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User Acceptance of Pervasive Computing in Healthcare: Main Findings of two Case Studies

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The successful implementation of pervasive computing technologies in healthcare does not only depend on technical issues but also on acceptability and acceptance issues. In this paper we focus on factors that facilitate or inhibit user acceptance of pervasive computing in healthcare. We present selected findings of the research project dasiaPerCoMed - Pervasive Computing in Healthcarepsila. The project is based on two case studies in pre- and post-clinical healthcare. In the first study, the potential of pervasive computing technologies for the treatment of acute cardiovascular diseases is investigated, in the second case study, the potential for the treatment of multiple sclerosis (MS) is evaluated. A qualitative user acceptance analysis of the two case studies shows the following results: the main factor of user acceptance is the perceived medical usefulness. Furthermore, acceptance is strongly inhibited if data privacy or if subjective norms are violated. Usability only presents a decisive factor of acceptance if problems with usability reduce the perceived usefulness.
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User Acceptance of Pervasive Computing in
Healthcare: Main Findings of two Case Studies
Mandy Scheermesser
Hannah Kosow
Institute for Futures Studies and Technology
Assessment (IZT), Berlin; Germany;
e-mail: m.scheermesser@izt.de, h.kosow@izt.de
Asarnusch Rashid
Dr. Carsten Holtmann
Research Center for Information Technologies
(FZI), Karlsruhe; Germany;
e-mail: rashid@fzi.de, holtmann@fzi.de
Abstract— The successful implementation of Pervasive Comput-
ing technologies in healthcare does not only depend on technical
issues but also on acceptability and acceptance issues. In this
paper we focus on factors that facilitate or inhibit user accep-
tance of Pervasive Computing in healthcare. We present selected
findings of the research project ‘PerCoMed – Pervasive Comput-
ing in Healthcare’. The project is based on two case studies in
pre- and post-clinical healthcare. In the first study, the potential
of Pervasive Computing technologies for the treatment of acute
cardiovascular diseases is investigated, in the second case study,
the potential for the treatment of multiple sclerosis (MS) is evalu-
ated. A qualitative user acceptance analysis of the two case stud-
ies shows the following results: the main factor of user acceptance
is the perceived medical usefulness. Furthermore, acceptance is
strongly inhibited if data privacy or if subjective norms are vio-
lated. Usability only presents a decisive factor of acceptance if
problems with usability reduce the perceived usefulness.
Keywords: user acceptance, case study, usefulness, usability,
data privacy
I. INTRODUCTION
‘Pervasive Computing’ is a technological vision or a para-
digm so far. Pervasive Computing technologies are perceived
as the next generation of innovative information and communi-
cation technologies (ICT). The vision of a pervasive environ-
ment is characterized by five criteria [1]. Very tiny sensors and
devices which are more portable than traditional ICT (minia-
turization), embedded into other devices and everyday objects
(embeddedness), are working invisibly in the background (in-
visibility). Pervasive Computing components can take up in-
formation about their environment (context-sensitivity) and
communicate them through wireless data exchange (network-
ing).
New technologies in healthcare mostly do not yet fulfill
these five criteria, i.e. – following this definition – one cannot
talk about genuine Pervasive Computing applications in health-
care today. But a strong tendency towards these characteristics
can already be observed, with existing devices and applications
that fulfill these characteristics in part and where the other
features are imaginable. We are facing a technological vision
on its way to be realized.
The research project ‘PerCoMed – Pervasive Computing in
healthcare’
1
analyzes the opportunities and risks of Pervasive
Computing technologies for healthcare in an interdisciplinary
approach. The aim of the project is a case-based analysis of the
use of ‘Pervasive Computing’ technologies for the integrated
medical care of various user- and interest-groups.
Thus, our perspective is proactive: we analyze a techno-
logical vision and aim to assess its consequences and condi-
tions before the technologies have become widely imple-
mented. To assess the phenomenon of Pervasive Computing in
healthcare, we focus on social, economic and technical aspects.
The aim of this ‘technology assessment’ is to create knowledge
that can be used in the further process of implementing Perva-
sive Computing in healthcare by anticipating contexts, avoiding
problems and adapting more closely to users’ needs, attitudes
and behavior.
In this paper, we first present our theory based focus on
four factors of acceptance of Pervasive Computing in health-
care namely ‘perceived usefulness’, ‘usability’, ‘data privacy
and ‘subjective norms’ (2), then we describe our design of two
case studies and of our user acceptance analysis (3). Subse-
quently we present the technology, processes and stakeholders
of the first case study ‘Stroke Angel’ (4) and the main findings
of the user acceptance analysis of this case study (5). In paral-
lel, the second case study ‘MS Nurses’ is described via its
technology, processes and stakeholders (6) and we summarize
the main results of user acceptance analysis in this case (7).
Afterwards, the findings of user acceptance of Pervasive Com-
puting in healthcare are generalized and discussed (8). To con-
clude, the central results are summed up and further fields of
research are opened up (9).
II. R
ESEARCH QUESTIONS AND THEORETICAL
BACKGROUND
A successful implementation of Pervasive Computing tech-
nologies in healthcare certainly depends on technical features
like interoperability, battery capacity, (data) security and con-
nectivity. But social and organizational environments, proc-
esses and stakeholders are decisive conditions, too. [2] To
1
This project is funded by the German Federal Ministry of Education
and Research under grant number 16I1546 (www.percomed.de). Partners are
the Institute for Futures Studies and Technology Assessment (IZT), Berlin; the
Research Center for Information Technologies, Karlsruhe (FZI) and the
Institute for Technology Assessment and System Analysis Karlsruhe (ITAS).
implement Pervasive Computing technologies in healthcare
successfully, issues of acceptability and acceptance cannot be
ignored as e.g. in Germany, the physicians’ opposition to the
implementation of the electronic patient card has shown [3]. In
healthcare, we are confronted with very particular environ-
ments and very particular users. There are diverse environ-
ments as accident settings, intensive care and adapted domestic
environments and there are patients on the one hand, people
mostly suffering, ill or handicapped and hospital professionals
on the other hand, people working under considerable stress
[2]. That is why we focus on the following research question:
Which factors facilitate or inhibit user acceptance of Perva-
sive Computing in healthcare?
Furthermore, we ask if it is possible to distinguish between
more fundamental and rather subordinate factors of acceptance
of Pervasive Computing in healthcare and if there are major
differences in acceptance of Pervasive Computing between
different groups of stakeholders.
Acceptance, defined as the adoption and the use of objects
by persons [4], depends on the subject of acceptance, the object
of acceptance and the context of acceptance: ‘Who does accept
what under which parameters?’.
To guide our analysis, we focus different factors of accep-
tance referring to two theoretical models. Our study refers to
the Technology Acceptance Model (TAM) [5, 6] and the The-
ory of Planned Behavior (TPB) [7, 8]. The TAM is a technol-
ogy adoption model that considers user acceptance of informa-
tion systems. This model posits that perceived ease of use and
perceived usefulness are the significant factors for the accep-
tance of information systems. The TPB is a model from social
psychology which is concerned with the determinants of in-
tended behavior. The model posits that behavioral intention is
determined by attitudes, subjective norms and perceived behav-
ior control.
We decide to examine four factors of acceptance: The first
factor of acceptance, referring to the TAM, is the factor ‘per-
ceived usefulness’. This factor means on the one hand “the
degree to which a person believes that using a particular system
would enhance his or her job performance” [6] and on the other
hand, perceived usefulness means medical or therapeutical use.
Hypothesis 1: Perceived usefulness facilitates the acceptance of
Pervasive Computing in healthcare.
The second factor, also proposed by the TAM, is the factor
‘usability’ (equivalent to ‘perceived ease of use’[6]). Usability
can be understood as a combination of the effectiveness and
efficiency of a technology and the users’ satisfaction (see EN
ISO 9241-11: 1998). Hypothesis 2: Perceived usability facili-
tates the acceptance of Pervasive Computing in healthcare.
For the third factor of acceptance ‘subjective norms’, we re-
fer to the TPB. Subjective norms define “the person’s percep-
tion that most people who are important to him think he should
or should not perform the behavior in question.” [9]. We under-
stand ‘subjective norms’ as perceived accordance to social
norms and satisfying interpersonal communication. Hypothesis
3: Accordance to subjective norms facilitates the acceptance of
Pervasive Computing in healthcare.
Furthermore, because in the field of ICT collecting personal
data is a very sensitive area [10] [11], we decided to include the
factor ‘data privacy’ and the ‘perception to be permanently
controlled’ into our focus. Hypothesis 4: Perceived threats for
data privacy inhibit the acceptance of Pervasive Computing in
healthcare. Likewise, the perception to be permanently con-
trolled inhibits the acceptance of Pervasive Computing in
healthcare.
These hypotheses are guiding our research. We apply the
following methodology.
III. M
ETHODS
Based on an analysis of trends and stakeholders in the field
of Pervasive Computing in healthcare, we first designed and
realized two case studies. Within each of these case studies, we
then performed a qualitative user acceptance analysis.
A. Design of the two Case Studies ‘Stroke Angel’ and ‘MS
Nurses’
In the case studies ‘Stroke Angel’ and ‘MS Nurses’ the us-
age of real Pervasive Computing technology is designed and
analyzed in different sections of healthcare.
The Stroke Angel project
2
intents to speed up the treatment
of patients in the intersectional stroke chain of survival with
help of a mobile stroke diagnosis and data transmission device
– called the Stroke Angel system – applied in Emergency Med-
ical Service (EMS). Different aspects of the Stroke Angel Sys-
tem are analyzed: First, to figure out the specifications and
functions required, a requirement and functional analysis is
conducted: Second, a process analysis delivers the different
impacts of such an introduction: This process analysis com-
prises modeling of stroke chain processes just as comparative
time measurements with and without Stroke Angel. [12]
In the case study ‘MS Nurses’
3
[13], we purpose to set up
an environment to support the Multiple Sclerosis (MS) diagno-
sis and treatment. The wearable actibelt system is utilized as
tool for movement analysis in a medical study. The aim of this
study is to analyze the correlation between MS-patients’ activ-
ity patterns and their state of health. Thus, we first exemplarily
analyze the implementation of pervasive technologies in a
medical study, second we anticipate the implications of its
future usage as an activity monitoring system in home care.
B. User Acceptance Analysis within the Case Studies
Within the two case studies, the qualitative user acceptance
analysis is based on several methods to collect and to interpret
empirical data.
2
www.strokeangel.de. In this case study we were collaborating with
Neurologische Klinik Bad Neustadt/ Saale, Bayerisches Rotes Kreuz Bad
Neustadt/ Saale, Heinrich-Heine-University in Düsseldorf, as well as Philips
Research from Aachen. Together, they join forces to apply the technical
solution from Philips Research in the two test regions Düsseldorf and Bad
Neustadt/ Saale with the support of Boehringer Ingelheim Pharma and the
Stiftung Deutsche Schlaganfall-Hilfe (German stroke foundation).
3
www.msnurses.de. This study is driven by the PerCoMed research
partner, Sylvia Lawry Multiple Sclerosis Research (SLMSR), Trium Analysis
Online, Neurologische Klinik Bad Neustadt/ Saale and Sanofi-Aventis Phar-
ma.
In the case of Stroke Angel, we first conducted qualitative
semi-structured interviews [14, 15] with all relevant stake-
holders affected by the Pervasive Computing application
(EMT-I, physicians, nurses). Second, to deepen the analysis of
the perception and the assessment of Pervasive Computing, two
focus group discussions [16] [17] have been conducted with
citizens in the role of ‘potential patients’. We addressed the
participants via notices in public spaces. We divided these
potential patients into two groups the following way: at the first
contact, we asked them to asses themselves as open minded
towards technology or as skeptical towards technology.
In the case study ‘MS Nurses’, we included the chronic MS
patients participating at the medical study at the hospital from
the outset. We first addressed them via questionnaires before
and after they had used the actibelt (n= 12). Second, we con-
ducted semi structured qualitative interviews with all relevant
stakeholders (patients, physicians, nurses and physiotherapists).
Third, we discussed with the patients during two focus groups.
In total, during the two case studies, we conducted n= 45 quali-
tative semi-structured interviews and did focus group discus-
sions with 24 persons. The perspective of the four focus group
discussions was opened up on Pervasive Computing in health-
care in general and not limited to the concrete technologies of
each of the case studies.
The records of the interviews and focus groups have been
treated by the method of qualitative content analysis [18], the
questionnaires have been analyzed by descriptive statistical
methods and we compared the results before and after the test-
ing.
IV. T
ECHNOLOGY, PROCESSES AND STAKEHOLDERS IN THE
CASE STUDY ‘STROKE ANGEL
Stroke is one of today’s most threatening diseases in Eu-
rope.
4
Even though the existing treatments of stroke are able to
provide substantial benefits, stroke still takes a leading position
on statistics of causes of death. Recent technical and medical
developments give reason to hope for improved stroke care.
One important step in this direction was done in Germany by
the approval of the thrombolysis for the treatment of the
ischemic stroke in 2000. This acute therapy for stroke improves
the chances for survival and healing
5
; but this treatment has to
be started if possible within a time-frame of three hours after
onset of symptoms. [19]
The aim of the Stroke Angel project is to speed up the treat-
ment of patients in the intersectional stroke chain of survival
with help of mobile technology. The Stroke Angel system
consists in a structured checklist for stroke diagnosis (Los An-
geles Prehospital Stroke Screen – LAPSS) on a portable com-
puter (PDA), a patient card reader and a mobile phone to send
the corresponding information through radio transmission from
EMS to the hospital.
4
About 575,000 case a year, a mortality rate of 29%, another 25% of
disablement within 12 months, and total costs of about 34 billion Euros in the
EU per year.
5
The thrombolytic therapy effectuates the blood clod that blocks the
arteries with the help of dissolving medicaments, so that the degree of conse-
quential damages can be reduced or rather completely avoid.
The stroke chain of survival commonly comprises the
whole process from discovering the affected person to the
patient’s admission and treatment in hospital. Figure 1 illus-
trates the architecture of the Stroke Angel system and the
communication links between the EMS which collects patients’
data and sends them via Bluetooth connection of the mobile
phone to the Stroke Angel server of the hospital.
Figure 1. Stroke Angel architecture with communication links [20]
The first step of the process analysis mentioned above
comprises the identification of the parties involved. In this
context, the Red Cross, which assumes the coordination be-
tween the EMS, the emergency physicians and the neurological
clinic have been identified as the four important stakeholders.
They are directly involved within the Stroke Angel system and
their interventions affects the workflow of decision making and
information.
In general, the results lead to the conclusion that Stroke
Angel has improved the emergency management in total. Sur-
prisingly, even though the ‘time in situ’ retards in average
around 5 minutes for all patients (4 minutes for all patients with
lysis), it is compensated by the shorter transportation time, i.e.
paramedics seem to need more time to enter the data, but they
get alarmed earlier by the LAPSS. Consequently, the signal
alarm was used more often in 2006/ 2007 in order to speed up
the transportation process. Even though the ‘time in situ’ using
Stroke Angel has been increased, the ‘time-to-imaging’ has
been improved due to more fundamental decision making and
due to earlier information and preparation of the neurological
clinic saving 9 minutes (mainly on treatments of lysis-
candidates).
Since the operation of the Stroke Angel system in
2006/ 2007, the total time entering the hospital to brain-
imaging by CT (computer tomography) or MRT (magnetic
resonance tomography) has been reduced from 32 to 23 min-
utes for around 30% of the patients with lysis, due to parallel
preparation and faster recording of the patient at the hospitals
reception desk. In case of incoming patients whose stroke
symptoms are less than three hours ago the ‘time-to-imaging’
could be decreased by around 25% from 48 to 36 minutes.
Hence, preliminary results of the process analysis offer
promising insights. Although not all influence factors and de-
pendencies are checked so far and statistical measures haven’t
been applied in detail, there is a strong indication that the sys-
tem allows for significant progress in decision making quality
and hence resource allocation arbitrations. Exact results will
have to be provided with the final data. Hopefully expectations
can then be approved and experiences of the Stroke Angel
study transferred to other emergencies scenarios.
Finding reliable diagnosis as early as possible in the preclinical
phase can clearly assure that treatment alternatives for the clin-
ical phase remain possible. This is likely to provide an overall
increase in quality of medical care.
V. M
AIN FINDINGS OF USER ACCEPTANCE WITHIN THE
CASE STUDY ‘STROKE ANGEL
First, the overall acceptance in the case study Stroke Angel is
described, then our findings regarding the four factors of accep-
tance ‘usefulness’, ‘usability’, ‘subjective norms’ and ‘data
privacy’ are presented.
A. Overall Acceptance
The object of acceptance in this case study is the Stroke
Angel system, the context of acceptance is the chain of survival
from the pre-clinical EMS to the clinical emergency care. The
main subjects of acceptance are emergency physicians, EMT-I
(Emergency Medical Technician, Intermediate) but also further
hospital staff as well as (potential) stroke patients.
The user acceptance analysis in the case study ‘Stroke An-
gel’ shows that all groups of medical professionals do have an
overall positive attitude towards the Stroke Angel system.
Emergency physicians, EMT-I and hospital staff would like to
continue the case study and favor an extension of the Stroke
Angel system beyond the pilot.
However, some stakeholders are more reluctant and con-
sider well trained medical personnel to be the better alternative
to optimize stroke care. They are afraid of structural changes in
healthcare and specifically in the EMS, which could favor the
cheaper technologies to the disadvantage of human work.
In the group of potential patients open minded towards
technology, the overall attitude towards Pervasive Computing
in healthcare is positive. They state enormous hopes towards
these technological applications. “I feel better since I know
there is Stroke Angel, if I get a stroke one day”, as one citizen
turned it. For the potential patients skeptical towards technol-
ogy, the overall attitude is similar. Even if they seriously dis-
cuss a lot of negative aspects, the balance finally drawn clearly
shows that the potential medical benefit is more important than
any concerns.
B. Factor of Acceptance: Usefulness
The decisive factor of acceptance of the ‘Stroke Angel’ ap-
plication is – and this holds true for physicians, hospital staff
and patients – the perceived usefulness. If the device is per-
ceived as useful, acceptance is high, if on the contrary, there
are doubts on the usefulness of the application, acceptance is
missing. Medical benefit is perceived to be the main and most
important use of Stroke Angel, organizational use as assistance
in daily work is manifestly of secondary importance.
Almost 75 % among the physicians and medical staff perceive
an important medical use, which consists – according to them
– in the possibility to save lives or to reduce remote damages
of stroke patients by shortening ‘time-to-imaging’. This time
gain is achieved by organizational changes at two levels, dur-
ing the emergency service (pre-clinical-level) and at the hospi-
tal (clinical-level): First, with the calculated stroke-probability,
the direct transport of patients to the specialized hospital
(stroke unit) is made without detours to other hospitals. Sec-
ond, with the patients data available via the Stroke Angel
report as the patients name, date of birth, address and health
insurance, the medical staff at the hospital can already prepare
the arrival of a stroke patient and e.g. by booting up CT. The
application of the Stroke Angel system provides quantitative
time improvements in the rescue chain and qualitative medical
benefits for stroke patients. Therefore, the effective usefulness
is perceived by most of the stakeholders.
But there are also skeptical voices that are not sure about
this medical use and express doubts about the gain of ‘time-to-
imaging’ because of two reasons: First, half of the interviewees
perceived a loss of time during the data entry. The process of
entering data over the PDA takes – according to the interview-
ees – between 3 and 15 minutes. Especially in the case of short-
distance transports, the time loss is perceived as unpropor-
tional. “This just doesn’t make any sense.”, as one EMT-I said
Some report that the data entry procedure had clearly delayed
the start of the transport. Second, some of the emergency medi-
cal service report of the frustrating experience that even though
the Stroke Angel report had been send to the hospital, they
arrived at an emergency room with closed doors. The survival
chain is sometimes blocked. This is due to different but all
organizational reasons: sometimes the physician on duty does
not cooperate; sometimes the physician himself is informed
and present but not the other relevant medical actors as nurses
or the radiologist, who finally has to allow the patient to the
CT. When Stroke Angel is thus perceived as a zero-sum appli-
cation, because the time gain is too small to compensate for the
time loss of data entry, or if the chain of survival does not work
well, the acceptance is clearly reduced.
Most of the potential patients do consider Stroke Angel to
be a very useful system. The idea that everybody could become
a stroke patient one day, and then he or she would hope for the
fastest help possible, fosters the acceptance of this application.
Health is considered to be the most important priority and the
aim to save more stroke patients is considered to be highly
valuable.
C. Factor of Acceptance: Usability
In the case of Stroke Angel, usability is a relevant factor of
acceptance, because it directly affects the perception of medical
use.
The Stroke Angel software is overall assessed as positive
by emergency medical technicians (EMT-I), the main users of
the system. The entry mask is characterized as user-friendly,
clear, appropriate and even outranging the classical paper re-
port, because it helps to collect more and more precise informa-
tion on the patient. In contrast, the hardware has room for im-
provement. The display is criticized by EMT-I as being too
small, as it is very difficult to enter data while the transport is
driving. The difficulty of slipping on the display and not
matching the correct input field is reported several times.
Sometimes, this leads to losses of time, which are perceived to
nullify the time gain for patients and thus to endanger the effi-
cacy of the system.
These problems with usability lower the perception of
medical use and consequently, the acceptance of the applica-
tion is endangered. Furthermore, the EMT-I criticize that
Stroke Angel is generally not perfectly adopted to the working
conditions in an ambulance, where more robust, chemical- and
waterproof, one-piece “plug and play” devices are required.
D. Factor of Acceptance: Subjective Norms
In the setting of this case study, the factor ‘subjective
norms’ applies above all job roles and job images of different
groups of medical professionals.
The Stroke Angel application interferes with the established
work and power relation between emergency physicians and
EMT-I and could induce changing job images: with the help of
Stroke Angel, EMT-I do not need an emergency physician
anymore to decide whether to bring a patient to a stroke unit or
not. Thus, they gain autonomy and competence. Some of the
interviewed EMT-I experienced Stroke Angel as a reassess-
ment of their role and position, classically subordinated and
dependent to the emergency physician. Emergency physicians
on the contrary, feel a loss of competence and of power for the
benefit of EMT-I. Furthermore, emergency physicians are
afraid, cost efficient technologies like Stroke Angel could in
the long run completely substitute their work. One emergency
physician said: “in the future, there will be no emergency phy-
sicians”.
Consequently, some emergency physicians are highly criti-
cal towards to the Stroke Angel system and could entirely
block its successful implementation.
E. Factor of Acceptance: Data Privacy
Overall, for the involved stakeholders, data privacy turned
out to be no issue with Stroke Angel.
Data privacy is no point of concern for any of the inter-
viewed medical professionals. Confronted with our questions
on data privacy, some report they not even had thought about it
and that “everybody uses mobile phones everyday” as a physi-
cian said. Three of the interviewees developed the ad hoc as-
sessment that data privacy may perhaps not be guaranteed with
the Stroke Angel application, but that data privacy has no prior-
ity at all in case of emergency.
Potential patients overall do share this attitude, but one can
distinguish between two groups of positions: People opened
minded towards technology clearly consider the medical use of
Stroke Angel to be more important than data privacy. Among
people skeptical towards technology, there are some who are
afraid about data privacy questions even in case of emergency
(e.g. a stroke) and demand transparent regulations which pre-
cisely and restrictively define the case of emergency in which
data are accessible without further consent.
After having presented the results of the case study ‘Stroke
Angel’, we expose in parallel our case study ‘MS Nurses’ and
the main findings of its acceptance analysis.
VI. T
ECHNOLOGY, PROCESSES AND STAKEHOLDERS IN THE
CASE STUDY ‘MS NURSES
MS is thought to be an autoimmune disorder that leads to the
destruction of myelin, oligodendrocytes and axons in the cen-
tral nervous system (CNS) [21]. MS primarily affects adults,
with an age of onset typically between 20 and 40 years, and is
more common in women than in men. MS may take several
different forms, with new symptoms occurring either in dis-
crete attacks or slowly accruing over time. Between attacks,
symptoms may resolve completely, but permanent neurological
problems often persist; especially as the disease advances. MS
currently does not have a cure, though several treatments are
available which may slow the appearance of new symptoms.
In MS the Expanded Disability Status Scale (EDSS) is a
frequently used disability score for the evaluation of clinical
disease burden on progression. [22] It helps monitoring the
course of MS and is part of the treatment optimization model
recommended for the observation of effectiveness of immuno-
modulatory therapies. The EDSS score ranges from 0 until 10.
Between the scores 0 and 3.5 patients are assessed being able to
walk on their own without limitations. Patients with the scores
4 to 5.5 are able to walk a maximum distance of less than 500
meters and above 6 they are unable to walk on their own.
Within periodic intervals (in most cases every three or six
months), the EDSS score is evaluated by physicians during
basic examination. But even though these examinations are
done regularly, they can only give an instant impression of the
disease’s status. It would therefore be desirable to learn more
about everyday occurrences between the examinations so that
tendency and progress of the patient’s condition can be moni-
tored more precisely. The sooner the tendency of the disease’s
progress can be detected, the better measures can be taken and
therapy can be adequately adapted to preserve the patients’ mo-
bility. Recently, medical studies were performed showing that
there are correlations between gait parameters and the EDSS
score. [23] It seems possible to stage the patients’ status of
health by his patterns of activity. The objective is to find a way
to monitor patients’ activity over a long period by using perva-
sive technologies. With the help of the measured parameters
and the comparison of activity and EDSS score physicians
could be supported in detecting tendencies of patients’ aggrava-
tions. Therefore, we set up a study which aims to associate the
data of the activity monitoring with specific MS symptoms.
The clinical study is divided into an ambulant and a station-
ary setting with patients with an EDSS score less than 5. In the
ambulant setting, MS patients wear the device one week at
home and bring the device back to the hospital. The stationary
patients wear the device over one day in the hospital.
Our scenario has to meet the critical factors according med-
ical, technical and user specific aspects. As the device will be
used in an ambulatory home care and will collect information
about every day activity of the patients, all techniques that need
a laboratory setting are not applicable. Furthermore, the device
should collect data about patient’s general activity that allow
for classifying activities he is performing. In particular running,
walking, sitting and standing activities should be detectable.
The device shall also include fall detection and gait asymmetry.
Additionally, it must be possible for the patient to use the de-
vice easily by himself at home. The device has to be wearable
wearable without disturbing the patient during his normal ac-
tivities and needs to provide a reliable informative base. [24]
Accelerometers are used most frequently in early activity stud-
ies. [25-27]
For our proposes, we use the actibelt
®
of Trium Analysis
Online GmbH
6
and a corresponding software environment to
collect and analyze the activity data of MS patients. It has the
advantage of being embedded in a daily wearable belt close to
the body’s centre of mass and therefore it is unobtrusive and
does not disturb the test person in its daily life. The belt is in
operation if the button on the backside is pressed for three
seconds and the LED close to the button blinks every three
seconds. The actibelt
®
is switched off if the button is pressed
again for three seconds and the LED stops blinking.
Figure 2. Belt buckle front side (i), belt buckle back side (ii) [13]
The belt can be connected to a PC via USB. The client soft-
ware allows belt administration (like downloading files, check-
ing battery status and/ or storage capacity). Additionally users
can upload files from PC to the actibelt
®
server to be analyzed.
Figure 3. Determination of movement with actibelt
®
[13]
The analysis software currently distinguishes 6 different
movement types: jogging, walking, standing, sitting, lying and
undefined movements as standing up or lying down (Figure
3).
7
6
The actibelt
®
has a triaxial accelerometer integrated into its buckle
(see Figure 2 (iii)). For further technical information see www.slcmsr.de.
7
For this segmentation the signal is broken up into a series of time
segments of 1 second. For all time segments the arithmetic mean value, the
robustified range, defined as the difference between the mean of the 10 largest
For the movement analysis the step number, the duration of
every step, the step frequency, the step amplitude and the
asymmetry depending on the difference between the duration
of right and left steps are calculated. The software also esti-
mates the traveled distance and the maximum distance walked
in one draught with an accuracy of approx. +/- 10 % [28] and
estimates the energy consumption. The results of the analysis
are summarized in a report and are transmitted to the web ap-
plication. The measured and calculated parameters are saved in
a text file to allow additional user-defined statistical analysis.
VII. M
AIN FINDINGS OF USER ACCEPTANCE WITHIN THE
CASE STUDY ‘MS NURSES
First, the overall acceptance in the case study ‘MS Nurses’
is described, then our findings regarding the four factors of
acceptance ‘usefulness’, ‘usability’, ‘subjective norms’ and
‘data privacy’ are presented. The technology actibelt
®
does not
yet fulfill the five criteria of Pervasive Computing mentioned
above. Consequently, during the interviews with all stake-
holders we asked not only about the existing technology, but
we also presented a scenario and asked our interviewees about
the vision of an ‘intelligent-Pervasive Computing-actibelt’.
This intelligent actibelt was described as wireless exchanging
data and interconnecting to data bases and medical technolo-
gies.
A. Overall Acceptance
In the case study ‘MS Nurses’, the object of acceptance is
the actual actibelt
®
but also the vision of the ‘intelligent acti-
belt’ described above. The context of acceptance today is a
medical study, with patients wearing the device at the hospital
and at home. In the future, the context of the actibelt will be the
ambulant medical care. The subjects of acceptance are first of
all MS patients but also physicians and medical staff (nurses
and physiotherapists).
The results of the user acceptance analysis show that most
of the patients, the physicians and the medical staff have an
open-minded attitude towards the actibelt and towards Perva-
sive Computing technologies in general. Almost all of the que-
ried patients believe that technologies as the actibelt are a posi-
tive technical development, “It could be a good and useful
thing for me and my chronic illness.” Only one patient out of
ten thinks the actibelt is a negative technical development.
However, there are differentiated assessments concerning the
different Pervasive Computing characteristics. Embeddedness
and context-sensitivity, characteristics already given with the
actibelt, are assessed positively by all stakeholders. Further
characteristics as wireless data exchange and networking are
assessed to be ambivalent; patients confront possible medical
use with threats of autonomy and self-determination; medical
staff hopes for better medical care but fears new work loads.
and 10 smallest measurement values, and the absolute deviation are calcu-
lated. A type of movement is assigned to every 1-s segment using a threshold
method with thresholds depending on multiples of g. These thresholds have
been determined by exploring measurement values taken from approximately
20 healthy volunteers of different gender, age, height and weight.
(
i
)
(
ii
)
B. Factor of Acceptance: Usefulness
Perceived medical usefulness is the most important factor
of user acceptance for all involved stakeholders.
For chronic patients, the crucial condition to accept new
technologies is to perceive personal and/ or medical benefit, i.e.
an amelioration of their state of health. In the MS Nurses case,
most of the patients expect pervasive technologies to be poten-
tially very helpful for their MS. If the technology actibelt
®
is
able to reveal a solid correlation between movement and effec-
tiveness of therapy, pervasive monitoring technologies could be
advantageous for diagnosis and therapy of MS. Long-term
objective data of individual movements are considered to be
essential to objectify subjective assessments. Patients also hope
that anamnesis (documentation of preliminary medical history)
will get easier and more certain because their personal medical
data could be up to date all time in the hospital data base. One
patient said, “For me it is very useful, if the doctor has a better
overview of consistent medical data about my course of dis-
ease.”
After having tested the actibelt, patients clearly and steadily
express a need for individual feedback, i.e. they want to know
“what is the result” of the actibelt
®
records. Without getting any
report, some patients start to develop severe doubts about the
usefulness of the device. Thus, an important condition for pa-
tients to perceive usefulness of pervasive technologies seems to
be the experience of individual feedback.
Physicians and medical staff also expect medical and – but
to a lower degree – organizational use from pervasive tech-
nologies. Most of the physicians and the medical staff see on
the one hand possibility to reduce efforts, thus to save time and
in consequence to save money. On the other hand, objective
clinical data and long-term-data are eagerly expected because
they could simplify diagnosis and treatment. Like patients,
physicians and medical staff expect a medical benefit from the
use of Pervasive Computing technologies for anamnesis, diag-
nosis and therapy. But medial professionals clearly state they
would only accept pervasive technologies if their medical use
is proved to be significant. Second important condition for this
group is that treatment and analysis of new data flows can be
done automatically and will not produce higher work load for
them.
C. Factor of Acceptance: Usability
In the case study ‘MS Nurses’, only patients directly use the
new technology actibelt
®
. In the main, patients do not have
serious problems using the new technology actibelt
®
and are
satisfied with its usability. “The belt is very discreet and easy to
handle.”, so a patient. Still, some patients criticize the size of
the buckle and the length of the belt. They also reported that
the buckle sometimes opens up of its own volition. Other pa-
tients explain that it is difficult to apply the belt if their clothes
have no or too small belt loops.
The physicians notice a problem that sometimes affects the
effectiveness of the actibelt: Some patients seem to have motor
problems with starting and stopping the actibelt
®
. Also the
flashing on/off signal is not always easily and clear enough to
understand, especially for MS patients, who sometimes suffer
from impaired vision. This sometimes leads to unwished bat-
tery discharges and missing data records – mostly unnoticed by
the patients themselves.
Furthermore, the software to readout data requires IT capabili-
ties which overburden some of the medical stuff.
D. Factor of Acceptance: Subjective Norms
In this case study, ‘subjective norms’ mainly concern satis-
faction with information and support as well as consequences
for the physician-patient relationship and also job roles and job
image of nurses.
The analysis shows that the acceptance of most patients is
linked to their satisfaction with information and support given
by their attending physicians. Acceptance is high between the
majority of patients who is satisfied with their medical care in
general and with the information they got about the technology
actibelt
®
and who have confidence in their attending physician.
Furthermore, they appreciate that a contact person has been
available for all possible problems at any time during the study.
This role is fulfilled today by physicians, but in the long run is
planned to be overtaken by MS-Nurses, which could disburden
the physicians.
A few patients but also some of the medical professionals
argue that Pervasive Computing technologies in healthcare
could substitute the personal dialog between chronic patient
and physician. A patient said, “It is important for me, that my
doctor sees me as a full person and doesn’t consider only my
medical parameters.” In particular for chronic patients, this
dialog is of crucial importance not only for a confiding physi-
cian-patient relationship but also for adherence and compliance
to the therapy and thus finally for a patient’s health and well
being. In sum, the effect on the physician-patient relationship is
seen to be open. The individual physician or staff member is
seen to be responsible to avoid negative developments and to
determine how Pervasive Computing influences the physician-
patient relationship.
As only group of medical professionals, nurses state some
fears, new technologies like the actibelt could substitute their
work. Consequently, they expect their job profile of the future
will comprise only duties of care and they will loose medical
responsibilities. This development is anticipated as loss of
status and as a de-qualification. Otherwise, nurses anticipate
also they could be the ones who explain new technologies to
patients and who support them in their ambulant use.
E. Factor of Acceptance: Data Privacy
Before testing the actibelt, almost none of the patients was
strongly concerned with questions of data privacy. Only some
of the patients had a negatively assessed perception to be per-
manently controlled during they wore the actual actibelt. In
contrast, most patients report about having forgotten during
their daily routine that the actibelt was more than a simple belt.
But after the test, the patients were clearly sensitized for pri-
vacy issues.
The idea of possible wireless data exchange amplified the
perception of being controlled and raised concerns of data
privacy. Some patients were really afraid of this option. “It
would be a nightmare for me, if the actibelt sends an emer-
gency call, unnoticed, and I don’t need medical help.” “Auto-
matic data exchange leaves a bad feeling, I feel too transpar-
ent.” Our findings suggest that this attitude depends on a pa-
tient’s concrete situation: The stronger a patient is suffering
and the more he hopes for the technologies to save or to ame-
liorate his life, the more the uncomfortable perception to be
observed and data privacy concerns become unimportant. “In
case of emergency it could be very helpful, when I’m alone at
home.”, so patient. Ergo, in case of emergency, chronic patients
agree with data exchange without specific accordance but for
daily routine, they reject it. In everyday life, chronic patients
want to decide when and to whom they send which personal
medical data and who has access to medical data bases. “I want
to know who will receive my medical data and what they want
to do with that. Do they have good or bad faiths?” On the one
hand, the respect of autonomy and self-determination seems to
be a crucial restriction for patients to accept Pervasive Comput-
ing technologies, on the other hand, data privacy regulations
are requested to deal with risks of misuse of data through
“wrong addressees” as insurance funds and employers.
Certainly, physicians and medical staff both agree that col-
lecting a huge amount of personal medical data could present
hazards of data privacy and data security. But they do not an-
ticipate the patients’ account of self-determination. Moreover,
physicians do consider data privacy questions from the oppo-
site perspective than patients. They argue that already today,
the respect of existing data privacy regulations does present an
important extra-workload which could even grow in the future.
Furthermore, these regulations could endanger the patients’
medical benefit because of incomplete information flows.
VIII. D
ISCUSSION AND GENERALIZATION
Most of the individual results of our user acceptance studies
of the two cases ‘Stroke Angel’ and ‘MS Nurses’ do not con-
tradict already existing findings or presumptions [29] [30]. But
our results allow to support these findings via the broad empiri-
cal base of our research and to consider user acceptance of
Pervasive Computing in healthcare in a generalized way. The
findings show first that all four factors under analysis – useful-
ness, usability, data privacy and accordance with social norms
– do influence the acceptance of pervasive technologies in
healthcare. This holds for patients, physicians, EMT-I, nurses
and physiotherapists. Second, it is possible to distinguish the
factor perceived usefulness as fundamental factor of accep-
tance.
The perceived usefulness seems to be the crucial factor of
acceptance. Within this factor, it is the aspect of medical use-
fulness that is the most important criteria for acceptance. When
there is no medical use perceived, the acceptance of all groups
of stakeholders is very low. Inversely, if medical use is seen,
the acceptance is high. The potentials for optimal health and
lifesaving are fundamental arguments in favor of Pervasive
Computing in healthcare of all the interviewed stakeholders.
One can thus deduce that pervasive technologies can easily be
accepted in healthcare, if – and only if – its medical use is
convincing and convincingly communicated. The organiza-
tional usefulness, i.e. the facilitation of daily work has lower
importance. Still, acceptance depends on the condition that
Pervasive Computing technologies do not require any extra-
effort as e.g. the treatment of higher amounts of data. Ergo
organizational efforts should not grow to assure acceptance of
hospital professionals.
Usability certainly influences user acceptance but does not
finally determine a users’ attitude. Usability seems to be less
important for user acceptance than the perceived use [31].
Usability only presents a decisive factor of acceptance, if
problems with usability harm the effectiveness or reduces the
perceived medical usefulness, as we have illustrated by the
case of Stroke Angel. The experience from both case studies
also shows that it is still important to demand to adopted every
Pervasive Computing application precisely to its specific
health care setting (ambulance, clinic, home etc.) and to the
patients’ and professionals’ abilities or disabilities and life or
working conditions.
The influence of data privacy issues on the acceptance of
pervasive technologies has to be considered in a differentiated
manner. Our findings suggest there are two distinctive contexts
of acceptance: In case of emergency, data privacy is no issue
but lifesaving has absolute priority; whereas in normal case, the
respect of data privacy is a necessary factor of acceptance. In
normal case, people demand to respect data privacy and the
possibility of self determination. In everyday life they want to
decide whom and when they send their personal medical data.
These conditions have to be considered to guarantee user ac-
ceptance in the context of normal. The case of emergency has
to be clearly defined in advance to allow different data privacy
standards. Furthermore, the perception of being permanently
observed could be an additional barrier of acceptance when
technologic devices are sending data automatically. Finally,
data privacy and self-determination turn out to be issues that
reveal opposing interests and perspectives among different
stakeholders and these issues might turn out to be the most
complex issues of user acceptance of Pervasive Computing in
healthcare.
The acceptance of Pervasive Computing applications de-
pends on the accordance of the technical system and its usage
with established social norms. When subjective norms are
violated, this can present serious barriers to acceptance. If so-
cial roles (as job images) are changing because of newly im-
plemented pervasive technologies, the opposition from stake-
holders, who feel they are loosing status or competences, is
possible and this opposition can inhibit the successful imple-
mentation of pervasive technologies. The opposition of occupa-
tion groups is thus one factor which can inhibit the implemen-
tation of Pervasive Computing in healthcare. Idem, an impair-
ment of the physician-patient relationship presents a risk which
has to be taken into consideration and the fear of loosing ‘hu-
man quality’ in healthcare should be taken seriously to stabilize
the acceptance of Pervasive Computing.
IX. C
ONCLUSION
In sum, medical usefulness seems to be the decisive factor
of acceptance of Pervasive Computing in healthcare for both,
hospital professionals and patients. The respect of data privacy
and the accordance to subjective norms are additional factors,
which surely could inhibit the acceptance of pervasive tech-
nologies. Patients demand to control the access at their personal
data; medical professionals demand not to augment their effort
as the treatment of data is concerned. Usability only presents a
decisive factor of acceptance, if problems with usability reduce
the perceived usefulness.
Thus, for a successful implementation of Pervasive Com-
puting in healthcare, a given and well communicated medical
use seems to be a stable starting basis. Furthermore, the active
integration of different stakeholders, physicians, medical staff
and patients, each with their specific priorities and needs, could
facilitate the acceptance of Pervasive Computing in healthcare.
In conclusion, these two case studies point out that there are
already possibilities to implement Pervasive Computing tech-
nologies in real world medical scenarios. The partners of the
case study ‘Stroke Angel’ plan to make the technology usable
for heart attacks emergency cases. Furthermore, they started
thinking about a roll-out in Bavaria and throughout Germany.
The case study ‘MS Nurses’ will be continued at least until
December 2008 and the participating doctors wished to extend
the application context to Parkinson and Trombolyse. Though
there is a need for new pervasive technologies, the implementa-
tion of this kind of technologies turned out to be challenging
and the consideration of user acceptance proved crucially.
A further important field of research is the question of fund-
ing and of cost-benefit ratio of Pervasive Computing in health-
care. And questions of funding certainly will influence user
acceptance and general social acceptability, too.
A
CKNOWLEDGMENT
The authors gratefully acknowledge funding from the Ger-
man Federal Ministry of Education and Research under grant
number 16I1546. Furthermore, we would like to thank our
Research Partner (Institute for Technology Assessment and
Systems Analysis (ITAS) of the Forschungszentrum Karlsruhe)
and our cooperate Partners.
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... User characteristics, such as age [12][13][14][15][16], gender [17,18], medical literacy [19,20], self-efficacy [21], technology expertise [12][13][14]19,22] and patient involvement [20,23], affect their willingness to use a PHD. For example, Gaul and Ziefle [12] carried out a survey with 280 participants to examine the acceptance motives for a medical stent implemented into the body. ...
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The vision of ubiquitous computing is increasingly picking up pace. An increasing number of everyday objects are equipped with smart technology and start to form the Internet of Things. Yet, interacting with these devices is based on conventional surfaces made of glass, metal, or plastic. We believe that textile interaction surfaces will be the next frontier of ubiquitous computing and identified many blank spots in the research landscape. Peoples’ perception and acceptance of smooth and soft interaction surfaces is insufficiently understood. In this paper we present a study in which 90 people of a wide age range evaluated the suitability of smart textiles in different usage scenarios in the home environment. Overall, a solid willingness to use smart textiles as input devices was found, even though there were conditional acceptance criteria which should be given before participants would be willing to buy them. In contrast to many other technology contexts, however, age is not decisive in the evaluation of the usefulness of smart textiles. Younger and older adults seem to have a quite similar evaluation, hinting at a quite generic acceptance pattern.
... An examination of existing literature on user acceptance (Wilkowska et al, 2010;Scheermesser and Rashid, 2008) and the empirical data generated, Schaar and Ziefle (2011a, p514) suggests that acceptance of medical technology is "… complex and strongly affected by health status, age, gender roles, culture, personal living conditions or care situation". ...
... Smart textiles in the field of healthcare are often focused on health monitoring. The integration of sensors into shirts [6] and other objects that are close to a persons body (e.g., belt [12], shoes [5], jewelry [1]) allow a continuous monitoring of vital parameter like temperature or the pulse rate. The vision that vital parameters could be easily permanently monitored could be a great support for medical care within the home environment. ...
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