Passive Sensor Technology Interface to Assess Elder Activity in Independent Living

Abstract

The effectiveness of clinical information systems to improve nursing and patient outcomes depends on human factors, including system usability, organizational workflow, and user satisfaction.
The aim of this study was to examine to what extent residents, family members, and clinicians find a sensor data interface used to monitor elder activity levels usable and useful in an independent living setting.
Three independent expert reviewers conducted an initial heuristic evaluation. Subsequently, 20 end users (5 residents, 5 family members, 5 registered nurses, and 5 physicians) participated in the evaluation. During the evaluation, each participant was asked to complete three scenarios taken from three residents. Morae recorder software was used to capture data during the user interactions.
The heuristic evaluation resulted in 26 recommendations for interface improvement; these were classified under the headings content, aesthetic appeal, navigation, and architecture, which were derived from heuristic results. Total time for elderly residents to complete scenarios was much greater than for other users. Family members spent more time than clinicians but less time than residents did to complete scenarios. Elder residents and family members had difficulty interpreting clinical data and graphs, experienced information overload, and did not understand terminology. All users found the sensor data interface useful for identifying changing resident activities.
Older adult users have special needs that should be addressed when designing clinical interfaces for them, especially information as important as health information. Evaluating human factors during user interactions with clinical information systems should be a requirement before implementation.

Full text

Nursing Research September/October 2011 Vol 60, No 5, 318–325
Passive Sensor Technology Interface to Assess
Elder Activity in Independent Living
Gregory L. Alexander 4Bonnie J. Wakefield 4Marilyn Rantz 4Marjorie Skubic
Myra A. Aud 4Sanda Erdelez 4Said Al Ghenaimi
bBackground: The effectiveness of clinical information sys-
tems to improve nursing and patient outcomes depends on
human factors, including system usability, organizational
workflow, and user satisfaction.
bObjective: The aim of this study was to examine to what extent
residents, family members, and clinicians find a sensor data
interface used to monitor elder activity levels usable and
useful in an independent living setting.
bMethods: Three independent expert reviewers conducted
an initial heuristic evaluation. Subsequently, 20 end users
(5 residents, 5 family members, 5 registered nurses, and
5 physicians) participated in the evaluation. During the
evaluation, each participant was asked to complete three
scenarios taken from three residents. Morae recorder soft-
ware was used to capture data during the user interactions.
bResults: The heuristic evaluation resulted in 26 recommen-
dations for interface improvement; these were classified
under the headings content, aesthetic appeal, navigation,
and architecture, which were derived from heuristic results.
Total time for elderly residents to complete scenarios was
much greater than for other users. Family members spent
more time than clinicians but less time than residents did to
complete scenarios. Elder residents and family members
had difficulty interpreting clinical data and graphs, experi-
enced information overload, and did not understand ter-
minology. All users found the sensor data interface useful
for identifying changing resident activities.
bDiscussion: Older adult users have special needs that should
be addressed when designing clinical interfaces for them,
especially information as important as health information.
Evaluating human factors during user interactions with clinical
information systems should be a requirement before
implementation.
bKey Words: gerontology &human factors &passive monitoring &
sensor networks &user-centered design
The use of clinical information systems to gather
health information is increasingly important for cli-
nicians and for patients and their families. Clinical infor-
mation systems are used by healthcare providers to support
clinical decision making to deliver appropriate patient care
and to alert clinicians to potential adverse events. Patients
and their family members are also increasingly savvy in the
use of information systems to access vital healthcare in-
formation (Cresci, Yarandi, & Morrell, 2010). The effec-
tiveness of clinical information systems to provide usable
information for clinicians to appraise and predict nursing
and patient outcomes depends on several factors, including
usability of the information system, presentation of data,
and satisfaction during interactions with the information
system (Alexander & Staggers, 2009).
In independent living settings, use of technology such
as nonwearable sensors can facilitate earlier detection of
changes like reduced activity in an elder’s apartment and
alert providers to intervene earlier (Courtney, Demiris, &
Hensel, 2007). These systems provide new ways of detecting
subtle changes that do not require traditional face-to-face
assessment of individual residents. However, these technol-
ogies must first be evaluated to understand human inter-
actions with them, how the technology functions, and the
environment. This article includes an evaluation of a clinical
information system composed of passive sensors used to
track human motion and physiologic parameters of elders
residing in an independent living facility called TigerPlace.
Residents who are living in independent settings such as
TigerPlace are typically vulnerable to decline, are frail, and
often require some nursing care; therefore, the terms patient
and resident are used interchangeably for the purposes of
this article. The specific aim of this research was to examine
to what extent residents, family members, and clinicians
318 Nursing Research September/October 2011 Vol 60, No 5
GregoryL.Alexander,PhD,RN,is Associate Professor, Sinclair
School of Nursing; Bonnie J. Wakefield, PhD, RN, FAAN,
is Associate Research Professor, Sinclair School of Nursing;
Marilyn Rantz, PhD, RN, FAAN, is Professor, Sinclair School of
Nursing; Marjorie Skubic, PhD, is Professor, Electrical and Com-
puter Engineering; Myra A. Aud, PhD, RN, is Associate Professor,
Sinclair School of Nursing; Sanda Erdelez, PhD, is Associate Pro-
fessor, Sinclair School of Nursing; and Said Al Ghenaimi, RN,
MSN, MED-Tech, is Graduate PhD Student, School of Informa-
tion Science and Learning Technologies, University of Missouri,
Columbia.
Supplemental digital content is available for this article. Direct
URL citations appear in the printed text and are provided in the
HTML and PDF versions of this article on the journal’s Web site
(www.nursingresearchonline.com).
DOI: 10.1097/NNR.0b013e318225f3e1
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

find the sensor data interface usable and useful in an inde-
pendent living setting.
Background
Clinicians and patients interact with computers on many
levels. Staff nurses and physicians collect information from
their clients, store client information in clinical information
systems, and then retrieve that information for making
clinical decisions. The information is used by clinicians to
keep an ongoing record of events, actions, behaviors, per-
ceptions, and progress made during healthcare encounters.
The effectiveness of clinical information systems that allow
nursing staff to predict nursing and patient outcomes
depends on the usability of the system, organizational work-
flow, and satisfaction with technology. Therefore, under-
standing human interactions with information technology
has the potential to improve clinical processes and patient
outcomes. The following research was guided by a frame-
work for nursing informatics called the NurseYPatient
Trajectory Framework (Alexander, 2007; Figure 1). The
framework utilizes nursing process theory, human factors,
nursing and patient trajectories, and outcomes to evaluate
clinical information systems.
The NurseYPatient Trajectory Framework
The NurseYPatient Trajectory Framework can be used to
predict which information structures, processes, and tech-
nologies can be used to achieve desired outcomes for patients
and providers (Bakken, Stone, & Larson, 2008). Research
using this framework was designed to focus specifically on
how nurses and patients interact with information systems
and how those interactions influence decision making and
outcomes. However, it is also important to consider other
people who interact with nurses and patients as they make
decisions and arrive at outcomes along their trajectories. For
example, nurses assess resident conditions, determine from
these assessments that resident needs may have changed,
take action, and alert physicians or family members that
needs have changed. A thorough discussion of the compo-
nents of the NurseYPatient Trajectory Framework has been
published elsewhere (Alexander, 2007) and will not be
discussed here. However, human factors are a central con-
cept for this article and will be described briefly.
Human Factors For the purpose of this article, human
factors refers to a discipline focused on optimizing the re-
lationship between technology and humans (Alexander,
in press; Czaja & Nair, 2006). For example, in a study com-
paring older users and nonusers in the use of Internet health
information resources, nonusers are more likely to make
healthcare decisions based on information found offline when
compared with Internet users with access to more information
(Taha, Sharit, & Czaja, 2009). Nonusers who do not use
the Internet typically obtain a larger percentage of health
information from newspapers, popular magazines, or the
television, but most of the time, they turn to family and
friends as a source of information (Taha et al., 2009). In
healthcare, human-factors researchers investigate the re-
lationships between patients, their families, and providers;
tools they use, such as the Internet; the environments in
which they live and work; and the tasks they perform.
Human factors concepts guided the selection of methods
for this study.
Passive Sensor Systems in TigerPlace
The purpose of this article was to evaluate whether res-
idents in an independent living center (TigerPlace), resi-
dents’ family members, and healthcare providers find an
interface designed to monitor resident activity levels useful
and usable. During the research, human factors that are
important for the design of sensor systems were examined.
A variety of passive infrared sensors are available for de-
tecting motion, location, falls, and functional activity and
are installed in resident’s apartments in an independent
living setting called TigerPlace. A thorough description of
all sensor modalities and other references used are described
elsewhere (Skubic, Alexander, Popescu, Rantz, & Keller,
2009). Sensor systems are becoming much more common
in research literature, but mostly in other disciplines than
nursing, such as engineering and lived environments and
aging. For example, remote monitoring technology has been
installed in community-living elders’ residences to monitor
meal preparation, physical activity, vitamin use, and per-
sonal care (Reder, Ambler, Philipose, & Hedrick, 2010).
Researchers indicated that elders and their caregivers
experienced peace of mind and greater perceived safety, well-
being, and independence as a result of new monitoring sys-
tems. Another instance includes an Automated Technology for
Elder Assessment, Safety and Environment remote monitoring
system for elders in independent living residences (Feeney-
Mahoney, Mahoney, & Liss, 2009). User subgroups inter-
viewed (residents, family members, management) all shared
concerns about safety and well-being and stated that there is a
need for enhanced monitoring using sensors.
In this study, sensor information was operationalized
as where a patient who is a resident in independent living
spends most of his or her time in an apartment, location
in his or her residence, and physiological data including
FIGURE 1. The NurseYPatient Trajectory Framework.
Nursing Research September/October 2011 Vol 60, No 5 Passive Sensor Interface 319
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

measures of restlessness and vital signs, and where certain
activities occur in the elder person’s apartment. For ex-
ample, functional activity data might show that a resident
spends some time during the day in the kitchen and uses
the stove, is frequently up and out of bed during the night
using the bathroom, and while in bed experiences some
periods of restlessness. These data provide an assessment
of the baseline trajectories experienced by patients in
TigerPlace, can be linked to declining activity levels, and
can be used as another source of information for the elec-
tronic health record (Rantz et al., 2010).
Every data element in the network of sensor data can
be visualized on a computer screen through a Web-based
interface that can be used to determine baseline activity
patterns. During this study, researchers wanted to provide
an opportunity for all types of end users, including resi-
dents who lived with the sensors and their family members,
nurses, and physicians, to interact with the interface using
scenario-based think-aloud methods. The aim was to deter-
mine if these end users perceived the sensor interface as
usable and useful.
Interface Development for Sensor Network A subgroup of
the larger Eldertech research team interested in human com-
puter interaction began meeting biweekly in April 2007.
Members of the interdisciplinary group included nurses
with specific expertise in gerontology and others with ex-
pertise in health informatics, engineering, clinical medi-
cine, information science, and physical therapy. By August
2007, the interdisciplinary researchers have developed a
data sensor interface with capability to illustrate sensor data
in different formats. For example, data could be viewed in
histograms, line graphs, and pie charts; users could query
sensor data by date and time of day for each sensor location
(e.g., bed, bedroom, bathroom, closet, front door, kitchen,
living room shower); and users could drill down into smaller
increments of time, ranging across month, week, day, and
hour, to observe trends in different sensor data over a length
of time. Maps of residents’ apartments indicating sensor
placement and range of sensors were integrated into the in-
terface for users. Development occurred through iterative
reviews of the sensor interface and discussions between cli-
nicians who had expertise in caring for gerontological pop-
ulations and designers of the information system. Examples
of the interface are shown in Figures 2a and 2b. The inter-
face was then evaluated using sequential approaches, includ-
ing an expert heuristic review.
Expert Review of the Sensor Interface Three reviewers
trained in usability research and who were independent of the
research team were hired to complete an expert review of the
sensor data interface (Alexander et al., 2008). This review
was completed prior to end user assessments. A heuristic
evaluation checklist was created to suit the interface being
evaluated, keeping in mind the audience of the Web siteV
elderly residents, family members, and healthcare providers.
Individual reviews were conducted before the reviewers
came together for a meeting to discuss each of the items
under the criteria. An overview of this heuristic evaluation is
provided (see Table, Supplemental Digital Content 1, which
includes a listing of heuristics used, with a brief heuristic de-
scription, ratings given by expert reviewers, and selected com-
ments made by the reviewers, http://links.lww.com/NRES/A56).
Expert Heuristic Evaluation of the Interface Therewere87
items distributed under the 16 heuristic criteria. Twenty-nine
of these items were not applicable to the interface, primarily
because the interface was at a very early stage of develop-
ment. The number of criteria meeting and not meeting
heuristic criteria (or not applicable), percentage agreement
with applicable criteria, and severity ratings have been
described previously (Alexander et al., 2008). On the basis
of this review, the expert team made 26 recommendations
for improvement; these recommendations were categorized
under four themes derived from the heuristic evaluation, in-
cluding content (n= 4), aesthetic appeal (n= 12), navigation
(n= 9), and architecture (n= 1). Interface improvements in
each of these categories would make the interface more
usable and useful for end users. Interface design issues were
addressed by the research team during iterative reviews prior
to the usability assessment.
Specific recommendations within the content area in-
cluded constructing clearer more descriptive titles, for ex-
ample, better labeling methods, adding a description of the
interface on the home page by including descriptions about
interface utilities, and consistently maintaining the same or-
der of legends and keys throughout.
Aesthetic appeal improves as dialogue on the interface
becomes more relevant to the users. The sensor interface
could be improved by adding error messages when a user
clicks on an item not associated with hyperlinks, such as a
graph title. Furthermore, aesthetic appeal would be im-
proved by minimizing the number of new windows that
open with every click of the mouse and when using
zooming in features such as date fields. Finally, providing
greater distinction between line colors on graphs would
improve visibility and readability.
Navigation through the sensor interface could be
enhanced by making the hyperlinked texts more visible with
conventional hyperlink colors, that is, blue for unvisited sites
and purple for visited sites. Another improvement would be
to add navigation options that direct users to specific parts of
the interface, such as a ‘‘Home’’ link that would send users
back to the home page of the interface where they can choose
a new resident. Furthermore, it was recommended to add
prompts that need to be approved by the user before the
application closes. This feature would prevent users from
accidently exiting from a page before they complete reviews.
Finally, the architecture of the interface could be im-
proved by giving users better options for viewing data, for
example, giving users the ability to decide how they want to
look at the data. Architectural options could enhance effi-
ciency of use by enabling users to look at graphs all at one
time on one scrollable page or individual graphs one at a
time. Anchors could be used after each graph to navigate the
user automatically and consistently back to the top of a page
to select preferred views of each graph.
After the expert heuristic review, researchers continued
to meet, and the interface was refined using some of the
suggestions mentioned. Once the research team had ad-
dressed all the recommendations through iterative reviews
of the interface, we began conducting usability assessments.
320 Passive Sensor Interface Nursing Research September/October 2011 Vol 60, No 5
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

Approval for all research was granted through the uni-
versity’s institutional review board.
Methods
Setting
Since 2005, the Eldertech research team has been develop-
ing a passive sensor technology system that detects motion
activities in residents in an independent living setting,
TigerPlace (Rantz et al., 2005). Sensor systems have been
installed in 34 residents’ apartments who desire to age in
place. The longevity of the sensor data collection enables
collection of data and developing algorithms for identifying
declining patient trajectories such as increased bed restless-
ness or decline in a resident’s normal daily activity patterns.
Usability Assessments
Both the expert review and end user assessments were
completed with the assistance of the Information Experience
(IE) Lab located on the campus of the University of Missouri.
The IE Lab functions as a testing and evaluation area with
network-enabled data collection workstations using ad-
vanced data collection software such as Morae usability as-
sessment software (Techsmith, Inc., Okemos, MI).
End User Usability Assessments After the expert review,
updates were made to the interface during iterative reviews
conducted by the research team to correct some of findings
from the heuristic evaluation before end user assessments
were conducted. For example, labels were added to graphs,
color schemes were modified, and font sizes were adjusted
so that users could find information more easily on the
interface. After the updates, four groups of end users
(residents, family members, nurses, and physicians) were
recruited to evaluate the interface. The target was five
members per group. This decision was based on evidence
that most usability problems are detected with the first five
FIGURE 2. (a) Activity Motion Sensor Data. (b) Bed breathing sensor data.
Nursing Research September/October 2011 Vol 60, No 5 Passive Sensor Interface 321
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

participants, that running additional participants is not
likely to reveal new information, and that great return on
investment is realized with small groups (Turner, Lewis, &
Nielsen, 2006).
Residents who participated in this usability evaluation
were frail and had multiple chronic conditions and most
had mobility problems, representative of most TigerPlace re-
sidents. Therefore, resident usability assessments were con-
ducted at the independent living facility with the assistance of
the IE Lab. Family members and healthcare providers came to
the IE Lab for their usability assessment.
Interviews were conducted with each participant indi-
vidually in a quiet private location. Individual assessments
were recorded directly to a computer hard drive for later
review and analysis. Participants were informed that they
would be participating in a usability study designed to
explore their interactions with a sensor data interface. Each
testing site had two rooms: one for private usability testing
and one for observation of usability participants and cap-
turing interactions using Morae software. Networked com-
puters were used by IE Lab staff and other researchers to
observe the interactions of participants and record obser-
vations simultaneously.
Data Acquisition Morae recorder software was used to
capture data during each interaction. Observational notes
were also recorded by remote observers during the testing.
The interaction data included a video of the participant using
the sensor data interface; audio in the immediate environ-
ment; quantitative data such as keystrokes, mouse clicks, and
window events; text presented to the user; and Web page
changes. Event markers and comments were recorded and
are summarized in the heuristic table (see Table, Supplemen-
tal Digital Content 1, http://links.lww.com/NRES/A56).
Event markers were preset to represent situations like ‘‘user
needs help,’’ ‘‘user frustrated,’’ ‘‘start task,’’ and ‘‘end task.’’
Test Setup Prior to testing, each participant was given in-
struction on the user interface by one of the research team
members (a registered nurse certified in humanYcomputer
interaction methods and who had experience with the
interface) using a training manual designed to provide stan-
dardized training about the sensor system for each partici-
pant. This instruction provided information on the purpose
of the sensor systems, how to navigate through the screens to
get through the data, and details about how the data can be
used. Each participant also was informed that Morae
software was used to capture and record his or her inter-
actions with the sensor data interface.
Participants were given three scenarios to complete on three
distinct residents who had sensors installed in their apartments
(see Table, Supplemental Digital Content 2, which provides
study training scenarios, http://links.lww.com/NRES/A57). Sce-
narios were developed by expert geriatric registered nurses
who were part of the research team investigating the sensor
data network. Scenarios were developed around periods
when residents experienced known sentinel events (e.g.,
hospitalization, decline in health, falls). Scenario 1 included
sensor data around a period when a resident experienced a
hospitalization after an acute illness at home. Scenario 2
included a period when a resident was not feeling well, had
decreased activity levels in the apartment, and was spending
more time in bed. Scenario 3 included information on a
restless resident who was moving back and forth between a
bed and a chair at night to get more comfortable during sleep.
Actual resident data were deidentified.
Participants were asked to complete each scenario using
the sensor data interface; they were asked to ‘‘think aloud’’
as they progressed through the scenarios, verbally indicat-
ing their thought processes as they began to search through
the sensor activity data using the scenarios as their guide.
The Morae software was activated at the beginning of the
instruction for the sensor data interface and digital record-
ings made throughout each scenario for later comparison.
Each participant’s interaction was timed using the Morae
software.
After the usability test, each participant was asked to
complete a short survey. The survey asked participants to
provide demographic information about their age, job ex-
perience, how much Web experience they have had, educa-
tion level, and daily use of Web.
Data Analysis Time statistics by activities and tasks were
analyzed, including total time spent on introduction and tu-
torials. Timed tests and interface-related issues were com-
pared within the group of residents and clinician users.
Results are described using the 16 heuristic categories and
summarized qualitative and quantitative data collected dur-
ing the interviews.
Results
Usability Assessments
A total of 20 end usersV5 from each group of residents,
family members, nurses, and physiciansVparticipated in
this study. The demographics of the users obtained from a
short survey collected at the time of assessments are de-
scribed next, followed by results of data collected during
user interactions. Total time on activities and scenarios was
collected using Morae software.
Residents Participating residents were all over the age of
70 years. Four female residents and one male resident com-
pleted usability assessments. Two of the users had used com-
puters before; three had never used computers. None of the
residents had seen the sensor data interface before.
Generally, the total time to complete activities and
tasks on the interface was higher for elderly residents when
compared with other users. Every resident took nearly an
hour to get through the tutorial. Interviewers halted the
interactions at an hour to limit respondent burden during
interactions. Most residents only partially completed the
scenarios in this amount of time. Residents required
assistance by interviewers during their interactions with
the sensor data interface. In fact, one resident did not get
through the tutorial to be able to use scenarios and view
sensor data. Residents had difficulty manipulating the
mouse, and the interviewer had to assist the residents in
entering date ranges into fields, which increased time to
complete activities. Residents experienced difficulty manip-
ulating the mouse over text boxes, often repeatedly clicking on
parts of the interface just next to text boxes. (For other issues,
322 Passive Sensor Interface Nursing Research September/October 2011 Vol 60, No 5
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

see Table, Supplemental Digital Content 3, which describes
resident interactions, http://links.lww.com/NRES/A58).
Family Members Participating family members were between
the ages of 35 and 65 years old. Four women and one man
were interviewed. Two of these participants had 10 or more
years, two had 5Y9 years, and one had less than 4 years of
Internet experience. Participant relations were son-in-law,
daughter, and stepdaughter. None of the family members had
used the interface before.
Family members’ time spent learning the interface during
the tutorial ranged from 7 to 19 minutes. Similarly, family
members individually spent 8Y15 minutes reviewing sensor
data after each scenario was read and after initial data entry
was accomplished to retrieve sensor data. Family members
spent more time reviewing sensor data on the Scenario 3
(average of 13 minutes) and the least time on Scenario 2
(average of 10 minutes).
Nurses Participating nurses ranged in age from 40 to 60 years
old. The nurses were women with 20 or more years of work
experience and included one clinical educator, two assistant
professors, one nurse clinician, and one nurse care coordina-
tor. All the nurse participants had 9 or more years of Internet
experience. None of the nurses had seen the sensor data in-
terface before.
Nurses took nearly 8 minutes to complete the sensor
interface tutorial. They consistently spent between 10 and
12 minutes completing each of the scenarios after reading
the scenario and providing initial data entry. Nurses spent
most of their time, nearly 12 minutes, on Scenario 1. They
spent the least amount of time, just under 11 minutes, on
Scenario 2.
Physicians Physicians were 40Y60 years old and included
two women and three men. Practice disciplines were family
practice, geriatrics, and general internal medicine. All par-
ticipants had 10 or more years of Internet experience. Only
one of the physicians had seen the interface.
Physicians spent 7Y12 minutes learning how to use the
interface before moving on to scenarios. Physicians each
averaged 9Y13 minutes completing the scenarios. They spent
the most time reviewing sensor data on Scenario 1 (just more
than 15 minutes). They took the least time (just less than
8 minutes) reviewing data related to Scenario 2.
Group Observations Observations from the four user groups
were performed (see Table, Supplemental Digital Content 3, which
categorized within heuristics, http://links.lww.com/NRES/A58).
Overall, and not surprisingly, a greater number of issues
were identified in the resident group, which substantially
increased their time to complete activities. For example,
residents had more difficulty viewing data; they had some
difficulty interpreting graphs, thought there was too much
information, and did not understand the terminology.
However, all of the users found the information com-
forting. Family members thought the interface would be
very useful for remotely monitoring the health and physi-
cal activities of their family; for example, one family
member expressed that she would use the sensor data to
monitor her mother’s sleep patterns because her mother has
Alzheimer’s and tends to forget (see Table, Supplemental
Digital Content 4, which provides comments by user group,
http://links.lww.com/NRES/A59). All residents stated that
collecting this information made them feel safer, knowing
that someone was watching for changes in activity levels and
potential health problems.
Users identified some persistent problems previously
identified by the expert review panel using the heuristic
criteria (see Table, Supplemental Digital Content 1,
http://links.lww.com/NRES/A56). For example, under Heu-
ristic 4, the expert reviewer noted color usage in the graphs as
problematic. In some instances, colors lacked adequate contrast
and were too similar, so locations of sensors within the apart-
ment could be confused. All user groups noted problems with
color selections on the interface (see Table, Supplemental
Digital Content 3, http://links.lww.com/NRES/A58). Another
color issue was identified via Heuristic 2, indistinctive color
(e.g., it is difficult to discriminate orange and red restlessness).
Size of the data points (too small) was identified in Heuristic 7,
and font size issues were addressed in Heuristic 12. Most
residents had difficulty seeing the information on the screen
because the font was too small and there was no way to adjust it
on the screen. To overcome this problem, some residents
selected contrasting backgrounds of black or white to enhance
visualization.
Under Heuristic 10, the experts identified that navigation
was difficult and information was not easy to find. Again, the
residents had problems because there was too much informa-
tion displayed or they felt overwhelmed by the charts. All
users found some parts of the display confusing; for exam-
ple, the stove temperature sensor was listed in the same
vicinity on the interface with other physiological parameters.
The placement and labeling of the stove sensor information
confused users because the labels did not represent the users’
terminology consistently (temperature in a healthcare record
usually means body temperature, but in this case, it meant
stove temperature).
During the test session, the nurses and physicians often
used the data to interpret clinical information. For example,
healthcare providers watched for warning signs by paying
close attention to resident activities within the apartment.
When clinicians noticed changes in activities, they attempted
to determine if it was prior to a change in patient status, such
as increasing bed restlessness prior to hospitalization. Both
nurses and physicians found the data useful for interpreting
health status. However, residents and family members had
more difficulty interpreting the sensor output because they
did not know what normal activity was for an individual
or age group (see Table, Supplemental Digital Content 4,
http://links.lww.com/NRES/A59). This could have contrib-
uted to their increased time to complete activities. Despite
problems noted by users, comments made during interactions
illuminate how useful the sensor data could be to assist in
monitoring for changing trajectories of frail elderly residents
living independently.
Discussion
The results support recent research advocating that older
adults have special needs that should be addressed when
designing interfaces for them, especially information as im-
portant as health information used to make decisions about
Nursing Research September/October 2011 Vol 60, No 5 Passive Sensor Interface 323
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

their healthcare (Czaja, Sharit, Nair, & Lee, 2008). Design-
ing health information systems with the needs of older end
users in mind is important to maximize their interactions
with these devices. Using these user-centric design principles,
human factors allow older end users to incorporate wanted
technologies into their daily lives more easily, and they find
the information more useful. A result of enhanced usefulness
and usability is greater acceptance and satisfaction with tech-
nology and potentially improved patient trajectories (Figure 1).
Evaluating human factors during user interactions with clin-
ical information systems should be a requirement before any
field implementation. In addition, end user involvement is
critical to identify important problem areas that can be over-
looked during iterative reviews by developers who have dif-
ferent requirements and needs.
In this study, residents, family members, and clinicians
found the interface useful for identifying the level of activ-
ities for residents who had the sensor systems installed;
residents found the data less useful. Residents indicated that
they already know their activity levels, and therefore, they
did not find the information helpful. However, residents did
report a sense of security knowing that someone was watch-
ing out for them when they were alone in their apartment.
The sensors and data provided a safer environment for them.
All resident users felt the sensor data would be useful for
healthcare providers and family members to monitor them
for changes in activity levels. All residents interviewed were
willing to share their sensor data information with their
families and providers.
Clinicians who had experience working with elderly in-
dividuals in the community claimed that access to this type of
sensor interface data would definitely help them make better
decisions in the field about the residents. Usability issues
encountered did not deter them from attempting and, in most
cases, successfully finding altered activity levels within res-
ident’s data. This discovery was completed with minimum
assistance on the part of the investigator conducting the us-
ability interviews. Clinician users discussed important issues
that still need to be addressed with the sensor data and inter-
face, including developing better ways to visualize the data
to detect changes from normal baseline activity levels and
using terminologies and parameters for physiological mea-
sures that match the real world of healthcare providers. Im-
proving visual acuity and screen readability has the effect of
improving clinician interactions with technology by making
clinical information more accessible, understandable, and
consistent with clinician work. These attributes of clinical
information improve information flow along trajectories and
can enhance nursing and patient outcomes (Figure 1).
Methodological Issues
Important methodological issues were discovered. First,
although a standardized training manual was used, the in-
troduction and tutorial to familiarize users with the interface
could have been delivered in a more standardized way. This
would ensure that the participants knew all the necessary
details about the interface before testing began. Second, to
conduct quantitative user analysis, clear instructions should
be given to the evaluator for the scenario start and end to
compare these times consistently between users and user
groups. The users should be asked to read the scenario and
their understanding of the scenario should be clarified before
starting the usability assessment. Third, evaluators should
decide beforehand when tasks or scenarios should begin.
Time frames to consider include when the user starts reading
the scenario, when the user starts typing in the details to
access the data related to the scenario, or when the user starts
interpreting the graphs that are illustrated. Finally, the
evaluator should decide beforehand what would be the task
end measure, for example, when the user says they are done
or when the user clicks on the Home button.
Conclusion
The effectiveness of clinical information systems to provide
useful information for clinical decision making is depen-
dent on the usability of system, data presentation, the match
between the real world of end users and the system, and the
satisfaction of users during interactions. There are important
considerations for different types of users based on age, vo-
cation, and experience. This is particularly true in healthcare
settings, where users are of a wide range of age and expe-
rience and have multiple types of jobs. Evaluation of human-
to-computer interactions in healthcare settings will lead to
improvements in the development of clinical information
systems and greater understanding of how information can
be used, which will positively impact care delivery processes,
clinical decision making, and healthcare outcomes.
q
Accepted for publication April 27, 2011.
This project was supported by Grant K08HS016862 from the
Agency for Healthcare Research and Quality, Grant 90AM3013
from the U.S. Administration on Aging’s Technology to Enhance
Aging in Place at TigerPlace, and Grant IIS-0428420 from the
National Science Foundation’s ITR Technology Interventions for
Elders With Mobility and Cognitive Impairments projects.
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the Agency for Healthcare
Research and Quality, the U.S. Administration on Aging, or the
National Science Foundation.
The authors have no conflicts of interest to disclose.
Corresponding author: Gregory L. Alexander, PhD, RN, Sinclair School
of Nursing, University of Missouri, Columbia, MO 65211 (e-mail:
alexanderg@missouri.edu).
References
Alexander, G. L. (2007). The NurseYPatient Trajectory Framework.
MEDINFO, 910Y914.
Alexander, G. L. (in press). Human factors. In V. Saba & K.
McCormick (Eds.), Essentials of nursing informatics (5th ed.,
chap. 8). New York: McGraw-Hill.
Alexander, G. L., Rantz, M. J., Skubic, M., Aud, M. A., Wakefield,
B., Florea, E., et al. (2008). Sensor systems for monitoring func-
tional status in assisted living facility residents. Research in Ger-
ontological Nursing,1,238Y244.
Alexander, G. L., & Staggers, N. (2009). A systematic review of the
designs of clinical technology: Findings and recommendations
for future research. Advances in Nursing Science,32,252Y279.
Bakken, S., Stone, P. W., & Larson, E. L. (2008). A nursing in-
formatics research agenda for 2008Y18: Contextual influences
and key components. Nursing Outlook,56, 206Y214.
Courtney, K. L., Demiris, G., & Hensel, B. K. (2007). Obtrusiveness
of information-based assistive technologies as perceived by older
adults in residential care facilities: A secondary analysis. Medical
Informatics and the Internet in Medicine,32,241Y249.
324 Passive Sensor Interface Nursing Research September/October 2011 Vol 60, No 5
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1

Cresci, M. K., Yarandi, H. N., & Morrell, R. W. (2010). The
digital divide and urban older adults. Computer, Informatics,
Nursing,28,88Y94.
Czaja, S. J., & Nair, S. N. (2006). Human factors engineering
and systems design. In G. Salvendy (Ed.), Handbook of human
factors and ergonomics (3rd ed., pp. 32Y53). Hoboken, NJ:
Wiley.
Czaja, S. J., Sharit, J., Nair, S. N., & Lee, C. C. (2008). Older adults
and the use of e-health information. Gerontechnology,7, 97.
Feeney-Mahoney, D., Mahoney, E. L., & Liss, E. (2009). AT
EASE: Automated Technology for Elder Assessment, Safety,
and Environmental monitoring. Gerontechnology,8,11Y25.
Rantz, M. J., Marek, K. D., Aud, M., Tyrer, H. W., Skubic, M.,
Demiris, G., et al. (2005). A technology and nursing collaboration
to help older adults age in place. Nursing Outlook,53,40Y45.
Rantz, M. J., Skubic, M., Alexander, G. L., Popescu, M.,
Aud, M. A., Wakefield, B. J., et al. (2010). Developing a com-
prehensive electronic health record to enhance nursing care
coordination, use of technology, and research. Journal of
Gerontological Nursing,36,13Y17.
Reder, S., Ambler, G., Philipose, M., & Hedrick, S. (2010).
Technology and long-term care (TLC): A pilot evaluation of
remote monitoring of elders. Gerontechnology,9,18Y31.
Skubic, M., Alexander, G. L., Popescu, M., Rantz, M. J., &
Keller, J. (2009). A smart home application to eldercare: Cur-
rent status and lessons learned. Technology and Health Care,
17,183Y201.
Taha, J., Sharit, J., & Czaja, S. J. (2009). Use of and satisfaction
with sources of health information among older internet users
and nonusers. Gerontologist,49, 663Y673.
Turner, C. W., Lewis, J. R., & Nielsen, J. (2006). Determining
usability test sample size. In W. Karwowski (Ed.), International
encyclopedia of ergonomics and human factors (2nd ed., pp.
3084Y3088). Boca Raton, FL: CRC Press.
Nursing Research September/October 2011 Vol 60, No 5 Passive Sensor Interface 325
Copyright @ 201 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
1