=Paper=
{{Paper
|id=Vol-2730/paper20
|storemode=property
|title='Paper and pencil' vs. 'online' assessment: exploring measurement invariance of the Yale Food Addiction Scale 2.0 in inpatients with severe obesity and the general population
|pdfUrl=https://ceur-ws.org/Vol-2730/paper20.pdf
|volume=Vol-2730
|authors=Alessandro Rossi,Ashley N. Gearhardt,Gianluca Castelnuovo,Stefania Mannarini
|dblpUrl=https://dblp.org/rec/conf/psychobit/RossiGCM20a
}}
=='Paper and pencil' vs. 'online' assessment: exploring measurement invariance of the Yale Food Addiction Scale 2.0 in inpatients with severe obesity and the general population==
https://ceur-ws.org/Vol-2730/paper20.pdf
‘Paper and pencil’ vs. ‘online’ assessment: exploring
measurement invariance of the Yale Food Addiction
Scale 2.0 in inpatients with severe obesity and the general
population
Alessandro Rossi1,2 [0000-0001-7000-5999], Ashley N. Gearhardt3 [0000-0003-3843-5731], Gianluca
Castelnuovo4,5 [0000-0003-2633-9822], Stefania Mannarini1,2 [0000-0002-8446-785X]
1 Department of Philosophy, Sociology, Education, and Applied Psychology, Section of
Applied Psychology, University of Padova, Padova, Italy
2Interdepartmental Center for Family Research, University of Padova, Padova, Italy
3Department of Psychology, University of Michigan, AnnArbor, MI, USA
4 Psychology Research Laboratory, Ospedale San Giuseppe, IRCCS, Istituto Auxologico
Italiano, Verbania, Italy
5Department of Psychology, Catholic University of Milan, Milan, Italy
alessandro.rossi.27@phd.unipd.it
Abstract.
During the last few years, food addiction (FA) increased its popularity both in
clinical and research practice. To date, the gold standard for the assessment of
FA is the Yale Food Addiction Scale 2.0 (YFAS2.0) – that conceptualizes FA as
a substance-related and addictive disorder (SRAD), according to the DSM-5 di-
agnostic criteria. Despite an intensive worldwide use across heterogeneous pop-
ulations, to date, no studies assessed the factorial validity and measurement in-
variance (MI) of the YFAS2.0 across samples that filled out the questionnaire
with different assessment methods. The present study aimed to: extend evidence
of YFAS2.0 factorial validity and explore its MI across four different groups.
Participants (N = 470) completed the Italian YFAS2.0. Participants were grouped
on the basis of their recruitment (inpatients with severe obesity vs. the general
population) and the assessment methodologies (‘paper and pencil’ assessment vs.
‘online’ assessment). The CFA showed good fit indexes for the overall sample
as well as for each of the different groups. Also, configural, metric, and strong
invariance were achieved across the four groups. Findings suggested that the Ital-
ian YFAS2.0 can be considered a good psychometrically-based and structural
invariant instrument for the assessment of FA in different samples and across
different methods of assessment.
Keywords: Food addiction, severe obesity, confirmatory factor analysis, meas-
urement invariance, assessment, online survey.
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2
1 Introduction
Food addiction (FA) has become more and more popular [1-3] and has received
increasing interest in both clinical and research practice [3, 4]. Its popularity could be
due to its dual nature [2-4]. Indeed, on one hand, FA seems to share the clinical char-
acteristics of some eating disorders [4-6]. On the other hand, some individuals seem to
be addicted to certain kinds of food [7, 8]: neuroscience showed a neural activation in
response to high-caloric palatable foods (e.g.: sweetened foods, foods with high levels
of refined carbohydrates, and food with added fat) comparable to what found in re-
sponse to addictive drugs [8-10].
Considering this background, to date, the gold standard for the assessment of FA at
the light of DSM-5 SRAD criteria is the second version of the Yale Food Addiction
Scale (YFAS 2.0 [11, 12]). The YFAS 2.0 concerns the key behavioral features of ad-
diction-like eating behaviors over the previous year: (A) ‘Substance taken in larger
amount and for a longer period than intended’; (B) ‘Persistent desire or repeated unsuc-
cessful attempts to quit’; (C) ‘Much time/activity to obtain, use, recover’; (D) ‘Im-
portant social, occupational, or recreational activities given up or reduced’; (E) ‘Use
continues despite knowledge of adverse consequences (e.g., emotional problems, phys-
ical problems)’; (F) ‘Tolerance’; (G) ‘Characteristic withdrawal symptoms; substance
taken to relieve withdrawal’; (H) ‘Continued use despite social or interpersonal prob-
lems’; (I) ‘Failure to fulfill major role obligation (e.g., work, school, home)’; (J) ‘Use
in physically hazardous situations’; (K) ‘Craving, or a strong desire or urge to use’; (L)
‘Significant distress/impairment’.
Simultaneously, during the last few years, several studies underlined the need for
an evolution of psychological interventions as well as psychological assessment, sug-
gesting increasing the use of technology-based tools – such as online psychotherapy
and/or online surveys [13-18].
In this context, despite a large number of studies tested psychometrical properties of
the YFAS 2.0, none of them explored its measurement invariance (MI) across samples
of individuals who filled out the YFAS 2.0 with different methodologies – namely, the
classical method (‘paper and pencil’ assessment) and a computer-based one (‘online’
survey/online assessment).
Thus, the present study aimed to assess for the first time the structural validity and
MI of the YFAS 2.0 in four samples of subjects assessed with different methodologies.
2 Material and Methods
2.1 Sample size
Considering the statistical analyses necessary for this study, the sample size was
calculated a priori according to the “n:q criterion”: n is the number of participants and
q is the number of (free) model parameters to be estimated [19]. Consequently, five
subjects per free parameter (5:22; nminimum = 110) were guaranteed.
3
2.2 Procedure and Participants
Four groups of individuals were enrolled: (A) a first sample of inpatients with se-
vere obesity who filled out the YFAS 2.0 with the ‘paper and pencil’ assessment; a
second sample of (B) inpatients with severe obesity who compiled the YFAS 2.0 with
an ‘online’ assessment; a third sample of (C) individuals from the general population
who filled out the YFAS 2.0 with the ‘paper and pencil’ assessment; a fourth sample of
(D) individuals from the general population who compiled the YFAS 2.0 with an
‘online’ assessment. Exclusion criteria were: (A) illiteracy; (B) inability to complete or
finish the assessment. All participants signed informed consent.
Concerning the ‘paper and pencil’ assessment method, the sample of inpatients with
severe obesity (Body Mass Index; BMI > 35) was recruited at the San Giuseppe Hos-
pital, IRCCS, Istituto Auxologico Italiano, Verbania (Italy) whether individuals from
the general population were enrolled in Padua (Italy).
Regarding the ‘online’ assessment method, an online survey was developed and
disseminated using the Qualtrics software for data collection. Moreover, the ‘snowball
sampling method’ was used to recruit participants through personal invitations or ma-
terials advertised via social media platforms (i.e., Facebook, Twitter).
The final sample comprised 470 participants [171 males (36.4%) and 299 females
(63.6%) aged from 18 to 84 years (mean = 45.02, SD = 17.715)].
More in detail, the first sample was composed of (A) inpatients with severe obesity
who filled out the YFAS 2.0 with the ‘paper and pencil’ assessment: n = 121; 43 males
(35.5%) and 78 females (64.5%) aged from 20 to 78 years (mean = 56.59, SD = 12.43),
with a BMI ranged from 35.06 to 65.82 (mean = 42.66, SD = 6.05).
The second sample was composed of (B) inpatients with severe obesity who com-
piled the YFAS 2.0 with an ‘online’ assessment: n = 114; 56 males (49.1%) and 58
females (50.9%) aged from 18 to 77 years (mean = 54.89, SD = 12.16), with a BMI
ranged from 35.16 to 80.11 (mean = 43.12, SD = 6.79).
The third sample was composed of (C) individuals from the general population who
filled out the YFAS 2.0 with the ‘paper and pencil’ assessment: n = 118; 39 males
(33.1%) and 79 females (66.9%) aged from 19 to 84 years (mean = 36.03, SD = 16.09),
with a BMI ranged from 15.37 to 34.37 (mean = 23.08, SD = 3.70).
The fourth sample was composed of (D) individuals from the general population
who compiled the YFAS 2.0 with an ‘online’ assessment: n = 117; 33 males (28.2%)
and 84 females (71.8%) aged from 22 to 79 years (mean = 33.32, SD = 15.64), with a
BMI ranged from 17.04 to 31.25 (mean = 22.38, SD = 3.54).
2.3 Measure
The Yale Food Addiction Scale 2.0 (YFAS2.0)
The Italian version of the YFAS 2.0 [4, 11, 12] is a 35-item self-report questionnaire
assessing FA symptoms in both general and clinical populations. The YFAS 2.0 as-
sesses the 11 DSM-5 diagnostic criteria for SRAD and the significant impairment
and/or distress related to food. The scale is scored on an 8-point Likert type scale (rang-
ing from 0 = “never” to 7 = “every day”). According to an item-specific cutoff, each
4
of the 35 items has to be dichotomized (0 = “non-endorsed” vs. 1 = “endorsed”) to
compute the two scoring options: the symptom count score and the diagnostic score
[11]. The first one is the symptom count score – namely – the number of FA criteria
(ranging from 0 to 11) experienced during the previous year. The ‘impairment/distress’
criterion should not be considered in this count [14]. The second one is the diagnostic
score: FA could be diagnosed as mild if there are 2 or 3 symptoms and clinically sig-
nificant impairment/distress, moderate if there are 4 or 5 symptoms and significant im-
pairment/distress, or severe if there are 6 or more symptoms and significant impair-
ment/distress [14].
2.4 Statistical Analyses
Statistical analyses were performed with R software and the following packages:
‘lavaan’, ‘semTools’, and ‘semPlot’.
A confirmatory factor analysis (CFA) was performed using the diagonally weighted
least square (DWLS) estimator. A single-factor first-order structure was specified [4,
11]: each of the eleven symptoms (from ‘Criterion A’ to ‘Criterion K’) loaded onto a
latent dimension.
Factorial validity was assessed using the Satorra-Bentler χ2 (a non-significant χ2
indicating a better model fit). Goodness-of-fit indices were also used, with the follow-
ing criteria as cutoffs for ideal fit [20]: the Root-Mean-Square Error of Approximation
(RMSEA < 0.05); the Comparative Fit Index (CFI > 0.95); and the ratio of χ2 to the
degrees of freedom (χ2/df < 3).
As reported in Figure 1, measurement invariance (MI) analysis was computed to
evaluate whether the aforementioned structure of the Italian version of the YFAS 2.0
was invariant between (A) a sample of inpatients with severe obesity who filled out the
YFAS 2.0 with the ‘paper and pencil’ assessment; (B) a sample of inpatients with se-
vere obesity who compiled the YFAS 2.0 with an ‘online’ assessment; (C) a sample of
individuals from the general population who filled out the YFAS 2.0 with the ‘paper
and pencil’ assessment; (D) a sample of individuals from the general population who
compiled the YFAS 2.0 with an ‘online’ assessment.
The “standard” procedure for structural models with categorical indicators was fol-
lowed [21]. First, the first-order model was constrained to equality between the four
groups (Configural Invariance). Second, both the factor loadings and items’ thresholds
were simultaneously constrained to equality across groups (Metric+Strong Invariance).
Third, the latent factor means (Latent Means Invariance) were constrained to equality
between groups.
Measurement invariance was assessed by using test differences in three fit indices
and with the following criteria as cutoffs for model equivalence: DIFFTEST (equal to
Δχ2; p-value > 0.050), ΔCFI (< 0.010), ΔRMSEA (< 0.015) [21]. An excess of the
cutoff in two out of these three indices, combined with worse fit indices, was considered
as the evidence of model non-invariance.
5
Figure 1. conceptual models for the four groups tested.
3 Results
3.1 Structural validity
The single-factor model showed a good fit to the data for the overall sample. Despite
the Chi-square statistic resulted to be statistically significant [χ2 (44) = 89.241; p <
0.001], all the other fit indices revealed a good fit to the data: the RMSEA = 0.047;
90%CI 0.033–0.061; p(RMSEA < 0.05) = 0.063, the CFI = 0.995, and the χ2/df = 2.028.
As reported in Table 1, all the items’ loadings were statistically significant and ranged
from 0.745 (Criterion A) to 0.911 (Criterion E); mean = 0.835; SD = 0.055.
Regarding (A) the sample of inpatients with severe obesity who filled out the YFAS
2.0 with the ‘paper and pencil’ assessment, all of the fit indices revealed a good fit to
the data: χ2 (44) = 26.588; p = 0.982 ns, the RMSEA = 0.000 [90%CI 0.000–0.000;
p(RMSEA < 0.05) = 0.999], the CFI = 1.000, and the χ2/df = 0.604. Items’ loadings
ranged from 0.754 (Criterion A) to 0.931 (Criterion K); mean = 0.833; SD = 0.071.
Regarding (B) the sample of inpatients with severe obesity who compiled the YFAS
2.0 with an ‘online’ assessment, all of the fit indices revealed a good fit to the data: χ 2
(44) = 55.081; p = 0.122 ns, the RMSEA = 0.047 [90%CI 0.000–0.083; p(RMSEA <
6
0.05) = 0.519], the CFI = 0.995, and the χ 2/df = 1.252. Items’ loadings ranged from
0.477 (Criterion H) to 0.919 (Criterion K); mean = 0.790; SD = 0.136.
Regarding (C) the sample of individuals from the general population who filled out
the YFAS 2.0 with the ‘paper and pencil’ assessment, all of the fit indices revealed a
good fit to the data: χ2 (44) = 38.999; p = 0.685 ns, the RMSEA = 0.000 [90%CI 0.000–
0.050; p(RMSEA < 0.05) = 0.949], the CFI = 1.000, and the χ2/df = 0.886. Items’ load-
ings ranged from 0.602 (Criterion B) to 0.982 (Criterion E); mean = 0.782; SD = 0.120.
Regarding (D) the sample of individuals from the general population who compiled
the YFAS 2.0 with an ‘online’ assessment, all of the fit indices revealed a good fit to
the data: χ2 (44) = 35.813; p = 0.805 ns, the RMSEA = 0.000 [90%CI 0.000–0.041;
p(RMSEA < 0.05) = 0.976], the CFI = 1.000, and the χ 2/df = 0.814. Items’ loadings
ranged from 0.695 (Criterion A) to 0.952 (Criterion K); mean = 0.867; SD = 0.092.
Overall Sample Sample Sample Sample
sample A B C D
Criterion A 0.745 0.754 0.880 0.683 0.695
Criterion B 0.795 0.855 0.790 0.602 0.735
Criterion C 0.883 0.856 0.883 0.900 0.933
Criterion D 0.818 0.900 0.601 0.712 0.913
Criterion E 0.911 0.882 0.856 0.982 0.934
Criterion F 0.864 0.870 0.871 0.907 0.772
Criterion G 0.809 0.779 0.844 0.766 0.944
Criterion H 0.759 0.695 0.477 0.821 0.842
Criterion I 0.857 0.862 0.735 0.786 0.903
Criterion J 0.844 0.781 0.832 0.623 0.917
Criterion K 0.901 0.931 0.919 0.818 0.952
Table 1. Standardized factor loadings for each sample.
3.2 Measurement invariance
Configural Invariance. A first-order configural invariance model was specified be-
tween groups. Good model fit indices were found (χ 2 (176) = 156.48, p = 0.852 ns; the
RMSEA = 0.000; the CFI = 1.000; and the χ2/df = 0.998), suggesting that the factor
structure was similar between the four groups.
Metric+Strong Invariance. Also the first-order metric plus strong invariance model
still fitted data well: χ2 (203) = 201.02, p = 0.526 ns; the RMSEA = 0.000; the CFI =
1.000; and the χ2/df = 0.990. Non-significant decreases – in two out of three fit indices
– were found (DIFTEST = 44.54; p = 0.018; ΔRMSEA = 0.000; ΔCFI = 0.001), indi-
cating that items were equivalently related to the latent factor between groups.
Latent Means Invariance. Finally, also the first-order latent means invariance model
revealed adequate fit indices: χ2 (206) = 274.34, p = 0.001; the RMSEA = 0.053; the
CFI = 0.993; and the χ2/df = 1.332. Moreover, statistically significant decreases in fit
indices compared to the previous invariance model were found (DIFTEST = 73.32, p <
0.001; ΔRMSEA = 0.053; ΔCFI = -0.007), suggesting that groups had not the same
expected item response at the same absolute level of the trait.
7
4 Discussion
To date, an increasing number of studies underline the necessity of an evolution of
psychological interventions as well as psychological assessment toward the use of tech-
nology-based tools – such as online psychotherapies and/or online surveys [13, 14, 17].
This necessity for technology-based change may be fostered by a significant number of
people avoid seeking psychological help and (social) support [22, 23] despite maladap-
tive behaviors as well as several related psychological issues [24-37]. On one hand,
some of these people may be reluctant to seek professional help due to the associated
stigma [38-41]. On the other hand, some individuals may deny the problem, leading
them to think that it will probably resolve itself naturally [23, 42, 43], thus choosing to
manage the psychological issue on their own instead of starting a structured psycholog-
ical intervention [23, 44]. Moreover, the urgency to improve technology-based assess-
ment and psychological intervention could be due to the new categories of patients who
often struggle to turn to clinical services in person – such as people with an infective
disease or chronic progressively disabling disease (i.e. severe obesity). Also, people
with severe obesity may show the comorbidity of unhealthy behavior and/or psycho-
pathological ones that exacerbate their illness – i.e. emotional eating and/or FA.
In this context, the YFAS 2.0 could be considered as the ‘gold standard’ for the
assessment of FA in both clinical and the general population [11, 12, 45]. However, no
previous study compared the factorial structure of YFAS 2.0 among samples that com-
plied this scale with different assessment methodologies – such as the classical ‘paper
and pencil’ assessment or a technology-based assessment (online survey). The present
study aimed to fill this gap assessing the MI of the YFAS 2.0 across four groups.
The CFA revealed that the Italian YFAS 2.0 showed a good fit to the data for the
overall sample. Also, the CFA showed that each aforementioned single group provided
good fit indices, in line with Italian validation studies. Statistical analyses successfully
replicated the original factorial structure of the YFAS 2.0 – suggesting that it could be
considered as a good psychometrically-based instrument for the assessment of FA.
Moreover, configural, metric, and scalar invariance were achieved across the four
abovementioned samples. These results are in line with previous research [4] and sug-
gest that individuals in the four samples interpreted the YFAS 2.0 items in the same
way, with the same strength, and with the same starting point – the factorial structure
was equal across samples and items were equally related to the latent construct with
equal thresholds. However, the latent trait was not equally distributed between groups:
latent means were different across samples.
Despite these interesting findings, some limitations have to be highlighted. First,
although the sample size was adequate to perform a CFA and MI, the number of indi-
viduals in each group was small. Also, this study lacks a second administration of the
scale – thus not allowing to perform longitudinal analyses.
Overall, these findings suggest that the comparisons between these samples should
be taken with caution (different latent means), but these groups were comparable (due
to equal items threshold). Finally, these results suggest Italian YFAS 2.0 should be
considered as a starting point for the assessment of FA and in the planning of psycho-
logical treatments in different samples and across different methods of assessment.
8
References
1. Meule, A.: Back by Popular Demand: A Narrative Review on the History of Food Addiction
Research. The Yale journal of biology and medicine 88, 295-302 (2015)
2. Albayrak, Ö., Wölfle, S.M., Hebebrand, J.: Does Food Addiction Exist? A Phenomenological
Discussion Based on the Psychiatric Classification of Substance-Related Disorders and
Addiction. Obesity facts 5, 165-179 (2012)
3. Gordon, E.L., Ariel-Donges, A.H., Bauman, V., Merlo, L.J.: What Is the Evidence for “Food
Addiction?” A Systematic Review. Nutrients 10, 477 (2018)
4. Manzoni, G.M., Rossi, A., Pietrabissa, G., Mannarini, S., Fabbricatore, M., Imperatori, C.,
Innamorati, M., Gearhardt, A.N., Castelnuovo, G.: Structural validity, measurement
invariance, reliability and diagnostic accuracy of the Italian version of the Yale Food
Addiction Scale 2.0 in patients with severe obesity and the general population. Eating and
Weight Disorders - Studies on Anorexia, Bulimia and Obesity (2020)
5. Brewerton, T.D.: Food addiction as a proxy for eating disorder and obesity severity, trauma
history, PTSD symptoms, and comorbidity. Eating and weight disorders : EWD 22, 241-247
(2017)
6. Davis, C., Curtis, C., Levitan, R.D., Carter, J.C., Kaplan, A.S., Kennedy, J.L.: Evidence that
‘food addiction’ is a valid phenotype of obesity. Appetite 57, 711-717 (2011)
7. Meule, A., Gearhardt, A.N.: Five years of the Yale Food Addiction Scale: Taking stock and
moving forward. Current Addiction Reports 1, 193-205 (2014)
8. Volkow, N.D., Wang, G.-J., Fowler, J.S., Telang, F.: Overlapping neuronal circuits in
addiction and obesity: evidence of systems pathology. Philosophical Transactions of the
Royal Society B: Biological Sciences 363, 3191-3200 (2008)
9. Davis, C., Levitan, R.D., Kaplan, A.S., Kennedy, J.L., Carter, J.C.: Food cravings, appetite,
and snack-food consumption in response to a psychomotor stimulant drug: The moderating
effect of “food-addiction.”. Frontiers in psychology 5, 403 (2014)
10. Davis, C., Loxton, N.J., Levitan, R.D., Kaplan, A.S., Carter, J.C., Kennedy, J.L.: “Food
addiction” and its association with a dopaminergic multilocus genetic profile. Physiology &
behavior 118, 63–69 (2013)
11. Gearhardt, A.N., Corbin, W.R., Brownell, K.D.: Development of the Yale Food Addiction
Scale Version 2.0. Psychology of addictive behaviors : journal of the Society of Psychologists
in Addictive Behaviors 30, 113-121 (2016)
12. Meule, A., Gearhardt, A.N.: Ten Years of the Yale Food Addiction Scale: a Review of
Version 2.0. Current Addiction Reports 1-11 (2019)
13. L'Hadi, B., Alain, J.: Can Online Peer Assessment be Trusted? Journal of Educational
Technology & Society 12, 257-268 (2009)
14. Vallejo, M.A., Jordán, C.M., Díaz, M.I., Comeche, M.I., Ortega, J.: Psychological
Assessment via the Internet: A Reliability and Validity Study of Online (vs Paper-and-Pencil)
Versions of the General Health Questionnaire-28 (GHQ-28) and the Symptoms Check-List-
90-Revised (SCL-90-R). J Med Internet Res 9, e2 (2007)
15. Rossi, A., Panzeri, A., Pietrabissa, G., Manzoni, G.M., Castelnuovo, G., Mannarini, S.: The
Anxiety-Buffer Hypothesis in the Time of COVID-19: When Self-Esteem Protects From the
Impact of Loneliness and Fear on Anxiety and Depression. Frontiers in psychology, Front.
Psychol. 11, 2177. (2020)
9
16. Parola, A., Rossi, A., Tessitore, F., Troisi, G., Mannarini, S.: Mental health through the
COVID-19 quarantine: a growth curve analysis on Italian young adults. Frontiers in
psychology (2020)
17. Skinner, H.A., Pakula, A.: Challenge of computers in psychological assessment. Professional
Psychology: Research and Practice 17, 44-50 (1986)
18. Parola, A.: Novel Coronavirus Outbreak and Career Development: a narrative approach into
the meaning of Italian university graduates. Frontiers in psychology (2020)
19. Kline, R.B.: Principles and practice of structural equation modeling. The Guilford Press, New
York (2016)
20. van de Schoot, R., Lugtig, P., Hox, J.: A checklist for testing measurement invariance.
European Journal of Developmental Psychology 9, 486-492 (2012)
21. Millsap, R.E.: Statistical approaches to measurement invariance. Routledge, New York
(2012)
22. Ratti, M.M., Rossi, A., Delli Zotti, G.B., Sarno, L., Spotti, D.: Social support, psychological
distress and depression in hemodialysis patients. Psicologia della Salute 112-122 (2017)
23. Rossi, A., Mannarini, S.: The Italian version of the attitudes toward seeking professional
psychological help scale – short form: The first contribution to measurement invariance.
TPM - Testing, Psychometrics, Methodology in Applied Psychology 26, 93-100 (2019)
24. Balestroni, G., Panzeri, A., Omarini, P., Cerutti, P., Sacco, D., Giordano, A., Pistono, M.,
Komici, K., Rossi Ferrario, S.: Psychophysical health of elderly inpatients in cardiac
rehabilitation: A retrospective cohort study. European journal of physical and rehabilitation
medicine 56, 197-205 (2020)
25. Balottin, L., Mannarini, S., Mensi, M.M., Chiappedi, M., Gatta, M.: Triadic interactions in
families of adolescents with anorexia nervosa and families of adolescents with internalizing
disorders. Frontiers in psychology 7, (2017)
26. Jackson, J.B., Pietrabissa, G., Rossi, A., Manzoni, G.M., Castelnuovo, G.: Brief strategic
therapy and cognitive behavioral therapy for women with binge eating disorder and comorbid
obesity: A randomized clinical trial one-year follow-up. Journal of consulting and clinical
psychology 86, 688-701 (2018)
27. Mannarini, S., Balottin, L., Munari, C., Gatta, M.: Assessing conflict management in the
couple: The definition of a latent dimension. Family Journal 25, 13-22 (2017)
28. Mannarini, S., Boffo, M.: The relevance of security: A latent domain of attachment
relationships. Scandinavian journal of psychology 55, 53-59 (2014)
29. Mannarini, S., Boffo, M., Bertucci, V., Andrisani, A., Ambrosini, G.: A Rasch-based
dimension of delivery experience: spontaneous vs. medically assisted conception. Journal of
clinical nursing 22, 2404-2416 (2013)
30. Manzoni, G.M., Rossi, A., Marazzi, N., Agosti, F., De Col, A., Pietrabissa, G., Castelnuovo,
G., Molinari, E., Sartorio, A.: Feasibility, Validity, and Reliability of the Italian Pediatric
Quality of Life Inventory Multidimensional Fatigue Scale for Adults in Inpatients with
Severe Obesity. Obesity facts 11, 25-36 (2018)
31. Panzeri, A., Rossi Ferrario, S., Vidotto, G.: Interventions for Psychological Health of Stroke
Caregivers: A Systematic Review. Frontiers in psychology 10, (2019)
32. Pietrabissa, G., Manzoni, G.M., Rossi, A., Castelnuovo, G.: The MOTIV-HEART study: A
prospective, randomized, single-blind pilot study of brief strategic therapy and motivational
interviewing among cardiac rehabilitation patients. Frontiers in psychology 8, (2017)
10
33. Pietrabissa, G., Rossi, A., Borrello, M., Manzoni, G.M., Mannarini, S., Castelnuovo, G.,
Molinari, E.: Development and Validation of a Self-Determination Theory-Based Measure
of Motivation to Exercise and Diet in Children. Frontiers in psychology 11, (2020)
34. Pietrabissa, G., Rossi, A., Simpson, S., Tagliagambe, A., Bertuzzi, V., Volpi, C., Fava, G.,
Manzoni, G.M., Gravina, G., Castelnuovo, G.: Evaluation of the reliability and validity of
the Italian version of the schema mode inventory for eating disorders: short form for adults
with dysfunctional eating behaviors. Eating and Weight Disorders 25, 553-565 (2020)
35. Pietrabissa, G., Sorgente, A., Rossi, A., Simpson, S., Riva, G., Manzoni, G.M., Prochaska,
J.O., Prochaska, J.M., Cattivelli, R., Castelnuovo, G.: Stages of change in obesity and weight
management: factorial structure of the Italian version of the University of Rhode Island
Change Assessment Scale. Eating and weight disorders : EWD 22, 361-367 (2017)
36. Simpson, S., Pietrabissa, G., Rossi, A., Seychell, T., Manzoni, G.M., Munro, C., Nesci, J.B.,
Castelnuovo, G.: Factorial Structure and Preliminary Validation of the Schema Mode
Inventory for Eating Disorders (SMI-ED). Frontiers in psychology 9, 600 (2018)
37. Tamburin, S., Lacerenza, M.R., Castelnuovo, G., Agostini, M., Paolucci, S., Bartolo, M.,
Bonazza, S., Federico, A., Formaglio, F., Giusti, E.M., Manzoni, G.M., Mezzarobba, S.,
Pietrabissa, G., Polli, A., Turolla, A., Sandrini, G.: Pharmacological and non-
pharmacological strategiesin the integrated treatment of pain in neurorehabilitation: Evidence
and recommendations from the Italian consensus conference on pain in neurorehabilitation.
European journal of physical and rehabilitation medicine 52, 741-752 (2016)
38. Faccio, E., Belloni, E., Cipolletta, S., Iudici, A., Castiglioni, M., Mannarini, S.: The power
of weight and the weight of power in adolescence: a comparison between young and adult
women. Journal of Families Studies 25, 46-60 (2019)
39. Mannarini, S., Rossi, A.: Assessing mental illness stigma: A complex issue. Frontiers in
psychology 9, (2019)
40. Mannarini, S., Rossi, A., Munari, C.: How do education and experience with mental illness
interact with causal beliefs, eligible treatments and stigmatising attitudes towards
schizophrenia? A comparison between mental health professionals, psychology students,
relatives and patients. BMC Psychiatry 20, 167 (2020)
41. Mannarini, S., Boffo, M., Rossi, A., Balottin, L.: Etiological beliefs, treatments, stigmatizing
attitudes toward schizophrenia. What do Italians and Israelis think? Frontiers in psychology
8, (2018)
42. Rossi Ferrario, S., Panzeri, A.: Exploring illness denial of LVAD patients in cardiac
rehabilitation and their caregivers: A preliminary study. Artificial Organs (2020)
43. Rossi Ferrario, S., Panzeri, A., Anselmi, P., Vidotto, G.: Development and Psychometric
Properties of a Short Form of the Illness Denial Questionnaire. Psychology research and
behavior management 12, 727-739 (2019)
44. Cattivelli, R., Pietrabissa, G., Ceccarini, M., Spatola, C.A.M., Villa, V., Caretti, A., Gatti, A.,
Manzoni, G.M., Castelnuovo, G.: ACTonFOOD: Opportunities of ACT to address food
addiction. Frontiers in psychology 6, (2015)
45. Manzoni, G.M., Rossi, A., Pietrabissa, G., Varallo, G., Molinari, E., Poggiogalle, E., Donini,
L.M., Tarrini, G., Melchionda, N., Piccione, C., Gravina, G., Luxardi, G., Manzato, E.,
Schumann, R., Innamorati, M., Imperatori, C., Fabbricatore, M., Castelnuovo, G.: Validation
of the Italian Yale Food Addiction Scale in postgraduate university students. Eating and
weight disorders : EWD 23, 167-176 (2018)