Access to this full-text is provided by MDPI.
Content available from Behavioral Sciences
This content is subject to copyright.
Citation: Felgueras, N.; López-Díaz,
J.M.; Garrote, I. Effects of
Developmental Timing on Cognitive
and Behavioral Profiles in Fetal
Alcohol Spectrum Disorder:
Considerations for Education. Behav.
Sci. 2024,14, 431. https://doi.org/
10.3390/bs14060431
Academic Editor: Xiaochun Xie
Received: 1 May 2024
Revised: 20 May 2024
Accepted: 21 May 2024
Published: 22 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
behavioral
sciences
Article
Effects of Developmental Timing on Cognitive and Behavioral
Profiles in Fetal Alcohol Spectrum Disorder: Considerations
for Education
Nerea Felgueras * , JoséMaría López-Díaz * and Inmaculada Garrote
High-Performance Research Group on Inclusive Education, People with Disabilities and Universal
Accessibility (DIVERSIA), Department of Educational Sciences, Faculty of Education and Sport and
Interdisciplinary Studies, King Juan Carlos University, 28933 Fuenlabrada, Spain; inmaculada.garrote@urjc.es
*Correspondence: nerea.felgueras@urjc.es (N.F.); josemaria.lopez@urjc.es (J.M.L.-D.)
Abstract: Associations and families demand the need to raise awareness of the implications in the
cognitive and behavioral development of children with Fetal Alcohol Spectrum Disorder (FASD) that
affect their learning and school participation. This study aims to generate a profile of executive and
behavioral functioning in children and adolescents diagnosed with FASD. A probabilistic sampling
by clusters (associations for individuals with FASD) is applied. The sample is composed of 66 families
from three associations. The BRIEF-2 and SENA tests were administered to assess executive and
behavioral functioning domains. Data analysis found that the executive and behavioral functioning
profile of individuals with FASD varies with age, with greater impairment in middle and late
adolescence. Likewise, the domain of executive functioning most affected in any of the developmental
stages is working memory. Finally, cognitive impairment in the executive functioning domains has a
direct impact on the social and adaptive functioning of people with FASD.
Keywords: childhood; adolescence; executive functions; behavioral problems; prenatal alcohol
exposure; educational needs
1. Introduction
Fetal Alcohol Spectrum Disorder (FASD) brings together a heterogeneity of condi-
tions related to prenatal alcohol exposure associated with facial abnormalities; unusual
growth and weight (considering established age parameters); and irregularities in brain
structures and neurocognitive difficulties, including impaired intellectual capacity, learning
difficulties, memory impairment, and deficits in visuospatial ability, executive functioning,
and self-regulatory capacity. It is considered the leading non-genetic cause of completely
preventable intellectual disability [1].
The literature surrounding this disorder calls for an accurate and universal detection
and diagnostic system that enables early identification of its core manifestations [
2
,
3
]. Based
on these arguments and considering the prevalence data in Spain, estimated at 22.2% cases
of FASD per 10,000 inhabitants [
4
], as well as the lack of knowledge among healthcare and
educational professionals regarding this disorder [
5
–
11
], it is essential to have a behavioral
and cognitive profile that considers the developmental stage.
Therefore, research on FASD needs to progress towards evaluating the cognitive and
behavioral domains affected by prenatal alcohol exposure, considering not only the clinical
perspective but also the viewpoint of caregivers of individuals affected by this disorder [
12
].
Available evidence indicates that families and teachers are the best informants of the
observable behaviors of children and adolescents with this diagnosis [13,14].
Regarding executive functions, these are defined as a multidimensional construct
referring to interrelated abilities that enable a person to direct their behavior towards a
goal [
15
]. The most representative impairments in executive functions in individuals with
Behav. Sci. 2024,14, 431. https://doi.org/10.3390/bs14060431 https://www.mdpi.com/journal/behavsci
Behav. Sci. 2024,14, 431 2 of 17
FASD are difficulties in organizational and planning abilities, concrete thinking, inhibition
problems, understanding cause–effect relationships, following instructions, developing
a plan of action with a predefined objective, making informed judgments, generalizing,
and understanding abstract concepts [
2
]. At the same time, behavioral impairments are
understood as a set of maladaptive behaviors depending on the specific circumstances,
where the common element lies in difficulties with behavior, self-control, and emotion
regulation [
16
]. The most probable behavioral impairments in FASD include difficulties
with emotion regulation, behavioral manifestations of executive function dysregulation,
attention deficit, hyperactivity, impulsivity, irritability, sleep disorders, deficits in social
skills, and difficulties in adaptive behavior and social communication [
2
]. Likewise, the
influence of gender on the expression of difficulties in executive and behavioral functioning
in cases of comorbidity with ADHD should be considered [
17
]. In this sense, gender plays
an important role in the profile of cognitive and behavioral functioning, with a greater
deterioration in males [18].
The most significant implications of behavioral disorders are the transgression of
people’s rights, as well as of established social norms, which often leads to repeated
problems with justice and serious difficulties in adapting to the surrounding environment.
Social ignorance about the implications of the behavioral alterations of this disorder in the
autonomous life of the affected persons favors limitations in participation in society [
19
–
21
].
This study aims to create a profile of executive and behavioral functioning of the
people with FASD according to developmental stage, gender, and diagnosis through the
contributions of the family member who has the greatest opportunities for observation of
the affected person.
The following working hypotheses are considered:
•
It is observed that the contribution of families through the BRIEF-2 and SENA assess-
ments allows for the development of a cognitive and behavioral functioning pattern
for children and adolescents with FASD.
•
It is contemplated that age is a factor that modulates significant differences in the
neurocognitive and behavioral profiles of individuals affected by FASD.
2. Materials and Methods
2.1. Study Design
The Research Ethics Committee of the Universidad Rey Juan Carlos issued a favorable
opinion in September 2020 on the adequacy of the planning and research design of this
study, meeting the necessary ethical requirements in relation to the objectives of the research
project. Furthermore, all participants in this study agreed to the requirements of this study
and consented to participate in this study.
It is a quasi-experimental design, being the only viable alternative in the impossibil-
ity of conducting a random assignment research, which could hinder the control of all
variables involved in families’ responses. Additionally, it is identified with a quantitative
methodology. It is characterized by its objectivity and the use of tools that allow the precise
measurement of specific psychological constructs, enabling the statistical treatment of the
data and the generalization of the results to the population.
The method followed in the study is shown in the flow chart below (Figure 1).
Behav. Sci. 2024,14, 431 3 of 17
Behav. Sci. 2024, 14, x FOR PEER REVIEW 3 of 17
The method followed in the study is shown in the flow chart below (Figure 1).
Figure 1. Research Method Flowchart.
2.2. Participants
The aainment of the primary research unit was carried out following a probabilistic
cluster sampling approach, resulting in five conglomerate groups. Each group corre-
sponds to one of the registered associations nationwide related to FASD and Fetal Alcohol
Syndrome (FAS), regulated under the provisions of the Organic Law 1/2002, dated 22
March, regulating the Right of Association. The sample size for each of the conglomerates
is distributed as follows: 250 cases registered in VISUAL TEAF (stands for FASD), 305
cases in AFASAF (stands for Association of Families Affected by Fetal Alcohol Syndrome),
80 cases in SAFGROUP (stands for FAS), 60 cases in Tolerancia-Cero, and 40 cases in Zero
FAS (stands for FAS). Subsequently, three groups were selected according to random sam-
pling rules: VISUAL TEAF, SAFGROUP, and AFASAF. The sample consists of a total of
66 families of children with FASD. Regarding the association of origin, 40.9% belong to
AFASAF (n = 27), 27.3% to SAFGROUP (n = 18), and 31.8% (n = 21) are associated with
VISUAL TEAF. The distribution of participants includes 57.6% men (n = 38) and 42.4%
women (n = 28). The breakdown by age groups is as follows: 19.7% (n = 13) in the child
group, 25.8% (n = 17) in the early adolescence group, 21.2% (n = 14) in the middle
ORGANIZATIONS
UNIT OF ANALYSIS
VISUAL
TEAF
AFASAF
(305)
SAF GROUP
(80)
NON-PARTICIPANTS
559 families do not initi-
ate any contact.
6 families initiate, but do
not continue contact.
2 families give IC, but do
not finalize collaboration
in the study.
2 families excluded.
PARTICIPANTS
66 families collaborate in
the study
635 contacted
76 located
AGE
13 cases in child group
17 cases in the early adolescence
group
14 in the middle adolescence group
22 in the late adolescence group
Families allow access to the
unit of analysis: children and
adolescents with FASD.
GENDER
38 men
28 women
DIAGNOSES
49 cases with complete
FAS
11 cases with partial FAS
6 cases with ARND
The self-administered version of the BRIEF-2 and
SENA standardized tests are administered to families
to report on the executive and behavioral functioning
of their children with FASD (unit of analysis).
Figure 1. Research Method Flowchart.
2.2. Participants
The attainment of the primary research unit was carried out following a probabilistic
cluster sampling approach, resulting in five conglomerate groups. Each group corresponds
to one of the registered associations nationwide related to FASD and Fetal Alcohol Syn-
drome (FAS), regulated under the provisions of the Organic Law 1/2002, dated 22 March,
regulating the Right of Association. The sample size for each of the conglomerates is
distributed as follows: 250 cases registered in VISUAL TEAF (stands for FASD), 305 cases in
AFASAF (stands for Association of Families Affected by Fetal Alcohol Syndrome), 80 cases
in SAFGROUP (stands for FAS), 60 cases in Tolerancia-Cero, and 40 cases in Zero FAS
(stands for FAS). Subsequently, three groups were selected according to random sampling
rules: VISUAL TEAF, SAFGROUP, and AFASAF. The sample consists of a total of 66 fami-
lies of children with FASD. Regarding the association of origin, 40.9% belong to AFASAF
(n = 27), 27.3% to SAFGROUP (n = 18), and 31.8% (n = 21) are associated with VISUAL
TEAF. The distribution of participants includes 57.6% men (n = 38) and 42.4% women
(n = 28). The breakdown by age groups is as follows: 19.7% (n = 13) in the child group,
25.8% (n = 17) in the early adolescence group, 21.2% (n = 14) in the middle adolescence
group, and 33.3% (n = 22) in the late adolescence group. There are 49 cases with a complete
Fetal Alcohol Syndrome (FAS) diagnosis (n = 49; 74.24%), 11 cases of partial FAS (n = 11;
16.66%), and 6 cases of Alcohol-Related Neurodevelopmental Disorder (ARND) (n = 6;
9.09%). If attention is paid to the co-morbidity situation, there are six cases presenting a
joint diagnosis with Attention Deficit and/or Hyperactivity Disorder (n = 6; 9.09%); four
cases showing Specific Learning Disorder (n = 4; 6. 06%); four cases showing Conduct
Behav. Sci. 2024,14, 431 4 of 17
Disorder (n = 4; 6.06%); two cases presenting hypoacusis (n = 2; 3.03%); a single case
presenting Specific Language Disorder (n = 1; 1.51%); and, finally, two cases presenting
comorbidity with Autism Spectrum Disorder (n = 2; 3.03%). The rest of the co-participants
did not present comorbidity with other disorders (n = 47; 71.21%). The diagnosis of all cases
was made based on the criteria established by Hoyme [
1
]. Finally, 78.8% had a disability
(n = 52), 48.5% were in a situation of dependency, and, lastly, all participants were adopted
(n = 66; 100%).
2.3. Variables
On the one hand, the developmental stage or age, gender, and the diagnosis of FASD
are considered independent variables. The developmental stage is organized into childhood
(7–10 years), early adolescence (11–14 years), middle adolescence (15–17 years), and late
adolescence (18–19 years). The gender variable distinguishes between men and women.
The variable diagnosis of FASD includes complete FAS, partial FAS, and ARND.
On the other hand, the dependent variables are identified with each of the executive
functioning domains of the BRIEF-2 scale: inhibition (INH), self-monitoring (SMO), flexibil-
ity (FLE), emotional control (EMC), initiative (INI), working memory (WM), planning and
organization (PLA), task monitoring (TAS), and materials organization (ORG). Finally, the
dependent variables are also associated with each of the domains assessed in the SENA
test: depression (DEP), anxiety (ANS), social anxiety (SCA), somatic complaints (SOM),
attention problems (ATE), hyperactivity–impulsivity (HIP), anger control problems (ANG),
aggression (AGG), challenging behavior (CHA), antisocial behavior (ANT), substance use
(SUB), eating behavior problems (EAT), unusual behavior (UNU), emotional regulation
problems (REG), rigidity (RIG), isolation (ISO), social integration and competence (SOC),
emotional intelligence (EMI), and study disposition (STU).
2.4. Measurement
In this study, the standardized test Behavior Rating Inventory of Executive Function,
Second Edition, hereinafter BRIEF-2, in its Spanish adaptation, was applied. In addition,
the standardized neuropsychological test ‘Sistema de Evaluación de Niños y Adolescentes’
(SENA) is applied for the Spanish-speaking population. This test is applied because of its
close parallelism with the diagnostic criteria included in the DSM-V [16] and its adequate
psychometric properties. The administration of both tests uses a self-administered version
aimed at families of children and adolescents with FASD.
The BRIEF-2 and SENA assessments are structured according to three age levels, which
primarily correspond to the educational stages of Early Childhood Education, from 3 to
6 years old; Primary Education, from 6 to 12 years old; and finally, Compulsory Secondary
Education, from 12 to 18 years old. However, they allow for some flexibility to adapt to the
specific assessment needs based on the stage of the developmental cycle.
The administration of the Behavior Rating Inventory of Executive Function, Second
Edition (BRIEF-2) provides a profile of impairment in different areas of executive function
in children and adolescents aged 5 to 18 years, with an approximate administration time
of 10 min. The BRIEF-2 consists of 63 items that are assessed on a Likert-type rating
scale with three response options: never, sometimes, and frequently. Additionally, the
System for Evaluating Children and Adolescents (SENA) is also administered. It evaluates
various psychological problems frequently encountered in children and adolescents from a
multidimensional perspective. The SENA family version for adolescents contains 154 items,
using a Likert-type rating scale with five response options, where respondents are asked
to select the frequency of a behavior. The SENA family version for the child population
(6 to 12 years old) includes 129 items, utilizing the same rating scale. Both versions have
an approximate administration time of 30 min. One aspect to highlight is that there is
consistent evidence of the validity and reliability of the results provided by both tests.
In addition, there is correlation between these two tests in studies of populations with
neurodevelopmental disorders [13,14].
Behav. Sci. 2024,14, 431 5 of 17
3. Statistical Methods and Results
To create the executive and behavioral functioning profile, the typical scores (T) ob-
tained from the BRIEF-2 and SENA tests will be used for each variable. Additionally, data
analysis is performed using the SPSS V27 software for Windows.
The normality of the independent variables (developmental stage and diagnosis) and
the dependent variables of executive and behavioral functioning are assessed using the
Kolmogorov–Smirnov test (D) for the total sample (n
≥
50) and the Shapiro–Wilk test (W) for
groups with n
≤
50. Regarding the sociodemographic variable of the developmental stage, it
is determined that all subgroups of this variable do not follow the assumption of normality, as
the significance level is
≤
0.05 (D = 0.170, p
total
< 0.000; W = 0.821, p
childhood
< 0.012; W = 0.874,
p
early-adolescence
< 0.025; W = 0.862, p
middle-adolescence
< 0.032; W = 0.628, p
late-adolescence
< 0.000).
The diagnostic variable does not meet the assumption of normality in its distribution, with
p< 0.000 (D = 0.448). Table 1shows the normality study for the dependent variables.
Table 1. Normality test.
Variable
EF
FE Statistics Variable
BF
BF Statistics
DpDp
INH 0.076 0.435 DEP 0.810 0.331
SMO 0.156 0.000 ANS 0.089 0.212
FLE 0.118 0.021 SCA 0.103 0.075
EMC 0.121 0.014 SOM 0.161 0.000
INI 0.094 0.150 ATE 0.079 0.376
WM 0.073 0.503 HIP 0.102 0.079
PLA 0.181 0.000 ANG 0.092 0.170
TAS 0.116 0.026 AGG 0.123 0.013
ORG 0.094 0.151 CHA 0.113 0.032
ANT 0.191 0.000
SUB 0.362 0.000
EAT 0.166 0.001
UNU 0.103 0.077
REG 0.124 0.011
RIG 0.110 0.043
ISO 0.106 0.062
SOC 0.133 0.005
EMI 0.091 0.191
STU 0.135 0.004
N = 66. D = Kolmogorov-Smirnov statistic with Lilliefors significance correction (n
≥
50) based on 10,000 Monte
Carlo samples with a starting seed of 2,000,000. If p
≥
0.05 H
0
is accepted so the observed values are distributed
under the assumption of normality.
To ascertain if there are significant differences in the variables of executive and be-
havioral functioning between the general population and the clinical population (FASD),
Student’s t-test for independent samples is applied in cases where the assumption of nor-
mality is met. It is important to note that the general population mean is 50, with a standard
deviation of 10, in both the BRIEF-2 test [
14
] and the SENA test [
13
]. Student’s test of
executive and behavioral functioning is presented in Table 2.
If p< 0.05, the data are inconsistent with the hypothesis that the population mean
value is as proposed. If the lower and upper bounds include the value zero, the sample
data are compatible with the proposed population value, so the H
0
is accepted, and vice
versa.
Bivariate correlation analysis is used to determine whether there are differences ac-
cording to age and diagnosis for each of the variables. It will allow us to find out whether
there is an association between the variable age or diagnosis and each of the variables, as
well as the strength and directionality of this correlation (bilateral probability) (Tables 3
and 4). The program automatically calculates the level of significance from the 99% and
Behav. Sci. 2024,14, 431 6 of 17
95% confidence interval, so that significant correlations at the 0.01 level will be identified
with two asterisks (**) and significant correlations at the 0.05 level with one (*).
Table 2. Student’s t-test of EF and BF domains.
Variable
EF
t-Test Results EF Variable
BF
t-Test Results BF
TpTp
INH ** 15.22 0.000 DEP ** 8.38 0.000
SMO * 17.71 0.000 ANS ** 11.37 0.000
FLE * 17.52 0.000 SCA ** 5.79 0.000
EMC * 11.86 0.000 SOM * 2.02 0.047
INI ** 23.33 0.000 ATE ** 20.22 0.000
WM ** 22.38 0.000 HIP ** 13.04 0.000
PLA * 26.81 0.000 ANG ** 10.7 0.000
TAS * 20.13 0.000 AGG * 8.45 0.000
ORG ** 12.87 0.000 CHA * 10.6 0.000
ANT * 9.32 0.000
SUB * 3.46 0.001
EAT ** 3.72 0.001
UNU ** 11.83 0.000
REG * 14.05 0.000
RIG * 16.77 0.000
ISO ** 13.66 0.000
SOC * −15.37 0.000
EMI ** −13.79 0.000
STU * −19.36 0.000
** normal distribution; * non-normal distribution; N = 66; df = 65; test value = 50. p= bilateral significance.
Table 3. Analysis of bivariate correlations in EF and BF variables according to age.
Variable
EF Pearson Spearman’s
Rho pEF Variable
BF Pearson Spearman’s
Rho pBF
INH 0.456 ** 0.000 ** DEP 0.105 0.401
SMO 0.529 ** 0.000 ** ANS 0.274 * 0.026 *
FLE 0.487 ** 0.000 ** SCA 0.019 0.877
EMC 0.473 ** 0.000 ** SOM −0.015 0.907
INI 0.153 0.219 ATE 0.290 * 0.018 *
WM 0.275 * 0.026 * HIP 0.251 * 0.042 *
PLA 0.335 ** 0.006 ** ANG 0.263 * 0.033 *
TAS 0.427 ** 0.000 ** AGG 0.379 ** 0.002 **
ORG 0.163 0.183 CHA 0.352 ** 0.004 **
ANT 0.566 ** 0.000 **
SUB 0.258 0.070
EAT 0.100 0.491
UNU 0.340 ** 0.005 **
REG 0.434 ** 0.000 **
RIG 410 ** 0.001 **
ISO 0.263 * 0.033 *
SOC 0.004 0.976
EMI −243 * 0.049 *
STU −0.152 0.222
** correlation is significant at the 0.01 level; * correlation is significant at the 0.05 level. N = 66. p= bilateral
significance. If p
≥
0.05, H
0
is accepted, i.e., there are no differences in the executive/behavioral functioning
variable as a function of age.
Behav. Sci. 2024,14, 431 7 of 17
Table 4. Analysis of bivariate correlations in EF and BF variables according to diagnosis.
Variable
EF Pearson Spearman’s
Rho pEF Variable
BF Pearson Spearman’s
Rho pBF
INH −0.034 0.786 DEP 0.021 0.434
SMO 0.030 0.813 ANS −0.144 0.124
FLE −0.085 0.497 SCA 0.045 0.359
EMC 0.044 0.726 SOM −0.043 0.366
INI −0.122 0.329 ATE −0.257 * 0.019
WM −0.090 0.471 HIP −0.262 * 0.017
PLA 0.054 0.668 ANG 0.228 0.033
TAS 0.001 0.996 AGG 0.097 0.220
ORG −0.240 0.052 CHA −0.010 0.468
ANT −0.075 0.275
SUB 0.110 0.190
EAT −0.062 0.312
UNU −0.264 * 0.016
REG 0.108 0.195
RIG −0.188 0.066
ISO 0.025 0.422
SOC −0.293 ** 0.008
EMI −0.085 0.249
STU 0.215 * 0.041
** correlation is significant at the 0.01 level; * correlation is significant at the 0.05 level. N = 66. p= bilateral
significance. If p
≥
0.05, H
0
is accepted, i.e., there are no differences in the executive/behavioral functioning
variable as a function of diagnosis.
It is noted that age is a factor that intervenes in almost all the variables of executive
control and behavioral functioning.
In addition, the diagnosis does not determine the level of executive functioning.
However, it does correlate with the behavioral functioning variables attention problems, hy-
peractivity, unusual behavior, integration problems, and social competence and willingness
to study.
This analysis is not sufficient to explain the nature of this correlation, so one-factor
analysis of variance (ANOVA) is applied. It determines whether the population means
of the independent variable (IV) and the dependent variable (DV) are equal. This test
assumes the principle of homoscedasticity, or equality of variances, and the normality of
distribution. The higher the F-statistic, the more the variables are related, i.e., DV values
differ between VI groups. If the pis less than 0.05, the null hypothesis (H
0
) of equal means
is rejected, concluding that not all compared population means are equal (Tables 5and 6).
Table 5. One-factor ANOVA for the age variable and the EF and BF variables.
ANOVA EF ANOVA BF
EF F pFpBF
INI 5.703 0.002 ANS 7.845 0.000
SMO 9.274 0.000 ATE 3.511 0.020
FLE 7.518 0.000 HIP 2.278 0.088
EMC 3.907 0.013 ANG 1.039 0.381
WM 4.430 0.007 AGG 2.610 0.059
PLA 3.534 0.020 CHA 2.911 0.041
TAS 6.075 0.001 ANT 5.695 0.002
UNU 3.955 0.012
REG 6.693 0.001
RIG 6.434 0.001
SOC 3.045 0.035
EMI 0.954 0.420
Inter-group df = 3; intra-group df = 62; total df = 65.
Behav. Sci. 2024,14, 431 8 of 17
Table 6. One-factor ANOVA for the diagnosis variable and the EF and BF variables.
ANOVA BF
FpBF
ATE 2.232 0.116
HIP 2.450 0.094
UNU 2.509 0.089
SOC .322 0.726
STU 2.365 0.102
Inter-group df = 2; intra-group df = 63; total df = 65.
In turn, the Kruskal–Wallis H-test is applied to determine whether there are significant
differences in the executive and behavioral functioning variables according to the type
of diagnosis (Tables 7and 8). If the critical level is less than 0.05, the null hypothesis of
equality of population means is rejected and it can be concluded that diagnostic types differ
on the cognitive and behavioral functioning variables.
Table 7. Kruskal–Wallis H-test of executive functioning variables according to diagnosis.
Variables EF Average Range Zp
FAS pFAS ARND
INH 33.97 34.41 28 0.548 0.760
SMO 33.28 32.23 37.67 0.341 0.843
FLE 34.43 31.36 29.83 0.474 0.789
EMC 33.18 31.27 40.17 0.889 0.641
INI 35.14 25.73 34.33 2.186 0.335
WM 34.07 35.86 24.50 1.536 0.464
PLA 33.20 29 44.17 2.504 0.286
TAS 33 43.45 19.33 6.310 0.043
ORG 36.01 29.86 19.67 4.373 0.112
NFAS = 49; NpFAS = 11; NARND = 6; df = 2. Significance level is 0.05.
Table 8. Kruskal–Wallis H-test of behavioral functioning variables according to diagnosis.
Variables BF Average Range Zp
FAS pFAS ARND
DEP 32.99 37.77 29.83 0.801 0.670
ANS 35.41 27.73 28.50 1.894 0.388
SCA 32.92 35.27 35.00 0.176 0.916
SOM 33.97 32.41 31.67 0.120 0.942
ATE 36.30 29.41 18.17 5.388 0.068
HIP 36.51 24.00 26.33 4.750 0.093
ANG 31.70 31.50 51.83 6.031 0.049
AGG 32.71 30.45 45.50 2.713 0.258
CHA 33.78 29.91 37.83 0.702 0.704
ANT 34.30 31.95 29.83 0.384 0.825
SUB 32.32 36.14 38.33 0.795 0.672
EAT 34.39 27.55 37.17 1.411 0.494
UNU 36.45 26.36 22.50 4.674 0.097
REG 32.65 29.55 47.67 3.845 0.146
RIG 35.71 25.45 30.17 2.792 0.248
ISO 35.81 31.50 18.33 4.581 0.101
SOC 33.20 34.64 33.83 0.052 0.974
EMI 34.71 28.82 32.17 0.884 0.643
STU 31.32 35.41 47.83 4.105 0.128
NFAS = 49; NpFAS = 11; NARND = 6; df = 2. Significance level is 0.05.
Behav. Sci. 2024,14, 431 9 of 17
Therefore, in the sample assessed there are no statistically significant differences in
cognitive and behavioral functioning according to diagnosis.
Finally, differences in gender in the variable of executive and behavioral functioning
will be tested by applying the non-parametric Kolmogorov–Smirnov test for independent
samples (n ≤30) (Table 9).
Table 9. Kolmogorov–Smirnov test for executive and behavioral functioning variables (grouping
variable: gender).
Variables EF ZpVariables BF Zp
INH 1.155 0.139 DEP 1.079 0.194
SMO 0.936 0.345 ANS 0.906 0.385
FLE 0.936 0.345 SCA 1.260 0.083
EMC 0.762 0.607 SOM 1.268 0.080
INI 0.694 0.721 ATE 1.743 0.005
WM 1.977 0.001 HIP 1.004 0.266
PLA 0.770 0.594 ANG 0.687 0.733
TAS 1.592 0.013 AGG 0.611 0.849
ORG 0.868 0.438 CHA 1.162 0.134
ANT 0.574 0.897
SUB 1.253 0.087
EAT 1.004 0.266
UNU 0.732 0.657
REG 0.634 0.816
RIG 1.230 0.097
ISO 1.608 0.011
SOC 0.845 0.473
EMI 0.981 0.291
STU 1.208 0.108
Ntotal = 66; Nmen = 38; Nwomen = 28. Significance level is 0.05.
In the executive functioning variables, no statistically significant differences were
observed between men and women. However, this is not true for the variables working
memory (p< 0.001) and task monitoring (p< 0.013), as they have a significance level of
less than 0.05. On this occasion, the null hypothesis is retained in which the behavioral
functioning variables belong to the same population, regardless of gender, except for the
variables ATE (attention problems (p< 0.005)) and ISO (isolation (p< 0.011)), which reflect
significant differences according to male or female sex.
4. Discussion
In the first instance, the BRIEF-2 neuropsychological test provides a measure of execu-
tive function assessment based on an ecological perspective [
14
]. There are three previous
studies that apply the BRIEF-2 test for the assessment of all executive functions in the
population affected by FASD in the United Kingdom and Canada, showing significant
alterations in most of the domains assessed by the test, except in the domain “organization
of materials” [
12
,
18
,
22
]. The results of this study follow a similar pattern to those obtained
in different works [
12
,
18
,
23
]. That is, participants affected by FASD show significant differ-
ences in the executive functioning domains assessed (inhibition, self-monitoring, flexibility,
emotional control, initiative, working memory, planning and organization, task monitoring,
and organization of materials), with working memory being the most impaired variable
and organization of materials the least impaired, although in some cases they continue
to belong to the clinical population. However, we find that the interaction of the age
variable is a moderating factor for executive functioning [
23
]. Thus, school-aged children,
which in this study comprises ages seven to ten, show less marked impairment in the
executive functions of inhibition, self-monitoring, cognitive flexibility, emotional control,
working memory, planning and organization, and task monitoring compared to their peers
in higher developmental stages (middle and late adolescence). The results of this study
Behav. Sci. 2024,14, 431 10 of 17
coincide with those obtained in the studies by [
23
], in which age is identified as a factor
that determines the deterioration of executive functioning, which is more notable at higher
ages, but which stabilizes in the adolescent period. This not only confirms the hypothesis
that the age variable is a determinant of the level of functioning of the different domains of
executive functions at a general level, but also achieves one of the objectives of this study:
the verification of significant differences in EF motivated by the evolutionary moment of
the person affected by FASD. Finally, studies by several authors ([
19
,
24
,
25
], among others)
analyze the comorbidity between FASD and ADHD and reveal that cases with a comorbid
diagnosis with ADHD compared to a single diagnosis of FASD show higher scores in all
domains of executive functions assessed by the BRIEF test, with the variables working
memory and inhibition being the most affected. Thus, the presence of comorbid ADHD is
associated with an exacerbation of impaired executive functioning [18,26].
Secondly, in the literature reviewed, there is no evidence of the application of the
SENA test in the population affected by FASD. The impairment of neurocognitive domains
affected by FASD, namely intellectual functioning, memory, attention, executive functions,
or communication skills, directly impacts the person’s social and adaptive functioning. In
other words, impaired executive functioning domains self-regulation, information process-
ing, inhibition, working memory, self-monitoring, and planning and organization impact
the ability to make decisions and regulate behavior effectively and adaptively [
27
]. Thus, ex-
ecutive functioning will condition behavioral functioning. Therefore, individuals exposed
to alcohol in the prenatal period with impaired inhibition and emotional control experience
serious behavioral problems, such as illogical thinking, difficulties in understanding cause–
effect relationships, aggressive and antisocial behavior, or other maladaptive behaviors,
irrespective of IQ. In this sense, most of the child-age population with severe adaptive
behavioral disturbances also show them in adulthood, often associated with problems with
justice [
28
]. This study shows the presence of alterations in all of the domains of adaptive
behavior assessed by means of the SENA test in the clinical population affected by FASD
in comparison with the general population. Specifically, alterations coinciding with the
clinical population were observed in the domains of depression, anxiety, social anxiety,
attention problems, hyperactivity and impulsivity, anger control problems, aggression, de-
fiant behavior, antisocial behavior, substance use, problems with eating behavior, unusual
behavior, rigidity, isolation, integration and social competence, emotional intelligence, and
willingness to study. As can be seen, the impairment of behavioral functioning in the
diagnosis of FASD is compatible with the impairments in the domains, as can be similarly
reflected in the studies of several authors ([
1
,
2
,
19
–
22
,
27
], among others). Depression poses
severe life-threatening risks for adolescents affected by FASD, such as suicidal ideation, self-
harming behaviors, or suicide attempts [
29
]. Deficits in behavioral control are explained by
deficits in behavioral self-regulation, behavioral inhibition, and adaptive functioning [
21
].
Problems with anger control, aggression, or defiant behavior towards authority figures
are common in the FASD population, especially with advancing age [
21
]. These adaptive
dysfunctions, together with behavioral inhibition and impulsive behaviors, are consistent
with impaired social functioning, intensifying at older ages [
20
]. All these findings are
confirmed by the results obtained in this study. Furthermore, all these impairments in
adaptive behavior and social competence lead to difficulties in community participation.
In addition, social deterioration with advancing age is also motivated by alterations in
the social and communication domain [
19
], revealing a halt in the progression of social
competence. Thus, since social and communication demands increase in adolescence and
adulthood, social deterioration is indeed confirmed as age increases [21].
The presence or absence of dysmorphic traits with prenatal alcohol exposure does
not necessarily imply significant differences in executive functioning [
19
,
30
]. These results
are supported by those obtained in the same study. Therefore, considering that the as-
sociation between neurocognitive impairment and dysmorphic traits is not statistically
significant, it is suggested that the diagnosis of FASD should prioritize central nervous
system deficits [
31
]. Other studies show differences in terms of which behavioral distur-
Behav. Sci. 2024,14, 431 11 of 17
bances manifested according to the diagnostic entity [
32
,
33
], with the genetic component,
the environment, and early adversity experiences becoming conditioning elements [21].
From another point of view, considering the gender variable as a moderating factor
in the impairment of executive and behavioral functioning, in this study, no significant
differences were observed in either the executive functioning or behavioral functioning
variables as a function of gender. These results are confirmed by those obtained in the work
of Herman and colleagues [
17
] or Rai and colleagues [
18
], which reveal no statistically sig-
nificant differences between boys and girls on measures of impaired executive functioning
using the BRIEF-2 test. Likewise, these results are consistent with those obtained in the
study by Sakano and colleagues [
21
], where possible differences in behavioral domains
could be due to other factors unrelated to FASD.
5. Conclusions
Firstly, it is considered that the contribution of the family through the BRIEF-2 and
SENA standardized assessment tests for the Spanish-speaking population allows for the
elaboration of a defined executive and behavioral profile of the child and adolescent with a
diagnosis of Fetal Alcohol Spectrum Disorder, thus fulfilling the first working hypothesis
of this study.
The domains of executive functioning that are affected in each of the profiles that
are formed from the three classifying factors can be found in Appendix A, expressed
through the qualitative indicators proposed by the BRIEF-2 test: typical scores between
0–59 are qualified as “no clinical significance”, T scores between 60–64 as “mild elevation”,
T scores between 65–69 as “potentially clinical elevations” and, finally, T scores equal to
or above 70 as “clinically significant elevations”. In contrast, it was determined that both
the gender variable and the diagnostic entity did not become conditioning factors for the
level of cognitive functioning or the level of executive function. Ultimately, the pattern of
executive functioning becomes very similar between the diagnostic entities considered in
this study, which allows us to confirm that the hypothesis about affinity between patterns
of functioning of the diagnostic entities is fulfilled.
It is also noted that the behavioral functioning of people with FASD is significantly
altered in comparison with their age and gender counterparts because of alterations in
executive functioning. This altered behavioral functioning mainly responds to problems of
self-regulation, evidencing problems in anger control or emotional regulation, hyperactivity
and impulsivity, problems in maintaining attention, cognitive rigidity, and alterations in
social interaction and competence related to unusual behavior, which on many occasions
leads to situations of marked social isolation. In addition, anxiety, depression, defiant
behavior, antisocial behavior, and aggressiveness may be observed. In all cases, the willing-
ness to study has been affected, remaining stable at higher ages. This fact highlights the
absence or insufficient support to offer experiences of academic success to people affected
by FASD within the educational system. In turn, the effect on behavioral functioning
worsens with increasing age in some domains of behavioral functioning: anxiety, attention
problems, challenging behavior, antisocial behavior, unusual behavior, problems in emo-
tional regulation, cognitive rigidity, and social isolation. This may be due to a multifactorial
origin, such as the person’s support network, experiences of academic success, or perceived
social support, among others (See Appendix B).
The scope of this research study in the field of research is promising. The development
of a neurocognitive and behavioral profile of the adopted person with a diagnosis of FASD
has shown that developmental timing is an element that modulates the expression of
alterations in executive and behavioral functioning. However, this finding is subject to
a more complex assessment that includes not only the analysis of the affected person’s
environment, but also its comparison with a clinical group of the biological population.
In any case, having a defined profile in terms of cognitive and behavioral functioning is
a very valuable resource for professionals in the educational community. It serves on the
one hand, as a guide to understanding how these students function and learn and, on the
Behav. Sci. 2024,14, 431 12 of 17
other hand, as a guideline for the implementation of inclusive educational practices. The
main methodological limitations identified during the development of the research are
the sample size, the lack of research, and the biases associated with the administration
of questionnaires. Considering that there are no real data describing the prevalence and
incidence data of this disorder in Spain, rather than the mere fact of an estimate of it [
4
], it
is difficult to define a representative sample size of the clinical population. Although it is
true that, considering the estimated data (22 people affected per 10,000 inhabitants), the
sample size is small (n = 66). In addition, the establishment of the correlation between the
alterations in executive and behavioral functioning and the age variable has been carried out
based on the available sample. However, the ideal would be to design a longitudinal study
to analyze the development of executive functions and the establishment of a behavioral
pattern in the same population, which would undoubtedly require an increase in economic
resources and time. In this sense, the conclusions drawn are limited to the conditions of
this study. Finally, the bias associated with the administration of questionnaires that use
the Likert scale as a recording measure can lead to the loss of information, since it is limited
to polarized categories and can even be affected by the statistical treatment carried out by
the researcher himself [34].
As lines of future research, it is recommended to continue with the design of specific
training on FASD that can be offered in educational centers, through associations, in
congresses or seminars, where the profiles generated in this study can be provided.
Author Contributions: Conceptualization, N.F.; methodology, N.F. and J.M.L.-D.; software, N.F. and
J.M.L.-D.; formal analysis, N.F. and J.M.L.-D.; investigation, N.F., J.M.L.-D. and I.G.; data curation, N.F.
and I.G.; writing—original draft preparation, N.F. and I.G.; writing—review and editing, I.G.; project
administration, N.F.; funding acquisition, N.F. All authors have read and agreed to the published
version of the manuscript.
Funding: This research project has received funding from the Institutional Chair on People with
Disabilities, Universal Accessibility and Inclusion of the Universidad Rey Juan Carlos for the purchase
of standardized assessment tests and correction pins, amounting to €615.22.
Institutional Review Board Statement: The research project was carried out in accordance with the
Research Ethics Committee of the Universidad Rey Juan Carlos with internal registration number
0403202007620, obtaining a favorable opinion for the study to be carried out on 16 September 2020.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement: The data presented in this study are available upon request to the
corresponding author due to the preservation of the health data protection of the study participants.
Acknowledgments: The authors thank all family and associations who participated in the research.
Conflicts of Interest: The authors declare no conflicts of interest.
Behav. Sci. 2024,14, 431 13 of 17
Appendix A
PROFILE OF EXECUTIVE FUNCTIONING BY AGE
CHILDHOOD (7–10 years)
Inhibition: PCE
Self-monitoring: ME
Flexibility: PCE
Emotional control: ME
Initiative: CSE
Working memory: CSE
Planning and organization: PCE
Task monitoring: PCE
Material organization: EL.
EARLY ADOLESCENCE (11–14 years)
Inhibition: PCE
Self-monitoring: ME
Flexibility: PCE
Emotional control: ME
Initiative: CSE
Working memory: CSE
Planning and organization: PCE
Task monitoring: PCE
Material organization: ME
MIDDLE ADOLESCENCE (15–17 years)
Inhibition: PCE
Self-monitoring: ME
Flexibility: PCE
Emotional control: ME
Initiative: CSE
Working memory: CSE
Planning and organization: PCE
Task monitoring: PCE
Material organization: ME
LATE ADOLESCENCE (18–19 years)
Inhibition: PCE
Self-monitoring: ME
Flexibility: EPC.
Emotional control: ME
Initiative: CSE
Working memory: CSE
Planning and organization: PCE
Task monitoring: PCE
Material organization: ME
PROFILE OF EXECUTIVE FUNCTIONING BY GENDER
WOMEN
Inhibition: CSE
Self-monitoring: CSE
Flexibility: CSE
Emotional control: PCE
Initiative: CSE
Working memory: CSE
Planning and organization: CSE
Task monitoring: CSE
Material organization CSE
MEN
Inhibition: CSE
Self-monitoring: CSE
Flexibility: CSE
Emotional control: PCE
Initiative: CSE
Working memory: CSE
Planning and organization: CSE
Task monitoring: CSE
Material organization: CSE
PROFILE OF EXECUTIVE FUNCTIONING BY DIAGNOSIS
Complete FAS
Inhibition: CSE
Self-monitoring: CSE
Flexibility: CSE
Emotional control: CSE
Initiative: CSE
Working memory: CSE
Planning and organization: PCE
Task monitoring: CSE
Material organization: CSE
Partial FAS
Inhibition: CSE
Self-monitoring: CSE
Flexibility: CSE
Emotional control: PCE
Initiative: CSE
Working memory: CSE
Planning and organization: CSE
Task monitoring: CSE
Material organization: CSE
ARND
Inhibition: CSE
Self-monitoring: CSE
Flexibility: CSE
Emotional control: CSE
Initiative: CSE
Working memory: CSE
Planning and organization: CSE
Task monitoring: PCE
Material organization: ME
NCS = no clinical significance (typical or T scores between 0–59); ME = mild elevation (T between 60–64);
PCE = potentially clinical elevation (T between 65–69); CSE = clinically significant elevation (T ≥70).
Behav. Sci. 2024,14, 431 14 of 17
Appendix B
PROFILE OF BEHAVIORAL FUNCTIONING BY AGE
CHILDHOOD (7–10 years)
Depression: 58.7
Anxiety: 56.9
Social anxiety: 53.9
Somatic complaints: 47
Attention problems: 67.7
Hyperactivity-impulsivity: 67.2
Anger control problems: 66.9
Aggression: 65.7
Challenging behavior: 66.5
Antisocial behavior: 0
Substance use: 0
Eating behavior problems: 0
Unusual behavior: 70.9
Emotional regulation problems: 59.5
Rigidity: 61.5
Isolation: 67.6
Social integration and competence: 29
Emotional intelligence: 38.1
Study disposition: 32.2
EARLY ADOLESCENCE (11–14 years)
Depression: 63.5
Anxiety: 65.6
Social anxiety: 62.4
Somatic complaints: 57.1
Attention problems: 72.7
Hyperactivity-impulsivity: 66.7
Anger control problems: 68.4
Aggression: 63.8
Challenging behavior: 63.8
Antisocial behavior: 50.7
Substance use: 36.1
Eating behavior problems: 41.9
Unusual behavior: 80.1
Emotional regulation problems: 66.7
Rigidity: 69.4
Isolation: 78.7
Social integration and competence: 30.4
Emotional intelligence: 34.4
Study disposition: 28.1
MIDDLE ADOLESCENCE (15–17 years)
Depression: 66.3
Anxiety: 75.8
Social anxiety: 66.5
Somatic complaints: 60.7
Attention problems: 78.6
Hyperactivity-impulsivity: 76.8
Anger control problems: 71.7
Aggression: 76.3
Challenging behavior: 69.1
Antisocial behavior: 86.5
Substance use: 51.1
Eating behavior problems: 59.9
Unusual behavior: 93.2
Emotional regulation problems: 72.9
Rigidity: 74.6
Isolation: 85.4
Social integration and competence: 24.3
Emotional intelligence: 35
Study disposition: 28.3
LATE ADOLESCENCE (18–19 years)
Depression: 63.4
Anxiety: 66.5
Social anxiety: 56.1
Somatic complaints: 49.9
Attention problems: 74.6
Hyperactivity-impulsivity: 73.6
Anger control problems: 75.6
Aggression: 80
Challenging behavior: 76.8
Antisocial behavior: 99.5
Substance use: 79
Eating behavior problems: 59.2
Unusual behavior: 96
Emotional regulation problems: 73.3
Rigidity: 73.2
Isolation: 82.3
Social integration and competence: 27.2
Emotional intelligence: 32.8
Study disposition: 28.1
PROFILE OF BEHAVIORAL FUNCTIONING BY GENDER
WOMEN
Depression: 60.5
Anxiety: 65.5
Social anxiety: 63.4
Somatic complaints: 50.1
Attention problems: 75.9
Hyperactivity-impulsivity: 68.9
Anger control problems: 69.9
Aggression: 73
Challenging behavior: 71.3
Antisocial behavior: 68.6
Substance use: 46.2
Eating behavior problems: 41.4
Unusual behavior: 82.8
Emotional regulation problems: 68.3
Rigidity: 68.4
Isolation: 85.5
Social integration and competence: 25.5
Emotional intelligence: 33.9
Study disposition: 26.3
MEN
Depression: 65.1
Anxiety: 66.9
Social anxiety: 56.6
Somatic complaints: 56
Attention problems: 71.9
Hyperactivity-impulsivity: 73
Anger control problems: 72.2
Aggression: 71.6
Challenging behavior: 68.7
Antisocial behavior: 61.6
Substance use: 46.7
Eating behavior problems: 44.6
Unusual behavior: 89
Emotional regulation problems: 69.2
Rigidity: 71.5
Isolation: 74.5
Social integration and competence: 29.4
Emotional intelligence: 35.3
Study disposition: 31
Behav. Sci. 2024,14, 431 15 of 17
PROFILE OF BEHAVIORAL FUNCTIONING BY DIAGNOSIS
Complete FAS
Depression: 62.6
Anxiety: 67.6
Social anxiety: 59.1
Somatic complaints: 54.2
Attention problems: 75
Hyperactivity-impulsivity: 73.3
Anger control problems: 69.8
Aggression: 71.5
Challenging behavior: 69.9
Antisocial behavior: 67.1
Substance use: 44.4
Eating behavior problems: 44.5
Unusual behavior: 90.3
Emotional regulation problems: 68.6
Rigidity: 71
Isolation: 81.9
Social integration and competence: 27.2
Emotional intelligence: 35.4
Study disposition: 28
Partial FAS
Depression: 67.3
Anxiety: 61.7
Social anxiety: 60.8
Somatic complaints: 52.6
Attention problems: 70.8
Hyperactivity-impulsivity: 65
Anger control problems: 69.7
Aggression: 67.3
Challenging behavior: 66.8
Antisocial behavior: 57.5
Substance use: 52.9
Eating behavior problems: 37.5
Unusual behavior: 76.6
Emotional regulation problems: 66
Rigidity: 66.7
Isolation: 74.2
Social integration and competence: 30.4
Emotional intelligence: 31.8
Study disposition: 29.7
ARND
Depression: 60.3
Anxiety: 64.7
Social anxiety: 60.3
Somatic complaints: 48.7
Attention problems: 67.7
Hyperactivity-impulsivity: 65.7
Anger control problems: 85.3
Aggression: 86.7
Challenging behavior: 74.3
Antisocial behavior: 57.3
Substance use: 51.7
Eating behavior problems: 43.7
Unusual behavior: 72.3
Emotional regulation problems: 76.3
Rigidity: 69.7
Isolation: 66
Social integration and competence: 27
Emotional intelligence: 34.7
Study disposition: 36
T-scores equal to or above 60 or equal to and below 39 should be considered with caution as they are relatively
infrequent in the population, indicating possible difficulties in that area; T-scores equal to or above 70 or equal to
and below 29 should be considered clinically significant as they imply a high level of impairment in that area.
Finally, T-scores equal to or above 80 or equal to and below 19 are truly extreme scores and should be given
priority attention.
References
1.
Hoyme, E.H.; Kalberg, W.; Elliott, A.; Blankenship, J.; Buckley, D.; Marais, A.S.; Nursing, C.; Manning, M.; Robinson, L.; Adam,
M.; et al. Updated clinical guidelines for diagnosing fetal alcohol spectrum disorders. Pediatrics 2016,138, e20154256. [CrossRef]
[PubMed]
2.
Maya-Enero, S.; Ramis-Fernández, S.; Astals-Vizcaino, M.; García-Algar, O. Perfil neurocognitivo y conductual del trastorno del
espectro alcohólico fetal. An. Pediatría2021,95, e1–e208. [CrossRef]
3.
Popova, S.; Dozet, D.; Burd, L. Fetal Alcohol Spectrum Disorder: Can We Change the Future? Alcohol Clin. Exp. Res. 2020,44,
815–819. [CrossRef] [PubMed]
4.
Popova, S.; Lange, S.; Probst, C.; Gmel, C.; Rehm, J. Estimation of national, regional, and global prevalence of alcohol use during
pregnancy and fetal alcohol syndrome: A systematic review and meta-analysis. Lancet Glob. 2017,5, e290–e299. [CrossRef]
5. Bombardelli, O. Inclusive Education and Its Implementation: International Practices. Educ. Self Dev. 2020,15, 37–46. [CrossRef]
6.
Coates, J.; Harris, J.; Waring, M. The effectiveness of a special school experience for improving preservice teachers’ efficacy to
teach children with special educational needs and disabilities. Br. Educ. Res. J. 2020,46, 909–928. [CrossRef]
Behav. Sci. 2024,14, 431 16 of 17
7.
Emmers, E.; Baeyens, D.; Petry, K. Attitudes and self-efficacy of teachers towards inclusion in higher education. Eur. J. Spec. Needs
Educ. 2019,35, 139–153. [CrossRef]
8.
Lees, B.; Riches, J.; Mewton, L.; Elliot, E.; Allsop, S.; Newton, N.; Thomas, S.; Rice, L.; Nepal, S.; Teeson, M.; et al. Fetal alcohol
spectrum disorder resources for educators: A scoping review. Health Promot. J. Austr. 2022,33, 797–809. [CrossRef] [PubMed]
9.
Taresh, S.; Aniza, N.; Roslan, A.S.; Ma’rof, A.M.; Zaid, S. Pre-School Teachers’ Knowledge, Belief, Identification Skills, and
Self-Efficacy in Identifying Autism Spectrum Disorder (ASD): A Conceptual Framework to Identify Children with ASD. Brain Sci.
2020,10, 165. [CrossRef] [PubMed]
10.
Taylor, T.; Enns, L.N. Factors predictive of a fetal alcohol spectrum disorder diagnosis: Parent and teacher ratings. Child
Neuropsychol. 2018,25, 507–527. [CrossRef] [PubMed]
11.
Reid, N.; White, C.; Hawkins, E.; Crawford, A.; Liu, W.; Shanley, D. Outcomes and needs of health and education professionals
following fetal alcohol spectrum disorder-specific training. J. Paediatr. Child Health 2019,56, 317–323. [CrossRef] [PubMed]
12.
Zameer, M.; Carlisle, A.C.S.; Livesey, A.C.; Mukherjee, R.A.S. Comparisons of the BRIEF parental report and neuropsychological
clinical tests of executive function in Fetal Alcohol Spectrum Disorders: Data from the UK national specialist clinic. Child
Neuropsychol. 2020,25, 648–663. [CrossRef] [PubMed]
13.
Fernández-Pinto, I.; Santamaría, P.; Sánchez-Sánchez, F.; Carrasco, M.; del Barrio, V. SENA. Sistema de Evaluación de Niños y
Adolescentes. Manual de Aplicación, Corrección e Interpretación; TEA Ediciones: Madrid, Spain, 2015.
14.
Gioia, G.; Isquith, P.; Guy, S.; Kenworthy, K. BRIEF-2. Evaluación Conductual de la Función Ejecutiva; Maldonado, M.J., Fournier,
M.C., Martínez-Arias, R., González-Marqués, J., Santamaría, J.M.E.-S.Y.P., Eds.; TEA Ediciones: Madrid, Spain, 2017.
15. Baddeley, A. Working memory: Theories, models, and controversies. Annu. Rev. Psychol. 2012,63, 1–29. [CrossRef] [PubMed]
16.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association:
Washington, DC, USA, 2013.
17.
Herman, L.E.; Acosta, M.; Chang, P.N. Gender and attention deficits in children diagnosed with a fetal alcohol spectrum disorder.
Can. J. Clin. Pharmacol. 2008,15, e411–e419. Available online: https://jptcp.com/index.php/jptcp/article/view/234 (accessed on
21 September 2023). [PubMed]
18.
Rai, J.; Abecassis, M.; Casey, J.; Flaro, L.; Erdodi, L.; Roth, R. Parent rating of executive function in fetal alcohol spectrum disorder:
A review of the literature and new data on Aboriginal Canadian children. Child Neuropsychol. 2017,23, 713–732. [CrossRef]
[PubMed]
19.
Mattson, S.; Bernes, G.; Doyle, L. Fetal Alcohol Spectrum Disorders: A review of the neurobehavioral deficits associated with
prenatal alcohol exposure. Alcohol Clin. Exp. Res. 2019,43, 1046–1062. [CrossRef] [PubMed]
20.
Rangmar, J.; Sandberg, A.D.; Aronson, M.; Fahlke, C. Cognitive and executive functions, social cognition, and sense of coherence
in adults with fetal alcohol syndrome. Nord. J. Psychiatry 2015,69, 1754–1760. [CrossRef]
21.
Sakano, M.; Mukherjee, R.A.S.; Turk, J. Behaviour and adaptive functioning in children and young people with fetal alcohol
spectrum disorders: A UK study. Adv. Dual Diagn. 2019,12, 62–72. [CrossRef]
22.
Rasmussen, C.; Andrew, G.; Zwaigenbaum, L.; Tough, S. Neurobehavioral outcomes of children with Fetal Alcohol Spectrum
Disorders: A Canadian perspective. Paediatr. Child Health 2008,13, 185–191. [CrossRef] [PubMed]
23.
McLachlan, K.; Paolozza, A.; Kully-Martens, K.; Portales-Casamar, E.; Pavlidis, P.; Andrew, G.; Hanlon-Dearman, A.; Loock, C.;
McFarlane, A.; Nikkel, S.; et al. Unpacking the heterogeneity of cognitive functioning in children and adolescents with fetal
alcohol spectrum disorder: Determining the role of moderators and strengths. Adv. Neurodev. Disord. 2017,1, 271–282. [CrossRef]
24.
Rasmussen, C.; Wyper, K.; Talwar, V. The relation between theory of mind and executive functions in children with fetal alcohol
spectrum disorders. J. Popl. Ther. Clin. Pharmacol. 2018,16, e370–e380. Available online: https://jptcp.com/index.php/jptcp/
article/view/561 (accessed on 30 January 2024).
25.
Rasmussen, C.; Tamana, S.; Baugh, L.; Andrew, G.; Tough, S.; Zwaigenbaum, L. Neuropsychological impairments on the NEPSYII
among children with FASD. Child Neuropsichol. 2013,19, 337–349. [CrossRef]
26.
Khoury, J.E.; Milligan, K. Comparing Executive Functioning in Children and Adolescents with Fetal Alcohol Spectrum Disorders
and ADHD: A Meta-Analysis. J. Atten. Disord. 2019,23, 1801–1815. [CrossRef] [PubMed]
27.
Brown, N.; Connor, P.; Adler, R. Conduct-disordered adolescents with fetal alcohol spectrum disorder. Intervention in secure
treatment settings. Crim. Justice Behav. 2012,39, 770–793. [CrossRef]
28.
Nash, K.; Stevens, S.; Clairman, H.; Rovet, J. Preliminary Findings that a Targeted Intervention Leads to Altered Brain Function in
Children with Fetal Alcohol Spectrum Disorder. Brain Sci. 2018,8, 7. [CrossRef] [PubMed]
29.
Xiang, X.; Cho, J.; Sun, Y.; Wang, X. Childhood adversity and cognitive impairment in later life. Front. Psychol. 2022,13, 935254.
[CrossRef]
30.
Kingdon, D.; Cardoso, C.; McGrath, J.J. Research Review: Executive function deficits in fetal alcohol spectrum disorders and
attention-deficit/hyperactivity disorder—A meta-analysis. J. Child Psychol. Psychiatry Allied Discip. 2016,57, 116–131. [CrossRef]
[PubMed]
31.
Chudley, A.E.; Conry, J.; Cook, J.L.; Loock, C.; Rosales, T.; Le Blanc, N. Fetal alcohol spectrum disorder: Canadian guidelines for
diagnosis. Can. Med. Assoc. J. 2005,1, s1–s21. [CrossRef] [PubMed]
32.
Kully-Martens, K.; Denys, K.; Treit, S.; Tamana, S.; Rasmussen, C. A Review of Social Skills Deficits in Individuals with Fetal
Alcohol Spectrum Disorders and Prenatal Alcohol Exposure: Profiles, Mechanisms, and Interventions. Alcohol Clin. Exp. Res.
2012,36, 568–576. [CrossRef]
Behav. Sci. 2024,14, 431 17 of 17
33.
Johnson, M.E.; Robinson, R.V.; Corey, S.; Dewane, S.L.; Brems, C.; Casto, L.D. Knowledge, attitudes, and behaviors of health,
education, and service professionals as related to fetal alcohol spectrum disorders. Int. J. Public Health 2010,55, 627–635. [CrossRef]
[PubMed]
34. Westland, J.C. Information loss and bias in Likert survey responses. PLoS ONE 2022,17, e0271949. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Available via license: CC BY 4.0
Content may be subject to copyright.
