Content uploaded by Elizabeth Broadbent
Author content
All content in this area was uploaded by Elizabeth Broadbent on Mar 16, 2016
Content may be subject to copyright.
ORIGINAL RESEARCH
Attitudes and Reactions to a
Healthcare Robot
Elizabeth Broadbent, Ph.D.,
1
I Han Kuo, B.E.,
2
Yong In Lee,
1
Joel Rabindran, B.Sc. (Hons),
3
Ngaire Kerse, M.B.Ch.B., Ph.D.,
4
Rebecca Stafford, M.Sc.,
1
and Bruce A. MacDonald, Ph.D.
2
1
Department of Psychological Medicine, Faculty of Medical
and Health Sciences,
2
Department of Electronic and Computer
Engineering, Faculty of Engineering,
4
Department of General
Practice, Faculty of Medical and Health Sciences, The University
of Auckland, Auckland, New Zealand.
3
Northern Clinical School, Sydney Medical School, University of
Sydney, Sydney, Australia.
Abstract
Objective: The use of robots in healthcare is a new concept. The
public’s perception and acceptance is not well understood. The ob-
jective was to investigate the perceptions and emotions toward the
utilization of healthcare robots among individuals over 40 years of
age, investigate factors contributing to acceptance, and evaluate
differences in blood pressure checks taken by a robot and a medical
student. Materials and Methods: Fifty-seven (n¼57) adults aged
over 40 years and recruited from local general practitioner or
gerontology group lists participated in two cross-sectional studies.
The first was an open-ended questionnaire assessing perceptions of
robots. In the second study, participants had their blood pressure
taken by a medical student and by a robot. Patient comfort with each
encounter, perceived accuracy of each measurement, and the quality
of the patient interaction were studied in each case. Readings were
compared by independent t-tests and regression analyses were con-
ducted to predict quality ratings. Results: Participants’ perceptions
about robots were influenced by their prior exposure to robots in
literature or entertainment media. Participants saw many benefits
and applications for healthcare robots, including simple medical
procedures and physical assistance, but had some concerns about
reliability, safety, and the loss of personal care. Blood pressure
readings did not differ between the medical student and robot, but
participants felt more comfortable with the medical student and saw
the robot as less accurate. Although age and sex were not significant
predictors, individuals who held more positive initial attitudes
and emotions toward robots rated the robot interaction more favor-
ably. Conclusions: Many people see robots as having benefits
and applications in healthcare but some have concerns. Individual
attitudes and emotions regarding robots in general are likely to in-
fluence future acceptance of their introduction into healthcare
processes.
Key words: technology, e-health, home health monitoring
Introduction
The world’s population is rapidly aging.
1
The U.S. population
aged over 65 is projected to increase from 39 million in
2010 to 69 million by 2030, and the population over 85 is
projected to double by 2025 and increase fivefold by 2050.
2
This is placing increased pressure on elderly care facilities and
healthcare services. In the 2000 U.S. census, 1.6 million people were
living in institutions, 45% of whom were over 85.
3
Further, half of
those over 85 require personal assistance even when not institu-
tionalized.
4
There is mounting concern over current and projected
shortages of healthcare professionals to care for the aging popula-
tion.
5
The United States is expected to have a shortfall of 400,000
registered nurses by 2020, which is 29% short of the projected demand.
6
Assistive technologies that can supplement human care-giving are
being increasingly promoted.
7
Robots are being developed for
physical monitoring and physical assistance, as well as for simple
tasks such as meal preparation and removing bed linen.
8,9
Socially
assistive robots are starting to be used in areas such as stroke reha-
bilitation
10
and dementia care.
11
A robot for guiding people around
and giving reminders in an assisted-living facility has been developed.
12
How recipients of healthcare will react to robotic technology is an
important consideration that is not well understood. Early trials
608 TELEMEDICINE and e-HEALTH JUNE 2010 DOI: 10.1089/tmj.2009.0171
suggest that robots can be acceptable to patients and improve health
outcomes. For example, a study of 92 gastric surgery patients showed
that bed stays were reduced on average by 1 day if doctors’ bedside
visits were supplemented with four additional ‘‘virtual’’ visits from
the doctor via an ambulatory robot, saving $US 219,578.
13
Aged
people, including those with dementia, have been shown to respond
favorably to robotic pets
14–16
and elderly users have reported
enthusiasm for robotic walkers.
17
Robots have been successfully used
for physiotherapy.
18
There is recognition that acceptance is an
important issue among staff and patients,
15
and careful design is
required.
19,20
As older people have high healthcare needs, age is an important
variable to consider in healthcare robotics. Previous research has
indicated that, compared with younger people, older people are less
comfortable with computers and new technology.
21
However, older
people are prepared to accept simple assistive devices in their home to
help maintain their independence,
22
especially when there is a per-
ceived need for the device.
23
The Unified Theory of Acceptance and Use of Technology model
stresses the importance of users’ performance expectancy, effort
expectancy, social influence, and facilitating conditions to accep-
tance and use of technology.
24
Age, sex, experience, and voluntari-
ness of use are key moderators in the model. Other models in the
adoption of technology have demonstrated the importance of intel-
ligence and attitudes toward computers to technology acceptance in
older adults.
25
However, these models are based on research with
simple information technologies such as phones and computers
rather than with robots. A recent review of acceptance of healthcare
robots reported that both user and robot variables were important to
acceptance, including user age, sex, experience, education, needs and
culture, and robot appearance, size, sex, ergonomics, role, and per-
sonality.
26
The review recommended more research into how user
expectations about robots predict acceptance.
Most people have no first-hand experience with robots, and we
speculate that the ideas people hold about them arise from their
exposure to robots in literature and entertainment media. Consider-
ing the variety of potential applications for robots for disabled
people, hospital patients, and elderly people at home and in assisted-
living facilities, it is important to determine how people feel about
and react to the use of robots in healthcare. This article reports two
studies. The first exploratory study aimed to investigate people’s
perceptions about robots in healthcare by using an open-ended
response format; because little is known in this area, much can be
gained from this approach compared with constrained fixed
choice response formats. The second study aimed to investigate the
importance of attitudes and emotions toward robots in the accep-
tance of a healthcare robot. It investigated how people reacted to the
healthcare robot compared with a medical student in a blood pressure
check. We hypothesized that blood pressure would not differ between
the robot and the medical student because they both used the same
type of automatic blood pressure monitor. However, because this was
the first time the participants had interacted with a healthcare robot
and they were unfamiliar with it, we hypothesized that patients
would be less comfortable with the robot than with the medical
student and would perceive the robot as less accurate. The study
recruited middle aged and older adults as these age groups are likely
to use such technologies in the short-term and medium-term time
frames when they become readily available. The studies were approved
by the Ministry of Health Northern Regional X Ethics Committee.
Study 1—Exploratory
MATERIALS AND METHODS
Participants were either patients of a local general practitioner in
the vicinity of the university campus, or in the university’s geron-
tology research group. Invitations to participate in a study that
assessed people’s thoughts and feelings about healthcare robots were
posted to 400 people who met inclusion criteria of being aged over 40
years. Only those who replied positively within 4 weeks (n¼57) were
asked to attend the university. On arrival, all individuals gave written
informed consent for both studies. Participants were 33 women and
24 men; 50 were New Zealand Europeans, 2 Maori, 2 Chinese, and 3
from other ethnic groups, with a mean age of 64.33 ( –10.05).
The participant completed the first section of Questionnaire 1.
Participants were asked whether they had seen a robot in real life
before and if they had heard of robots being used in healthcare. Five
open-ended items were included: ‘‘When you think of robots, what
comes to mind?’’ ‘‘Can you think of the names of any robots you
might have seen in movies, on TV, or in books or newspapers?’’
‘‘What do you think a robot might be able to help you with in
healthcare?’’ ‘‘Can you think of any benefits of having a robot help
you with healthcare?’’ ‘‘Do you have any reservations or fears about
having a robot help you in healthcare?’’
RESULTS
Fifteen participants reported having seen a robot in real life (6 in
industry, 5 in entertainment, and 4 in both). Twenty participants had
heard about healthcare robots (11 general assistance, 7 surgeries, 1
geriatric, and 1 pediatric care).
Thoughts about robots included conceptual definitions (n¼23),
movies or novels (n¼21), industrial uses, robot looks and movement
ATTITUDES AND REACTIONS TO A HEALTHCARE ROBOT
ªMARY ANN LIEBERT, INC. .VOL. 16 NO. 5 .JUNE 2010 TELEMEDICINE and e-HEALTH 609
(n¼6), reservations (n¼2), and no answer (n¼2). Names of robots
people reported included R2D2 (n¼24) and C3PO (n¼10) from the
movie Star Wars, the Daleks from the television series Doctor Who
(n¼4), the television series Lost in Space (n¼4), Tripods (n¼2), and
Robo-Cop (n¼2). Other robots mentioned were Number 5, the AI
movie, the iRobot movie, Asimo, Nara Robb, Optimus Prime and
Robbie, Lunar-Rover, and explosive robots. Twenty-one (n¼21)
participants did not give a name.
Sixty-three percent of participants (36/57) did not have any fears
or reservations about robots in healthcare. Those who did, mentioned
reliability issues (n¼9), error and safety (n¼8), loss of personal
interaction (n¼7), and having to learn how to use it (n¼1). Two
participants commented that robots needed to be used wisely as they
could only be as ethical as the programmers, and two commented
that patients should be given choices for human or robotic care.
Answers about useful tasks for healthcare robots were categorized
into ‘‘medical procedures’’ (e.g., taking blood pressure, oxygen, pulse,
respiration, diabetic levels, blood gas, blood flow, temperature,
weight, height, electrocardiogram, blood analysis, diagnostics,
ordering tests, taking history, drug administration, fine surgery, and
closing wounds), ‘‘physical assistance’’ (directing blind people,
reaching and moving heavy/high things, doing housework, moving
bedridden people, mobility, personal care, dental care, dealing with
contagious illnesses, and medical alert), and ‘‘miscellaneous’’ (games
and answer phones). Fourteen participants did not list any tasks.
Participants listed many benefits of healthcare robots: cost
advantage, never going on strike, no waiting, always available, no
travel, accuracy, speed, allowing a human free time, relief from
mundane tasks, avoiding undesirable contact, reducing workload,
overcoming staff shortages, allowing doctors to focus on the
emotional relationship with the patient, not wasting a medical pro-
fessionals time, resolving simple problems without having to visit a
doctor, organization of daily routine, reminders, reducing surgical
incisions and speeding recovery, decisions based on database, robots
not distracted by other patients, no personality factors, honesty,
perseverance, and commitment. Nine participants could not think of
any benefit.
DISCUSSION
Many participants’ were familiar with robots from popular science
fiction. They saw a wide variety of applications, including simple
procedures to relieve burden on doctors, even seeing superior qual-
ities in robots, such as honesty, and lack of distraction. Many patients
had no concerns but those who did mentioned the reliability and
capability of robots as well as the personal element being lost in
healthcare. These findings suggest that even though participants had
little personal experience with healthcare robots they held mental
representations about them.
Study 2—Experiment
MATERIALS AND METHODS
After completing study 1, all participants took part in study 2. The
medical student asked the participants to complete the remaining
sections in Questionnaire 1, which included one item on how expe-
rienced the participants were with computers from 1 ‘‘not at all’’ to 8
‘‘extremely.’’ It also included the Positive and Negative Affect
Schedule (PANAS), which was used to rate responses to ‘‘how you
currently feel toward using a healthcare robot,’’ similar to previous
research.
27,28
This scale contains a list of 10 positive and 10 negative
emotion words such as ‘‘enthusiastic’’ and ‘‘afraid,’’ which are rated
on a 5-point scale from ‘‘very slightly or not at all’’ to ‘‘extremely.’’
They also completed the 11-item ‘‘Robot Attitudes Scale,’’ which asks
the person about their views toward robots in general on 8-point
scales where lower scores represent more positive perceptions;
Cronbach’s alpha for the scale was 0.85.
29
The medical student measured the participant’s blood pressure and
heart rate using an Omron Digital Automatic Blood Pressure Monitor
model M10-IT, and reported the result to the patient. The assistant
then introduced the participant to the robot in another room. The
robot spoke to the participant, took their blood pressure and heart
rate (using another Omron Digital Automatic Blood Pressure Monitor
Model M10-IT that was attached to the robot), and reported the result
verbally and on the screen. The robot was a Peoplebot (Mobile
Robots, Inc.) named Charles, from mobilerobots.com (Fig. 1). The
interaction took 163 s on average ( –36 s). The participant then
completed Questionnaire 2, which included readministration of the
PANAS scale twice to measure emotions during and after the inter-
action, and questions about how accurate they thought the readings
were from 1 ‘‘very inaccurate’’ to 8 ‘‘very accurate,’’ and comfort with
the blood pressure measurement from 0 ‘‘very uneasy’’ to 8 ‘‘very
comfortable,’’ for both the robot and the medical student. Participants
rated the quality of the interaction with the robot by using a modified
version of the Social Interaction Scale by Berry and Hansen to assess
how enjoyable was the interaction with the robot.
30
The 8-item
Quality of Experience questionnaire assessed how the participant
appraised the robot (e.g., as friendly, natural).
31
Using a power of 0.8, alpha of 0.05, G-Power estimated that a total
sample size of 55 participants were required to detect a correlation of
r¼0.33 between initial emotions and quality of the interaction, an
effect size based on previous research.
28
Data were analyzed using
BROADBENT ET AL.
610 TELEMEDICINE and e-HEALTH JUNE 2010
SPSS software (Version 15). Repeated measures t-tests and analysis
of variance (ANOVA) were used to test for differences within groups.
Multiple linear regression analyses were performed to assess the
contribution of the predictors to ratings of the quality of interaction
and the quality of experience.
RESULTS
Repeated measures t-tests showed that there were no significant
differences between the participants’ blood pressure levels or pulse
taken by the robot and the medical student (Fig. 2) (diastolic, t¼
0.28, p¼0.78, Cohen’s d¼0.03; systolic, t¼0.38, p¼0.71,
d¼0.04; pulse, t¼1.16, p¼0.25, d¼0.11). However, the partici-
pants felt more comfortable with the medical student (robot 7.09
[0.99]; medical student 7.58 [0.68], Z¼3.85, p¼0.000, d¼0.64)
and thought that the medical student was more accurate (robot 6.72
[1.60]; medical student 7.07 [1.49], Z¼2.80, p¼0.005, d¼0.29).
Emotions during interaction with robot. Repeated measures ANOVA
with repeated contrasts showed that positive affect toward the
healthcare robot significantly improved from before (mean
30.24 –7.68) to during the interaction (mean 32.35 –8.68), and this
was maintained immediately after the interaction (mean
31.90 –9.65, F¼6.52, p¼0.005). Negative affect toward the robot
significantly reduced from before (mean 11.84 –2.35) to during the
interaction (mean 10.97 –1.77) and immediately after the interac-
tion (mean 10.64 –1.44, w
2
¼22.39, p¼0.000).
Quality of interaction and quality of experience. Two hierarchical
linear regression analyses were performed to assess how age, sex,
computer experience, attitude toward robots, and initial positive and
negative affect scores toward healthcare robots from the initial
questionnaire were related to the quality of interaction and the
quality of experience. Age, sex, and computer experience were
entered in block 1 and did not significantly predict the quality of
interaction [F(3,53) ¼0.78, R
2
¼0.02]; when attitude and emotions
were added to the regression in block 2, the model became significant
[F(6,50) ¼7.59, p¼0.000, R
2
¼0.48]. Initial positive emotions and
attitudes toward robots before the interaction were significant
Fig. 1. The robot ‘‘Charles’’ used in study 2.
Fig. 2. No significant differences in blood pressure readings
and pulse taken by the robot and the medical student.
ATTITUDES AND REACTIONS TO A HEALTHCARE ROBOT
ªMARY ANN LIEBERT, INC. .VOL. 16 NO. 5 .JUNE 2010 TELEMEDICINE and e-HEALTH 611
predictors of the quality of interaction. Similarly, age, sex, and com-
puter experience did not significantly predict the quality of experience
[F(3,53) ¼0.71, R
2
¼0.04], but the model became significant with the
addition of attitude and emotions toward robots [F(6,50) ¼3.80,
p¼0.003, R
2
¼0.31]. Initial positive emotions and attitudes toward
robots were significant predictors of the quality of experience.
DISCUSSION
The small differences in blood pressure readings between the
robot and medical student were within interobserver errors of
measurement found in a previous work, which suggests that robots
do not cause elevated blood pressure readings.
32
This is an impor-
tant finding to reassure clinicians that the use of robotics in
healthcare is appropriate. In this study, the same model of auto-
mated blood pressure device was used by both the robot and the
medical student. It is not clear whether the results are generalizable
to manual blood pressure readings, or to those taken by more senior
medical staff.
Participants’ lower comfort and accuracy ratings for the robot
compared with the medical student may reflect a lack of familiarity
with the robot, as some participants wrote comments on the ques-
tionnaire that the robot reading would get more accurate as they got
more practiced with the robot. This is consistent with the finding that
feelings toward the robot became more positive and less negative
during the interaction. The most important predictors of the quality
of interaction and experience were positive emotions and attitudes
toward robots prior to the interaction. Age was not associated with
outcome ratings, which suggests that older age can be compatible
with robot use. Healthcare providers need to be aware that peoples’
preexisting attitudes and feelings toward robots may be important to
acceptance.
General Discussion
This study supports continued development of healthcare robots
for middle-aged and older persons, showing a positive attitude
amongst many people and acceptance of a robot to measure blood
pressure. People may take some time to get comfortable with and
trust robots in healthcare, as they are currently more familiar with
human medical staff. Healthcare robot manufacturers need to take
steps to ensure that people’s preconceived ideas do not present bar-
riers to acceptance, perhaps by eliciting and addressing negative
perceptions prior to the introduction of robots in specific settings.
The strengths of this article are that it combined an exploratory
design and an experimental design. It systematically assessed peo-
ples’ perceptions and feelings before and after having an encounter
with a healthcare robot and also included a physiological measure. A
weakness is that while people were systematically invited, people with
more positive attitudes toward robots may have been more likely to
participate. The study had a relatively small sample size, although it is
larger than many sample sizes in published user studies of robots.
The findings add to the literature on the acceptance of healthcare
technologies by demonstrating the importance of attitudes toward
robots and positive emotions toward robots for the acceptance of
healthcare robots. They demonstrate that age and sex are not barriers
to acceptance and that many people foresee benefits from robotic
healthcare.
Acknowledgments
This study was supported by a summer studentship from the HOPE
Foundation for Research on Ageing and a grant from the University
of Auckland (no. 3608916). The authors thank the general practi-
tioner for inviting patients to take part, and the National Institute of
Health Innovation for use of their offices.
Disclosure Statement
No competing financial interests exist.
REFERENCES
1. Lutz W, Sanderson W, Scherbov S. The coming acceleration of global population
ageing. Nature 2008;451:716–719.
2. Day JC. U.S. Bureau of the Census, Current Population Reports, P25-1130,
Population projections of the United States by age, sex, race, and hispanic
origin: 1995 to 2050. Washington, DC: U.S. Government Printing Office. 1996.
Available at www.census.gov/prod/1/pop/p25-1130.pdf (last accessed June 6, 2009).
3. We H, SenGupta M, Velkoff VA, DeBarros KA. National Institute on Aging and
U.S. Census Bureau. 65þin the United States. December 2005. Available at
www.census.gov/prod/2006pubs/p23–209.pdf (last accessed May 1, 2010).
4. Hobbs FB. The elderly population. The U.S. Census Bureau. 2001. Available at
www.census.gov/population/www/pop-profile/elderpop.html (last accessed
May 1, 2010).
5. Fleming KC, Evans JM, Chutka DS. Caregiver and clinician shortages in an aging
nation. Mayo Clin Proc 2003;78:1026–1040.
6. Murray M. The nursing shortage past, present, and future. J Nurs Adm
2002;32:79–84.
7. Pollack M. Intelligent technology for an aging population: The use of AI to
assist elders with cognitive impairment. AI Mag 2005;26:9–24.
8. Dario P, Guglielmelli E, Laschi C, Teti G. MOVAID: A personal robot in everyday
life of disabled and elderly people. Technol Disabil 1999;10:77–93.
9. Noury N. AILISA: Experimental platforms to evaluate remote care and assistive
technologies in gerontology. Proc.—HEALTHCOM 2005;67–72.
10. Mataric
´MJ, Eriksson J, Feil-Seifer DJ, Winstein CJ. Socially assistive robotics for
post-stroke rehabilitation. J Neuroeng Rehabil 2007;4:5.
BROADBENT ET AL.
612 TELEMEDICINE and e-HEALTH JUNE 2010
11. Wada K, Shibata T, Musha T, Kimura S. Effects of robot therapy for demented
patients evaluated by EEG. Proc.—IROS 2005;1552–1557.
12. Pineau J, Montemerlo M, Pollack, ME, Roy N, Thrun S. Towards robotic
assistants in nursing homes: Challenges and results. Robot Auton Sys
2003;42:273–282.
13. Gandsas A, Parekh M, Bleech MM. Robotic telepresence: Profit analysis in
reducing length of stay after laparoscopic gastric bypass. J Am Coll Surg
2007;205:72–77.
14. Tamura T, Yonemitsu S, Itoh A, Oikawa D, Kawakami A, Higashi Y, Fujimoto T,
Nakajima K. Is an entertainment robot useful in the care of elderly people with
severe dementia? J Gerontol A Biol Sci Med Sci 2004;59A:83–85.
15. Brewer BR, McDowell SK, Worthen-Chaudhari LC. Poststroke upper extremity
rehabilitation: A review of robotic systems and clinical results. Top Stroke
Rehabil 2007;14:22–44.
16. Maeda T, Yoshida K, Hisao N, Kayashima K, Maeda Y. Net-accessible pet-type
robot for aged people’s welfare. Proc.—CIRA 2003;130–133.
17. Glover J, Holstius D, Manojlovich M, Montgomer K, Powers A, Wu J, Kiesler S,
Matthews J, Thrun S. A robotically-augmented walker for older adults. Technical
Report CMU-CS-03-170. 2003. Available at http://robots.stanford.edu/papers/
glover.walker-tr.pdf (last accessed May 1, 2010).
18. Fazekas G, Horvath M, Toth A. A novel robot training system designed to
supplement upper limb physiotherapy of patients with spastic hemiparesis. Int J
Rehabil Res 2006;29:251–254.
19. DiSalvo CF, Gemperle F, Forlizzi J, Kiesler S. All robots are not created equal: The
design and perception of humanoid robot heads. DIS 2002;6:321–326.
20. Goetz J, Kiesler S, Powers A. Matching robot appearance and behaviour to tasks
to improve human-robot cooperation. ROMAN 2003;IXX:55–60.
21. Czaja SJ, Sharit J. Age differences in perceptions towards computers. J Gerontol
B Psychol Sci Soc Sci 1998;53:329–340.
22. Pain H, Gale CR, Watson C, Cox V, Cooper C, Sayer AA. Readiness of elders to
use assistive devices to maintain their independence in the home. Age Ageing
2007;36:465–467.
23. Tinker A, Lansley P. Introducing assistive technology into the existing homes of
older people: Feasibility, acceptability, costs and outcomes. J Telemed Telecare
2005;11(Suppl 1):1–3.
24. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information
technology: Towards a unified view. MIS Q 2003;27:425–478.
25. Czaja SJ, Charness N, Fisk AD, Hertzog C, Nair SN, Rogers WA, Sharit J. Factors
predicting the use of technology: Findings from the center for research and
education on aging and technology enhancement (CREATE). Psychol Aging
2006;21:333–352.
26. Broadbent E, Stafford R, MacDonald B. Acceptance of healthcare robots for the
older population: Review and future directions. Int J Soc Robot 2009;1:319–
330.
27. Watson D, Clark LA, Tellegen A. Development and validation of brief measures
of positive and negative affect: The positive and negative affect scales. J Pers
Soc Psychol 1988;54:1063–1070.
28. Broadbent E, MacDonald B, Jago L, Juergens M, Mazharullah O. Human
reactions to good and bad robots. Proc.—IROS 2007;3703–3708.
29. Broadbent E, Tamagawa R, Kerse N, Knock B, Patience A, MacDonald B.
Retirement home staff and residents’ preferences for healthcare robots.
Proc.—ROMAN 2009;645–650.
30. Berry DS, Hansen JS. Positive affect, negative affect, and social interaction.
J Pers Soc Psychol 1996;71:796–809.
31. Wang E, Lignos C, Vatsal A, Scassellati B. Effects of head movements on
perceptions of humanoid robot behavior. Proc.—HRI 2006;180–185.
32. Adams C, Burke V, Beilen LJ. Accuracy of blood pressure measurement and
anthropometry among volunteer observers in a large community survey. J Clin
Epidemiol 2002;55:338–344.
Address correspondence to:
Elizabeth Broadbent, Ph.D.
Department of Psychological Medicine
Faculty of Medical and Health Sciences
The University of Auckland
Private Bag 92019
Auckland 1142
New Zealand
E-mail: e.broadbent@auckland.ac.nz
Received: November 30, 2009
Revised: January 25, 2010
Accepted: January 25, 2010
ATTITUDES AND REACTIONS TO A HEALTHCARE ROBOT
ªMARY ANN LIEBERT, INC. .VOL. 16 NO. 5 .JUNE 2010 TELEMEDICINE and e-HEALTH 613
