research papers on eating disorders

• Publications
• Published Papers, 2024

Published Papers by year

Walter Kaye and the UCSD Eating Disorders Research team have published over 250 papers on the neurobiology of eating disorders. These publications include behavioral, treatment, and cognitive neuroscience studies that have improved understanding of the clinical presentation, genetics, neurotransmitter systems, and neural substrates involved in appetite dysregulation and disordered eating. These studies are guiding the development of more effective, neurobiologically informed interventions.

• Change in motivational bias during treatment predicts outcome in anorexia nervosa
• Sophie R. Abber MS, Susan M. Murray PhD, Carina S. Brown MS, Christina E. Wierenga PhD
• doi: 10.1002/eat.24156. Epub 2024 February 01.
• Wiley Online Library
• The acceptability, feasibility, and possible benefits of a neurobiologically-informed 5-day multifamily treatment for adults with anorexia nervosa
• Christina E. Wierenga, Laura Hill, Stephanie Knatz Peck, Jason McCray, Laura Greathouse, Danika Peterson, Amber Scott, Ivan Eisler, Walter H. Kaye
• oi: 10.1002/eat.22876. Epub 2018 May 2.
• Research Program
• Current Research Studies
• Genetic Studies
• Participate in Our Studies

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 19 July 2021

Analyzing the link between anxiety and eating behavior as a potential pathway to eating-related health outcomes

• Felix S. Hussenoeder 1 ,
• Ines Conrad 1 ,
• Christoph Engel 2 ,
• Silke Zachariae 2 ,
• Samira Zeynalova 2 ,
• Heide Glaesmer 3 ,
• Andreas Hinz 3 ,
• Veronika Witte 4 ,
• Anke Tönjes 5 ,
• Markus Löffler 2 ,
• Michael Stumvoll 5 ,
• Arno Villringer 4 &
• Steffi G. Riedel-Heller 1  

Scientific Reports volume  11 , Article number:  14717 ( 2021 ) Cite this article

8605 Accesses

11 Citations

4 Altmetric

Metrics details

• Public health
• Risk factors

Anxiety is a widespread phenomenon that affects various behaviors. We want to analyze in how far anxiety is connected to eating behaviors since this is one potential pathway to understanding eating-related health outcomes like obesity or eating disorders. We used data from the population-based LIFE-Adult-Study (n = 5019) to analyze the connection between anxiety (GAD-7) and the three dimensions of eating behaviors (FEV)—Cognitive Restraint, Disinhibition, and Hunger—while controlling for sociodemographic variables, smoking, physical activity, personality, and social support. Multivariate regression analyses showed significant positive associations between anxiety and Disinhibition as well as Hunger, but not between anxiety and Cognitive Restraint. Interventions that help individuals to better regulate and cope with anxiety, could be one potential pathway to reducing eating disorders and obesity in the population.

Similar content being viewed by others

Microdosing with psilocybin mushrooms: a double-blind placebo-controlled study

Adults who microdose psychedelics report health related motivations and lower levels of anxiety and depression compared to non-microdosers

Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses

Introduction.

Anxiety is a widespread phenomenon. For example, while data show that the lifetime prevalence of generalized anxiety disorder (GAD) is around 3.7% 1 , a review finds a lifetime prevalence of subthreshold GAD of around 12.4% 2 . A study of community-dwelling older adults (65+) found a 12 month prevalence of 26.2% for all subthreshold manifestations of anxiety 3 . While anxiety can be a mental health problem by itself, it is also connected to problematic eating-related health outcomes and behaviors. Anxiety has repeatedly been connected to obesity 4 , 5 and to a variety of other eating disorders, i.e., anorexia nervosa 6 , 7 , bulimia nervosa 8 , 9 , and binge eating 10 , 11 as well as to the sub-clinical forms of these disorders and weight concerns 12 . In the light of the strong empirical connection between anxiety and eating-related health outcomes our study is interested in how far anxiety is related to specific, detrimental eating behaviors. We assume that these behaviors play a crucial role in connecting anxiety on the one side with obesity and eating disorders on the other side. Furthermore, anxiety-related eating behaviors could also contribute to the connection between anxiety and other widespread diseases like cardiovascular disorder and diabetes that is reflected in meta-analyses 13 , 14 .

Identifying anxiety-related eating behaviors can contribute to the development of targeted interventions to combat obesity and other disorders and health problems in the population. There are already examples in the literature that connect anxiety with specific eating behaviors like selective eating in children 15 , disordered eating behaviors in adolescents 16 , unhealthy eating in university students 17 , and the increased consumption of saturated fats and added sugars 18 . If we also take into account the studies on eating behaviors in the context of eating disorders that we introduced above, it is clear that the focus of the literature is on inherently problematic—often highly specific—eating behaviors, and/ or young individuals. Since we are interested in a public health perspective and the broad applicability of our results, in our study, we want to shift focus to less clinical, broader eating behaviors and a more inclusive representation of the population. The three-factorial approach to eating behaviors was established in the 1980`s by Stunkard and Messick 19 and has since then been applied around the globe and to a variety of research topics, from weight reduction and BMI to sleep and chrono type 20 , 21 , 22 , 23 . It refers to the following three domains of eating behavior: (1) Cognitive Restraint, i.e., the tendency of someone to restrict food consumption, a form of cognitive control that is associated with daily food intake; (2) disinhibition, i.e., an overconsumption of food due to a variety of stimuli and a loss of control with regard to food intake; and (3) Hunger, i.e., the susceptibility for internal or external hunger signs 19 , 24 , 25 .

The three-factors of eating behavior have already been linked to anxiety in recent studies. Janjetic et al. 26 showed a relationship between all three factors and state anxiety in a sample of Argentinian women between 40 and 65 years, and Aoun et al. 27 linked anxiety symptoms to uncontrolled eating, an amalgamation of the two factors Disinhibition and Hunger, in a sample of Lebanese university students. While these studies point towards a meaningful link between anxiety and the three factors of eating behavior, they were both conducted with highly specific samples from non-European countries. Since eating behaviors are embedded in and shaped by their cultural and social contexts, these results cannot easily be applied to a European context. Hence, the goal of this study is to analyze the connection between anxiety and all three factors of eating behavior in a European context and with a large sample that is broad and heterogeneous in terms demographics and social status.

Study design

The Adult Study of the Leipzig Research Centre for Civilization Diseases (LIFE) is a large population-based cohort study in the city of Leipzig, Germany, and a collaboration of several clinical and epidemiological research teams.

10,000 participants between 18 and 80 years were recruited through age- and gender-stratified random selection by the local residents’ registry office. The majority of participants were above 40, and the only exclusion criterion was being pregnant. The LIFE-Adult baseline examination took place between 2011 and 2014, and every participant provided written informed consent prior to participation. The participants underwent a set of assessments, including interviews, questionnaires, and medical examinations. Details on study design and assessments can be found elsewhere 28 .

The LIFE-Adult-Study complies with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by the ethics committee of the University of Leipzig.

Sociodemographic variables

Participants provided information on age, gender, medical history, marital status, smoking, and living situation in standardized interviews to trained study personnel. They also provided information on education, occupational status, and equivalent household income that was used to compute socioeconomic status (low, medium, high 29 ).

We used the Generalized Anxiety Disorder Scale-7 (GAD-7 30 , 31 ) to measure the level of anxiety. The GAD-7 contains seven items that can be answered on a scale from “0” (= never) to “3” (= almost every day). These items refer to typical anxiety symptoms, like worrying, nervousness, and irritability.

Eating behavior

We used the modified and extended German version of the three-factor-eating questionnaire (German: Fragebogen zum Essverhalten, FEV) to asses three factors of eating behavior 25 , 32 . The test is the German version of the Three-Factor-Eating-Questionnaire 19 , measuring Cognitive Restraint (21 items), Disinhibition (16 items), and Hunger (14 items). Items are in the form of statements like “I eat small portions on purpose because I do not want to gain weight” (Cognitive Restraint), “When I am sad, I often eat too much” (Disinhibition), or “Because I am hungry all the time, it is hard for me to stop eating before the dish is empty.” (Hunger).

Participants completed the revised German version of the Ten Item Personality Inventory (TIPI 33 ). The revised test comprises 16 items (scale range 1–7) assessing the Big Five personality traits, i.e. Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Physical activity was assessed via the 7-item short form of the International Physical Activity Questionnaire (IPAQ-SF, https://sites.google.com/site/theipaq/ ), and social support via the 5-item ENRICHD Social Support Scale 34 . We selected personality, physical activity, and social support as covariates based on our own theoretical considerations as well as on the literature 35 , 36 , 37 .

Statistical analyses

Statistical analyses were performed using IBM SPSS (Version 25). From the original sample of 10,000 participants we had to exclude participants who were living in retirement/nursing homes, with relatives or in some form of supported living because we assumed that this would affect their eating behaviors (N = 127). We also excluded people with diabetes (N = 1072), and those that were treated for diseases, when treatment or disease were likely to have an impact on eating behaviors, like ulcer or cancer (N = 1055). We then removed participants with missing values for GAD-7 (N = 332) FEV-scales (N = 1176/199/168), SES (N = 3), smoking (N = 75), IPAQ (N = 611), ESSI (N = 37), personality (N = 126). The persons that we removed due to missing values were significantly older, and more likely to be female and to exhibit a lower social economic status than the participants included in the final sample. The final sample contained 5019 participants.

We conducted linear regression analyses with anxiety as a predictor of (1) Cognitive Restraint, (2) Disinhibition, and (3) Hunger with and without sociodemographic variables, personality, physical activity, and social support as control variables. Before the regression analysis, we checked assumptions (linearity, homoscedasticity, multicollinearity, independence of residuals, normally distributed errors).

Our 5019 participants were 53.5 years old on average, and 51.6% were female. Table 1 gives an overview on the general characteristics of our sample.

Table 2 shows that anxiety positively predicted all three factors of eating behavior. Once control variables were added, anxiety still predicted Disinhibition and Hunger but no longer Cognitive Restraint. Male gender and smoking exhibited a negative and Neuroticism a positive relationship with all three dimensions of eating behavior. Age, conscientiousness, and intense physical activity all exhibited a positive association with Cognitive Restraint and a negative relationship with Disinhibition and Hunger. Socioeconomic status had a positive association with Cognitive Restraint and Disinhibition, Openness a positive one with Cognitive Restraint and a negative one with Hunger. In addition, Extraversion and Agreeableness both had a positive relationship with Cognitive Restraint, and Social Support a negative one with Disinhibition.

Our results showed a positive association between anxiety and all three domains of eating behavior. After controlling for sociodemographic variables, smoking, physical activity, personality, and social support, anxiety remained significantly associated with both Disinhibition and Hunger, but not Cognitive Restraint.

Cognitive Restraint refers to an individual`s cognitive effort that is targeted at reducing food intake and that involves elements like planning and decision making, e.g., in the form of a planned diet. Hence, Cognitive Restraint items address plans to eat small portions or to avoid specific foods, knowledge about calories, and attitudes towards dieting. Accordingly, some studies point in the direction that anxiety is connected to risk-avoidant decision making and higher rates of dieting 38 , 39 . The fact that there is no significant association between anxiety and Cognitive Restraint in our data may be explained by the inclusion of the Big Five personality traits, all of which exhibited significant associations with the outcome. It suggests that Cognitive Restraint represents a rather stable general tendency or predisposition that lacks the volatility and reactiveness to be affected by rather temporary fluctuations in anxiety, leading to a loss of significance of the association with anxiety once personality is included in the regression. This interpretation matches with similar results from a study on the effects of Neuroticism, Conscientiousness, and anxiety on Cognitive Restraint in morbidly obese patients 40 .

Unlike Cognitive Restraint, Disinhibition refers to an overconsumption of food in response to a variety of internal and external stimuli 19 , i.e., it represents a more volatile and reactive factor. Our results suggest that anxiety acts as an internal stimulus that increases Disinhibition and could therefore temporally counteract the more stable effects of Cognitive Restraint. It matches with research that connects anxiety to impulsive forms of food consumption like binge or night eating 10 , 11 and that indicates that some forms of overeating that are a part of eating pathologies may function as a way to regulate and deal with negative emotions 41 , 42 . Disinhibition could therefore be a form of anxiety regulation or coping. Since overeating 43 , 44 as well as binge eating 45 , 46 have been linked to overweight and obesity, Disinhibition could be a valuable starting point for public health interventions targeting eating-related health outcomes. If further research strengthens the assumption of a causal effect of anxiety on Disinhibition, this could be the basis for interventions to promote alternative ways of anxiety regulation for individuals with subthreshold anxiety symptoms, ranging from physical activity and autogenic training to muscle relaxation and cognitive interventions.

While clinical research is pointing in the direction of a causal link from anxiety to eating behaviors and experimental studies show an effect of emotional state and anxiety on food intake 47 , 48 , we cannot rule out that this also goes in the opposite direction as other studies show effects of nutrition on anxiety 49 , 50 and a connection between household food insecurity and anxiety 51 . Therefore, eating behaviors that trigger the consumption of specific foods could thereby affect anxiety levels. Future longitudinal research has to clarify in how far that holds true for Disinhibition.

Our results showed a significant positive relationship between anxiety and Hunger. This connection is supported by recent research on the neurobiological foundations of both phenomena. A current review shows that not only specific nuclei of the hypothalamus and extended amygdala are activated by both hunger and fear, but also that neuropeptides that are released during states of hunger can reduce anxiety 52 . From that perspective, Hunger could be seen as a form of subconscious, neurobiological anxiety regulation. The authors of the review conclude that neuropeptides released during states of satiety could increase anxiety, which could additionally reinforce hunger as a form of emotion regulation. Hence, individuals would experience both, elevated states of hunger as well as anxiety, at different times which matches with our results. Future research needs to assess both variables in a synchronic way and over a longer period of time to strengthen our interpretation of the results.

This study has several advantages, e.g., multiple control variables, and the exclusion of participants with diseases/treatments that could affect the outcomes. In addition, we used a large and diverse sample from a German cohort study, and therefore our results can be generalized to some extent to a German and even Western European level. However, there are also certain limitations. First, future research needs to apply different and longitudinal research designs in order to further strengthen causal interpretations. Second, we removed a large part of the original sample due to missing values, resulting in a younger sample with a higher socioeconomic status and a more balanced distribution of gender. While this could be seen as a limitation of our study, it is not necessarily limiting the generalizability of our results since our final sample is even closer to the general population in terms of age and gender.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ruscio, A. M. et al. Cross-sectional comparison of the epidemiology of DSM-5 generalized anxiety disorder across the globe. JAMA Psychiat. 74 , 465–475. https://doi.org/10.1001/jamapsychiatry.2017.0056 (2017).

Article   Google Scholar  

Haller, H., Cramer, H., Lauche, R., Gass, F. & Dobos, G. J. The prevalence and burden of subthreshold generalized anxiety disorder: A systematic review. BMC Psychiatry 14 , 128 (2014).

Grenier, S. et al. The Impact of DSM-IV symptom and clinical significance criteria on the prevalence estimates of subthreshold and threshold anxiety in the older adult population. Am. J. Geriatr. Psychiatry 19 , 316–326. https://doi.org/10.1097/JGP.0b013e3181ff416c (2011).

Article   PubMed   PubMed Central   Google Scholar  

Amiri, S. & Behnezhad, S. Obesity and anxiety symptoms: A systematic review and meta-analysis. Neuropsychiatrie 33 , 72–89. https://doi.org/10.1007/s40211-019-0302-9 (2019).

Article   PubMed   Google Scholar  

Gariepy, G., Nitka, D. & Schmitz, N. The association between obesity and anxiety disorders in the population: A systematic review and meta-analysis. Int. J. Obes. 34 , 407–419. https://doi.org/10.1038/ijo.2009.252 (2010).

Article   CAS   Google Scholar  

Meier, S. M. et al. Diagnosed anxiety disorders and the risk of subsequent anorexia nervosa: A Danish population register study. Eur. Eat. Disord. Rev. 23 , 524–530. https://doi.org/10.1002/erv.2402 (2015).

Guarda, A. S., Schreyer, C. C., Boersma, G. J., Tamashiro, K. L. & Moran, T. H. Anorexia nervosa as a motivated behavior: Relevance of anxiety, stress, fear and learning. Physiol. Behav. 152 , 466–472. https://doi.org/10.1016/j.physbeh.2015.04.007 (2015).

Article   CAS   PubMed   Google Scholar  

Levinson, C. A. & Rodebaugh, T. L. Social anxiety and eating disorder comorbidity: The role of negative social evaluation fears. Eat. Behav. 13 , 27–35. https://doi.org/10.1016/j.eatbeh.2011.11.006 (2012).

Koskina, A., van den Eynde, F., Meisel, S., Campbell, I. C. & Schmidt, U. Social appearance anxiety and bulimia nervosa. Eat. Weight Disord.-Stud. Anorexia Bulimia Obes. 16 , e142–e145 (2011).

Rosenbaum, D. L. & White, K. S. The relation of anxiety, depression, and stress to binge eating behavior. J. Health Psychol. 20 , 887–898. https://doi.org/10.1177/1359105315580212 (2015).

Sassaroli, S. et al. Daily and nightly anxiety among patients affected by night eating syndrome and binge eating disorder. Eat. Disord. 17 , 140–145 (2009).

Touchette, E. et al. Subclinical eating disorders and their comorbidity with mood and anxiety disorders in adolescent girls. Psychiatry Res. 185 , 185–192. https://doi.org/10.1016/j.psychres.2010.04.005 (2011).

Emdin, C. A. et al. Meta-analysis of anxiety as a risk factor for cardiovascular disease. Am. J. Cardiol. 118 , 511–519. https://doi.org/10.1016/j.amjcard.2016.05.041 (2016).

Smith, K. J. et al. Association of diabetes with anxiety: A systematic review and meta-analysis. J. Psychosom. Res. 74 , 89–99. https://doi.org/10.1016/j.jpsychores.2012.11.013 (2013).

Farrow, C. V. & Coulthard, H. Relationships between sensory sensitivity, anxiety and selective eating in children. Appetite 58 , 842–846. https://doi.org/10.1016/j.appet.2012.01.017 (2012).

Lee, K. S. & Vaillancourt, T. A four-year prospective study of bullying, anxiety, and disordered eating behavior across early adolescence. Child Psychiatry Hum. Dev. 50 , 815–825. https://doi.org/10.1007/s10578-019-00884-7 (2019).

Ramón-Arbués, E. et al. Eating behavior and relationships with stress, anxiety, depression and insomnia in university students. Nutr. Hosp. 36 , 1339–1345. https://doi.org/10.20960/nh.02641 (2019).

Masana, M. F. et al. Dietary patterns and their association with anxiety symptoms among older adults: The ATTICA study. Nutrients https://doi.org/10.3390/nu11061250 (2019).

Stunkard, A. J. & Messick, S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J. Psychosom. Res. 29 , 71–83 (1985).

Boschi, V., Iorio, D., Margiotta, N., D’Orsi, P. & Falconi, C. The three-factor eating questionnaire in the evaluation of eating behaviour in subjects seeking participation in a dietotherapy programme. Ann. Nutr. Metab. 45 , 72–77. https://doi.org/10.1159/000046709 (2001).

Schubert, E. & Randler, C. Association between chronotype and the constructs of the three-factor-eating-questionnaire. Appetite 51 , 501–505. https://doi.org/10.1016/j.appet.2008.03.018 (2008).

Gallant, A. R. et al. The three-factor eating questionnaire and BMI in adolescents: Results from the Québec family study. Br. J. Nutr. 104 , 1074–1079. https://doi.org/10.1017/S0007114510001662 (2010).

Blumfield, M. L., Bei, B., Zimberg, I. Z. & Cain, S. W. Dietary disinhibition mediates the relationship between poor sleep quality and body weight. Appetite 120 , 602–608. https://doi.org/10.1016/j.appet.2017.10.022 (2018).

Kruger, R., de Bray, J. G., Beck, K. L., Conlon, C. A. & Stonehouse, W. Exploring the relationship between body composition and eating behavior using the three factor eating questionnaire (TFEQ) in young New Zealand women. Nutrients 8 , 386 (2016).

Löffler, A. et al. Age- and gender-specific norms for the German version of the three-factor eating-questionnaire (TFEQ). Appetite 91 , 241–247. https://doi.org/10.1016/j.appet.2015.04.044 (2015).

Janjetic, M. A. et al. Association between anxiety level, eating behavior, and nutritional status in adult women. J. Am. Coll. Nutr. 39 , 200–205. https://doi.org/10.1080/07315724.2019.1633970 (2020).

Aoun, C. et al. The cognitive, behavioral, and emotional aspects of eating habits and association with impulsivity, chronotype, anxiety, and depression: A cross-sectional study. Front. Behav. Neurosci. 13 , 204. https://doi.org/10.3389/fnbeh.2019.00204 (2019).

Loeffler, M. et al. The LIFE-Adult-Study: Objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15 , 691. https://doi.org/10.1186/s12889-015-1983-z (2015).

Lampert, T., Kroll, L., Müters, S. & Stolzenberg, H. Measurement of socioeconomic status in the German Health Interview and Examination Survey for Adults (DEGS1). Bundesgesund. Gesundh. Gesundh. 56 , 631–636. https://doi.org/10.1007/s00103-012-1663-4 (2013).

Spitzer, R. L., Kroenke, K., Williams, J. B. W. & Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 166 , 1092–1097 (2006).

Hinz, A. et al. Psychometric evaluation of the generalized anxiety disorder screener GAD-7, based on a large German general population sample. J. Affect. Disord. 210 , 338–344 (2017).

Pudel, V. & Westenhöfer, J. Fragebogen zum Essverhalten (FEV): Handanweisung (Verlag für Psychologie Hogrefe, 1989).

Google Scholar  

Herzberg, P. Y. & Brähler, E. Assessing the Big-Five personality domains via short forms: A cautionary note and a proposal. Eur. J. Psychol. Assess. 22 , 139–148. https://doi.org/10.1027/1015-5759.22.3.139 (2006).

Berkman, L. F. et al. Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The enhancing recovery in coronary heart disease patients (ENRICHD) randomized trial. JAMA 289 , 3106–3116 (2003).

Allen, M. S., Robson, D. A. & Laborde, S. Normal variations in personality predict eating behavior, oral health, and partial syndrome bulimia nervosa in adolescent girls. Food Sci. Nutr. 8 , 1423–1432. https://doi.org/10.1002/fsn3.1425 (2020).

Andrade, A. M. et al. The effect of physical activity on weight loss is mediated by eating self-regulation. Patient Educ. Couns. 79 , 320–326. https://doi.org/10.1016/j.pec.2010.01.006 (2010).

Conceição, E. M. et al. Perceived social support before and after bariatric surgery: Association with depression, problematic eating behaviors, and weight outcomes. Eat. Weight Disord. Stud. Anorexia Bulimia Obes. 25 , 679–692. https://doi.org/10.1007/s40519-019-00671-2 (2020).

Fitzsimmons-Craft, E. E., Bardone-Cone, A. M., Brownstone, L. M. & Harney, M. B. Evaluating the roles of anxiety and dimensions of perfectionism in dieting and binge eating using weekly diary methodology. Eat. Behav. 13 , 418–422. https://doi.org/10.1016/j.eatbeh.2012.06.006 (2012).

Maner, J. K. et al. Dispositional anxiety and risk-avoidant decision-making. Pers. Individ. Differ. 42 , 665–675. https://doi.org/10.1016/j.paid.2006.08.016 (2007).

Gade, H., Rosenvinge, J. H., Hjelmesæth, J. & Friborg, O. Psychological correlates to dysfunctional eating patterns among morbidly obese patients accepted for bariatric surgery. Obes. Facts 7 , 111–119 (2014).

Leehr, E. J. et al. Emotion regulation model in binge eating disorder and obesity—A systematic review. Neurosci. Biobehav. Rev. 49 , 125–134. https://doi.org/10.1016/j.neubiorev.2014.12.008 (2015).

Meule, A. et al. Emotion regulation and emotional eating in anorexia nervosa and bulimia nervosa. Eat. Disord. https://doi.org/10.1080/10640266.2019.1642036 (2019).

Ledoux, T., Watson, K., Baranowski, J., Tepper, B. J. & Baranowski, T. Overeating styles and adiposity among multiethnic youth. Appetite 56 , 71–77. https://doi.org/10.1016/j.appet.2010.11.145 (2011).

Eloranta, A.-M. et al. Dietary factors associated with overweight and body adiposity in Finnish children aged 6–8 years: The PANIC study. Int. J. Obes. 36 , 950–955. https://doi.org/10.1038/ijo.2012.89 (2012).

Sonneville, K. R. et al. Longitudinal associations between binge eating and overeating and adverse outcomes among adolescents and young adults: Does loss of control matter? JAMA Pediatr. 167 , 149–155. https://doi.org/10.1001/2013.jamapediatrics.12 (2013).

He, J., Cai, Z. & Fan, X. Prevalence of binge and loss of control eating among children and adolescents with overweight and obesity: An exploratory meta-analysis. Int. J. Eat Disord. 50 , 91–103. https://doi.org/10.1002/eat.22661 (2017).

Wilkinson, L. L., Rowe, A. C. & Heath, G. H. Eating me up inside: Priming attachment security and anxiety, and their effects on snacking. J. Soc. Pers. Relat. 30 , 795–804. https://doi.org/10.1177/0265407512468371 (2013).

Cardi, V., Leppanen, J. & Treasure, J. The effects of negative and positive mood induction on eating behaviour: A meta-analysis of laboratory studies in the healthy population and eating and weight disorders. Neurosci. Biobehav. Rev. 57 , 299–309. https://doi.org/10.1016/j.neubiorev.2015.08.011 (2015).

Agarwal, U. et al. A multicenter randomized controlled trial of a nutrition intervention program in a multiethnic adult population in the corporate setting reduces depression and anxiety and improves quality of life: The GEICO study. Am. J. Health Promot. 29 , 245–254. https://doi.org/10.4278/ajhp.130218-QUAN-72 (2015).

Zhang, L. et al. Multiple micronutrient supplementation reduces anemia and anxiety in rural China’s elementary school children. J. Nutr. 143 , 640–647. https://doi.org/10.3945/jn.112.171959 (2013).

Maynard, M. S., Perlman, C. M. & Kirkpatrick, S. I. Food insecurity and perceived anxiety among adolescents: An analysis of data from the 2009–2010 National Health and Nutrition Examination Survey (NHANES). Journal of Hunger & Environmental Nutrition 14 , 339–351. https://doi.org/10.1080/19320248.2017.1393363 (2019).

Comeras, L. B., Herzog, H. & Tasan, R. O. Neuropeptides at the crossroad of fear and hunger: A special focus on neuropeptide Y. Ann. N. Y. Acad. Sci. 1455 , 59–80. https://doi.org/10.1111/nyas.14179 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

We thank the research teams and the participants of the LIFE-Adult-Study.

Open Access funding enabled and organized by Projekt DEAL. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by funds of the Free State of Saxony within the framework of the excellence initiative (Project Numbers 713-241202, 14505/2470, 14575/2470). We acknowledge support from Leipzig University for Open Access Publishing.

Author information

Authors and affiliations.

Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Ph.-Rosenthal-Str. 55, 04103, Leipzig, Germany

Felix S. Hussenoeder, Ines Conrad & Steffi G. Riedel-Heller

Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany

Christoph Engel, Silke Zachariae, Samira Zeynalova & Markus Löffler

Department of Medical Psychology and Medical Sociology, University of Leipzig, Leipzig, Germany

Heide Glaesmer & Andreas Hinz

Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Veronika Witte & Arno Villringer

Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig, Leipzig, Germany

Anke Tönjes & Michael Stumvoll

You can also search for this author in PubMed   Google Scholar

Contributions

F.S.H., I.C. and S.R.H. designed the study and F.S.H. conducted the statistical analysis and literature searches. F.S.H. wrote the first draft of the manuscript and C.E., S.Za., S.Ze., H.G., A.H., V.W., A.T., M.L., M.S. and A.V. contributed data and/or expertise. All authors contributed to and have approved the final manuscript.

Corresponding author

Correspondence to Felix S. Hussenoeder .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Hussenoeder, F.S., Conrad, I., Engel, C. et al. Analyzing the link between anxiety and eating behavior as a potential pathway to eating-related health outcomes. Sci Rep 11 , 14717 (2021). https://doi.org/10.1038/s41598-021-94279-1

Download citation

Received : 09 April 2021

Accepted : 07 July 2021

Published : 19 July 2021

DOI : https://doi.org/10.1038/s41598-021-94279-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Beyond generalized anxiety: the association of anxiety sensitivity with disordered eating.

• Maria Bazo Perez
• Timothy B. Hayes
• Leslie D. Frazier

Journal of Eating Disorders (2023)

Irritable bowel syndrome and mental health comorbidity — approach to multidisciplinary management

• Heidi M. Staudacher
• Christopher J. Black
• Laurie Keefer

Nature Reviews Gastroenterology & Hepatology (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Open access
• Published: 27 January 2022

The future of eating disorders research: an editorial

• Stephen Touyz 1 &
• Phillipa Hay 2  

Journal of Eating Disorders volume  10 , Article number:  10 ( 2022 ) Cite this article

4326 Accesses

3 Citations

4 Altmetric

Metrics details

Much has changed since the first months of 2020 when we penned one of the first editorials to be published on COVID-19 and how it might impact on those with the lived experience of an eating disorder. At that time as Editors-in-Chief of the Journal of Eating Disorders , we instigated one of the first themed special issue entitled “Eating Disorders in the time of COVID-19 outbreak—Implications for now and the future”. The number of papers submitted and since published has exceeded all anticipations, demonstrating the attention that has been directed to this important topic and the emergence of systematic reviews [ 1 ]. There can now be no doubt that this pandemic has had a devastating impact on those with the lived experience of eating disorders with distress calls to the Butterfly Foundation phone hotline in Australia and similar access points around the globe soaring to a height of almost 200% in the UK [ 2 ]. We in no way want to detract from the urgency in ensuring that all of those experiencing distress from an eating disorder and wanting treatment, succeed in doing so despite the lockdowns and self-isolation imposed by health orders. On the other hand, however, the opportunities that COVID-19 has presented in terms of innovative health care delivery should be grasped at a time in our history when we have witnessed the greatest transformation in the innovation of advanced digital technologies. Such technology was responsible for the launch of the world’s first ever open access journal dedicated to eating disorders on the 22nd January 2013. It is hard to believe that a decade has elapsed since that time and that in 2022, the Journal of Eating Disorders will be in its10th year.

Looking over the topics of those publications that appeared in early years of this journal, there is clearly an ever greater need for research in our field. Those working in the field of family based treatments for adolescents with eating disorders particularly anorexia nervosa are exploring new avenues to develop better clinical efficacy such as multiple family therapy [ 3 , 4 ], family based treatment (FBT) in the home, and combining FBT with dialectical behaviour and other therapies (see [ 5 ]). There has been precious little research to date that has transformed treatments for adults with eating disorders who are presenting much more of a challenge to those researching this field. In a recent Lancet Psychiatry meta-analyses of all the relevant randomised controlled trial research data on the role of cognitive behaviour therapy (CBT) in adult anorexia nervosa (AN), the authors concluded that in terms of outcome, it was not superior to treatment as usual [ 6 ]. Those venturing into novel areas for AN deserve our highest praise as it does take courage and fortitude to revisit theoretical understandings and venture into new avenues of neuro-modulation such as deep brain stimulation and rapid Transcranial Magnetic Stimulation and pharmacological agents such as ketamine and psilocybin [ 7 , 8 ] when gold standards such as CBT prevail. This year also saw the opening of the first Australian residential centre for people with eating disorders which has embraced the concept of therapists with lived experience providing care [ 9 ]. Other areas are innovating. Those in our field who care for people with binge eating spectrum and comorbid metabolic disorders will be aware of the rapid rise of new glucagon-like peptide-1 receptor agonists used in combination with increasingly sophisticated behavioural weight loss therapies to treat type 2 diabetes. We cannot rest on our laurels.

Just published in the Journal of Eating Disorders , Levinson et al. [ 10 ] report on a proof of concept study and initial data in using networks to identify treatment targets for eating disorder treatment. They are adopting individualised network analyses utilising comprehensive longitudinal data via ecological momentary assessment to “model how dynamic systems of symptoms interrelate with each other to maintain pathology, within one person”. We need to truly embrace personalised care models for recovery and to learn what works for whom and when. It is no longer good enough to present data on attrition with the concept that people needed to stay with treatment and all would be well. Rather than people failing treatment, there is an imperative to refine the treatment to meet the person and their family’s needs. As enunciated by Gustafsson et al . [ 11 ] it is incumbent on us to increase our understanding of the treatment experience and to learn with those experiencing an eating disorder how to refine approaches and therapies.

We urgently need paradigm shifts to do for our field what has been done in developing vaccines and treatments for Covid-19. To mark the 10th year of the Journal of Eating Disorders, a series of special themed issues are planned. These include collections on medical assessment and management, environmental influences on eating disorders, disordered eating and body image, a trans-diagnostic understanding of binge eating, and a series of rapid reviews and updates for the field. It is hoped that the papers published in these as well as invited commentaries and editorials will be part of a global transformation of research to ultimately deliver more effective outcomes for people living with eating disorders. The researchers are here, as well as organisations such as the Academy of Eating Disorders and the Eating Disorders Research Society, other high calibre clinical/research bodies in many counties such as ANZAED (see the recent conference proceedings [ 12 ]) and a plethora of regular international and national conferences to rapidly disseminate research findings. If there was ever a time to feel optimistic about our field it is now.

Availability of data and materials

Not applicable.

Abbreviations

Anorexia Nervosa

Australian and New Zealand Academy for Eating Disorders

Cognitive Behaviour Therapy

Coronavirus disease

Family Based Treatment

McLean C, Utpala R, Sharp G. The impacts of COVID-19 on eating disorders and disordered eating: A mixed studies systematic review and implications for healthcare professionals, carers, and self. Preprint. file://ad.uws.edu.au/dfshare/HomesCMB$/30021271/Downloads/McLean,%20Utpala,%20and%20Sharp,%202021.pdf. Accessed 28 Dec 2021.

The Butterfly Foundation. Butterfly Foundation unites with international network to champion equity for eating disorders on World Eating Disorders Action Day. June 2 2021 https://butterfly.org.au/news/worldeatingdisordersactionday/ . Accessed 28 Dec 2021.

Smith C, Potts J, Hoiles K, O’Sullivan U. A grandparent in the room: multiple family therapy for adolescents. J Eat Disord. 2014;2(1):13.

Article   Google Scholar  

Baudinet J, Eisler I, Simic M, Schmidt U. Brief early adolescent multi-family therapy (BEAM) trial for anorexia nervosa: a feasibility randomized controlled trial protocol. J Eat Disord. 2021;9(1):71.

Couturier J, Isserlin L, Norris M, Spettigue W, Brouwers M, Kimber M, McVey G, Webb C, Findlay S, Bhatnagar N, Snelgrove N. Canadian practice guidelines for the treatment of children and adolescents with eating disorders. J Eat Disord. 2020;8(1):1–80.

Solmi M, Wade TD, Byrne S, Del Giovane C, Fairburn CG, Ostinelli EG, De Crescenzo F, Johnson C, Schmidt U, Treasure J, Favaro A. Comparative efficacy and acceptability of psychological interventions for the treatment of adult outpatients with anorexia nervosa: a systematic review and network meta-analysis. Lancet Psychiatry. 2021.

Bang L, Treasure J, Rø Ø, Joos A. Advancing our understanding of the neurobiology of anorexia nervosa: translation into treatment. J Eat Disord. 2017;5(1):1–3.

Treasure J, Willmott D, Ambwani S, Cardi V, Clark Bryan D, Rowlands K, Schmidt U. Cognitive interpersonal model for anorexia nervosa revisited: the perpetuating factors that contribute to the development of the severe and enduring illness. J Clin Med. 2020;9(3):630.

Wandi Nerida https://wandinerida.org.au/ Accessed 29/12/2021.

Levinson CA, Hunt RA, Keshishian AC, Brown ML, Vanzhula I, Christian C, Brosof LC, Williams BM. Using individual networks to identify treatment targets for eating disorder treatment: a proof-of-concept study and initial data. J Eat Disord. 2021;9(1):1–8.

Gustafsson SA, Stenström K, Olofsson H, Pettersson A, Wilbe RK. Experiences of eating disorders from the perspectives of patients, family members and health care professionals: a meta-review of qualitative evidence syntheses. J Eat Disord. 2021;9(1):1–23.

ANZAED 2021 hybrid conference: oral and poster abstracts. J Eating Disorders. 2021;9(1):162. https://doi.org/10.1186/s40337-021-00505-6

Download references

Acknowledgements

None received.

Author information

Authors and affiliations.

InsideOut Institute, Charles Perkins Centre, University of Sydney, Camperdown, Australia

Stephen Touyz

Translational Health Research Institute, School of Medicine, Western Sydney University, Locked Bag 1797, Penrith South, NSW, 2715, Australia

Phillipa Hay

You can also search for this author in PubMed   Google Scholar

Contributions

ST and PH conceived this publication; ST wrote the first draft, ST and PH edited the manuscript and both authors read and approved the final manuscript.

Corresponding author

Correspondence to Phillipa Hay .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

ST and PH are Editors-in-Chief of Journal of Eating Disorders. ST and PH are also consultants to Takeda Pharmaceuticals.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Touyz, S., Hay, P. The future of eating disorders research: an editorial. J Eat Disord 10 , 10 (2022). https://doi.org/10.1186/s40337-022-00538-5

Download citation

Published : 27 January 2022

DOI : https://doi.org/10.1186/s40337-022-00538-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Journal of Eating Disorders

ISSN: 2050-2974

• General enquiries: [email protected]

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

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

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

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

• Carlos A. Almenara

• Published: December 15, 2022
• https://doi.org/10.1371/journal.pone.0278981
• Peer Review
• Reader Comments

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.

Citation: Almenara CA (2022) 40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling. PLoS ONE 17(12): e0278981. https://doi.org/10.1371/journal.pone.0278981

Editor: Alberto Baccini, University of Siena, Italy, ITALY

Received: February 5, 2021; Accepted: November 27, 2022; Published: December 15, 2022

Copyright: © 2022 Carlos A. Almenara. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: 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 ).

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

Competing interests: The author has no competing interest to declare.

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 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0278981.g001

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 ).

https://doi.org/10.1371/journal.pone.0278981.t001

https://doi.org/10.1371/journal.pone.0278981.t002

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.

https://doi.org/10.1371/journal.pone.0278981.g002

https://doi.org/10.1371/journal.pone.0278981.t003

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 ).

https://doi.org/10.1371/journal.pone.0278981.t004

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 ).

https://doi.org/10.1371/journal.pone.0278981.g003

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

S1 file. most local cited documents..

https://doi.org/10.1371/journal.pone.0278981.s001

S2 File. Most global cited documents.

https://doi.org/10.1371/journal.pone.0278981.s002

S3 File. Network statistics.

https://doi.org/10.1371/journal.pone.0278981.s003

S4 File. Network of collaboration including close to one hundred authors.

https://doi.org/10.1371/journal.pone.0278981.s004

S5 File. Data preprocessing and text representation in Python.

https://doi.org/10.1371/journal.pone.0278981.s005

S6 File. Sum of NMF results for topic dominance per year and per topic.

https://doi.org/10.1371/journal.pone.0278981.s006

S7 File. Number of documents per year and per journal.

https://doi.org/10.1371/journal.pone.0278981.s007

S8 File. Trends over time in number of documents per journal.

https://doi.org/10.1371/journal.pone.0278981.s008

• View Article
• PubMed/NCBI
• Google Scholar
• 4. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Arlington, VA: American Psychiatric Association; 2013.
• 6. Mennell S. All manners of food: Eating and taste in England and France from the Middle Ages to the present. Oxford, UK: Basil Blackwell; 1985.
• 21. Savoy J, Naji N. Comparative information retrieval evaluation for scanned documents. In: Proceedings of the 15th WSEAS International Conference on Computers. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS); 2011. p. 527–534. https://doi.org/10.5555/2028299.2028394
• 23. De S, Stanley RJ, Cheng B, Antani S, Long R, Thoma G. Automated text detection and recognition in annotated biomedical publication images. Information Resources Management Association, editor. Medical Imaging: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global; 2017. p. 457–489. https://doi.org/10.4018/978-1-5225-0571-6.ch018
• 28. Hlava MMK. The taxobook: Principles and practices of building taxonomies. Morgan & Claypool; 2015. (Synthesis Lectures on Information Concepts, Retrieval, and Services). https://doi.org/10.2200/S00603ED1V02Y201410ICR036
• 33. Crane D. Invisible colleges: Diffusion of knowledge in scientific communities. University of Chicago Press; 1972.
• 34. Merton RK. The sociology of science: Theoretical and empirical investigations. University of Chicago Press; 1973.
• 35. Börner K, Boyack KW, Milojević S, Morris S. An introduction to modeling science: Basic model types, key definitions, and a general framework for the comparison of process models. In: Scharnhorst A, Börner K, van den Besselaar P, editors. Models of science dynamics: Encounters between complexity theory and information sciences. Berlin: Springer; 2012. p. 3–22. (Understanding Complex Systems). https://doi.org/10.1007/978-3-642-23068-4
• 36. Whitley R. The intellectual and social organization of the sciences. 2nd ed. Oxford University Press; 2000.
• 37. Hjørland B. Informetrics needs a foundation in the theory of science. In: Sugimoto CR, editor. Theories of informetrics and scholarly communication. De Gruyter; 2016. p. 20–46. https://doi.org/10.1515/9783110308464-005
• 38. Vitanov NK. Science and society: Assessment of research. In: Vitanov NK, editor. Science dynamics and research production: Qualitative and quantitative analysis of scientific and scholarly communication. Cham: Springer International Publishing; 2016. p. 3–52. (Qualitative and Quantitative Analysis of Scientific and Scholarly Communication). https://doi.org/10.1007/978-3-319-41631-1_1
• 40. McGinty S. Gatekeepers of knowledge: Journal editors in the sciences and the social sciences. Westport, CT: Bergin & Garvey; 1999.
• 53. Chen C, Song M. Representing scientific knowledge: The role of uncertainty. New York, NY:Springer; 2017. https://doi.org/10.1007/978-3-319-62543-0
• 54. Carmichael T, Hadžikadić M. The fundamentals of complex adaptive systems. In: Carmichael T, Collins AJ, Hadžikadić M, editors. Complex adaptive systems: Views from the physical, natural, and social sciences. New York, NY: Springer; 2019. p. 1–16. (Understanding Complex Systems). https://doi.org/10.1007/978-3-030-20309-2_1
• 55. Wolfram S. Complex systems theory. In: Pines D, editor. Emerging syntheses in science: Proceeding of the founding workshops of the Santa Fe Institute. Santa Fe, NM: Santa Fe Institute; 2019. p. 299–307. https://doi.org/10.1201/9780429492594-18
• 56. Wolfram S. A project to find the fundamental theory of physics. Manitoba, Canada: Wolfram Media, Inc.; 2020.
• 57. Northrop RB. Introduction to complexity and complex systems. Boca Raton, FL: CRC Press; 2011. https://doi.org/10.1201/9781439894989
• 62. Mobus GE, Kalton MC. Principles of systems science. Springer; 2015. (Understanding Complex Systems). https://doi.org/10.1007/978-1-4939-1920-8
• 63. Hara S, Imura J, Tsumura K, Ishizaki T, Sadamoto T. Glocal (global/local) control synthesis for hierarchical networked systems. In: 2015 IEEE Conference on Control Applications (CCA). Sydney, Australia: IEEE; 2015. p. 107–12. https://doi.org/10.1109/CCA.2015.7320618
• 65. R Core Team. R: A language and environment for statistical computing. Vienna, Austria; 2020.
• 68. Menche J, Barabási A-L. Introduction to network analysis. In: Loscalzo J, Barabási A-L, Silverman EK, editors. Network medicine: Complex systems in human disease and therapeutics. Cambridge, MA: Harvard University Press; 2017. p. 17–43. https://doi.org/10.4159/9780674545533-003
• 75. Milojević S. Network analysis and indicators. In: Ding Y, Rousseau R, Wolfram D, editors. Measuring scholarly impact: Methods and practice. Cham: Springer International Publishing; 2014. p. 57–82. https://doi.org/10.1007/978-3-319-10377-8_3
• 76. Chen Z, Liu B. Topic models for NLP applications. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning and data mining. 2nd ed. New York, NY: Springer; 2017. p. 1276–80. https://doi.org/10.1007/978-1-4899-7687-1_906
• 77. Alelyani S, Tang J, Liu H. Feature selection for clustering: A review. In: Aggarwal CC, Reddy CK, editors. Data clustering: Algorithms and applications. Boca Raton, FL: CRC Press; 2014. p. 29–60. (Data Mining and Knowledge Discovery Series). https://doi.org/10.1201/9781315373515-2
• 79. Landauer TK. LSA as a theory of meaning. In: Landauer TK, McNamara DS, Dennis S, Kintsch W, editors. Handbook of latent semantic analysis. Mahwah, NJ: Lawrence Erlbaum Associates; 2011. p. 3–34. https://doi.org/10.4324/9780203936399-5
• 80. Wang C, Paisley J, Blei DM. Online variational inference for the Hierarchical Dirichlet Process. In: Gordon G, Dunson D, Dudík M, editors. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (PMLR). PMLR; 2011. p. 752–60.
• 84. Řehůřek R, Sojka P. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: ELRA; 2010. p. 45–50. Available from: http://www.fi.muni.cz/usr/sojka/presentations/lrec2010-poster-rehurek-sojka.pdf
• 85. Röder M, Both A, Hinneburg A. Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. New York, NY, USA: Association for Computing Machinery; 2015. p. 399–408. (WSDM ‘15). https://doi.org/10.1145/2684822.2685324
• 87. Tijssen R. Re-valuing research excellence: From excellentism to responsible assessment. In: Kraemer-Mbula E, Tijssen R, Wallace ML, McLean R, editors. Transforming research excellence: New ideas from the global south. Cape Town, South Africa: African Minds; 2020. pp. 59–78.
• 88. Thomas JJ, Roberto CA, Berg KC. Assessment measures, then and now: A look back at seminal measures and a look forward to the brave new world. In: Walsh BT, Attia E, Glasofer DR, Sysko R, editors. Handbook of assessment and treatment of eating disorders. Arlington, VA: American Psychiatric Publishing; 2016.
• 109. Levine MP, Smolak L. Introduction to prevention. In: Levine MP, Smolak L, editors. The prevention of eating problems and eating disorders: Theories, research, and applications. 2nd ed. New York, NY: Routledge; 2020. p. 3–16. https://doi.org/10.4324/9781315401065-2
• 110. Mrazek PJ, Haggerty RJ. Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press; 1994.
• 111. Piran N, Levine MP. Introduction. Eat Disord. 1996 Dec 1;4(4):291–3. https://doi.org/10.1080/10640269608249189
• 112. Smolak L, Levine MP, Striegel-Moore RH. The developmental psychopathology of eating disorders: Implications for research, prevention, and treatment. Mahwah, NJ: Lawrence Erlbaum Associates; 1996. https://doi.org/10.4324/9780203763506
• 113. Piran N, Levine MP, Steiner-Adair C. Preventing eating disorders: A handbook of interventions and special challenges. Ann Arbor, MI: Brunner/Mazel; 1999. https://doi.org/10.4324/9780203778081
• 115. Ulijaszek SJ. Models of obesity: From ecology to complexity in science and policy. Cambridge, UK: Cambridge University Press; 2017. https://doi.org/10.1017/9781316338650
• 117. Attia E, Becker AE, Bulik CM, Field AE, Golden NH, Kreipe RE, et al. Research agenda for eating disorders. In: Evans DL, Foa EB, Gur RE, Hendin H, O’Brien CP, Romer D, et al., editors. Treating and preventing adolescent mental health disorders: What we know and what we don’t know. 2nd ed. New York, NY: Oxford University Press; 2017. p. 363–7. https://doi.org/10.1093/med-psych/9780199928163.003.0016
• 121. Greene D, O’Callaghan D, Cunningham P. How many topics? Stability analysis for topic models. In: Calders T, Esposito F, Hüllermeier E, Meo R, editors. Machine learning and knowledge discovery in databases. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 498–513. https://doi.org/10.1007/978-3-662-44848-9_32