Feeding and Eating Disorder list). The diagnostic criteria for anorexia nervosa and bulimia nervosa were slightly expanded to capture a few more patients in each category. But two other changes had a greater impact in reducing the use of nonspecific diagnoses.

The first of these was the addition of binge eating disorder (BED), which had previously been described in an appendix of DSM-IV . BED is the most common eating disorder in the United States, so its official recognition in DSM-5 led to a substantial reduction in the need for nonspecific diagnoses.

DSM-5 Feeding and Eating Disorder

Rumination Disorder

Avoidant/restrictive food intake disorder

Anorexia nervosa

Bulimia nervosa

Binge-eating disorder

Other specified feeding or eating disorder

Unspecified feeding or eating disorder

The second important change was the combination of the DSM-IV section titled “Feeding and Eating Disorders of Infancy or Early Childhood” with “Eating Disorders” to form an expanded section, “Feeding and Eating Disorders.” This change thereby included three diagnostic categories: pica, rumination disorder, and feeding disorder of infancy or early childhood. Pica and rumination disorder are infrequently diagnosed.

The other category, feeding disorder of infancy or early childhood, was rarely used and had been the subject of virtually no research since its inclusion in DSM-IV . The Eating Disorders Work Group responsible for reviewing the criteria for eating disorders for DSM-5 realized that there was a substantial number of individuals, many of them children, who severely restricted their food intake but did not have anorexia nervosa. For example, after a severe bout of vomiting after eating, some individuals attempt to prevent a recurrence by no longer eating at all, leading to potentially serious nutritional disturbances. No diagnostic category in DSM-IV existed for such individuals. Therefore, the DSM-IV category, feeding disorder of infancy or early childhood, was expanded and retitled “avoidant/restrictive food intake disorder” (ARFID). Combined, these changes led to a substantial reduction in the need for nonspecific diagnostic categories for eating disorders.

In the course of assessing the impact of the recommended changes in the diagnostic criteria for eating disorders, the Eating Disorders Work Group became aware of another group of individuals presenting for clinical care whose symptoms did not quite fit any of the existing or proposed categories. These were individuals, many of them previously overweight or obese, who had lost a substantial amount of weight and developed many of the signs and symptoms characteristic of anorexia nervosa. However, at the time of presentation, their weights remained within or above the normal range, therefore not satisfying the first diagnostic criterion for anorexia nervosa. The work group recommended that a brief description of such individuals be included in the DSM-5 diagnostic category that replaced DSM-IV ’s EDNOS: “other specified feeding and eating disorders” (OSFED); this description was labeled atypical anorexia nervosa. The degree to which the symptoms, complications, and course of individuals with atypical anorexia nervosa resemble and differ from those of individuals with typical anorexia nervosa remains an important focus of current research.

Epidemiology

Although eating disorders contribute significantly to the global burden of disease, they remain relatively uncommon. A study published in September 2018 by Tomoko Udo, Ph.D., and Carlos M. Grilo, Ph.D., in Biological Psychiatry examined data from a large, nationally representative sample of over 36,000 U.S. adults 18 years of age and older surveyed using a lay-administered diagnostic interview in 2012-2013. The 12-month prevalence estimates for anorexia nervosa, bulimia nervosa, and BED were 0.05%, 0.14%, and 0.44%, respectively. Although the relative frequencies of these disorders were similar to those described in prior studies, the absolute estimates were somewhat lower for unclear reasons. Consistent with clinical experience and prior reports, the eating disorders, especially anorexia nervosa and bulimia nervosa, were more prevalent among women (though men are also affected). Although eating disorders occurred across all ethnic and racial groups, there were fewer cases of anorexia nervosa among non-Hispanic and Hispanic Black respondents than among non-Hispanic White respondents. Consistent with long-standing clinical impression, individuals with lifetime anorexia nervosa reported higher incomes.

Finally, when BED was under consideration for official recognition in DSM-5 , some critics suggested that, since virtually everyone occasionally overeats, BED was an example of the misguided tendency of DSM to pathologize normal behavior. The low prevalence of BED reported in the study by Udo and Grilo documents that, when carefully assessed, BED affects only a minority of individuals and is therefore distinct from normality.

A subject of some debate and substantial uncertainty is whether the incidence of eating disorders (the number of new cases a year) is increasing. Some studies, such as that of Udo and Grilo, have found that the lifetime rates of eating disorders among older individuals are lower than those among younger individuals, suggesting that the frequency of eating disorders may be increasing. However, this might also reflect more recent awareness and knowledge of eating disorders. Other studies that conducted multiple examinations of the frequency of eating disorders in the same settings over time appear to suggest that, in the last several decades, the incidence of anorexia nervosa has remained roughly stable, whereas the incidence of bulimia nervosa has decreased. Presumably, this reflects changes in the sociocultural environment such as an increased acceptance of being overweight and reduced pressure to engage in inappropriate compensatory measures such as self-induced vomiting after binge eating.

The COVID-19 pandemic has impacted virtually every facet of life across the world and has produced severe financial, medical, and psychological stresses. Preliminary research suggests that such stresses have exacerbated the symptoms of individuals with preexisting eating disorders and have led to increased binge eating in the general population. Hopefully, these trends will improve with successful control of the pandemic.

Neurobiology

Much recent research on the mechanisms underlying the development and persistence of eating disorders has focused on the processing of rewarding and nonrewarding/punishing stimuli. Several studies have suggested that individuals with anorexia nervosa are less able to distinguish among stimuli with varying probabilities of obtaining a reward. Other studies suggest that, when viewing images of food during MRI scanning, individuals with anorexia nervosa tend to show less activation of brain reward areas than do controls. Such deficits may be related to disturbances in dopamine function in areas of the brain known to be involved in reward processing. Research based on emerging methods in computational psychiatry suggests that individuals with anorexia nervosa may be particularly sensitive to learning from punishment; for example, they may be very quick to learn what stimuli lead to a decrease in the amount of a reward. Conceivably, they may learn that eating high-fat foods prevents weight loss and produces undesirable weight gain, and they begin to avoid such foods. These studies, and a range of others, focus on probing basic brain mechanisms and how they may be disrupted in anorexia nervosa. A challenge for this “bottom-up” approach is to determine how exactly disturbances in such mechanisms are related to the eating disturbances characteristic of anorexia nervosa.

Other recent studies take a “top-down” approach, focusing on the neural circuitry underlying the persistent maladaptive choices made by individuals with anorexia nervosa when they decide what foods to eat. Such research successfully captures the well-established avoidance of high-fat foods by individuals with anorexia nervosa and has documented that such individuals utilize different neural circuits in making decisions about what to eat than do healthy individuals. These results are consistent with suggestions that the impressive persistence of anorexia nervosa in many individuals may be due to the establishment of automatic, stereotyped, and habitual behavior surrounding food choice. A challenge for such top-down research strategies is to determine how these maladaptive patterns develop so rapidly and become so ingrained.

Research on the neurobiology underlying bulimia nervosa is broadly similar. Although the results are complex, individuals with bulimia nervosa appear to find food stimuli more rewarding, and there are indications of disturbances in reward responsiveness to sweet tastes. Several studies have documented impairments in impulse control assessed using behavioral paradigms such as the Stroop Task. In this task, individuals are presented with a word naming a color (for example, “red”) but asked to name the color of the letters spelling the word (for example, the letters r, e, and d are green). Increased difficulties in performing such tasks have been described in individuals with bulimia nervosa and linked to reduced prefrontal cortical thickness.

It has long been known that eating disorders tend to run in families, and there has been strong evidence that this in part reflects the genes that individuals inherit from their parents. In recent decades, it has become clear that the risk of developing most complex human diseases, including obesity, hypertension, and eating disorders is related to many genes, each one of which contributes a small amount to the risk. Because the contribution of a single gene is so small, the DNA from a very large number of individuals with and without the disorder needs to be examined. For instance, genomewide association studies (GWAS) in schizophrenia have examined tens of thousands of individuals with schizophrenia and over 100,000 controls and identified well over 100 genetic loci that contribute to the risk of developing schizophrenia.

GWAS examining the genetic risk for eating disorders are under way but to date have focused primarily on anorexia nervosa. The Psychiatric Genetics Consortium has collected information from 10,000 to 20,000 individuals with anorexia nervosa and over 50,000 controls and has, so far, identified eight loci that contribute to the genetic risk for this disorder. In addition, this work has identified genetic correlations between anorexia nervosa and a range of other disorders known to be comorbid with anorexia nervosa such as anxiety disorders as well as a negative genetic correlation with obesity. These data suggest that the genetic risk for anorexia nervosa is based on a complex interplay between loci associated with a range of psychological and metabolic/anthropometric traits.

Although there have been no dramatic developments in our knowledge of how best to treat individuals with eating disorders, there have been some significant and useful advances in recent years.

For anorexia nervosa, arguably the most significant advance in treatment in the last quarter century has been family-based treatment for adolescents. In this approach, sometimes referred to as the “Maudsley method,” the family, guided by the therapist, becomes the primary agent of change and responsible for ensuring that eating behavior normalizes and weight increases. This approach differs markedly from prior treatment strategies that assumed parental involvement was not helpful or even detrimental. Family-based treatment is now widely viewed as a treatment of first choice for adolescents with anorexia nervosa and has also been adapted to treat bulimia nervosa.

Family-based treatment can be quite challenging for parents. The entire family is asked to attend treatment sessions, and one session early in treatment includes a family meal during which the parents are charged with the difficult task of persuading the adolescent to consume more food than he/she had intended. An alternative but related model, termed “parent-focused treatment,” has recently been explored in a few studies. In this approach, parents meet with a therapist without the affected adolescent or other members of the family and receive guidance regarding how to help the adolescent to alter his or her behavior following techniques virtually identical to those provided in traditional family-based treatment. Several small studies have examined this approach, and results suggest similar effectiveness. Although more research is needed, these findings suggest that parent-focused treatment may be an attractive alternative to family-based treatment for many parents and practitioners.

The COVID-19 pandemic has led to a dramatic acceleration in the provision of psychiatric care remotely, including family-based treatment. Work on providing family-based treatment via videoconference had begun prior to the arrival of COVID-19, as this specialized form of care is not widely available, and its provision via HIPAA-compliant video links would offer a substantial increase in accessibility. Several small studies suggested that remote provision of family-based treatment is feasible and likely to be efficacious. The restrictions imposed by COVID-19 on face-to-face contact have accelerated the remote delivery of family-based treatment; hopefully, new research will document its effectiveness. It should be noted, however, that, in most cases, local contact with a medical professional who can directly measure weight and oversee the patient’s physical state is required.

The treatment of adults with anorexia nervosa, who typically developed the disorder as teenagers and have been ill for five or more years, remains challenging. Structured behavioral interventions, such as those available in specialized inpatient, day program, or residential centers, typically lead to significant weight restoration and psychological and physiological improvement. However, the rate of relapse following acute care remains substantial. Furthermore, most adult patients with anorexia nervosa are very reluctant to accept treatment in such structured programs. A recent helpful development is evidence that olanzapine, at a dose of 5 mg/day to 10 mg/day, assists modestly with weight gain in adult outpatients with anorexia nervosa and is associated with few significant side effects. Unfortunately, it does not address core psychological symptoms and must be viewed as adjunctive to standard care.

There have been fewer recent developments in the treatment of patients with bulimia nervosa and of BED. For bulimia nervosa, cognitive-behavioral therapy remains the mainstay psychological treatment, and SSRIs continue to be the first-choice class of medication. For BED, multiple forms of psychological treatment are associated with substantial improvement in binge eating, and, in 2015, the FDA approved the use of the stimulant lisdexamfetamine (Vyvanse) for individuals with BED. Unlike most psychological treatments, lisdexamfetamine is associated with modest weight loss but has effects on pulse and blood pressure that may be of concern, especially for older individuals.

Also noteworthy are the development and application of new forms of psychological treatment for individuals with eating disorders. These include dialectical behavior therapy (DBT), acceptance and commitment therapy (ACT), and integrative cognitive-affective therapy (ICAT). Although only a few controlled studies have examined the effectiveness of these treatments, anecdotal information and the results of these studies suggest that such methods may be useful alternatives to more established interventions.

Conclusions

Eating disorders remain uncommon but clinically important problems characterized by persistent disturbances in eating or eating-related behavior. Cutting-edge research focuses on neurobiology and genetics, utilizing novel and rapidly evolving methodology. There have been modest advances in treatment approaches, including the COVID-19 pandemic’s acceleration of treatment delivery via video-link. Future studies will hopefully clarify the nature of ARFID and of atypical anorexia nervosa and lead to the development of more effective interventions, especially for individuals with long-standing eating disorders. ■

Additional Resources

Walsh BT. Diagnostic Categories for Eating Disorders: Current Status and What Lies Ahead. Psychiatr Clin North Am . 2019; 42(1):1-10.

Udo T, Grilo CM. Prevalence and Correlates of DSM-5 -Defined Eating Disorders in a Nationally Representative Sample of U.S. Adults. Biol Psychiatry . 2018; 84(5):345-354.

Van Hoeken D, Hoek HW. Review of the Burden of Eating Disorders: Mortality, Disability, Costs, Quality of Life, and Family Burden. Curr Opin Psychiatry . 2020; 33(6):521-527.

Bernardoni F, Geisler D, King JA, et al. Altered Medial Frontal Feedback Learning Signals in Anorexia Nervosa. Biol Psychiatry . 2018; 83(3):235-243.

Frank GKW, Shott ME, DeGuzman MC. The Neurobiology of Eating Disorders. Child Adolesc Psychiatr Clin N Am . 2019; 28(4):629-640.

Steinglass JE, Berner LA, Attia E. Cognitive Neuroscience of Eating Disorders. Psychiatr Clin North Am . 2019; 42(1):75-91.

Bulik CM, Blake L, Austin J. Genetics of Eating Disorders: What the Clinician Needs to Know. Psychiatr Clin North Am . 2019; 42(1):59-73.

Attia E, Steinglass JE, Walsh BT, et al. Olanzapine Versus Placebo in Adult Outpatients With Anorexia Nervosa: A Randomized Clinical Trial. Am J Psychiatry . 2019; 176(6):449-456.

Le Grange D, Hughes EK, Court A, et al. Randomized Clinical Trial of Parent-Focused Treatment and Family-Based Treatment for Adolescent Anorexia Nervosa. J Am Acad Child Adolesc Psychiatry . 2016; 55(8):683-92.

Pisetsky EM, Schaefer LM, Wonderlich SA, et al. Emerging Psychological Treatments in Eating Disorders. Psychiatr Clin North Am . 2019; 42:219-229.

B. Timothy Walsh, M.D., is a professor of psychiatry at the Columbia University Irving Medical Center and the founding director of the Columbia Center for Eating Disorders at the New York State Psychiatric Institute. He is the co-editor of the Handbook of Assessment and Treatment of Eating Disorders from APA Publishing.

Dr. Walsh reports receiving royalties or honoraria from UpToDate, McGraw-Hill, the Oxford University Press, the British Medical Journal, the Johns Hopkins Press, and Guidepoint Global

• Research article
• Open access
• Published: 15 September 2015

Perceptions of the causes of eating disorders: a comparison of individuals with and without eating disorders

• Elizabeth H. Blodgett Salafia 1 ,
• Maegan E. Jones 1 ,
• Emily C. Haugen 1 &
• Mallary K. Schaefer 1  

Journal of Eating Disorders volume  3 , Article number:  32 ( 2015 ) Cite this article

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In this study, we examined perceptions regarding the causes of eating disorders, both among those with eating disorders as well as those without. By understanding the differences in perceived causes between the two groups, better educational programs for lay people and those suffering from eating disorders can be developed.

This study used open-ended questions to assess the beliefs of 57 individuals with self-reported eating disorders and 220 without. Participants responded to the questions, “What do you think was (were) the cause(s) of your eating disorder?” and “What do you think is (are) the cause(s) of eating disorders?”.

A list of possible codes for the causes of eating disorders was created based on a thorough review of the literature. A manually-generated set of eight codes was then created from individuals' actual responses. Frequencies and chi square analyses demonstrated differences in rates of endorsement between those with eating disorders and those without. Participants with eating disorders most frequently endorsed psychological/emotional and social problems, with genetics/biology and media/culture ideals least endorsed. Participants without eating disorders most frequently endorsed psychological/emotional problems and media/culture ideals, with traumatic life events and sports/health least endorsed. There was a difference between groups in the endorsement of the media as a cause of eating disorders, suggesting that those without eating disorders may overly attribute the media as the main cause while those with eating disorders may not be fully aware of the media’s impact. Additionally, while both groups highly endorsed psychological/emotional problems, there was a noticeable stigma about eating disorders among those without eating disorders.

Conclusions

There were noteworthy differences between samples; such differences suggest that there is a need for more education on the topic of eating disorders. Furthermore, despite empirical support for the effects of genetics, sports, and family factors, these were infrequently endorsed as causes of eating disorders by both groups. Our results suggest that there is a need for more education regarding the factors associated with eating disorders, in order to reduce the stigma surrounding these disorders and to potentially aid the treatment process.

Eating disorders have increasingly become the focus of research studies due to their prevalence, especially in Western cultures. Of the adolescent and young adult populations in the United States, for example, between .3 and .9 % are diagnosed with anorexia nervosa (AN), between .5 and 5 % with bulimia nervosa (BN), between 1.6 and 3.5 % with binge eating disorder (BED), and about 4.8 % with eating disorder otherwise not specified (EDNOS) [ 1 – 4 ]. According to the fifth edition of the DSM, individuals that do not fit the criteria for AN, BN or BED are diagnosed with sub-threshold or atypical conditions that fit under other specified feeding or eating disorder (OSFED) [ 5 ]. Due in part to decreased thresholds for the diagnoses of AN, BN or BED in the DSM-V, rates of OSFED have been found to be lower than previous rates of EDNOS, while the rates of AN, BN or BED have stayed the same or slightly increased [ 6 ]. Furthermore, the age at onset is concerning, as most eating disorders originate during adolescence [ 4 ]. Despite the potentially serious health consequences that result from disordered eating [ 7 ], many in the general public believe that issues with eating are due to personal shortcomings [ 8 , 9 ]. This creates a foundation of stigma regarding why individuals develop an eating disorder (e.g., to be “skinny”) and the purpose the disorder serves (e.g., to gain control). Such stigma may dishonor the actual experience of those who have lived with an eating disorder, as people could assume eating disorders are self-inflicted. In turn, those developing unhealthy habits may be discouraged from seeking help [ 10 ].

Previous research has identified biological, psychological, and sociocultural factors related to the development of eating disorders. However, it is important to explore individual narratives to identify similarities and differences among individuals with and without eating disorders. Obtaining such knowledge can help scholars determine the public’s educational needs and better target missing gaps in their knowledge. More accurate information may reduce stigma regarding eating disorders, which may in turn encourage those experiencing symptoms to seek help sooner, as they may no longer fear the negative feedback from peers and family that such stigma causes.

Factors that contribute to eating disorders identified by research

Research has identified many risk factors, ranging from individual to sociocultural, that contribute to the development of eating disorders. Based on empirical literature, we present some of the most salient factors below.

Individual factors

Genetics and biology are individual factors that play a role in the development of eating disorders. Genetic contributions to the development of eating disorders have been suggested by twin studies, with heritability estimates ranging from 0.39 to 0.74, depending on the disorder [ 11 ]. Abnormalities in the regulation of certain neurochemicals, such as 5-Hydroxytryptamine (HT) and the serotonin-transporter-linked polymorphic region (5-HTTLPR), have been closely linked with eating disorders [ 11 – 13 ]. Further, recent research has identified mutations on two specific genes that have been associated with increased risk of developing eating disorders in families: estrogen-related receptor α (ESRRA) and histone deacetylase 4 (HDAC4) [ 14 ]. In addition, early puberty has also been associated with disordered eating behaviors, potentially due to increases or irregularities in circulating sex hormones, especially estrogen [ 15 , 16 ].

Body dissatisfaction has been commonly identified as an influential risk factor for eating disorders. Individuals dissatisfied with their bodies are at an increased risk of engaging in disordered eating behaviors such as bingeing and purging in order to gain satisfaction and move closer to the thin ideal [ 14 , 17 ]. Engaging in dieting behaviors also increases the risk for the occurrence of eating pathology such as binge eating and purging [ 15 , 18 ].

Researchers have recognized perfectionism as a specific risk factor in the development of eating disorders, as this personality trait may lead to a persistent pursuit of the thin ideal [ 15 , 19 , 20 ]. Perfectionism can also be a maintenance factor for disordered eating since it promotes dieting, bingeing, and purging, and enhances eating disorder symptoms, particularly when combined with low self-esteem [ 12 , 15 ]. Similarly, research has shown that negative affect in general, such as high levels of stress, guilt, hostility, anger, anxiety, and depressed mood, is associated with increases in eating disorder symptoms [ 12 , 13 , 17 – 21 ].

Sexual, physical, and emotional abuse have all received empirical support as risk factors for psychiatric difficulties, which can include eating disorders [ 22 ]. Specifically, research has shown that sexual abuse can occur in 29 % of individuals with eating disorders, and physical abuse may occur in 57 % of individuals [ 23 , 24 ]. Additionally, emotional abuse is a significant predictor of eating disorder symptoms among women when other types of abuse are controlled for, suggesting that emotional abuse may be particularly salient [ 12 , 25 ].

Sociocultural factors

Many sociocultural factors affect the development of eating disorders. In families, for example, mothers’ and fathers’ own body dissatisfaction and dieting behaviors have been associated with their children’s eating-related attitudes and behaviors [ 26 , 27 ]. Parental weight-related teasing, negative comments about body shape, pressure to lose weight, and encouragement to diet have also been associated with body dissatisfaction, dieting, disordered eating behaviors, and eating disorders among both females and males [ 12 , 15 , 26 , 28 – 31 ]. Furthermore, parents who engage in high levels of parental control, expressed emotionality, critical comments, hostility, or emotional overinvolvement and negate their child’s emotional needs are more likely to have children who develop eating disorders [ 12 , 32 ].

Peer influences on the development of eating disorders can also be broken down into a variety of factors. Peer pressure to conform to cultural ideals has been consistently identified as an important factor associated with the development of disordered eating behaviors, especially among adolescents [ 29 , 33 ]. In particular, girls may learn attitudes and behaviors from their peers, such as the importance of being thin and dieting behaviors, through modeling, teasing, and conversations about body image and eating [ 12 , 33 ]. Similarly, romantic partners play a significant role in the development of eating disorders through negative comments about appearance and encouragement to lose weight, which can lead to weight concerns, body dissatisfaction, and disordered eating behaviors among both men and women [ 34 , 35 ].

It is also worth mentioning that eating disorders among athletes are common, as there is a large focus not only on being in shape, but on being the fittest and therefore the “best” [ 36 , 37 ]. There is an even greater risk of developing an eating disorder with participation in certain competitive sports that focus on leanness, such as gymnastics [ 38 ]. Athletes who believe that being leaner will increase their performance are more likely to engage in disordered eating [ 39 ]. This belief may be encouraged or reinforced by coaches and instructors, further increasing athletes’ risk for developing disordered habits [ 40 ].

Lastly, the media has an influential, if often controversial, role in the development of eating behaviors due its representation of the thin ideal. There is support that, regardless of the level of internalized thin ideal, women who were warned that a thin media image was altered experienced lower body dissatisfaction in comparison to those who were not warned the image was altered [ 41 ]. A preference for a thin and virtually unattainable body has been associated with the development of eating disorders, particularly AN [ 42 ].

A relatively small number of studies have examined individuals’ perceptions regarding the causes of eating disorders [ 10 , 36 , 43 – 55 ]. Some studies have solely focused on the perceptions of either the general public [ 10 , 43 – 47 ] or those with eating disorders [ 36 , 37 , 50 – 55 ]. Both types of studies have identified a common set of risk factors, with public perceptions and the perceptions of individuals with eating disorders varying slightly [e.g. 48,49]. Overall, both populations have a basic understanding of what eating disorders are and characteristics of each eating disorder [ 10 , 36 , 43 – 55 ]. However, despite this knowledge, many adults without eating disorders may be unsympathetic to those suffering from eating disorders, believe that having an eating disorder would not be distressing, and report that eating disorders are not difficult to treat [ 9 ].

Public perceptions of factors that contribute to eating disorders

The studies to date that have focused on identifying public perceptions of the factors associated with the development of eating disorders have surveyed individuals drawn from communities or schools. Typically, these samples have been quite large, numbering over 100 [ 43 , 44 ] or even several hundred [ 10 , 45 , 46 ], and have included both females and males [ 10 , 43 , 44 , 46 – 48 ]. Despite the importance of large samples, all of these studies have been limited in that the researchers did not ask open-ended questions; rather, participants responded to forced-answer questions where they either had to identify which item was a cause of eating disorders or identify to what degree a particular item was a cause.

The public commonly places blame on individuals with eating disorders, suggesting that they have control over their “self-inflicted” illnesses [ 48 ]. Of the individual factors associated with the development of eating disorders, the majority of people who do not have eating disorders identify psychological explanations such as emotional state, personality, and low self-esteem [ 10 , 43 , 46 – 48 ]. The general public also believes that individuals’ own behaviors and attitudes related to body image such as dieting, a desire to be thin, and body image distortion are important factors in the development of eating disorders [ 43 , 47 , 48 ]. Traumatic events, genetics, and sexual abuse were rarely discussed or, if they were mentioned, rated low on the level of significance in causing eating disorders [ 10 , 47 ].

Although sociocultural factors are less commonly identified as causal factors of eating disorders among the general public, a few factors have received support. Of all the sociocultural factors, family issues were the factors most often identified [ 43 , 46 , 48 , 49 ]. Pressure from friends as well as social isolation and loneliness were also perceived to be factors contributing to eating disorders [ 46 , 47 ]. In one study, the portrayal of thin women in the media was a highly significant cause endorsed by adult women [ 45 ].

Perceptions of individuals with eating disorders regarding causes

In contrast to studies investigating the perceptions of the general public regarding factors associated with the development of eating disorders, most studies we found that focused on individuals with eating disorders used open-ended measures, either via interview or questionnaires. Despite this, one pitfall of the research to date is that it has often involved relatively small sample sizes, ranging from 15 to 36 [ 36 , 37 , 49 – 51 ]. Only two studies have included samples over 50 individuals [ 52 , 53 ]. Additionally, almost all of these studies have focused exclusively on women, with only two including a limited number of men [ 37 , 50 ]. Furthermore, although research has included assessments of individuals with AN [ 36 , 50 , 54 ] and BN [ 53 , 55 ] or both [ 49 , 51 , 52 ], studies have failed to examine if differences existed in the perceptions of those with AN versus BN, or include individuals with other eating disorders such as BED, EDNOS, or OSFED.

Similar to public perceptions of causal factors, people with eating disorders also identify individual and sociocultural factors. Individual factors commonly identified among samples of those who were diagnosed with eating disorders include perfectionism, emotional problems or distress, stress, unhappiness with appearance, high expectations of self, and lack of control [ 36 , 48 , 50 – 54 ]. Behaviors and attitudes related to body image, such as weight loss activities, body image distortion, and a belief that thinness equals happiness, were also frequently identified as factors that related to the development of their disorders [ 48 , 53 , 55 ]. Hereditary factors and sexual abuse were not indicated.

Sociocultural influences identified by individuals with eating disorders included the media, family, peers, and sports. Although rarely mentioned, the media was occasionally identified as playing a role through the importance it places on thinness and self-comparison to the thin ideal [ 36 , 37 ]. Family factors, in contrast, were often cited and included poor parental care, controlling parents, poor relationship with parents, family tension or high amounts of conflict, critical family environment, emotional abuse, and an emphasis on weight [ 36 , 37 , 48 – 51 , 53 , 55 ]. Factors associated with peers and sports were also common and included receiving comments or pressure from friends and coaches about appearance, a need to lose weight for sports performance, and poor relationships with peers [ 36 , 37 , 53 , 55 ].

Comparisons of individuals with and without eating disorders

We could only find two studies that examined the perceptions of both individuals with and without eating disorders. First, Haworth-Hoeppner [ 49 ] interviewed 21 women with an eating disorder (either AN or BN) and 11 without, asking open-ended questions about the development of eating disorders. In this study, no comparisons were made across the two groups, likely due to the qualitative nature of the project as well as the small sample size. Second, Holliday and colleagues [ 48 ] used larger samples of individuals with and without AN and made comparisons across groups regarding the causes of eating disorders and the most important causes. However, this study was limited in that it did not allow participants to describe their own beliefs. Instead, participants responded to a list of eighteen pre-identified causes of eating disorders, which did not allow for individual perspectives and greater depth into the complexity of eating disorders.

The present study

With the prevalence of eating disorders and young age of onset, examining people’s perceptions of the factors contributing to eating disorders is important. Such efforts can enhance public education and potentially decrease the stigma surrounding eating disorders. The present study specifically examined the differences between what people with and without eating disorders perceived to be the causes of eating disorders in order to better understand people’s experiences with eating disorders as well as to better educate the larger population. We also examined differences regarding the causes of eating disorders according to type of eating disorder, including AN, BN, both, and other (e.g., BED, EDNOS, or OSFED). This study strengthens existing research by utilizing qualitative, open-ended responses as opposed to forced-answer questionnaires so that participants could identify causes using their own opinions.

Participants and procedure

This study was reviewed and approved by the university’s Institutional Review Board. Our sample was recruited from flyers and emails distributed at local universities as well as from flyers distributed to local hospitals and clinics in a medium-sized, Midwestern U.S. city. A secure Internet link was provided, which participants used to indicate consent, provide demographic information, and answer several open-ended questions. All participants were first asked, “Do/did you have an eating disorder?” with the answer choices of “yes, currently,” “yes, in the past,” and “no.” Individuals who answered as having an eating disorder, whether past or current, were asked to specify which eating disorder they had/have and for how long.

The total sample consisted of 277 participants: 57 individuals who had a past or current eating disorder and 220 who did not. Consistent with the ethnic composition of the city, most of the sample identified themselves as White (93 %). There were 234 females (84.5 %) and 43 males (15.5 %). The age range of participants was from 18 to 51 (M = 22.39, SD = 5.77).

Sample with eating disorders

Of the 57 individuals who had an eating disorder, 26 had AN (46 %), 12 had BN (21 %), 11 had both AN and BN (19 %), and 8 had another type of eating disorder such as BED or EDNOS/OSFED (14 %). Participants reporting having an eating disorder from between 4 months and 22 years (M = 3.70 years, SD = 4.55 years). Similar to the demographics of the entire sample, 93 % identified as White, and the majority of individuals in this sample were female (96.5 %; n  = 55). Participants ranged in age from 18 to 47 (M = 23.70, SD = 5.84).

Sample without eating disorders

Of the 220 individuals who did not have an eating disorder, 93 % identified as White. In addition, 81 % identified as female ( n  = 179). Participants ranged in age from 18 to 51 (M = 22.05, SD = 5.71). In terms of ethnicity and age, both samples were similar; there were no statistically significant differences between samples ( p  = .80 and p  = .11, respectively). There was, however, a statistically significant difference in gender ( p  = .01).

Survey questions and compensation

After completing a series of demographic questions using the secure Internet link, individuals who had an eating disorder were asked the open-ended question, “What do you think was (were) the cause(s) of your eating disorder?” Individuals who did not have an eating disorder were asked a similar open-ended question, “What do you think is (are) the cause(s) of eating disorders?” These participants were then asked to report why they believed that these were the causes or how they learned about them. All participants were invited to participate in a random drawing for one of four $50 giftcards. Interested individuals were given another secure Internet link to provide their contact information if they wished to enter the drawing; this was done to keep the survey responses anonymous.

Coding of participants’ reponses

We initially created a list of possible codes for the causes of eating disorders commonly specified in previous research articles (as identified by overview articles on the risk factors or causes of eating disorders [e.g., 12, 15]). This provided us with a basic framework for content analysis [ 56 ]. Next, we manually generated a set of codes from actually reading individuals’ responses to the questions, “What do you think was (were) the cause(s) of your eating disorder?” and “What do you think is (are) the cause(s) of eating disorders?” Thus, we were able to identify a unique but relevant set of eight key themes. The eight themes that emerged from the data were: 1) traumatic life events, 2) family problems, 3) social problems, 4) psychological and emotional problems, 5) genetics and biology, 6) media and culture ideals, 7) sports and health, and 8) body image and eating.

Participants’ responses were then grouped under each of these categories. Many participants identified multiple causes of eating disorders, which were therefore grouped under multiple categories. The responses were coded independently by three research assistants, then checked by an additional research assistant and the first author for consistency. This was done to ensure interrater reliability [ 56 ]. When a difference in coding existed, the research team discussed the differences and mutually agreed upon a solution. See Table  1 for sample responses in each category.

Frequencies of individuals reporting each cause

A Chi square test for goodness of fit indicated that the participants in this sample showed significantly different rates of endorsement among the causes of eating disorders, χ 2 (7, n  = 108) = 41.63, p  < .05. Specifically, psychological and emotional ( n  = 30) and social problems ( n  = 22) were most frequently endorsed, with the lowest number of endorsements for genetics and biology ( n  = 2) and media and culture ideals ( n  = 5).

Individuals with AN most commonly indicated psychological and emotional problems as the cause ( n  = 13), followed by body image and eating problems ( n  = 9). Individuals with BN reported psychological and emotional ( n  = 8) and social ( n  = 7) as the primary causes. Those with both AN and BN listed all types of problems as causes, so there was not a clear primary cause, although social ( n  = 5) and psychological and emotional problems ( n  = 4) were slightly more frequently endorsed. Finally, those with other eating disorders most frequently cited psychological and emotional problems ( n  = 5) and traumatic life events ( n  = 3). See Table  2 for a complete listing of the frequencies of individuals citing each causal category.

A Chi square test for goodness of fit indicated that the participants in this sample showed significantly different rates of endorsement among the causes of eating disorders, χ 2 (7, n  = 414) = 326.95, p  < .05. Specifically, psychological and emotional problems ( n  = 141) and media and culture ideals ( n  = 104) were most frequently endorsed, with the lowest number of endorsements for family problems ( n  = 28), genetics and biology ( n  = 18), traumatic life events ( n  = 5), and sports and health ( n  = 4). Clearly, this sample differed from the sample of individuals with eating disorders in what they viewed as the primary causes. See Table  2 for the frequencies.

Differences between samples

Chi square tests for independence indicated that there was not a significant relationship between type of eating disorder (AN, BN, both, or other) and the causes specified. Furthermore, there were no significant relationships among each pairing of eating disorder sub-groups. The lack of statistically significant findings here could be the result of our small sample sizes for each group. See Table  3 for a summary of results from these chi square tests for independence.

Of particular noteworthiness, results from a chi square test of independence indicated that there was a significant relationship between eating disorder versus non-eating disorder groups and the causes specified, χ 2 (7, n  = 522) = 77.96, p  < .05, Phi = .39. This suggests that individuals with and without eating disorders had significantly different views regarding the causes of eating disorders, with each group likely to endorse causes at different rates. In conducting follow-up analyses of each cause separately, we found significant differences in the endorsement of family problems (χ 2 (1, n  = 39) = 7.41, p  < .05), social problems (χ 2 (1, n  = 79) = 15.51, p  < .05), psychological and emotional problems (χ 2 (1, n  = 171) = 72.05, p  < .05), genetics and biology (χ 2 (1, n  = 20) = 12.80, p  < .05), media and culture (χ 2 (1, n  = 109) = 89.92, p  < .05), and body image and eating (χ 2 (1, n  = 71) = 26.04, p  < .05) among those with and without eating disorders. More specifically, individuals with eating disorders more often endorsed family problems, and social problems while individuals without eating disorders more often endorsed psychological and emotional problems, genetics and biology, media and culture, and body image and eating.

Additionally, there were significant relations between each individual type of eating disorder versus non-eating disorder and the causes specified. See Table  3 for these results. This suggests, for example, that individuals without eating disorders had different levels of endorsement for each cause than the group of individuals with AN. The same was true for the sub-groups of BN, both, and other, when compared to individuals without eating disorders.

This is the only known study that assessed subjective perceptions of the causes of eating disorders among a relatively large sample of individuals with and without eating disorders. The results support differences between the general public and individuals suffering from eating disorders, which hopefully can be used to provide proper education. Specifically, the general public largely believed that the media causes eating disorders, a perception that is not shared among individuals with an eating disorder. Similarly, sports, body image, and traumatic events were listed less frequently by participants without eating disorders than participants with eating disorders. However, psychological and emotional problems were highly endorsed by all. Together, these findings indicate differences in opinion regarding the causes of eating disorders between those who have an eating disorder and those who do not.

The open-ended questions used in the present study enabled us to gain insight into individuals’ personal opinions regarding factors associated with the development of their disorders, ultimately providing a greater understanding for both clinicians and lay people. Psychological and emotional problems were the most frequently reported causes for those with an eating disorder, supporting the need for greater availability of support systems. In considering the perspectives of individuals who had an eating disorder, it is difficult to know if their perceptions align accurately with the actual causes. However, professionals working with these individuals could help assess the discrepancy between perceived and actual causes. For many postmodern therapists, understanding the perception of the eating disorder from the client perspective and helping him or her make meaning of the experience is more important than determining the actual cause of the disorder [ 57 , 58 ]. This, therefore, provides reinforcement for the role of psychologists and family therapists within the field of eating disorders, yet many currently lack sufficient training to address eating disorders and instead must refer clients to specialists, who are often expensive and not widely located.

The role of the media

Our findings revealed a definite contrast between how people with and without eating disorders perceive media as a risk factor for developing an eating disorder. A large percentage of people without eating disorders identified media as a cause (47 %), but only five total participants with eating disorders did. There is a clear separation in the experience of those with eating disorders and with society’s conceptualization of them [ 36 , 37 ]. Thus, it seems that lay individuals may overemphasize the role of the media as one of the main causes of eating disorders, while those with eating disorders may not be fully aware of the potential impact of the media [ 50 ]. Whereas specific media variables such as depiction of the thin ideal and unrealistic body standards may be correlated with eating disorders [ 42 ], they do not fully explain disordered behaviors. Our findings should be used to educate consumers of media on the complexity of eating disorders, and as evidence for the need to change the types of messages regarding body image ideals that are currently available in the media.

• Psychological and emotional problems

Psychological and emotional problems were one of the highest named causes of eating disorders by both groups, which is consistent with prior research [ 43 , 46 , 48 ]. However, upon close examination of the data, we noticed a contrast between the written answers of those who had eating disorders and those who did not. More specifically, individuals with eating disorders listed personal reasons, such as “a bad relationship that caused a lot of low self-esteem,” or simple statements such as “stress, depression.” In contrast, there was a negative stigma surrounding some of the answers from participants without eating disorders. These answers included phrases such as “no self-confidence” and “mental disabilities.” This difference is worth noting, because it demonstrates a stigma towards those with eating disorders, which may result in a fear of judgment from others that often prevents those suffering from eating disorders to seek help [ 59 ]. Reduction of this stigma through educational programs could encourage individuals who are developing disordered eating habits to speak up, as well as encourage friends and family to begin a non-judgmental, supportive dialogue with individuals about their habits.

Other factors

Traumatic life events were only listed by 2 % of the non-eating disorder group, versus 23 % of those with eating disorders. This once again emphasizes the need for education geared towards the general public. However, there is also a need for better education for those with eating disorders, as the number of people listing traumatic events was quite low. Many individuals may not make the connection between a traumatic event, such as sexual assault, and the beginning of their disorder, despite empirical support for the effects of abuse [ 22 ].

Similar to previous studies, genetics as a cause of disordered eating was only listed by two participants with eating disorders and eight participants without eating disorders [ 10 , 47 ], making it the least endorsed cause. This indicates a need for the dissemination of information regarding the genetic component of eating disorders, as this could potentially help with the negative stigma surrounding eating disorders [ 60 ].

Similarly, and in line with previous studies, only twelve participants with eating disorders and 28 participants without listed family problems as a cause of disordered eating [ 43 , 46 , 48 , 49 ]. There are numerous studies, however, that show the impact that mothers, fathers, and siblings can have on the development of disordered eating in an individual (e.g., [ 26 , 27 ]). If education efforts could help improve understanding of how eating disorders develop within families, parents and siblings can take steps towards preventing the occurrence of these issues and can work towards developing healthier habits for themselves as well.

Sports and health were also listed more frequently as causes by those with eating disorders (19 %), whereas only 2 % of those without eating disorders mentioned them. However, these numbers are both still low. The general public, and specifically coaches, need to be aware of how an intense focus on the body can lead to negative outcomes and strive to support healthy methods of getting and staying in shape.

Body image was listed as a cause of eating disorders by 26 % of participants without an eating disorder, and 25 % of those with experience with disorder eating; these numbers represent a substantial portion of participants. Poor body image often provides a foundation for the development of an eating disorder [ 15 , 17 ], and understanding what issues underlie an eating disorder can help not only those struggling to recover, but those trying to assist them.

Another highly-endorsed cause of eating disorders was social problems, as 26 % of those without eating disorders and 39 % of those with eating disorders listed them. While these numbers are considerably higher than other groups, only one fourth of those without eating disorders acknowledged social problems as a cause, while a much larger number of those with eating disorders indicated social problems as a cause. However, many individuals may not realize the effect that external events can have on their internal belief systems, once again indicating the need to incorporate this finding into general education, as well as into the treatment process as a way of lessening the blame that those suffering may place on themselves.

Summary of findings

This study provides insight into the educational resources needed to inform the lay audience regarding eating disorders as well as some factors to consider in the education or prevention of eating disorders among those affected. There is a clear difference between perceived causes of eating disorders from those who have experienced them and those who have not. Those who had not struggled with an eating disorder were more likely to believe that media and cultural ideals influenced eating disorders. For those who had lived with an eating disorder, this was one of the least likely perceived causes. Social problems, in contrast, were frequently listed by participants with eating disorders and less frequently listed by participants without. Genetics and traumatic events were listed most infrequently by both groups, and there were also relatively low levels of endorsement for traumatic life events, sports and health, and family problems among both groups. Both groups listed body image as a fairly frequent cause, and although both groups highly endorsed psychological and emotional problems as causes, there was a clear negative stigma surrounding psychological and emotional problems when listed by non-disordered participants. Improved educational programs should seek to give those who are uninformed a greater understanding of how psychological, social, and relational factors influence those with eating disorders. Increased opportunities for those who have lived with eating disorders to share their stories and perspectives are also needed. With the opportunity to provide first-hand knowledge, these individuals can be an excellent asset for researchers, professionals, and lay people.

Limitations

Our sample was a relatively homogenous group in terms of gender and ethnicity, so separate analyses could not be conducted examining differences among men and women or among various ethnic groups. Thus, care should be taken when generalizing the results to males and non-white individuals. Furthermore, in order to utilize open-ended questions, no measurement scales were used to determine eating disorder pathology. Therefore, eating disorder status was determined solely by self-report and may not be clinically accurate. In retrospect, it may have been useful to at least provide participants with a self-report survey to assess their eating disorder symptomatology. However, we do note that our sample was recruited not only from local universities but directly from hospitals and clinics that included eating disorder treatment facilities. As a result, we hope that participants were able to appropriately reflect on the nature of their symptomatology. Further, our type of questioning allowed for only two groups of samples, those with eating disorders and those without; individuals who have subclinical symptoms or undiagnosed eating disorders may have been inaccurately placed in the category of non-eating disorder due to their own assessment. Similarly, those who identified themselves as having an eating disorder may have been self-diagnosed, and therefore may not technically meet clinical standards for a disorder.

Additionally, two different questions were asked of participants. Specifically, we asked participants with an eating disorder: “What do you think was (were) the cause(s) of your eating disorder?”, and we asked participants without an eating disorder: “What do you think is (are) the cause(s) of eating disorders?” This allows individuals to add a personal dimension to their analysis of the causes of eating disorders. As such, they may believe that the cause of their disorder is very different than the cause of someone else’s disorder. Similarly, individuals with an eating disorder may have focused more on life events or recent triggers without a reflection on more general risk factors.

Lastly, because this study was completed online, it could be considered relatively impersonal, whereas in-person interviews would have most likely been more in depth. However, because the main interest of the study was to examine participants’ instinctive reactions to eating disorders, the completely anonymous online survey was the most beneficial means of execution.

Despite limitations, this study contributes to the field in a variety of ways. The sample size of those with eating disorders ( n  = 57) is somewhat larger than samples currently in the literature. Furthermore, while many studies focus only on AN or BN, this study included those with self-reported AN, BN, BED and EDNOS/OSFED, allowing for more inclusive results. It also allowed us to separately assess perceived causes of eating disorders according to the type of eating disorder. For example, individuals with AN most frequently indicated psychological and emotional problems as well as body image and eating problems; individuals with BN often reported psychological and emotional problems as well as social problems; individuals with both AN and BN listed all types of problems; and individuals with BED, EDNOS, or OSFED primarily cited psychological and emotional problems as well as traumatic life events. Although these differences in perceptions were not statistically significant, it may suggest that each type of disorder is unique, with potentially unique causes attributed to the disorder. Future research should continue to examine these differences, and education should focus on the unique nature of each type of eating disorder.

The use of an open-ended qualitative assessment allowed for a complete picture of individuals’ perceptions of the causes of eating disorders. It also allowed individuals to write about more than one perceived cause of the disorders, which is not always possible with close-ended questions with limited answer options. An additional strength of this study is that it contributes to the relatively small pool of current literature discussing perceptions of eating disorders. Within this limited research, most examine perceptions of the general public or perceptions of those with eating disorders separately. Our study is also one of very few studies to examine differences between these two groups.

Overall, it appears that all individuals would benefit from learning more about eating disorders and their causes. Knowing this could be particularly helpful for individuals going through eating disorder treatment, especially for therapists to use when educating those close to someone struggling with an eating disorder. This could help facilitate greater support and connection between family members and friends, and help to end the stigma surrounding these problems and allow those in trouble to seek help.

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We thank our undergraduate and graduate research assistants at the North Dakota State University Eating Disorders and Body Image Lab for their assistance with coding the data.

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EHBS conceived of and designed the study, developed codes for and analysed the data, and wrote the manuscript. MEJ co-wrote and formatted the manuscript. ECH coded data and co-wrote the manuscript. MKS coded data and co-wrote the manuscript. All authors read and approved the final manuscript.

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Blodgett Salafia, E.H., Jones, M.E., Haugen, E.C. et al. Perceptions of the causes of eating disorders: a comparison of individuals with and without eating disorders. J Eat Disord 3 , 32 (2015). https://doi.org/10.1186/s40337-015-0069-8

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DOI : https://doi.org/10.1186/s40337-015-0069-8

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Healthy female volunteers age 13-70 without a history of an eating disorder are needed at JHMI East Baltimore campus.

Participants will be compensated $200 for three in-person visits: Visit (1) 0.5 hr eligibility screen that includes drinking an Ensure, Visit (2) 3.5 hr visit includes blood draws and computer tasks, and Visit (3) 0.5 hr MRI scan (~5 hrs total).

This research study is looking at how the gastrointestinal response to food (Ensure) contributes to the maintenance of AN. At all three visits, participants will be asked to consume an Ensure. An IV will be used to collect blood samples at multiple timepoints over the course of 3 hours (Visit 2). In between the blood collection intervals, participants will be given questionnaires to answer. A couple of weeks following the draw, an MRI scan will be conducted (Visit 3).

If you are interested in participating, call 443-287-6853 or email jkantne5@jh.edu with the subject line "Gut Peptide Study."

Principal Investigator: Dr. Kimberly Smith, IRB# 00214417

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40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

Carlos A. Almenara

School of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima, Perú

Associated Data

The data that support the findings of this study are publicly available from the OSF repository: https://osf.io/5yzvd/ (DOI: 10.17605/OSF.IO/5YZVD ).

Previous studies have used a query-based approach to search and gather scientific literature. Instead, the current study focused on domain-specific journals in the field of eating disorders. A total of 8651 documents (since 1981 to 2020), from which 7899 had an abstract, were retrieved from: International Journal of Eating Disorders (n = 4185, 48.38%), Eating and Weight Disorders (n = 1540, 17.80%), European Eating Disorders Review (n = 1461, 16.88%), Eating Disorders (n = 1072, 12.39%), and Journal of Eating Disorders (n = 393, 4.54%). To analyze these data, diverse methodologies were employed: bibliometrics (to identify top cited documents), network analysis (to identify the most representative scholars and collaboration networks), and topic modeling (to retrieve major topics using text mining, natural language processing, and machine learning algorithms). The results showed that the most cited documents were related to instruments used for the screening and evaluation of eating disorders, followed by review articles related to the epidemiology, course and outcome of eating disorders. Network analysis identified well-known scholars in the field, as well as their collaboration networks. Finally, topic modeling identified 10 major topics whereas a time series analysis of these topics identified relevant historical shifts. This study discusses the results in terms of future opportunities in the field of eating disorders.

Introduction

There are a large and growing number of scientific publications on eating disorders (ED) [ 1 – 3 ]. ED are mental disorders characterized by a continuous disturbance in eating behavior, such as Anorexia Nervosa [ 4 ]. ED are usually defined according to manuals like the Diagnostic and Statistical Manual of Mental Disorders (DSM) [ 4 ]. The spectrum of ED can share some symptoms (e.g., fear of fatness ), and these symptoms negatively impact psychosocial functioning and physical health. Due to the complexity of ED like Anorexia Nervosa, scholar literature about them covers different disciplines, such as ED related to: visual arts (e.g., art history) [ 5 ], sociology (e.g., social history) [ 6 ] and even dentistry (e.g., oral health) [ 7 ]. Thus, ED literature has a broad diversity.

Previous bibliometric studies about ED have focused on: identifying the distribution by language, region and country, as well as topics and their trends [ 1 ], productivity trends and collaboration patterns [ 2 ], most cited works in Anorexia Nervosa research [ 8 ], cross-cultural aspects of ED [ 3 ], comparison of citations between types of journals [ 9 ], female authorship [ 10 ], secular trends in the scientific terminology [ 11 , 12 ], the gap between scientific research and clinical practice [ 13 ], the use of keywords [ 14 ], and network analyses of common terms used in the field [ 15 ]. In particular, the current study complements the work by He et al. [ 1 ].

A standard practice of these studies is to retrieve the literature by performing a systematic search in databases like Web of Science or Scopus (i.e., employing a query-based approach), although there are some caveats worth mentioning. As noted elsewhere [ 16 , 17 ], those two databases differ in journal coverage and their use can introduce bias favoring science publications (e.g., biomedicine) in detriment of arts and humanities, other than overrepresenting English-language journals. Second, databases in general (including others like PubMed, Dimensions, JSTOR), differ in their search engine functionality and information retrieval capabilities.

For example, some databases offer a controlled vocabulary like a thesaurus or taxonomy from which to choose the search terms (e.g., the Medical Subject Headings [MeSH] in PubMed), whereas others offer a full text search. Regarding the latter, indexing scanned documents to offer a full text search, requires pre-processing methods like optical character recognition (OCR), known to include typos, and post-OCR processing, both affecting information retrieval accuracy [ 18 – 23 ].

In other words, a query-based approach, although widely used, can be affected by several factors, including: domain expertise to design the most appropriate search strategy, the characteristics of the selected database(s), including indexation accuracy (e.g., due to OCR typos). The former is particularly important because scholars are not always consistent in using the terminology [ 24 ]. In fact, their selection of keywords is not systematic, but rather influenced by factors like their background knowledge and previous experience [ 25 ]. In this regard, within the field of ED, scholars are encouraged to use appropriate terminology [ 26 , 27 ], usually a controlled vocabulary such as the Thesaurus of Psychological Index Terms. This helps to optimize the Knowledge Organization Systems (KOS) of journals and databases, such as a controlled vocabulary for information retrieval [ 14 , 28 ].

In sum, most previous studies have employed a query-based search, being compelled to choose among different databases, search terms, and search strategies [ 29 ]. Nevertheless, this approach not necessarily recognizes the boundaries and limitations of both databases and we as humans interacting with machines, using diverse information retrieval strategies, and dealing with information overload [ 30 , 31 ].

An alternative to the query-based approach is the one proposed in this study: to select a set of specialty journals exclusively devoted to the study of ED. Although this sampling could seem arbitrary, it was adopted: (1) to complement the findings of previous studies [ 1 , 2 ] and (2) because it has in fact a sound base: the intellectual and social structure of knowledge [ 32 – 36 ]. We must recognize that documents need to be understood with regard to "the broader contexts in which they are produced, used, and cited" [ 37 , p. 42]. Thus, the following sections will explain how domain-specific journals are tightly tied to an organized social and disciplinary structure. Moreover, I will explain how this approach does not necessarily exclude all ED literature from non-domain-specific journals, but rather incorporates part of it into their citations. Finally, from a complex systems perspective, I will show how domain-specific journals can be conceived as a specialized subset from the larger and more complex network comprising all ED literature.

Domain-specific journals and its social structure

From a scientometric perspective, science, metaphorically conceived as a knowledge space or knowledge landscapes , can be defined in terms of a network of scholars that produce a network of knowledge [ 35 ]. In the former case, the social function of science has long been recognized (e.g., by Thomas Kuhn): scholars produce and communicate scientific knowledge and this organized activity has the characteristics of a social process [ 36 , 38 ]. More importantly, the patterns of interactions and communication within this social organization are tightly tied, rather than isolated, to the knowledge they produce [ 36 ].

An exemplary case is the role of journal editors as gatekeepers, with studies identifying editorial gatekeeping patterns [ 39 , 40 ]. According with the Network Gatekeeping Theory, inspired by the work of Kurt Lewin, gatekeeping refers to the control in the flow of information [ 41 , 42 ]. In the field of ED, this intellectual and social organization of knowledge can be seen in professional societies like the Academy of Eating Disorder, which since 1981 publishes the most renowned scientific journal: The International Journal of Eating Disorders. Within its editorial board, there are distinguished scholars that can act as gatekeepers to ensure quality control and that manuscripts published by the journal are in line with the aims and scope of it.

In sum, domain-specific journals have the goal of publishing information within the boundaries of their aims and scope, allowing the diffusion of specialized knowledge.

Domain-specific journals and its disciplinary organization

From a network perspective, specialty journals are also indicators of disciplinary organization [ 43 ], which exerts a non-trivial influence at both the global and local level of the network. To be more precise, if we visualize a network [e.g., 2 , 44 , 45 ], the local density of specialty journals evidence emerging patterns such as citation patterns by articles from the same journal or group of journals [ 43 ]. At the author level, these patterns reflect the local influence of specialty journals on scholars who adhere to their research tradition and their contributions help to advance a research agenda [ 46 ].

For example, domain-specific journals on ED often publish curated information from conferences [e.g., 47 ] or special issues about a specialized topic [e.g., 48 ], which commonly include a research agenda [ 48 ], setting the stage for future research. As we mentioned above, similar literature, such as special issues about ED published in other journals [e.g., 49 ], is not necessarily excluded in the analysis of domain-specific journals. Rather, such literature is commonly cited in documents from domain-specific journals and can be included in a citation analysis. Importantly, these citation patterns suggest that the former intellectual and social structure of knowledge constrains what is being studied in the future [ 46 ]. Thus, in the upcoming years, most of this specialized literature is expected to become an active research front [ 32 ], as evidenced by its high number of citations.

Finally, it is worth mentioning that the analysis of these patterns can reveal latent hierarchies and topological properties of journal networks. In fact, domain-specific journals can be identified through the study of the hierarchical organization of journal networks. When hierarchical network analysis is used to identify the capability of journals to spread scientific ideas, multidisciplinary journals are found at the top of the hierarchy, whereas more specialized journals are found at the bottom [ 50 , 51 ]. Similarly, significant articles from a specific domain have unique topological properties that can affect the dynamic evolution of the network [ 52 ]. In sum, it is important to recognize the topological properties of networks and their latent hierarchies, both at the journal level and document level. In our case, focusing on domain-specific journals, it would be like zooming into the most central part (core) of the network topology to analyze its organization and distinctive features. Indeed, this approach is commonly employed, for example, when studying network subsets such as niches or communities in complex systems.

Domain-specific journals and complex adaptive systems

Domain-specific journals can also be comprehended from a complex systems standpoint, as the aggregation of the intellectual, social, and citation patterns outlined above. According to the Structural Variation Theory [ 53 ], the body of scientific knowledge can be conceived as a complex adaptive system (CAS). As such, it can be described and studied as a complex network with a series of characteristics like non-linearity, emergence, and self-organization; and a series of social, conceptual, and material elements that evolve over time [ 46 ]. Ideally, we must study CAS holistically to understand the properties of the system at the macrolevel [ 54 ]. In our case, this would require including all scholar literature on ED, which could be attempted using a query-based approach and employing ad hoc methodologies (e.g., iterative citation expansion) [ 45 ]. However, complex systems emerge from rules and behavior of lower-level components, and there is growing interest in understanding complexity from its simplest and fundamental elements and patterns [ 55 , 56 ]. In our case, this can be accomplished by zooming into domain-specific patterns that emerge from the relational structure and organization of journals and papers [ 46 ], rather than focusing on the whole system which comprises all the scientific literature on ED.

This approach can be described in terms of modularity , a structural property of systems: the local density of specialty journals is indicative of a structural module or subsystem [ 57 ]. This property of complex systems is important because it recognizes, as we did above, the existence of subsets within networks. Indeed, scientometric studies usually attempt to detect communities based on the principle of modularity by grouping similar literature (i.e., clustering) [ 44 , 58 ]. However, in the approach used in this study, rather than using bibliographic connections (e.g., through co-citation analysis) to detect domain-specific literature, we can use logical connections [ 59 ], to identify modules that operate as domain-specific representations [ 60 ]. In other words, domain-specific journals can be seen as clusters of articles that are logically linked because they all pertain to a given domain, which is explicitly stated in the aims and scope of the journals.

This modular organization has some advantages over others such as a hierarchy (e.g., Scimago categorization of journals) or a cluster obtained by literature partitioning algorithms. First, it has the advantage of reducing both complexity bias and hierarchical bias . The former is the tendency to assume and adopt a more complex system (the opposite to Occam’s Razor: prefer the simplest explanation), which means to analyze all ED literature. The latter assumes that behavior is directed in a hierarchical fashion, where a central authority passes instructions to all agents in the system [ 54 ]. Second, although it still recognizes a hierarchical structure composed by diverse classes of subsystems, it assumes heterarchy [ 43 , 61 ], which means that both hierarchical and nonhierarchical elements can be present in a system; holarchy , which means that systems are composed of components that can be recognized as subsystems [ 62 ]; and glocal control , which means that local and global phenomena in a system are achieved by local actions [ 63 ]. In simple words, sampling a set of domain-specific journals reduces complexity without affecting assumptions such as a categorical hierarchy of journals.

The current study

To expand on previous studies [ 1 , 2 ], the current study aims to answer the following research questions:

Which are the most cited documents in this domain-specific corpus of articles?

Which are the most important authors and their collaboration networks?

Which are the most relevant topics in this domain-specific corpus of articles?

How have the identified topics evolved over time (since 1981 to 2020)?

To answer these questions, this study employs a hybrid methodology. First, basic bibliometrics will be performed to identify the most cited documents. Second, network analysis will be employed to identify the most important authors and their networks of collaboration. Third, text mining, natural language processing, and machine learning algorithms will be used to identify the most relevant topics (i.e., topic modeling). Finally, a simple time series analysis will be performed to examine the evolution of these topics over time. The procedure employed for the analyses is detailed in the methods section below (and S5 File ), whereas the dataset and the code to perform the analyses are shared in a public repository ( https://doi.org/10.17605/OSF.IO/5YZVD ), allowing the reproducibility of results [ 64 ].

Data collection

The methodology workflow is presented in Fig 1 .

First, in May 2020, a search of journals was performed in Scimago Journal Reports (SJR, https://www.scimagojr.com/ ), using the term “eating disorders”. In this step, the following five journals were identified: International Journal of Eating Disorders (ISSNs: 0276–3478, 1098-108X), European Eating Disorders Review (ISSNs: 1072–4133, 1099–0968), Eating Disorders (ISSNs: 1064–0266, 1532-530X), Eating and Weight Disorders (ISSNs: 1124–4909, 1590–1262), and Journal of Eating Disorders (ISSN: 2050-2974). The official website of each journal was then visited to confirm that the scope of the journal specifically includes the publication of research articles on eating disorders. It should be noted that the journal Advances in Eating Disorders (ISSNs: 2166–2630, 2166–2649) was not included because it was not found in SJR, it was published only between 2013 and 2016, it was incorporated into the journal Eating Disorders , and by the time of writing this article, it was not indexed neither in Scopus ( https://www.scopus.com ) nor in Web of Science ( https://www.webofknowledge.com ).

Next, also in May 2020, the Scopus database was chosen to retrieve the document records from the aforementioned journals. The election was made for no other reason than the capability of Scopus to retrieve several structured information (metadata, such as the abstract), and the file types for download are easy to manage, such as comma-separated values (CSV). Therefore, all document records published by these journals were searched in Scopus using the ISSN as the search term (e.g., ISSN (0276–3478) OR ISSN (02763478) OR ISSN (1098-108X) OR ISSN (1098108X) ). A total of 8651 documents between 1981 and 2020 were retrieved (of which 7899 had an abstract): 4185 (48.38%) from the International Journal of Eating Disorders, 1540 (17.80%) from Eating and Weight Disorders, 1461 (16.88%) from the European Eating Disorders Review, 1072 (12.39%) from Eating Disorders, and 393 (4.54%) from the Journal of Eating Disorders. These 8651 documents included a total of 213,744 references. It should be noted that the International Journal of Eating Disorders is the oldest of these journals, established in 1981. The S7 and S8 Files provide the number of documents per year and per journal. The document records were downloaded from Scopus both as comma separated values (CSV) and as BibTex ( http://www.bibtex.org/ ), and selecting all fields available (i.e., title, author, abstract, etc.). Due to copyright, the full text of all documents was not retrieved but rather their metadata (i.e, title, author, date, abstract), whilst the dataset shared online ( https://doi.org/10.17605/OSF.IO/5YZVD ) is the one obtained after the preprocessing procedures detailed below.

Analyses were performed using open software: R Statistical Software 4.0.3 (Bunny-Wunnies Freak Out) [ 65 ], and Python programming language version 3.9.1 ( https://www.python.org/ ).

Bibliometric analysis and network analysis in R

The biblioshiny application from the R package bibliometrix [ 66 ] was used to preprocess the CSV file. Next, it was used to identify the most cited documents. Local citations (i.e., citations only from documents whithin the dataset), and global citations (i.e., citations made by any document from the whole Scopus database), were computed. Biblioshiny was also used for network analysis as described by Batagelj & Cerinšek [ 67 ], and Aria & Cuccurullo [ 66 ]. Regarding the network, it is defined as a pair of sets: a set of nodes or vertices and a set of edges (link between nodes) [ 68 ]. In this study, when authors were treated as nodes, a link would represent co-authorship or collaboration [see 69 ]. More precisely, the Louvain algorithm for community detection [ 70 ] was used to identify communities within the collaboration network. This algorithm identifies densely connected nodes within the network (i.e., communities) [e.g., 71 ]. It works unconstrained to automatically extract a number of clusters although it requires basic network parameters as input. These network parameters were: up to 100 nodes, a minimum of two edges by node, and the removal of isolated nodes. For network layout visualization, the Fruchterman & Reingold [ 72 ] algorithm was chosen. Finally, common centrality measures were calculated: betweenness, closeness, and PageRank. Betweenness centrality refers to “the frequency that a node is located in the shortest path between other nodes” [ 73 , p. 772]. Closeness centrality refers to nodes that can easily reach others in the network, whilst PageRank , originally created to rank websites [ 74 ], has been used to rank authors because it takes into account the weight of influential nodes [ 75 ].

Topic modeling: Dimensionality reduction and matrix factorization

As can be seen in the workflow ( Fig 1 ), once network analysis was finished, a series of steps (detailed in S5 File ) were necessary to preprocess the dataset prior to topic modeling. Topic modeling refers to applying machine learning techniques to find topics by extracting semantic information from unstructured text in a corpus [ 76 ]. As we explain in S5 File , to this point we end up with a high-dimensional and sparse document-term matrix. In other words, we have many features (columns) each corresponding to a term in our corpus, and for a given document (rows) we have many columns with zero values meaning the term of that column is not in the given document. To deal with sparsity, we can perform dimensionality reduction to obtain a representation that effectively captures the variability in the data. In summary, dimensionality reduction can be categorized in feature extraction and feature selection ; the former combines the original feature space into a new one, whereas the latter selects a subset of features [ 77 ].

As explained in S5 File , the term frequency (TF) and the term frequency-inverse document frequency (TF-IDF) were used as feature extraction for vectorization. Then, the following machine learning algorithms were applied for topic modeling: Latent Dirichlet Allocation (LDA) [ 78 ], Latent Semantic Analysis (LSA or Latent Semantic Indexing) [ 79 ], Hierarchical Dirichlet Process (HDP) [ 80 ], and Non-negative Matrix Factorization (NMF) [ 81 ]. LDA is a generative probabilistic model that decomposes the document-term matrix into a topic-term matrix and a document-topic matrix, and it is commonly used for topic discovering from a corpus [e.g., 82 ]. LSA utilizes a truncated Singular Value Decomposition for decomposition and can work efficiently on TF or TF-IDF sparse matrices. In a fully unsupervised framework, the HDP model is characterized by inferring the number of topics on its own. Finally, NMF is an alternative approach that implements the Nonnegative Double Singular Value Decomposition, an algorithm suitable for sparse factorization [ 83 ].

First, the GENSIM library [ 84 ] was used for topic modeling because it provides a way to calculate topic coherence , an index to compare models based on measures of segmentation, probability estimation, confirmation measure, and aggregation [see 85 ]. Therefore, based on a TF matrix, HDP, LSA, NMF, and LDA were performed in GENSIM and compared in topic coherence. Once identified the topic modeling algorithms with the highest topic coherence, scikit-learn [ 86 ] was used because it provides an Exhaustive Grid Search option for ensemble learning the models (i.e., automatically fine-tuning the parameters to find the most optimal). Finally, once the topics were extracted, a simple time series analysis was performed to visualize the changes over time in the topics found. This analysis consisted of simply plotting the number of documents for each topic across years, from 1981 to 2020.

First, bibliometric analyses were performed to identify the most cited documents. Local citations are presented in Table 1 (and the S1 File ), whereas global citations are in Table 2 (and the S2 File ).

a Local citations are citations only from documents whithin the dataset.

a Global citations are citations made by any document from the whole Scopus database.

Next, a network analysis was performed to identify the most important authors ( Table 3 ) and their collaboration networks ( Fig 2 , see also S3 File , a dataset, and S4 File , an interactive visualization in HTML and JavaScript, also available online: https://osf.io/5yzvd/ ). This collaboration network analysis identified eight clusters with 96 authors: (1) red color, 4 authors; (2) blue, 15 authors; (3) green, 17 authors; (4) purple 21 authors; (5) orange, 2 authors; (6) brown, 18 authors; (7) pink, 2 authors; (8) grey, 17 authors.

Note . The order of authors is sorted by the value in betweenness centrality.

a Original closeness values were multiplied by 100 to display values with only two decimals.

b Original PageRank values were multiplied by 10 to display values with only two decimals

Regarding the most relevant topics, LDA and NMF were superior to HDP and LSA in topic coherence. Then, when ensemble learning was used for LDA (based on TF) and NMF (based on TF-IDF), NMF provided the most meaningful results, and 10 topics were identified ( Table 4 ).

Note . KW = Keyword. Numbers below each keyword indicate their weight within the topic they belong.

The labels for the topics were manually added based on the top 10 keywords and their respective weights. Thus, each topic was manually labeled as follows: (1) risk factors for eating disorders, (2) body image dissatisfaction, (3) Binge Eating Disorder diagnosis, (4) weight loss, weight control, and diet, (5) clinical groups, (6) treatment outcome, (7) family and parent-child, (8) binge and purge episodes, (9) gender and subgroups, (10) EDNOS.

To examine how these topics have evolved over time, a simple time series analysis plot was created ( Fig 3 and S6 File ).

Note . Values in the y-axis are the sum of the weight values from the NMF analysis for topic dominance, per year and per topic. Values go from minimum 0 to maximum 11.2 (see S6 File ).

This study analyzed 8651 documents between 1981 and 2020 from domain-specific journals in the field of eating disorders. The aims were: to identify the most cited documents, the most important authors and their collaboration networks, and the most relevant topics and their evolution over time. The results expand previous findings of studies that employed a query-based approach and included articles dating back as far as 1900 [ 13 ]. In particular the results expand the studies by Jinbo He et al. (2022) and Juan-Carlos Valderrama-Zurián, et al. (2017), which employed a similar methodology [ 1 , 2 ]. For example, He et al. (2022) created a collaboration network, although it was based on countries rather than authors [ 1 ]. Therefore, the results obtained here (e.g., author centrality measures, author clusters) provide a more fine grained understanding of the relevance and contribution of individual authors and their collaboration networks. Furthermore, He et al. (2022) [ 1 ] identified top authors based on traditional performance metrics (e.g., h-index), and it should be noted that there is some criticism towards their use and a claim to shift towards more responsible metrics of research excellence [ 87 ]. Then, He et al. (2022) [ 1 ] employed LDA for topic modeling, whilst this study employed NMF. Although LDA is largely used, in this study NMF outperformed LDA in interpretability, reproducibility, and as we said above, it suits better for short texts, as is the case of article abstracts used here. Finally, the top journals identified by He et al. (2022) confirmed that the five journals selected for this study are in fact among the most important in the field of eating disorders [ 1 ]. In the case of Valderrama-Zurián, et al. (2017) [ 2 ], they also focused on authors’ productivity trends whereas their social network analysis was focused on network metrics such as the number of nodes and edges over time, which precludes to inspect the social network at the author level. Therefore, this study also expands on the findings of Valderrama-Zurián, et al. (2017) [ 2 ].

Below, we discuss in more detail the results of the analysis employed to answer the four research questions outlined in the introduction.

Bibliometric analysis

The top cited documents were all from the International Journal of Eating Disorders. As noted above, this journal is the oldest one (it started in 1981), and it has the largest number of articles per year, with the exception of the year 2019 when it was outperformed by the Eating and Weight Disorders journal (see S7 and S8 Files). The majority of top cited documents were related to the development of instruments for the assessment of eating disorders or the course and outcome of eating disorders. For example, we can see in the results the most common instruments used for the screening of eating disorders, as well as the evaluation of its core symptoms: Eating Disorder Inventory (EDI), Body Shape Questionnaire (BSQ), Dutch Eating Behavior Questionnaire (DEBQ), and Eating Disorder Examination Questionnaire (EDE-Q). These instruments are widely used to screen the general population, as well as in clinical settings, together with more recent instruments [ 88 ]. It should be noted, however, that in clinical practice settings the use of instruments for the diagnosis and the different phases of the treatment process is not necessarily widespread [ 89 , 90 ]. To reduce this gap, some authors suggest to provide assessment training and/or assessment guidelines for mental health professionals and general practitioners in primary health care [ 91 , 92 ]. This can help obtain a comprehensive clinical assessment, particularly of individuals with higher risk such as young adolescents with restrictive Anorexia Nervosa [ 93 ]. The instruments mentioned above are reliable measures, and they could be used online for a quick screening or session by session for ongoing monitoring, although further research is necessary [e.g., 94 – 96 ].

The rest of most cited documents include important review articles on epidemiology (Hoek & van Hoeken, 2003, in Table 1 ); the course and outcome of eating disorders (Berkman, Lohr & Bulik, 2007; Strober, Freeman & Morrell, 1997; in Table 1 ); and the diagnosis of Binge Eating Disorder (Spitzer et al., 1992, 1993, in Table 1 ). These results are similar to previous studies in which measurement methods (including instrument development), epidemiology, and review articles were the most common type of document [ 8 , 9 ].

Finally, the large number of articles on the diagnosis of Binge Eating Disorder, which was not fully recognized as a mental disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM) until its fifth edition [ 4 ], reveal that the recognition of Binge Eating Disorder as an own disorder took several years. To reach expert consensus in a shorter time, eating disorder professionals should pay special attention to emerging eating problems, such as Orthorexia Nervosa [ 97 ].

Network analysis

The network analysis identified eight clusters with 96 authors. Previous studies have examined the network of authors in the field in terms of network statistics such as number of edges or network density [ 2 ]. By contrast, this study provides a more fine-grained network analysis, identifying experts and group of experts in the field of eating disorders. As seen in the results section, the majority are distinguished authors with contributions dating back to the early 1980s.

The author with the largest betweenness centrality was Ross D Crosby (Sanford Center for Biobehavioral Research, United States), followed by James E Mitchell (University of North Dakota, United States) which has the largest value in PageRank. Authors with high betweenness centrality can act as both enablers and gatekeepers of information flow between communities [ 75 ]. Moreover, it has been found that authors with high betweenness centrality establish more collaborations than those high in closeness centrality [ 75 ]. In summary, the results of centrality measures can help to identify experts in the field of eating disorders, particularly authors that can quickly reach other authors in the network (high in closeness), act as gatekeepers (high in betweenness), or relate to influential others (high in PageRank).

Regarding the clusters identified by the network analysis, in the same cluster of Ross D Crosby and James E Mitchell are found other renowned authors like Daniel Le Grange (University of California, San Francisco, United States), Stephen A Wonderlich (Sanford Center for Biobehavioral Research, United States), and Carol B Peterson (University of Minnesota, United States). Among the most relevant results of collaboration of this cluster we can find studies on the ecological momentary assessment of eating disorders [ 98 ], the psychometric properties of the EDE-Q [ 99 ], and the diagnosis of Binge Eating Disorder [ 100 ].

The second largest cluster includes authors like Cynthia M Bulik (University of North Carolina at Chapel Hill, United States), Walter H Kaye (University of California, San Diego, United States), and Katherine A Halmi (Weill Cornell Medical College, United States). The results of their collaboration include studies related to the phenotypic characterization of eating disorders, such as the International Price Foundation Genetic Study, a multisite study that included a large sample of patients with eating disorders and their families [e.g., 101 ].

Finally, the third largest cluster includes authors like Janet Treasure (King’s College London, England), Ulrike Schmidt (King’s College London, England), and Tracey D Wade (Flinders University, Australia), which are widely recognized by the Maudsley Model for Treatment of Adults with Anorexia Nervosa (MANTRA) [ 102 , 103 ]. Interestingly, this is the only cluster that includes collaborations with authors from non-English speaking countries, more specifically from Spain. Examples of these collaborations include studies resulting from the Wellcome Trust Case Control Consortium 3 (WTCCC3) and the Genetic Consortium for AN (GCAN) [ 104 ], and other studies with clinical samples in Spain [e.g., 105 ].

On the other hand, the results reveal the importance of multisite studies that strengthen collaboration and originate in relevant outcomes for the prevention and treatment of eating disorders. Research groups could look for opportunities to collaborate in multisite studies and strengthen both their interdisciplinary and transdisciplinary collaboration, and their collaboration with less common partners such as stakeholders and policy makers [ 106 , 107 ]. By establishing these integrative and strategic collaborations we can promote translational research, and thus helping to reach broader public health goals [ 108 ].

Topic modeling

The combination of TF-IDF and NMF provided meaningful results, identifying 10 topics. After labeling these topics based on the first 10 keywords and their respective weights, we can see that most of the research on eating disorders done in the past 40 years has focused on their prevention and treatment. Interestingly, the time trend analysis of these topics revealed a noticeable change in the first lustrum of the 1990s. Whereas during the early 1980s the study of clinical groups (topic 5) was the most dominant topic, from the mid-1990s, this topic was surpassed by the study of risk factors of eating disorders (topic 1). This indicates an increasing interest for the prevention rather than solely the treatment of eating disorders. This result is consistent with the historical shift that occurred in the United States when in 1992 the Institute of Medicine (IOM) Committee on Prevention of Mental Disorders was created [ 109 ]. Then two years later, a report on reducing risk factors for mental disorders and promoting a preventive approach in research was published [ 110 ]. As expected, this shift had echo in several scholars at the time, became a research front, and relevant publications started to include more information on the prevention of eating disorders, including a special issue [ 111 ], book chapters [ 112 ], and progressively entire books [ 113 ]. It is important to note that this historical shift, as well as later others like in 2017 [ 114 ], were favorable, because in other cases like obesity, it took more time to focus on its prevention due to different issues, including the pressure of the weight loss industry and its commercial interest [ 115 ].

Another interesting finding was that the outcome of the treatment of eating disorders (topic 6), is the second most important topic of 2013, and this finding has important aspects to discuss. First, the surge of state-of-the-art machine learning algorithms provide several opportunities to build intelligent systems for precision medicine. Thus, the treatment course and outcome of eating disorders can be more personalized, guided, and enhanced with the help of predictive technologies and intelligent systems [e.g., 116 ]. Second, as suggested elsewhere [ 117 ], the advantages of technology can be particularly relevant for certain age groups like adolescents, and when a digital intervention is employed [ 118 ]. In summary, treatment outcome is currently an important topic, and future studies can deploy digital interventions and machine learning algorithms for a more precise treatment planning.

Limitations and conclusions

Although this study has strengths, such as using data and code that allows the reproducibility of the results, readers should consider some limitations. First, the analysis of most cited documents is for all the time span, and more recent highly cited documents are underrepresented. Moreover, the journal Advances in Eating Disorders was not included due to indexing issues. Nevertheless, this study provides the code and a detailed procedure to allow researcher to perform further analyses, such as document co-citation analysis. Future studies can also evaluate the Mexican Journal of Eating Disorders ( Revista Mexicana de Trastornos Alimentarios , ISSN 2007-1523), which has published articles primarily in Spanish [ 119 ]. Second, the network analysis included close to 100 scholars mostly with a long trajectory in the field, and this can be a limitation in representing more younger scientists or newcomers [ 2 ]. Future studies can focus on a larger number of scholars and apply different techniques in network analysis, such as other community detection techniques [e.g., 120 ]. Finally, the results of topic modeling suggested a solution of 10 topics out of up to 30 topics solution models tested. Although there is not a universally accepted approach to establish the number of topics, this study relied on several strategies, including ensemble learning, to automatically fine-tune the parameters of the machine learning algorithms, stability, and heuristic approaches [ 121 ]. Future studies can try other machine learning algorithms and techniques to retrieve topics [ 121 ].

In conclusion, this study analyzed 40 years of research on eating disorders, identified the most cited articles, networks of collaboration, experts in the field, and the 10 major topics in the field.

Supporting information

Funding statement.

Funding for this study was obtained from Universidad Peruana de Ciencias Aplicadas (A-006-2021).

Data Availability

• PLoS One. 2022; 17(12): e0278981.

Decision Letter 0

17 Jun 2021

PONE-D-21-04053

40 years of research on eating disorders: Bibliometrics, network analysis, and topic modeling

Dear Dr. Almenara,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please carefully consider all the concerns raised by #Reviewer 1.

The acceptance of the paper will require a solid justification of  the adoption of a narrow definition of 'eating disorders literature', a major issue highlighted by both reviewers.

Consequently, you should also introduce more cautions in the discussion of the overall relevance of  your work.

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Reviewer #1: Please, see the attached file "Review Comments to the Author.pdf"

Reviewer #2: The present manuscript presents a rather comprehensive but superficial analysis of three interrelated indexes of the eating disorders literature. I have two main criticisms. The first is that I’m not convinced or do not quite understand why the author focused on just papers published in eating-disorders specialty journals. There could be a good reason to do so—but they explanation given was unconvincing. I would argue that the most “central” or “key” works are those that, regardless of where they are published, reach the broader audiences and are the most highly cited globally. My second criticism is that there is little effort to interrelate or integrate the results from the three analyses—that is, what is the nature of the relationship between being the most cited article, the most productive-impactful network, and the topic of the work?

Overall, the data analyses and the results are interesting, but the author needs to do a better job at explaining what the work attempts to do and why, the extent to which the mission was accomplished, and what limitations the current work has—including its narrow focus on literature published in “specialty” journals.

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Submitted filename: Review Comments to the Author.pdf

Author response to Decision Letter 0

10 May 2022

Please see the attached document "Responses_to_Reviewers.pdf"

Submitted filename: Responses_to_Reviewers.pdf

Decision Letter 1

27 Jun 2022

PONE-D-21-04053R140 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modelingPLOS ONE

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Reviewer #1: See attached file

Reviewer #2: I find the author was very responsive to the reviewer's comments and critiques. Whereas the sampling method could be questioned, I believe the data are valid and reliable for the scope of journals included. Of all the questions addressed, the most novel and substantive contribution is the analysis of topic trends over time-this alone makes the manuscript worthy of publication.

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Author response to Decision Letter 1

Please see attached PDF file "Responses to Reviewer".

Submitted filename: responses_to_reviewer.pdf

Decision Letter 2

15 Nov 2022

PONE-D-21-04053R240 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modelingPLOS ONE

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. I have a couple of  very minor points that I Think you should fix before the paper is accepted for publication.

1. The description of a network is not standard. You wrote: . "In network analysis, there are two main components: nodes (vertices) and edges (links between nodes)". Here you are mixing the notion of network with the analysis of networks. Moreover "components"  is a technical term in network analysis: better to avoid confusion. So, sraphs or networks are the object studied by network analysis. A network is defined as a a pair of sets: a set of nodes  or vertices and a set of edges (link between nodes). 

2. In describing the methodology you wrote:  "More precisely, the Louvain algorithm for community detection [70] was used to create a collaboration network. This algorithm identifies densely connected nodes within the network (i.e., communities) [e.g., 71]". Please note that the collaboration network is not created by the Louvain algorithm. The Louvain algorithm is used for identyfying communities in the collaboration network.  

Please submit your revised manuscript by Dec 30 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Reviewer #2: Reviewer 1 was amazingly generous in providing very detailed and good guiding recommendations. I also believe the authors have been highly accommodating and the manuscript has improved substantively. Thus, I continue to be supportive of accepting the manuscript for publication.

Reviewer #2:  Yes:  Antonio Cepeda-Benito

Author response to Decision Letter 2

24 Nov 2022

Reviewers didn't make any further comments.

Submitted filename: Response to Reviewers.docx

Decision Letter 3

29 Nov 2022

PONE-D-21-04053R3

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Healthy Living with Diabetes

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On this page:

How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.