Skip to main content
Download PDF
Download ePub

Eye movements and eating disorders: protocol for an exploratory experimental study examining the relationship in young-adult women with subclinical symptomatology

Journal of Eating Disorders volume 10, Article number: 47 (2022) Cite this article

Abstract

Background

Recent research indicates that patients with anorexia (AN) show specific eye movement abnormalities such as shorter prosaccade latencies, more saccade inhibition errors, and increased rate of saccadic intrusions compared to participants without AN. However, it remains unknown whether these abnormal eye movement patterns, which may serve as potential biomarkers and endophenotypes for an early diagnosis and preventive clinical treatments, start to manifest also in people with subclinical eating disorders (ED) symptomatology. Therefore, we propose a protocol for an exploratory experimental study to investigate whether participants with subclinical ED symptomatology and control participants differ in their performance on several eye movement tasks.

Methods

The sample will be recruited through convenience sampling. The Eating Disorder Examination Questionnaire will be administered as a screening tool to split the sample into participants with subclinical ED symptomatology and control participants. A fixation task, prosaccade/antisaccade task, and memory-guided task will be administered to both groups. Additionally, we will measure anxiety and premorbid intelligence as confounding variables. Means comparison, exploratory Pearson's correlations and discriminant analysis will be performed.

Discussion

This study will be the first to elucidate the presence of specific eye movement abnormalities in participants with subclinical ED symptomatology. The results may open opportunities for developing novel diagnostic tools/therapies being helpful to the EDs research community and allied fields.

Background

Eating disorders (EDs) such as anorexia or bulimia nervosa are a serious problem in our society. They are relatively common, especially among young-adult women [1, 2]; are associated with severe quality-of-life impairments [3], medical and psychopathological comorbidities [4]; have the highest mortality rates of all psychiatric illnesses [5]; and require their own specialized treatments, which results in elevated care costs [6]. Further, EDs are an underestimated problem since a big part of the adult outpatients with EDs do not fully meet DSM criteria [7] and, despite advances in clinical research on EDs, a subset of patients does not improve after receiving treatment [8]. Accordingly, efficacious treatment for EDs is therefore of high importance.

In this context, a vast body of evidence indicates that an earlier diagnosis is considered essential to get more favourable therapeutic outcomes and cost-effective treatments [9,10,11]. Notwithstanding such advances in prevention research, a challenge reported in ED field is that somatic and psychosocial symptoms, as well as their comorbidities, can be difficult to detect in routine clinical care hindering their preventive treatment [12, 13]. On the other hand, individuals with EDs may fail to report their psychological symptoms [14], experience feelings of stigma and shame about seeking help [15] or have poor insight into perceiving the severity of the illness [14, 16].

To overcome this critical situation, a growing body of literature is focusing on the identification of distinctive biomarkers and endophenotypes associated with EDs [17, 18]. In broad terms, the term biomarkers refers to objectively measured indicators that indicate biological processes [19], while the term endophenotypes refers to quantitative traits in the causal pathway from the genotype to the phenotype [19]. Endophenotypes are distinguished from biomarkers by their causative role. On the contrary, biomarkers are just risk indicators of biological processes and do not play a causal role [19]. Despite their substantive differences, together, biomarkers and endophenotypes offer the potential to improve population-based screens for identifying people who are at risk for psychopathological disorders and can benefit from preventative treatments [20, 21].

While the findings should be interpreted with caution, some authors have speculated that EDs may be linked to a variety of biomarkers and endophenotypes, such as specific metabolic components [22], altered inner body perceptions [23, 24], or neuropsychological weaknesses (e.g., cognitive inflexibility or weak central coherence [25]). More specifically, studies using eye-tracking point out a possible attentional bias linked to disorder-related stimuli, such as food or bodies [26,27,28]. However, few eye-tracking studies have addressed the role of saccadic eye movements using non-disorder-related procedures (e.g., fixation or pro-/anti-saccade tasks). For example, a recent systematic review of the topic found 28 studies using disorder-related stimuli and only three were involved in the study of saccadic eye movements [28]. Paradoxically, this trend contrasts with the wide body of evidence in psychiatric research that suggests that specific disorders (e.g., depression or schizophrenia) are accompanied by specific patterns of saccadic abnormalities [29, 30]. As a result, in methodological terms, the use of non-disorder-related procedures aimed to identify saccadic eye movement abnormalities is a potential avenue for further research in the ED field. This is because it opens the possibility to provide objective tests and, ultimately, to improve prognostic and preventive treatments, which is an urgent call in the field as previously stated.

In this regard for example, Phillipou et al. [31] found that patients with AN showed poor performance on ocular fixations tasks, i.e., an impaired ability to maintain fixation on a single dot for a longer time compared with control participants. Specifically, they showed an increased rate of saccadic intrusions named square wave jerks (SWJs; these are horizontal, involuntary, saccadic intrusions that interrupt fixation). Recently, Phillipou et al. [32] identified the state independence and heritability of this possible biomarker. According to the results, they found that patients with AN, patients’ weight-restored from the illness, and sisters of people with AN, made significantly more SWJs than healthy controls. Furthermore, the combination of SWJ rate and anxiety showed high accuracy levels to discriminate between the different groups tested (≥ 70%). Another study using prosaccade/antisaccade and memory-guided saccade tasks showed the existence of specific eye movement anomalies in patients with AN [33]. The prosaccade task is stimulus-driven as it requires to perform a saccade (i.e., a ballistic movement of the eyes that shifts the centre of gaze) to an onset peripheral stimulus, a process which is difficult to inhibit. On the contrary, the antisaccade task is goal-driven since it requires volitional processing, a voluntary saccade to the opposite direction of the stimulus. The memory-guided saccade task requires a saccade towards a remembered onset peripheral stimulus after a brief delay. Compared to control participants, patients with AN tended to show shorter saccade latencies in the prosaccade/antisaccade task and more inhibitory errors in the memory-guided saccade task [33].

In sum, the potential clinical value of this research is noteworthy since it may allow screening people at risk of developing AN [18, 34]. The aforementioned research has placed a great focus on clinical and recovered AN samples. Nevertheless, there is a call to focus not only on AN samples, but also including diverse samples such as other ED subtypes [18]. According to this transdiagnostic perspective, it would be possible to find potential biomarkers or endophenotypes to identify "particular symptoms across heterogeneous illness profiles” [18]. In this context, it is not yet known whether these eye movement patterns start to manifest also in heterogeneous samples with subclinical ED symptomatology. Furthermore, studies using subclinical ED samples could help to disentangle a possible influence of starvation (which presumably is less extended under this population than in clinical ED) on these eye-movement patterns and shed light on whether these eye movement abnormalities may be a reflection of the condition itself. On the other hand, given the relative novelty of these results, there is a call in the field to replicate this underpinning research [18, 28], as well as to standardize some of these eye movement tasks (i.e., prosaccade/antisaccade tasks) [35].

Study objectives and hypothesis

As Malcom and Phillipou [18] noted in their recent review, demonstrating a relationship with illness is one of the first steps in determining biomarkers and endophenotypes. As illustrative example, first Phillipou et al. [33] showed the existence of specific eye movement anomalies in patients with AN and only after these findings, they amplified the scope of their study and were able to identify the state-independence of these eye movement abnormalities [32]. Following this line of reasoning, we will investigate whether the aforementioned eye movement abnormalities detected are, in this case, associated in young-adult women with subclinical symptomatology. Therefore, this study will take as primary aim to investigate whether participants with subclinical ED symptomatology and control participants differ in their performance on a range of eye movement tasks. We hypothesize that participants with subclinical ED symptomatology compared to controls would be prone to show shorter saccade latencies and more inhibitory errors in a prosaccade/antisaccade task and in a memory-guided saccade task, respectively, and an increased rate of SWJs in a fixation task. Additionally, as secondary aim, and following the same approach of Phillipou et al. [31, 32], we will test the potential role of SWJ rate and anxiety for discriminating the different groups tested in our study (participants with subclinical ED symptomatology vs. control participants).

Methods/design

Participants and procedure

A young adult female sample will be chosen due to the higher prevalence of EDs in this population [1, 2, 7]. Thus, the sample will comprise young adult women in the 18–25 age range. The sample procedure will be through public advertisements and social media posts (convenience sampling). Inclusion criteria will be: (a) normal (or corrected) visual acuity; (b) gender: women; (c) current body mass index (BMI) in the normal range according to World Health Organization (WHO) (between 18.5–24.9). Exclusion criteria: (a) self-reported lifetime history of significant brain injury, neurological condition, ocular and/or visual pathology; (b) self-reported lifetime history of an ED or other mental illness; (c) self-reported current use of psychotropic drugs (e.g., antidepressants) or intake of recreational synthetic or natural drugs; (d) incomplete data collection or eye-tracker calibration failure; (e) inability to understand Spanish; (f) out of range age. The Spanish Eating Disorder Examination Questionnaire (S-EDE-Q) [36] will be administered since it is considered the gold standard for assessment of ED pathology [37]. The EDE-Q have shown good predictive as well as concurrent validity [37, 38]. Further, has been shown good psychometric properties in young adults in Spain [36]. This tool will serve as a pre-screening tool, allowing us to split the sample into two groups of participants with subclinical ED symptomatology and control participants according to their EDE-Q score, as defined by the study criteria (see below). A global EDE-Q score ≥ 2.8 has been shown to provide an optimal trade-off between sensitivity and specificity when it is used for screening in primary care [38]. The pre-screening will allow to form two groups of similar size: once the desired participant sample size has been reached for one of the groups, only participants falling into the other group defined for the study will be invited to take part.

At the beginning of the experiment a written informed consent will be obtained from all participants. Then, sociodemographic data collection and confounding variables like anxiety and premorbid intelligence quotient (IQ) will be measured. The instructions and questionnaires will be administered by a postgraduate student trained by senior members of the research team. Next, a battery of eye movement tasks presented in the same order will be administered (see the Measures section for more details). After completing the study, a debriefing session will be carried out. The full procedure will take approximately 90 min.

Measures

Psychometric measures

Eating disorder symptomatology

Spanish Version of the Eating Disorder Examination Questionnaire (S-EDE-Q) [36]. Composed by 28 7-point Likert-type response items ranging from 0 (not at all) to 6 points (markedly). Four subscales are measured, including dietary restraint, shape concerns, weight concerns, and eating concerns. The global score ranges from 0 to 6 points. As in Mond et al.’s [37], we will use a cut-off point ≥ 2.8 as clinically significant. Participants will be grouped according to this global index score.

To identify possible confounders, we will explore variables that may affect performance in eye movement tasks (premorbid intelligence and anxiety) [33]. Only those with significant relationships between groups will be deemed potential confounders and included in the analysis.

Premorbid intelligence

The Word Accentuation Test [39]. Composed by 30 low frequency Spanish words whose accents have been removed. Participants must demonstrate their knowledge of the correct accentuation of each word. The total score is the number of words correctly read (from 0 to 30). The test is administered individually and takes 2–3 min.

Anxiety levels

State-Trait Anxiety Inventory (STAI) [40]. The two forms of anxiety (state and trait) are separated in the inventory, and both have their own 20 separate questions. The questionnaire is therefore composed of 40 items ranging from 0 (almost never) to 4 (almost always). The score ranges from 0 to 80 points. For both forms, higher scores indicate higher levels of anxiety.

Eye movement measures

Data acquisition

Participants will sit in a bright room with constant lighting and temperature in front of a 24″ LED 144 Hz monitor (Asus VG248QE; 60-Hz refresh rate) with resolution 1920 × 1080 pixels. An adjustable chin rest will be used to minimize head movements and to ensure a constant distance between the participants’ eyes and the screen (90 cm). Eye movements will be recorded with the EyeLink Portable Duo (SR Research, Ontario, Canada). Both eyes will be recorded at a sampling rate of 500 Hz. The device uses a dark pupil-to-cornea reflection method. A saccade velocity threshold of 30°/sec, an acceleration threshold of 8000°/s2 and a motion threshold of 0.15° will be set. Before each task, an automatic randomized 9-point calibration will be conducted on a black background screen and drift correction will be performed throughout the task when necessary.

Eye movement tasks

Fixation task [31]. The participant is asked to look at a centred fixation dot (diameter of 0.5 degrees) for 1 min (Fig. 1a). The task comprises three trials with a short resting period between them.

Fig. 1
figure 1

Eye-tracking tasks. a Fixation task, b Prosaccade/Antisaccade task, c Memory-guided task

Full size image

Prosaccade/antisaccade task [35]. Each trial begins with a centred fixation cross (random period 1000–3000 ms) followed by a dot (diameter of 0.5 degrees) that appears in a random location either 8º left or right of the fixation cross (1000 ms). In the prosaccade condition the participant is required to direct her gaze towards the dot as quickly as possible, while in the antisaccade condition the participant is required to direct her gaze to the opposite position of the dot (Fig. 1b). First, participants are required to complete a practice phase consisting of five prosaccade trials and five antisaccade trials. Once practice trials are completed, a total of 240 trials are presented organized in five blocks as follows: 60 prosaccades; 40 antisaccades; 40 antisaccades; 40 antisaccades; 60 prosaccades (Fig. 1b).

Memory-guided saccade task [33]. The participant is asked to look at a centred fixation cross for a random period from 1000 to 3000 ms. Next, a 50 ms-dot (diameter of 0.5 degrees) is presented in a random location in the periphery (5–10° left or right) while the central fixation remains for a random period in the interval 1000–3000 ms. The participant is instructed to keep her eyes on this central fixation during the entire delay and to trigger a saccade towards the memorised location of the peripheral dot as soon as the central fixation disappears. First, participants are required to complete a practice phase consisting of eight trials. Once practice trials are completed, a total of 52 trials are presented, with an equal number of target presentations for each peripheral location (Fig. 1c).

For all the tasks: cross fixation colour, font type and size (RGB: 255,255,255; Font: Times New Roman, 28). Background screen colour (RGB: 51,51,51).

Other measures

Age, socioeconomic status, and highest level of education completed will be collected as sociodemographic variables. BMI will be measured and computed according to population norms and adjusted for age, sex, and height.

Data analysis

Eye-tracking data will be analysed with SR Research’s program DataViewer and Microsoft Excel. See Table 1 for a detailed overview of the outcome measures associated with the eye-tracking data. The proportion of participants and excluded trials, and the percentage of lost data will be reported.

Table 1 Outcome measures associated with the eye-tracking data
Full size table

Sample size calculation

An 80% of power analysis and a significant criterion of α = 0.05 were set since these are considered a convention for general use specifically in the psychology field [41, 42]. Regarding the effect size, based on previous literature on the topic [34], we choose a large effect size (d = 0.8) [41]. Thus, a priori t-test G*Power v3.1.9.2 (Dusseldorf University, Germany, http://www.gpower.hhu.de/en.html) analysis yields a minimum sample of 26 participants in each group (N = 52, two groups: participants with subclinical ED symptomatology vs. control participants). Nevertheless, if significant results were not obtained after completion of the study, a retrospective power analysis will be carried out with the participants responses to verify whether the non-significant result is due to lack of relationship between the groups or due to lack of statistical power. In this case, more participants will be recruited.

Statistical analysis

Statistical analyses will be performed with SPSS v.28. Normality and outliers will be checked through graphic tests such as QQ plots, histograms, and box plots. Cronbach’s α for each questionnaire will be reported. Group analyses will be performed with Student's t-test paired comparison (for normal distribution) or Wilcoxon tests (for non-normal distribution). We will apply a correction for multiple t-test comparisons. Cohen’s d will be reported for variables showing significance. In case of significant differences between study groups, we will then include confounding variables (premorbid intelligence and anxiety) as covariates in the analysis described above. Across groups, exploratory Pearson's correlation analyses will also be conducted between S-EDE-Q, STAI scores, and BMI and the outcome measures associated with the eye-tracking data (see Table 1, for a detailed view). Additionally, if significant differences are found between STAI scores and SWJ rate, consistent with previous literature [31, 32], a discriminant analysis will be performed on the entire sample to elucidate to what extent both measures correctly classify group membership (participants with subclinical ED symptomatology vs. control participants). Alpha will be set at 0.05 for all analyses.

Discussion

Basic research using non-disorder-related eye-tracking procedures indicates that patients with AN show specific eye movement abnormalities. Taken together, these results may indicate the existence of specific eye-tracking measures that could be used as screening tools of people at risk of AN, although replications with larger sample sizes are needed. Therefore, the upshot of this promising line of research may help to deepen our understanding of the nature of these eye-movement abnormalities as potential biomarkers and endophenotypes to develop future preventive treatments. In our view, exploring the presence of these patterns of eye movements in a subclinical ED phase is also crucial since once individuals develop an ED, it might be more difficult, or even too late, to improve treatment outcomes. Thus, our exploratory research aims to extend previous literature to shed new light on whether the presence of distinctive eye movement patterns is also present in participants with subclinical ED symptomatology. Additionally, we will test the accuracy of SWJ rate and anxiety to discriminate the different groups tested (participants with subclinical ED symptomatology vs. control participants). The findings may be the starting point to lay the basis for future studies aimed to clarify the state-indepence or the heritability of these eye movements abnormalities in subclinical populations. By doing so, the existence of putative biomarkers and endophenotypes under this population could be argued with a higher degree of evidence.

Availability of data and materials

The datasets generated and/or analysed during the current study will be available in the Open Science Framework.

Abbreviations

EDs:

Eating disorders

AN:

Anorexia nervosa

SWJs:

Square wave jerks

BMI:

Body mass index

IQ:

Intelligence quotient

WHO:

World Health Organization

References

  1. Fitzsimmons-Craft EE, Karam AM, Monterubio GE, Taylor CB, Wilfley DE. Screening for eating disorders on college campuses: a review of the recent literature. Curr Psychiatry Rep. 2019. https://doi.org/10.1007/s11920-019-1093-1.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Santomauro DF, Melen S, Mitchison D, Vos T, Whiteford H, Ferrari AJ. The hidden burden of eating disorders: an extension of estimates from the Global Burden of Disease Study 2019. The Lancet Psychiatry. 2021. https://doi.org/10.1016/S2215-0366(21)00040-7.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mond J, Mitchison D, Latner J, Hay P, Owen C, Rodgers B. Quality of life impairment associated with body dissatisfaction in a general population sample of women. BMC Public Health. 2013;13(1):1. https://doi.org/10.1186/1471-2458-13-920.

    Article  Google Scholar 

  4. Samnaliev M, Noh HLA, Sonneville KR, Austin SB. The economic burden of eating disorders and related mental health comorbidities: an exploratory analysis using the U.S. Medical Expenditures Panel Survey. Prev Med Rep. 2015;2:32–4. https://doi.org/10.1016/j.pmedr.2014.12.002.

    Article  PubMed  Google Scholar 

  5. Fichter MM, Quadflieg N. Mortality in eating disorders—results of a large prospective clinical longitudinal study. Int J Eat Disord. 2016;49:391–401. https://doi.org/10.1002/eat.22501.

    Article  PubMed  Google Scholar 

  6. Streatfeild J, Hickson J, Austin SB, Hutcheson R, Kandel JS, Lampert JG, et al. Social and economic cost of eating disorders in the United States: evidence to inform policy action. Int J Eat Disord. 2021;54:851–68. https://doi.org/10.1002/eat.23486.

    Article  PubMed  Google Scholar 

  7. Touchette E, Henegar A, Godart NT, Pryor L, Falissard B, Tremblay RE, et al. Subclinical eating disorders and their comorbidity with mood and anxiety disorders in adolescent girls. Psychiatry Res. 2011;185(1–2):185–92. https://doi.org/10.1016/j.psychres.2010.04.005.

    Article  PubMed  Google Scholar 

  8. Ghaderi A. Does individualization matter? A randomized trial of standardized (focused) versus individualized (broad) cognitive behavior therapy for bulimia nervosa. Behav Res Ther. 2006;44:273–88. https://doi.org/10.1016/j.brat.2005.02.004.

    Article  PubMed  Google Scholar 

  9. Nina K, France H, Anne S, Huas C, Nathalie G. Early detection of eating disorders : a scoping review. Eat Weight Disord Stud Anorexia Bulim Obes. 2021. https://doi.org/10.1007/s40519-021-01164-x.

    Article  Google Scholar 

  10. Stice E, Becker CB, Yokum S. Eating disorder prevention: current evidence-base and future directions. Int J Eat Disord. 2013;46:478–85. https://doi.org/10.1002/eat.22105.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Watson HJ, Joyce T, French E, Willan V, Kane RT, Tanner-Smith EE, et al. Prevention of eating disorders: a systematic review of randomized, controlled trials. Int J Eat Disord. 2016;49:833–62. https://doi.org/10.1002/eat.22577.

    Article  PubMed  Google Scholar 

  12. Kästner D, Weigel A, Buchholz I, Voderholzer U, Löwe B, Gumz A. Facilitators and barriers in anorexia nervosa treatment initiation: a qualitative study on the perspectives of patients, carers and professionals. J Eat Disord. 2021;9:1–11. https://doi.org/10.1186/s40337-021-00381-0.

    Article  Google Scholar 

  13. Schaumberg K, Welch E, Breithaupt L, Hübel C, Baker JH, Munn-chernoff MA, et al. The science behind the academy for eating disorders ’ nine truths about eating disorders. Eur Eat Disord Rev. 2017;25:432–50. https://doi.org/10.1002/erv.2553.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ali K, Farrer L, Fassnacht DB, Gulliver A, Bauer S, Griffiths KM. Perceived barriers and facilitators towards help-seeking for eating disorders: a systematic review. Int J Eat Disord. 2017;50:9–21. https://doi.org/10.1002/eat.22598.

    Article  PubMed  Google Scholar 

  15. Fursland A, Watson HJ. Eating disorders: a hidden phenomenon in outpatient mental health? Int J Eat Disord. 2014;47:422–5. https://doi.org/10.1002/eat.22205.

    Article  PubMed  Google Scholar 

  16. Kazdin AE, Fitzsimmons-Craft EE, Wilfley DE, Haven N. Addressing critical gaps in the treatment of eating disorders. Int J Eat Disord. 2018;50:170–89. https://doi.org/10.1002/eat.22670.

    Article  Google Scholar 

  17. Lopez C, Roberts M, Treasure J. Biomarkers and endophenotypes in eating disorders. In: Ritsner MS, editor. The handbook of neuropsychiatric biomarkers, endophenotypes and genes. Dordrecht: Springer; 2009. p. 227–37.

    Chapter  Google Scholar 

  18. Malcolm A, Phillipou A. Current directions in biomarkers and endophenotypes for anorexia nervosa: a scoping review. J Psychiatr Res. 2021;137(March):303–10. https://doi.org/10.1016/j.jpsychires.2021.02.063.

    Article  PubMed  Google Scholar 

  19. Ritsner MS, Gottesman II. Where do we stand in the quest for neuropsychiatric biomarkers and endophenotypes and what next. In: Ritsner MS, editor. The handbook of neuropsychiatric biomarkers, endophenotypes and genes. Dordrecht: Springer; 2009. p. 3–23.

    Chapter  Google Scholar 

  20. Beauchaine TP. Role of biomarkers and endophenotypes in prevention and treatment of psychopathological disorders. Biomark Med. 2009;3(1):1–3. https://doi.org/10.2217/17520363.3.1.1.

  21. Peterson BS. Editorial: biomarkers in precision medicine for mental illnesses. J Child Psychol Psychiatry Allied Discip. 2020;61:1279–81. https://doi.org/10.1111/jcpp.13357.

    Article  Google Scholar 

  22. Watson HJ, Yilmaz Z, Thornton LM, Hübel C, Coleman JRI, Gaspar HA, et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat Genet. 2019;51(8):1207–14. https://doi.org/10.1038/s41588-019-0439-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Malighetti C, Gaudio S, Di Lernia D, Matamala-Gomez M, Riva G. Altered inner body perception in anorexia and bulimia nervosa: a systematic review. PsyArXiv. 2020. https://doi.org/10.31234/osf.io/2x4em.

  24. Peyser D, Scolnick B, Hildebrandt T, Taylor JA. Heart rate variability as a biomarker for anorexia nervosa: a review. Eur Eat Disord Rev. 2020;29(1):20–31. https://doi.org/10.1002/erv.2791.

    Article  PubMed  Google Scholar 

  25. Keegan E, Tchanturia K, Wade TD. Central coherence and set-shifting between nonunderweight eating disorders and anorexia nervosa: a systematic review and meta-analysis. Int J Eat Disord. 2021;54(3):229–43.

    Article  Google Scholar 

  26. Cornelissen KK, Cornelissen PL, Hancock PJB, Tovée MJ. Fixation patterns, not clinical diagnosis, predict body size over-estimation in eating disordered women and healthy controls. Int J Eat Disord. 2016;49(5):507–18. https://doi.org/10.1002/eat.22505.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Jiang MYW, Vartanian LR. A review of existing measures of attentional biases in body image and eating disorders research. Aust J Psychol. 2018;70(1):3–17. https://doi.org/10.1111/ajpy.12161.

    Article  Google Scholar 

  28. Kerr-Gaffney J, Harrison A, Tchanturia K. Eye-tracking research in eating disorders: a systematic review. Int J Eat Disord. 2019;52(1):3–27. https://doi.org/10.1002/eat.22998.

    Article  Google Scholar 

  29. Bittencourt J, Velasques B, Teixeira S, Basile LF, Salles JI, Nardi AE, Budde H, Cagy M, Piedade R, Ribeiro P. Saccadic eye movement applications for psychiatric disorders. Neuropsychiatr Dis Treat. 2013;9:1393. https://doi.org/10.2147/NDT.S45931.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Rommelse NN, Van der Stigchel S, Sergeant JA. A review on eye movement studies in childhood and adolescent psychiatry. Brain Cogn. 2008;68(3):391–414. https://doi.org/10.1016/j.bandc.2008.08.025.

    Article  PubMed  Google Scholar 

  31. Phillipou A, Rossell SL, Castle DJ, Gurvich C, Abel LA. Square wave jerks and anxiety as distinctive biomarkers for anorexia nervosa. Investig Ophthalmol Vis Sci. 2014;55(12):8366–70. https://doi.org/10.1167/iovs.14-15807.

    Article  Google Scholar 

  32. Phillipou A, Rossell SL, Gurvich C, Castle DJ, Meyer D, Abel LA. A biomarker and endophenotype for anorexia nervosa? Aust N Z J Psychiatry. 2021. https://doi.org/10.1177/00048674211047189.

    Article  PubMed  Google Scholar 

  33. Phillipou A, Rossell SL, Gurvich C, Hughes ME, Castle DJ, Nibbs RG, et al. Saccadic eye movements in Anorexia Nervosa. PLoS ONE. 2016;11(3):1–16. https://doi.org/10.1371/journal.pone.0152338.

    Article  Google Scholar 

  34. Phillipou A, Abel LA, Gurvich C, Castle DJ, Rossell SL. Eye movements in anorexia nervosa: State or trait markers? Int J Eat Disord. 2020;53(10):1678–84. https://doi.org/10.1002/eat.23345.

    Article  PubMed  Google Scholar 

  35. Antoniades C, Ettinger U, Gaymard B, Gilchrist I, Kristjánsson A, Kennard C, et al. An internationally standardised antisaccade protocol. Vis Res. 2013;84:1–5. https://doi.org/10.1016/j.visres.2013.02.007.

    Article  PubMed  Google Scholar 

  36. Peláez-Fernández MA, Labrador FJ, Raich RM. Datos normativos de la versión española del eating disorders examination questionnaire (S-EDE-Q). Psicothema. 2013;25:107–14.

    PubMed  Google Scholar 

  37. Mond JM, Myers TC, Crosby RD, Hay PJ, Rodgers B, Morgan JF, et al. Screening for eating disorders in primary care: EDE-Q versus SCOFF. Behav Res Ther. 2008;46:612–22. https://doi.org/10.1016/j.brat.2008.02.003.

    Article  PubMed  Google Scholar 

  38. Mond JM, Hay PJ, Rodgers B, Owen C, Beumont PJV. Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behav Res Ther. 2004;42(5):551–67.

    Article  Google Scholar 

  39. Del Pino R, Peña J, Ibarretxe-Bilbao N, Schretlen DJ, Ojeda N. Demographically calibrated norms for two premorbid intelligence measures: the word accentuation test and pseudo-words reading subtest. Front Psychol. 2018;9:1–11. https://doi.org/10.3389/fpsyg.2018.01950.

    Article  Google Scholar 

  40. Spielberger CD, Gorsuch RL, Lushene RE. Manual STAI, Cuestionario de ansiedad estado rasgo. Madrid: TEA Ediciones; 1982.

    Google Scholar 

  41. Cohen J. A power primer. Psychol Bull. 1992;112(1):155. https://doi.org/10.1037//0033-2909.112.1.155.

    Article  Google Scholar 

  42. Dunn DS, Smith RA, Beins B. Best practices in teaching statistics and research methods in the behavioral sciences [with CD-ROM]. Lawrence Erlbaum Associates (Bks). 2007.

Download references

Acknowledgements

Not applicable.

Funding

This publication is supported by the project grant “MAGIC outFIT” I+D+i /PID2019-105579RB-I00, funded by the Spanish Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033/, where AT and MS are Principal Investigators. SN is supported by the FPU program from the Spanish Ministerio de Educación, Cultura y Deporte (FPU20/00089). LC acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538.

Author information

Authors and Affiliations

  1. Department of Psychology, Universidad Loyola Andalucía, Sevilla, Spain

    Sergio Navas-León, Milagrosa Sánchez-Martín & Luis Morales

  2. DEI Interactive Systems Group, Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, Spain

    Ana Tajadura-Jiménez

  3. UCL Interaction Centre (UCLIC), University College London, University of London, London, UK

    Ana Tajadura-Jiménez & Lize De Coster

  4. Department of Psychology, Universidad de Sevilla, Sevilla, Spain

    Mercedes Borda-Más

Authors
  1. Sergio Navas-León
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milagrosa Sánchez-Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana Tajadura-Jiménez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lize De Coster
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mercedes Borda-Más
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luis Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. Conceptualization: SN, LM, MS, AT; Methodology: SN, LM; Writing–Original Draft Preparation: SN; Writing, Review and Editing: LM, MS, AT, LD, MB; Supervision: LM; Funding Acquisition: AT, MS. LM is the chief investigator on this study. MS, AT, LC and MB are co-investigators. This study will contribute towards SN’s Doctor of Philosophy thesis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Luis Morales.

Ethics declarations

Ethics approval and consent to participate

This study has been approved by the Research Ethics Committee of the Autonomous Region of Andalucía (code: 2354-N-20) and Ethics Committee of the University of Loyola Andalucía. The data collection in this study follows the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The ethics commission will be notified in case of any amendments to the study protocol. Prior to participation, written informed consent is obtained from all participants after a comprehensive explanation of the study procedures. The informed consent form can be requested from the corresponding author.

Consent for publication

Not applicable.

Competing interests

The authors have no conflict to declare.

Study status

Data collection will be in February 2022.

Additional information

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

Verify currency and authenticity via CrossMark

Cite this article

Navas-León, S., Sánchez-Martín, M., Tajadura-Jiménez, A. et al. Eye movements and eating disorders: protocol for an exploratory experimental study examining the relationship in young-adult women with subclinical symptomatology. J Eat Disord 10, 47 (2022). https://doi.org/10.1186/s40337-022-00573-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40337-022-00573-2

Keywords

  • Antisaccade
  • Eating disorders
  • Inhibitory control
  • Memory-guided saccade
  • Prosaccade
  • Saccades
  • Subclinical population
  • Square wave jerks
  • Visual memory

Journal of Eating Disorders

ISSN: 2050-2974