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Abstract—mHealth (healthcare using mobile wireless
technologies) has the potential to improve healthcare and
the quality of life for elderly and chronic patients. Many
studies from all over the world have addressed this issue in
view of the aging population in many countries. However,
there has been a lack of any consolidated evidence-based
study to classify mHealth from the dual perspectives of
healthcare and technology. This paper reports the results
of an evidence-based study of mHealth solutions for
chronic care amongst the elderly population and proposes
a taxonomy of a broad range of mHealth solutions from
the perspective of technological complexity. A systematic
literature review was conducted over 10 online databases
and the findings were classified into four categories of
predominant mHealth solutions, that is, self-healthcare,
assisted healthcare, supervised healthcare and continuous
monitoring. The findings of the study have major
implications for information management and policy
development in the context of the Millennium
Development Goals (MDGs) related to healthcare in the
world.
Index Terms—chronic, elderly, IT artifact, mobile
health, taxonomy, technologies, ubiquitous health.
I. INTRODUCTION
health (healthcare using mobile wireless
technologies, also called mobile health
technologies) has the potential to transform the
healthcare system in aging societies by opening up
novel opportunities for global access to health services
and medical care for chronic diseases.
Manuscript received April 16, 2012.
Giovanni Chiarini and Cristina Masella are with the Dept. of
Management, Economics and Industrial Engineering (DIG),
Politecnico di Milano, Italy (e-mail: giovanni.chiarini@mail.polimi.it).
Pradeep Ray is with the School of Information Systems, University
of New South Wales, NSW 2052, Australia (e-mail:
p.ray@unsw.edu.au).
Shahriar Akter is with the School of Management & Marketing,
University of Wollongong, Australia (e-mail: skater@uow.edu.au).
Aura Ganz is with the Electrical & Computer Engineering
Department, University of Massachusetts (e-mail:
ganz@ecs.umass.edu).
According to the United Nation’s 2009 World
Population Ageing report, the number of people aged 60
years or over was 600 million in 2000, a tripling of what
it was in 1950, and over the span of the next 40 years,
this number is projected to triple once again, taking the
count to 2 billion. Furthermore, the average age of
people over 60 is increasing: currently, one in every
seven people in this age group is 80 years or above and
by 2050, one in five will be 80 or over, with nearly four-
fifths of them living in less developed regions [1].
Additionally, the total number of persons globally who
report a long-standing health problem or disability is
860 million, with NCDs (i.e. non-communicable
diseases, such as, cardiovascular diseases and diabetes,
cancers and chronic respiratory diseases) still the
leading cause of death in the world [2]. In this context,
mobile health technologies are playing an instrumental
role in serving patients by making healthcare more
affordable, accessible and available. The ITU report [3]
shows that at the end of 2009, there were approximately
4.6 billion mobile cellular subscriptions, with the
average penetration rate, in developed countries, of
above 100%. Moreover, the latest generation of
smartphones are increasingly viewed as handheld
computers rather than as phones, due to their powerful
on-board computing capability, capacious memories,
large screens and open operating systems that encourage
application development [4]. Therefore, it is clear that
the potential for mobile technologies to transform
healthcare and clinical intervention in the community is
tremendous (between $1.96 billion and $5.83 billion in
saved healthcare costs worldwide by 2014 [5])
especially in assisting elders and people with chronic
conditions to live independently. In fact, in a recent
Price Waterhouse Cooper report, the global mobile
health market is expected to reach US$23 billion by
2017. Among the various categories, monitoring
services will account for the largest share globally
(approximately 65%), and they will be driven primarily
by solutions that aid chronic disease management
(US$10.7 billion) and independent aging (US$4.3
billion), with revenues accruing from both developed
countries and large developing countries, such as China
and India [6].
mHealth Technologies for Chronic Diseases and
Elders: A Systematic Review
Giovanni Chiarini, Pradeep Ray, Senior Member, IEEE, Shahriar Akter, Cristina Masella and
Aura Ganz, Fellow, IEEE
M
Currently, the key stakeholders–mobile operators,
device vendors, healthcare providers, content players,
foundations and governments–have already launched
several mHealth services and applications worldwide
[6] and, at the moment, the GSMA tracker [7] reports
more than 300 commercial deployments globally. In
particular, developments in new mHealth solutions and
technologies specifically for the elderly are steadily
proliferating [8–10] and to date, they have targeted a
wide range of applications such as: medication
adherence, vital signs’ monitoring, activity monitoring
and alert systems, wellness and rehabilitation, remote
consultation, and solutions for caregivers [11].
However, at the moment, the most successful
smartphone applications (apps) are generally targeted
only to younger and healthier populations [4], while the
solutions for seniors face resistance due usually to
preconceptions about cost, lack of awareness about what
is available, and caution about sharing personal health
information [11]. In fact, the higher adoption rate of
smartphones by older people and people with chronic
disease will depend on cost, easy to use apps, awareness
and the type of technology [4]. It is noteworthy that
technologies differ on a broad scope and scale, ranging
from simple “stand-alone” direct-to-individual
smartphone applications to a more complex mobile-
based system, enabling continuous interactions amongst
patients, caregivers and clinicians anytime and
anywhere. As a result, the increasing number of
applications, the variety of technologies, and newly
introduced terminology (e.g. mHealth; u-health;
wireless health [12]; m-IoT [13] etc.) make it difficult to
understand these solutions under an hierarchy of IT
artifacts [14].
The aim of this paper is primarily to propose a
taxonomy of different categories of mobile platforms
currently implemented in this area through a systematic
review of experiences reported in the literature in the
last five years. We believe that the taxonomy can
represent an information management strategy to
improve knowledge sharing, facilitate policy initiatives,
and provide some guidance for the orderly development
of new mobile health solutions for the elderly [15].
Furthermore, for practitioners and managers, the
systematic review helps in developing a reliable
evidence base by providing collective insights through
theoretical synthesis [16].
II. R
ESEARCH METHOD
A systematic literature review is a means of
identifying, evaluating and interpreting all available
research relevant to a particular research question, or
topic area, or phenomenon of interest [17]. Although
this rigorous evidence-based approach has been used
especially in medical science research, the movement to
base practice on the best available evidence has
migrated from medicine to other disciplines [16]. In this
study, the steps used to perform the systematic review
are based on the original guidelines proposed by
Kitchenham [17], [18], for software engineering
research combined with the systematic review process
applied in the management field [16]. In particular, the
phases undertaken are as follows:
- Planning the review (Section III)
- Conducting the review (Section IV)
- Reporting the review (Section V).
III. P
LANNING THE REVIEW
In order to determine the most appropriate search
strategy, an initial scoping study was conducted, and the
outcomes of this process were discussed with other
researchers and captured in a review protocol with
explicit descriptions of the methods used and the steps
to be taken. A pre-defined protocol is often necessary to
reduce the possibility of researcher bias [18]. The main
information about the search strategy contained in the
protocol were: (1) the most appropriate search terms
identified, (2) the resources to be searched (including
databases, specific journals, and conference
proceedings), and (3) the criteria for inclusion/exclusion
of studies in the review.
A. Searched terms
In order to identify the most appropriate search terms,
we adapted the experimental findings proposed by
Dieste et al. [19] concerning the development of an
optimum search strategy. Taking the objective of this
review to survey the largest possible number of
empirical mHealth solutions, the term “mobile health”
was searched
1
, and in each of the first 100 results, all
the terms related to “mobile health” were identified.
Based on the most recurring terms retrieved,
“application”, “system”, “device” and “sensor” were
finally considered. Intentionally, due to our objective to
review every possible type of mobile-based platform,
we did not use terms referring only to a specific
category of mobile technologies (e.g. PDAs, tablets, cell
phones, smartphones, etc.). Similarly, in the effort to be
comprehensive, we also considered all the possible
1
The database used for all the trial pilot searches was Google
Scholar, considering publications in which the keyword occurs
“anywhere in the article”, written in English, between 2008 and 2012
and in the field of ‘‘Engineering, Computer Science, and
Mathematics.’’
3
abbreviations, alternative spellings, and combinations of
terms usually related to the meaning of “mobile health”
and extracted from the scoping study, the literature and
discussions with other researchers. Afterwards, we
ranked this list of terms, selecting those words that
maximized the sensitivity rate (estimated by the total
number of articles retrieved
1
with such keywords, see
Table 2). Finally the terms “chronic” and “elderly” were
added with the expectation that publications relating to
these categories of patients would contain these terms at
least once in the full text. To summarize, depending on
the search services offered by each selected search
engine, the full text of the journal articles and
conference proceedings were searched using the
following search strings:
chronic AND (application OR system OR
device OR sensor) AND (“pervasive
healthcare” OR “mobile health” OR “m-
health” OR “wireless health” OR “pervasive
health” OR “mobile healthcare” OR
“ubiquitous healthcare” OR “wearable
health”)
elderly AND (application OR system OR
device OR sensor) AND (“pervasive
healthcare” OR “mobile health” OR “m-
health” OR “wireless health” OR “pervasive
health” OR “mobile healthcare” OR
“ubiquitous healthcare” OR “wearable
health”)
B. Resources searched
The journals and conference proceedings published in
English between 2008 and 2012 were searched with the
keywords noted in the previous section using 10 online
databases: SpringerLink; ScienceDirect; Wiley
InterScience; Liebert Online; Journal of Telemedicine
and Telecare; Scirus; IEEE Xplore; ACM Digital
Keywords
Articles
retrieved:
Total
citations:
Years
range
pervasive healthcare
1040
3718
5
mobile health
908
2090
5
m-health
718
2044
5
wireless health
683
1537
5
pervasive health
679
2818
5
mobile healthcare
667
1668
5
ubiquitous healthcare
591
1410
5
ubiquitous health
507
1358
5
wearable health
418
1364
5
mobile telemedicine
311
894
5
u-healthcare
298
506
5
wireless healthcare
249
596
5
wireless telemedicine
245
856
5
u-health
237
270
5
mhealth
175
495
5
wearable healthcare
174
818
5
uhealthcare
12
16
5
mhealthcare
8
23
3
uhealth 6 11 5
TABLE 1
- SETTINGS USED FOR SEARCHES ON ONLINE DATABASES AND THE ARTICLES FOUND.
Database Subjects Field Document Type
Numbers of
non- repeated
articles
Number of
repeated articles
SpringerLink
Engineering, Computer Science
Full Text
Journal Articles
34
-
ScienceDirect
Computer Science, Decision Science, Eng.
Full Text
Journals
61
-
Wiley InterScience
ALL
Full Text
Journals
205
-
Liebert Online
Engineering/Informatics
ALL Fields
Journals
44
-
J. of Telemedicine
ALL
Full Text
Articles
8
-
Scirus ALL
Title,
Keywords
Articles,
Conferences
34
15 from the above
databases
IEEE Xplore
Computing & Processing - Components,
Circuits, Devices & Systems - Communication,
Networking & Broadcasting - Bioengineering
Title,
Keywords,
Abstract
Journals,
Conferences
487
8 from the above
databases
ACM Digital
Library
ALL ALL Fields
Journals,
Proceedings
140
8 from the above
databases
CiteSeer ALL Full Text ALL 13
4 from the above
databases
Google Scholar
Engineering, Computer Science, and
Mathematics
Full Text ALL 1395
331 from the
above databases
Total
2421
366
Number of non-repeated papers found 2055
TABLE 2 - KEYWORDS SELECTION STUDY.
