Available online at www.sciencedirect.com
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC)
August 9-12, 2021, Leuven, Belgium
Literature-based requirements analysis review of persuasive systems
design for mental health applications
Abdul Rahman Idreesa,b,∗, Robin Krafta,b, Rüdiger Pryssc, Manfred Reicherta, Harald
Baumeisterb
aInstitute of Databases and Information Systems, Ulm University, Germany
bDepartment of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Germany
cInstitute of Clinical Epidemiology and Biometry, University of Würzburg, Germany
Abstract
Mental health problems are becoming more common while access to treatment is often not available to everyone who needs
help. Recent advances in information technology, the wide availability of the internet, the emergence of smartphones and their
common usage worldwide raise hope for more treatment options for mental health disorders. Many mobile phone apps that claim
to assist in treating a variety of mental health disorders are already available, and the number of such apps continues to increase.
The availability of such apps raises many questions about their effectiveness, suitable treatment methods, possibilities for use
alongside traditional treatment methods, possible risks and other uncertainties. Beside mobile apps, internet-based apps are also
being introduced with similar sets of challenges and ambiguities. One area of research that is gaining a lot of attention recently
is Persuasive System Design and Behavior Change. Persuasive System Design is considered one solution that has the potential
to help solve the challenges of lack of user motivation and adherence when utilizing mental health applications. The goal of this
paper is to perform a literature review, in order to determine the most essential requirements for a persuasively designed mental
health application. As part of this process, the challenges and requests of the end-user will be taken into account in order to make
recommendations for the future design of such applications.
©2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Keywords: Persuasive Systems; Mental Health application; Web-based Interventions;
1. Introduction
Recent advances in internet and mobile technologies have the potential to offer new opportunities in the way that
we treat mental disorders [1]. The number of individuals using messaging apps in 2019 is understood to be around 2.18
∗Corresponding author. Tel.: +49 731 50 24 131 ; fax: +49 731 50 24 134.
1877-0509 ©2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
2A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000
billion worldwide [2]. This indicates that the use of mobile phone apps is now widespread. Such numbers provide an
opportunity for the use of internet and mobile technologies to deliver mental health support to individuals who require
it. However, the use of internet technology and mobile apps comes with its own set of challenges. User adherence to
a mental health app is one challenge when it is used as part of a treatment procedure. In a survey of mood app usage,
it was found that the apps are used no longer than two weeks after download on average [3]. Similar results have been
reported for anxiety, depression, or well-being apps [4]. There are other challenges facing both users and mental health
professionals regarding the use of such apps, such as concerns regarding efficacy, ethical issues, users’ discomfort,
data safety and security concerns [5] [6], to name only a few. Moreover, several studies highlighted a missing link
between user and expert ratings, with only modest average content quality of mental health apps as rated by domain
experts [7] [8] [9]. To overcome users’ lack of interest or decreased motivation after starting treatment, [10] and [11]
suggest the use of persuasive technology to increase adherence. Persuasive design technologies have the potential to
change or influence the behaviour of the user [12]. In the case of mental health apps, [13] states that persuasive design
technologies can be used to alter the behaviour of the user in order to support them to keep using the app and achieve
their goals. This paper provides a requirements analysis based on available literature for mental health applications
that use Persuasive System Design (PSD). These requirements can be taken into consideration by anyone developing
a mental health computer system, irrespective whether it is a web app, a mobile app, a desktop application or an
internet intervention. Hence, this paper attempts to provide a contribution that can be used in practical settings in the
development process of mental health persuasive applications. The remainder of the paper is organized as follows:
Section 2offers background information regarding mental health applications and Persuasive System Design, as well
as describing the needs behind using Persuasive Design Technologies in mental health applications. The requirements
discovery process is presented in Section 3. Furthermore, in Section 4, the identified requirements are discussed.
Finally, Section 5concludes the paper and suggests possible further areas of research.
2. Background and Motivation
The concept of Persuasive Design has already been put into practice through many notable research projects and
in several different fields [14]. Persuasive technology as defined by Fogg in [15], is “the use of computer-mediated
products in order to change people’s cognition and behaviors”. Furthermore, Fogg developed the widely-known Fogg
Behavior Model (FBM) [16]. This model consists of three components: motivation, ability, and triggers [16]. Accord-
ing to the FBM, for people to reach their goals, all three components of the Behavior Model must be present. Aside
from Fogg, other researchers have tried to put together definitions for persuasive systems. [12] states that persuasive
systems are “computerized software or information systems designed to reinforce, change or shape attitudes or behav-
iors or both without using coercion or deception”. Using persuasive technologies is suggested by [10] to raise user’s
adherence to mental health applications.
In terms of software engineering for behavior change and persuasive systems, [17] provides a discovery process for
non-functional requirements. The discovery process contains three steps: 1) identifying unfulfilled user needs, 2) map-
ping to the PSD premises, and 3) categorizing user feedback to the PSD categories as described by [17]. Moreover, [17]
explains that identifying the unfulfilled needs of users could be achieved by utilizing assessment questionnaires. Other
studies have also investigated how gamification could be used to alter people’s behaviors. [18] provides a framework
for software with gamification elements to influence the behavior of the user and make the software more engaging.
Furthermore, [19] provides a software architecture design, which can support the development of behavior change
support systems. It relies on understanding the user context, i.e., the differences amongst users and their individual
cases. Additionally, it also relies on understanding the intent and the desired behavior change, and deciding on the
technology stack and the implementation approach.
Based on our literature review, research into the area of software engineering and requirements analysis for Per-
suasive System Design is still growing. However, resources are more limited when it comes to software engineering
for persuasive mental health applications. This work tries to provide recommended requirements that should exist in
e-mental health applications. These requirements are elicited based on the experiences and feedback of the end-users
of such applications.
A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000 3
3. Requirements Discovery
[20] provides a description for the requirements discovery process in general. It starts with the following: Identify
customers and stakeholders, understand the customer’s needs, define and state the problem and write system require-
ments as stated by [20]. The focus of this work will only be on two categories of stakeholders: users and service
providers (clinicians, therapists, etc.). The first step deals with users’ experiences, understanding user’s needs, gather-
ing information regarding what problems users faced when using mental health applications, what affected the usage
of such applications negatively, and what suggestions users may have.
3.1. Method
Several studies have already been carried out to investigate users’ experiences with e-mental health apps and
to try to identify the most common problems faced by the end-users. Using IEEE Xplore, the National Center for
Biotechnology Information (NCBI), the databases of PubMed and Google Scholar, a literature review was performed
to identify papers published in English from the year 2014 to the end of 2020. The papers describe the opinions and
experiences of adult users in using internet and mobile technologies to access mental health. As mentioned in [20],
understanding customers’ needs is the second step in requirements analysis. In order to search for user feedback,
the following terms are used: user’s reviews, user’s opinions, therapists’ feedback, service providers, blended therapy
combined with mental health apps, internet-based interventions and web-based mental health app. Since smartphones
are now widely used [2], the search first focused on studies where users sought help using their mobile apps to
access treatment courses or support materials. Further to this, the search looked for other types of technologies used
to facilitate access for users to mental health support such as web-based apps, online forums, discussion groups,
websites, and social media.
The next step was to identify studies that provided data on the opinions of clinical practitioners who provide
mental health support. The search was divided into two parts; the first part sought to identify the opinions of therapists
regarding therapy provided solely via internet and mobile technologies, and the second part sought to identify the
opinions of therapists regarding the use of blended therapy, in which support is provided to patients using traditional
therapy methods as well as using internet and mobile technologies. When similar results are identified in different
papers, only one is included. The selected paper is the one that has the largest number of reviewed users. The previous
workflow is presented in Fig. 1.
3.2. Results
10 studies were identified, but two of them were excluded due to being outdated. One was rejected because it deals
with adolescents’ and not adults. Another was excluded because it only reviewed a small number of users’ opinions.
In total, six studies were shortlisted and the final list excluded one of these six studies for repeated outcomes and
similarities to another study. Using machine learning and thematic analysis, [21] carried out a study to discover
factors influencing effectiveness of mental health apps based on users’ reviews. The total number of apps included in
the study amounted to 105 apps and up to 88,125 reviews. The study discovered both negative and positive factors.
Moreover, the study provides suggestions based on users’ reviews. Results of the survey can be classified into liked
features, suggestions, missing features and concerns. Among the features that users requested or found useful while
using the apps were the following:
•Simplicity
•Personalized Content
•Logging
•Ease of Use
•Reminders
•Customizability
•Social Effect
•Analytics
•Enjoyability
•Virtual Reward
Regarding missing features and concerns, users reported the following:
•Lack of Customization
4A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000
Fig. 1. Literature Review Workflow
•Navigation Issues
•Data privacy and Security
•Non-personalized Content
Since this paper does not focus on a specific platform, such as mobile or web, it is useful to look at findings from
studies that focus on more varying technologies. [22] conducted an online survey to explore the use of technology to
support individual’s mental health. Unlike the previous studies, which focused on mobile apps, this study included a
variety of technologies, such as mobile apps, web-based apps, online forums, discussion groups, websites, and social
media. The study analyzed the responses of 81 participants to a survey consisting of 59 questions regarding their
use of different types of technology to support their mental health. Among the features that users requested or found
useful when using the apps were the following:
•Connecting with others
•Expert Interaction
•Tracking and Analyzing
•Recommendations based on Monitoring
•Personalization
•Customization
•Professional Input
While missing features and concerns were as follows:
•Trust (in the system, the content and the community)
•Fears of Overuse
•Non-personalized Content
Furthermore, [23] was conducted to understand the user’s view on what impacted their interaction and its frequency
with a web-based psychosis intervention application. 17 individuals (males and females) participated in the study with
ages ranging from 18 to 65, with varying levels of education. Users reported the following positive influences:
•Incorporating it into the Daily Routine
•System-automated support Emails
A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000 5
•Connecting with Peers
•Flexibility of the Intervention (feeling in control)
While the negative influences were as follows:
•Outside Factors (levels of willingness, lack of time or space)
•Overwhelmed by Navigation
•Feeling the content is irrelevant
•Lack of connection with other users
•Content-related Factors (triggering contents)
Another important group of end-users are the therapists that use the platform to help their patients to overcome their
mental health disorders. [24] conducted a study to explore the opinions and experiences of mental health providers on
using web and mobile tools to help patients. A total of 15 participants were included in the interviews. Six questions
were asked and articulated in a way that allowed room for discussion. The features requested by users were as follows:
•More support for complex presentations (more than one diagnosis and changing situation over the course of
treatment)
•Remote Monitoring (behavior and symptoms)
•Colleague Interaction Tools (between providers)
•More support outside the office
•Supervision
While concerns and missing features were as follows:
•Confidentiality and security of patient data
•Security of Communications
•Ease of Use
•Low adherence to treatment activities
Another relevant study is [25], in which nine therapists were interviewed about blended care in clinical practice.
The therapists identified several main areas that needed attention during the development of apps for clinical use: 1)
The need to increase the ease of the patient’s adherence to the program by providing information via different tools
such as videos, animation, or expressive examples. They identified that this was preferred by patients over big blocks
of texts. 2) The option for online feedback and reminders. 3) The integration of encouraging tools and features such
as gamification. 4) The incorporation of a motivational aspect for the therapist within the app. 5) The inclusion of
flexibility for the therapist, such that they can have a wide selection of tools to avail of, in order to tailor the app to
the individual needs of each patient. Overall, therapists expressed an interest in motivating patients and encouraging
them to finish their assignments [25].
Considering the studies previously discussed, closely related or similar requests can be grouped together, for ex-
ample, enjoyability while using the application and virtual rewards can be grouped under gamification. Ease of use,
simplicity, and navigation are grouped under usability. Furthermore, professional feedback, supervision and support
outside the office is grouped under professional support. Data security related concerns were grouped under security
category. Finally, monitoring user’s situation, data logging, and analysis are grouped under monitoring and analyzing.
Table 1shows how many times each category was mentioned over the five studies. From the table it can be seen
that having highly customized apps was requested by both patients and therapists in all studies above. Tracking
and analyzing user activity was mentioned by patients and therapists in three out of five studies. Supervision, expert
feedback, data protection, and security concerns were also requested in three studies. Usability concerns, gamification,
personalized content and recommendations, social interaction tools, and reminders were all mentioned in two out of
five studies. Finally, a wide selection of media options, provider to provider interaction tools, and low adherence to
treatment activities were mentioned in one of the five studies.
6A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000
Table 1. Requested features occurrences over the five studies.
Request Category Study 1 Study 2 Study 3 Study 4 Study 5 Total
Usability 1 1 1 3
Customization 1 1 1 1 1 5
Gamification 1 1 2
Personalization & Recommendation 1 1 1 3
Monitoring & Analyzing 1 1 1 3
Social Support 1 1 1 3
Professional Support 1 1 1 3
Reminders notifications 1 1 1 3
Security 1 1 1 3
Rich media choices 1 1
Colleagues interaction tools 1 1
Low adherence to treatment activities 1 1
3.3. Suggested Requirements
Based on the aforementioned literature analysis, the following requirements are suggested:
1. Easy and intuitive user interfaces
Usability was mentioned at least three times in the studies above. Many of the users’ reviewed in the studies above
requested applications that were simple and easy to use. Some users even described their navigation experience
through the web-app from study [23] as overwhelming.
2. Customization
Customization is the feature mostly requested by users over the five analyzed studies. Therapists should be
enabled to customize and adjust the content based on the specific needs of each patient. This could also counter
the complaints from some users in study [23] that certain features felt irrelevant to their requirements.
3. Protecting users’ privacy
Privacy concerns were also mentioned by many users over at least 3 of the five studies. Either as lack of trust
in the app, or as explicit privacy concerns as in [21]. Mental health applications should try to gain the trust of
their users by being transparent about what data is being collected, i.e., the sharing data policy. Additionally,
encryption is advised whenever possible to provide another layer of privacy and protection.
4. Flexible notifications and reminders
Notifications and reminders are among the features most frequently requested by users. They can be helpful in
reminding users of their tasks [26], but they should be used with care so that the user does not feel disturbed
by their occurrence, as stated in [12], i.e., persuasive systems should not disturb their users. To avoid such a
scenario, notifications and reminders should be personalized, based on the user’s usage routines of the app [27],
while providing users with the option to adjust reminders and notifications.
5. Promoting users’ autonomy
[28] suggests offering users more choice to set their own sub-goals and to work with the app according to their
own pace. Additionally, [29] states that offering users more choices can be motivational. As people acquire skills
at different speeds, it is important to have each user choose their own time frame to reach each small goal. This
becomes more important when discussing ways to implement a reward and a gamification system, which is a
feature often requested by the service providers. Forcing users to follow one pre-defined workplan could result
in some users feeling “left behind” or unable to keep up with the speed suggested by the app.
6. Track changes in users’ status
As mentioned by the service providers in the studies above, users can sometimes have more than one diagnosis
and their diagnosis can change over the course of time. The application should be able to adjust its activities and
interventions accordingly. As mentioned in [29], tailoring should be dynamic and adapt to the changes of the
user. This works hand in hand with other requirements.
A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000 7
7. Users inter-communication tools
Some users in [22] and [23] expressed interest in being able to communicate with other users of the app in
order to exchange experiences. As [23] states, some people registered on the website for the sole purpose of
hearing what other people have experienced and sharing their own experiences as well. It also states that people’s
motivation dropped when they tried to get in touch with other users, but were not able to do so.
4. Discussion
The requirements suggested in the previous section can be helpful when trying to introduce persuasive design
strategies into an e-mental health application. The requirements were discovered based on the suggestions of patients
who use digital technologies and mental health applications to improve their mental health as well as service providers
who assist patients during their treatment. The list of requirements is not final and can be adjusted according to each
application-specific needs. There is always room for improvement based on the latest developments in the theory of
persuasive design, technological advances and the varying needs of the users. This list offers a starting point and, to
the best of our knowledge, it is the first list of its kind in the available literature. Furthermore, these requirements are
aimed at e-mental health applications in general. While these recommendations can apply to many different software
categories, it is not necessarily a definitive list of requirements for all e-mental health applications. For example, in
some cases, mobile apps are developed to deal with one specific diagnosis, such as people being treated for post-
traumatic stress disorder (PTSD) [8] or depression [7]. In this situation, the requirements recommended above can
be a helpful starting point, but the app may have further requirements specific to helping users who are dealing with
this disorder. Furthermore, as can be seen in the above requirements, collecting user data and behavior patterns is
the cornerstone and the basis for many other requirements and functionalities of such applications. It is important to
understand the legal requirements of such data collection based on the country of operation, as regulations can vary
across countries.
5. Future Work &Conclusion
The requirements suggested above were based on utilizing persuasive design strategies to overcome challenges
faced by patients and service providers. In some cases, the requirements of further stakeholders must also be taken
into consideration (e.g., operating facilities, fundraisers, governments). These stakeholders can have their own set
of requirements, challenges, and needs. This review focused on users who can directly affect the workflow of the
treatment and are directly impacted by it. Furthermore, as suggested by [30], requirements should be further processed
and reviewed in an iterative process in order to discover more requirements. This process will be discussed in more
detail in future work. Another factor to consider constitutes the platform upon which the application is being developed
on. This review did not put much emphasis on the application type. In a real-world scenario, the application can be
designed to run on mobile devices, in a browser, in a smartphone, as a desktop application, or on a combination of
more than one platform, such as a mobile application and a web app. Additionally, in the case of blended therapies,
the application could have more than one part, for example, one app for the participants and one for the service
provider. Such a scenario should be taken into account when designing an e-mental health application. Depending on
the nature of the system being developed, all these requirements can be used together or individually. For example,
when the application has a need for personalization, then the second, the fifth and the sixth requirements can be
helpful. Another case is when user and service provider each use a different application with different needs. In this
case, the requirements can be more specific for each application. Finally, more research could be done to validate
the requirements and fine-tune them, by utilizing them in a persuasive mental health application to examine their
effectiveness. This step is outside the scope of this work and will be addressed in future publications.
References
[1] J. A. Naslund, P. P. Gonsalves, O. Gruebner, S. R. Pendse, S. L. Smith, A. Sharma, and G. Raviola, “Digital innovations for global mental
health: opportunities for data science, task sharing, and early intervention,” Current treatment options in psychiatry, vol. 6, no. 4, pp. 337–351,
2019.
8A. R. Idrees et al. /Procedia Computer Science 00 (2019) 000–000
[2] C. Bröhl, P. Rasche, J. Jablonski, S. Theis, M. Wille, and A. Mertens, “Desktop PC, tablet PC, or smartphone? An analysis of use preferences
in daily activities for different technology generations of a worldwide sample,” in International Conference on Human Aspects of IT for the
Aged Population. Springer, 2018, pp. 3–20.
[3] E. Chiauzzi and A. Newell, “Mental health apps in psychiatric treatment: a patient perspective on real world technology usage,” JMIR mental
health, vol. 6, no. 4, p. e12292, 2019.
[4] A. Baumel, F. Muench, S. Edan, and J. M. Kane, “Objective user engagement with mental health apps: systematic search and panel-based
usage analysis,” Journal of medical Internet research, vol. 21, no. 9, p. e14567, 2019.
[5] F. Alqahtani and R. Orji, “Insights from user reviews to improve mental health apps,” Health informatics journal, vol. 26, no. 3, pp. 2042–2066,
2020.
[6] M. Bauer, T. Glenn, J. Geddes, M. Gitlin, P. Grof, L. V. Kessing, S. Monteith, M. Faurholt-Jepsen, E. Severus, and P. C. Whybrow, “Smart-
phones in mental health: a critical review of background issues, current status and future concerns,” International journal of bipolar disorders,
vol. 8, no. 1, pp. 1–19, 2020.
[7] Y. Terhorst, E.-M. Rathner, H. Baumeister, and L. Sander, “«Hilfe aus dem App-Store?»: Eine systematische Übersichtsarbeit und Evaluation
von Apps zur Anwendung bei Depressionen,” Verhaltenstherapie, vol. 28, no. 2, pp. 101–112, 2018.
[8] L. B. Sander, J. Schorndanner, Y. Terhorst, K. Spanhel, R. Pryss, H. Baumeister, and E.-M. Messner, “‘Help for trauma from the app stores?’A
systematic review and standardised rating of apps for Post-Traumatic Stress Disorder (PTSD),” European journal of psychotraumatology,
vol. 11, no. 1, p. 1701788, 2020.
[9] D. Schultchen, Y. Terhorst, T. Holderied, M. Stach, E.-M. Messner, H. Baumeister, and L. B. Sander, “Stay present with your phone: A
systematic review and standardized rating of mindfulness apps in european app stores,” International Journal of Behavioral Medicine, pp. 1–9,
2020.
[10] S. M. Kelders, R. N. Kok, H. C. Ossebaard, and J. E. Van Gemert-Pijnen, “Persuasive system design does matter: a systematic review of
adherence to web-based interventions,” Journal of medical Internet research, vol. 14, no. 6, p. e152, 2012.
[11] H. Baumeister, R. Kraft, A. Baumel, R. Pryss, and E.-M. Messner, “Persuasive e-health design for behavior change,” in Digital Phenotyping
and Mobile Sensing. Springer, 2019, pp. 261–276.
[12] H. Oinas-Kukkonen and M. Harjumaa, “Persuasive systems design: Key issues, process model, and system features,” Communications of the
Association for Information Systems, vol. 24, no. 1, p. 28, 2009.
[13] F. Alqahtani, G. Al Khalifah, O. Oyebode, and R. Orji, “Apps for mental health: An evaluation of behavior change strategies and recommen-
dations for future development,” Frontiers in Artificial Intelligence, vol. 2, p. 30, 2019.
[14] K. Torning and H. Oinas-Kukkonen, “Persuasive system design: state of the art and future directions,” in Proceedings of the 4th international
conference on persuasive technology, 2009, pp. 1–8.
[15] B. J. Fogg, “Persuasive technology: using computers to change what we think and do,” Ubiquity, vol. 2002, no. December, p. 2, 2002.
[16] ——, “A behavior model for persuasive design,” in Proceedings of the 4th international Conference on Persuasive Technology, 2009, pp. 1–7.
[17] N. Condori-Fernandez, J. Araujo, A. Catala, and P. Lago, “Towards a non-functional requirements discovery approach for persuasive systems,”
in Proceedings of the 35th Annual ACM Symposium on Applied Computing, 2020, pp. 1418–1420.
[18] T. Dal Sasso, A. Mocci, M. Lanza, and E. Mastrodicasa, “How to gamify software engineering,” in 2017 IEEE 24th International Conference
on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2017, pp. 261–271.
[19] T. Alahäivälä, H. Oinas-Kukkonen, and T. Jokelainen, “Software architecture design for health bcss: case onnikka,” in International Conference
on Persuasive Technology. Springer, 2013, pp. 3–14.
[20] A. T. Bahill and F. F. Dean, “The requirements discovery process,” in INCOSE International Symposium, vol. 7, no. 1. Wiley Online Library,
1997, pp. 340–347.
[21] O. Oyebode, F. Alqahtani, and R. Orji, “Using machine learning and thematic analysis methods to evaluate mental health apps based on user
reviews,” IEEE Access, vol. 8, pp. 111 141–111 158, 2020.
[22] K. Stawarz, C. Preist, and D. Coyle, “Use of smartphone apps, social media, and web-based resources to support mental health and well-being:
Online survey,” JMIR mental health, vol. 6, no. 7, p. e12546, 2019.
[23] C. Arnold, A. Williams, and N. Thomas, “Engaging with a web-based psychosocial intervention for psychosis: Qualitative study of user
experiences,” JMIR mental health, vol. 7, no. 6, p. e16730, 2020.
[24] S. M. Schueller, J. J. Washburn, and M. Price, “Exploring mental health providers’ interest in using web and mobile-based tools in their
practices,” Internet interventions, vol. 4, pp. 145–151, 2016.
[25] R. van der Vaart, M. Witting, H. Riper, L. Kooistra, E. T. Bohlmeijer, and L. J. van Gemert-Pijnen, “Blending online therapy into regular face-
to-face therapy for depression: content, ratio and preconditions according to patients and therapists using a delphi study,” BMC psychiatry,
vol. 14, no. 1, pp. 1–10, 2014.
[26] D. Bakker, N. Kazantzis, D. Rickwood, and N. Rickard, “Mental health smartphone apps: review and evidence-based recommendations for
future developments,” JMIR mental health, vol. 3, no. 1, p. e4984, 2016.
[27] Q. Yang and S. K. Van Stee, “The comparative effectiveness of mobile phone interventions in improving health outcomes: meta-analytic
review,” JMIR mHealth and uHealth, vol. 7, no. 4, p. e11244, 2019.
[28] M. Z. Huber and L. M. Hilty, “Gamification and sustainable consumption: overcoming the limitations of persuasive technologies,” in ICT
innovations for sustainability. Springer, 2015, pp. 367–385.
[29] L. Yardley, B. J. Spring, H. Riper, L. G. Morrison, D. H. Crane, K. Curtis, G. C. Merchant, F. Naughton, and A. Blandford, “Understanding
and promoting effective engagement with digital behavior change interventions,” American journal of preventive medicine, vol. 51, no. 5, pp.
833–842, 2016.
[30] K. Wiegers and J. Beatty, Software requirements. Pearson Education, 2013.
