# Personal Data Visualisation on Mobile Devices: A Systematic Literature
Review ††thanks: Citation: Authors. Title. Pages…. DOI:000000/11111.

Yasmeen anjeer alsehhhi, Mohamed Abdelrazek

Deakin Univeristy
{Yasmeen,
Mohamed}yanjeeralshehhi@deakin.edu.au<EMAIL_ADDRESS>
Alessio Bonti
Deakin University
<EMAIL_ADDRESS>

###### Abstract

Personal data cover multiple aspects of our daily life and activities,
including health, finance, social, Internet, Etc. Personal data visualisations
aim to improve the user experience when exploring these large amounts of
personal data and potentially provide insights to assist individuals in their
decision making and achieving goals. People with different backgrounds, gender
and ages usually need to access their data on their mobile devices. Although
there are many personal tracking apps, the user experience when using these
apps and visualisations is not evaluated yet. There are publications on
personal data visualisation in the literature. Still, no systematic literature
review investigated the gaps in this area to assist in developing new personal
data visualisation techniques focusing on user experience. In this systematic
literature review, we considered studies published between 2010 and 2020 in
three online databases. We screened 195 studies and identified 29 papers that
met our inclusion criteria. Our key findings are various types of personal
data, and users have been addressed well in the found papers, including
health, sport, diet, Driving habits, lifelogging, productivity, Etc. The user
types range from naive users to expert and developers users based on the
experiment’s target. However, mobile device capabilities and limitations
regarding data visualisation tasks have not been well addressed. There are no
studies on the best practices of personal data visualisation on mobile
devices, assessment frameworks for data visualisation, or design frameworks
for personal data visualisations.

_K_ eywords Personal data visualization $\cdot$ mobile visualization $\cdot$
tracking visualization

##  1 Introduction

Personal analytics, personal visual analytics [1], Personal data
visualisation, quantified self’s [2], or self-tracking are interchangeable
terms[3][4]. It is used to define the science of analytical reasoning
displayed using visual representations to explore personal context internally
such as goals, skills and preferences and externally that are related to
physical constraints such as devices or social influence[1]. Lee et al.
divided personal information into five groups: body information, psychological
state and traits, activity, environmental property status, social interaction
and other life logging aspects such as tracking financial data [3].
Individuals may be interested in tracking various conditions [5]. However, the
main self-tracking motivation is health conditions using different
applications through mobile devices [5], and wearable devices such as
smartwatches [5]. Adopting mobile devices to track personal data became one of
the open research areas [6]. Personal data visualisation has been investigated
by Choe et al. in which quantified selfers have recorded their experiences in
tracking their data, this paper has only summarised the number of
visualisation charts used and how frequently each of these types has been used
[5]. Oh et al. have developed a study that investigated the UX issues for
tracking personal data and found that simple charts can not be understood by
everyone due to graph literacy, on the other hand, it is required to develop
advanced data visualisation to enrich expert users experience when exploring
their data [3]. Katz et al have specifically investigated the effectiveness of
diabetes commercial mobile applications in terms of data visualisation and
other aspects[4]. However, this was limited research in which it covered only
one specific type of personal data to be visualised and tracked. The provided
reviews were positive in understanding the presented information but not
sufficient context information and interaction with data visualisation.

Still, there are gaps in personal data visualisation to be adopted on mobile
devices. There are open research areas related to the smart phones’ capability
and compatibility to adopt data visualisation charts, tasks and interactions.
Another research area is the guidelines for developing complete, correct and
consistent data visualisation on mobile devices. Additionally, there is no
detailed systematic literature review of the state of the art and state of
practice regarding personal data visualisations.

In this paper, we introduce a systematic literature review (SLR) on personal
data visualisation. The SLR addresses the following research questions:

  * •

What personal data does end-users want to track? this research question aims
to collect and show the different kinds of tracked personal data so we know
which of these we should do more research on.

  * •

What are the different backgrounds and skills of personal data visualisation
end users? As personal visualisations are targeting non-expert audience, we
need to understand to know who are they and what works for them and what
dimensions did existing studies consider.

  * •

What are the advantages and disadvantages of adopting mobile devices in
tracking and visualising personal data? We need to investigate and understand
mobile devices pros and cons to adopt personal data visualisation focusing on
both hardware and software.

  * •

What are the visualisation techniques and visualisation tasks covered in the
literature in personal data visualisation? The target of this question is to
acknowledge the adopted graphs, tasks and interaction by the end users and the
provided applications.

The rest of the paper is structured as follows: first, we discuss the related
work done in personal and mobile data visualisation research areas. Second, we
present the methodology we followed in conducting the SLR. Third, we present a
summary of the results we found. Finally, we provide a detailed discussion on
the findings from the SLR process.

##  2 Related Work

There are existing research and experiments related to personal data
visualisation and adoption by users. However, the number of systematic
literature reviews developed to cover this research field is limited. We found
one SLR paper covered the design criteria to develop data visualisation
published in 2015 [7], and another two review papers developed in the area of
evaluating software visualisation and learning analytic visualisation [8],[9].
The recent SLR that we found published in 2020 covered the type of health data
for elderly people [10].

Out of the reviews mentioned above, there is only one review that is related
to our topic [10]. It reviewed 95 papers that were published between 2009 and
2019. The purpose of the review was to assess the proposed technology in terms
of its easiness and allowance of entering health data and evaluating the
appropriateness of the information presented with data visualisation. There
are various data identified to track health data, including emotion, sleep,
pain and time. Smartphones are the top devices used by older people to
visualise their health data. Although this review claimed that information
visualisation is straightforward in terms of understanding and interpretation
of a displayed image, there are limited papers that describe the
characteristics of good data visualisation. There were two main methods
identified to evaluate mobile health technology, either quantitative or mixed,
of both quantitative and qualitative [10]. In terms of data visualisation
evaluation, Merino et al. conducted a literature review in which they reviewed
181 papers out of 387 papers published in SOFTVIS/VISSOFT conferences [8].
They divided the evaluation methods into two groups traditional and non-
traditional. The researchers found three ways: conducting interviews with
participants or developers to gain hypotheses to be tested in the experiment,
developing questionnaires to reaa ch a large number of participa,nts and
recordisessionsions to evaluate user performance. There are three methods
classified as non-traditional to evaluate software visualisation eye tracking
to capture how participants see the elements in visualisation, log analysis to
investigate how participants navigate visualisations, and emotion cards to
help participants measurably report their feelings. Although these methods are
listed to be effective to evaluate software visualisation, yet three main
barriers are limiting the evaluation of these tools:

  * •

The lack of ready to use an evaluation tool

  * •

The lack of benchmark to compare with the developed tool

  * •

The difficulty to find the right type of participants to share their knowledge

In terms of visualisation techniques, two reviews agreed on classifying
visualisation techniques into traditional and modern techniques[7], [9] While
traditional visualisation could be developed using a spreadsheet such as a bar
chart, line chart, box chart, pie chart. The higher-level visualisation
requires special tools to create charts like geo charts (Google maps), time
charts, running race charts, time logs, spiral displays, cluster maps and
component planes. In terms of modern visualisation techniques, art and
technology are combined to show energy consumption. For example, a floor plan
can be presented, and red spots are presented in different areas of the floor
to show power consumption[7]. Designing a visualisation interface is another
challenge reviewed in 2015 by covering 22 studies to develop a list of
visualisation design guidelines to show energy consumption[7]. In their
review, the researcher found that guidelines should be grouped into functional
and non-functional criteria. The functional criteria involve the type of
information displayed in the visualisation, techniques, and modes: the non-
functional criteria are hardware and software integration, portability, and
extensibility. Based on the findings, there are five recommendations developed
based on the design requirement. An example of these recommendations is if the
visualisation should be portable and displayed on different platforms, the
recommendation was to focus on the scalability, flexibility and
accessibility[7].

However, there are some missing challenges related to the type of users,
visualisation techniques that can be used to show personal data, and mobile
devices’ ability to adopt this kind of visualisation.

##  3 Methodology

In order to conduct this systematic literature review, the researcher has
followed kitchenham’s guidelines [11] to evaluate the relevant data by
developing a protocol that includes research questions, the inclusion and
exclusion criteria, quality assessment, data extraction and identifying the
selected studies.

###  3.1 Research Questions

In this SLR, we have focused on the following research questions:

  * •

SLR RQ1: What personal data do users prefer to view on their mobile phone?
Health? Personal development? Utility usage? Financial spending? Lifestyle
monitoring?

  * •

SLR RQ2: What are the groups of users primarily interested in personal data
visualisation?

  * •

SLR RQ3: To what extent are mobile devices capable of adopting personal
visualisation? In terms of efficiency, scalability, memory, user experience,
Etc.

  * •

SLR RQ4: What are the existing techniques in the literature to visualise
personal data on mobile devices?

  * •

SLR RQ5: What is the feedback on the available applications/techniques that
support personal visualisation?

  * •

SLR RQ6: what are the challenges to design a visualisation interface in mobile
devices?

###  3.2 Inclusion and exclusion criteria

The researcher has adopted the following string to be applied as a search
query to be used in the following search engines: ("mobile data
visualisation") OR ("personal analytics" AND "data visualisation" AND
"mobile").  The search engines are Google Scholar, IEEE Explore, and ACM
digital library, including peer-reviewed articles. Inclusion criteria:

  * •

It should be in the English language

  * •

It should relate to the topic scope

  * •

It should be within the years from 2010 to 2020

  * •

The full text should be available

  * •

It should be a peer-reviewed journal or conference article

###  3.3 Selection Process

In order to have a well-built selection process, we screened the results of
the search string, using the inclusion and exclusion criteria provided
previously. After that, we read the titles and abstracts of the publication to
check the eligibility. Finally, we filled Table 1 to filter out the paper used
in the systematic literature review.

Table 1: Selection Process Title | Publication Venue | Reasons of inclusion or exclusion
---|---|---
—– | —– | —–
—– | —– | —–

###  3.4 Study Quality Assessment

In addition to the inclusion and exclusion criteria, the papers selected will
undergo a quality assessment process to ensure their valuable contribution to
the SLR. Based on the search we have done using the queries search mentioned
above, the results can be classified into quantitative and qualitative
studies. The following criteria are used to evaluate the quality of the papers
generally [12],

  * •

Does the paper have a description of the research method?

  * •

Does the paper describe an explicit research question/goal/purpose?

  * •

Does the paper describe the motivation for the research question(s)?

  * •

Does the paper discuss limitations or validity?

  * •

Does the paper describe the context of the research?

  * •

Does the paper describe data collection?

  * •

Does the paper describe data analysis?

  * •

Does the paper describe sampling or selection of the study object(s)?

  * •

Does the paper present any data?

###  3.5 Data Extraction

Usually data extraction needs a second reviewer to establish inter-rater
reliability. Thus, the co-authors performed data extraction on random samples
of the primary study to cross-check the results. Table 2 shows the data types
to be collected:

Table 2: Data Extraction Reference | Study Parameters | Focus | Methodology | Findings
---|---|---|---|---
—– | —– | ——- | —– | —-

###  3.6 Selected Studies

Based on the inclusion criteria, out of 194 papers, only 25 papers have met
the criteria. Additionally, we added three more papers we identified while
reviewing the 25 papers. Figure 1 shows the process of selecting the used
papers in this SLR.

Figure 1: The Process of Selecting Studies & [Source], via Research gate
website, From Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group
(2009). Preferred Reporting Items for Systematic Reviews and MetaAnalyses.

The twenty-eight selected research papers have been published at 25 different
venues; IEEE Transaction on visualisation and Computer Graphics has published
4 out of these 28 papers. Another two papers have been published through IEEE
Transactions on Computer Graphics and Applications. In addition to IEEE
Transactions, the Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems have published two research studies. While the mentioned
venues have published more than one paper, the other venues in Table 3 have
published 1 of the research study related to our topic.

Table 3: Venues of selected papers Venue Name | Counts
---|---
2015 Eighth International Conference on Mobile Computing& and Ubiquitous Networking (ICMU) | 1
Behaviour Information Technology | 1
Computers in Human Behaviour | 1
Economy and Society | 1
eHealth 360°,Springer | 1
Human–Computer Interaction | 1
IEEE 16th International Conference on Smart City | 1
IEEE Computer Graphics and Applications | 2
IEEE International Conference on &Computer and Information Technology |
Ubiquitous Computing and Communications | 1
IEEE transactions on visualisation and computer graphics | 4
International Journal of Mobile Human & and Computer Interaction | 1
International Journal on Advances in Intelligent System | 1
International society for optics and photonics | 1
Journal of biomedical informatics | 1
Journal of Comparative Research& in Anthropology and Sociology | 1
Journal of the Association for & Information Science and Technology | 1
Journal of Visual Languages and Computing | 1
Proceedings of SIGRAD 2015 | 1
Proceedings of the 2018 CHI Conference & on Human Factors in Computing Systems | 1
Proceedings of the 22nd International& Academic Mindtrek Conference | 1
Proceedings of the ACM on Interactive, Mobile,& Wearable and Ubiquitous Technologies | 1
Proceedings of the SIGCHI Conference& on Human Factors in Computing Systems | 2
Societies | 1
Big data | 1
Total | 29

##  4 Results

We now detail and group the results of the research questions that have been
found in the literature. The following sections will present the results based
on five aspects: types of self-tracked data, user profile, target mobile
devices, visualisation tasks and techniques, and user feedback. Firstly, we
will show the types of personal data tracked and the types of users targeted
or included in the found studies. This information is presented in Tables 4
and 5. Secondly, we will present the various devices included in the studies
as mobile devices and elaborate their benefits and challenges to adopting
self-tracking and data visualisation; tables 6 and 7 present these two
aspects. Thirdly, we will explain the visualisation tasks and techniques in
the selected research papers and their adoption in different self-tracking
applications. This information is presented in Tables 8 and 9. Finally, we
will discuss the feedback given by users in terms of UX design, application
functionality and essential features.

Table 4: Tracked Personal Data  Tracked Personal Data | Total
---|---
Activity - Fitness | 4
Diet | 2
Driving habits | 1
Energy consumption | 1
Environmental property statue | 2
Financial | 2
Geo location | 1
Health - Body information | 7
Life logging | 1
Productivity | 2
Psychological data and traits | 2
Sleeping pattern | 1
Smartphone usage | 1
Social connectedness | 2
Not mentioned | 17
Total | 46
Table 5: Type of Users Type of Users | Total
---|---
-Developer | 1
Diabetes users | 2
Expert in data visualisation | 1
Novice users | 2
Not specified | 3
Public users | 6
Students | 1
Not mentioned | 13
Total | 29
Table 6: Devices Devices | Total
---|---
Large display | 1
Mobile devices | 4
Smart phones | 3
Tablets | 1
Wearable devices | 4
Not specified | 14
Total | 31
Table 7: Mobile Benefits Challenges  Pros and Cons | Total
---|---
Benefits | 6
Challenges | 4
Table 8: visualisation Types  visualisation Types | Total
---|---
Animation visualisation | 1
Info graphic | 2
Line Graph | 2
Mapping visualisation | 1
Multivariate | 1
Pie chart | 1
Time Series Data | 1
Bar chart | 1
Table | 1
Not mentioned | 20
Table 9: visualisation tasks  visualisation Tasks | Total
---|---
Analytics tasks |
(select – encode – reconfigure – elaborate -filter -connect) | 2
Auto -generation visualisation type-recommended | 1
Change colour scheme and design - recommended | 1
Custom- Create their own novel -recommended | 2
Read-only | 2
Not mentioned | 21

###  4.1 Self-Tracked Personal Data

Table 4 shows the number of papers that indicated various types of personal
data. Please note that the total number exceeds 28 because some studies
considered multiple self-tracked data. Thus, there are ten papers out of 28
that have involved various types of tracked personal data. In addition, while
6 out of 10 papers have directly stated the types of tracked personal data,
the other four papers have mentioned personal data as the type of data to be
tracked using developed applications or prototypes.

As Swan stated [13]that personal data are related to an individual’s daily
life aspects, 6 of the found papers have listed ten different kinds of
personal data that are related to daily life aspects. Interestingly, there are
3 papers that stated special kind of personal data, visualizing moments by
posting memory photos on a timeline [14], smartphones usage [15], tracking Geo
location [16] and driving habits [16]. By summarising the findings, there are
14 kinds of information that have been listed as personal data: Body
information, health, diet, fitness, physical activity, social connectedness,
visual moments, sleep quality, financial aspects, smartphone usage,
psychological data and traits, productivity, lifelogging, environmental
property statue, energy consumption and driving habits. Among these personal
data, health condition was the most data that have been studied. Seven studies
have focused on this kind of personal data as tracked data using Desktop or
mobile applications. For instance, 2 studies focused on diabetes tracking [4],
[17], 1 study focused on chronic disease students to help them moving to
adult-oriented health care[18], 2 studies focused on maintaining good sleep
pattern[19], [20] and 2 studies involved Diet and fitness along with other
kinds of tracked personal data [5], [21].

###  4.2  Type of Users

Based on the papers found, Table 5 represents that 15 papers addressed the
second research question. We grouped the users into two groups. Firstly, the
public who are interested in self-tracking. Secondly, users who have been
recruited to evaluate an application or framework. There are two attributes
considered of the target audience in these papers: users’ age and experience
level.

In terms of the first group, six papers covered interested public participants
to explore their interaction, engagement and feedback on self-tracking
applications or prototypes. In addition, two papers [5], [3] investigated
public users’ -who have been encouraged by the quantified self-movement that
was established in 2008- interaction with personal data collected and feedback
on application reviews. In these studies, users recorded their experience in
collecting their data, stating the data items been tracked, listing the tools
been used to collect these data, explaining the benefits of tracking personal
data, and exploring the UX issues when using self-tracking tools. Another
paper covered public people who are interested in tracking specific kinds of
personal data such as health and physical activity [22]. Study [23] explored
the effectiveness of elicitation interviews to explore users experience in
collecting and visualising data by interviewing ten public individuals who are
interested in data visualisation. Study [24] involved people who were
motivated to self-track to find out the effectiveness of building their
tracking activity. Finally, the study [20] focused on developing an
application that supports users’ sleeping patterns. The participants involved
evaluated the application based on its learnability, appeal, and distraction.

In terms of the second group, two papers targeted diabetes people and
educators: One paper targeted diabetes people who do not know self-tracking
habits to discover the effectiveness of data visualisation adopted in diabetes
commercial applications [4]. The other study targeted diabetes people who have
been trained to track their health data in order to understand how these
groups of people are tracking their habits for health improvement [17]. One
paper recruited students to act as designers to specify the suitable
visualisation interface [18]. Another paper recruited individuals from the HCI
lab to conduct the study of evaluating smartphone usage data visualisation
[15]. Another couple of papers recruited a naive individual to understand
users needs and expectations concerning personalising data visualisation [25],
[26]. There is one paper that involved a group of developers who are
interested in building self-tracking tools in order to track the mood[16].
Finally, two papers have not mentioned the users’ types involved in the study.
These studies focused on finding out the guidelines and recommendations in
designing visualisation interfaces on tablets, coping challenges of
visualising big data on mobile devices [27],[28].

According to the focus points that we have decided, only six have indicated
the users’ age ranges from 22 to above 50 years. Furthermore, only four papers
have indicated the knowledge level of the users that ranges from naïve to
expert users. Although six papers have focused on public users, these studies
focused on types of tracked personal data but not visualising these data.
Therefore, more studies are needed on how public users interact with
visualising their data.

###  4.3 Mobile Devices: Benefits and Challenges

To address the third research question, we found 15 papers covering various
types of mobile devices adopted in self-tracking personal data, the advantages
of using mobile devices and the challenges of mobile devices in terms of
hardware and data visualisation. As shown in Table 6, out of 15 papers, 13
studies have specified the types of devices included in the study. We
classified these papers into two groups, the former is related to papers that
targeted smartphone applications in terms of building and evaluating them, and
the latter is related to combining smartphone and wearable devices.

To elaborate on the first group, We found eight studies focused on developing
tracking applications that suit the currently available mobile devices’
operating systems. One paper involved a group of developers who are interested
in building self-tracking tools in order to track the mood and explored the
interface issues when adopting data visualisation on tablets [27]. Another
study included creating an application on mobile devices with different
Operating systems[28]. Furthermore, we found study evaluated diabetes
commercial application and its data visualisation effectiveness to deliver
information [4] and a study evaluated small multiples and animation data
visualisation on a couple of operating system (Android and ISO) [29]. We found
a study focused on smartphone usage by building a prototype of a person
analytics tool to get insights on personal life through smartphone usage[15].
Similar to the previously mentioned study, there is another paper discussed
how smartphone usage affects sleeping habits by approaching three services:
triggering for more walk, smartphone time usage and sleeping duration and
relaxing before bedtime [19]. In addition to these studies, there is another
study that helped people to sleep better by developing applications [20]. The
latest study focused on evaluating the usability of 62 applications in order
to build an application that enables users to customise their tracking
activity and study[24].

In terms of the second group, we found that Samsung smartwatch and Fitbit were
involved in the following study as personal data collection sources. For
instance, study [25] adopted wearable devices as a personal data source. The
application built in this study has used these wearable devices to collect
personal data and visualise it using timeline and colours to enable users to
see their photos at different moments. Study [30] adopted a Fitbit bracelet to
collect and visualise data doubles. Study [26] familiarised mobile
applications and bracelets to enhance and explore users experiences to collect
and visualise their data, and study [31] introduced smartwatch as a controller
when visualising data on a large screen. Study [32] presented various types of
wearable devices such as Samsung smartwatch, Fitbit HR, spire devise and mood
metric ring. There are 3 papers that have not mention the type of devices (
[3] , [17] , [18])

There are indeed various studies that cover smartphones and tablets in
different areas. These areas included developing personal data applications
for different purposes, evaluating and reviewing current self-tracking
applications, and exploring UX issues in data visualisation. Nevertheless,
there is limited focus on the device performance and capability to adopt
personal data visualisation.

To summarise the findings of this section, smartphones, tablets, and
smartwatches or bracelets are identified as mobile devices. While mobile
devices as device have been mentioned clearly in 4 studies [28][29][20][24],
smartphone as a general term has been clearly identified in three papers
[4][15] [32] . Tablets have been identified clearly in one study [27].
Interestingly, smartwatches or bracelets have been included in five papers as
a wearable devices that can be used to track physical activity as personal
data.

Table 7 has shown that six studies mentioned the benefits of adopting mobile
devices, and another four mentioned mobile devices’ issues when visualising
personal data and using self-tracking applications.

We found that, small screen is the main issues of adopting personal data
visualisation on mobile devices [4][22][24]. Furthermore, scale-ability [29],
battery drain [24] and limited functionality issues were results of studies
conducted in reviewing applications [4] .

In contrast, the built in sensors[16], powerful processor[29], high resolution
display [29], providing personal space [18], low maintenance[22] and easy
access to data [6][22] have supported mobile devices to be adopted in
visualizing personal data.

###  4.4 Visualisation Tasks and Techniques

To address the fourth research question, we classify visualisation features
into three general aspects: visualisation tasks, visualisation types, and
interaction techniques. The first aspect is visualisation tasks which are
shown in table 9. We found 14 papers that have covered visualisation tasks.
These tasks are either adopted or need to be adopted to get better
visualisation interaction. The second aspect is visualisation types which are
shown in table 8. Nine studies have evaluated various visualisation types
either directly or as part of a self-tracking application. Finally, the third
aspect is related to visualisation interaction techniques, in which four
papers have suggested that there is a need for further visualisation
interaction techniques.

The adopted visualisation tasks are read-only, in which users can interact
with their self-tracking application by reading the available graphs. For
example, a spatial map is utilised to show users photos based on a timeline
and enable them to remember facts that happened on a specific day [3]. There
is a limited number of tasks attempted to interact with personal information
such as sharing information [16],[14], adjusting visualisation [14], filtering
[1], [31] , elaborating [14] , selecting [14], and configuring data [14].
Furthermore, identify and compare trends [29] adopted to compare between two
visualisation types performance in mobile devices. On the other hand, some
tasks have been required to be adopted, which are building visualisation [20]
and setting self-tracking goals by oneself [33].

Regarding the visualisation types that have been mentioned in the papers. All
of them were 2D charts, line graph [5][3], pie charts [4], info-graphic
visualisation [13] [2], map visualisation [14], colours[23] [18], patterns
[23] , time-series graph [3], Multivariate and animation visualisation [29].

In terms of the visualisation techniques, we found four different techniques
that need to be explored. The first is data storytelling, which emerged a new
class of self-service application, and include dynamic query, visual analytics
and interactive presentation[2]. Furthermore, the same source opened a new
research area related to representing data physically, in which data can be
represented in 5 forms of physical modalities such as data sculptures, Ambient
display, pixel sculpture, object augmentation, wearable visualisation [34].

The other two techniques that are needed to support understanding
visualisation are adding natural language to give a description of the
presented visualisation types and avoid various understanding [33], [26], and
auto-generated visualisation [24]. From our point of view, there are some
missing techniques to be adopted to visualise personal data on mobile devices
such as augmented reality. As the current smartphones have already adopt
augmented reality, it would be valuable and exciting to adopt personal data
visualisation using this technique.

###  4.5 Feedback

We found only eight papers that have included users reviews of data
visualisation that have been adopted in self-tracking applications. There is
one study that has focused on building an application but, it has not been
reviewed yet[18].

These eight papers have summarised users feedback in which we can discuss them
from two perspectives. Firstly, there are three papers involved in reviewing
used visualisation types and users engagement. In study [3], the researcher
collected information from the quantified-self website in which public users
posted their experiences and shared knowledge related to UX issues when using
Personal informatics applications. To summarise the results of this study, the
researcher has collected the users’ feedback and found that line graph is not
easy to be understood by everyone, time series as a graph is the most used
visualisation type as it is easy to track various progressing. In the
quantified self-study that collected information from user experience and
shared knowledge, it is noticed that not all users can understand the simple
graphs such as line graphs and most of the users liked the sharing feature
[5]. In the same study, users developed some suggestions, including fun
factors such as rewards and budges when they achieve goals to increase the
level of engagement and simplify user inputs. Another study showed different
user feedback compared two visualisation types (small multiples and animation
visualisation). Users preferred small multiples scatterplots because it was
faster to find information than the animation. However, the group that
experienced animation visualisation reported more confidence than the first
group in terms of confidence. It can be noticed that both visualisations
suited mobile devices, with large datasets citer10. Further studies need to be
done to check the compatibility of various visualisation types on mobile
devices.

Secondly, four papers summarised the recommendation that users stated in the
application/prototype that they reviewed. 2 of them have supported the need
for natural language to be integrated with data visualisation to have better
understanding [26], [33]. The context of both studies was distinguished; in
the study, [26] 14 naive participants were recruited to share their experience
in collecting their data and visualised it. Therefore, they have suggested
that adding natural language with data visualisation would improve their
engagement and understanding. In study, [33] researcher designed a hypothesis
for better data visualisation understanding. Thus, they suggested adding
natural language to explain the presented data visualisation so users get a
similar understanding of the presented data. Based on the feedback collected
from quantified selves in study [3], fun factors need to be considered in
designing personal data visualisation applications as it encourages users to
be engaged with their data. These fun factors can be getting budges for goal
achievement or enabling sharing their achievements with others. In paper [4],
13 diabetes users reviewed three commercial applications and filled a survey
that is related to their interaction with these applications. They stated that
data visualisation was not offering the help of understanding the information,
not only showing numbers, but they want something that graphically tells them
what they do is right or wrong, they want to know how their choice would
affect their sugar level. In the same study, the pie chart took the high rates
in terms of being easy to read as users claimed that it helped in gaining
cause and effect concept. There were some recommendations as well. That there
is a need for natural language to support data visualisation interpretation.
In the activity tracking technology study, the participants preferred websites
over mobile devices as they showed graphs with more details. Although this
research paper had multiple screenshots that presented data visualisation
graphs, the research did not focus on this aspect when reviewing the
application [22]. Although testing visualisation was not an aspect of being
examined in the field study for OMNI application, participants commented
actively on the visualisation aspect. Therefore, researchers agreed that the
application needs better data visualisation [24]. In the smartphone usage
study, the interviewed volunteers were asked about three issues, usability,
visual design and other suggestions. In terms of usability, most of the
volunteers recorded positive responses to learning more about themselves using
the tool. In addition, they were interested in exploring their smartphone
usage data. In terms of visual design, most users presented a positive
attitude towards the interface in general. The flexibility, friendly and
smooth interaction with the application make users confident to use the
application. In contrast, there were a couple of issues related to the same
aspect. For instance, colour coding was confusing for some users, and other
users claim that the application was limited in terms of further exploring
data [15].

##  5 Discussion

###  5.1 Self-tracked personal data

Based on the above section, we think that the analysed studies have perfectly
answered the research question that we proposed related to the types of
tracked personal data. However, the only personal data examined and attempted
in different studies were limited. For example, diabetes, smartphone usage,
mood and sleeping pattern were the personal data tracked using mobile
applications. We expect this review to be used as a start for researchers to
acknowledge the various types of personal data individuals would like to
track. Furthermore, it opens another research area in investigating more
personal data to be tracked and visualised, especially with the emergence of
mobile devices and wearable devices and their built-in sensors.

###  5.2 Type of Users

In terms of types of users, we observed that participants involved in the
studies were varied. Most of them have been chosen based on specific inclusion
criteria that suited the study target and type or recruited from the same team
that was developing the project. Researchers may have guidance on various
types of users and participants that would be recruited. However, from our
point of view, we would prefer to focus more on non-expert individuals of
various ages and levels of experiences to explore their interaction and
interest to interact with personal data visualisation. Furthermore, we would
focus on mobile device users to understand how frequently they are using self-
tracking applications and how they interact with data visualisation presented
in such applications.

###  5.3 Mobile Devices, Benefits and Challenges

We found that smartphones as mobile devices were adopted in various studies.
However, limited papers have focused on the benefits and challenges of
adopting data visualisation in mobile devices. Furthermore, the available
papers indeed specified the type of mobile device used in the study, yet, few
of them specified the capability of these devices to adopt data visualisation.
Thus, we need more research on mobile phones’ capability to adopt data
visualisation and specify visualisation types, tasks, and techniques that
could be adopted using mobile devices.

###  5.4 Visualisation Tasks and Technique

The role of data visualisation to present tracking personal data is important.
Therefore, we discussed visualisation tasks, techniques, and types mentioned
amongst the reviewed papers in this section.

####  5.4.1 Visualisation Tasks:

Although there are some visualisation tasks mentioned in various papers, we
recommend that a large study is needed to examine these tasks and its adoption
to mobile devices environment.Furthermore, we recommend that it is needed to
compare these tasks with the ten frequent tasks that Saket et al. published in
their paper[35] to have a full list of basic visualisation types and analytic
tasks that are required to analyse personal data.

####  5.4.2 Visualisation Types:

We recommend to do more research on suitable visualisation charts that match
users preferences as visualisation types in the result section do not
sufficiently address the data visualisation research question. As there are
still missing visualisation types that have not been mentioned across the
selected studies. We expect that it would be useful to investigate how to
select the best visualisation type to present the needed data and the
visualisation type that might suit various types of personal data.

####  5.4.3 Visualisation Interaction Techniques:

We found a limited number of papers that mentioned visualisation interaction
techniques, and all of them are suggestions from users feedback. The suggested
techniques were adding natural language to interpret the information
represented in the visualisation type and auto-generation visualisation types
to better select the best visualisation to present personal data. We think
that this part requires more research studies, as some techniques have not
been investigated yet. For example, augmented reality, in which charts can be
represented as 3D charts. Augmented reality has been applied already to
present big data in different sectors. We are suggesting to examine its role
in visualising personal data [34].

###  5.5 Feedback

Three interesting recommendations came out of the studies found, such as
adopting natural language to support understanding the presented
visualisation, adding budges for completing goals, and auto-generating for
visualisation types as it would better select the suitable visualisation type.
Yet, from our point of view, the available feedback from users regarding data
visualisations is not sufficient. The focus of the papers we found was to
evaluate self-tracking applications in general. However, one paper mentioned
that participants did evaluate data visualisation, although that was not part
of the application evaluation. Therefore, we suggest exploring personal data
visualisation on mobile devices and how people are interacting with it is
needed to understand the public’s requirement in terms of visualising personal
data.

The limitations of our SLR are, most of the papers found focused on self-
tracking aspects. Data visualisation as part of self-tracking were discussed
as a minor aspect of visualisation types, tasks and interface design.
Furthermore, although many papers found mentioned mobile applications, none of
them focused mainly on the benefits and challenges of adopting personal data
visualisation on mobile devices. Therefore, this information is limited, and
research needs to be conducted to summarise the best practice of personal data
visualisation using mobile devices. A further limitation is the found user
reviews of self-tracking applications, we tried to find out the feedback
related to data visualisation, but it was limited as the focus was reviewing
the entire application.

##  6 Conclusion

To summarise this SLR, the papers that have been selected are based on the
inclusion criteria provided and the search phrase adopted. Then, the
researcher summarised the found papers and found out the results for the
research questions. Many papers have addressed the first research questions
related to the type of tracked personal data. However, the found papers have
not sufficiently addressed the other research questions. Therefore, to bridge
this gap, more studies need to be done in the following areas: exploring the
public’s attitude toward visualising personal data and engaging with it.
Another research area that needs to be focused on is the capability of mobile
devices to adopt various visualisation techniques and identify the basic and
advanced tasks that users can do using data visualisation.

Table 10: A list of papers Title | coding
---|---
Reaching Broader Audiences With Data Visualization | P1
An evaluation-guided approach for effective data visualization on tablets | P2
Making Sense: An Innovative Data Visualization Application Utilized Via Mobile Platform | P3
Understanding quantified-selfers’ practices in collecting and exploring personal data | P4
Exploring UX issues in Quantified Self technologies | P5
Questioning the Reflection Paradigm for Diabetes Mobile Apps | P6
Personal discovery in diabetes self-management: discovering cause and effect using self-monitoring data | P7
The KOOLO app | P8
A comparative evaluation of animation and small multiples for trend visualization on mobile phones | P9
A personal visual analytic on smartphone usage data | P10
Civic Participation and Empowerment through Visualization | P11
Nudge Better Quantified-Self with Context-Aware and Proactive Services | P12
The quantified self: Fundamental disruption in big data science and biological discovery | P13
A quantum of self: A study of self-quantification and self-disclosure | P14
The diverse domains of quantified selves: self-tracking modes and data veillance | P15
Regaining control–the “quantified self” movement and the creation of the postmodern human | P16
Envisioning the future of personalization through personal informatics: A user study | P17
Quantified factory worker-expert evaluation and ethical considerations of wearable self-tracking devices | P18
When david meets goliath: Combining smartwatches with a large vertical display for visual data exploration | P19
Visual mementos: Reflecting memories with personal data | P20
Revisiting personal information management through information practices with activity tracking technology | P21
The elicitation interview technique: Capturing people’s experiences of data representations | P22
OmniTrack: A flexible self-tracking approach leveraging semi-automated tracking | P23
Visualized and interacted life: Personal analytics and engagements with data doubles | P24
Designing a personal informatics system for users without experience in self-tracking: a case study | P25
Know thyself: a theory of the self for personal informatics | P26
Personal visualization and personal visual analytics | P27
ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display | P28
Expanding research methods for a realistic understanding of personal visualization | P29

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