Internet of Health Things for Quality of Life:
Open Challenges based on a Systematic Literature Mapping
Pedro Almir M. Oliveira
1 a
, Rossana M. C. Andrade
1 b
Pedro A. Santos Neto
2 c
and Breno S. Oliveira
1 d
Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Cear
a, Cear
a, Brazil
Laboratory of Software Optimization and Testing (LOST), Federal University of Piau
ı, Piau
ı, Brazil
eHealth, Quality of Life, Internet of Health Things, Systematic Mapping.
Internet of Health Things (IoHT) related papers has produced valuable knowledge concerning applications
such as monitoring vital signs and predicting diseases. However, this knowledge is dispersed in the literature
and, to the best of our knowledge, we could not find a recent study summarizing it. Thus, this work presents
a systematic mapping conducted to organize the challenges regarding IoHT applied to QoL. As a result, we
highlight a growing interest in developing health monitoring tools, but without many real-world validations.
The most mentioned challenges were well-known IoT challenges, security and privacy, data science, and
networks. Moreover, despite many studies discussing proposals for improving QoL, few papers sought to
measure this gain, and none addressed the semantic organization of QoL data obtained from smart objects.
Finally, it is expected for future a strengthening regarding elderly healthcare solutions, data science usage for
personalized systems; smart models to predict health problems; and QoL continuous monitoring.
Quality of Life can be defined as the perception of
life in a sociocultural context, concerning goals and
personal standards (WHOQoL Group, 1994). World
Health Organization (WHO) states that it is crucial
to measure the QoL because it has a close relation-
ship with the health status, and it provides valuable
data to medical practice (WHO, 1998). However, de-
spite the expressive number of initiatives to improve
the citizens’ QoL, there is still room for opportunities,
especially regarding the continuous measurement of
these data and strategies for adapting the environment
to improve the QoL (Oliveira et al., 2021).
Thus, due to the need for solutions that provide
broad access to healthcare and even more accurate
monitoring methods, the Internet of Things (IoT)
has been applied in healthcare (Islam et al., 2015).
The IoT enables interaction among physical things
through the Internet to achieve common goals. Thus,
the Internet of Health Things (IoHT) emerges from
the application of IoT in healthcare (Rodrigues et al.,
2018). As IoHT examples, there are non-invasive glu-
cose sensing, electrocardiogram monitoring, oxygen
saturation monitoring, medication management, and
elderly fall detection (Araujo et al., 2020).
The previously mentioned studies represent a tiny
snapshot of IoHT applications. Thus, considering the
studies published in this area, valuable knowledge is
spread in the IoHT literature. Due to this, many re-
views have been published to summarize it.
However, despite the high interest in this area, it
was not found a recent study that presents a compre-
hensive picture of the IoHT applied for the Quality
of Life. Some reviews mention the QoL term, but
they are not focused on this topic. Also, the works
published in this area bring a common idea that tech-
nologies such as IoT or Machine Learning improve
the users’ Quality of Life. Nevertheless, few studies
present measures that corroborate this idea or strate-
gies to monitor the QoL variation over time.
Therefore, in this work, we conducted a Sys-
tematic Literature Mapping (SLM) to summarize the
challenges in this area. Our main contribution is to
present a systematic map of the IoHT literature ap-
plied on Quality of Life, grouping the results and
identifying gaps that can be further investigated.
Oliveira, P., Andrade, R., Neto, P. and Oliveira, B.
Internet of Health Things for Quality of Life: Open Challenges based on a Systematic Literature Mapping.
DOI: 10.5220/0010812400003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 397-404
ISBN: 978-989-758-552-4; ISSN: 2184-4305
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The process of this study follows the guidelines pro-
posed by (Petersen et al., 2008). In addition, to sup-
port the replicability and expansion of this study, all
artifacts and raw data can be publicly accessed (see
our Data Availability section).
2.1 Research Questions
This review’s need relies on the increasing interest in
IoHT and the absence of studies focused on the sys-
tematization of IoHT applied in Quality of Life. Thus,
three RQ were defined: RQ1 What is the context of
the papers published in the QoL-related IoHT litera-
ture? RQ2 What are the challenges related to IoHT
for QoL? RQ3 What is the evidence that IoHT can
monitor and improve people’s Quality of Life?
2.2 Search Strategy
Four scientific databases were chosen based on their
representativeness: Scopus, Web of Science (WoS),
Compendex, and PubMed.
This work applied the PICO methodology (Pai
et al., 2004) to elaborate the search string due to its
wide acceptance in the literature. According to PICO,
the search string must be composed of four parts:
Population, Intervention, Comparison, and Outcome.
Then, the Population was described as “IoT papers”,
the Intervention as “Quality of Life”, and the Out-
come was defined as “Challenges”.
2.3 Eligibility Criteria
This work included papers that discuss IoHT solu-
tions, challenges, or open questions focused on Qual-
ity of Life. In contrast, we excluded papers that: i)
do not present an IoHT system focused on Quality of
Life; ii) be available only in the form of abstract; iii)
do not be written in English; iv) be a short paper; v)
do not be available on the Web; vi) not published in a
workshop, conference, or journal.
2.4 Study Selection
Two researchers performed the study selection in
three steps. The major concern here is to mitigate the
researcher’s bias during this process (Wohlin et al.,
2012). Thus, it was executed the following steps:
1. Read the Title and Abstract: the studies were
selected by the reading of titles and abstracts. In
this step, an agreement analysis was conducted
considering 10% of the papers. This agreement is
essential to check the protocol’s consistency. The
agreement level achieved was considered good
with a Kappa value of 0.8.
2. Full Reading: a full reading step was conducted
to verify the relevance to answer our questions.
3. Final Review: responsible for resolving doubts
and reviewing the selection process.
2.5 Data Extraction
Data extraction included many fields, such as goals,
research questions, and challenges. Also, topic mod-
eling was performed to cluster the studies.
Regarding topic modeling, it is widely used to get
insights into textual data. In this work, we adopted
one of the most used algorithms called LDA (Blei
et al., 2003), which was implemented using the de-
fault parameters and English stopwords provided by
the Scikit-learn and using as input the paper’s title and
2.6 Synthesis Strategy
After Data Extraction, synthesis was performed to
build the systematic map. This phase includes i) a
classification analysis considering the primary data
and the fields with closed options, and ii) a summa-
rization of the textual attributes using manual content
Initially, it was recovered 378 papers in June 2020.
After duplicates removal, 187 studies remained.
Then, 55 were removed while reading titles and ab-
stracts, and another 20 were excluded because they
were only available as abstract (1), were short papers
(13), and were not available for download (6). Fi-
nally, 18 were excluded for not meeting the inclusion
criteria. Thus, 94 relevant works were selected.
In this section, we present our results, discuss their
implications, and answer our research questions. All
data extracted for this research and data visualizations
can be publicly available (see Data Availability). It is
also important to highlight that the papers selected in
this mapping were referenced using a numerical cita-
tion due to space restrictions. In our repository, there
is a table with all the 94 selected papers.
3.1 The Context of the Papers
The papers’ context is one of the most relevant aspects
to understand a research area. However, this context
HEALTHINF 2022 - 15th International Conference on Health Informatics
can be complex because it is possible to involve many
aspects. In order to provide greater detail without in-
creasing complexity, this work considered eight (8)
aspects for the context:
Aspects obtained directly from scientific bases:
year of publication
Aspects built using clustering algorithms:
papers’ hot topics
Aspects that consider published taxonomies:
research type (Wieringa et al., 2006)
contribution (Breivold, 2017)
empirical validation (Wohlin et al., 2012)
QoL data (WHOQoL Group, 1994)
Aspects extracted as open fields:
health issue addressed
Together, these aspects assist in building a robust
answer for RQ1. We argue that without this compre-
hensive analysis, it would not be possible to observe
research gaps. In addition, these aspects enable us
to analyze the temporal distribution of the papers, in
which venue they are being published, which topics
are most discussed, the research type and proposed
contributions, the validation strategies, the focus re-
garding QoL domains and facets, and what health is-
sues are being addressed.
Figure 1: The distribution of papers over the years.
Regarding the temporal distribution, Figure 1
presents a graph with the number of works published
over the years. It is possible to observe an increase
in the number of publications except for 2020. How-
ever, this low number in 2020 is due to the period in
which the search was performed. It is highly proba-
ble (given the analysis of the works already accepted
for publication but which are not yet available in the
databases) that the number of papers in 2020 will ex-
ceed the other years. Moreover, concerning the venue,
55% (52/94) of the studies were published in journals.
Concerning the hot topics, nine clusters were pro-
posed based on the LDA results. Figure 2.A shows the
distribution of papers by each cluster. In this graphic,
it is possible to observe a high interest in IoT Health-
care Services (38%), followed by Elderly Healthcare
(17%), Big Data (11%), Sensors and Wearable (9%).
The Security and Privacy, Network and Communica-
tion, and Health Activity Monitoring clusters have six
studies each, and the last two categories are Health
Prediction (3%) and Well-being and Comfort (1%).
These results indicate an interest in IoT services
for healthcare, activity monitoring, and disease pre-
diction with a special focus on the elderly. Other re-
search areas have also been strengthened to support
the development of these services, such as Big Data,
Sensors and Wearable, Security and Privacy, and Net-
work and Communication. Another interesting point
to highlight is that although Machine Learning and
Cloud Computing did not appear as topics, they are
fundamental in IoHT. Many works mention the use of
Machine Learning techniques (Study IDs: 78, 89) and
cloud capabilities (Study IDs: 14, 48).
As regards the research type, we used the taxon-
omy proposed by (Wieringa et al., 2006) that has
six categories: solution proposal; validation research;
evaluation research; experience papers; and, opin-
ion papers. In Figure 2.B, it is possible to observe
the number of papers for each research type. Most
works were classified as solution proposal (29), fol-
lowed by evaluation research (21), validation research
(17), conceptual proposal (9), experience paper (5),
and two studies were classified as opinion papers. For
11 papers, this aspect was not clearly identified.
The contribution also has six categories accord-
ing to (Breivold, 2017): method; model; tool; formal
study; experience; and others for those that do not
fit in any of these categories. Usually, this last cat-
egory encompasses secondary studies and papers fo-
cused on discussing challenges. This mapping found
25 tools, 21 models, 13 methods, 7 formal studies,
and 3 experiences (Figure 2.C).
To deepen the analysis of the contributions, we
decided to classify them into only monitoring, or
monitoring and acting. In this way, 47 papers pro-
posed only monitoring solutions, and 6 studies pre-
sented monitoring and acting solutions. For 41 stud-
ies, this aspect was not clearly identified. Thus, this
result reinforces the discussion made by (Al-Fuqaha
et al., 2015) that there are many information ag-
gregation services, and the IoT needs to evolve for
more collaborative-aware services and ubiquitous ser-
vices. Also, the low number of collaborative-aware
and ubiquitous services can be seen as a gap for the
Internet of Health Things area.
Regarding the empirical validation, we selected
the taxonomy proposed by (Wohlin et al., 2012),
including usability evaluation, proof-of-concept
(PoCs), and simulation. These three last categories
were included to expand our classification scheme.
Internet of Health Things for Quality of Life: Open Challenges based on a Systematic Literature Mapping
Figure 2: Number of papers considering (A) the proposed clustering, (B) research type, and (C) contribution type.
Thus, only 68 papers (72%) present a well-described
empirical validation. There are 23 case studies, 19
PoCs, 13 experiments, 8 surveys, and 5 simulations.
This result directly correlates with the research type
since case studies usually evaluate a solution in
practice. Moreover, two papers used case studies to
present lessons learned. Hence, considering that 28%
of works did not present a strong validation and that
54,4% were evaluated in controlled environments
with PoCs, experiments, and simulations, there is
a large room of challenges for partnerships with
industry to conduct practical validations.
Concerning QoL data (domains and facets), we
considered the WHO’s QoL definition (WHOQoL
Group, 1994). For WHO, there are four domains
(physical, psychological, social, and environment)
and 24 facets in QoL. The results showed that despite
the large number of studies that use the term “Quality
of Life”, few studies (11,7%) correlate this term to the
definition proposed by WHO. In addition, although
there are proposals for individualized monitoring of
aspects related to QoL, no initiatives have been found
for comprehensive Quality of Life monitoring using
intelligent objects in IoT environments.
Finally, we also decided to extract the
health issues have been addressed in the QoL-
related IoHT literature. It was found 33 health issues
and were clustered into six groups: diseases, health
monitoring, elderly healthcare, illness detection,
ethics, and health at work.
Regarding the diseases, studies about sleep apnea
syndrome, stress, cancer, cardiovascular and cardio-
respiratory diseases, chronic diseases, hypertension,
and diabetes were found. In the health monitoring cat-
egory, the most recurrent issue is how to provide con-
tinuous and real-time monitoring of vital signs. For
elderly healthcare, dementia and falls were the most
mentioned. Finally, it was also found studies focused
on early illness detection, ethical responsibility over
health data, and how to provide a better environment
to employees. Thus, the answer for RQ1 can be sum-
marized as follows.
RQ1: What is the context of the papers published
in the QoL-related IoHT literature?
Summarized Answer:
Year and Venue: in the last years, the num-
ber of studies has grown. Also, 55% and 27%
of the papers were published in journals and
conferences, respectively. However, no venue
stood out.
Hot Topics: IoT Healthcare Services (38.3%),
Elderly Healthcare (17%), Big Data (11.7%),
Sensors and Wearable (9.6%), Security and
Privacy (6.4%), Network and Communication
(6.4%), Health Activity Monitoring (6.4%),
Heath Prediction (3.2%), and Well-being and
Comfort (1%).
Research Type and Contribution: many pa-
pers were classified as solution proposals, and
that have monitoring tools as a contribution.
Empirical Validation: the distribution was
33,8% of case studies, 27,9% of proofs-of-
concept, 19,1% of experiments, 11,8% of sur-
veys, 7,4% of simulations, and was not found
usability evaluations. This result reinforces the
need to conduct practical validations.
QoL Domains and Facets: many studies
have investigated strategies to improve people’s
QoL, but these strategies are generally focused
on a specific QoL aspect or specific health is-
sues. Moreover, it was not found studies fo-
cused on seamless QoL monitoring.
Health Issues: it was found 33 health issues
into six categories: diseases, health monitoring,
elderly healthcare, illness detection, ethics, and
health at work.
3.2 Challenges
The challenges were identified by paper’ excerpts us-
ing an open field on the extraction form. Then, they
were iteratively refined in order to make them more
concise and to categorize them. This process resulted
HEALTHINF 2022 - 15th International Conference on Health Informatics
in 182 challenges grouped into eight categories, and
due to the large number of challenges found, the dis-
cussion will be made by category mentioning the most
recurring challenges.
3.2.1 IoT Challenges
This category, which had the most mentions (71), en-
compasses well-known and intrinsic IoT challenges
that have been studied. Unfortunately, despite the ad-
vances, there is still no “killer solution” for them.
The Lack of Interoperability is a well-known
difficulty for those who work with the IoT. This chal-
lenge has many facets, and one of its major causes is
the huge heterogeneity found in IoT environments. It
is possible to find devices from many different compa-
nies with specific protocols and data structures. Het-
erogeneity combined with the lack of widely accepted
standards and the vendor lock-in improve this prob-
lem. In this way, middleware platforms (e.g., FI-
WARE) and data standardization (such as FHIR) are
the most common solutions. The study (Study ID: 32)
used a subset of FHIR to overcome the data interop-
erability in an IoT system for dementia care, and this
kind of initiative has been strengthened by private sec-
tor initiatives like the Argonaut Project. Other authors
have proposed smart gateways capable of supporting
interoperability among heterogeneous sensors (Study
ID: 48), and solutions based on the idea of plug-and-
play, extensible components (Study ID: 50).
Trustworthiness is a critical issue in IoT systems,
and even more when these systems are responsible
for people’s healthcare. This aspect deals with the
user’s expectation of the service competency (Study
ID: 81). Moreover, the trustworthiness is a challenge
in this context due to its close relation with the data
quality (Study ID: 55), privacy (Study ID: 81), and
quality of network (Study ID: 69). The solution to
this problem involves strengthening verification and
validation techniques, fault tolerance strategies, in ad-
dition to regulations that seek to protect users in case
of failures.
Some medical services are restricted to rich peo-
ple in underdeveloped countries due to their high-cost
(Study ID: 87). Then, the Cost Efficiency is the de-
sired goal for IoT systems applied on remote health
(Study ID: 57). Thus, we observed the development
of low-cost solutions.
Healthcare should be highly personalized (Study
ID: 30). In this way, the Personalized IoT-Health
is a challenge that has been more explored recently.
The authors of the paper (Study ID: 19) stated, for
example, that in general-propose elderly monitoring
systems, several presumptions are made, and these
presumptions can result in inefficiencies in the long-
term. Therefore, investigating these challenges rep-
resents an opportunity to develop self-adaptive IoHT
systems or reinforce data analytics strategies.
Standardization is another well-known challenge in
the IoT-related area. The definition of standards for
data representation, data exchange, communication
protocols, quality of service, development method-
ologies, and many others has the potential to re-
move barriers in the development of many IoHT so-
lutions. In this way, there are initiatives such as the
ISO/IEEE 11073 standards for point-of-care medical
device communication; HL7, FHIR, and OpenEHR
standards for electronic health records; and the DI-
COM for medical images. However, this area still has
opportunities to improve the existing standards or to
propose new ones.
3.2.2 Security and Privacy
Security and privacy challenges were the second most
mentioned (64). The challenges found in this category
are closely related to the three primary security goals:
confidentiality, integrity, and availability. In this way,
the study (Study ID: 87) mentioned the Data Secu-
rity as a critical requirement for IoHT systems and
that it is necessary both develop new solutions to keep
the data consistent and train healthcare professionals
to be aware of this criticality. In addition, problems
with the data can hinder decision-making regarding
the treatment of a patient.
For the Access control, it was found mentions
for the Identify Establishment and Capability-based
Access Control (IECAC) protocol and the Elliptical
Curve Cryptography (ECC) to protect the IoT from
the man-in-the-middle, replay, and denial-of-service
(DDoS) attacks (Study ID: 29). The paper (Study
ID: 52) proposed a framework to preserve privacy
in patient-doctor communication based on public and
private keys.
For the Confidentiality, the investigation (Study
ID: 29) mentioned solutions using the Datagram
Transport Layer Security (DTLS) protocol, and cryp-
tography based on symmetric encryption and elliptic
curve. Other researchers have also investigated the
usage of Blockchain for this purpose (Study ID: 90).
In addition to the previously mentioned chal-
lenges, it was found papers mentioning more spe-
cific issues such as the problems with methods to
de-identify data without introducing noise and re-
identification attacks in anonymization techniques
(Study ID: 17), and security issues in scenarios with
heterogeneous resource-constrained devices (Study
ID: 76). Considering this context, it is crucial to adopt
an expanded view for security and privacy in IoHT
(Study ID: 33).
Internet of Health Things for Quality of Life: Open Challenges based on a Systematic Literature Mapping
3.2.3 Data Science
The Data Science category was mentioned 55 times,
and it includes challenges related to Big Data, Data
Analytics, and the usage of Artificial Intelligence to
support decision making in healthcare. These three
areas are fundamental to move from reactive health,
in which the diagnosis and treatment are defined in
response to symptoms, to proactive health, which is
focused on early warnings (data inference) using the
data collected by smart objects (Study IDs: 82, 17).
Regarding Big Data, it is estimated that the
healthcare industry produces 30% of the entire
world’s data volume. In addition, this data also has a
great variety as it may involve medical images, moni-
toring vital signs, sleep data, location, medical notes,
laboratory test results, among others. Furthermore,
the veracity is related to the quality of data, and it can
impact clinical decision-making (Study ID: 23). So
then, the volume, variety, velocity, and veracity are
concerns that should be addressed during the IoHT
development (Study ID: 62).
Concerning the Data Analytic, the challenge is to
analyze the massive amount of data. Usually, this in-
volves data acquisition, filtering, cleaning and trans-
formation, application of statistical methods and data
mining algorithms, interpretation, and formatting of
results (Study ID: 10). In the results, it was found pa-
pers discussing new algorithms for data cleaning and
improvements in the mining approaches to deal with
heterogeneous data. Also, the work (Study ID: 17)
highlighted issues about data silos.
Another challenge found was the use of Artifi-
cial Intelligence (AI) techniques in healthcare. These
techniques can bring advantages both for the quality
of the services and cost reduction. The challenges re-
lated to AI can be summarized as the application of
Machine Learning and Deep Learning to monitor pa-
tients, recognize user activities, and predict diseases.
For example, the study (Study ID: 5) has used logistic
regression and artificial neural networks (ANN) for
early detection of hypertension. However, as stated
by (Study ID: 87), there are still challenges to guar-
antee accuracy since the false alerts or the absence of
warnings are critical factors for the IoHT.
3.2.4 Network and Communication
We found 50 mentions of network and communica-
tion issues. The first and most mentioned challenge
was Real-time probably because the latency is criti-
cal for healthcare systems. It is currently common to
observe applications in which the data are collected
by sensors, then transmitted to gateways or cloud in-
frastructures to be processed. In some cases, this ap-
proach can introduce an impracticable latency. To
tackle this issue, it has been proposed many strate-
gies, such as the usage of efficient processing units
close to the sensors (Study ID: 49), new system ar-
chitectures to use concepts of edge and fog comput-
ing (Study IDs: 42, 48, 56), or adaptive data trans-
mission policies using mist, fog, and cloud Comput-
ing (Study ID: 84). Although 5G technology was not
much mentioned (only two papers), it is expected that
this new technology will make profound changes in
digital healthcare through its high-throughput, low-
latency wireless connectivity (Study ID: 27).
In addition, we also found mentions for scalabil-
ity (Study ID: 62), availability, and network design is-
sues. These issues are relevant because it is expected
that IoHT systems be able to keep their availability
even with more smart objects and users, and this re-
quirement impacts the network design (such as the
choice for wireless communication and architecture)
(Study ID: 77).
3.2.5 Sensors and Wearable
This category has many challenges due to the grow-
ing interest in wearables. For example, the authors
of the paper (Study ID: 16) estimated that, in the fu-
ture, wearables could represent 30% of health track-
ing. The mapping result found 38 papers discussing
challenges, mainly focused on resource constraints.
Regarding the Device Resources Constraint, the
energy consumption is the major issue (Study ID:
62). The limited power can restrict the transmis-
sion and processing capabilities. As stated by the
study (Study ID: 77), wearables should operate con-
tinuously with minimal human intervention, and the
adoption of large-capacity batteries makes them un-
comfortable. As a practical example, the paper writ-
ten by (Study ID: 76) reinforces these difficulties dur-
ing the development of healthcare systems in Canada.
This context paves the way for developing protocols
that support low energy consumption (Study IDs: 74,
87) and for strategies to harvest energy (Study ID:
77). In this way, the work (Study ID: 74) proposed
a routing protocol for body area sensor networks that
uses the bio-inspired multi-objective algorithm to im-
prove data reliability, reducing power consumption.
Another relevant characteristic for wearable is its
User Acceptance. In some case, this requirement can
be a barrier to its usage (Study ID: 38). Thus, it is
also important to investigate less invasive methods to
collect data (Study ID: 16).
Finally, the recent emergence of various fitness
tracking, there is a demand for the development of
approaches for Testing and Validating these devices
(Study ID: 82). In addition, the (Study ID: 72) also
HEALTHINF 2022 - 15th International Conference on Health Informatics
reinforces the need for methods to verify the effec-
tiveness of mobile health applications.
3.2.6 Software Engineering
In the studies selected by this mapping, seventeen
(17) pointed out software engineering challenges.
The most mentioned were reliability (Study ID: 42),
reconfigurability and remote configuration (Study
IDs: 36, 7), easy installation (Study ID: 80), legacy
systems and technical debts (Study ID: 82), system
compatibility (Study ID: 41), and fault tolerance
(Study ID: 57). In this way, many solutions have been
proposed. For example, reliability and fault tolerance
can be addressed with testing techniques and adaptive
models for this kind of system. The reconfigurabil-
ity and easy installation require design solutions; the
legacy systems and technical debts can be mitigated
by decoupling the data from the legacy system and
adopting debt management processes. Finally, there
are also many solutions using middleware platforms
to overcome heterogeneity.
3.2.7 Human-Computer Interaction
Fourteen papers mentioned the following challenges:
usability (Study IDs: 87, 32, 93), non-invasive care
technologies (Study IDs: 75, 34), empowered users
(Study ID: 90), engagement in health interventions
(Study ID: 86), and the acceptance of the elderly
(Study ID: 25). As the adoption of IoHT systems
increases, interest in technologies that provide a bet-
ter user experience should also grow, leading to stud-
ies focused on the impact of functional and non-
functional requirements in that experience.
3.2.8 Cloud Computing
The last category is cloud computing with 6 men-
tions. For example, the complexity of integration
and management of different layers of cloud and
IoT for healthcare systems (Study ID: 62), delay in
cloud computing (Study ID: 91), offloading (Study
ID: 70), the usage of fog computing in IoT-Health
(Study ID: 43), and the synchronization between
different cloud vendors (Study ID: 46).
RQ2: What are the challenges related to IoHT for
Quality of Life?
Summarized Answer: Among the large room of
challenges to be addressed, there is a high inter-
est in personalized IoT-Health applications, data
security and privacy, the usage of wearables to
monitor patients, and machine learning to predict
health issues. Also, the strengthening of mobile
health is expected. Naturally, this strengthening
will demand new software engineering methods,
mainly focused on testing and systems’ usability.
3.3 IoHT to Monitor and Improve the
People’s Quality of Life
It is a consensus that health and QoL are closely re-
lated (Study ID: 72). However, there is no significant
interest in strategies to measure this QoL gain. Cur-
rently, the most known strategies to measure QoL are
based on questionnaires, which are tiring, and hard to
engage the user (Sanchez et al., 2015). In this way,
a seamless and unnoticeable IoHT-based monitoring
can be more helpful to promote early interventions.
The RQ3 was proposed from the hypothesis that
many works are proposing IoHT solutions to improve
people’s QoL, but only a few studies are concerned
with measuring this indicator. In our results, only
11 papers explicitly mentioned some QoL domain or
facet. However, none have proposed a method, or tool
to ubiquitously infer users’ QoL using IoHT data.
The study published by (Study ID: 75) presents
a broad discussion about QoL for the elderly and its
relation to health. In addition, it was identified a set
of instruments to measure QoL, such as EQ-SD-3L,
SF-36, and WHOQOL-BREF. Finally, they conclude
by proposing an architecture for non-intrusive mon-
itoring of older adults. The main drawback of this
proposal is that the monitoring module still uses ques-
tionnaires and was not presented any strategy focused
on the semantic structure of the QoL domain and how
the IoHT can produce data to infer the QoL.
The other works address specific points, such as
QoL of hospitalized children (Study ID: 3), plant
management for indoor comfort (Study ID: 51), sleep
monitoring for apnea treatment (Study ID: 48), and
recognition of emotions (Study ID: 70).
RQ3: What is the evidence that IoHT can monitor
and improve the people’s Quality of Life?
Summarized Answer: most studies did not seek to
measure QoL gain using IoHT. Few studies have
proposed QoL automated monitoring approaches.
Finally, there is a lack of works focused on QoL,
providing models for the semantic relation of the
QoL-related data, and using AI to infer its value.
This work was conducted to summarize the literature
about the IoHT applied to the Quality of Life. As
our main highlights, we found a growing interest in
Internet of Health Things for Quality of Life: Open Challenges based on a Systematic Literature Mapping
IoHT studies, mainly focused on the elderly. In gen-
eral, there is still room for more partnerships with the
industry to perform validations in practice. Finally,
several solutions for monitoring and diagnosing dis-
eases have been proposed, but an increase in machine
learning solutions for early diagnosis is expected.
Regarding the challenges, there is a desire for per-
sonalized and intelligent services that provide contin-
uous, fast, secure, and effective health data monitor-
ing. Regarding our last research question, the results
show that few studies seek a general approach to deal-
ing with QoL, and the works closer to this proposal
still use questionnaires to collect data. Probably, this
happens due to the difficulty in developing this kind of
approach since a large amount of user data is needed,
in addition to the validation complexity, which is done
through longitudinal studies. Thus, this question can
validate the beginning of a more in-depth investiga-
tion towards a semantic structure of the QoL domains
and facets and an intelligent model for capturing and
inferring this metric.
Concerning the gaps, we realized a need for
collaborative-aware and ubiquitous services able to
anticipate events and to act in the environment to im-
prove the living conditions; partnerships with the in-
dustry to conduct validations in practice; methods and
protocols capable of guaranteeing data security and
privacy even on restricted devices; network designs
to ensure low latency and high reliability; techniques
for validation and verification of fitness tracking apps;
approaches to the development of intelligent systems
integrated with the domain experts; and studies fo-
cused on user experience.
Our data are public. It is also important to highlight
that the papers selected in this mapping were linked
using the study ID due to space restrictions.
- Protocol:
- Selected Papers: /
- Images (higher resolution):
- Raw Data (codes and tables):
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HEALTHINF 2022 - 15th International Conference on Health Informatics