Received: 3 August 2020 Accepted: 4 August 2020 DOI: 10.1002/erv.2784 SPECIAL ISSUE COVID Isolation Eating Scale (CIES): Analysis of the impact of confinement in eating disorders and obesity—A collaborative international study Fernando Fernández-Aranda1,2,3 | Lucero Munguía1,3 | 1,4 5 Gemma Mestre-Bach | Trevor Steward | Mikel Etxandi1 | Isabel Baenas1,2 | Roser Granero2,6 | Isabel Sánchez1,2 | Emilio Ortega7,8 | Alba Andreu7 | Violeta L. Moize7,8 | Jose M Fernández-Real2,9 | Francisco J. Tinahones2,10 | Carlos Diegüez2,11 | 2,12 13 14,15 Gema Frühbeck | Daniel Le Grange | Kate Tchanturia | Andreas Karwautz16 | Michael Zeiler16 | Angela Favaro17 | 18,19 19,20 15 Laurence Claes | Koen Luyckx | Ia Shekriladze | 21 Eduardo Serrano-Troncoso | Teresa Rangil22,23 | Maria Eulalia Loran Meler22 | Jose Soriano-Pacheco24,25 | 24,25 26 Mar Carceller-Sindreu | Sara Bujalance-Arguijo | Meritxell Lozano27 | 27 28,29,3 28,29,3 Raquel Linares | Carlota Gudiol | Jordi Carratala | 1 30 Jessica Sanchez-Gonzalez | Paulo PP Machado | Anders Håkansson31,32 | 33 33,34 35 36 Ferenc Túry | Bea Pászthy | Daniel Stein | Hana Papezová | Brigita Bax37 | Mikhail F. Borisenkov 38 | Sergey V. Popov38 | Youl-Ri Kim39 | Michiko Nakazato40 | Nathalie Godart41,42,43 | 44 45 46 Robert van Voren | Tetiana Ilnytska | Jue Chen | Katie Rowlands14 | 14 1,2,3 Janet Treasure | Susana Jiménez-Murcia 1 Department of Psychiatry, Bellvitge University Hospital-IDIBELL, Barcelona, Spain 2 CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain 3 Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain 4 Universidad Internacional de La Rioja, Logroño, La Rioja, Spain 5 Melbourne School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia 6 Department of Psychobiology and Methodology, School of Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain 7 Endocrinology and Nutrition Division, Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain 8 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain 9 Unit of Diabetes, Endocrinology and Nutrition, Hospital de Girona Dr. Josep Trueta and Institut d'Investigació Biomèdica de Girona (IDIBGI), Girona, Spain 10 Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital and Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain 11 Department of Physiology, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela, Spain 12 Metabolic Research Laboratory, Clínica Universidad de Navarra, University of Navarra-IdiSNA, Pamplona, Spain 13 Eating Disorders Program, Department of Psychiatry, University of California, San Francisco, California Eur Eat Disorders Rev. 2020;1–13. wileyonlinelibrary.com/journal/erv © 2020 Eating Disorders Association and John Wiley & Sons Ltd 1 2  FERNANDEZ-ARANDA ET AL. 14 Section of Eating Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK 15 D. Uznadze Institute of Psychology, Ilia State University, Tbilisi, Georgia 16 Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria 17 Department of Neuroscience, University of Padua and Neuroscience Center (PNC), Padua, Italy 18 Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium 19 Faculty of Medicine and Health Sciences, University Antwerp, Antwerp, Belgium 20 UNIBS, University of the Free State, Bloemfontein, South Africa 21 Child and Adolescent Psychiatry and Psychology Department, Hospital Sant Joan de Déu and Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain 22 Department of Psychiatry, Germans Trias i Pujol University Hospital-IGTP, Barcelona, Spain 23 Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain 24 Department of Psychiatry, Hospital de la Santa Creu i Sant Pau e Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain 25 Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), ISCIII, Madrid, Spain 26 Associació Contra l'Anorèxia i la Bulímia (ACAB), Barcelona, Spain 27 FITA Foundation, Barcelona, Spain 28 Infectious Diseases Department, Hospital Universitari Bellvitge, Institut de Investigació Biomedica de Bellvitge (IDIBELL) and Institut Català d'Oncologia-Hospitalet, IDIBELL, Barcelona, Spain 29 REIPI (Spanish Network for Research in Infectious Disease), Instituto de Salud Carlos III, Madrid, Spain 30 Psychotherapy and Psychopathology Research Unit – Psychology Research Center, School of Psychology, University of Minho, Braga, Portugal 31 Department of Clinical Sciences Lund, Psychiatry, Faculty of Medicine, Lund University, Lund, Sweden 32 Gambling Disorder Unit, Malmö Addiction Center, Malmö, Sweden 33 Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary 34 1st Department of Paediatrics, Semmelweis University, Budapest, Hungary 35 Safra Children's Hospital, Chaim Sheba Medical Center, Tel Hashomer, Israel 36 Department of Psychiatry, 1st Medical Faculty of Charles University, Prague, Czech Republic 37 Vilnius University Eating Disorders Center, Vilnius, Lithuania 38 Institute of Physiology Komi Science Center UB RAS, Syktyvkar, Russian Federation 39 Department of Psychiatry, Seoul Paik Hospital, Inje University, Seoul, South Korea 40 International University of Health and Welfare – Psychiatry, Narita, Japan 41 CESP, Univ. Paris-Sud, UVSQ, INSERM U 1178, Université Paris-Saclay [Paris-Saclay University], Villejuif, France 42 Department of Psychiatry, Institut Mutualiste Montsouris, School of Medicine, Université Paris Descartes, Paris, France 43 UFR des Sciences de la Santé Simone Veil (UVSQ), Praticienne Hospitalière, Fondation Santé des Etudiants de France, Paris, France 44 Vytautas Magnus University, Kaunas, Lithuania 45 Institute of Psychiatry of Taras Shevchenko, National University of Kyiv, Kyiv, Ukraine 46 Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China Correspondence Fernando Fernández-Aranda, Department Abstract of Psychiatry, Bellvitge University Confinement during the COVID-19 pandemic is expected to have a serious and Hospital-IDIBELL and CIBEROBN, c/ complex impact on the mental health of patients with an eating disorder FeixaLlarga s/n, 08907 L'Hospitalet de Llobregat, Barcelona, Spain. (ED) and of patients with obesity. The present manuscript has the following Email: ffernandez@bellvitgehospital.cat aims: (1) to analyse the psychometric properties of the COVID Isolation Eating Scale (CIES), (2) to explore changes that occurred due to confinement in eating Funding information Fondo Investigación Sanitario- FIS, Grant/ symptomatology; and (3) to explore the general acceptation of the use of tele- Award Numbers: FIS, INT19/00046, medicine during confinement. The sample comprised 121 participants (87 ED PI17/01167; Ministerio de Economía y Competitividad, Grant/Award Number: patients and 34 patients with obesity) recruited from six different centres. Con- PSI2015-68701-R; Portuguese Foundation firmatory Factor Analyses (CFA) tested the rational-theoretical structure of the  FERNANDEZ-ARANDA ET AL. 3 for Science and Technology grant, Grant/ Award Number: POCI-01-0145-FEDER- CIES. Adequate goodness-of-fit was obtained for the confirmatory factor analy- 028145; Consejo Nacional de Ciencia y sis, and Cronbach alpha values ranged from good to excellent. Regarding the Tecnología; Generalitat de Catalunya; European Regional Development Fund effects of confinement, positive and negative impacts of the confinement depends of the eating disorder subtype. Patients with anorexia nervosa (AN) and with obesity endorsed a positive response to treatment during con- finement, no significant changes were found in bulimia nervosa (BN) patients, whereas Other Specified Feeding or Eating Disorder (OSFED) patients endorsed an increase in eating symptomatology and in psychopathology. Fur- thermore, AN patients expressed the greatest dissatisfaction and accommoda- tion difficulty with remote therapy when compared with the previously provided face-to-face therapy. The present study provides empirical evidence on the psychometric robustness of the CIES tool and shows that a negative confinement impact was associated with ED subtype, whereas OSFED patients showed the highest impairment in eating symptomatology and in psychopathology. KEYWORDS COVID Isolation Eating Scale, COVID-19, eating disorders, obesity 1 | B ACKGROUND Wang, Di et al., 2020), but also mental health deteriora- tion (Pierce, Hope, Ford, et al., 2020) and nutritional and In just a few months, COVID-19 has become a global pan- activity patterns changes (Papandreou, Tsilidis, Arija, demic that has brought numerous challenges to health Aretouli, & Bulló, 2020), have been reported. Healthcare professionals and their patients. To stop the speed of the workers have emerged as a specific population in danger spread of the virus, most governments have chosen to of suffering from psychological distress, depression, anxi- place their populations under confinement, which has ety and insomnia (Inchausti, García-Poveda, Prado- implied radical changes in social interactions and the way Abril, & Sánchez-Reales, 2020; Lai et al., 2020; Rossi work was conducted. The final repercussions of confine- et al., 2020) and populations with a prior mental health ment are still under investigation, though it is expected to condition may experience an increase in their symptom- have a significant impact on mental health for many. atology related to changes in the delivery of their usual Considering other health and humanitarian crises, such treatment (De Girolamo et al., 2020; Fernández-Aranda as the Ebola and H1N1 influenza epidemics, depression et al., 2020). (Hewlett & Hewlett, 2005; Kinsman, 2012), isolation, fear of It is important to consider that stay-at-home and being infected, posttraumatic stress (Li et al., 2018; Raven, social-distancing mandates have increased the frequency Wurie, & Witter, 2018; Main, 2011) and health anxiety of several risk behaviours. The time available for engag- (McDonnell, Nelson, & Schunk, 2012; Mihashi, 2009) have ing in potentially addictive behaviours, such as online been some of the negative effects on mental health reported gambling, gaming, and pornography (Håkansson, by the general population, health workers and survivors of Fernández-Aranda, Menchón, Potenza, & Jiménez- past epidemics (Wu, 2009). Confinement has been found to Murcia, 2020; Király et al., 2020; Mestre-Bach, Blycker, & increase the incidence of negative states such as irritability, Potenza, 2020) has increased and these behaviours, along insomnia, anger, depression symptoms, amongst others with the use of alcohol or other substances, may be used (Mihashi, 2009). as coping strategies to avoid negative emotional states In response to the COVID-19 pandemic, several stud- (Håkansson et al., 2020; King, Delfabbro, Billieux, & ies have already been carried out to explore the psycho- Potenza, 2020; Király et al., 2020; Sun et al., 2020). logical effects of the pandemic and confinement. In the Although these behaviours do not constitute a risk for all general population, anxiety, depression, stress, worry individuals, the current situation may increase the risk about being infected, worry about family members being for onset, maintenance, and relapse, especially for the infected, worry about financial stress stability, post- most vulnerable individuals (Columb, Hussain, & traumatic stress (Cao et al., 2020; Wang, Pan, et al., 2020; O'Gara, 2020; Marsden et al., 2020), such as patients with 4  FERNANDEZ-ARANDA ET AL. an eating disorders and patients with obesity (Baenas concerns, anxiety and depressive symptomatology and et al., 2020; Graell et al., 2020; Cornejo-Pareja et al., 2020), addictive behaviours); (3) to explore the potential difficul- as well as patients with less favourable family environ- ties for participants in using telemedicine during confine- ments (Vintró-Alcaraz et al., 2018). ment when usual health care was interrupted. Based on It has been hypothesised that the COVID-19 pandemic previous literature (Baenas et al., 2020; Fernández-Aranda may exacerbate the risk factors for overeating and et al., 2020; Temorshuizen et al., 2020; Mallorqui-Bague, unhealthy weight gain, especially in vulnerable populations 2018), we hypothetised that eating disorder subtype and such as children and individuals with an eating disorder obesity would achieve a moderator/interaction role in the (ED) and obesity (Rundle, Park, Herbstman, Kinsey, & changes occurred during the confinement: behavioural Wang, 2020, Graell et al., 2020). ED patients have already and emotional reactions, eating patterns-weight, anxiety reported increased worries about the risk of being infected and affective symptoms should be different depending on with COVID-19, disruptions in their work and treatment, the ED subtype previous to the COVID-19 pandemic. the worsening of their ED symptoms, as well as heightened anxiety and stress (Fernández-Aranda et al., 2020). ED patients and individuals with obesity constitute vulnerable 2 | METHODS populations who require targeted approaches (Cornejo- Pareja et al., 2020). As an immediate emergency measure to The data collection of this study was conducted between address this situation, different telemedicine tools during June and July 2020. Our study sample comprised 121 par- the pandemic have been described within this population ticipants (87 ED patients and 34 patients with obesity), rec- (Cooper et al., 2020; Smith, Ostinelli, Macdonald, & ruited from six different child-adolescent and adult units Cipriani, 2020), however there is a lack of studies looking at from the Barcelona (Spain) region. These centres are rep- their effectiveness and acceptability by users. resentative of the public and private sectors of ED treat- New psychometric instruments have been developed ment services in Barcelona. Participants were involved in to measure different features related to the Covid-19 pan- ED treatment before the outbreak of COVID-19 and were demic, such as the contextual situation lived during the asked by therapists from each centre if they were willing confinement, the changes occurring in the individuals' to voluntarily complete the study questionnaire. The ED life and the impact on their health state. Some of these sample was diagnosed according to DSM-5 criteria (Ameri- new scales are: the Fear of COVID-19 Scale (FCV-19S; can Psychiatric Association, 2013) by means of a semi- (Ahorsu et al., 2020; Sakib et al., 2020; Soraci et al., 2020), structured interview. Obese participants were bariatric sur- the COVID-19 Anxiety Scale (CAS; (Chandu, Pachava, gery candidates who were recruited at the Endocrinologi- Vadapalli, & Marella, 2020), the COVID-19-PTSD (Forte, cal Unit at the Clinic Hospital of Barcelona. Favieri, Tambelli, & Casagrande, 2020), the CoViD-19 The sex distribution was 104 women (86.0%) and Peritraumatic Distress Index (CPDI; (Costantini & 17 men (14.0%). The age range was broad (mean = 33.7, Mazzotti, 2020), the Coronavirus Impact Scale (CIS; SD = 15.8), ranging from 13 to 77. The distribution of the Kaufman & Stoddard, 2020) and the COVID Stress Scales ED diagnoses was as follows: 55 cases met criteria for (CSS; (Taylor et al., 2020), including COVID danger and anorexia nervosa (AN), 18 for bulimia nervosa (BN), contamination fears, COVID fears about economic conse- 14 were diagnosed with an Other Specified Feeding or Eat- quences, COVID xenophobia, COVID compulsive check- ing Disorder (OSFED). Table S1 (supplementary material) ing and reassurance seeking, and COVID traumatic stress contains the distribution of sex and age within the groups symptoms. However, to the best of our knowledge, no defined by diagnosis. No statistical differences by gender tool has yet been developed and validated to assess eating were obtained (χ 2 = 4.06, df = 3, p = .256). However, chro- disorder symptomatology and treatment-related aspects nological age was not equally distributed between groups in patients with an ED and obesity. (F = 30.08, df = 3/117, p < .001): obese patients were the oldest (mean = 48.8, SD = 12.9), followed by OSFED (mean = 36.9, SD = 16.4), BN (mean = 31.5, SD = 10.1) 1.1 | Goals and AN (mean = 24.2, SD = 10.7) patients. The aims of this exploratory study were threefold: (1) to analyse the psychometric properties of the COVID Isola- 2.1 | Assessment tion Eating Scale (CIES), a newly created scale for measur- ing the impact of confinement; (2) to explore changes that The COVID Isolation Eating Scale (CIES) is a self- occurred due to confinement (eating symptomatology and report questionnaire that assesses the impact of con- weight, attitudes and dysfunctional emotions, worries and finement in patients with an eating disorder and/or  FERNANDEZ-ARANDA ET AL. 5 obesity. This measure has been designed to be 2.4 | Statistical analysis answered both with paper and pencil or online. The scale was generated by experts in the ED area through a Statistical analyses were carried out with Stata16 for Win- theoretical-rationale procedure based on the next steps: dows (Stata Press, 2019). Based on the theoretical-rational (a) identification of the domains (constructs/areas) method used to develop the CIES, confirmatory factor ana- endeavouring to measure; (b) deciding the number of fac- lyses (CFA) was used to verify the factor structure of the tors/dimensions and their position into a meaningful order questionnaire. CFA was implemented through structural and format into the questionnaire; and (c) generating the equation modelling, defining the maximum likelihood, items, as well as their distribution within each factor/ and considering adequate goodness of fit for Root Mean dimension. After the development of the item pool, inde- squared Error of Approximation RMSEA < 0.08, Compar- pendent expert judges assessed whether the items ade- ative Fit Index CFI > 0.90, Tucker-Lewis Index TLI > 0.90 quately represented the domains of interest. The final and Standardised Root Mean Squared Residuals CIES, besides exploring patient sociodemographic infor- SRMR<0.10 (Barrett, 2007). mation, the scale was divided into four sections: An initial CFA assumed the existence of five latent (I) Circumstances during confinement (eight items theoretical factors: Factor 1 was defined by 10 items characterising the circumstances of confinement). measuring eating related symptoms (such as concerns (II) Effects of confinement on eating disorder symptoms about weight, attempts to reduce eating quantities and (13 items relating to the eating symptomatology of meals, presence of bingeing/purging, use of laxatives/ anorexia nervosa, bulimia nervosa, binge eating disorder diuretics, and exercise or other activities to control and other specified feeding or eating disorder, according to weight); Factor 2 was defined by 10 items measuring the the DSM-5 and comorbid physical and psychiatric disor- effects of confinement on the eating related style (such ders. (III) Behavioural and psychopathological impact of as being unable to stop eating during the day, or confinement (34 items covering the effects of confinement between meals or certain foods); Factor 3 was defined on eating patterns, attitudes and habits, anxiety-depression by 11 items assessing anxiety and depressive symptoms symptomatology, emotion dysregulation, and other (sleep problems, upsetting thoughts, loneliness, limited symptomatology associated with substance use disorders social contact, health concerns related to COVID-19, or and non-substance related addictive behaviours. sexual problems); Factor 4 was defined by five items (IV) Evaluation of remote interventions (13 items related to emotion regulation (emotional control/aware- assessing the feasibility, acceptance and satisfaction). The ness, anger/shame, aggressive behaviours and irritabil- three last scales are answered on a five-point Likert scale. ity); and Factor 5 was defined by 10 items to evaluate Sections 2 and 3 consider two moments of time: before telemedicine (adequacy, motivation, satisfaction and confinement and now, the present (for the English version usefulness). Factors F1, F2, F3 and F4 were also devel- of CIES Scale and translation in 18 additional languages oped to allow for assessment prior and following see Supplementary information online). COVID-19 confinement. However, it was not possible to test the initial single CFA for the five-dimension model, because the sample size was too small to verify this com- 2.2 | Additional information plex structure (fit was not obtained). According to the sample size, separate models were tested for each Socio-demographic/clinical information, including age, dimension, programming within the same model the affiliation to the patient with an ED, and marital status, measures for the pre- and the post-confinement to but also variables related to COVID and duration and account for the expected correlations between both type of the confinement was obtained along with the periods. Figure S1 (supplementary material) contains CIES questionnaire. the path diagrams for the individual constructs tested in this manuscript. The pre-post changes in the quantitative variables of 2.3 | Procedure the study were based on paired-sample t-tests for interval scaled variables, and on the McNemar test for categorical The present study was approved by the Clinical measures. The comparison between groups defined by Research Ethics Committee of Bellvitge University Hos- the diagnosis (AN, BN, OSFED and obese) was based on pital (PR239/20). Written and signed informed consent analysis of variance analyses (ANOVAs), with post-hoc was obtained from all participants before taking part in pairwise comparisons estimated via Bonferroni's proce- the study. dure. For all these analyses, significant tests were 6  FERNANDEZ-ARANDA ET AL. complemented with the estimation of the effect size supplementary material, contains the frequency distribu- through Cohen's-d coefficient, considering null effect for tion within the groups). jdj < 0.20, low-poor for jdj > 0.20, mild-medium for jdj > 0.50 and large-high for jdj > 0.80) (Cohen, 1988; Kelley & Preacher, 2012; Granero, 2020). 3.2 | Psychometric properties of the CIES 3 | R E SUL T S The factorial analyses confirmed the rational-theoretical structure for the CIES. All the items obtained significant 3.1 | Characteristics of the participants factor loadings for their specific factor (Table S2, supple- during the COVID-19 confinement mentary material, includes the standardised coefficients, standard errors, p-values and 95% confidence intervals). During the lockdown period, 30 (24.8%) participants lived The upper block of Table 1 includes the fit indices of the alone, and 32 (26.4%) lived with one or two people at CFAs and the internal consistency coefficients of the fac- home, 29 (24.0%) lived with three people, and the tors/scales: adequate goodness-of-fit was obtained, and remaining 30 participants (24.8%) lived with more than Cronbach alpha coefficients ranged from good (α = 0.81, three people. Most participants were not infected by for the factor F1-impacts at pre-confinement) to excellent COVID-19 (n = 115, 95.0%) and most had no infections (α = 0.92, for F2-changes in eating at pre-confinement). amongst people close to them (n = 94, 77.7%). Most par- The bottom block of Table 1 contains the correlation ticipants did not report a caring role during the confine- matrix for the factor scores. Relevant positive correlations ment (n = 84, 69.4%). 58% (70 participants) continued to (with jRj coefficients above 0.24) were obtained for the fac- work and 69% (83 participants) had no financial loss. No tors F1, F2, F3 and F4, for both at pre-confinement and statistical differences between groups were observed for after the confinement, with the only exception of F1-post these contextual variables during lockdown (Table S1, which did not correlate with F3-pre and F4-pre. However, TABLE 1 Internal consistency, fitting indexes in the CFA and correlation-matrix for CIES scores Fitting indices Cronbach-alpha Pre Post RMSEA CFI TLI SRMR F1. Impact on ED symptoms .805 .806 .061 .937 .917 .088 F2. Changes - eating .922 .910 .072 .941 .924 .080 F3. Changes- anxiety/depression .865 .857 .093 .961 .950 .089 F4. Changes- emotion regulation .826 .836 .012 .999 .998 .042 F5. Evaluation telemedicine .939 .033 .983 .964 .036 Correlation-matrix F2pre F3pre F4pre F1post F2post F3post F4post F5 F1. Pre-impact on ED symptoms .675 .368 .401 .547 .480 .278 .256 .006 F2. Pre-changes—eating — .489 .498 .451 .657 .341 .338 .160 F3. Pre-changes—anxiety/depression — .736 .144 .345 .729 .549 −.032 F4. Pre-changes—emotional regulation — .154 .281 .553 .765 −.145 F1. Post-impact on ED symptoms — .656 .365 .331 −.120 F2. Post-changes—eating — .528 .444 −.008 F3. Post-changes—anxiety/depression — .746 −.208 F4. Post-changes—emotion regulation — −.183 F5. Evaluation telemedicine Abbreviations: CFI, comparative fit index; RMSEA, root mean squared error of approximation; SRMR, standardised root mean squared resid- ual; TLI, Tucker-Lewis Index. Note: Bold values indicate correlation coefficient with an effect size in the moderate (jRj > 0.24) to high (jRj > 0.37) range.  FERNANDEZ-ARANDA ET AL. 7 TABLE 2 Changes during confinement stratified by diagnostic subtype Pre Post Anorexia (n = 55) Mean SD Mean SD p jdj Weight (kg) 49.16 6.91 50.27 6.52 .056 0.17 2 BMI (kg/m ) 18.25 2.25 18.67 2.14 .058 0.19 CIES-F1 impact ED symptoms 11.87 6.79 9.40 5.61 .015 0.40 CIES-F2 changes—eating 8.76 9.61 6.11 6.94 .023 0.32 CIES-F3 changes—anxiety-depression 18.29 9.69 17.80 9.64 .662 0.05 CIES-F4 changes—emotion regulation 9.47 4.63 8.33 4.86 .046 0.24 n % n % P jdj Tobacco 15 27.3% 15 27.3% 1.00 0.00 Alcohol 6 10.9% 5 9.1% 1.00 0.06 Other illegal drugs 4 7.3% 3 5.5% 1.00 0.07 Addictive behaviours 25 45.5% 31 56.4% .180 0.22 Bulimia (n = 18) Mean SD Mean SD p jdj Weight (kg) 65.26 10.93 66.22 11.81 .230 0.08 2 BMI (kg/m ) 24.13 3.75 24.47 4.02 .243 0.09 CIES-F1 impact ED symptoms 15.72 6.31 14.94 6.04 .617 0.13 CIES-F2 changes—eating 19.50 9.75 17.72 7.51 .306 0.20 CIES-F3 changes—anxiety-depression 18.61 8.89 20.22 7.12 .125 0.20 CIES-F4 changes—emotion regulation 9.33 4.19 9.56 4.26 .521 0.05 n % n % p jdj Tobacco 5 27.8% 5 27.8% 1.00 0.00 Alcohol 6 33.3% 6 33.3% 1.00 0.00 Other illegal drugs 1 5.6% 1 5.6% 1.00 0.00 Addictive behaviours 12 66.7% 13 72.2% 1.00 0.12 OSFED (n = 14) Mean SD Mean SD p jdj Weight (kg) 63.07 8.99 62.91 8.48 .926 0.02 2 BMI (kg/m ) 23.40 3.38 23.30 2.84 .886 0.03 CIES-F1 impact ED symptoms 12.29 6.60 13.14 8.37 .602 0.11 CIES-F2 changes—eating 11.57 10.80 13.36 12.68 .140 0.15 CIES-F3 changes—anxiety-depression 14.07 10.23 18.21 10.54 .071 0.40 CIES-F4 changes—emotion regulation 5.14 4.28 6.36 4.80 .066 0.27 n % n % p jdj Tobacco 6 42.9% 6 42.9% 1.00 0.00 Alcohol 3 21.4% 4 28.6% 1.00 0.17 Other illegal drugs 0 0.0% 0 0.0% 1.00 0.00 Addictive behaviours 7 50.0% 9 64.3% .500 0.29 OBESE (n = 34) Mean SD Mean SD p jdj Weight (kg) 109.62 20.51 106.46 19.61 .035 0.16 2 BMI (kg/m ) 41.15 7.37 39.94 6.86 .037 0.17 CIES-F1 impact ED symptoms 13.29 6.06 13.56 6.21 .645 0.04 CIES-F2 changes—eating 14.00 10.40 9.82 9.40 .017 0.42 CIES-F3 changes—anxiety-depression 14.29 9.80 14.00 10.33 .765 0.03 CIES-F4 changes—emotion regulation 4.65 4.69 4.06 4.36 .280 0.13 (Continues) 8  FERNANDEZ-ARANDA ET AL. TABLE 2 (Continued) Pre Post Anorexia (n = 55) Mean SD Mean SD p jdj n % n % p jdj Tobacco 5 14.7% 4 11.8% 1.00 0.09 Alcohol 6 17.6% 4 11.8% .500 0.17 Other illegal drugs 1 2.9% 0 0.0% 1.00 0.34 Addictive behaviours 23 67.6% 23 67.6% 1.00 0.00 Abbreviations: OSFED, other specified feeding eating disorders. Note: Bold values indicate significant comparison. Figure 1 displays the means of the pre-post changes in the main variables of the study: weight, BMI, and CIES factors F1 to F4 (Table 3 contains the ANOVA comparing the changes between the groups). The obese patients were characterised by the highest change in weight (with a significant decrease of 3.2 kg, compared to an increase of nearly 1 kg for AN and BN patients and 0 kg for OSFED patients). Patients with AN also improved on other CIES factors. Although for the OSFED patients there were no pre- post changes in weight and BMI, all other CIES factors deteriorated. 3.4 | Evaluation of telemedicine FIGURE 1 Differences (post-pre changes) in weight, BMI and Table 4 contains information about the evaluation of CIES factors amongst the groups alternatives to face to face therapy. Patients with AN, found these alternatives the least acceptable whereas peo- ple with obesity and OSFED patients were more satisfied no relevant correlations emerged between F5 (evaluation with these alternatives. of telemedicine) and the other factors. 4 | DISCUSSION 3.3 | Impact of the COVID-19 on eating related behaviours This study was prompted by the need to assess the effects of confinement due to the COVID-19 pandemic in vulnerable Table 2 contains the main changes in weight, BMI, CIES patients with ED and obesity (Cornejo-Pareja et al., 2020; factors F1 to F4, the consumption of substances and the Fernández-Aranda et al., 2020; Todisco & Donini, 2020). presence of other addictive behaviours. Separate analyses The three main aims were to first establish the psychomet- were performed according to the diagnostic subtype, since ric properties of the specifically developed assessment mea- it was hypothesised that the diagnosis could influence pre- sure (CIES), second to measure the changes in eating and post differences. Within AN patients, significant decreases general symptomatology, and thirdly to establish the accept- after the confinement due to COVID-19 were found for the ability of remote interventions. factors F1 (impact on eating symptoms), F2 (changes in The CFA confirmed the rational-theoretical structure eating style) and F4 (changes in emotion regulation). Obese of CIES into five-factors (impact on eating symptoms, patients also reported a significant decrease in weight, BMI changes in eating style, changes in anxiety/depression and changes in the eating style. However, no significant symptoms, changes in emotion regulation and evaluation changes emerged for the BN and OSFED patients. of telemedicine), obtaining adequate goodness-of-fit.  FERNANDEZ-ARANDA ET AL. 9 TABLE 3 Comparison of the differences (post-pre changes) for the weight and the CIES factor scores Anorexia (AN) Bulimia (BN) OSFED Obesity (OBES) Significant N = 55 N = 18 N = 14 N = 34 Pairwise Mean SD Mean SD Mean SD Mean SD Comparisons Weight (kg) 1.11 4.23 0.96 3.27 −0.16 6.48 −3.16 8.38 OBES ≠ (AN=BN=OSFED) BMI (kg/m )2 0.43 1.63 0.35 1.22 −0.10 2.51 −1.21 3.24 OBES ≠ (AN=BN=OSFED) CIES-F1 impact ED symptoms −2.47 7.31 −0.78 6.48 0.86 6.00 0.26 3.31 AN ≠ (BN=OSFED = OBES) CIES-F2 changes—eating −2.65 8.38 −1.78 7.14 1.79 4.25 −4.18 9.70 OSFED ≠ (AN=BN) ≠ OBES CIES-F3 changes—anx-dep. −0.49 8.29 1.61 4.23 4.14 7.89 −0.29 5.70 OSFED ≠ (AN=BN=OBES) CIES-F4 changes—emotion −1.15 4.15 0.22 1.44 1.21 2.26 −0.59 3.12 OSFED ≠ (AN=BN=OBES) Abbreviations: OSFED, other specified feeding eating disorder. T A B L E 4 Comparison of the CIES Anorexia (AN) Bulimia (BN) OSFED Obesity F5 evaluation of telemedicine between the groups n = 55 n = 18 n = 14 n = 34 Mean SD Mean SD Mean SD Mean SD 25.58 10.40 28.61 6.46 29.50 9.53 28.97 7.25 Pairwise comparisons p jdj Anorexia vs bulimia .218 0.35 Anorexia vs OSFED .148 0.39 Anorexia vs obesity .047* 0.38 Bulimia vs OSFED .782 0.11 Bulimia vs obesity .891 0.05 OSFED vs obesity .853 0.06 Abbreviation: OSFED, other specified feeding eating disorder. Other attributes of validity of this tool should be analysed dysregulation after confinement. Factors such as youn- in future research (such as the convergent, discriminant ger age and how participants were dealing with exter- and predictive validity, assessing the relationship nal control environments, might be associated between the CIES scores with other external measures (Darrow, Accurso, Nauman, Goldschmidt, & Le related with eating behaviours). Grange, 2017; Treasure, Gavan, Todd, & The impact of confinement was mixed and varied by Schmidt, 2003). Interestingly, OSFED patients reported diagnosis. In contrast to other studies (Rodgers et al., 2020), most adverse effects on eating behaviours and anxiety- the disordered eating improved during the COVID-19 pan- depressive symptoms after confinement. As reported pre- demic. Patients with obesity had a significant decrease both viously in the literature (Riesco et al., 2018), clinicians in weight/BMI and in eating psychopathology. This may be may need to pay special attention to subthreshold cases, due to the fact that candidates for bariatric surgery were who may be more sensitive to adverse environments receiving ongoing nutritional management, and were (Claes, Boekaerts, Verschueren, Boukaert, & selected based on minimal psychopathology. Also the find- Luyckx, 2019; Strand, von Hausswolff-Juhlin, Fredlund, & ings from AN and BN patients do not align with previous Lager, 2019; Vanzhula, Calebs, Fewell, & Levinson, 2019) studies which found a worsening in dietary restriction Finally, although most patients reported being sat- (Temorshuizen et al., 2020) and heightened psychologi- isfied with the remote treatment used during the pan- cal distress (Clark Bryan et al., 2020; Pierce demic, in concordance with previous studies et al., 2020). In the present study, AN and BN partici- (Linardon, Shatte, Tepper, & Fuller-Tyszkiewicz, 2020), pants, did not present significant changes in weight/ patients with AN were the least comfortable with the BMI. In this study people with AN, reported a signifi- change. Other studies have reported on the distress cant decrease in ED symptomatology and in emotion caused by premature discharge from inpatient care 10  FERNANDEZ-ARANDA ET AL. with a lack of preparation (Clark Bryan et al., 2020). Research Committee of the University Hospital of This sensitivity to change may be related with specific Bellvitge (HUB; Barcelona, Spain) 2019-2020. The temperamental traits, such as managing uncertainty funders had no role in the study design, data collection and may be specific therapy targets in future interven- and analysis, decision to publish, or preparation of the tions (Baenas et al., 2020; Brown et al., 2017; manuscript. Kannarkat, Smith, & McLeod-Bryant, 2020). ORCID Fernando Fernández-Aranda https://orcid.org/0000- 4.1 | Limitations 0002-2968-9898 Lucero Munguía https://orcid.org/0000-0002-9751- Despite the novelty of this study, several limitations 810X should be considered in the present study: memory bias Gemma Mestre-Bach https://orcid.org/0000-0001-5345- due to the retrospective nature of the assessment, limited 0484 sample size and heterogeneity of the patient groups Trevor Steward https://orcid.org/0000-0003-3116-8175 analysed. Isabel Baenas https://orcid.org/0000-0001-7415-0616 Roser Granero https://orcid.org/0000-0001-6308-3198 Isabel Sánchez https://orcid.org/0000-0001-5874-8204 5 | C ON C L U S I ON S Emilio Ortega https://orcid.org/0000-0002-2217-8905 Jose M Fernández-Real https://orcid.org/0000-0002- The present study provides empirical evidence on the 7442-9323 psychometric robustness of the CIES tool. The effects Francisco J. Tinahones https://orcid.org/0000-0001- of confinement, varied by ED subtype. Patients with 6871-4403 AN and those with obesity endorsed a positive Carlos Diegüez https://orcid.org/0000-0002-0919-4337 response whereas OSFED patients showed the highest Gema Frühbeck https://orcid.org/0000-0002-8305-7154 deterioration in eating symptomatology and in psycho- Daniel Le Grange https://orcid.org/0000-0001-7293- pathology. Furthermore, AN patients expressed the 9496 greatest dissatisfaction with adjustment to remote Kate Tchanturia https://orcid.org/0000-0001-8988-3265 therapy. Andreas Karwautz https://orcid.org/0000-0001-9053- The administration of the CIES in populations with 998X ED and obesity may inform clinicians about how to pre- Michael Zeiler https://orcid.org/0000-0002-7087-6134 pare for adjustments to future environmental challenges. Angela Favaro https://orcid.org/0000-0002-6540-5194 Further studies may need to be conducted in different Laurence Claes https://orcid.org/0000-0002-2287-3158 countries with larger samples in order to be able to gener- Koen Luyckx https://orcid.org/0000-0001-8862-5598 alise these results. Eduardo Serrano-Troncoso https://orcid.org/0000-0002- 4935-7348 ACK NO WLE DGE MEN TS Jose Soriano-Pacheco https://orcid.org/0000-0002-5995- We thank CERCA Programme/Generalitat de Catalu- 6977 nya for institutional support. This manuscript and Carlota Gudiol https://orcid.org/0000-0003-3095-4422 research was supported by grants from the Ministerio Jordi Carratala https://orcid.org/0000-0003-3209-2563 de Economía y Competitividad (PSI2015-68701-R), Paulo PP Machado https://orcid.org/0000-0003-4277- Instituto de Salud Carlos III (ISCIII) ( INT19/00046 9203 and PI17/01167) and co-funded by FEDER funds Anders Håkansson https://orcid.org/0000-0002-5800- /European Regional Development Fund (ERDF), a 8975 way to build Europe. CIBERobn, CIBERsam and Hana Papezová https://orcid.org/0000-0001-8145-3295 CIBERDEM are all initiatives of ISCIII. GMB is Mikhail F. Borisenkov https://orcid.org/0000-0002- supported by a postdoctoral grant from FUNCIVA. 4310-2010 This initiative is supported by Generalitat de Catalu- Youl-Ri Kim https://orcid.org/0000-0002-5538-7180 nya. LM is supported by a postdoctoral grant of the Nathalie Godart https://orcid.org/0000-0002-7233-6991 Mexican institution Consejo Nacional de Ciencia y Robert van Voren https://orcid.org/0000-0001-9010- Tecnología (CONACYT). PPM was supported, in part, 3115 by a Portuguese Foundation for Science and Technol- Janet Treasure https://orcid.org/0000-0003-0871-4596 ogy grant (POCI-01-0145-FEDER-028145). IB was par- Susana Jiménez-Murcia https://orcid.org/0000-0002- tially supported by a Post-Residency Grant from 3596-8033  FERNANDEZ-ARANDA ET AL. 11 R EF E RE N C E S (2020). Coronavirus disease 2019 (COVID-19) and obesity. Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Impact of obesity and its main comorbidities in the evolution of Pakpour, A. H. (2020). The fear of COVID-19 scale: Develop- the disease. European Eating Disorders Review, 28; https:// ment and initial validation. 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