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Acceptability of robot-assisted therapy for disruptive behavior problems in children.


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Technological innovations have changed the way that mental health care interventions are delivered. In recent years, robots have been integrated into treatments for multiple mental health problems. To clarify public opinion regarding the integration of robots into psychological treatments, this study assessed parents’ reaction to robot-assisted therapy as a treatment option for children with disruptive behavior problems. Parents from a community sample (N = 100) were presented with a brief clinical description of a child with disruptive behavior problems and evaluated (through treatment acceptability ratings and positive–negative evaluation scores) 3 different treatment options for that child: a robot-assisted therapy, an Internet-based treatment, and a no-treatment comparison group. Robot-assisted therapy was rated as a highly acceptable form of treatment. Parents rated it as significantly more acceptable than the no-treatment comparison group (F(1, 96) = 88.90, p
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Acceptability of Robot-Assisted Therapy for Disruptive Behavior Problems
in Children
Sarah M. Rabbitt
Akron Children’s Hospital, Akron, Ohio
Alan E. Kazdin
Yale University
Joanna H. Hong
University of California, Irvine
Psychotherapy for children, adolescents, and adults increasingly draws on technology as reflected in treatments available
on the Internet for all sorts of psychological problems (e.g., depression, anxiety). A relatively new technology is the use
of social robots to teach specific skills that can reduce psychological and behavioral problems. The adoption of such
treatments depends heavily on whether people find them acceptable and a reasonable approach to clinical problems. In this
study, we had parents evaluate 3 strategies to treat disruptive behavioral problems in children. Our primary interest was
seeing the extent to which parents would see robots as an acceptable form of treatment. Three treatment conditions were
compared. The first strategy was a cognitively based treatment administered through a robot; the second was the same
treatment administered through the Internet. A third condition was no treatment at all but seeing if parents viewed the other
treatments as better than just waiting and seeing if the child grows out of the problem. In fact, most children experiencing
psychological problems do not receive any treatment, so waiting and seeing if the child gets better is a common practice.
Parents evaluated the treatments after learning how the treatments were applied to children with behavioral problems
commonly seen in psychological services. The results indicated that social robots were very acceptable as a form of
treatment for children. The more familiar use of technology through the Internet was viewed as more acceptable than the
use of robotics. Both treatments were seen as more acceptable than waiting for the child to get better. As technology is
increasingly applied to help with psychological problems, we will need to know more about the conditions that make them
acceptable and how to ensure that treatments are seen as viable options when help is needed.
Technological innovations have changed the way that mental health care interventions are delivered. In recent years, robots
have been integrated into treatments for multiple mental health problems. To clarify public opinion regarding the
integration of robots into psychological treatments, this study assessed parents’ reaction to robot-assisted therapy as a
treatment option for children with disruptive behavior problems. Parents from a community sample (N100) were
presented with a brief clinical description of a child with disruptive behavior problems and evaluated (through treatment
acceptability ratings and positive–negative evaluation scores) 3 different treatment options for that child: a robot-assisted
therapy, an Internet-based treatment, and a no-treatment comparison group. Robot-assisted therapy was rated as a highly
acceptable form of treatment. Parents rated it as significantly more acceptable than the no-treatment comparison group
(1, 96)
88.90, p.001, partial
.48) but less acceptable than an Internet-based treatment program (F
(1, 96)
p.05, partial
.05). Parents also evaluated the treatment quite positively. They viewed it as significantly more
positive than the no-treatment comparison group (F
(1, 96)
153.20, p.001, partial
.62) but less acceptable than
an Internet-based treatment (F
(1, 96)
9.11, p.005, partial
.09). These results suggest that robot-assisted therapy
is viewed positively by the public and that it merits more attention as a treatment platform for mental health problems.
This article was published August 3, 2015. It was accepted under the editorial term of Harris Cooper and Gary R. VandenBos.
Sarah M. Rabbitt, Division of Pediatric Psychiatry and Psychology, Akron Children’s Hospital, Akron, Ohio; Alan E. Kazdin, Department of Psychology, Yale
University; Joanna H. Hong, Department of Psychology and Social Behavior, University of California, Irvine.
This research was supported in part by a grant from the Jack Parker Corporation to the second author.
For further discussion on this topic, please visit the Archives of Scientific Psychology online public forum at
Correspondence concerning this article should be addressed to Alan E. Kazdin, Department of Psychology, 2 Hillhouse Avenue, Yale University, New Haven,
CT 06520-8205. E-mail:
Archives of Scientific Psychology 2015, 3, 101–110 © 2015 American Psychological Association
DOI: 2169-3269
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Archives of Scientific Psychology
Keywords: robot-assisted therapy, treatment acceptability, child treatment
Supplemental materials:
Data repository: (see Kazdin & Rabbitt, 2014)
Unmet mental health care needs have been well documented in the
United States and worldwide (Kessler & Wang, 2008; Kessler et al.,
2009). Approximately 70% of individuals diagnosed with a psychiat-
ric disorder in the United States will not receive any formal treatment
for their mental health problems (Kessler et al., 2005). As a field,
mental health care has tackled this problem from several perspectives,
including expanding efforts to develop and disseminate evidence-
based interventions (e.g., Weisz, Ng, & Bearman, 2014), turning
toward novel models of treatment (e.g., Kazdin & Rabbitt, 2013), and
increasing use of Internet-based and mobile technology to reach
underserved populations (e.g., Cummings, Wen, & Druss, 2013).
Advances in technology already have dramatically changed the land-
scape of mental health care. Internet-based interventions have become
increasingly popular for the treatment of a wide range of mental health
problems, including mood, anxiety, and eating disorders (Andersson
et al., 2005, 2006; Carlbring & Andersson, 2006; Ljotsson et al.,
2007). These web-based treatments are effective at producing clini-
cally meaningful changes in symptoms and improving functioning
among clients, with observed effects comparable to in-person treat-
ments (Andrews, Cuijpers, Craske, McEvoy, & Titov, 2010). Perhaps
even more importantly, these interventions are well received by cli-
ents and are characterized by high levels of treatment adherence and
positive ratings of treatment acceptability (Andrews et al., 2010;
Kaltenthaler et al., 2008).
Technological innovations are not limited to computer- and
Internet-based treatment. In recent years, robots have been incorpo-
rated into treatment paradigms for several different mental health
problems such as autism spectrum disorder, dementia and related
cognitive impairment, and depressed mood (e.g., Moyle et al., 2013;
Vanderborght et al., 2012). Although still a relatively nascent litera-
ture, a growing number of research studies have demonstrated the use
of robots in a variety of treatment relevant roles, including compan-
ion, coach, and therapeutic play partner (Rabbitt, Kazdin, & Scassel-
lati, 2015). Positive outcomes and encouraging initial results indicate
that treatments incorporating robots are an exciting area of growth in
mental health care interventions, and expanded evaluation efforts are
underway (e.g., Scassellati, Admoni, & Matari´
c, 2012; Tapus, Tapus,
& Matari´
c, 2009).
The positive reaction that human users (including both children and
adults) typically have to robots outside of the health care context lends
support to efforts to expand the use of robots in the treatment of
mental health problems (e.g., Melson et al., 2009; Smarr et al., 2012).
While users may rate certain robot appearances to be distasteful or
eerie (e.g., as noted in work on the “uncanny valley”; Mori, MacDor-
man, & Kageki, 2012), most people are quite receptive to interactions
with robots. Adults often rate their interactions with robots as enjoy-
able and demonstrate increasingly positive reactions to the robots over
time (e.g., Koay, Syrdal, Walters, & Dautenhahn, 2007). These pos-
itive reactions have been noted across users of different ages and in
different cultures, further adding to the robust nature of these findings
(e.g., Melson et al., 2009; Smarr et al., 2012).
Although preliminary data suggest positive reactions to these ro-
botic systems, additional work is needed to determine how the public
views these treatments for at least two important reasons. First, many
people are reticent to have robots perform certain personal and sen-
sitive tasks (e.g., Pew Research Center, 2014a). For example, a large
public opinion survey found that most people (65%) responded neg-
atively to the idea of using robots for the care of elderly or physically
ill people. Negative reactions may not extend to applications of robots
to mental health care, which were not specifically included in that
survey. Even so, the results suggest that the positive reactions ob-
served in robotics research may not accurately reflect the opinion of
the general public. Understanding such public attitudes is a critical
component to expanding robot-based interventions and will influence
if and how these treatments are introduced to clients.
Second, the use of robotics in mental health care has focused on
important but restricted child populations, primarily children with
autism spectrum disorder (Scassellati, 2007). The potential for use of
robotics could be broad, with robots serving in such roles as an aid to
traditional treatment (e.g., as partner for practicing skills learned in
therapy sessions) as well as a helpful companion in the home (e.g., in
a role similar to a therapy animal) and with a variety of clinical
diagnoses (e.g., mood disorders, disruptive behavior problems; Rab-
bitt et al., 2015). Children from both clinical and community samples
respond very well to robotics in laboratory studies (Kim et al., 2013;
Melson et al., 2009). In the context of mental health services, parents
make decisions about treatment options for children and hence their
views about the role of robotics are critically important and, at this
point, unknown. Acceptability of different forms of treatment has
been well studied (Carter, 2007; Miltenberger, 1990). Understand-
ably, less is known about the acceptability of new and emerging
treatment modalities, especially technologically innovative ones as the
use of robots or Internet-based treatment.
The purpose of this study is to better understand how parents view
the use of robots in treatment for mental health problems for children.
Specifically, we assessed attitudes toward robot-assisted therapy for
the treatment of disruptive behavior problems in children. The study
focused on disruptive behavior problems because they are among the
most common mental health concerns identified in children and,
therefore, are an important target for expanded treatment efforts
(Nock, Kazdin, Hiripi, & Kessler, 2006). We evaluated the extent that
robot-assisted treatment and Internet-based treatment were viewed as
acceptable forms of treatment by parents of children in the general
population. Internet-based treatment is a meaningful comparison
group because it, like robot-assisted therapy, represents a departure
from traditional face-to-face treatment with a service provider. How-
ever, unlike robot-assisted therapy, this treatment platform has been
more extensively evaluated (e.g., through a large and growing number
of randomized controlled trials; L’Abate & Kaiser, 2012). Comparing
robot-assisted treatment to an Internet-based treatment contextualizes
the acceptability ratings of a robot-assisted intervention with another
technologically innovative treatment platform. Given the decades of
research supporting traditional psychotherapy (i.e., face-to-face treat-
ment with a human therapist) and the popularity of this method of
treatment in Western medicine, it would not be an appropriate com-
parison at this point for such a new form of treatment. That is, it would
be reasonable to predict that parents would consider traditional psy-
chotherapy to be superior in acceptability at this stage. In addition, our
focus was on evaluating the format of treatment (i.e., computer, robot)
rather than the specific clinical techniques used in the treatment.
Therefore, the specific treatment techniques described in the two
active treatments focused on problem-solving skills training, an
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
evidence-based treatment for children with disruptive behavior prob-
lems (Kazdin, 2010). By keeping the specific clinical techniques
consistent, we were able to evaluate the delivery format and platform,
which was our primary focus in this study.
In addition to the two active interventions, we also evaluated a
“wait-and-see” approach to the identified problem, in which no spe-
cific treatment was offered. This type of approach often is used by
families because of the large number of childhood problems that remit
with the passage of time and without any specific intervention (e.g.,
stuttering, lying, noncompliance; Achenbach, 1991; Mesman et al.,
2009). Moreover, as highlighted previously, most people who need
services do not receive them, making no treatment the most common
response to psychiatric diagnoses (Kessler et al., 2005; Kazdin &
Rabbitt, 2013). For the present study, the inclusion of a wait-and-see
condition provides an important base from which to judge the other
two interventions. Indeed, we have noted elsewhere that “no treat-
ment” actually is the usual care in mental health care in the United
States (Kazdin, 2013). Even when people do seek and obtain treat-
ment, typically several years have elapsed since the onset of the
problem (Jorm, 2012). It is quite possible that these technology-
focused interventions would be viewed more negatively than moni-
toring a problem to see if it improves with the passage of time. This
might be especially the case with childhood disorders where many
marked changes in mental health-related domains (e.g., anxieties,
tantrums, stuttering, school refusal) normally occur over the course of
early development and often decrease or remit completely over time.
This study tested three interrelated hypotheses. First, we predicted
that the robot-assisted therapy would be a highly acceptable form of
treatment. Second, we predicted that the robot-assisted treatment
would be equally acceptable as another technology-facilitated treat-
ment, Internet-based treatment. Third, we predicted that robot-assisted
treatment would be more acceptable than monitoring the problem
without formal treatment. We evaluated these hypotheses in the con-
text of a community sample of parents whose opinions on treatment
and acceptability are likely to reflect attitudes of the general public.
A total of 100 parents completed the study and provided data that
were included in the study analyses. Participants were required to: (a)
be 18 years or older, (b) reside in the United States, and (c) have a
child under 18 years old. One hundred twelve participants consented
to participate in the project and met the inclusion criteria. Twelve of
these participants did not complete most of the study (e.g., they
responded to a single question and then discontinued participation);
these individuals were excluded from study analyses. All participants
were recruited through the Amazon’s Mechanical Turk (MTurk;, a crowdsourcing Internet-based workplace
where individuals and businesses can request registered workers to
complete tasks online. MTurk has been used in a wide variety of
research activities, including tasks similar to the treatment acceptabil-
ity paradigm used in the current study (e.g., Rabbitt, Kazdin, & Hong,
2014; Shapiro, Chandler, & Mueller, 2013).
Participants ranged in age between 21 and 62 years old (M31.83,
SD 7.93). Fifty-four (54%) of participants identified as male; one
participant declined to provide information related to gender. Partic-
ipants responded to separate questions about race and ethnicity. Most
of the participants identified their race as White or Caucasian (83%).
Eight participants (8%) identified as Asian, five participants (5%)
identified as Black or African American, two participants (2%) iden-
tified as Native Hawaiian or Pacific Islander, one participant (1%)
identified Native American, and one participant (1%) identified as
biracial. In terms of ethnicity, six participants (6%) identified as
Hispanic or Latino. Most participants were either married (49%) or
partnered (28%). In terms of their children, participants had between
one and six children (M1.59, SD 0.89).
The majority of participants reported being employed (89%). Of the
employed participants, 76% described their employment as full-time
(N68) and 20% described their employment as part-time, with an
additional three participants describing their employment circum-
stances as “other,” (e.g., self-employment). Thirty-seven percent of
participants were college graduates and 7% completed graduate
school (e.g., a doctoral degree). An additional 43% completed some
college or technical training after high school. Two participants did
not graduate from high school. The median monthly incomes for the
sample were $1,501–$2,000 per month and $2,001–$2,500 per month.
All participants completed a series of questionnaires including a
demographics questionnaire, an assessment of treatment acceptability,
and measures assessing experiences with technology and with mental
health care services.
Demographic information. A brief Demographics Form was
used to provide basic descriptive information on study participants.
The form included questions about participants’ gender, race and
ethnicity, age, education level, employment, and marital status. Par-
ticipants also were asked about the number and the ages of their
Treatment acceptability. Treatment acceptability was assessed
through two measures. The Treatment Evaluation Inventory (TEI)
assesses the degree to which an intervention is viewed as a fair,
humane, and appropriate treatment for an identified mental health
problem (see Appendix; Kazdin 1980a, b). The TEI has been used to
evaluate parent reactions to treatments in both clinical and community
samples (e.g., Kelley, Heffer, Gresham, & Elliott, 1989; Miltenberger,
1990; Kazdin, 2000). The measure comprises 15 items rated on a
7-point scale, yielding a maximum total acceptability score of 105; a
higher score indicates a more acceptable treatment. The measure
displays adequate reliability as reflected in high internal consistency
(e.g., ␣⫽.95 in the present study). The TEI also has demonstrated the
ability to discriminate among alternative treatments (e.g., medication,
positive reinforcement, time-out) by different consumers (e.g., par-
ents, children, therapists) of psychiatric treatments (Kazdin 1980a,
1980b; Kazdin, French, & Sherick, 1981).
A subset of items from the Semantic Differential also was used as
a measure of acceptability in this study (Osgood, Suci, & Tannen-
baum, 1957). From the Semantic Differential, bipolar adjectives were
selected for the Evaluative Scale and included six pairs of positive–
negative adjectives: good– bad, pleasant– unpleasant, kind– cruel,
valuable–worthless, fair– unfair, and sincere–insincere. For each ad-
jective pair, participants rated the treatment on a 7-point scale, with
one anchor point representing a positive reaction (e.g., good) and one
anchor point representing a negative reaction (e.g., bad), yielding a
maximum total evaluation score of 42; a higher score indicates a more
negative evaluation. The Semantic Differential was included as a
dependent measure because the measure does not ask specific ques-
tions about treatment, is in a different rating format from the TEI, and
provides a separate method of assessing reactions to treatment. Prior
research has shown that the measure discriminates among psychoso-
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cial treatments that vary in acceptability and by parents as well as
clinicians (e.g., Kazdin et al., 1981).
Pertinent experiences that may influence acceptability
evaluations. The evaluation of robots as an intervention might be
influenced by many factors. We did not have specific hypotheses
about predictors of acceptability, but included two constructs that
might well contribute to acceptability. First, we evaluated the extent to
which participants had experience with mental health care services.
Because parents were evaluating treatment options in this study, their
own personal experiences in treatment (rather than experiences of
their children) were evaluated. To that end, participants completed the
Mental Health Service Utilization Survey, a self-report questionnaire
based on an interview used in the National Comorbidity Survey The present self-report ver-
sion has been used in multiple studies as a way to assess individuals’
experiences with different types of treatment providers (e.g., Elhai,
Patrick, Anderson, Simons, & Frueh, 2006). The measure comprises
items across four broad categories of treatment services: inpatient
hospitalization, self-help groups, hotlines, and outpatient therapy and
counseling services.
A second construct explored was the extent to which participants
had experience with technology. Conceivably, acceptability of robot-
ics as a treatment would be in keeping with other experiences partic-
ipants had related to other personal devices (e.g., laptops, smart-
phones) and other technologies (e.g., social media, computer
software) used in everyday life. Knowledge about and use of a variety
of electronic devices was assessed though the Technology Use and
Experience Questionnaire. This information was collected in order to
determine if this sample is representative of most Americans’ tech-
nology use and experience. This measure was designed specifically
for this study and includes items related to gadget ownership, amount
of time spent using electronic devices, and comfort and skill with
devices, software, and related technology.
All study procedures were reviewed by the Yale University Human
Subjects Committee and were based on similar procedures used in our
prior work on treatment acceptability (Rabbitt et al., 2014). None of
the study participants complained or reported adverse experiences as
part of the study. Participants were recruited through the MTurk
website. A brief description on the website included basic information
about the study and inclusion criteria. The description also included a
hyperlink where interested MTurk workers were directed to a secure
online survey website ( At that point, all
participants were provided with an informed consent that explained
the purpose of the research and the study protocol. Participants then
responded to a series of yes–no questions to ensure that they met all
of the study’s inclusion criteria. Participants who did not meet criteria
were redirected to a web page where they were informed that they did
not meet criteria and thanked for their time. Participants who met
criteria were given detailed instructions on the study procedures and
were randomly assigned one of two brief case vignettes describing a
child with disruptive behavior problems.
Each case vignette was followed by descriptions of three treatment
options (i.e., robot-assisted treatment, Internet-based treatment, and
wait and see [no active treatment]) that could be used to treat the
behavior problems described in the vignette. The treatments were
presented in two different sequences, and participants were randomly
assigned to one of the sequences. Participants rated the acceptability
of a given treatment option immediately after that treatment option
was presented. The participants also completed a demographics ques-
tionnaire, mental health care service utilization form, and the tech-
nology experience questionnaire after rating the acceptability of all of
the treatment options. Participants were paid a total of $5 upon
completion of the study.
Treatment conditions. Three treatment options were evaluated
and compared. First, robot-assisted treatment was presented as a
12-week program focused on problem-solving skills. In this treatment,
the child was given a small robot that was specifically designed to
deliver and practice this treatment intervention with children and
adolescents. The robot presented problem-solving skills to the child
and coached the child in the use of the skills in a variety of situations.
The child was scheduled to interact with the robot twice each week.
Second, Internet-based treatment involved participating in an
Internet-based treatment program for 12 weeks, much like the robot-
assisted treatment. For this treatment, the child learned problem-
solving skills through a secure treatment website specifically designed
for clinical use with children and adolescents. With the help of a
caregiver, he logged onto the website and then learned and practiced
specific problem-solving strategies. He was presented with a variety
of situations in which to practice the strategies and typed his responses
directly into the program. The child was scheduled to log into the
program and participate twice each week. Because of our specific
interest in exploring acceptability of different treatment platforms
(rather than specific clinical techniques), the details of the treatment
intervention were kept consistent across the two active treatment
Third, a wait-and-see approach was described as one of the viable
treatment options. For this condition, no active intervention was
provided. The child’s behavior problems were observed over a 12-
week period and an alternative treatment could be considered if no
improvement was noted during that time. As noted previously, most
children like those described in the vignettes in fact are not likely to
receive special care or interventions. Given this fact, any type of
intervention might be viewed as less acceptable than monitoring the
problem to determine if it remits over time.
Treatment and clinical case presentation. Two different clini-
cal case vignettes were presented to participants. These were based on
vignettes used in prior research on treatment acceptability for disrup-
tive behaviors (Kazdin et al., 1981; Rabbitt et al., 2014). Two children
with two different clinical problems were used to address concerns
related to narrow stimulus sampling and to ensure that acceptability
ratings were not restricted to the characteristics of one single vignette.
Both vignettes described a 12-year-old male child of average intelli-
gence who was experiencing disruptive behavior problems. Males
were used because they represent a disproportionate number of clin-
ical referrals for these clinical concerns (e.g., Nock et al., 2006). Both
vignettes described a 12-year-old because younger children may not
be seen as having the skills (e.g., reading ability, technological expe-
rience) to participate in either robot-assisted or Internet-based treat-
One vignette described a child named Michael who was having
significant behavioral problems at school. His problem behaviors
included hyperactivity (e.g., fidgeting, jumping out of his seat) as well
as impulsive and disruptive behavior in the classroom (e.g., shouting
out answers, interrupting his peers). Michael was described as having
an extensive history of these problems. His teacher was extremely
concerned about these issues and worried that the behavioral problems
were having a significant negative impact on his academic perfor-
mance and social functioning. In terms of the Diagnostic and Statis-
tical Manual of Mental Disorders (DSM–IV–TR; American Psychiat-
ric Association, 2000), Michael would meet criteria for attention-
deficit/hyperactivity disorder (ADHD), predominantly hyperactive-
impulsive subtype.
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The other vignette described a child named William who was engaging
in frequent oppositional behavior at home. He refused to follow his
parents’ instructions and often had severe temper tantrums (which were
described as atypical for his age). William was easily annoyed and often
responded to simple requests with rude and disrespectful comments.
William’s parents were very concerned about the negative impact of his
behavior on his relationship with his family and his interactions with his
peers. In terms of DSM–IV–TR criteria, William would meet criteria for
oppositional defiant disorder (ODD). Half of the participants (n50)
were randomly assigned to Michael’s vignette, and half (n50) were
randomly assigned to William’s vignette.
Three treatment descriptions were presented following the case
vignette. All participants were initially presented with the wait-and-
see treatment option, which was conceptualized as a baseline for
comparison for the active treatments. The order of the two active
treatment descriptions (i.e., Internet-based treatment and robot-
assisted treatment) was counterbalanced after the presentation of the
wait-and-see condition, which served as a baseline comparison. Thus,
through random assignment, approximately half of participants (n
48) were presented with wait and see, Internet-based treatment, and
robot-assisted treatment; approximately half of the participants (n
52) were presented with wait and see, robot-assisted treatment, and
Internet-based treatment. Our prior work has indicated that sequence
does not reliably influence acceptability ratings (Kazdin et al., 1981;
Rabbitt et al., 2014).
Each participant was presented with all three treatment descriptions
to allow more powerful (i.e., within-subject) statistical tests than
provided by a between-subjects design. The treatment descriptions
were specific to the case vignette, but the descriptions were consistent
across the two cases (with the exception of reference to the child’s
name). All of the treatment descriptions were designated for a 12-
week period after which the child’s progress would be evaluated and
the treatment plan revised if needed.
Descriptive Information
In addition to basic demographic information, participants also
reported on their experiences with various types of mental health care
services and their knowledge and use of technology in everyday life.
In terms of mental health care, seven of the study participants (7%)
had ever been hospitalized due to psychiatric problems; one of those
hospitalizations occurred in the past 6 months. Nine participants (9%)
endorsed attending self-help groups for mental health issues, and four
participants (4%) reported calling a hotline for help with mental health
problem at any point in their lives. Thirty-five participants (35%)
reported talking to any type of a professional (e.g., clergy member,
primary care doctor, therapist) about a mental health concern. The
most common professional that participants turned to for support were
primary care physicians (17% of the sample), counselors (14% of the
sample), and psychiatrists (13% of the sample). Number of profes-
sionals sought for support was not correlated with TEI acceptability
scores for any of the three treatment options (robot-assisted therapy:
r(98) ⫽⫺.10, ns; Internet-based treatment: r(98) ⫽⫺.01, ns; wait
and see: r(98) ⫽⫺.14, ns). Similarly, number of professionals sought
for support was not correlated with Semantic Differential evaluation
scores for any of the three treatment options (robot-assisted therapy:
r(98) .16, ns; Internet-based treatment: r(98) .10, ns; wait and
see: r(98) .12, ns). These findings indicate that prior experience
with mental health services is not related to parents’ ratings of the
treatment options. Consequently, these variables were not included in
subsequent analyses.
Participants also provided information on their knowledge of and
experiences with technology. All participants (100%) reported owning
either a desktop or laptop computer, with 52% owning both a desktop
and a laptop computer. In addition, nearly half of participants (49%)
reported owning a tablet or e-reader. Most participants (73%) also
reported owning a smartphone. In terms of daily activities, 95% of
participants use technology as part of their jobs. Most participants also
use technology for paying bills (96%), reading books or magazines
(84%), watching TV and movies (89%), listening to and downloading
music (92%), and using social media (95%). Amount of time spent
using technology in daily life was not correlated with TEI acceptabil-
ity scores for robot-assisted therapy (r(98) ⫽⫺.07, ns). It was
correlated with TEI acceptability scores for Internet-based treatment
(r(98) ⫽⫺.26, p.01) and for waiting and seeing if the problem
improves (r(98) ⫽⫺.51, p.01). In terms of Semantic Differential
scores, amount of time spent using technology in daily life was not
correlated with evaluation scores for robot-assisted therapy (r(98)
.11, ns) or for Internet-based treatment (r(98) .18, ns). It was
correlated with evaluation scores for waiting and seeing if the problem
improves (r(98) .49, p.001). These findings related to technol-
ogy suggest that for the two active treatment groups (i.e., robot-
assisted therapy and Internet-based treatment) experience with tech-
nology was either unrelated to treatment acceptability ratings or only
correlated with a relatively small magnitude of effect (Cohen, 1988).
Acceptability of Robot-Assisted Treatment
The first hypothesis predicted that robot-assisted treatment would
be viewed as an acceptable intervention to the parents included in the
study. Two different types of acceptability ratings (TEI score and
Semantic Differential score) were used to evaluate acceptability of the
treatment. First, participants were classified into two groups based on
their mean TEI item score for robot-assisted treatment. The proportion
of participants with a low-to-moderate score (indicating a more neg-
ative view; mean item rating of 1.00 4.99; n27) was compared
with the proportion of participants with a high score (indicating a
more positive mean item rating of 5.00 –7.00; n73). Significantly
more participants rated robot-assisted treatment in the high accept-
ability range than the low acceptability range (
(1) 21.16, p
.001, r.46). Over 70% of participants had a mean item TEI rating
of 5.00 or higher. Only 7% of the sample had a mean item rating
below 4.00, which would indicate a less acceptable reaction to the
treatment. Second, a similar set of analyses was completed using the
mean Semantic Differential item score. Participants also were classi-
fied into two groups based on their mean Semantic Differential item
rating. The proportion of participants with a high-to-moderate score
(indicating a more negative view; mean item rating of 4.00 –7.00; n
10) was compared with the proportion of participants with a low score
(indicating a more positive view; mean item rating of 1.00 – 3.99; n
90). Significantly more participants rated the robot-assisted treatment
in the positive evaluation range than in the negative evaluation range
(1) 64.00, p.001, r.80). Nearly all participants (90%) rated
the treatment positively, and most participants (69%) rated it very
positively (mean item rating of 1.00 –2.99). Taken together, these
findings indicate that a clear majority of participants viewed robot-
assisted treatment as an acceptable intervention for children with
disruptive behavior problems.
The second and third study hypotheses focused on the relative
acceptability of robot-assisted treatment. More specifically, these hy-
potheses predicted that (a) robot-assisted treatment would be viewed
more positively than waiting and seeing if the problem improves over
time and (b) robot-assisted treatment would be equally acceptable as
Internet-based treatment. In order to test these hypotheses, two series
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
of 2 (Case Vignette) 2 (Sequence) 3 (Treatment Option) repeated
measures analyses of variance (ANOVA) were conducted. In the first
repeated-measures ANOVA, participants’ TEI ratings were the pri-
mary outcome of interest. The main effect of treatment option on TEI
rating was significant (F
(2, 192)
99.00, p.001, partial
see Figure 1), indicating an overall difference among the three treat-
ment options in terms of acceptability. Follow-up contrasts were
conducted to compare robot-assisted treatment to the other two treat-
ment options (i.e., wait and see and Internet-based treatment). Con-
sistent with our hypotheses, robot-assisted treatment was rated as
significantly more acceptable than waiting and seeing if the problem
improves (F
(1, 96)
88.90, p.001, partial
.48). Contrary to
study hypotheses, Internet-based treatment was rated as slightly more
acceptable than robot-assisted treatment (F
(1, 96)
4.73, p.05,
.05). The interaction between case vignette and treatment
option was not significant (F
(2, 92)
0.86, ns, partial
indicating that participants who received the ADHD case vignette did
not rate the acceptability of the treatment options different than the
participants who received the ODD case vignette. In addition, the
interaction between sequence of presentation and treatment option
was not significant (F
(2, 92)
1.41, ns, partial
.01), indicating
that the participants rated the acceptability of the treatments similarly
regardless of the specific sequence in which the treatments were
In the second repeated-measures ANOVA, participants’ Semantic
Differential ratings were used as the primary outcome of interest. The
pattern of findings was consistent with the results from the TEI. The
main effect of treatment option on Semantic Differential rating was
significant (F
(2, 192)
98.86, p.001, partial
.51; see Figure
2), indicating an overall difference among the three treatment options
in terms of participant evaluation. Follow-up contrasts comparing the
robot-assisted treatment to the other two options yielded partial sup-
port for study hypotheses. Robot-assisted treatment was rated more
positively than waiting to see if the problem improves over time
(1, 96)
153.20, p.001, partial
.62). However, it was rated
less positively than Internet-based treatment (F
(1, 96)
9.11, p
.005, partial
.09). The interaction between case vignette and
treatment option (F
(2, 92)
0.81, ns, partial
.00) and between
sequence of presentation and treatment option (F
(2, 92)
0.81, ns,
.00) were not significant. Considering the ANOVA
results from both the TEI and the Semantic Differential, robot-assisted
treatment was viewed as a more acceptable and positive intervention
than waiting and seeing if the problem improves. However, robot-
assisted treatment was viewed as less acceptable than the Internet-
based intervention.
The results of this study indicate that a robot-assisted treatment was
evaluated positively as an intervention for disruptive behavior prob-
lems in children. Most parents viewed this treatment quite favorably,
and they rated robot-assisted treatment as significantly more accept-
able than waiting and seeing if a problem improves over time. In spite
of these positive evaluations, robot-assisted treatment was not viewed
as positively as Internet-based interventions in the treatment of dis-
ruptive behavior problems. The generally positive reactions that par-
ents had to both the robot-assisted treatment and the Internet-based
treatment suggest that many caregivers are receptive to interventions
that include a technologically innovative platform (e.g., computers,
robots, the Internet). Given the particularly novel nature of robotics in
the use of any health care applications and the lack of familiarity with
robotics in the general population, the positive reactions to robot-
assisted treatment are quite promising. As the public becomes more
knowledgeable about robots and how they can be used in mental
health care interventions, reactions to robots in this domain may
become even more positive.
This study’s findings are consistent with robotics research that has
indicated the public’s openness to the use of robots in different aspects
of daily living and self-care (e.g., medication reminders; Smarr et al.,
2012). The relatively positive reactions that parents had to the robot-
Figure 1. Mean acceptability rating for each treatment option and case
vignette. 95% confidence intervals are displayed by the error bars; TEI
Treatment Evaluation Inventory.
Figure 2. Mean semantic differential evaluation rating for each treatment
option and case vignette. 95% confidence intervals are displayed by the error
bars; higher score indicates more negative rating.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
based treatment in the present study may be related to the background
information provided about the robot (e.g., the details of the interven-
tion, how the robot would be used in treatment). Studies that have
found relatively less positive reactions to robots (e.g., Pew Research
Center, 2014a) have not provided this type of contextual information
when assessing public opinions. Given the relative novelty of the use
of robotics, it may be particularly important to provide background
information to orient the public and help them to understand how and
why a robot will be used. Without this information, the public may
react more negatively to using robots in mental health interventions
and other sensitive applications.
There are limitations to the current study that merit discussion. First,
the sample included in this study may not be representative of parents of
children with mental health problems. The sample was relatively small
and was recruited online, which may make them more technologically
savvy than the average American, and consequently more receptive to
technologically innovative treatments for psychological problems in chil-
dren. There is some evidence to suggest that this is the case for the current
sample given that participants in this study own computers and other
gadgets at rates higher than most Americans (Pew Research Center,
2014c). In addition, these parents were part of a community sample.
Parents who have children with significant mental health problems and
have more experience with interventions for children may have different
perceptions of treatment acceptability.
Second, the treatment descriptions included in the study focused on
one specific treatment program for children with disruptive behavior
problems: problem-solving skills training. Even though problem-solving
skills is an evidence-based treatment for disruptive behavior problems in
children, this is not the only treatment that is effective for these problems
(Eyberg, Nelson, & Boggs, 2008). Parents may have reacted differently
to the treatment platforms (i.e., computer and robot) if the specific
treatment techniques were different. We chose to hold the specific treat-
ment technique constant across the two active interventions to prevent the
possibility of a confounding variable (i.e., treatment technique) influenc-
ing parent reactions to the technology used to deliver the intervention
(i.e., robot or computer). At this point, we have no reason to predict that
the specific treatment technique would influence views of the treatment
delivery platform. Even so, it remains possible that parents may have
different reactions to the interventions if the techniques focused on a
different type of evidence-based treatment.
Finally, each of the two treatment platforms could have been
depicted in many different ways. Both robot-assisted treatments and
Internet-based treatments include a wide variety of specific programs
and procedures. We provided an example of how each of these
treatment platforms can be used to deliver an evidence-based treat-
ment. Of course, this does not come close to representing all possible
applications for robot-assisted and Internet-based interventions. And,
it is possible that participants could have reacted to these treatment
platforms very differently if different examples of their applications
were used. On a related point, many different types of robots have
been used in mental health care applications. Depending on the
specific robot used and their characteristics (e.g., appearance, size,
mobility, voice), participants may rate a treatment as more or less
acceptable. In general, users rate robots as more or less acceptable
depending on a variety of different robot traits (e.g., Leite, Pereira,
Martinho, & Paiva, 2008; Powers & Kiesler, 2006). In the present
study, relatively little information was provided on the physical form
of robot itself besides noting that it was “small.” If participants were
given more specific information about the robot, including a photo-
graph or video, they may have had a different reaction to it, either
more positive or more negative. Additional studies are needed in
which a variety of robot forms and characteristics are explored to
determine if varying such features influence perceived acceptability of
robot-assisted therapy.
In addition to this important area of research, future studies are needed
to understand if parents in clinical samples would view robot-assisted
treatment as acceptable as parents in a community sample. Parents of
children who have been identified as having the type of mental health
problems treated by the robot-assisted programs described in this study
might reasonably find treatments to be either more or less acceptable than
parents whose children are not living with such problems. Related, in
these clinical samples, it would also be valuable to know if parents whose
children had personal experience with a variety of treatment options
viewed acceptability differently than those parents whose children had
not received services in the past. Additional studies that explore how
treatment providers (e.g., therapists, counselors, psychologists) react to
robot-based treatment would be helpful to get a better sense of whether
practitioners would be willing to integrate these machines into their
treatment programs at some point in the future. Finally, as robots become
increasingly visible in health care applications (and in a variety of other
roles in society), studies that compare robot-assisted treatments to other
more widely accepted interventions (including those that use human
therapists) are needed to understand how this type of treatment fits into
the broader context of psychological interventions.
Even with the previously noted limitations, the current study provides
important information about the acceptability of technologically novel
treatment platforms in the treatment of disruptive behavior problems in
children. It is unsurprising that participants in this study rated Internet-
based treatment as more acceptable than robot-assisted treatment. After
all, all participants in the study own at least one computer and use of the
Internet is nearly ubiquitous in the United States (Pew Research Center,
2014b). Most Americans do not own a robot, although exposure to novel
technological devices is increasingly common (Pew Research Center,
2014c). The increased knowledge about and exposure to computers may
explain why a treatment using this platform was rated even more posi-
tively than a robot-based treatment.
Given the significant mental health care needs in the United States and
throughout the world, society must explore a variety of treatment options
to expand the reach of treatments for mental health problems so that
children who are not served by current systems receive the care that they
need. Undoubtedly, part of this effort will include traditional psychother-
apy and increasing access to in-person treatment with a trained health
care professional. However, psychologists have already identified a va-
riety of novel models of service delivery, including technologically
innovative treatment resources that can be used in this effort (Kazdin &
Rabbitt, 2013). The positive reaction that parents demonstrated to robot-
assisted therapy suggests that robots may be part of the solution.
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Treatment Evaluation Inventory
Please complete the items listed below. The items should be completed by clicking on the space under the question that best indicates how you
feel about this treatment. Please read the items very carefully because a checkmark accidentally placed on one space rather than another may
not represent the meaning you intended.
1. How acceptable do you find this treatment to be for the child’s problem behavior?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not At All
2. How willing would you be to carry out this procedure yourself if you had to change the child’s problems?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not At All
3. How suitable is this procedure for children who might have other behavioral problems than those described for this child?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not At All
4. If children had to be assigned to treatment without their consent, how bad would it be to give them this treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not Bad
At All
5. How cruel or unfair do you find this treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not Cruel
At All
6. Would it be acceptable to apply this procedure to children living in institutions, people with significant developmental delays, or other individuals who are
not given the opportunity to choose treatment for themselves?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not At All
To Apply This
To Apply This
7. How consistent is this treatment with common sense or everyday notions about what treatment should be?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Very Different
Or Inconsistent
8. To what extent does this procedure treat the child humanely?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Does Not Treat
Them Humanely
At All
Treats Them
Treats Them
(Appendix continues)
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9. To what extent do you think there might be risks in undergoing this kind of treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
A Lot Of Risks
Are Likely
Some Risks
Are Likely
No Risks
Are Likely
10. How much do you like the procedures used in this treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Do Not Like
Them At All
Like Them
Like Them
Very Much
11. How effective is this treatment likely to be?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Not At All
12. How likely is this treatment to make permanent improvements in the child?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Unlikely Moderately
13. To what extent are undesirable side effects likely to result from this treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Side Effects
Side Effects
Side Effects
14. How much discomfort is the child likely to experience during the course of treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Very Much
No Discomfort
At All
15. Overall, what is your general reaction to this form of treatment?
______________ ______________ ______________ ______________ ______________ ______________ ______________
Ambivalent Very
Received December 6, 2014
Revision received January 30, 2015
Accepted February 9, 2015
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... Multimodal feedback is particularly meaningful when the end users are unfamiliar with technologies or are limited in cognitive capabilities, such as older adults with dementia. Examples of feedback include voice, video, gesture, and physical appearance, all of which can affect users' perception of the robot during their interaction and thus the effectiveness of cognitive training/rehabilitation (Ab Aziz et al., 2015;Rabbitt et al., 2015). The following list shows popular modalities for robotic feedback during interaction with human users. ...
... Many experimental studies have been conducted to evaluate the important properties of robotic rehabilitation, such as feasibility, safety, usability, performance, etc. On the one hand, exploratory studies including surveys and interviews with users (e.g., patients, caregivers, and therapists) have been conducted to inform the next stage of study (Rabbitt et al., 2015;Coeckelbergh et al., 2016;Salichs et al., 2016;Darragh et al., 2017;Kohori et al., 2017;Korchut et al., 2017;Law et al., 2019b). On the other hand, researchers have conducted experimental studies to verify and/or validate robot-assisted cognitive training systems. ...
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A large and increasing number of people around the world experience cognitive disability. Rehabilitation robotics has provided promising training and assistance approaches to mitigate cognitive deficits. In this article, we carried out a systematic review on recent developments in robot-assisted cognitive training. We included 99 articles in this work and described their applications, enabling technologies, experiments, and products. We also conducted a meta analysis on the articles that evaluated robot-assisted cognitive training protocol with primary end users (i.e., people with cognitive disability). We identified major limitations in current robotics rehabilitation for cognitive training, including the small sample size, non-standard measurement of training and uncontrollable factors. There are still multifaceted challenges in this field, including ethical issues, user-centered (or stakeholder-centered) design, the reliability, trust, and cost-effectiveness, personalization of the robot-assisted cognitive training system. Future research shall also take into consideration human-robot collaboration and social cognition to facilitate a natural human-robot interaction.
... Introduction Currently, digital technologies for mental health care are on the rise [1,2]. Examples include internetdelivered cognitive behavioral therapy, but also virtual reality and artificial intelligence enabled programs [3], mobile applications [4], socially assistive robotics [5], and serious games [6,7]. Given the increasing costs of care and growing waiting lists in this field, technology could be an important element of sustainable mental health care [3]. ...
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BACKGROUND An essential value in mental health care is compassion: awareness of suffering, tolerating difficult feelings in the face of suffering, and acting or being motivated to alleviate suffering. Currently, technologies for mental health care are on the rise and could offer several advantages, such as more options for self-management by clients and more accessible and economically viable care. However, digital mental health interventions (DMHI’s) have not yet been implemented widely into daily practice. Developing and evaluating DMHI’s around important mental health care values, such as compassion, could be key for a better integration of technology in the mental health care context. OBJECTIVE The current systematic scoping review explored the literature for previous instances where technology for mental health care has been linked to compassion or empathy, to investigate how DMHI’s can support compassionate mental health care. METHODS Searches were conducted in the databases PsycINFO, PubMed, Scopus and Web of Science, and screening by two reviewers resulted in 40 included articles. From these articles we extracted the following data: goals and target groups of the technologies, types of technologies, study designs, outcome measures, and the extent to which the technologies met a five-step proposed definition of compassion. RESULTS We found three main ways in which technology can contribute to compassion in mental health care; by showing compassion, by enhancing (self-)compassion in people, or by facilitating compassionate care between a client and caretakers. However, none of the included technologies met all five elements of compassion, nor were they evaluated on compassion. CONCLUSIONS We make the case for the potential of compassionate technology, it’s challenges, and the need to evaluate technology for mental health care on compassion. Our findings could contribute to the development of fully compassionate technology, where all elements of compassion are embedded in its design, use, and evaluation.
... Results were positive regarding these constructs, showing social robotics as a potential tool in pediatric rehabilitation. In the same way, Rabbit et al. [18] observed the acceptance of social robot-based therapy in procedures for children with disruptive behaviors through an evaluation of the parents. The results indicated that social robots were highly acceptable. ...
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Purpose of Review This work presents a comprehensive overview of social robots in therapy and the healthcare of children, adults, and elderly populations. According to recent evidence in this field, the primary outcomes and limitations are highlighted. This review points out the implications and requirements for the proper deployment of social robots in therapy and healthcare scenarios. Recent Findings Social robots are a current trend that is being studied in different healthcare services. Evidence highlights the potential and favorable results due to the support and assistance provided by social robots. However, some side effects and limitations are still under research. Summary Social robots can play various roles in the area of health and well-being. However, further studies regarding the acceptability and perception are still required. There are challenges to be addressed, such as improvements in the functionality and robustness of these robotic systems.
... Results were positive regarding these constructs, showing social robotics as a potential tool in pediatric rehabilitation. In the same way, Rabbit et al. [18] observed the acceptance of social robot-based therapy in procedures for children with disruptive behaviors through an evaluation of the parents. The results indicated that social robots were highly acceptable. ...
Robot-assisted therapy for gait rehabilitation of patients with neurological disorders usually combines a body weight support system with a treadmill system. Lokomat is one of the most used devices for gait rehabilitation. This device allows therapists to focus on the patient and the therapy. However, this therapy session is based on multi-tasking processes, which are often difficult for a therapist to manage. In this work, a Socially Assistive Robot (SAR) was integrated into a neurorehabilitation program as a collaborator agent to promote patient engagement and performance during the therapy. This short-term study presents the effects comparing the social robot condition and control condition with a group of four neurological patients using repeated measurement design. As a remarkable result, patients improved thoracic 18.44% and cervical 32.23% posture on average with SAR assistance. This study demonstrated the feasibility of the integration of a social robot as a complement of gait rehabilitation programs.
... 30 An online survey about hypothetical robot therapy for children with disruptive behavioural problems found that while the treatment was considered more acceptable than no treatment, it was less acceptable than internet-based treatment. 31 Other publications conducted significance testing, but did not find significant effects. 18 ...
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Objective To review research on social robots to help children in healthcare contexts in order to describe the current state of the literature and explore future directions for research and practice. Design Scoping review. Data sources Engineering Village, IEEE Xplore, Medline, PsycINFO and Scopus databases were searched up until 10 July 2017. Only publications written in English were considered. Identified publications were initially screened by title and abstract, and the full texts of remaining publications were then subsequently screened. Eligibility criteria Publications were included if they were journal articles, conference proceedings or conference proceedings published as monographs that described the conceptualisation, development, testing or evaluation of social robots for use with children with any mental or physical health condition or disability. Publications on autism exclusively, robots for use with children without identified health conditions, physically assistive or mechanical robots, non-physical hardware robots and surgical robots were excluded. Results Seventy-three publications were included in the review, of which 50 included user studies with a range of samples. Most were feasibility studies with small sample sizes, suggesting that the robots were generally accepted. At least 26 different robots were used, with many of these still in development. The most commonly used robot was NAO. The evidence quality was low, with only one randomised controlled trial and a limited number of experimental designs. Conclusions Social robots hold significant promise and potential to help children in healthcare contexts, but higher quality research is required with experimental designs and larger sample sizes.
... Studies have shown that social robots can be a useful resource for social skills and communication therapies for children with autism [15]. Rabbitt et al. [16] argue that parents view social robots as a very acceptable form of treatment for children with disruptive behaviour problems. As a result of the positive feedback obtained from existing case studies, researchers continue to develop frameworks designed for robots to engage in social interactions with potential users. ...
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We have recently seen significant advancements in the development of robotic machines that are designed to assist people with their daily lives. Socially assistive robots are now able to perform a number of tasks autonomously and without human supervision. However, if these robots are to be accepted by human users, there is a need to focus on the form of human–robot interaction that is seen as acceptable by such users. In this paper, we extend our previous work, originally presented in Ruiz-Garcia et al. (in: Engineering applications of neural networks: 17th international conference, EANN 2016, Aberdeen, UK, September 2–5, 2016, proceedings, pp 79–93, 2016., to provide emotion recognition from human facial expressions for application on a real-time robot. We expand on previous work by presenting a new hybrid deep learning emotion recognition model and preliminary results using this model on real-time emotion recognition performed by our humanoid robot. The hybrid emotion recognition model combines a Deep Convolutional Neural Network (CNN) for self-learnt feature extraction and a Support Vector Machine (SVM) for emotion classification. Compared to more complex approaches that use more layers in the convolutional model, this hybrid deep learning model produces state-of-the-art classification rate of \(96.26\%\), when tested on the Karolinska Directed Emotional Faces dataset (Lundqvist et al. in The Karolinska Directed Emotional Faces—KDEF, 1998), and offers similar performance on unseen data when tested on the Extended Cohn–Kanade dataset (Lucey et al. in: Proceedings of the third international workshop on CVPR for human communicative behaviour analysis (CVPR4HB 2010), San Francisco, USA, pp 94–101, 2010). This architecture also takes advantage of batch normalisation (Ioffe and Szegedy in Batch normalization: accelerating deep network training by reducing internal covariate shift., 2015) for fast learning from a smaller number of training samples. A comparison between Gabor filters and CNN for feature extraction, and between SVM and multilayer perceptron for classification is also provided.
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Socially assistive robots (SARs) present a promising intervention for addressing the increasing prevalence of childhood stress. This innovative technology has become increasingly common in practical implementation. However, empirical support has not kept pace with the robots’ growing popularity. The present study set out to provide an empirical test of the stress-buffering capability of this technology. We examined the effects of the presence of an SAR on self-reported measures of stress in a community sample of 70 children (40 girls, 30 boys) between the ages of 7 and 10 years old. Child participants were randomly assigned to either a robot-present or a robot-absent condition during the Trier Social Stress Test for Children. Contrary to our predictions, we did not detect a stress-buffering effect of the presence of the robot. Instead, the primary analysis showed that the robot’s presence led to a larger decrease in positive affect following the task. However, further analyses found no significant difference in positive affect when accounting for baseline group differences. Exploratory analyses of the nature of children’s interactions with the SAR during the stressful task found that higher levels of parent-reported social anxiety were associated with greater prosocial behaviors towards the SAR, thus signifying a potential target for future interventions. Further work on SARs is needed to determine the optimal timing and robotic specifications that would maximize the potential of this developing technology to improve children’s mental health care.
Conference Paper
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The fundamental rights of hospitalized minors include receiving educational and emotional attention during the illness situation. Currently the teaching of computational thinking and programming constitute a new literacy that must be carried to all classrooms, including hospital classrooms. This paper presents an exploratory case study on an educational intervention that is inclusive and adaptable to the characteristics of hospital classrooms for the teaching of computational thinking in a transversal way and programming without screens and robots, including working with emotions. In this study, 22 minors participated in five sessions developed in a hospital classroom. The results indicate that the intervention has improved the emotional state of the children, as well as their knowledge regarding computational thinking.
Objective: We tested whether people are prone toward positive perceptions of Animal-Assisted Interventions (AAIs). We additionally evaluated whether this effect is stronger for people who have positive attitudes toward companion animals. Method: We presented 210 participants with fictitious news reports, each describing a study of an AAI or a control intervention. Participants rated the news reports on their credibility, acceptability, and general appeal and completed a measure of attitudes toward companion animals. Results: Individuals with positive attitudes toward companion animals evaluated AAIs as more credible, acceptable, and positive than did individuals with negative attitudes toward companion animals. There was no difference in how people with positive and negative attitudes toward companion animals evaluated control treatments. Conclusion: We found that individuals with positive attitudes toward companion animals perceived AAIs as more credible, acceptable, and positive, relative to individuals with more negative attitudes toward companion animals.
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Animal-assisted therapies (AATs) are not widely promoted in routine mental healthcare but represent a viable treatment option given positive perceptions of pets and growing evidence that animals provide meaningful contribution to psychological wellbeing. Relatively little is known about the general public's attitude toward AATs, especially in relation to more commonly used alternatives. This study compared the acceptability of four different treatment options (AAT, medication, psychotherapy, and no active treatment) for common externalizing behaviors in children. Parents from a community sample (N = 189) were presented with vignettes describing a child with symptoms of an externalizing behavior disorder and were asked to rate the acceptability of the four different treatment options. Participants rated AAT as a highly acceptable form of treatment and more acceptable than no active treatment and medication. However, AAT was rated as less acceptable than psychotherapy. Experiences with animals (both positive and negative) were unrelated to the acceptability of AAT. These findings suggest that AAT is viewed as a highly acceptable form of treatment for common externalizing behaviors and should be more systematically investigated as a treatment for children's mental health problems.
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This paper aims to study the role of the social robot Probo in providing assistance to a therapist for robot assisted therapy (RAT) with autistic children. Children with autism have difficulties with social interaction and several studies indicate that they show preference toward interaction with objects, such as computers and robots, rather than with humans. In 1991, Carol Gray developed Social Stories, an intervention tool aimed to increase children’s social skills. Social stories are short scenarios written or tailored for autistic individuals to help them understand and behave appropriately in social situations. This study shows that, in specific situations, the social performance of autistic children improves when using the robot Probo, as a medium for social story telling, than when a human reader tells the stories. The robot tells Social Stories to teach ASD children how to react in situations like saying “hello”, saying “thank you” and “sharing toys”. The robot has the capability of expressing emotions and attention via its facial expressions and its gaze. The paper discusses the use of Probo as an added-value therapeutic tool for social story telling and presents the first experimental results. Keywords: social robot; ASD children; social story; robot assisted therapy
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Decades of clinical psychological science have produced empirically supported treatments that are now undergoing dissemination and implementation (DI) but with little guidance from a science that is just taking shape. Charting a future for DI science (DIS) and DI practice (DIP), and their complex relationship, will be complicated by significant challenges—the implementation cliff (intervention benefit drops when tested practices are scaled up), low relevance of most clinical research to actual practice, and differing timetables and goals for DIP versus DIS. To address the challenges, and prepare the next generation of clinical psychological scientists, we propose the following: making intervention research look more like practice, solving the “too many empirically supported treatments” problem, addressing mismatches between interventions and their users (e.g., clients, therapists), broadening the array of intervention delivery systems, sharpening outcome monitoring and feedback, incentivizing high-risk/high-gain innovations, designing new professional tracks, and synchronizing and linking the often-insular practice and science of DI.
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Although participants with psychiatric symptoms, specific risk factors, or rare demographic characteristics can be difficult to identify and recruit for participation in research, participants with these characteristics are crucial for research in the social, behavioral, and clinical sciences. Online research in general and crowdsourcing software in particular may offer a solution. However, no research to date has examined the utility of crowdsourcing software for conducting research on psychopathology. In the current study, we examined the prevalence of several psychiatric disorders and related problems, as well as the reliability and validity of participant reports on these domains, among users of Amazon’s Mechanical Turk. Findings suggest that crowdsourcing software offers several advantages for clinical research while providing insight into potential problems, such as misrepresentation, that researchers should address when collecting data online.
Our world is changing and changing quickly. One can be certain that the impact of technology will only become more pervasive in the decades to come and we cannot ignore how it will impact our profession. The juggernaut of technological development has and will continue to dramatically alter how we as health professionals in the neurosciences pursue our efforts in research, training and treatment. This book provides a rare and needed analysis of the history of technology in our field, the current state-of-the-art as well as a vision for the future. The contributors of this book critically examine how recent technological developments can contribute to advances in a range of topics including educational technology, assessment and treatment. These advances include a wide range of new approaches to communication and extend to advances in clinical care such as Diffusion Tensor Imaging and Transcranial Magnetic and Deep-brain stimulation.
With recent increases in the use of positive approaches to treatment for individuals with developmental disabilities, it seems appropriate to review the variables that have been found to influence the acceptability of various treatments. Programmatic treatments for problematic behaviors that incorporate primarily positive (reinforcement) components rather than negative (punishment) components may still be susceptible to variables found to influence the acceptability of treatments. Although more positive reinforcement based approaches are certainly preferred, the need to consider the right to effective treatment is also an important component of any intervention for problematic behavior. To continually assure the right to effective treatment, the examination of variables affecting the acceptance of treatments continues to be an important area of research. This paper reviews the instruments that have been used to evaluate the acceptability of treatments as well as the variables that have shown demonstrated influence on the acceptability of treatments for problematic behavior.
As a field, mental health care is faced with major challenges as it attempts to close the huge gap between those who need services and those who receive services. In recent decades, technological advances have provided exciting new resources in this battle. Socially assistive robotics (SAR) is a particularly promising area that has expanded into several exciting mental healthcare applications. Indeed, a growing literature highlights the variety of clinically relevant functions that these robots can serve, from companion to therapeutic play partner. This paper reviews the ways that SAR have already been used in mental health service and research and discusses ways that these applications can be expanded. We also outline the challenges and limitations associated with further integrating SAR into mental health care. SAR is not proposed as a replacement for specially trained and knowledgeable professionals nor is it seen as a panacea for all mental healthcare needs. Instead, robots can serve as clinical tools and assistants in a wide range of settings. Given the dramatic growth in this area, now is a critical moment for individuals in the mental healthcare community to become engaged in this research and steer it toward our field’s most pressing clinical needs.
This study examined the role of perceived barriers to participation in treatment and the acceptability of treatment among children and parents. Children (N = 144, ages 6–14) referred for outpatient treatment for oppositional, aggressive, and antisocial behavior and their families participated. The main findings were that: (a) perceived barriers to participation in treatment predicted treatment acceptability as rated by children and parents; (b) the effect was not accounted for by socioeconomic disadvantage, parent psychopathology and stress, and severity of child dysfunction; and (c) treatment acceptability was related to therapeutic change in the children over the course of therapy but the relation was small. Overall, the findings indicate that families vary considerably in the barriers they perceive in coming to treatment and that these barriers influence the extent to which they and their children evaluate the acceptability of the treatments they receive. The implications of treatment acceptability for evaluation and delivery of psychotherapy are discussed.
Most individuals in both developing and developed countries who experience mental illness do not receive psychological services. The dominant model of delivering services used in developed countries (individual therapy by a highly trained mental health professional) can provide effective (i.e., evidence-based) treatments but is greatly limited as a means of reaching the large swath of individuals in need. We highlight several models outside the mental health professions (e.g., public health, medicine, business) that are more affordable and accessible and can be scaled up to reach many individuals in need. These models include task shifting, disruptive innovations, interventions in everyday settings, best-buy interventions, lifestyle changes, and social media. We convey their key characteristics and how they have been or can be applied to mental health. We end by discussing challenges in applying the models, critical issues on which effective treatment delivery depends, and paths to make progress.