Impact of a TV-based Assistive Technology on Older People’s Ability to
Self-manage Their Own Health
Daniela Loi
, Silvia Macis
, Danilo Pani
, Andrea Ulgheri
, Romina Lecis
, Mauro Murgia
Marco Guicciardi
and Luigi Raffo
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza dArmi, Cagliari, Italy
Department of Pedagogy, Psychology, Philosophy, University of Cagliari, Cagliari, Italy
Assistive Technology, Active Aging, Patient Activation, Self-management.
Nowadays, special emphasis is being focused on involving people on their own health and care. The use of
digital technologies in the home-care management process is increasingly contributing to the maintenance of
quality of life and preservation of functional independence in older adults. There is a huge number of available
m-health applications for self-tracking health parameters, but the majority of them are inconsistent with the
needs of older adults who do not currently use technologies such as computers, smartphones or tablets. The
aim of this work is to present a pilot study, which included 19 older adults, that was conducted to objectively
measure the effect of a TV-based assistive system on the improvement of older adults’ activation levels about
self-management of health. The correlation with the usage of specific digital services provided by the system
was also investigated. The results reveal how the impact is limited by the aspecific nature of the intervention
with respect to the participants’ health condition. At the same time, they are encouraging and indicate that
there is the potential for the system to impact on older people’ self-management skills.
Encouraging people to take an active role in the man-
agement of their health condition provides several ad-
vantages: it helps achieving quality goals and im-
proved clinical outcomes, enhances people’s expe-
rience in the care process and, consequently, their
quality of life, and contributes to the reduction of
the costs of the healthcare systems (Greene and Hi-
bbard, 2012). The concept of being an informed and
activated patient refers to people’s knowledge, abil-
ity, willingness and confidence to exercise indepen-
dent choices to manage their health and care (Hibbard
et al., 2004). Self-management skills include inter-
action with healthcare providers, self-monitoring of
own health conditions, dietary restrictions and healthy
choices, medication adherence, as well as manag-
ing stress and frustration which may come from liv-
ing with diseases and may impact on physical func-
tioning, psychological well-being and social relation-
ships. Self-management practices and skills offer
people an increased level of autonomy in their health,
especially in those facing age-related conditions such
as diabetes, renal and cardiac dysfunctions, hyper-
tension, cardiovascular diseases (Young et al., 2017),
(Johnson et al., 2015). Several studies have recently
shown that the use of information and communica-
tion technology (ICT) tools in the home-care manage-
ment process can help in improving patients’ activa-
tion and in influencing their attitudes and behaviours
(Kim et al., 2016), (Whitehead and Seaton, 2016).
During the last decade, there has been a trend in de-
veloping telehealth services and telemonitoring tech-
nologies to empower ordinary people to have more
knowledge and control over their own personal health
data. These technologies allow to remotely moni-
tor key physiological parameters such as blood sugar
levels, blood pressure and oxygenation levels, heart
rate and weight, as well as physical activity levels,
opening up new opportunities for self-care and self-
management. Typically, telemonitoring services are
accessible by users through PCs, smartphones and
tablets devices (Anderson et al., 2016), (Morrison
et al., 2014). An overwhelming volume of health-
related smartphone apps are available for download
today and new connected health devices are released
all the time from manufacturers such as A&D Med-
ical, Fitbit, iHealth, Medisana and Withings. How-
Loi, D., Macis, S., Pani, D., Ulgheri, A., Lecis, R., Guicciardi, M., Murgia, M. and Raffo, L.
Impact of a TV-based Assistive Technology on Older People’s Ability to Self-manage Their Own Health.
DOI: 10.5220/0006538802930299
In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 293-299
ISBN: 978-989-758-281-3
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
ever, mobile interface are often unsuitable for older
people with limited experience or confidence in using
new technologies, then missing the main target. The
use of a much more familiar and convenient interface
such as the TV, can help to address the usability needs
of the older users (Costa et al., 2017), (Raffaeli et al.,
This paper presents the results of a four-month pi-
lot study aimed at investigating the effect of a TV-
based assisted living system on older people’s activa-
tion and on their self-care behaviors. The system has
been developed in the framework of the HEREiAM
European project, financed by the Active and Assisted
Living (AAL) Programme (, call
5). The system has been designed specifically for
older people and exploits an Android set-top-box, a
smart card and a simplified remote control device,
to offer a combination of health, social, comfort and
safety services (Macis et al., 2017). The assessment
was mainly based on the different services usage and
on the patient activation measure (PAM) question-
naire, adopted to measure the level of activation of
participants toward their care management. In partic-
ular, the expected outcomes were an improved PAM
score over time and a significant correlation with the
use of the different services provided by the system.
This section provides information on the technology
adopted and on the methodologies followed in the
study for the evaluation of its impact on the users. It
includes a description of the system architecture, an
overview of the population study and of the experi-
mental setup, and an outline of the tools and methods
used for data collection and analysis.
2.1 System architecture
The system in its entirety consists of three main com-
a Home based kit (Figure 1), which includes an
Android set-top-box with installed a set of appli-
cations reflecting the interests and the needs of
each user, a personal smart card, a custom remote
control, a portable webcam, some proximity in-
frared sensors and two personal self-care devices
(body scale and non-invasive blood pressure me-
a cloud-based infrastructure, called Remote Ser-
vices Platform, that centralizes both the user pro-
file management and access control (authentica-
tion, authorization, user’s relationships) and the
interoperability services (data and information
sharing, messages exchanging, resources plan-
a Web Portal, available for external individuals
such as family members, informal and profes-
sional caregivers, that allows accessing shared
information (health status data, room presence,
usage statistics), according to user authorization
Smart card
Remote control
Android set-top-box
Proximity sensors
Medical devices
Figure 1: Home based kit.
2.1.1 Supported Services
The system basically assists older adults in their daily
life activities by offering a combination of different
digital home-based care services. Using the system
through their own television, older adults are able to:
Make or receive video calls from a personalized
network of caregivers (Videocall service);
Get notifications for upcoming appointments or
events that are scheduled for a specific time of the
day (Agenda service);
Access a grocery shopping service from home and
get products delivered right to the door (Shopping
See the latest news and events happening around
their city, insert events in their Agenda or share
them with friends (News & Events service);
Receive health coaching and reminder messages
for medication intake, and register how they feel
or behave in relation to a health condition and its
therapy (Coaching service);
Self-monitor their health status by measuring
blood pressure, heart rate and body weight using
the integrated Bluetooth medical devices with the
TV interface (Health service);
Be monitored by pre-authorized caregivers in
their day-time and night-time mobility trends
HEALTHINF 2018 - 11th International Conference on Health Informatics
around the house through the integrated network
of motion sensors (Tracking service).
All these services were offered with the purpose of
motivating older adults to be more involved in their
health care and to remain independent and socially
active for as long as possible.
2.2 Population Study
A group of 19 older adults (8 males, 11 females)
was recruited for the study by the local Municipal-
ity of Cagliari among the subjects enrolled in the so-
cial services department activities. Inclusion crite-
ria were: participant’s age of 65 years or over, abil-
ity to live independently and engage in normal ver-
bal communication, and familiarity with basic TV
functionalities. Exclusion criteria were: participant’s
age less than 65 years, any kind of cognitive impair-
ments or serious visual or hearing impairments, any
severely limited dexterity in one or both hands. The
study was performed following the principles outlined
in the Helsinki Declaration of 1975, as revised in
2000. Subjects were asked to read and sign an in-
formed consent form prior to their participation in the
study and to complete a pre-participation question-
naire, which inquired about basic demographic as-
pects, background information and medical history.
Sociodemographic and clinical characteristics of the
selected sample can be summarized as follows:
Age, years (range): 73.15 ± 6.36, (65 87);
Marital status: 65% married, 30% widowers and
5% separated;
Educational level: 15% completed a primary
school, 40% completed middle school, 30% had
a high school diploma and 15% graduation;
Health problems in the last 6 months: 80% suf-
fered from some disease;
Drugs assumptions: 75% reported regular intake
of drugs for conditions such as type 2 diabetes,
hypertension, heart problems, musculoskeletal
2.3 Study Design
Subjects who consented to the study were contacted
by the research team to schedule a home installation
appointment. The Home based kit was installed in
each house by a technician, assuring that everything
was fully functional and properly working. All partic-
ipants received a user’s manual with all the necessary
information for the proper use of the system. Partic-
ipants with no broadband Internet connection in their
homes were provided with a TP-LINK 4G Wi-Fi don-
gle. The Android TV-box was connected to the HDMI
port of the participant’s TV and to the Internet. Par-
ticipants accessed all the functionalities of the system
by simply inserting their personal smart card, with-
out the need to remember and type any passwords. A
dedicated remote control was entrusted to them in or-
der to control the Android TV-box and navigate the
different services. A portable webcam with built-in
microphone was connected to the TV-box, to exploit
videocall functionalities. The set of medical devices
to monitor the participants’ physical conditions was
configured and the network of environmental sensors
providing information about participants movements
around the house was installed. The sensors were
connected to the Android TV-box via Bluetooth or
ZigBee. For the whole duration of the study, each
TV-box collected all the different types of data gath-
ered from the network of sensors and instruments in-
stalled at home, and transmitted them to the Remote
Services Platform using the latest medical and secu-
rity standards. These data were made available on the
Internet through the Web portal for remote monitor-
ing purposes. At least one informal caregiver for each
older adult participant was invited to take part in the
study. In total, a sample of 26 informal caregivers (9
males, 17 females, age 43± 7.85 years) expressed in-
terest in participating and in accessing up-to-date in-
formation about their relatives. They received login
credentials and instructions to access the Web portal.
2.4 Assessment Plan
The assessment focused on finding out if the proposed
services could help in incorporating self-management
support into the daily routine of older adults. The
PAM questionnaire was employed to measure the
level of participants’ activation toward their care man-
agement. All older adult participants were asked to
fill in the PAM questionnaire three times during the
study period: baseline (T
), midterm (T
) and final
evaluation (T
). Moreover, the assessment investi-
gated potential correlations between the PAM scores
and the usage time of specific services (in minutes).
2.4.1 Activation Assessment
The PAM consists of 13 items that are related to pa-
tients’ knowledge of their health condition or disease,
skills and confidence in their management of health
related tasks, and personal beliefs about health care
(Hibbard et al., 2005). For each statement, there are
four response options which are assigned scores from
1 to 4: (1) “Strongly Disagree”, (2) “Disagree”, (3)
Agree” and (4) “Strongly Agree”. If all questions are
Impact of a TV-based Assistive Technology on Older People’s Ability to Self-manage Their Own Health
answered, the total raw score is calculated by adding
all the responses to the 13 items. The range of PAM
raw scores is 13 to 52. In the case of PAM items with
missing data, the total raw score is calculated by di-
viding the sum of the all the responses by the number
of items answered (excepting non-applicable items),
and by multiplying the result by 13. The higher the
PAM scores, the higher patient activation.
2.4.2 Services Usage and Trends Assessment
The system allows collecting statistics related to us-
age of the offered services. The following informa-
tion can be obtained from each Android TV box:
number of times each service was used;
total time and the mean time of usage by the single
This is possible thanks to a dedicated application in-
stalled in the TV box that exploits an internal database
and a service running in background to save statistics
of the actions performed by the older adults using the
system’s applications. When a system application is
opened by the user, the system automatically saves the
following information in the database of the monitor-
ing application:
name of the launched application;
a sequence of encoded information identifying
when the event occurred;
a sequence of encoded information identifying the
user logged on the platform.
The recorded information is saved in real time and a
daily log file is sent, every 24 hours, to the Remote
Services Platform.
2.5 Data Analysis Methods
All the daily usage information stored in the server
were retrievable through the Web Portal. At the end
of the study, these records were exported on a Mi-
crosoft Excel sheet for statistical analysis. False pos-
itives (applications launched by mistake and closed
after no more than 30 seconds) were manually dis-
carded and the quantitative data obtained were coded
and exported from Excel into SPSS Release 18.
Data related with the system usage were analyzed
with descriptive statistics, in order to identify the av-
erage time and frequency of usage. For each service,
the average session duration was calculated by divid-
ing the total duration of all the sessions performed by
the population study (measured in seconds) by the to-
tal number of sessions, as shown in (1):
Avg Session Duration =
Total Duration
Number of sessions
A statistical analysis was also performed on the
questionnaires data to describe the psychosocial char-
acteristics of the study population. All variables were
checked about univariate and multivariate normality.
Since data have shown a normal distribution, para-
metric analyses were used. An analysis of variance
(ANOVA) on repeated measures was performed to
analyze the changes of PAM scores from baseline.
The significance level was set at p < 0.05. Pearson’s
correlation coefficient was used to assess the correla-
tion between average PAM scores and average time
use of the services: Videocall, Agenda, Shopping,
News&Events, Health.
Descriptive statistics on the usage of the proposed ser-
vices are shown in figures 2, 3 and 4.
News &
Average number of sessions
Figure 2: Average number of sessions for each service. Er-
ror bars represent one standard deviation.
Table 1 details the descriptive statistics for the
PAM scores in each phase of the assessment (T
, T
and T
). The repeated measures ANOVA shows no
significant differences between PAM raw scores from
baseline to 4 months, F
= 1.55, p = 0.24. This
is partially due to the limited trial duration and sam-
ple size. However, it is also worth noting that the
high baseline PAM levels among participants may
have determined a “ceiling effect” in the reported re-
sults. Furthermore, the aspecific nature of the inter-
vention, with respect to the participants’ health con-
dition, could have determined an underestimation of
the system usefulness, with a consequent limited im-
pact on the subject’s activation. However, a general
improvement in the mean values of PAM, despite the
absence of statistical significance, supports the cur-
rent search for eHealth solutions for patient empower-
ment, i.e., the delivery or enhancement of health care
HEALTHINF 2018 - 11th International Conference on Health Informatics
News &
Average total duration in hours
Figure 3: Average total duration in hours of all sessions.
Error bars represent one standard deviation.
News &
Average session duration in minutes
Figure 4: Average session duration in minutes for each ser-
Table 1: Comparisons of PAM scores (Mean and Standard
Deviation) obtained in the three phases of the study: T
, T
and T
Patient Activation Measure
43 (SD 4.9)
43.1 (SD 6.9)
45.2 (SD 4.4)
services or health care information through the Inter-
net and related technologies (Lettieri et al., 2015).
Table 2 displays the correlations between the
mean PAM scores and time use (minutes) of differ-
ent services. The correlations analysis showed a pos-
itive relationship between the use of the applications
Videocall and News&Events. There is some evidence
that participants with higher levels of activation in
managing health care have spent more time using
these two applications. According to these findings,
greater contact and interaction with friends and rela-
tives and higher interest in local news and city activi-
ties can be associated with higher patient activation.
The aim of the study was to assess the level of ac-
tivation of the elderly toward their care management
when using a multi-purpose TV-based assistive tech-
nology. An improved PAM score over time was ex-
pected, correlated with the use of the services embed-
ded in the system.
However, after 4 months, there was only a slight
improvement in PAM scores, not reaching statistically
significant values. Main limitations of the study were:
the short duration due to the project schedule, the re-
duced number of subjects limiting, among the other
things, any gender-specific analysis, the absence of
specific disease whose quantitative monitoring was
perceived by the elderly as worthy of attention. Due
to such study limitations, it is difficult to draw a fi-
nal conclusion on the PAM changes, but the overall
high scores at baseline may have limited the potential
increase in participants’ activation. Moreover, the as-
pecific nature of the intervention, with respect to the
users’ health condition, may have determined an un-
derestimation of the system usefulness, with a conse-
quent limited impact on the subject’s activation. This
makes the achieved results not comparable to those
of studies involving chronic patients (diabetes, heart
failure, etc.), where a telemonitoring is perceived as a
need primarily by the patients themselves.
Descriptive statistics on the usage time high-
lighted that Health was the service most used by par-
ticipants during the study, followed by News&Events,
Videocall, Shopping and Agenda. The Health ser-
vice result is particularly encouraging, taking into ac-
count that there was no supervision on the health data
by professional caregivers. Over the reported period,
participants spent on average a total of 1 hour and 42
minutes in self-monitoring their health data. This is
not a very long amount of time, but considering that
the time needed to complete a measurement session
ranged from 1 to 2 minutes, this result indicates that
on average each participant accessed the Health ser-
vice between two and three times per week. Such
regular self-monitoring of physiological parameters
gave participants some degree of responsibility for
their own care during the trial, and a more tangible
understanding of their health status. Results also re-
vealed that the more engaging services with longer
sessions were VideoCall, News&Events and Shop-
ping. The average amount of time a participant spent
in the VideoCall service during a single session was
about 7.5 minutes.
Impact of a TV-based Assistive Technology on Older People’s Ability to Self-manage Their Own Health
Table 2: Correlations matrix between variables.
Variables Health Videocall Agenda Shopping News&Events
PAM 0.23 0.60 (p <0.01) 0.25 0.17 0.43
Health 0.17 0.26 0.40 0.18
Videocall 0.20 0.16 0.32
Agenda 0.02 0.18
Shopping 0.04
In this paper, the results of a 4-month pilot study, in
which a TV-based system and a set of home-care ser-
vices were introduced into the daily life of 19 older
adults, were presented. Assessment was carried out
with the objective of evaluating how a digital system
developed specifically for older people changes the
PAM scores, and to understand how the PAM scores
are related to the system usage. The approach, and
the achived results, are important for the development
of similar projects, enabling to address at the time of
the study design some criticalities. Results reveal a
non-intensive use of the system and negligible corre-
lation between the access to the health service provide
within the platform and the PAM score.
In the light of presented results, future investiga-
tions will be performed on people living with specific
chronic conditions, including asthma, diabetes, and
hypertension. These patients must have an active role
in making decisions about their health, under the su-
pervision of the family doctor or the specialist. To
avoid a ceiling effect, patients whose scores at base-
line are already fairly high will be excluded from the
This work is part of a project financed by the AAL
Programme (AAL-2012-5-064). The authors would
like to thank all the partners involved in the project
for their contribution, and the elderly citizens of the
Municipality of Cagliari who took part in the trial, for
their time and willingness to complete questionnaires
over the lifetime of this study.
Anderson, K., Burford, O., and Emmerton, L. (2016). Mo-
bile health apps to facilitate self-care: A qualitative
study of user experiences. Public Library of Science
One, 11(5):e0156164.
Costa, C., Anido-Rifon, L., and Fernandez-Iglesias, M.
(2017). An open architecture to support social and
health services in a smart tv environment. IEEE Jour-
nal of Biomedical and Health Informatics, 21(2):549–
Greene, J. and Hibbard, J. (2012). Why does patient acti-
vation matter? an examination of the relationships be-
tween patient activation and health-related outcomes.
Journal of General Internal Medicine, 27(5):520–6.
Hibbard, J., Mahoney, E., Stockard, J., and Tusler, M.
(2005). Development and testing of a short form of
the patient activation measure. Health Services Re-
search, 40(6 Pt 1):1918–1930.
Hibbard, J., Stockard, J., Mahoney, E., and Tusler, M.
(2004). Development of the patient activation mea-
sure (pam): conceptualizing and measuring activation
in patients and consumers. Health Services Research,
39(4 Pt 1):1005–1026.
Johnson, M., Zimmerman, L., Welch, J., Hertzog, M.,
Pozehl, B., and Plumb, T. (2015). Patient acti-
vation with knowledge, self-management and confi-
dence in chronic kidney disease. Journal of Renal
Care, 42(1):15–22.
Kim, J., Wineinger, N., and Steinhubl, S. (2016). The influ-
ence of wireless self-monitoring program on the rela-
tionship between patient activation and health behav-
iors, medication adherence, and blood pressure levels
in hypertensive patients: A substudy of a randomized
controlled trial. Journal of Medical Internet Research,
Lettieri, E., Fumagalli, L., Radaelli, G., Bertele, P., Vogt,
J., Hammerschmidt, R., Lara, J. L., Carriazo, A., and
Masella, C. (2015). Empowering patients through
ehealth: a case report of a pan-european project. BMC
Health Services Research, 15:309.
Macis, S., Loi, D., and Raffo, L. (2017). The HEREiAM
tele-social-care platform for collaborative manage-
ment of independent living. In Proceedings of the
International Conference on Collaboration Technolo-
gies and Systems, CTS ’16. IEEE.
Morrison, L., Hargood, C., Lin, S., Dennison, L., Joseph, J.,
Hughes, S., Michaelides, D., Johnston, D., Johnston,
M., Michie, S., Little, P., Smith, P., Weal, M., and
Yardley, L. (2014). Understanding usage of a hybrid
website and smartphone app for weight management:
a mixed-methods study. Journal of Medical Internet
Research, 16(10):e201.
Raffaeli, L., Gambi, E., and Spinsante, S. (2014). Smart
tv based ecosystem for personal e-health services.
HEALTHINF 2018 - 11th International Conference on Health Informatics
In Proceedings of the 8th International Symposium
on Medical Information and Communication Technol-
ogy, (ISMICT). IEEE.
Whitehead, L. and Seaton, P. (2016). The effective-
ness of self-management mobile phone and tablet
apps in long-term condition management: A system-
atic review. Journal of Medical Internet Research,
Young, L., Kupzyk, K., and Barnason, S. (2017). The im-
pact of self-management knowledge and support on
the relationships among self-efficacy, patient activa-
tion, and self-management in rural patients with heart
failure. Journal of Cardiovascular Nursing, 32(4):E1–
Impact of a TV-based Assistive Technology on Older People’s Ability to Self-manage Their Own Health