33
Self-Care Technologies in HCI: Trends, Tensions, and Opportunities
FRANCISCO NUNES, Vienna University of Technology
NERVO VERDEZOTO, Aarhus University
GERALDINE FITZPATRICK, Vienna University of Technology
MORTEN KYNG, Aarhus University and The Alexandra Institute
ERIK GR
¨
ONVALL, IT University of Copenhagen
CRISTIANO STORNI, University of Limerick
Many studies show that self-care technologies can support patients with chronic conditions and their carers
in understanding the ill body and increasing control of their condition. However, many of these studies
have largely privileged a medical perspective and thus overlooked how patients and carers integrate self-
care into their daily lives and mediate their conditions through technology. In this review, we focus on how
patients and carers use and experience self-care technology through a Human-Computer Interaction (HCI)
lens. We analyse studies of self-care published in key HCI journals and conferences using the Grounded
Theory Literature Review (GTLR) method and identify research trends and design tensions. We then draw
out opportunities for advancing HCI research in self-care, namely, focusing further on patients’ everyday life
experience, considering existing collaborations in self-care, and increasing the influence on medical research
and practice around self-care technology.
CCS Concepts:
r
Applied computing → Consumer health;
r
Human-centered computing →
Interaction design process and methods; HCI theory, concepts and models;
Additional Key Words and Phrases: Chronic conditions, chronic diseases, home monitoring, home care,
pervasive health, self-care, self-care design, self-care technology, self-management, telecare, telehealth,
literature review
ACM Reference Format:
Francisco Nunes, Nervo Verdezoto, Geraldine Fitzpatrick, Morten Kyng, Erik Gr
¨
onvall, and Cristiano Storni.
2015. Self-care technologies in HCI: Trends, tensions, and opportunities. ACM Trans. Comput.-Hum. Inter-
act. 22, 6, Article 33 (December 2015), 45 pages.
DOI: http://dx.doi.org/10.1145/2803173
1. INTRODUCTION
Current generations enjoy living longer lives than ever before due to advancements in
medicine, technology, and social structures that occurred in the last decades. Although
F. Nunes was supported by the Vienna PhD School of Informatics (www.informatik.tuwien.ac.at/teaching/
phdschool). N. Verdezoto was partly supported by the Lev Vel Consortium (funded by the Danish Council for
Technology and Innovation and The Capital Region of Denmark).
Authors’ addresses: F. Nunes (corresponding author) and G. Fitzpatrick, Institute for Design and Assess-
ment of Technology, Vienna University of Technology (TU Wien), Argentinierstrasse 8, 2. Stock, 1040 Vienna,
Austria; email: francisco.nunes@igw.tuwien.ac.at; N. Verdezoto, Department of Computer Science, Aarhus
University, Aabogade 34 D, 8200 Aarhus N, Denmark; M. Kyng, Department of Computer Science, Aarhus
University and The Alexandra Institute, Aabogade 34 D, 8200 Aarhus N, Denmark; E. Gr
¨
onvall, IT Univer-
sity of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen S, Denmark; C. Storni, Interaction Design
Centre, Computer Science Building CS2-032, Department of Computer Science and Information Systems,
University of Limerick, Limerick, Ireland.
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DOI: http://dx.doi.org/10.1145/2803173
ACM Transactions on Computer-Human Interaction, Vol. 22, No. 6, Article 33, Publication date: December 2015.
33:2 F. Nunes et al.
the increase in life expectancy is a great achievement of our time, it is also related to
a greater prevalence of chronic conditions [Ben-Shlomo and Kuh 2002; IHME 2013]. A
chronic condition, or chronic disease, is a long-lasting disorder that, although maybe
controllable with care interventions, cannot be cured. Thus, the goal of chronic disease
care is to promote independence and quality of life for as long as possible [Holman and
Lorig 2000]. Chronic conditions are likely to affect the lives of patients
1
[Wagner et al.
2001], challenging them to deal with symptoms, face some sort of disability, deal with
the emotional impact, cope with complex medication schemes, perform radical lifestyle
changes, and obtain useful medical advice from clinicians. These elements of living with
a chronic condition largely require patients to manage their disease by themselves—
through self-care. Self-care refers to the ability of individuals to manage symptoms,
treatment, emotions, and lifestyle changes as part of living with a chronic condition
[Barlow et al. 2002]. The goal of self-care is not to learn facts about a condition but
to influence its course through practical everyday actions, maintaining a satisfactory
quality of life for as long as possible [Clark et al. 1991; Barlow et al. 2002]. But, self-care
does not imply that patients manage their conditions in isolation, as they are likely
to collaborate with informal carers in their day-to-day self-care activities. Clinicians
are also likely to collaborate with patients’ self-care by, for example, providing advice
during consultations.
In addition, technology can greatly support patients in their self-care management.
A common self-care device is, for instance, the blood glucose meter that enables people
with diabetes to monitor their glucose level and thus potentially control their condition.
Self-care technologies are not restricted to medical devices and can materialise as
online communities for discussion of issues related to living with a condition [Mo and
Coulson 2010], web applications for obtaining feedback from clinicians [Mamykina
et al. 2008], or smartphone applications for monitoring disease symptoms [Bardram
et al. 2013]. Despite this widespread interest in self-care technology, no review in HCI
has focused on the use of self-care technologies for chronic disease management.
Previous work in medical informatics has reviewed technology for chronic condition
management (e.g., Par
´
e et al. [2007] and El-Gayar et al. [2013]). These reviews pro-
vided an understanding of the existing technological solutions in medical informatics,
together with an analysis of their health impact. In many cases, though, the reviews
disregarded the evaluation of technologies from the perspective of the everyday life of
patients [Koch 2006]. In HCI, reviews are not very common, but some prior work has
reviewed technology for chronic conditions (e.g., Tentori et al. [2012] and Kientz et al.
[2013]), taking a different perspective than the ones from medical informatics. In these
reviews, the main goal was to describe the functionalities and interaction patterns
promoted by the technology. In Computer-Supported Cooperative Work (CSCW) litera-
ture, Fitzpatrick and Ellingsen [2013] conducted a large review of healthcare research
articles, pointing to the growth in self-care technologies, and argued for the need to
further research in the area.
On the anniversary of 30 years of the CHI (Human Factors in Computing Systems)
conference (1983–2013), we reviewed HCI research on technologies for self-care, as
documented in its key journals and conferences. We focused on papers that discuss
the use of technology, targeted at chronic care, for patients and informal carers. And
we followed the Grounded Theory Literature Review (GTLR) method [Wolfswinkel
et al. 2013] to map the landscape and identify directions for future research. The
1
In this article, we define patients as people that have at least one chronic condition. We recognise that
having the chronic condition does not define a person. Being a patient is just one of the many roles (mother,
teacher, etc.) a person with a chronic condition encounters in her or his everyday life. However, for clarity,
we use the word patient in the text.
ACM Transactions on Computer-Human Interaction, Vol. 22, No. 6, Article 33, Publication date: December 2015.
Self-Care Technologies in HCI: Trends, Tensions, and Opportunities 33:3
technologies that appear in the review are organised according to the categories that
emerged from the analysis of the identified papers, namely opportunities explored, the
technical systems employed, the different configurations of users and roles supported,
and methods used as part of their research.
In addition, six tensions emerged from the analysis, namely: six tensions emerged,
namely: autonomy, appropriation, choosing strategy, choosing devices, game-based ap-
proaches, and visualising chronic condition data. These tensions foreground critical
issues of designing for the self-care setting and are presented as a tool “to think with,”
especially useful for young practitioners. Based on the insights from the review, we
suggest key opportunities to advance HCI research in self-care technologies, namely,
focus further on patients’ everyday life experience, consider the existing collaborations
in self-care, and increase the impact of more medical self-care technology.
The aim of this review is not to show a complete
2
picture of the work done on
self-care technologies but to complement the existing body of work. As the number
of patients using self-care technologies is growing, it becomes increasingly relevant
to study how technologies shape the ways they manage their conditions. With this
in mind, we focused on HCI studies that discuss the influence and role of technology
in managing chronic care. Our work should be useful for researchers, designers, and
practitioners who are involved in the conceptualisation, design, and evaluation of self-
care technologies by providing them with an overview of the related work in HCI,
implications for design, and current challenges. This review represents an additional
step towards achieving a more complete picture of research in self-care technologies.
As reviews from medical informatics and HCI are becoming available, future work may
move towards a more complete meta-review spanning work in the different areas.
In the following sections, we provide details of the methodology of the review (Section
2) and an overview of the papers included in the review (Section 3). Then, we describe
trends identified in the reviewed papers (Section 4) and present a number of design
tensions to consider in the design of self-care technology (Section 5). We discuss and
reflect on the overall methodological approach and conclude by presenting a set of
unexplored opportunities for future work (Section 6).
2. METHODS
Our review followed the GTLR method [Wolfswinkel et al. 2013]. This method adopts
a Grounded Theory [Glaser and Strauss 1967] approach to add rigour to the process
of searching, selecting, and analysing studies in a review. GTLR uses the content
from the papers as empirical material that is coded and constantly compared, thus
grounding the insights of the review. This method is composed of five orienting stages,
namely, (i) defining the scope of the review (inclusion and exclusion criteria, sources
of information, search terms); (ii) search for the potential papers; (iii) selection of the
papers for the review (filtering, refine sample based on title and abstract); (iv) in-depth
analysis of the papers (through different coding levels); and (v) present the emerging
categories from the papers. This method is not “prescriptive,” but rather informative,
providing suggestions on how to conduct the different phases of a review to ensure it
is grounded in the selected papers.
In this section, we describe the setup of our literature review, specifically the focus,
index databases and search keywords used, the filtering and selection process, and the
analysis process. We also position our own stance towards self-care technologies and
our epistemological stance.
2
Obtaining a complete picture is never possible with a review, as choices always constrain and direct the
research in specific ways [Wolfswinkel et al. 2013].
ACM Transactions on Computer-Human Interaction, Vol. 22, No. 6, Article 33, Publication date: December 2015.
33:4 F. Nunes et al.
2.1. Focus of the Review
We defined the focus of the review as the literature that discussed the use of a specific
technology, targeted at the care of chronic conditions, by patients and informal carers.
This focus enabled us to concentrate on how technologies are used for self-care, but
also meant that a number of potentially related studies were deemed out of scope. In
this regard, we make three notes.
First, the review was concerned with the use of technology in self-care practices.
This excluded a number of papers that would otherwise be featured in the review,
such as those contextualising the design space of caring for a chronic condition (e.g.,
Barnes et al. [2013]), those suggesting design concepts without evaluating them (e.g.,
Lee et al. [2010]), and those describing algorithms or software architectures to solve
specific self-care problems (e.g., Schaeffer-Filho et al. [2009]). These types of studies are
very relevant, but they were not about technology in use and, therefore, were excluded
from the review. By drawing on the insights from the use of technology, we hope to
uncover experienced-based design opportunities, tensions, and consequences.
Second, the focus was on the self-care of chronic conditions. This excluded self-
management technologies focusing on ageing in place (e.g., Gr
¨
onvall and Verdezoto
[2013b]), wellness (e.g., Grimes et al. [2010]), or quantifying habits for health (e.g.,
Consolvo et al. [2008]). Therapy was only included if it was performed in the context of
living with a chronic condition, but not when it was focused on recovering functionality
(e.g., as in poststroke rehabilitation [Balaam et al. 2011]). By keeping the focus on
chronic conditions, the motivation for using the technology was to perform care, and
not pursuing personal interest, leisure, general well-being, or recovery of function,
which would likely bring different principles for design and use.
Third, the focus was on self-care, active role of patients living with chronic conditions
and their (potential) informal carers. This excluded a number of technologies for clinical
settings in which patients have a (more) passive role (e.g., Bardram et al. [2005]).
Excluding these studies enabled us to focus on the lived experience rather than the
medical view of self-care.
2.2. Sources of Information—Index Databases
The papers in the review were collated in October 2013, primarily using the HCI
Bibliography search engine.
3
HCI Bibliography was selected for its broad coverage of
literature in HCI, including the key conferences and journals, according to the Mi-
crosoft Academic Ranking.
4
This includes, for example, CHI, CSCW, Ubicomp, and
ECSCW conferences,
5
as well as IJHCS, TOCHI, JCSCW journals.
6
The IEEE Xplore
7
was also used for accessing the Pervasive Health conference proceedings. The Pervasive
Health proceedings were included due to its specific focus on technologies for health-
care, including technologies for chronic care, and because of its close relationship to
HCI/technology design concerns as indicated by the number of HCI/CSCW researchers
who also publish in this venue and its IEEE affiliation (the first edition was also spon-
sored by ACM/SIGCHI). Not including this conference would provide a narrower view
3
HCI Bibliography is a search engine for publications in the area of Human-Computer Interaction and
related fields. The service is available at http://hcibib.org.
4
Microsoft Academic top conferences and journals: http://academic.research.microsoft.com/RankList?
entitytype=4\&topDomainID=2\&subDomainID=12 and http://academic.research.microsoft.com/RankList?
entitytype=3\&topDomainID=2\&subDomainID=12.
5
Refer to footnote 17 for the meaning of the different conference acronyms.
6
Complete journal names: IJHCS—International Journal of Human-Computer Studies; TOCHI—ACM
Transactions on Computer-Human Interaction; JCSCW—Computer Supported Cooperative Work.
7
IEEE Xplore is a search engine for publications of the IEEE association. The service is available at
http://ieeexplore.ieee.org/.
ACM Transactions on Computer-Human Interaction, Vol. 22, No. 6, Article 33, Publication date: December 2015.
Self-Care Technologies in HCI: Trends, Tensions, and Opportunities 33:5
Table I. Number of Papers Retrieved with the Different Keywords
Total 1,022 entries, reduced to 795 after removing duplications (see Table II).
chronic disease management 37 self-care 28 telehealth 31
home care 296 self-manage* 68 telecare 31
home monitoring 226 self-monitor* 47 telemonitoring 8
patient-cent* 27 self-reflection 30 pervasive health
11
193
Table II. Papers in the Search Set, Preselection, and Fitting the Selection Criteria
HCI bibliography Pervasive healthcare Total
Search set (unique and filtered) 638 157 795
Included for preselection 64 20 84
Fitting the selection criteria 25 4 29
of the research that is done in the HCI community. We had also considered includ-
ing other conferences or journals (e.g., ICHI,
8
JAMIA
9
), and other index databases
(e.g., PubMed). However, for this review, we wanted to keep the focus on how the HCI
community is engaging with the theme of self-care technologies.
10
The earlier-mentioned sources returned results based on title and abstract search.
2.3. Search Terms
Through multiple sessions among the authors, who have all worked with healthcare
IT for years, we selected 12 keywords (see Table I) that we considered most relevant
to identify papers of interest, based on studies we were familiar with. As our set of
keywords evolved, we made sure they followed three main criteria. The first one was to
ensure that both recent (e.g., self-care) and older studies (e.g., home care) were included
by using keywords that were popular in different periods. The second was that more
medically oriented work would also be included (e.g., using the terminology of “patient”
in patient-cent*). And the third was that more technically oriented work would be
accounted for (e.g., pervasive health*). Some of the keywords include an asterisk to
obtain all possible results that start with a specific search keyword entered (e.g., self-
manage*, self-manage, self-management). The search results were transformed to a
spreadsheet for analysis. Using the current keywords and the earlier-mentioned index
databases, we obtained papers in different areas of self-care that capture a range of
conditions (see Table VI).
2.4. Selection Criteria
Each entry in the spreadsheet included the title, abstract, DOI or URL, publication
year, publication venue, and keywords that retrieved it. Using this information, the
first two authors preselected the papers to be considered for the review
12
(see Table II).
The first step was to clean the dataset. In this process, we removed entries concerning
workshop, panel, or talk abstracts, as well as proceedings index and title pages that
only contained keywords and outlines. We also joined repeated entries of the same
8
ICHI stands for the IEEE International Conference on Healthcare Informatics.
9
JAMIA stands for the Journal of the American Medical Informatics Association.
10
We recognise that many HCI researchers are increasingly choosing to publish in both HCI and medical
informatics forums, which we applaud but scope the review here to the HCI venues.
11
The keyword pervasive health was only searched in HCI Bibliography database, as it would be too present
in papers of Pervasive Health conference.
12
In the auxiliary materials of this article, the reader can find a spreadsheet with the papers that were
collated, preselected for reading, and selected to be part of the review.
ACM Transactions on Computer-Human Interaction, Vol. 22, No. 6, Article 33, Publication date: December 2015.
![Fig. 6. Using TiY [Storni 2011], patients were able to reflect about their condition in different ways, which enabled them actively to prepare for their clinical appointments, c©Cristiano Storni.](/figures/fig-6-using-tiy-storni-2011-patients-were-able-to-reflect-3d9yjdbw.webp)
![Fig. 7. MAHI [Mamykina et al. 2008] enabled an alternative way of communicating with clinicians, which provides regular feedback, c©ACM.](/figures/fig-7-mahi-mamykina-et-al-2008-enabled-an-alternative-way-of-17wwevxz.webp)