Software Design Principles for Digital Behavior Change Interventions
Lessons Learned from the MOPO Study
Lauri Tuovinen
1
, Riikka Ahola
2,3
, Maarit Kangas
2,3
, Raija Korpelainen
3,4,5
, Pekka Siirtola
1
,
Tim Luoto
6
, Riitta Pyky
2,3,4,5
, Juha R
¨
oning
1
and Timo J
¨
ams
¨
a
2,3
1
Department of Computer Science and Engineering, University of Oulu, Oulu, Finland
2
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
3
Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
4
Centre for Life Course Health Research, University of Oulu, Oulu, Finland
5
Department of Sports and Exercise Medicine, Oulu Deaconess Institute, Oulu, Finland
6
Department of Cultural Anthropology, University of Oulu, Oulu, Finland
Keywords:
Health Intervention, Behavior Change, Web Application, Software Design, Data Collection.
Abstract:
Using the Internet as a delivery channel has become a popular approach to conducting health promotion in-
terventions, and the evidence indicates that such interventions can be effective. In this paper we propose a set
of design principles and a generic architectural model based on experiences accumulated while developing a
Web-based application for a physical activation intervention. The proposed principles address the develop-
ment of an intervention application as an abstract entity, a platform for gathering data for the needs of three
principal stakeholder groups. The principles are derived from the purposes for which the data is gathered and
the constraints that may limit the availability of desired data; by observing these principles, developers of inter-
vention applications can identify the design trade-offs they need to make to ensure that all stakeholder needs
are adequately fulfilled. An evolutionary development process is proposed as a way of gradually working
toward an application that induces the desired effect on the behavior of the users.
1 INTRODUCTION
Computer software, and especially Web applications,
can be convenient and effective tools for the execution
of intervention studies that promote health behavior
change. However, the task of translating the objec-
tives of the intervention into software requirements is
far from trivial. The standard software engineering
practice of eliciting requirements from the customer
is not appropriate in this context, because there is no
customer in the conventional sense.
In the MOPO study
1
(Ahola et al., 2013), we have
developed and deployed a gamified Web portal in-
tended to persuade young men to improve their health
behavior, especially their physical activity. In this pa-
per we review the design challenges we needed to
overcome and the approach by which we chose to
address them. The paper focuses on problems that
1
http://www.tuunaamopo.fi/sivu/fi/mopo-
study in english/
we perceive as universal to all Internet-based behav-
ior change interventions, and on generalizing our ex-
periences as guidelines for developers of intervention
applications. The principal contribution of the paper
is a conceptual framework for designing the core ar-
chitecture of an intervention application and identify-
ing factors to be taken into account in the functional
specification of the application.
In particular, we observe that when considered in
isolation of intervention objectives, a software appli-
cation developed for a health promotion intervention
is essentially a platform for collecting data from vari-
ous sources and delivering it to various stakeholders.
With all the details specific to a given intervention
thus abstracted away, specifying such an application
is reduced to identifying the information needs of the
stakeholders and the sources from which the required
information can be acquired. On the other hand, it is
equally important to identify the constraints that may
prevent the fulfilment of some of those needs. If there
Tuovinen, L., Ahola, R., Kangas, M., Korpelainen, R., Siirtola, P., Luoto, T., Pyky, R., Röning, J. and Jämsä, T.
Software Design Principles for Digital Behavior Change Interventions - Lessons Learned from the MOPO Study.
DOI: 10.5220/0005656101750182
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 175-182
ISBN: 978-989-758-170-0
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
175
is some factor limiting the amount of data that it is
feasible to collect, then it may be necessary to bal-
ance what a stakeholder would ideally expect against
what is achievable in practice. Therefore, in order to
discover useful design guidelines, we will need to ex-
amine both the expectations and the limitations.
Regarding the scope of the paper, our intent is not
to claim that the abstraction of intervention applica-
tions as data collection platforms yields a compre-
hensive view, or that the list of stakeholders identi-
fied in the paper is exhaustive. What we do argue is
that the abstraction provides one useful view that can
and should be supplemented with views addressing
other aspects and stakeholders of intervention soft-
ware. The result is not a complete methodology or
a universal architecture, but rather an exploration of
what can be learned about the design of intervention
software by adopting this particular perspective.
Section 2 of this paper presents an overview of
the MOPO study and the Web application developed
for it, along with a review of related work. Section
3 examines the expectations of different stakeholders
and how they affect the design of intervention appli-
cations. Section 4 considers major trade-offs that may
need to be made in order to design a feasible solution.
Section 5 discusses the findings and some directions
for future work, and Section 6 concludes the paper.
2 BACKGROUND
The medical research community has been aware
of the potential of the Internet as a communica-
tions channel in public health promotion interventions
since the late 1990s (Cassell et al., 1998). A vast
number of Internet-based interventions have been car-
ried out since then, targeting a variety of unhealthy
behavioral patterns such as physical inactivity [e.g.,
(Napolitano et al., 2003; Van Den Berg et al., 2006)],
smoking [e.g., (Graham et al., 2007; Saul et al.,
2007)], heavy drinking [e.g., (Linke et al., 2007;
Cunningham et al., 2009)], and high fat intake [e.g.,
(Frenn et al., 2005; Oenema et al., 2008)]. Several
reviews and meta-analyses have also been conducted,
indicating that Internet-based interventions have sig-
nificant effects and providing recommendations for
future research and practice [e.g., (Bennett and Glas-
gow, 2009; Tate et al., 2009; Webb et al., 2010;
Brouwer et al., 2011)].
In the MOPO study, the principal target was to
persuade conscription-aged Finnish men to increase
their physical activity by means of a gamified Web
application. The effects of the application were stud-
ied in a 6-month randomized controlled trial (Ahola
et al., 2013). The study participants were recruited
at the annual conscription call-ups of the Finnish De-
fence Forces, which every male citizen of Finland is
required by law to attend in the year of his 18th birth-
day. Following two pilot studies carried out in 2011
and 2012 (Jauho et al., 2015), a full-scale interven-
tion was launched in 2013, with 248 participants in
the intervention group and 244 in the control group.
The interventions were carried out before the par-
ticipants began their national service, so that the study
tracked their activity as they went about their regular
daily lives. To measure their initial physical fitness, a
series of fitness tests were performed for each partic-
ipant and the results were recorded. At the end of the
study period, the same measurements were performed
again to see if there had been any significant change
in the fitness of the participants. To collect physical
activity data from the study participants, each partici-
pant was loaned a Polar Active activity monitor (Kin-
nunen et al., 2012) and asked to wear it for the du-
ration of the study; blinded devices, displaying only
date and time, were issued to the control group.
The application developed for the study is a Web
portal that provides the users with information on top-
ics related to health, well-being and fitness, generates
visualizations and verbal feedback on their physical
activity, allows them to play a location-based digi-
tal game that rewards activity and mobility, and pro-
vides them with links to other relevant services and
resources. To collect the activity data recorded by
the study participants into a database, the participants
were instructed to periodically upload the data logged
by the activity monitor to the intervention server.
Manual logging of exercises was also possible, to ac-
count for situations where the wearable monitor was
unavailable or did not generate useful data. In addi-
tion to the activity data, information on how the ap-
plication was used and perceived was collected in the
study. The application itself was designed to log a
number of events and statistics, and the study partic-
ipants were also requested to voice their opinions on
what they liked and disliked about the application.
As the number of Internet-based health promotion
interventions such as the MOPO study continues to
increase, we argue that there is a need for a develop-
ment methodology that addresses the challenges spe-
cific to intervention applications. There is some pre-
vious work in this area that should be noted. Some
authors have created primarily descriptive conceptu-
alizations and critical reviews of various aspects of
the field (Strecher, 2007; Barak et al., 2009; Danaher
and Seeley, 2009; Klasnja and Pratt, 2009), while oth-
ers have presented case reports of specific tools and
systems (Koskinen and Salminen, 2007; Alah
¨
aiv
¨
al
¨
a
HEALTHINF 2016 - 9th International Conference on Health Informatics
176
et al., 2013). Most pertinent to our work are process
models, frameworks and design principles for the de-
velopment of persuasive systems for health behavior
change (Skinner et al., 2006; LaMendola and Krysik,
2008; Mohr et al., 2014; Nguyen et al., 2015), along
with more generic design methodologies considered
suitable for persuasive technology, such as participa-
tory design (Kensing and Blomberg, 1998).
Perhaps the single most relevant piece of previous
research is the BIT model (Mohr et al., 2014), which
addresses the software design of intervention appli-
cations among other considerations. However, the
model concentrates on a single perspective, namely
building a system capable of effecting health behav-
ior change, and this is generally true of the related
work cited above. Based on what we learned from the
MOPO study, we argue that the development of inter-
vention software should be approached from multiple
perspectives to account for the fact that designing an
application for a health intervention is an optimization
problem where maximizing the efficacy of the inter-
vention is but one of several objectives. Another re-
cent and relevant study is (Nguyen et al., 2015), but its
topic is design of health information portals, whereas
our work concerns applications that are more interac-
tive and have more advanced application logic. On the
other hand, compared to more universal design prin-
ciples of persuasive technology or Web applications,
our work is more attuned to the problems and require-
ments specific to software for behavior change inter-
ventions. There is likely to be some overlap, but we
argue that intervention software is enough of a special
case to merit special treatment.
3 DESIGN PERSPECTIVES
Having established that the purpose of an interven-
tion application, when viewed on a high level of ab-
straction, is to collect, store and provide access to data
considered important for the success of the interven-
tion, we need to ask what exactly, then, is important.
The answer depends on which perspective the appli-
cation is viewed from; we discern three perspectives
that should be considered:
From the perspective of the study participants, the
primary concern is operation of the application:
what its features are and how they are presented
to and used by the participants. The application
must be able to collect and store all data required
for it to deliver its functionality such that the ap-
plication is relevant and interesting to the users.
From the perspective of the researchers, the pri-
mary concern is evaluation of the application:
how it performs with respect to the objectives set
for the intervention. The application must be able
to collect and store all data required for the re-
searchers to track relevant attributes of the study
participants and assess the performance of the ap-
plication objectively and reliably.
From the perspective of the developers, the pri-
mary concern is modification of the application:
how it could be improved to make it more effec-
tive as an intervention tool. The application must
be able to collect and store all data required for the
developers to clearly identify directions for fur-
ther development.
Although these are technically three distinct per-
spectives, in practice there is likely to be a substantial
amount of overlap. In the MOPO study, the physi-
cal activity data was used by both the researchers, to
enable them to see whether the interventions had any
effect, and the study participants, in the form of feed-
back intended to motivate them to achieve and retain a
healthy activity level. Similarly, the application usage
data was used by both the researchers, to enable them
to assess the extent to which the effects of the inter-
ventions were caused by the software, and the devel-
opers, to enable them to diagnose technical problems
and identify potential improvements.
Among the stakeholder groups identified above,
the researchers are in a key position when the func-
tionality of the application is specified. This is be-
cause all the requirements of the application are ulti-
mately dependent on the objectives of the study, and
it is up to the researchers to hypothesize on how to
achieve the objectives by means of software (opera-
tion), design a method for testing the hypothesis (eval-
uation), and draw conclusions on how to proceed af-
ter the study is finished (modification). Translating
this knowledge into software specifications requires
close cooperation between the researchers and the de-
velopers, preferably with at least some members of
the development team having a dual role.
Probably the most elusive of the three design per-
spectives is the perspective of the study participants.
Defining requirements to represent this perspective
involves a considerable amount of speculation on how
a given feature of the application would affect the be-
havior of the participants, and therefore a consider-
able amount of uncertainty. Consequently, this task
is substantially more difficult than specifying features
required for the application to support evaluation and
interpretation of the study results. In the MOPO
study, representatives of the target demographic par-
ticipated in small-scale live testing of various devel-
opment versions of the intervention application, but
even with the feedback from these tests, it was diffi-
Software Design Principles for Digital Behavior Change Interventions - Lessons Learned from the MOPO Study
177
cult to predict how the participants of the intervention
would ultimately respond to the application.
The challenging nature of developing software ca-
pable of inducing behavior change emphasizes the
importance of the developer perspective. With so
much uncertainty concerning the impact of the soft-
ware, there is a high probability that the desired effect
will not be achieved on the first attempt. This can be
responded to by adopting an evolutionary approach
where data collected during the study is used to make
decisions on how the software should be revised to
improve its impact on the behavior of the study par-
ticipants. The development process can thus be repre-
sented as a cycle of 4 Ds, as shown in Figure 1.
Achieving behavior change in the study partici-
pants is, of course, the primary goal in an interven-
tion study, but this does not mean that the other two
perspectives are of secondary importance: the im-
pact of the study cannot be reliably evaluated unless
the necessary data is available to the researchers, and
the evolutionary development process cannot advance
beyond the initial cycle unless the necessary data is
available to the developers. All three perspectives are
thus equally indispensable, and the needs of the corre-
sponding stakeholders are the fundamental forces that
shape the intervention application when considered as
a data collection platform. However, there are practi-
cal limits to what data, and how much data, it is feasi-
ble to collect in any given study; the factors that limit
data collection are discussed in the next section.
4 CONSTRAINTS AND
COMPROMISES
Analyzing the needs of the stakeholder groups yields
a specification that states what data should ideally be
collected in order to maximize the likelihood that all
the identified needs can be fulfilled. In reality, how-
ever, it may be necessary to settle for a suboptimal
Figure 1: 4D model of the development process of interven-
tion applications. During each cycle of the process, a new
version of the application is developed and deployed, data
is collected for evaluation purposes, and decisions on what
to do in the next cycle are made based on the findings.
but acceptable solution. We discern three major fac-
tors that may force such trade-offs to be made:
Cost: the resources available for data collection
and storage are limited by the budget of the study.
Privacy: the study participants may not be willing
to disclose some data that would be desirable to
have for the purposes of the study.
Convenience: the study participants may not be
willing to participate in data collection that re-
quires them to sacrifice their personal resources.
The cost of the resources required for data col-
lection and storage affects both which kinds of data
are available for collection and how much of them
can be collected during the study. If sensor devices
are to be used to collect data, then it is generally the
case that the more sophisticated the desired data is,
the more costly the required hardware will be. Data
storage, while less likely to prove prohibitively expen-
sive, nevertheless also needs some resources allocated
to it. Furthermore, both quantity and quality of data
may be constrained by personnel costs.
The privacy issue arises because some poten-
tial participants will object to being monitored, even
when given the assurance that their data will be used
only for the purposes of the study and kept strictly
separate from any information that might be used to
identify an individual participant. The more sensitive
the data that the participants are requested to give ac-
cess to, the more difficult it will be to find willing
participants, and the fewer participants there are, the
smaller the resulting body of data. Information con-
cerning the health of an individual is generally re-
garded as sensitive data, making privacy a potential
constraint in any study where some form of health in-
formation is collected from the participants.
Convenience is, in a sense, even more difficult to
factor in than privacy, because its effect on data col-
lection cannot be known until when the study is al-
ready underway. Even a relatively small investment
of time or effort may prove enough of an inconve-
nience to cause some study participants to drop out of
the study, at which point it may be too late to replace
them. Using data collection methods that depend on
the participants actively providing data therefore runs
the risk of losing participants over the course of the
study such that it is not easy to predict how many will
stay committed. The usability of the main application
is also an important factor, since the participants are
likely to have a low tolerance to poor usability in an
application they use mainly because they are expected
to, not because they have a genuine use for it.
The MOPO study provides examples of compro-
mises made with respect to each of the constraints
HEALTHINF 2016 - 9th International Conference on Health Informatics
178
identified above. For instance, we wanted to keep
track of how certain key application usage statistics
changed over the course of the study. Instead of sim-
ply sampling these numbers at regular intervals, we
took periodic snapshots of the application database to
account for additional research ideas that might occur
during or after the collection of the dataset. However,
to reduce the need for storage resources, the most vo-
luminous component of the database time series of
the study participants’ activity levels, sampled at 30-
second intervals was left out of the snapshots. This
was judged an acceptable trade-off, given that includ-
ing this data in the snapshots would have multiplied
their size by two orders of magnitude.
An example of a trade-off between data collec-
tion and privacy is the collection of data represent-
ing the application usage behavior of the study partic-
ipants. From a technical point of view, it would have
been relatively simple to log the navigation history of
each participant individually, but there is a significant
risk that this would have been viewed as overly in-
vasive. As a compromise, only statistical usage logs
were maintained, enabling the researchers and devel-
opers to see, for example, how much activity there
had been in each section of the application. An excep-
tion was made in the case of user logins; these were
logged individually, since this information was neces-
sary for the purpose of identifying and possibly con-
tacting study participants who had not used the appli-
cation, and also useful for troubleshooting purposes.
The convenience of data collection is the area
where it proved the most difficult to find a good trade-
off. Uploading activity data from Polar Active to the
application server required the study participants to
log in to a standalone client application that commu-
nicates with the monitor through Flowlink, a small
peripheral plugged into the USB port of the computer
running the client. To avoid overrunning the memory
buffer of Polar Active, each study participant needed
to repeat the upload process at least once every 21
days. In theory, this would not seem to be an exces-
sive inconvenience, but in practice, it was necessary
to send out reminders to the participants and to offer
them chances to win prizes by uploading their activ-
ity data. It was also found that any glitches in the
upload process, or in any other application function,
could have a substantial demoralizing effect, caus-
ing some participants to stop trying rather than con-
tact the developers for a solution (Luoto et al., 2014).
The precarious motivation of the participants poses a
considerable problem, since it would be important for
the developers to get their feedback on how to make
the application more captivating, but lacking the mo-
tive, the participants may not even use the application
enough to be able to give any meaningful feedback.
With regard to each constraining factor, it is im-
perative to make a deliberate decision on a trade-
off, to ensure that the outcome is both acceptable
and achievable. Overemphasizing either of these puts
the success of the study at risk: if too much weight
is placed on achievability, there is a risk of diluting
the objectives of the study, whereas if acceptability is
weighted too heavily, there is a risk that the study will
fail to collect enough data to support the objectives.
Either way, the further off balance the trade-off is, the
more difficult it will be to achieve good results.
5 DISCUSSION
Table 1 shows some metrics based on login statis-
tics that can be used to evaluate the acceptance of our
application in the 2012 and 2013 interventions. The
most notable change is the increase in the percentage
of the intervention group who tried the application at
least once; there is no improvement when we look at
the ratio of the number of users who logged in mul-
tiple times to the number of all users. Thus it seems
that although the 2013 study had more success in per-
suading participants to start using the application, the
users lost interest in the application at a similar rate,
with only a relatively small number of them becoming
regulars. This does not mean that the studies did not
yield any positive results, but data from other sources
suggests that these were mostly caused by other mo-
tivating factors such as the activity monitor.
Some of the difficulty of motivating the partici-
pants of the MOPO study can probably be attributed
to the target population being particularly challeng-
ing to address effectively. The adverse health effects
of sedentary lifestyle generally do not occur until later
in life, so it is difficult to get young people to feel that
Table 1: Some metrics computed from application login
statistics for the 2012 and 2013 interventions. Applica-
tion users are defined as intervention group members who
logged in at least once, returning users as those who logged
in at least twice, and monthly users as those who logged in
at least four times between the beginning of the intervention
in September and the end of the year.
2012 2013
Intervention group (N) 141 248
Application users (N
u
) 66 160
Ratio of N
u
to N 0.47 0.65
Returning users (N
r
) 49 117
Ratio of N
r
to N
u
0.74 0.73
Monthly users (N
m
) 24 59
Ratio of N
m
to N
u
0.36 0.37
Software Design Principles for Digital Behavior Change Interventions - Lessons Learned from the MOPO Study
179
participating in the intervention is important to them
personally, if their only motivation is the abstract
knowledge that they have an elevated risk of suffering
from certain medical conditions in the future. Under
these circumstances, when executing a study based on
monitoring the participants while they act freely in an
uncontrolled environment, it may prove a consider-
able challenge to ensure that the participants do not
forget or neglect their commitment.
Related to this is our observation that it is not ad-
visable to rely on study participants to report prob-
lems encountered while using the application. People
who use an application because they need it for some-
thing have an incentive to report glitches, namely the
hope that they will be patched in a future update, but
this logic fails in the case of intervention applica-
tions, because the users may have no particular inter-
est in using the application. In this case their willing-
ness to report problems again depends on the depth of
their commitment, and an uncommitted study partic-
ipant, when encountering a problem that effectively
prevents them from using the application, may sim-
ply give up rather than seek help to solve the prob-
lem. Lesser usability issues, such as an unintuitive or
unattractive user interface, are similarly more discour-
aging if the user is not internally motivated to con-
tinue using the application.
This uncertainty concerning the commitment of
the study participants makes it all the more impor-
tant to design the application to automatically gather
data for development purposes. Automatic data col-
lection cannot completely substitute human feedback,
especially on subjective issues such as how interest-
ing or persuasive the application is, but it can be suf-
ficient for the developers to diagnose situations where
the application performs inadequately. Most impor-
tantly, automatic notification, whenever possible, is
the fastest way to inform the developers when a fail-
ure has occurred.
We can summarize the design imperatives derived
from our experience as follows:
Designers of intervention applications should
identify the data required to fulfill the needs of all
stakeholders, and design the application to collect
all the required data. In particular, the application
should collect:
all the data required for the application to de-
liver its specified functionality to the users.
all the data required for the successful execu-
tion of the scientific work of the researchers.
all the data required to inform the future deci-
sions and actions of the developers.
Designers of intervention applications should
identify the constraints that may prevent the ap-
plication from collecting desired data, and design
trade-offs to ensure that the outcome is satisfac-
tory to all stakeholders. In particular, the data col-
lection objectives should be set such that:
achieving them is possible using the resources
available for the execution of the intervention.
achieving them is possible without unduly lim-
iting the privacy of the study participants.
achieving them is possible without unduly in-
conveniencing the study participants.
Although these principles are all technically de-
rived from a single study, the fact that there were two
pilots before the full-scale intervention means that we
had multiple opportunities to accumulate experience
on the design and execution of Internet-based health
promotion interventions. This was particularly use-
ful in that it enabled us to observe the importance
of collecting data to support the specification of the
next version of the intervention application, both au-
tomatically and by eliciting input from members of
the target population. Thus we came to apply the evo-
lutionary development approach outlined in Section
3, and as a result of this, both the first pilot study
and the second one led us to identify major software
modifications that should be made before the next it-
eration. They also improved our understanding of
what data should be collected; for example, the full
metabolic equivalent (MET) data from Polar Active
was not used in the first pilot, and some additional
digested values calculated from the MET data during
upload were used only in the final intervention.
Given the amount of development work done in
the MOPO study, along with the fact that we have
abstracted away all application-specific details, we
would argue that the design principles proposed here
are generalizable to other Internet-based interven-
tions. Furthermore, the architecture of the MOPO
software is also potentially generalizable to a wider
range of applications. If we apply the abstraction used
in this paper to the architecture, we can reduce it to a
data repository and a set of interfaces through which
the three stakeholder groups access the data:
The data upload interface, by which whatever
data on the study participants is being monitored
in the intervention is sent to the application.
The user interface, by which the the study partic-
ipants submit requests to the application and have
information presented to them.
The administrator interface, by which the re-
searchers and developers have unlimited access to
the data stored by the application.
HEALTHINF 2016 - 9th International Conference on Health Informatics
180
To benefit from this abstract architecture, it could
be implemented in the form of a software framework
that would provide a generic foundation on which ap-
plications for many different digital behavior change
interventions could be built. The architecture of an
intervention application would then look like Figure
2, where the data interface APIs are provided by the
framework and the interface modules, which imple-
ment the application-specific functionality, are coded
by the application developers, using data import and
export services provided by the APIs. The inter-
face APIs handle communication with the application
database, either directly or via the intermediate an-
alytics API, which provides services for generating
various digested presentations from the original data.
The data repository is divided into two conceptu-
ally distinct sections: static content, which only the
administrators can write to, and collected data, which
holds the data acquired over the course of the study
by monitoring the participants. The internal archi-
tecture of the repository should reflect the modularity
of the data interfaces; due to the evolutionary nature
of the development process, data collection require-
ments are likely to change and the core architecture
must be able to accommodate these changes. With
several different stakeholder needs to be fulfilled, and
several factors constraining the quality or quantity of
the data that can be acquired to fulfill them, it is es-
pecially important to ensure that the design of the
application does not introduce any additional con-
straints. Making the data interfaces and storage struc-
tures modular will eliminate or at least significantly
reduce the risk of not being able to use an important
data source because the developers were not aware of
it when the application was originally specified.
6 CONCLUSION
Web applications hold considerable potential as tools
for executing effective health interventions promot-
ing behavior change. In this paper we proposed a
set of design principles for such applications based on
the observation that an intervention application can be
viewed as an abstraction whose principal functional-
ity consists of gathering data from various sources and
presenting it to stakeholders in various formats. From
this observation, we derived three principles that pre-
scribe identification of stakeholder needs, and another
three that prescribe identification of constraints. Fur-
thermore, we concluded that an evolutionary devel-
opment approach is suitable for tackling the problem
of predicting how the users of the application will re-
spond to it. These findings are the result of hands-on
Figure 2: A conceptual model of the architecture of an in-
tervention software application developed using a software
framework. The interface APIs are generic components
provided by the underlying framework; the application-
specific modules use the APIs to implement the function-
ality of the application.
experience of developing and deploying three itera-
tions of a Web-based application designed to persuade
young men to change their physical activity behavior.
ACKNOWLEDGEMENTS
The MOPO study was supported by the Finnish Min-
istry of Education and Culture; Juho Vainio Founda-
tion; Centre for Military Medicine Finland; Northern
Ostrobothnia Hospital District; Centre for Economic
Development, Transport and the Environment of
North Ostrobothnia; European Social Fund; Finnish
Funding Agency for Technology and Innovation; and
European Regional Development Fund.
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