JMIR MEDICAL INFORMATICS

Theis et al

Original Paper

Task-Data Taxonomy for Health Data Visualizations: Web-Based
Survey With Experts and Older Adults
Sabine Theis, MSc; Peter Wilhelm Victor Rasche, MSc; Christina Bröhl, MSc; Matthias Wille, Dr Phil; Alexander
Mertens, Dipl-Inform, Dr Ing, Dr rer medic
Human Factors Engineering and Ergonomics in Healthcare, Chair and Institute of Industrial Engineering and Ergonomics, RWTH Aachen University,
Aachen, Germany

Corresponding Author:
Sabine Theis, MSc
Human Factors Engineering and Ergonomics in Healthcare
Chair and Institute of Industrial Engineering and Ergonomics
RWTH Aachen University
Bergdriesch 27
Aachen, 52062
Germany
Phone: 49 492412418099469
Email: s.theis@iaw.rwth-aachen.de

Abstract
Background: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation.
Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the typeof data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-datataxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task
taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent
prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists.
Objective: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion ofolder adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking
background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empiricalevaluation and to facilitate a common understanding and language in eHealth data visualization.
Methods: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively
analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations.
Results: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts andolder adults disagreed on the importance of mentoring (χ24=14.1, P=.002) and monitoring (χ24=22.1, P<.001). The answers to
the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality
management to the aforementioned tasks. Here, group differences in normalized code counts were identified for “monitoring”
between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02).
Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned toeHealth tasks by both experimental groups.
Conclusions: We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides
a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At thesame time, the functionality dimension of the taxonomy for telemedicine—chosen as the basis for the construction of present
taxonomy—was confirmed.
(JMIR Med Inform 2018;6(3):e39) doi: 10.2196/medinform.9394
KEYWORDS
classification; data display; computer graphics; task performance and analysis; medicine; telemedicine; user/machine systems;
human factors

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Introduction
Overview
Health care services are currently undergoing a digitaltransformation that is increasing the amount of clinical andpersonal health data. Data visualizations enable people toanalyze and understand these data to make more informed
decisions and to promote health-improving behavior [1-3].
Information and communication technology (ICT) development
is the driving force behind the digitization of health services.
In the 1990s, digital tools were differentiated from their analog
counterparts with the prefix “e-.” Mail became email andcommerce became e-commerce. Likewise, health became
eHealth. The term describes all health services supported by
ICT [4]. A definition covering all aspects of the term has notbeen achieved to date because it depends on ongoing
technological development and diversity [5]. The major part ofeHealth systems processes data to make it accessible to the user.

Data Visualization
But what does the term data actually mean? Data—as the pluralof the Latium datum—labels “factual information such asmeasurements or statistics used as a basis for reasoning,
discussion, or calculation” [6]. Data results from a measurement[7]. In computer science, “data” is understood asmachine-readable, digital representation of information encoded
into character(s) (strings) following a syntax [8]. In order toabstract the information from data, it must be interpreted in acontext of meaning; therefore, the user must be able to perceive
and understand it [9]. Data visualizations are a way to make useof the effective visual perception channel to exchange
information inherited in data [7]. By assigning graphicalattributes to data, users can grasp data characteristics or identify
new patterns [10-12]. As a graphical representation of data andstatistical concepts, data visualizations particularly support
decision making [13]. Data analysts, scientists, and statisticalexperts have been among the primary users of data visualization
to date [14], but digitization of health services together withdemographic change [15] and the recently observable shift
toward patient empowerment are leading to an increase in thenumber of older adults without special background knowledge
using data visualizations [16-21]. Accordingly, research on thevisualization of health data is increasingly taking into accountthe perspective of older adults for design and evaluation [22-24].

Task Models and Taxonomies
Before developers visualize data, they identify tasks relevant
to users and data relevant to these tasks [25]. This ensures thatvisualization dashboards optimally support users in reaching
their goals. In user-centered development, this is called task
analysis as one method of the requirement analysis [26-28].
Thus, knowledge of visualization tasks is important for theselection or construction of suitable visual representations, atthe same time it supports the empirical visualization evaluation
during the selection of experimental tasks.
Tasks differ in their granularity and degree of abstraction[29,30]. For example, “curing a disease” is a domain task withlow granularity (high-level task), whereas “compare a patient’s
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Theis et alheart rate variability data to detect anomalies” describes a
granular domain task (low-level task). Visualization tasks are
determined by the user perspective [31] and numerous models
exist to capture those inferring layers of data visualization tasks
or processes [32-35]. Our work refers to Munzner’s model ofnested layers [36]. Munzner’s nested model describes theprocedure of data visualization design, starting with theinvestigation of domain tasks and data, because users have theirown vocabulary to describe it. Subsequently, the domain
problems have to be translated into abstract visualization tasks
and data types as a vocabulary for data visualization. Data types
in this context are defined by the kind of data to be visualized.
In the third layer of Munzner’s nested model, visual encodings
and interaction methods for data and task abstractions aredeveloped so that corresponding algorithms can be developed
at the innermost level. In this model, the output of one layer isthe input for the subsequent one.
Abstract visualization tasks have often been listed alone or
together with data types in the form of taxonomies [37].
Taxonomies are hierarchical structures originally used to classify
organisms. Later, computer science used them to structureknowledge within knowledge-based systems or forsoftware-testing research [38]. They provide conceptual clarityof a domain and categorize information for increased theoreticalunderstanding. Another advantage is that taxonomies foster
generalizability in empirical research if evaluation considers its
tasks and data types [27,37,39-43]. Taxonomies also allow
precise comparisons across different visualization tools andapplication domains. Work procedures can be analyzed using
a domain-independent language, so that comparative analysesof tasks involving different visualization tools in different
disciplines can be carried out [38,39]. A taxonomy is empiricallybuilt as the hierarchy of the concepts are classified by reason
or measured similarity found in observed variables. A typology,
in contrast, classifies various types that have equalcharacteristics and splits concepts into different types along atleast two dimensions. It does not necessarily rely on empiricalmethods, and elements are less strictly reliant on the hierarchy
as with a taxonomy.
An abstract task typology emerged from Munzner’s [36] nested
model and was developed by Brehmer and Munzner [44]. Theirtypology includes a set of visualization tasks and data types
with different levels of granularity (high level to low level),covering objectives on the “why dimension,” actions on the“how dimension,” and data types on the “what dimension.” We
adopt their definition of data types: kind of data that can bevisualized. The authors state that their typology is relevant fornearly all application domains. Thus, it might be assumed that
it is also relevant for the eHealth domain. Empirical evidence
has yet to be provided and it is one of the objectives of theinvestigation presented in this paper. The typology by Brehmer
and Munzner partly overlaps with the data types from
Shneiderman’s task-by-data-type taxonomy [37]. In a
subsequently published article, Brehmer et al [45] characterizedtask sequences related to the visualization of dimensionallyreduced data. Brehmer et al [46] also encourage detailed
investigations of domain problems and tasks before the actual
design and evaluation.
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In the health and eHealth domain, taxonomies of general tasks
have so far been applied to make concepts and their relation
understandable. Furthermore, they are applied to differentiate
ambiguous medical vocabulary [47-51]. For example, Bashshur
et al [47] focused on the differentiation of different terms
describing ICT-mediated health. The authors constructed ataxonomy of telemedicine by differentiating the subdomains
telemedicine, telehealth, eHealth, and mHealth. They
differentiated, as a part of the functionality dimension, theabstract tasks consultation, diagnosis, mentoring, andmonitoring. The described taxonomy was built based on theexpertise of the authors. A user study or literature review was
not undertaken.

the domain of eHealth as possible. Therefore, the purpose ofthis study is to construct a taxonomy of abstract domain andabstract visualization tasks and data types. To the best of ourknowledge, we are the first to investigate the relation between
abstract visualization tasks and data types in the eHealth context
and thus the first to create a taxonomy that has domain relevancebut remains general across different eHealth applications. In
contrast to existing work, we construct the task taxonomy withthe help of prospective eHealth users (older adults), so that it
can foster the understanding of the user, the users’ tasks, andthe users’ domain understanding in order to become a language
among researchers from different domains. In this regard, thestudy will answer the following questions:

Problem Statement

1.

Previous literature illustrated the importance of task analysis
with users for the description, evaluation, and creation of datavisualizations. The problem is that if someone wants to develop
a data visualization system, he or she must first find out whichtasks the users consider relevant by means of user studies.
Abstract visualization tasks as well as data andapplication-specific tasks play a role here. However, if all users
had already been asked for their opinion on relevant tasks anddata, developers could spare this time-consuming step of task
analysis or at least parts of it.
In addition, it is almost impossible for scientists to adhere to
the tasks that are relevant for users during an empiricalevaluation of health data visualizations because this would
require a separate study as a preanalysis of relevant user tasks.
We believe not only developers may profit from using generaltasks relevant to users as input for a more specific requirement
analysis, but also researchers may consider them to selectexperimental tasks so that results from their evaluation becomecomparable and more generalizable across applications [52].
Although an extensive list of task taxonomies for datavisualization exists, they are not suitable to lead developers andscientists to select tasks relevant to users because they are basedon authors’ experience or on literature studies. They lack users’
perspectives. Another problem is that existing health taxonomies
do not consider visualization-specific tasks and data, andtaxonomies or typologies of abstract visualization tasks anddata lack a definition of the domain problem and correspondinguser tasks. Additionally, it remains unclear to what extent
existing visualization task and data type classifications [44,47]
are relevant to prospective eHealth users, who we—given thecontext of demographic change—consider to be older adults.
Older adults are the ones who will use the future systems thatdevelopers can build based on the output of current research
efforts. Furthermore, incidence, prevalence, and mortality arestrongly age dependent. For this reason, the risk of developing
age-dependent chronic diseases or psychological decline isrising. Thus, older adults are more likely to use eHealth systems
than younger people are.

Purpose of the Study
With this study, we want to make a first step toward
generalizable results of user-centered task analysis, so that
results are valuable to as many developers and researchers in
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2.

3.

Which abstract eHealth tasks do older adults consider
relevant for eHealth systems?
Which abstract visualization tasks and data types do older
adults consider relevant for medical consultation, diagnosis,
mentoring, and monitoring?
Does the rating from older adults differ from that of eHealthexperts?

Methods
Study Design
We devised a structured cross-sectional study with a nonrandom
sample to collect data from prospective eHealth users (older
adults) and eHealth experts.

Participants
Prospective eHealth users were targeted by focusing on
participants older than 50 years because they are the ones whowill use the future systems that developers can build based on
the output of current research efforts. Furthermore, incidence,prevalence, and mortality are strongly age dependent with risks
rising, for example, for chronic diseases or cognitive andphysical decline [53]. Finally, yet importantly, the handling andperception of technology or relevant tasks is strongly influenced
by the experiences individuals have made with technologicalartifacts during their lives. The so-called technology generations
represent a major influence here [54]. We wanted to focus on
the third group, called the “generation of technology spread”
aged between 53 and 67 years. Thus, a perspective uninfluenced
from existing digital technology could be taken, so thatdevelopers and researchers are able to orient toward the users’
native needs.
We additionally approached eHealth experts to provide evidence
for the validity of the answers from the group of older adults.
Basically, the expert’s answers served as baseline information
to show if and where background knowledge has an impact ornot.

Recruitment
The sampling procedure was nonprobabilistic and purposiveand respondents were selected based on their voluntary
willingness to participate [55,56]. To approach described
experimental groups with differing eHealth backgroundknowledge, different recruitment channels were applied. For
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only one question instead of with a battery of standard eHealthliteracy questions. This way we could keep the questionnaireas short as possible.
We sent the link to an online survey to eHealth experts fromour existing network in Germany. The survey was presented in
the German language. Then we automatically extracted
additional expert email addresses from the e-health-com
webpage, where readers recommend experts. Editors of thewebsite review the propositions and, if they consider a person
an expert, the website lists them all alphabetically and provides
one profile page per expert containing the name and position
together with a short description, contact information, andaffiliation description. We extracted all email addresses ofexperts automatically from the website by means of a Python
script. We subsequently sent the link to the online questionnaire
to 70 of these experts by email. Of these 70, 24 came fromeHealth industry companies either as chief executive officer of
a company selling eHealth products or as consultant active inthe domain, and 40 came from research institutes working withinformation technology in the health sector. The remainder were
medical experts from various domains or politics.
Older adults were selected by a clickworker platform [57]according to the demographic characteristic of being older than
50 years. Only participants who stated they were 50 years orolder were able to access the survey. The link to the survey wasdisplayed as a task on the website of the platform. At the endof the survey, participants were provided with an individually
generated password. The participants had to provide thepassword to be credited with money to their accounts. We opted
for a fee of €3 for completing the survey, which is relatively
high because it was an abstract, and probably a more difficult
subject, for participants not familiar with it.

Survey Instrument
Data were collected via an online survey. The rationale for theuse of an online questionnaire was that abstract tasks could be
investigated by means of a sample larger than would have beenpossible with observations or qualitative in-depth interviews.
The survey instrument was programmed and made available ona website using SurveyMonkey software [56].
The survey was introduced as a study “improving digital healthcare systems according to user needs” and consisted of fivequestions (for introduction text and survey questions see
Multimedia Appendix 1). All participants were informed about
the duration of the survey, data storage, and the leading
investigator. After an introductory page, individual pages with
one question per screen were displayed. The participant wasable to skip to the next question, but was not able to return tothe previous one. On all survey pages, it was ensured that theuser could see all answer options without the need for scrolling.
The answer options for all questions contained a checkbox withthe label “no answer” (n/a) to keep track if the participant justforgot, or could not, or did not want to provide an answer.
Therefore, answering a question was not mandatory in order to
not frustrate participants and to collect as much information as
the participants wanted to provide.

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Theis et al
Subsequent to the introductory page, experts and older adults
were asked to list medical tasks that they considered relevant
for health systems (see question #1 in the questionnaire in
Multimedia Appendix 1). This was presented as an open
question to not restrict the participants’ views and to collect asmuch input as possible, while excluding priming effects that
may occur if a list of possible answers was given. The second
question was a closed question asking users to rate the relevanceof consultation, diagnosis, mentoring, and monitoring for
eHealth on a five-point Likert scale (question #2). Subsequently,
participants had to rate the importance of abstract visualization
tasks (“why” dimension) [44] for each task in Bashshur et al’sfunctionality dimension (consultation, diagnosis, mentoring,and monitoring; question #3). Finally, the relevance of datatypes [37,44] for consultation, diagnosis, mentoring, andmonitoring [47] was assessed by means of a checkbox matrix(see question #4) and the background knowledge was assessedby a five-point Likert scale (see question #5). The survey wastested by two independent examiners with regard to wording
and technical functionality.

Data Collection
Data were collected between February 29 and March 14, 2016,
from a sample of eHealth experts, and on November 16, 2016,
from a sample of people older than 50 years without experiencein eHealth. The time interval between the elicitation with experts
and the one with older adults was because of prolonged approval
for using the clickworker portal.
In total, 163 unique individuals visited the website of our
Web-based survey. Identifying individuals was ensured by using
the IP address and cookie function. Of these 163 visitors, 65
never started the survey. In total, 98 visitors participated in thesurvey; the participation rate was 74.4%. The average time spentcompleting the survey was 16 minutes 52.96 seconds.

Analysis
The open-ended answer (see Multimedia Appendix 1, question#1) was first analyzed in terms of the overall word frequencies
with the help of MaxQDA software [58]. Word frequencies
were computed and all occurring words were listed. After theelimination of stop words (eg, in, on, where, why), the resulting
word list was manually scanned for activities and tasks. The
most frequent tasks became an item within a hierarchicaldictionary. The dictionary items were named and structured
referring to Bashshur et al’s [47] functionality dimensions. Eachdimension (consultation, diagnosis, mentoring, or monitoring)
became an item in the dictionary as a child of the root nodeeHealth tasks as soon as it occurred in the word list. Tasks fromthe word frequency list that did not have a “part of” relation
with existing categories were considered the child of the root
node eHealth tasks—and thus a sibling of consultation,
diagnosis, mentoring, or monitoring. Two experienced
qualitative analysts conducted the manual scanning of tasks andthe structuring of the dictionary independently. The two analysts
then discussed differing opinions when they assigned an itemfrom the frequency list to the dictionary or when they sorted
the dictionary and then implemented a common solution. Then,each item (task) in the dictionary contained a list of synonyms
from the word frequency list. For example, the dictionary item
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JMIR MEDICAL INFORMATICS“prevention” contained the words from the frequency list:
prevention, explanatory work, hospital stay, tertiary prevention,
avoidance, and care.
Subsequently, the MaxQDA software automatically coded allwords in the answer texts with the item name from the dictionary
they were assigned to. As a result, the dictionary contained codefrequencies per dictionary item, which added up from lower tohigher structural levels. Consequently, lower levels meant lower
code frequencies. Code frequencies of items on higher levels
were a sum of the item’s own code frequency together with thecode frequencies of all subordinate levels (child items).
For the statistical computation of code count differences among
the two experimental groups, the root level was included up toa maximum of the third level down the hierarchical structure.
For statistical computation, the code frequencies werenormalized with the total number of words the participants gavein their answer. Therefore, for the analysis of the answers on
the closed questions, we used SPSS software, version 22 (IBMCorp, Armonk, NY, USA). To compare answers of eHealthexperts and older adults, t tests for independent samples andchi-square tests were calculated, both at a significance level of.05.

Taxonomy Construction
Our taxonomy for eHealth visualization tasks and data included
the perspective of both experimental groups: the tasks and datatypes that they agreed on and group differences. Individual items
have been ranked from top to bottom, according to task
relevance. The more important an element was, the higher it
was positioned.
Taxonomy construction started with abstract eHealth tasks
resulting from closed question # 2 (see Multimedia Appendix
1) that participants rated as relevant. Tasks resulting from theopen question #1 that were not already referred to by resultsfrom question #2 were then added as siblings. Subsequently,
we added data types from question #3 and the top-ranked
abstract visualization tasks resulting from question #4 to eachof the four abstract eHealth tasks from question #2 (consultation,
diagnosis, mentoring, and monitoring).
Group differences were reflected by the outline of a taxonomy
item. Thick outlines of items illustrated that there were no
significant differences between older adults and eHealth experts,
whereas dotted outlines were significantly more important forexperts and thin-outlined items were significantly more
important for older adults.
Abstract visualization tasks that users most frequently
considered relevant were included in the taxonomy. To
determine the most relevant, we initially ranked all visualization
tasks based on the amount they were considered relevant(“relevance count”). Then we computed the difference between
the relevance counts of consecutive tasks (“relevance countdifference”). The relevance count difference measure served tointensify the differentiation between relevant and nonrelevant
abstract visualization tasks. This reinforcement of the distance
between abstract visualization tasks became necessary in order
to not include too many of them.
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All abstract visualization tasks mentioned more frequently thanthe one with the second-biggest relevance count difference toits successor were included in the taxonomy. For example, therelevance of visualization tasks for consultation exhibited thetwo biggest differences between perceive information and search
information (relevance count difference=8) and query
information and lookup information (relevance countdifference=6). In this case, query information and all tasks withhigher total frequency exhibiting no group differences became
part of the taxonomy.

Approval and Informed Consent
The Ethics Committee at RWTH (Rheinisch-Westfälische
Technische Hochschule) Aachen Faculty of Medicine, Germany,
authorized this study and its ethical and legal implications in
its statement EK236/16.

Results
Participants
A total of 98 people participated: 47 eHealth experts and 51
older (≥50 years) adults. The mean age of the eHealth experts
was 42.3 (SD 7.3) years, and the mean age for the older adults
was 55.8 (SD 5.9) years. The eHealth knowledge of the eHealthexperts was comprehensive (8/47, 17%) or very good (39/47,83%), whereas for the older adults it was neutral (15/51, 29%),
low (27/51, 53%), or very low (9/51, 18%).

Relevance of Medical Tasks
The most frequently mentioned eHealth tasks in open-answer
texts were cooperation, consultation, mentoring, monitoring,
documentation, communication, therapy, and quality
management (see Table 1). In contrast to Bashshur et al [47],diagnosis constituted a subtask of therapy. Of all therapy
subtasks, it had the highest frequency, followed by treatment.
Extensions of the original taxonomy could be made concerning
the scope of eHealth tasks, their structure, their validity, andtheir user relevance.
Group differences in the code count were computed on the firstand second level except for the functionality dimension
subconcept therapy, which together with all its child nodes
reached a triple-digit code count. All normalized frequencies
showed a normal distribution. An independent sample t test was
conducted—as the normalized code frequencies were continuous
variables not originating from predefined categories—tocompare the code count of tasks and all child nodes of “therapy”
between older adults and the eHealth experts. There was asignificant difference in the scores for the code frequency ofmonitoring for eHealth experts (mean 0.18, SD 0.23) and older
adults (mean 0.08, SD 0.15; t96=2.43, P=.02). Monitoring wasmore important for experts than for older adults.
The closed question on eHealth task relevance revealed thatacross groups the relevance of eHealth systems for consultationand monitoring was most frequently considered very high. We
received 70 valid answers, of which 51 came from older adults
and 19 from the eHealth expert group (Figure 1, Table 2).

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Table 1. Task relevance based on code frequencies in open answers in older adults and eHealth experts.
eHealth tasks and subtasks

Word frequencies in older adults, n

Word frequencies in experts, n

Total, N

Cooperation

10

14

24

Consultation (total)

39

66

105

Consultation

25

37

62

Physician-physician

3

18

21

Physician-patient

10

11

21

Physician-pharmacist

1

0

1

Monitoring (total)

42

82

124

Monitoring

23

48

71

Patient condition

0

1

1

Observation

3

0

3

Interpreting data

2

1

3

Data transmission

3

4

7

Data collection

6

8

14

Patient behavior

0

1

1

Medication

0

1

1

Therapy progression

0

4

4

Vital signs

0

13

13

Health condition

1

0

1

Wound surveillance

1

0

1

Identifying saliences

3

1

4

Patient condition

0

1

1

Mentoring (total)

22

21

43

Mentoring

11

11

22

Assistance

5

2

7

Health suggestions

0

2

2

Instructions

6

2

8

Education

0

4

4Documentation (total)

12

11

23

Documentation

6

7

13

Symptoms

101Surgery10

1

Wound documentation

0

2

2

Experience reports

2

0

2

Patient information

2

2

4

Communication (total)

44

52

96

Communication

25

29

54

Data handling/review

7

16

23

Information search

10

3

13

Date arrangement

2

3

5

Billing0

1

1Therapy (total)

98

165

263

Therapy

54

95

149

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eHealth tasks and subtasks

Theis et al
Word frequencies in older adults, n

Word frequencies in experts, n

Total, N

Home care2

57

Diagnosis

30

37

67

After treatment

2

4

6

Treatment

6

12

18

Rehabilitation

2

3

5

Prevention

2

9

11

1

3

4

Quality

Figure 1. Mean relevance of individual eHealth tasks according to older adults and eHealth experts. Task relevance rated from 0=very low to 5=veryhigh. Error bars represent 95% CI.

Table 2. Relevance of eHealth tasks in older adults (older) and eHealth experts(expert).
eHealth task

Very low, n (%)

Low, n (%)

Neutral, n (%)

High, n (%)

Very high, n (%)

Total, N

Experts

Older

Experts

Older

Experts

Older

Experts

Older

Experts

Older

Experts

Older

Consultation

0 (0)

3 (6)

1 (5)

1 (5)

2 (11)

8 (16)

7 (37)

19 (37)9 (47)

20 (38)19

51

Diagnosis

0 (0)

2 (4)

2 (11)

7 (14)

4 (21)

7 (14)

9 (47)

21 (41)

4 (21)

14 (28)19

51

Mentoring

0 (0)

2 (4)

2 (11)2 (4)

2 (11)

20 (39)7 (37)

21 (41)8 (42)

3 (6)

19

48

Monitoring

0 (0)

3 (6)

0 (0)

5 (10)0 (0)

11 (22)3 (16)

20 (38)16 (84)12 (24)

19

51

A chi-square test of independence was performed to examine
the relation between relevance counts and user group (older
adults, eHealth experts). The relation between these variables
was highly significant for mentoring (χ24=14.1, P=.002) andmonitoring (χ24=22.1, P<.001). Descriptive values of significant
relevant differences are illustrated in Figures 2 and 3.

Relevance of Abstract Visualization Tasks
The tasks perceive, search, record, present, annotate, and query
information were most important for consultation across thewhole sample. For diagnosis, the priorities were perceive,
discover, search, locate, and identify information. For mentoring,the most relevant abstract visualization tasks were present,
compare, generate, browse, and select information, whereas
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monitoring included generate, encode, consume, select, browse,
and compare information (Table 3).

Relevance of Data Types
A chi-square test of goodness-of-fit revealed that data types
relevant to consultation, diagnosis, mentoring, and monitoring
differed significantly between groups for most data types. The
five most relevant data types were included into the taxonomy.
Additionally, the data type relevance for eHealth tasks (Tables
4-7) exhibited few cases in which the relevance frequency
exceeded half the number of valid answers. The most relevant
data types for consultation were quantitative data, nominal data,time-dependent data, points in time, and single values.

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For diagnosis, time-dependent data, quantitative data, anomalies,
single values, and points in time were most important acrossgroups. Mentoring exhibited time-dependent data, rates ofchange, single values, quantitative data, and points in time as
the most relevant data types.

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According to the participants, monitoring required
time-dependent data as the most important data type, followedby temporal patterns, rates of change, and quantitative data, andsingle values. In total, time-dependent and quantitative data
could be numbered among the types with the highest
frequencies.

Figure 2. Relevance of eHealth for mentoring.

Figure 3. Relevance of eHealth for monitoring.

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Table 3. Abstract visualization tasks relevant for consultation, diagnosis, mentoring, and monitoring in older adults and eHealth experts (N=68).
N

Older adults, n (% from group)eHealth experts, n (% from group)

χ21

P value

Perceive information

53

39 (74)14 (26)0.3.75

Search information

41

27 (66)

14 (34)

4.6.05

Record information41

31 (76)

10 (24)0.1.88Present information40

27 (68)13 (33)

2.9.15

Annotate information40

29 (73)

11 (28)0.3.78

Query information

39

29 (74)10 (26)0.1.88Perceive information47

34 (72)13 (28)0.6.55

Discover information47

34 (72)13 (28)0.6.45

Search information46

33 (2)

13 (28)0.8.37Locate information

43

33 (77)10 (23)0.2.66

Identify information

42

34 (81)8 (19)

2.0.15

Present information36

24 (67)

12 (33)2.8.09Compare information36

27 (75)9 (25)

0.0.99

Generate information

33

23 (70)10 (30)

1.0.33

Browse information

33

22 (67)

11 (33)

2.4.12

Select information

33

22 (67)

11 (33)

2.4.16

Generate information

38

25 (66)

13 (34)3.9.05

Encode information37

33 (89)4 (10)

8.7.01

Consume information35

21 (60)14 (40)8.7.01

Select information35

26 (74)9 (26)

0.2.89

Browse information34

24 (71)

10 (29)0.7.40Compare information34

24 (71)

10 (29)0.7.40Visualization task
Consultation

Diagnosis

Mentoring

Monitoring

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Table 4. Data types relevant for consultation.
Data types

N

Older adults,

eHealth experts,

Total relevant,n (% from group)n (% from group)n (% from N)

χ21

P value

Quantitative data92

32 (71)15 (32)47(43)

14.1.001

Time dependent95

27 (56)

13 (32)40 (42)8.0.001Single values91

25 (57)13 (28)38 (42)

7.9.01

Points in time92

28 (62)9 (18)

37 (40)

17.7

.001

Nominal data79

19 (59)13 (28)32 (40)

7.9.01

Ordinal data77

16 (53)

13 (28)29 (38)5.1.03Time spans

92

22 (49)7 (15)

29 (32)

12.3

.001

Temporal patterns

90

19 (43)9 (17)

28 (31)

6.6.01Time intervals

91

20 (46)7 (15)

27 (30)

10.2

.001

Anomalies88

19 (46)8 (17)

27 (31)8.9.01Outlier82

14 (30)7 (39)

22 (27)

1.7.21

1-D data

74

9 (33)

12 (26)21 (28)0.5.59Distributions

79

14 (44)7 (15)

21 (27)

8.1.01

Rates of change

91

21 (30)10 (28)31 (34)7.1.01Groups69

8 (15)

8 (17)

16 (23)

3.2.12Time sequences88

14 (34)5 (11)

15 (17)

7.2.01

Synchronizations

82

9 (26)

6 (13)

15 (18)2.3.16Multidimensional data75

5 (18)

10 (21)

15 (20)0.1.78

Clusters

68

6 (29)

5 (11)

11 (16)

3.4.08

2-D data

76

3 (10)

7 (15)

10 (21)

0.3.73

3-D data

74

2 (7)

8 (17)

10 (21)1.4.31

Tree data73

7 (27)

7 (15)

14 (19)

1.6.23

Network data70

3 (13)

7 (15)

10 (14)0.1>.99Graphs78

7 (14)

9 (19)

16 (21)0.1.78

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Table 5. Data types relevant for diagnosis.
Data types

N

Older adults,

eHealth experts,

Total relevant,n (% from group)n (% from group)n (% from N)

χ21

P valueTime dependent95

37 (77)16 (34)53 (56)

17.8

.001

Quantitative data92

32 (71)16 (34)48 (52)

12.7

.001

Anomalies88

36 (88)12 (25)48 (55)

34.3

.001

Single values

91

32 (73)

13 (28)45 (50)

18.5

.001

Points in time92

32 (71)

11 (23)43 (47)

21.0

.001

Outliers82

24 (69)14 (30)38 (46)

12.1

.001

Time intervals

91

27 (61)

10 (21)37 (41)

15.1

.001

Time spans

92

27 (60)9 (19)

36 (39)

16.1

.001

Nominal data79

24 (75)

11 (23)35 (44)

20.5

.001

Rates of change

91

23 (52)11 (23)34 (37)

8.1.01

Temporal patterns

90

26 (61)7 (15)

33 (37)

20.1

.001

Time sequences

88

24 (59)7 (15)

31 (37)

18.3

.001

Ordinal data77

18 (60)11 (23)29 (38)10.5.01

2-D data

76

17 (59)12 (26)29 (38)8.3.01

1-D data74

17 (63)

11 (23)28 (38)

11.0

.001

Graphs78

19 (61)9 (19)

28 (36)

14.4

.001

3-D data

74

15 (56)

11 (23)26 (35)7.8.01

Distributions97

17 (53)9 (19)

26 (33)10.0.01Multidimensional data75

12 (43)

12 (26)24 (32)2.4.13Groups96

15 (68)8 (17)

23 (33)

17.7

.001

Clusters86

13 (62)9 (19)

22 (32)

12.1

.001

Synchronizations82

13 (37)5 (11)

18 (22)8.2.01

Net data

70

8 (35)

9 (19)

17 (24)

3.0.23

Tree data73

10 (39)6 (13)

16 (22)

6.5.02

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Table 6. Data types relevant for mentoring.
Data types

N

Older adults,

eHealth experts,

Total relevant,n (% from group)n (% from group)n (% from N)

χ21

P valueTime dependent95

18 (38)13 (28)31 (33)

1.1.38

Rates of change

91

19 (43)

12 (26)31 (34)3.1.08Single values

91

23 (52)8 (17)

31 (34)

12.6

.001

Quantitative data92

17 (38)12 (26)29 (32)

1.6.26

Points in time92

18 (40)11 (23)29 (32)

2.9.12Time spans

92

19 (42)9 (19)

28 (32)

5.8.02

Temporal patterns

90

17 (40)11 (23)28 (31)

2.0.12Anomalies88

18 (40)9 (19)

27 (31)

6.3.02

Nominal data79

13 (41)

13 (28)26 (33)

1.5.33Time intervals

91

14 (32)10 (21)24 (36)

1.3.34Time sequences

88

16 (39)8 (17)

24 (27)

5.3.03

Graphs78

15 (48)8 (17)

23 (30)8.8.01

Ordinal data77

9 (30)

13 (28)22 (29)0.1.99

1-D data

74

12 (44)9 (19)

21 (29)

5.4.03

Clusters68

10 (48)9 (19)

19 (28)5.

.02

2-D data

76

10 (35)8 (17)

18 (24)3.0.10Distributions

79

10 (31)8 (17)

18 (23)2.2.18

3-D data

74

8 (30)

9 (19)

17 (23)

1.1.39

Synchronizations82

12 (34)5 (11)

17 (21)

6.8.01Multidimensional data79

10 (36)7 (15)

17 (21)

4.3.05

Outlier82

10 (29)7 (15)

17 (21)

2.3.02Tree data73

10 (39)6 (13)

16 (22)6.5.02Groups69

8 (36)

8 (17)

16 (23)3.2.12

Net data

70

8 (35)

7 (15)

15 (21)

3.6.07

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Table 7. Data types relevant for monitoring.
Data types

N

Older adults,

eHealth experts,

Total relevant,n (% from group)n (% from group)n (% from N)

χ21

P valueTime dependent95

22 (46)15 (32)37 (40)1.9.21

Temporal patterns

90

22 (51)

13 (28)35 (39)5.2.03

Rates of change

91

21 (48)12 (26)33 (36)

4.8.03

Quantitative data92

14 (31)

17 (26)31 (34)

0.3.66

Points in time92

20 (44)11 (23)31 (34)4.6.05

Single values91

18 (41)

13 (28)31 (34)

1.8.19Time spans

92

19 (43)

11 (23)30 (33)3.7.08Graphs78

20 (65)10 (21)30 (39)

14.6

.001

Synchronizations82

21 (60)8 (17)

29 (35)

16.2

.001

Multidimensional data75

16 (57)13 (28)29 (39)6.4.02Time intervals

91

16 (36)

12 (26)28 (31)

1.3.36

2-D data

76

18 (46)9 (19)

27 (36)

14.4

<.001

Time sequences88

17 (42)10 (21)27 (31)

4.5.06Outliers82

14 (40)12 (26)26 (32)

1.9.23

Anomalies88

17 (42)9 (19)

26 (30)5.2.03

3-D data

74

14 (53)

11 (23)25 (34)

6.2.02

Distributions

79

16 (50)9 (19)

25 (32)

8.4.01

Nominal data79

15 (47)9 (19)

24 (30)

6.9.01

Ordinal data77

9 (30)

12 (26)21 (27)0.2.79Groups69

11 (50)10 (21)21 (30)5.8.02

1-D data

74

10 (37)10 (21)20 (27)

2.2.18

Clusters68

10 (48)9 (19)

19 (28)5.8.02

Tree data73

11 (42)7 15

18 (25)6.8.01

Net data

70

9 (39)

8 (17)

17 (24)

4.1.07

Task-Data Taxonomy for eHealth Visualizations
The task-data taxonomy for eHealth visualizations wasconstructed as described in the Methods section of our paper.
It shows which health tasks are important for them and which
abstract visualization tasks and data types are relevant for theabstract health tasks monitoring,” consultation, diagnosis, andhttp://medinform.jmir.org/2018/3/e39/

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mentoring. Group differences within the taxonomy are marked
with different outline characteristics of the taxonomy item,
which gives it higher meaning (dotted line=experts, thinline=older adults, thick line=no difference). It is striking that
all relevant abstract visualization tasks were considered relevant
by both groups, so there were no significant differences in
relevance (see Figure 4).

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Figure 4. The eHealth visualization task-data taxonomy.

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Discussion

discrepancy between groups regarding the importance of thetask monitoring was replicated as well.

Principal Findings

Against the background of current work on the development of
eHealth applications [9,62-66], we would have expected
monitoring to be the most relevant eHealth task. The results ofcode count frequencies do not match this expectation. Because
the results of previous studies on the investigation ofhealth-related information need are consistent with the fact that,for adults older than 50 years, diagnosis is the most important
information during the maintenance and administration of theirpersonal health [67], it can be assumed that older adults regard
the relevance of individual eHealth tasks less from a technology
perspective. Tasks that are important for personal health haveincreased importance for older adults.

This section offers a discussion and interpretation of the results
regarding the task analysis of eHealth and visualization tasks
and the corresponding data types across the two experimentalgroups: eHealth experts and older adults. We additionallyelaborate on the limitations of our findings and describe futurework.
The eHealth experts’ answer texts led to a total of 244 codes,
whereas 155 codes could be derived from the older adults’answer texts. Here, therapy was most frequently mentioned
across the whole sample with a number of 263 counts including
all subtasks (see Table 1), followed by monitoring (n=124),
consultation (n=105), communication (n=96), mentoring (n=43),
documentation (n=23), and quality management (n=4).
Monitoring was seen differently across user groups: it wassignificantly more important to the experts than to the older
adults. Diagnosis was found to be the most frequently mentioned
subtask of therapy followed by treatment, prevention, homecare, aftertreatment, and rehabilitation. The tasks at the second
level were cited less frequently. The therapeutic tasks users
considered most important were diagnosis and treatment. The
former is important for both groups, whereas medical or eHealthexperts cited treatment and prevention twice or more frequently.
Collecting data as well as monitoring of vital data were the mostcommonly mentioned subtasks of monitoring in the participants’opinions. Similar to the task at the first level (monitoring), there
is a clear group difference with a focus on the maximum in theexpert group.
It appears that code frequencies are relatively low compared to
the whole sample size. This can be explained by the short,keyword-like answers most participants gave. For example, thesample group of 98 mentioned monitoring only 61 times.
Considering that each code count cannot even be exclusively
assigned to one person, results from a starting point for
taxonomy construction requires future iterative improvementwith a larger sample size as well as a validation of thehierarchical arrangement of individual elements [59,60].
Results of the open answers confirm the relevance of thefunctionality dimension within the taxonomy of telemedicine
and that given task classification could be extended by the tasks
therapy, cooperation, documentation, communication, andquality management. Results regarding confirmation of thefunctionality dimension of the taxonomy of telemedicine are inline with the results of our previous work [61].
The abstract eHealth tasks of the functional dimensions formed
the root nodes of our taxonomy by their later assignment toabstract visualization tasks and data types; therefore, the validityof the analysis of the open and uninfluenced responses wasvalidated by directly querying their importance with five-point
Likert scales. The analysis of those closed questions on therelevance of the tasks consultation, diagnosis, mentoring, andmonitoring supported results from the qualitative content
analysis of open questions. Here, both user groups considered
monitoring and diagnosis the most important eHealth tasks. The
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The background knowledge of older adults regarding thetechnical possibilities of eHealth systems differs from that ofeHealth experts. Conventional constant monitoring or medical
control has been less important to laypeople because it might
be unclear to them that when it comes to continuous monitoringof sensor data, technical systems are often more accurate andstable at monitoring patients than medical personnel. The mental
model that seems to influence the answer—even if the term was
explained at the beginning of the survey—is more strongly
characterized by health-relevant tasks users know from theireveryday life, where the extensive introduction of digitalmonitoring systems is still pending in Germany.
At first sight, one might suspect this is a problem for the utilityof the developed task-data taxonomy. However, this is only thecase if one assumes that our taxonomy should precisely represent
the tasks currently present in systems. However, the aim of thetaxonomy is—as described at the outset—an increase in theuser-centricity of future systems. For our taxonomy, it is notimportant which tasks and data actually exist, but which are
relevant for prospective users, so that systems developed basedon presented taxonomy have the greatest possible value.
However, users’ perceptions of the relevance of individual tasks
and data types are of great importance.
The question on the relevance of abstract visualization tasks
was not answered by nearly a third of the participants (30/98).
Whether a lack of knowledge or a lack of motivation is
responsible cannot be determined on the basis of the data.
Because 75% of older adults and only 25% of experts answered
the question, despite experts having higher backgroundknowledge, motivation seems to be more likely an influencing
factor here.
We also assume that the eHealth systems including suchvisualizations are not available to some participants.
Therefore—as in the case of the abstract eHealth tasks—theresults identify potential areas where data visualizations could
enable experts or patients to be supported in the corresponding
medical task.
Our ranking of general eHealth tasks supports the general
understanding of the application context of eHealth and eHealthvisualizations from the perspective of prospective users (older
adults). Visualizations that support those general domain tasks
are expected to have a stronger impact. The intention here is
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not to invite visualization researchers to contribute designs to
the eHealth domain, but to identify potential for the applicationof visualizations within eHealth systems, an aspect that hasoften been overlooked.

Transfer of Knowledge
The presented results add to the increasing number of papers
that target hierarchical task structures to establish a common
vocabulary and understanding of visualization tasks and data
[68,69]. This work goes beyond that by considering the context
of eHealth including the perspective of the prospective user andsynthesizing their input in the form of eHealth task-datataxonomy. In this way, eHealth system developers andresearchers can use it as an orientation during requirement
analysis or as a guideline for the definition of experimental tasks
in visualization evaluation experiments.

Limitations
We consider the described eHealth task-data taxonomy asprovisional and subject to validation in the field. In addition,
we only tracked the subjectively perceived knowledge about
eHealth systems, so participants might lack familiarity with
abstract data types or task-data taxonomies or they may not befamiliar with online surveys and interactive Web tools such as
those used for our Web survey. Thus, we are not able to quantify
participants’ familiarity with concepts mentioned in this study
and this may have influenced our findings. Familiarity with
abstract data types and visualization tasks and styles common
to the survey website would have likely reduced some of thebarriers participants might have experienced.

Theis et al
Furthermore, as with subjective methods in general, results arelimited in a way that they reflect the perspective and mental
model of the participants together with their experiences. But
observations will have the drawback that achievable sample
sizes are much smaller, so that the results are hardly
generalizable to the whole eHealth domain.
Additional limitations of our study lie in the selective sample
caused by using an online questionnaire. People who are familiar
with technology are more likely to answer the questionnairethan people who are not. Additionally, the older adults were
paid, whereas the experts were not. This leads to different
motivations between the two groups, which could be an
influencing variable. This might have been the reason why the
numbers of completed answers varied in the expert group overthe length of the questionnaire (more were answered at the
beginning than at the end).

Conclusion
We successfully constructed a task-data taxonomy for eHealthdata visualizations by providing a general description of tasks
and data useful for health data visualizations. We have shownthat semantic approaches [26] are feasible to generally performtask analysis. Furthermore, the results empirically validated andranked Brehmer and Munzner’s [44] typology of abstract
visualization tasks, as well as the functionality dimension of
Bashshur et al’s [47] taxonomy of telemedicine. Time-dependent
data and searching for information within visualizations ofmonitoring data had the highest relevance across user groups.

Acknowledgments
This publication is part of the research project “TECH4AGE,” financed by the Federal Ministry of Education and Research(BMBF, under grant no: 16SV7111) and promoted by VDI/VDE Innovation + Technik GmbH.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Text survey introduction and questionnaire.
[PDF File (Adobe PDF File), 355KB-Multimedia Appendix 1]

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Abbreviations
ICT: information and communication technology

Edited by G Eysenbach; submitted 12.11.17; peer-reviewed by U Backonja, V West; comments to author 09.12.17; revised version
received 14.03.18; accepted 03.04.18; published 09.07.18
Please cite as:
Theis S, Rasche PWV, Bröhl C, Wille M, Mertens A
Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults
JMIR Med Inform 2018;6(3):e39
URL: http://medinform.jmir.org/2018/3/e39/
doi: 10.2196/medinform.9394
PMID: 29986844

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Theis et al©Sabine Theis, Peter Wilhelm Victor Rasche, Christina Bröhl, Matthias Wille, Alexander Mertens. Originally published in JMIR
Medical Informatics (http://medinform.jmir.org), 09.07.2018. This is an open-access article distributed under the terms of theCreative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
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