Appetite 160 (2021) 105122

Contents lists available at ScienceDirect

Appetite
journal homepage: www.elsevier.com/locate/appet

Increased emotional eating during COVID-19 associated with lockdown,

psychological and social distress
Cinzia Cecchetto a, Marilena Aiello b, Claudio Gentili a, c, Silvio Ionta d, Sofia Adelaide Osimo d, *
a
Department of General Psychology, University of Padova, Via Venezia 8 - 35131, Padua, Italy
b
Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
c
Padova Neuroscience Center (PNC), University of Padova, Via Orus 2/B - 35131, Padua, Italy
d
Sensory-Motor Lab (SeMoLa), Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland

A R T I C L E I N F O A B S T R A C T

Keywords: Due to the spread of COVID 2019, the Italian government imposed a lockdown on the national territory. Initially,
COVID-19 pandemic citizens were required to stay at home and not to mix with others outside of their household (Phase 1); even­
Lockdown tually, some of these restrictions were lifted (Phase 2). To investigate the impact of lockdown on emotional and
Binge eating
binge eating, an online survey was conducted to compare measures of self-reported physical (BMI), psychological
Emotional eating
Negative emotions
(Alexithymia), affective (anxiety, stress, and depression) and social (income, workload) state during Phase 1 and
BMI Phase 2. Data from 365 Italian residents showed that increased emotional eating was predicted by higher
depression, anxiety, quality of personal relationships, and quality of life, while the increase of bingeing was
predicted by higher stress. Moreover, we showed that higher alexithymia scores were associated by increased
emotional eating and higher BMI scores were associated with both increased emotional eating and binge eating.
Finally, we found that from Phase 1 to Phase 2 binge and emotional eating decreased. These data provide evi­
dence of the negative effects of isolation and lockdown on emotional wellbeing, and, relatedly, on eating
behaviour.

1. Introduction well as some public parks and gardens re-opened. In the media, these
two periods of time have been called respectively Phase 1 and Phase 2 of
After China, Italy was the first country in which the COronaVIrus the lockdown (“Europe is in a new phase of reopening, but it’s hardly a
Disease 2019 (COVID-19) pandemic rapidly spread. As a consequence, return to normal,” 2020; “Fase 2: prove di normalità, runner e bici nei
the Italian government was the first country in Europe to impose a total parchi, primi funerali,” 2020).
lockdown in the entire national territory to reduce the spread of in­ Even though these restrictions were required to prevent people from
fections. From the March 10, 2020 citizens were required to socially being infected, the prolonged lockdown, social isolation, uncertainty,
isolate themselves and were not allowed to leave their homes except for and the potential negative consequences in the near future triggered a
documented reasons (such as serious health reasons or to shop for ne­ variety of psychological problems: for example, results from a survey
cessities). Non-essential activities (e.g. schools, universities, gyms, res­ during COVID-19 epidemic including more than 50.000 Chinese re­
taurants, commercial activities, companies, and industries selling or spondents showed that almost 35% of the participants experienced
producing non-essential goods) were moved on-line or closed, so most psychological distress (Qiu et al., 2020), while a similar survey on the
people either worked from home or stopped working (“Cities deserted, Italian population during Phase 1 of the lockdown, which collected more
families separated and social life on hold in Italy’s first day of lockdown, than 18,000 answers, reported that 37% of the participants experienced
” 2020; “Conte annuncia l’inasprimento delle misure: ‘Italia zona pro­ post-traumatic stress symptoms, and around the 20% encountered
tetta,’” 2020). This dramatic and extraordinary situation was extended depression, anxiety or high perceived stress (Rossi et al., 2020). The
until the 4th of May. From that day on, some of the restrictions were incidence found in Italy was in line with other studies as confirmed by a
lifted: people were allowed to leave their houses once more to visit meta-analysis published in July 2020 (Salari et al., 2020). In general, the
families and to do physical activity, and some non-essential activities as prevalence of stress was 29.6% (5 studies, 9074 participants), the

* Corresponding author. avenue de France 15, 1002 Lausanne, Switzerland.

E-mail address: sofia.osimo@gmail.com (S.A. Osimo).

https://doi.org/10.1016/j.appet.2021.105122
Received 11 August 2020; Received in revised form 3 January 2021; Accepted 10 January 2021
Available online 14 January 2021
0195-6663/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C. Cecchetto et al. Appetite 160 (2021) 105122

prevalence of anxiety was 31.9% (17 studies, 63,439 participants) and social isolation, and the quality of home residency. Moreover, we
that of depression was 33.7% (14 studies, 44,531 participants) (Salari intended to investigate how these aspects interact with personal char­
et al., 2020). acteristics such as BMI and level of alexithymia. Crucially, unlike other
The combination of stress, anxiety, and depression due to this un­ studies that focused only on Phase 1 of the lockdown and its effect on
precedented situation had an impact also on the eating behaviours. individual well-being, we aimed at testing how the difference in the
Indeed, the sudden start of the lockdown triggered panic buying and restrictions during Phase 1 and Phase 2 of the lockdown differently
stockpiling of food and daily supplies, as a coping mechanism in reaction affected eating behaviour, to better understand the implication of the
to the uncertainty of the duration of the pandemic and to the stress of the lockdown rigidity and provide some tools to guide future lockdown
incessant news of rising numbers of infected individuals and deaths policies. We hypothesized that emotional distress and poor quality of life
(Baker et al., 2020; Sim, Chua, Vieta, & Fernandez, 2020). Panic buying during lockdown would lead to increased self-reported emotional eating
and stockpiling lead to a supply shock (Baldwin 2020, but see Benker, and more frequent binge eating. Furthermore, we expected that the
2020 for resilience aspects that some stockpiling represents), quickly lockdown restrictions would impact more individuals with higher BMI
emptying supermarkets shelves, and to the temporary unavailability of and higher levels of alexithymia. Moreover, we hypothesized that the
some food products, which in turn created a more stressful situation partial lift of the restrictions during Phase 2 of the lockdown would
(Barua, 2020; Di Renzo, Gualtieri, Pivari, et al., 2020). This scenario, allow individuals to better cope with negative emotions and therefore to
combined with the changes in eating habits and routines, such as a reduce emotional eating and binge eating, compared to Phase 1. To
higher percentage of meals cooked and consumed at home (such as pizza investigate stressors and eating behaviors in the two Phases of the
and pasta; International Food Council Information, 2020; Statista, lockdown, we administered the online survey during the second week of
2020a, b), affected individual behaviours: many of them reported hav­ Phase 2, and we asked participants to respond to questions about their
ing eaten more during the lockdown and having had overall more un­ experiences during the two Phases, i.e. Phase 1, i.e. recalling their
healthy eating habits, such as consuming comfort foods (Robinson et al., experience from the 10th of March until the 3rd of May, and Phase 2,
2020; Scarmozzino & Visioli, 2020). Crucially, some of them attributed from the 4th of May until the day the questionnaire was filled in.
these changes to higher anxiety (Ammar et al., 2020; Scarmozzino &
Visioli, 2020; Scharmer et al., 2020). 2. Materials and methods
Dysfunctional eating habits, such as binge eating and emotional
eating have been shown to be predicted by both stress (Freeman & Gil, 2.1. Participants
2004; Lattimore, 2001; Levine & Marcus, 1997; Michels et al., 2012;
Talbot, Maguen, Epel, Metzler, & Neylan, 2013; van Strien, Herman, The anonymous online Survey (hosted by Qualtrics XM Platform)
Anschutz, Engels, & de Weerth, 2012; Wallis & Hetherington, 2004) and was shared via social media from the 14th of May to the May 19, 2020,
negative emotions, such as anxiety and depression (Goossens, Braet, targeting Italian residents and Italian speakers 18 or more years old.
Vlierberghe, & Mels, 2009; Nguyen-Rodriguez, Unger, & Spruijt-Metz, Participants were invited to complete a survey on the changes in eating
2009; Rosenbaum & White, 2015; Schulz & Laessle, 2010). Binge behaviours during the lockdown. There was no compensation for
eating consists of ingesting a large amount of food in a short amount of participating in the study. The study protocol was approved by the
time, combined with a sense of lack of control during the episode Ethics Committee of the University of Padova and was conducted in
(American Psychiatric Association, 2013), while emotional eating con­ accordance with the Declaration of Helsinki. All respondents read the
sists of excessive eating in response to arousal states such as anger, fear, written consent form and explicitly agreed to participate before starting
and anxiety (van Strien, Frijters, Bergers, & Defares, 1986). Two factors, the survey.
in particular, seem to play a primary role in mediating the relationship A total of 635 participants started the survey. From this sample, we
among stress, anxiety, depression, and dysfunctional eating: body excluded 194 participants because they did not complete the survey, 7
weight status, and the subjects’ ability to correctly perceive and inter­ because of missed information (five because of missed information
pret their emotional sensations, distinguishing them from their physical about gender), five because of pregnancy, two because they reported
ones, a condition called alexithymia (Sifneos, 1973). Previous literature having been infected by COVID- 19, and 23 because they spent part or all
has shown that people who have overweight are particularly at risk for of the lockdown outside the Italian territory. Moreover, 35 participants
emotional eating during negative emotional states (Geliebter & Aversa, were excluded from the main analyses because they reported currently
2003), and that high trait anxiety is associated with food intake for having or having had an eating disorder in the past; we report in the
people living with obesity, but not their lean counterparts (Schneider, supplementary results the analyses run separately on this sample.
Appelhans, Whited, Oleski, & Pagoto, 2010). The relationship between
stress and emotional eating has also been shown to be modulated by 2.2. Measures
Body Mass Index (BMI; Nguyen-Rodriguez, Chou, Unger, &
Spruijt-Metz, 2008; Tchanturia et al., 2012; Torres & Nowson, 2007). The online survey was divided into three parts. In the first part,
Additionally, alexithymia has been related to higher levels of obesity, participants answered questions regarding socio-demographic infor­
emotional eating, and more in general to impulsivity and negative affect mation (age, gender, education, family income, body weight and height,
(Casagrande, Boncompagni, Forte, Guarino, & Favieri, 2019; Pink, Lee, pregnancy, presence of pathologies, type of occupation before the
Price, & Williams, 2019). It has been suggested that higher alexithymia lockdown, presence of eating disorders or history of eating disorders,
reduces the ability to identify emotional states and to distinguish them COVID-19 infection) and filled in the Toronto Alexithymia Scale (TAS-
from internal signals of hunger and satiety, therefore leading individuals 20; Bressi et al., 1996). The second and the third parts included ques­
to regulate their emotions through food intake (Pink et al., 2019; Tan & tions and questionnaires that referred respectively to the first and sec­
Chow, 2014), increasing their BMI (Casagrande et al., 2019; Tan & ond Phases of the lockdown. They included questions regarding the
Chow, 2014; Taylor, Parker, Bagby, & Bourke, 1996). present home residence (dimensions of the house, presence of an
Given the literature reviewed so far, the present study aimed at external space such as a garden or a balcony), the number of people
investigating, through an online survey, how COVID-19 lockdown living with the participant (including the type and the quality of the
affected emotional eating and binge eating in the Italian residents. In relationships), the Type of Occupation (TO; home working or
particular, we wanted to analyze the effect of the level of anxiety, stress, not-working, desk job, public-facing job, a job in contact with COVID
and depression on eating habits. Particular attention was given to the patients), and how occupation changed from the previous Phase (not
social features that characterized the quality of life during the lockdown, working anymore, working less, working more or no changes). Also,
such as the changes in workload and type of occupation, the level of both parts included the 7-Item Binge-Eating Disorder Screener (BEDS-7,

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Herman et al., 2016), the subsection of the Dutch Eating Behaviour 1 for partners, by 0.85 for parents, 0.70 for siblings, 0.55 for friends, 0.4
Questionnaire investigating Emotional Eating (DEBQ; Dakanalis et al., for relatives, 0.25 for housemates. The relationships scores of multiple
2013), the Patient Health Questionnaire (PHQ-2; Kroenke, Spitzer, & cohabitants were then averaged per each respondent. Respondents who
Williams, 2003), investigating depressive symptoms, the Generalized were living alone were given a score of 0, equal to having neutral re­
Anxiety Disorder scale (GAD-2; Kroenke, Spitzer, Williams, Monahan, & lationships. The Workload (WL) was computed to measure the workload
Löwe, 2007), and the Perceived Stress Scale (PSS-10; Mondo, Sechi, & in each Phase, from not employed to working full time, and was rated
Cabras, 2019). from 0 to 2. The Changes in Workload (CW) is the measure of how much
the workload changed compared to before the lockdown, and it was
2.3. Questionnaires rated from − 1.5 to 0.5, with 0 indicating no change in workload.

The TAS-20 is a self-report scale measuring the general level of 2.4. Statistical analyses
alexithymia. Each item is scored from 1 (strongly disagree) to 5 (strongly
agree), for a total score of 100. The cut-off for alexithymia is 61 (Bressi Analyses were designed to test our predefined hypotheses that the
et al., 1996). To assess the presence of binge eating during each Phase, lockdown rigidity would influence emotional and social well-being and
we included the 7-Item Binge-Eating Disorder Screener (BEDS-7). Only if to investigate the effect of these cumulative factors on eating habits.
participants answered “yes” to the first question, inquiring into having Data were cleaned and analyzed using the software R (Team, 2017). All
experienced episodes characterized by eating an amount of food defi­ continuous variables were centered and scaled, and participants who
nitely larger than most people would eat in the same period of time, and deviated more than 4 standard deviations from the mean of any
to the second question, describing a sense of lack of control over eating continuous predictor were removed from the sample (N = 5). The final
during these episodes, the other 5 questions, which inquired into the sample was constituted of 365 participants, having excluded any
features of the binging episodes (as per the description of the binge participant with a current or past eating disorder. A second database
eating disorder in the DSM-5; American Psychiatric Association, 2013), that included only participants who had or had had a self-reported
were presented. The total score ranged from 0 to 5: if one of the first two eating disorder (N = 35) was created, to conduct separate additional
questions was answered negatively or none of the 5 criteria was selected, analyses (reported in the supplementary results).
the score was 0; if both the first two questions were answered positively, Descriptive analyses on the experimental measures were run using t-
the score was equal to the number of criteria met (Shankman et al., tests (stats package; R Core Team, 2017). The DEBQ emotional eating
2018). The DEBQ is a self-report questionnaire that contains 33 items, and BEDS-7 scales were investigated through two separate linear mixed
rated on a 5-point Likert scale (from “never” to “very often”; Dakanalis models (LMMs) with the same predictors. LMMs were computed using
et al., 2013). In this study, we administered only the “emotional eating” the “lmer” function (lme4 package, Bates, Mächler, Bolker, & Walker,
subscale which included 13 items (e.g., “Do you have a desire to eat 2015) and explored using the Anova function type 3 of the car package
when you are irritated?“). The PHQ-2 is a two-item screening tool (Fox, Weisberg, & Price, 2019). Predictors consisted of four categories:
inquiring about the frequency of depressed mood and anhedonia over the lockdown Phase (first or second), personal traits that did not vary
the previous two weeks. Each item is scored from 0, “not at all”, to 3, with the lockdown, and emotional states and social variables during the
“nearly every day”. A PHQ-2 ≥ 3 showed a sensitivity of 83% for major two Phases of the lockdown. Personal traits included participants’ BMI
depression (Kroenke et al., 2003). The GAD-2 scale is represented by the and TAS scores. Emotional variables included the GAD, PHQ, and PSS
first 2 items of the GAD-7 and it describes core anxiety symptoms. Each questionnaires scores. Social variables included the aggregated variables
item is scored from 0, “not at all”, to 3, “nearly every day”, and total QL, QR, OC, WL, and TO. To explore the interaction between personal,
scores range from 0 to 6; 3 is considered the cut-off for anxiety disorder emotional, and social characteristics in influencing emotional eating and
(Kroenke et al., 2007). The PSS-10 is a ten-item scale that evaluates bingeing in the two Phases of the lockdown, each of these categories was
thoughts and feelings related to stressful events that occurred one month put in interaction with the other categories. To avoid overly complicated
before. It has six negatively- and four positively-stated items rated on a and uninterpretable models, interactions between items of the same
5-point Likert scale ranging from 0, “never”, to 4, “very often”. Higher category were not computed, and only second-level interactions were
scores indicate higher levels of perceived stress (Mondo et al., 2019). entered into the models. A random intercept for participant ID was
Differing from the validated procedure, for the specific purposes of this added to account for within-subject measures, and two further in­
study, the DEBQ, the BEDS-7, the PHQ-2, the GAD-2, and the PSS-10 tercepts for sex and age were included in the initial models:
were repeated twice to collect participants’ experiences in the two ~ Phase * (BMI + TAS-20) + Phase * (GAD + PSS + PHQ) + Phase *
phases: once to record their current experience and once asking them to (QL + QR + WL + CW + TO) + (BMI + TAS-20) * (GAD + PSS + PHQ) +
recollect their experience one week prior. (BMI + TAS-20) * (QL + QR + WL + CW + TO) + (GAD + PSS + PHQ) *
(QL + QR + WL + CW + TO) + (1|ID) + (1|Gender) + (1|Age)
2.3.1. Indices To ensure that each random intercept and each personal trait,
To simplify statistical analyses, we combined some measures into emotional states, and social predictor measure improved the models’ fit,
indexes. We computed the BMI dividing the body weight by the square one factor at a time was removed from the model, and the resulting
of the body height (World Health Organization, 2018), as a measure of model was compared with the initial one on the basis of the AIC criterion
body weight status. We defined Quality of Life (QL) as a measure that (Bolker et al., 2009), using the “anova” function (lmerTest package,
combines the quantity and quality of the personal space at home and the Kuznetsova, Brockhoff, & Christensen, 2017). The random intercept for
family income. It was computed as the standard deviation from the participant ID was always kept in the models to ensure the correct
population mean of m2 available per person in the house, to which a unit computation of within-subject measures. Factors that did not signifi­
was added per each number of accesses to an external space (such as a cantly improve the model’s fit were removed, starting with the ones with
balcony, a garden, or a courtyard), and one more unit was added if the the highest p value (indicating the lowest chance that the factor
family income was greater than 36.000 euro. We defined Quality of the improved the model’s fit) and repeating the procedure until all the
Relationships (QR) as a measure that combines the quality of the re­ factors included in the models significantly improved their fit. Post-hoc
lationships and the relationship type (e.g. parents, partner, friends) that tests of interactions that included categorical factors were corrected
each respondent experienced with the people they were living with. using Benjamini & Hochberg’s False Discovery Rate method (Benjamini
Participants rated how good the relationship with each cohabitant was: & Hochberg, 1995), and interactions including continuous factors were
3 “not good all”, − 1 “okay”, 1 “quite good”, 3 “very good”. This score analyzed according to Aiken & West’s method (Aiken, West, & Reno,
was then multiplied by a coefficient depending on the relationship type: 1991). The same procedures were used in the supplementary analysis

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run on the eating disorders dataset. also compared the prevalence of binge eating in our data with the
normative data (Kessler et al., 2013; see Table 1) and found a signifi­
3. Results cantly higher prevalence of binge eating both in Phase 1 [χ2 (1) = 17.25,
p < 0.001] and in Phase 2 [χ2 (1) = 12.67, p < 0.001].
3.1. Sample characteristics
3.2. Emotional eating during Phase 1 and Phase 2
The main sample (n = 365) included only respondents who declared
not having a current or past eating disorder. See Table 1 for the mean The final model investigating the DEBQ emotional eating scale
and standard deviations of demographic and psychological character­ included Phase, BMI, TAS, GAD, PHQ, QL, and QR as predictors, and ID
istics. Fifty-five participants reported that, before the lockdown, they and gender as random intercepts (initial AIC = 1245.4, final AIC =
had a part-time job, 166 that they had a full-time job, 106 reported to be 1215.2, p = 0.10).
students, and 39 that they were not employed (retired or unemployed). ~ Phase * (BMI + TAS-20) + Phase * (GAD + PHQ) + Phase * (QL +
During the lockdown, the type of occupation slightly changed between QR) + (BMI + TAS-20) * (GAD + PHQ) + (BMI + TAS-20) * (QL + QR)
Phase 1 and 2: in Phase 1, 280 respondents were home working or not + (GAD + PHQ) * (QL + QR) + (1|ID) + (1|Gender)
working, 50 were employed at a desk job and not working from home, Conditional R2 was equal to 0.88, and marginal R2 was equal to 0.24.
23 were employed in public-facing jobs and 12 had jobs which put them Results showed a main effect of Phase [χ2 (1) = 15.53, p < 0.001],
in contact with COVID patients; in Phase 2, 221 were home working or illustrating a higher emotional eating during Phase 1 (mean = 2.14, SD
not working, 96 were employed at a desk job and not working from = 0.87) than during Phase 2 (mean = 1.99, SD = 0.86). Moreover,
home, 39 were employed in public-facing jobs and 9 had jobs that put higher emotional eating was found among individuals with a higher BMI
them in contact with COVID patients. For the variables measured in the [χ2 (1) = 23.60, p < 0.001], higher alexithymia score [TAS-20; χ2 (1) =
two Phases, we compared the mean, SD, and cut-off for each Phase; see 7.91, p = 0.005], higher anxiety [GAD; χ2 (1) = 20.83, p < 0.001], and
Table 1 and Fig. 1 for these descriptive results. We then compared our higher depressive symptoms [PHQ; χ2 (1) = 21.25, p < 0.001; see Fig. 3].
data on emotional eating with the normative data from Dakanalis et al. The model showed a significant Phase by QR interaction [χ2 (1) = 7.34,
(2013); (see Table 1) throught t.tests and found that emotional eating p = 0.007]. Post-hoc tests found that QR did not predict emotional
was significantly higher than normative data in Phase 1 [t (1282) = eating in Phase 1 [t (579) = − 1.38, p = 0.17] nor in Phase 2 [t (595) =
7.85, p < 0.001] but not in Phase 2 [t (1282) = − 0.58, p = 0.562]. We 0.46, p = 0.65]; however, while in all participants emotional eating was
higher in Phase 1 than Phase 2, this difference was bigger among par­
Table 1 ticipants with low QR [t (391) = 2.98, p = 0.003] than in participants
Mean and SD (Standard Deviation) of demographic and psychological variables with a high QR [t (371) = 2.29, p = 0.022]. There was also a significant
of the main sample without participants reporting any current or past eating TAS-20 by QL interaction [χ2 (1) = 4.70, p = 0.030]. Post-hoc showed
disorder. For PHQ and GAD, the percentages of participants above the cut-off for
that among individuals with higher QL, higher alexithymic scores pre­
each Phase are reported.
dicted higher emotional eating [t (482) = 3.88, p < 0.001], while
Sample N = 365 alexithymic scores did not have significant effects in individuals with
Gender 267 women (73.1%) low QL [t (480) = 0.84, p = 0.40]. High and low alexithymic scores did
Age mean = 35.09, SD = 13.59 (18–74 years) not predict differences in emotional eating depending on the QL [t (552)
BMI mean = 23.08, SD = 3.81 (15.05–37.50)
= 1.79, p = 0.07 and t (523) = − 1.23, p = 0.22, respectively]. Finally,
TAS-20 mean = 46.21, SD = 11.70 (0–100)
Phase Phase 2 Phase comparisons
there was a significant QL by anxiety interaction [χ2 (1) = 4.26, p =
1 0.039]. Post-hoc tests showed that QL did not predict emotional eating
Mean (SD) Mean in individuals with low [t (712) = 1.58, p = 0.11] or high [t (699) =
(SD) − 0.98, p = 0.33] anxiety, but that higher anxiety significantly predicted
DBEQ, Emotional eatingþ 2.14 (0.87) 1.99 t (364) = 6.49, p <
emotional eating both in individuals with low QL [t (608) = 5.03, p <
(0.86) 0.001
BEDS-7 0.26 (0.73) 0.16 t (364) = 3.59, p < 0.001] and high QL [t (565) = 1.99, p = 0.047], with a stronger effect
% with an episode of (0.63) 0.001 among individuals with low QL. All main and interaction effects are
binge eating*þ 3.01% * 2.46%* depicted in Fig. 2.
PHQ 2.18 (1.51) 1.86 t (364) = 5.86, p <
% above cut-off (1.43) 0.001
32% 22% 3.3. Binge eating during Phase 1 and Phase 2
GAD 2.04 (1.51) 1.82 t (364) = 3.97, p <
% above cut-off (1.52) 0.001
27% 20%
The final model investigating binge eating included Phase, BMI, and
PSS 18.70 18.09 t (364) = 3.99, p < PSS as predictors, and ID as random intercept (initial AIC = 1298, final
(3.04) (2.89) 0.001 AIC = 1246, p = 0.18).
QL 2.74 (1.13) 2.76 t (364) = − 1.51, p =
(1.10) 0.13 ~ Phase × BMI + Phase × PSS + BMI × PSS + (1|ID)
QR 1.16 (1.20) 1.14 t (364) = 1.07, p =
(1.70) 0.28 Conditional R2 was equal to 0.71, and marginal R2 was equal to 0.05.
WL 1.12 (0.90) 1.24 t (364) = − 5.19, p < Results showed a main effect of Phase [χ2 (1) = 9.02, p = 0.003],
(0.82) 0.001
indicating higher binge eating in Phase 1 (mean = 0.26, SD = 0.73) than
CW − 0.31 − 0.26 t (364) = − 3.97, p <
(0.42) (0.38) 0.001
Phase 2 (mean = 0.16, SD = 0.63), a main effect of BMI [χ2 (1) = 9.67, p
= 0.002], indicating higher binge eating among individuals with a
Note: BMI = body mass index; TAS-20 = 20 items Toronto Alexithymia Scale; higher BMI, and a main effect of PSS [χ2 (1) = 17.87, p < 0.001],
QL = Quality of Life; QR = Quality of the Relationships; WL = Workload; CW =
indicating higher binge eating in individuals who reported a higher level
Changes in Workload; * percentage of individuals who had an episode of binge
of stress. There was also a Phase by BMI significant interaction [χ2 (1) =
eating, who meet the first two criteria of DSM-V for binge eating disorders,
during Phase 1 or Phase 2. þNormative data: mean of emotional eating of 2.00 3.91, p = 0.048]. Post-hoc test showed that higher BMI significantly
(sd = 0.84) in a sample of 919 Italian respondents (517 women and 473 men, predicted a higher binge eating in Phase 1 [t (499) = 3.11, p = 0.002],
aged 20–63 M = 34.9, SD = 8.0; Dakanalis et al., 2013); 0.2% of prevalence in but not in Phase 2 [t (496) = 1.56, p = 0.12; see Fig. 2]. Finally, we found
Italy in the normal population according to the World Health Organization a BMI by stress interaction [χ2 (1) = 7.10, p = 0.008]. Post-hoc tests
(WHO) (World Mental Health Surveys; Kessler et al., 2013). showed that higher stress lead to a higher binge eating score among

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Fig. 1. Distribution of the questionnaire data during Phase 1 and Phase 2 of the lockdown. The boxplots depict the median and quartile ranges of the distribution; the
white diamonds indicate the mean.

individuals with a higher BMI [t (687) = 4.90, p < 0.001], but not with higher BMI (Geliebter & Aversa, 2003) and with a higher level of
among individuals with a lower BMI [t (653) = 1.53, p = 0.13], and that alexithymia (Pink et al., 2019). Moreover, we found that anxiety
BMI only predicted higher binge eating among individuals with a higher significantly interacts with the Quality of Life, which was an index of the
stress score [t (604) = 3.99, p < 0.001], and not among individuals with quantity and quality of the personal space at home and the family in­
a lower stress score [t (593) = 1.20, p = 0.23]. come: higher levels of anxiety had stronger effects in those individuals
who reported having lower QL. This suggests that lower quality or
4. Discussion smaller personal space, the absence of access to external space, or a
lower family income made individuals more vulnerable to the negative
The present study was designed to investigate how the negative consequences of anxiety. In developing countries, socioeconomic
emotions raised by the lockdown and the social features that charac­ disadvantage has been strongly correlated with a higher propensity to­
terized the quality of life during lockdown interacted with individual ward obesity (Sobal & Stunkard, 1989; Spinosa, Christiansen, Dickson,
characteristics to affect the eating behaviour during the lockdown. Our Lorenzetti, & Hardman, 2019), in particular, low socioeconomic status
main hypothesis, that emotional distress and poor quality of life during seems to affect BMI through increased psychological distress and
lockdown would lead to increased self-reported emotional eating and emotional eating (Spinosa et al., 2019). Additionally, we found a sig­
more frequent binge eating was confirmed. Indeed increased emotional nificant effect on emotional eating of the Quality of the Relationships
eating was significantly predicted by higher level of anxiety, depression, concerning the Phase: individuals who reported a lower QR presented
and, partially, by Quality of Life and Quality of the Relationships. higher emotional eating during Phase 1 as compared to Phase 2.
Moreover, increased binge eating was predicted by higher stress. Our Unsupportive social interactions have been proved to be associated with
second hypothesis, that the lockdown restrictions would impact more emotional eating in healthy participants and are considered an effective
individuals with higher BMI and higher levels of alexithymia, was also coping resource to deal with the effects of stressful events (Raspopow,
confirmed as we showed that higher alexithymia scores were associated Matheson, Abizaid, & Anisman, 2013). Accordingly, in our data, nega­
by increased emotional eating and higher BMI scores were associated tively perceived social interactions influenced emotional eating espe­
with both increased emotional eating and binge eating. Finally, in line cially during Phase 1, which was characterized by a higher level of
with our third hypothesis, we showed that emotional eating and binge negative emotions. Finally, emotional eating was predicted by the sig­
eating decreased significantly in Phase 2 compared to Phase 1. nificant interaction between alexithymia and Quality of Life. Unex­
In line with the literature, we found that emotional eating signifi­ pectedly, and in contrast with previous explained results, we found that,
cantly increased with a higher level of negative emotions, i.e. anxiety among individuals with higher QL, higher alexithymic scores predicted
(Nguyen-Rodriguez et al., 2009), and depression (Goossens et al., 2009), higher emotional eating. One possible explanation is that high

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C. Cecchetto et al. Appetite 160 (2021) 105122

Fig. 2. Main and interaction effects between predictors of emotional eating. In the first row, the main effects, with data distribution as black dots and fit lines in red.
In the second row, fit lines of the interaction effects. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of
this article.)

availability of resources, among which there is probably high avail­ Importantly, we found that the partial lift of the restrictions during
ability of good quality food, leads alexithymic individuals, which pre­ Phase 2 of the lockdown influenced emotional and binge eating. This
sent difficulties in identifying, describing, and expressing their emotions result suggests that the loosening of some restrictions helped people to
as well as in recognizing or experiencing emotional bodily responses as better deal with lockdown. On a more general note, we found that the
emotional feeling states (Lane et al., 1997), to try regulating their bodily mean of emotional eating during the lockdown was significanlty higher
emotional responses through eating. It is worth noting that our Quality during Phase 1 compared to the normative data collected in the Italian
of Life index was computed based on both income and the space avail­ population (Dakanalis et al., 2013), while our data pointed towards a
able during the lockdown. Future research should specifically address more consistent increase in the percentage of individuals who presented
the effects of each of these factors separately. at least one episode of binge eating during both phases of lockdown
The analysis of binge eating revealed that, in line with previous compared to the prevalence in Italy in the normal population (Kessler
literature (Palmisano, Innamorati, & Vanderlinden, 2016; Torres & et al., 2013; see Table 1). To our knowledge, this is one of the few reports
Nowson, 2007), higher stress led to higher binge eating score among (see Al-Musharaf, 2020) of emotional eating and binge eating on in­
individuals with a higher BMI, and that BMI predicted higher binge dividuals without eating disorders during the lockdown and, even
eating among individuals with a higher stress score. A recent though more investigations and additional data are needed for com­
meta-analysis reported that living a stressful experience is a risk factor parisons, these data suggest that the negative feelings that individuals
for developing obesity and binge eating disorder (Palmisano et al., had to face during the lockdown may have increased dysfunctional
2016). Our data strongly support this association and highlight the ne­ eating behaviour. This is in line with a previous report showing that
cessity for further investigations on the possibility that isolation and lockdown leads to an unhealthy pattern of food consumption (e.g.
lockdown would become a key factor for the development of an eating consuming unhealthy food, eating out of control, snacking between
disorder, in particular in vulnerable individuals (Brown et al., 2020; meals) and that these changes were exhibited in people from different
Fernández-Aranda et al., 2020; Fernández-Aranda et al., 2020). On the continents (Ammar et al., 2020; Di Renzo, Gualtieri, Cinelli, et al., 2020;
other side, our results on binge eating showed that the final model, that Pietrobelli et al., 2020; Robinson et al., 2020).
unexpectedly did not include most of the initial factors, explained only a As additional analyses, we applied the same models for emotional
small percentage of the variance, in contrast with the results on eating and binge eating on the group of participants that had been
emotional eating. This indicates that factors that were not investigated excluded from the main analyses because they had reported currently
in the study may be involved in binge eating. As binge eating is a clinical having or having had an eating disorder in the past (see Supplementary
disorder, this result may indicate the role of deeper psychological fac­ Results). Interestingly, even though none of the predictors were linked
tors, such as trauma, attachment patterns, and significant relationships to binge eating, for emotional eating we found significant predictors that
with caregivers, in the development of an eating disorder (Dominy, differed from the ones influencing healthy participants. In particular,
Johnson, & Koch, 2000; Harrington, Crowther, Henrickson, & Mick­ while alexithymia did not influence emotional eating, stress, in inter­
elson, 2006; Maxwell, Tasca, Ritchie, Balfour, & Bissada, 2013; Pace, action with Quality of Life, was associated with it, and anxiety and
Cacioppo, & Schimmenti, 2012; Ward, Ramsay, & Treasure, 2000). depression have only a marginal role. This result indicates that the two

6
C. Cecchetto et al. Appetite 160 (2021) 105122

Fig. 3. Main and interaction effects between predictors of binge eating. In the first row, the main effects, with data distribution as black dots and fit lines in red. In
the second row, fit lines of the interaction effects. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of
this article.)

groups are indeed samples from different populations that responded In conclusion, our study shows evidence of the negative effects of
differently to stressful situations such as lockdown. However, since it isolation and lockdown on eating behaviour in the Italian population.
was outside the aim of the present study, this sample was quite small and Even though these restrictions were needed to prevent the spread of the
this analysis must be considered with caution. pandemic, these and previous observations act as warnings that careful
This study presents some limitations. First, we relied on social net­ monitoring and nutritional as well as health recommendations are
works to recruit participants, which could have introduced some bias in important to mitigate the impact of negative effects of possible future
our sample as it excluded those people that are not on social networks or lockdowns. Future policies during lockdown should also take into
that could not use personal computers or smartphones. Second, we used consideration the emotional toll on individual well-being and should
an online survey which was based on self-report questionnaires. This include measures of psychological support. Future studies should
aspect could have been particularly problematic for the binge eating consider whether the effects of these two months’ lockdown caused
questionnaire which was adapted from the structured interview of DSM; long-term consequences on eating behaviour.
however, this procedure was imposed by the exceptionality of the
moment and the lockdown restrictions. Third, there was an uneven Acknowledgment
number of males and female participants; we however tried to account
for this issue by including gender as a random factor in the initial C.C. was supported by a grant from MIUR (Dipartimenti di Eccel­
models. For the same reason, we also include age in the initial models. lenza DM May 11, 2017 n. 262) to the Department of General Psy­
Fourth, our sample could be considered small for an online survey; chology. S.A.O. was supported by the Swiss National Science Foundation
however, we would like to point out that the survey was kept available (grant PP00P1_170506/1 to Silvio Ionta). We thank Prof. Bruno Osimo
only for six days to be still able to collect reliable answers related to for the English revision of the manuscript.
Phase 1 but at the same time to have people already felt the effects of
Phase 2. Five, while our experimental design allowed us to collect in­ Appendix A. Supplementary data
formation from the same participants for the two phases of the lock­
down, it should be noted that data is not acquired longitudinally, but Supplementary data to this article can be found online at https://doi.
rather participants were asked to recall at the beginning of Phase 2 how org/10.1016/j.appet.2021.105122.
they felt a week earlier (during Phase 1). This limitation needs to be
carefully considered in particular for the validated instruments (DEBQ, Author contributions
BEDS-7, GAD-2. PSS-10, PHQ-2) that were used following a different
procedure from the validated one. M.A., C.C, S.A.O development of the study concept and the study

7
C. Cecchetto et al. Appetite 160 (2021) 105122

design; C.C, M.A., S.A.O. data collection; S.A.O and C.C data analysis Fase 2. (2020). Prove di normalità, runner e bici nei parchi, primi funerali. https://www.
ansa.it/sito/notizie/cronaca/2020/05/04/fase-2-milano-e-roma-piu-auto-in-giro-_9f
under the supervision of M.A.; S.A.O. and C.C data interpretation and
7f5a07-49c4-4dd8-930c-0d6d32ab3c1b.html. (Accessed 6 November 2020).
manuscript writing; M.A., C.G., and S.I. review and editing; M.A. and C. Fernández-Aranda, F., Casas, M., Claes, L., Bryan, D. C., Favaro, A., Granero, R., et al.
G. supervision and project administration. All authors approved the final (2020). COVID-19 and implications for eating disorders. European Eating Disorders
version of the manuscript. Review, 28(3), 239.
Fox, J., Weisberg, S., & Price, B. (2019). carData: Companion to applied regression data
sets. https://cran.r-project.org/package=carData.
Conflicts of interest Freeman, L. M. Y., & Gil, K. M. (2004). Daily stress, coping, and dietary restraint in binge
eating. International Journal of Eating Disorders, 36(2), 204–212. https://doi.org/
10.1002/eat.20012
Authors reported no conflict of interest. Geliebter, A., & Aversa, A. (2003). Emotional eating in overweight, normal weight, and
underweight individuals. Eating Behaviors, 3(4), 341–347. https://doi.org/10.1016/
s1471-0153(02)00100-9
Ethical statement Goossens, L., Braet, C., Vlierberghe, L. Van, & Mels, S. (2009). Loss of control over eating
in overweight youngsters: The role of anxiety, depression and emotional eating.
The study protocol was approved by the Ethics Committee of the European Eating Disorders Review, 17(1), 68–78. https://doi.org/10.1002/erv.892
Harrington, E. F., Crowther, J. H., Henrickson, H. C. P., & Mickelson, K. D. (2006). The
University of Padova (Reference n◦ : 3628) and was conducted in relationships among trauma, stress, ethnicity, and binge eating. Cultural Diversity and Ethnic
accordance with the Declaration of Helsinki. All respondents read the Minority Psychology, 12(2), 212–229. https://doi.org/10.1037/1099-9809.12.2.212
written consent form and explicitly agreed to participate before starting Herman, B. K., Deal, L. S., DiBenedetti, D. B., Nelson, L., Fehnel, S. E., & Brown, T. M.
(2016). Development of the 7-item binge-eating disorder screener (BEDS-7). The
the survey.
Primary Care Companion for CNS Disorders, 18(2).
International Food Council Information. (2020). 2020 food and health survey. htt
References ps://foodinsight.org/2020-food-and-health-survey/. (Accessed 28 July 2020).
Kessler, R. C., Berglund, P. A., Chiu, W. T., Deitz, A. C., Hudson, J. I., Shahly, V., et al.
(2013). The prevalence and correlates of binge eating disorder in the World health
Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting
organization World mental health surveys. Biological Psychiatry, 73(9), 904–914.
interactions. Sage.
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2003). The patient health questionnaire-
Al-Musharaf, S. (2020). Prevalence and predictors of emotional eating among healthy
2: Validity of a two-item depression screener. Medical Care, 1284–1292.
young Saudi women during the COVID-19 pandemic. Nutrients, 12(10), 2923.
Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & Löwe, B. (2007). Anxiety
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental
disorders in primary care: Prevalence, impairment, comorbidity, and detection.
disorders DSM-5 (5th ed.). Washington, DC: American Psychiatric Association.
Annals of Internal Medicine, 146(5), 317–325.
Ammar, A., Brach, M., Trabelsi, K., Chtourou, H., Boukhris, O., Masmoudi, L., et al.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests
(2020). Effects of COVID-19 home confinement on eating behaviour and physical
in linear mixed effects models. Journal of Statistical Software, 82(13). https://doi.org/
activity: Results of the ECLB-COVID19 international online survey. Nutrients, 12(6),
10.18637/jss.v082.i13
1583. https://doi.org/10.3390/nu12061583
Lattimore, P. J. (2001). Stress-induced eating: An alternative method for inducing ego-
Baker, S. R., Farrokhnia, R. A., Meyer, S., Pagel, M., Yannelis, C., & Pontiff, J. (2020).
threatening stress. Appetite, 36(2), 187–188. https://doi.org/10.1006/
How does household spending respond to an epidemic? Consumption during the
appe.2000.0387
2020 COVID-19 pandemic. The Review of Asset Pricing Studies, 10(4), 834–862.
Levine, M. D., & Marcus, M. D. (1997). Eating behavior following stress in women with
Barua, S. (2020). Understanding coronanomics: The economic implications of the
and without bulimic symptoms | SpringerLink. Annals of Behavioral Medicine: A
coronavirus (COVID-19) pandemic. SSRN Electronic Journal. , Article 3566477.
Publication of the Society of Behavioral Medicine, 19(2), 132–138. https://doi.org/
https://doi.org/10.2139/ssrn.3566477
10.1007/BF02883330
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects
Maxwell, H., Tasca, G., Ritchie, K., Balfour, L., & Bissada, H. (2013). Change in
models using {lme4}. Journal of Statistical Software, 67(1), 1–48. https://doi.org/
attachment insecurity is related to improved outcomes 1-year post group therapy in
10.18637/jss.v067.i01
women with binge eating disorder. Psychotherapy, 51(1), 57–65. https://doi.org/
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery rate: A practical
10.1037/a0031100
and powerful approach to multiple testing. Journal of the Royal Statistical Society:
Michels, N., Sioen, I., Braet, C., Eiben, G., Hebestreit, A., Huybrechts, I., et al. (2012).
Series B, 57(1), 289–300. https://www.jstor.org/stable/2346101.
Stress, emotional eating behaviour and dietary patterns in children. Appetite, 59(3),
Benker, B. (2020). Stockpiling as resilience: Defending and contextualising extra food
762–769. https://doi.org/10.1016/j.appet.2012.08.010
procurement during lockdown. Appetite, 104981.
Mondo, M., Sechi, C., & Cabras, C. (2019). Psychometric evaluation of three versions of
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H.,
the Italian perceived stress scale. Current Psychology, 1–9.
et al. (2009). Generalized linear mixed models: A practical guide for ecology and
Nguyen-Rodriguez, S. T., Chou, C.-P., Unger, J. B., & Spruijt-Metz, D. (2008). BMI as a
evolution. Trends in Ecology & Evolution, 24(3), 127–135.
moderator of perceived stress and emotional eating in adolescents. Eating Behaviors,
Bressi, C., Taylor, G., Parker, J., Bressi, S., Brambilla, V., Aguglia, E., et al. (1996). Cross
9(2), 238–246. https://doi.org/10.1016/j.eatbeh.2007.09.001
validation of the factor structure of the 20-item Toronto alexithymia scale: An Italian
Nguyen-Rodriguez, S. T., Unger, J. B., & Spruijt-Metz, D. (2009). Psychological
multicenter study. Journal of Psychosomatic Research, 41(6), 551–559.
determinants of emotional eating in adolescence. Eating Disorders, 17(3), 211–224.
Brown, S., Opitz, M.-C., Peebles, A. I., Sharpe, H., Duffy, F., & Newman, E. (2020).
https://doi.org/10.1080/10640260902848543
A qualitative exploration of the impact of COVID-19 on individuals with eating
Pace, U., Cacioppo, M., & Schimmenti, A. (2012). The moderating role of father’s care on
disorders in the UK. Appetite, 156, 104977.
the onset of binge eating symptoms among female late adolescents with insecure
Casagrande, M., Boncompagni, I., Forte, G., Guarino, A., & Favieri, F. (2019). Emotion
attachment. Child Psychiatry and Human Development, 43(2), 282–292. https://doi.
and overeating behavior: Effects of alexithymia and emotional regulation on
org/10.1007/s10578-011-0269-7
overweight and obesity. Eating and weight disorders-Studies on anorexia. Bulimia and
Palmisano, G. L., Innamorati, M., & Vanderlinden, J. (2016). Life adverse experiences in
Obesity, 1–13.
relation with obesity and binge eating disorder: A systematic review. Journal of
Cities deserted. (2020). Families separated and social life on hold in Italy’s first day of
Behavioral Addictions, 5(1), 11–31.
lockdown. Retrieved November 6, 2020, from CNN website: https://edition.cnn.co
Pietrobelli, A., Pecoraro, L., Ferruzzi, A., Heo, M., Faith, M., Zoller, T., et al. (2020).
m/2020/03/10/europe/italy-coronavirus-quarantine-europe-intl/index.html.
Effects of COVID-19 lockdown on lifestyle behaviors in children with obesity living in
Conte annuncia l’inasprimento delle misure. (2020). “Italia zona protetta.”. https://www
verona, Italy: A longitudinal study. Obesity.
.ansa.it/sito/notizie/cronaca/2020/03/09/borrelli-salgono-a-7985-i-malati-1598-ri
Pink, A. E., Lee, M., Price, M., & Williams, C. (2019). A serial mediation model of the
spetto-a-ieri.-_dd169084-d4a9-4391-9d1c-2d9f42bace3f.html. (Accessed 6
relationship between alexithymia and BMI: The role of negative affect, negative
November 2020).
urgency and emotional eating. Appetite, 133, 270–278.
Dakanalis, A., Zanetti, M. A., Clerici, M., Madeddu, F., Riva, G., & Caccialanza, R. (2013).
Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020). A nationwide survey of
Italian version of the Dutch Eating Behavior Questionnaire. Psychometric proprieties
psychological distress among Chinese people in the COVID-19 epidemic:
and measurement invariance across sex, BMI-status and age. Appetite, 71, 187–195.
Implications and policy recommendations. General Psychiatry, 33(2).
Di Renzo, L., Gualtieri, P., Cinelli, G., Bigioni, G., Soldati, L., Attinà, A., et al. (2020).
Raspopow, K., Matheson, K., Abizaid, A., & Anisman, H. (2013). Unsupportive social
Psychological aspects and eating habits during COVID-19 home confinement: Results
interactions influence emotional eating behaviors. The role of coping styles as
of EHLC-COVID-19 Italian Online Survey. Nutrients, 12(7), 2152.
mediators. Appetite, 62, 143–149.
Di Renzo, L., Gualtieri, P., Pivari, F., Soldati, L., Attinà, A., Cinelli, G., et al. (2020).
Robinson, E., Boyland, E., Chisholm, A., Harrold, J., Maloney, N. G., Marty, L., et al.
Eating habits and lifestyle changes during COVID-19 lockdown: An Italian survey.
(2020). Obesity, eating behavior and physical activity during COVID-19 lockdown: A
Journal of Translational Medicine, 18(1), 229. https://doi.org/10.1186/s12967-020-
study of UK adults. Appetite, 104853.
02399-5
Rosenbaum, D. L., & White, K. S. (2015). The relation of anxiety, depression, and stress to
Dominy, N. L., Johnson, W. B., & Koch, C. (2000). Perception of parental acceptance in
binge eating behavior. Journal of Health Psychology, 20(6), 887–898. https://doi.org/
women with binge eating disorder. Journal of Psychology, 134(1), 23–36. https://doi.
10.1177/1359105315580212
org/10.1080/00223980009600846
Rossi, R., Socci, V., Talevi, D., Mensi, S., Niolu, C., Pacitti, F., et al. (2020). COVID-19
Europe is in a new phase of reopening. (2020). But it’s hardly a return to normal. https:
pandemic and lockdown measures impact on mental health among the general
//edition.cnn.com/2020/05/04/europe/europe-coronavirus-reopening-lockdown-i
population in Italy. Frontiers in Psychiatry, 11, 790.
ntl/index.html. (Accessed 6 November 2020).

8
C. Cecchetto et al. Appetite 160 (2021) 105122

Salari, N., Hosseinian-Far, A., Jalali, R., Vaisi-Raygani, A., Rasoulpoor, S., Statista. (2020b). Sales value growth of fresh food products during the coronavirus
Mohammadi, M., et al. (2020). Prevalence of stress, anxiety, depression among the (COVID-19) outbreak in Italy in 2020. https://www.statista.com/statistics/1111159
general population during the COVID-19 pandemic: A systematic review and meta- /fresh-food-products-during-the-coronavirus-outbreak-italy/. (Accessed 6 November
analysis. Globalization and Health, 16(1), 1–11. 2020).
Scarmozzino, F., & Visioli, F. (2020). Covid-19 and the subsequent lockdown modified van Strien, T., Frijters, J., Bergers, G. P.a., & Defares, P. B. (1986). The Dutch Eating
dietary habits of almost half the population in an Italian sample. Foods, 9(5), 675. Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and
https://doi.org/10.3390/foods9050675 external eating behavior. International journal of eating disorders, 5(2), 295–315.
Scharmer, C., Martinez, K., Gorrell, S., Reilly, E. E., Donahue, J. M., & Anderson, D. A. https://doi.org/10.1002/1098-108X(198602)5:2<295. AID-EAT2260050209>3.0.
(2020). Eating disorder pathology and compulsive exercise during the COVID-19 CO;2-T.
public health emergency: Examining risk associated with COVID-19 anxiety and van Strien, T., Herman, C. P., Anschutz, D. J., Engels, R. C. M. E., & de Weerth, C. (2012).
intolerance of uncertainty. International Journal of Eating Disorders, 53(12), 2049–2054. Moderation of distress-induced eating by emotional eating scores. Appetite, 58(1),
Schneider, K. L., Appelhans, B. M., Whited, M. C., Oleski, J., & Pagoto, S. L. (2010). Trait 277–284. https://doi.org/10.1016/j.appet.2011.10.005
anxiety, but not trait anger, predisposes obese individuals to emotional eating. Talbot, L. S., Maguen, S., Epel, E. S., Metzler, T. J., & Neylan, T. C. (2013). Posttraumatic
Appetite, 55(3), 701–706. https://doi.org/10.1016/j.appet.2010.10.006 stress disorder is associated with emotional eating. Journal of Traumatic Stress, 26(4),
Schulz, S., & Laessle, R. G. (2010). Associations of negative affect and eating behaviour in 521–525. https://doi.org/10.1002/jts.21824
obese women with and without binge eating disorder. Eating and Weight Disorders - Tan, C. C., & Chow, C. M. (2014). Stress and emotional eating: The mediating role of
Studies on Anorexia. Bulimia and Obesity, 15(4), e287–e293. https://doi.org/ eating dysregulation. Personality and Individual Differences, 66, 1–4. https://doi.org/
10.1007/BF03325311 10.1016/j.paid.2014.02.033
Shankman, S. A., Funkhouser, C. J., Klein, D. N., Davila, J., Lerner, D., & Hee, D. (2018). Taylor, G. J., Parker, J. D. A., Bagby, R. M., & Bourke, M. P. (1996). Relationships
Reliability and validity of severity dimensions of psychopathology assessed using the between alexithymia and psychological characteristics associated with eating
Structured Clinical Interview for DSM-5 (SCID). International Journal of Methods in disorders. Journal of Psychosomatic Research, 41(6), 561–568.
Psychiatric Research, 27(1). e1590. Tchanturia, K., Davies, H., Roberts, M., Harrison, A., Nakazato, M., Schmidt, U., et al.
Sifneos, P. E. (1973). The prevalence of ‘alexithymic’ characteristics in psychosomatic (2012). Poor cognitive flexibility in eating disorders: Examining the evidence using
patients. Psychotherapy and Psychosomatics, 22(2–6), 255–262. https://doi.org/ the Wisconsin card sorting task. PloS One, 7(1). https://doi.org/10.1371/journal.
10.1159/000286529 pone.0028331. e28331.
Sim, K., Chua, H. C., Vieta, E., & Fernandez, G. (2020). The anatomy of panic buying Team, R. C. (2017). R: A language and environment for statistical computing, 3.4.3
related to the current COVID-19 pandemic. Psychiatry Research, 288. https://doi. http://www.r-project.org/.
org/10.1016/j.psychres.2020.113015, 113015. Torres, S. J., & Nowson, C. A. (2007). Relationship between stress, eating behavior, and
Sobal, J., & Stunkard, A. J. (1989). Socioeconomic status and obesity: A review of the obesity. Nutrition, 23(11), 887–894. https://doi.org/10.1016/j.nut.2007.08.008
literature. Psychological Bulletin, 105(2), 260. Wallis, D. J., & Hetherington, M. M. (2004). Stress and eating: The effects of ego-threat
Spinosa, J., Christiansen, P., Dickson, J. M., Lorenzetti, V., & Hardman, C. A. (2019). and cognitive demand on food intake in restrained and emotional eaters. Appetite, 43
From socioeconomic disadvantage to obesity: The mediating role of psychological (1), 39–46. https://doi.org/10.1016/j.appet.2004.02.001
distress and emotional eating. Obesity, 27(4), 559–564. Ward, A., Ramsay, R., & Treasure, J. (2000). Attachment research in eating disorders.
Statista. (2020a). Percentage growth in sales of food products during the coronavirus British Journal of Medical Psychology, 73(Pt 1), 35–51. https://doi.org/10.1348/
(COVID-19) outbreak in Italy between February and March 2020, by category. https 000711200160282
://www.statista.com/statistics/1109965/best-selling-food-products-during-cor World Health Organization. (2018). Obesity and overweight, Key facts. http://www.
onavirus-italy/. (Accessed 11 November 2020). who.int/news-room/fact-sheets/detail/obesity-and-overweight.