Interaction Design Issues in the Development and Assessment of

Stress Management Apps: A Scoping Literature Review and Analysis

Elpida Bampouni

and Victor Kaptelinin

Department of Psychology, Umeå University, 901 87 Umeå, Sweden

Department of Informatics, Umeå University, 901 87 Umeå, Sweden

Keywords: Stress Management Apps, mHealth, Interaction Design, Scoping Literature Review.

Abstract: A number of smartphone apps have been developed in recent years to help people cope with stress and

promote mental well-being. Such apps have attracted significant attention in current research. However,

interaction design issues, such as usability and user experience, have so far been relatively unexplored. This

paper presents a meta-analysis of studies of mobile apps for stress management and mental well-being,

specifically focusing on interaction design issues. Through a scoping literature search we selected the total

of 46 articles, published in the last decade, for qualitative in-depth analysis. The analysis reveals that the

main interaction design issues addressed in the papers are ease of use, user engagement, and privacy. Key

opportunities and challenges for future work are discussed.

1 INTRODUCTION

Stress has been a growing societal phenomenon of

concern, with a significant mental health and

financial impact (Kalia, 2002). A recent trend in

helping people deal with stress is leveraging the

affordances of modern mobile technologies.

Increasingly powerful and affordable, mobile

technologies, especially smartphones, can be almost

constantly available, are capable of supporting

advanced screen-based and voice-based interaction,

and can collect rich information about the user.

These advantages of smartphones have been

capitalized upon in numerous applications, or apps,

promoting mental health and well-being (thereafter,

“mHealth apps.”). A substantial and ever-growing

number of apps (approximated as 20,000 (American

Psychology Association, 2020)) are available now in

various app repositories, such as Apple App Store

and Google Play.

The covid-19 pandemic adds urgency to

exploring the potential of using technology for

dealing with stress. The pandemic dramatically

increases the level of stress in many groups, while

simultaneously limiting opportunities for patient-

therapist communication. Multiple sources (both

academic and non-academic) are urging the public to

turn to technology for their stress management, if

they deem necessary (e.g. Tay, 2020). It is hardly

surprising that the use of mobile mental health apps

has been recently on the rise, with an increase of

about 30% just from January to April 2020

(Herzog,

2020). Notably, many apps have adapted their

content or changed their payment options to free for

all, or free for specific populations in order to

support users’ needs (Umoh, 2020).

The current trend toward “pocket psychiatry”

(Anthes, 2016) raises questions about the overall

feasibility of this approach (American Psychiatric

Association, n.d.), as well as the advantages and

disadvantages of such apps compared to more

traditional stress management. While recent research

has made progress in addressing mHealth topics,

there still need for further studies. In particular,

interaction design

(ID) issues, involved in the

design and evaluation of mobile stress apps, have

been relatively unexplored. The therapeutic strategy,

implemented in a particular app, is, undoubtedly, an

absolutely crucial factor that determines the

usefulness and success of the app, or the lack

thereof. Arguably, however, ID aspects are also

critically important. Apps, which are not designed

In this paper, ”interaction design” is understood in a

broad sense, as also encompassing related fields, such as

“Human-Computer Interaction” (HCI).

Bampouni, E. and Kaptelinin, V.

Interaction Design Issues in the Development and Assessment of Stress Management Apps: A Scoping Literature Review and Analysis.

DOI: 10.5220/0010167402330243

In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2020), pages 233-243

ISBN: 978-989-758-480-0

233

for optimal usability and user experience (UX), are

likely to fail even if the underlying strategy is sound.

ID (e.g., Preece et al., 2015) is a field of research

and practice, which adopts a user-centered design

perspective and offers a range of methods and

concepts for ensuring systems’ usability and positive

UX.

A range of questions, relevant to interaction

design, such as why people prefer and like stress

management apps, why interaction with such apps is

perceived as meaningful, and how healthcare

professionals’ view the apps and their potential,

have been explored in existing literature (e.g.

Apolinário-Hagen et al., 2019; Proudfoot et al.,

2010). However, to the best of our knowledge, there

have been no systematic analyses of ID issues in the

development and assessment of mobile stress apps.

In this paper, we address the above limitation of

current research by presenting a meta-analysis of

studies of mobile apps for stress management and

mental well-being conducted in the last decade,

specifically focusing on interaction design issues.

We summarize key findings in current research,

identify problems and challenges regarding ID, and

discuss problem areas in need of improvement to be

considered in future research and practice.

2 METHOD

2.1 Two Phases of the Analysis

This paper reports a qualitative meta-analysis of

contemporary research papers (that is, papers

published between 2009 and early 2020), dealing

with mobile apps intended for promoting mental

health and well-being. The analysis proceeded

through two phases. The first phase included a

scoping literature search, during which we started by

identifying a large number of potentially relevant

papers, and then narrowed down our search to 46

representative and relevant papers, which were

analyzed during the second phase. During the

second phase we conducted an in-depth analysis of

the selected papers: we formed thematically related

groups and developed a set of analytical dimensions

for describing each of the papers.

2.2 Scoping Literature Search

The literature search was conducted during

November 2019-January 2020 in order to identify a

representative set of potentially relevant literature

sources. We adopted the following 4-step version of

a scoping literature search workflow. At the first

step the database and the time scope of the search

were decided upon. The search was conducted in

Google Scholar, as it is the most inclusive database.

The results of trial searches conducted in PubMed

and ACM Digital Library did not produce a

significant number of new publications compared to

Google Scholar, so we selected the latter as our main

database. The time scope of the search was from

2009 to 2020. We included the year of 2020 in order

to monitor upcoming projects.

Second, the following search terms and term

combinations were used: (a) “stress”/”stress

management”/ “stress treatment”, (b) “app”/ “mobile

app”, and (c) “intelligent”/ “artificial intelligence”.

The numbers of returned results (indicated in

parentheses) were: “stress management apps” (94),

“stress management app” (63),“mobile app” AND

“stress management” (1440), intelligent assistant”

AND “ stress management” (9), “intelligent

assistant” AND “ stress apps” (0), “artificial

intelligence” AND “stress app” (7), “stress” AND

“mobile app” (16900), “stress treatment” AND

“mobile app” (12).

The third step comprised screening the results, on

the basis of publications’ titles and abstracts. The

screening was selective: when a search returned a

large number of results, the screening was limited to

the first 30-50 publications (which were presumably

most relevant). The aim of the screening was to

include representative papers from a variety of

different kinds of publications. For that reason we

employed the criteria of “type variety” and “content

variety”. Type variety refers to the type of

publication, for example a literature review, an

efficacy examination study (quantitative and

qualitative), evaluation and influence factors reports,

etc. Content variety refers to the information those

studies brought forth. For example, if the studies that

emerged were all assessing app efficacy but varied

in some factors, e.g. population samples, e.g., PTSD,

children, distant college students, caretakers, etc., we

tried to include all of those studies as they bring

novel information and context about mHealth

applications. The screening phase produced 52

publications. The first three steps of the literature

search were performed by the first author.

At the fourth step, the publications, selected at

previous phases, were analyzed by both authors to

identify thematic groups and more specifically

assess the relevance of the publications to research

on stress management apps. The groups were

initially produced independently and then discussed

until the authors reached a consensus. The resulting

CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications

234

grouping included publications which deal with

using mobile apps for: stress management (A),

mobile apps for mental health in general (B), various

technologies, not limited to mobile apps, for mental

well-being (C), and mobile apps for various health-

related purposes (D) (see Figure 1).

Figure 1: Categories of reviewed papers.

Within each of these groups we identified different types

of publications: literature reviews, technology reviews,

empirical studies, and design and evaluation studies.

Figure 2: The analytical template used in in-depth

analysis.

2.3 In-depth Analysis

The authors read and evaluated 52 papers selected

through the scoping search, described above. The

analytical template, shown in Figure 2, was

employed to produce consistently structured

descriptions of each paper, which descriptions were

used to create systematic overviews of each of the

groups shown in Figure 1. At this stage, 6 papers

were additionally excluded on the basis of low

relevance, after reading the entire papers. Therefore,

the total of 46 papers

were included in the results of

the in-depth analysis, presented below.

Marked with an “*” in the References section.

3 RESULTS

In this section we report a collective description

from all the papers included in categories A, B, C

and D.

3.1 A: Stress Management Apps

In this category we identified a total of 28 papers, of

which 17 were empirical studies, some of which also

introduce new app designs (e.g. Børøsund et al.,

2018; Serino et al., 2014), 8 app technology reviews

and 3 literature and app reviews/ overviews. A few

of the papers have high relevance to the fields of

HCI and interaction design, often citing the literature

in the fields and using related terminology (e.g.

persuasive technologies, gamification). It is also

often noted that it is important to bring together

different research areas, such as IT, Psychology and

Behavioral Sciences (Børøsund et al., 2018).

The empirical studies use both quantitative and

qualitative measures such as log files (e.g. response

to notifications or logging durations), various

questionnaires and self-assessment scales,

biofeedback information (e.g. glucose blood levels,

weight, heart rate), expert evaluations, including

evaluations of prototypes (Børøsund et al., 2018),

interviews and focus groups. The majority of sample

populations are healthy adults and several studies

included younger participants (e.g. Chittaro & Sioni,

2014; Paredes et al., 2014; Schnall et al., 2015). A

few papers involved participants living with

illnesses, e.g., cancer (Børøsund et al., 2018) and

HIV (Schnall et al., 2015), and 2 involved caretakers

and medical staff (Carr et al., 2019; Hwang & Jo,

2019). The reviews used researchers, authors and

independent coders in order to assess sets of mobile

apps. Common measures used by the review studies

were user ratings and expert evaluations, usually

based on predetermined scales or domain-specific

criteria (e.g., Coulon et al., 2016; Hoffmann et al.,

2017; Michelle et al., 2014; Rodriguez-Paras et al.,

2017)

Some of the key findings from studies in

category “A” are as follow. It was found that users

were receptive to stress management apps and often

preferred them over other psychological services

(Apolinário-Hagen et al., 2019). An overall

improvement to psychological distress was reported

in several studies (e.g. Hwang & Jo, 2019; Ly et al.,

2014; Munster-Segev et al., 2017). There are also

specialized apps, e.g., those intended to deal with

pediatric pain and PTSD, but not many of them

GENERAL INFORMATION

Reference details

Study type

Key aims

Intervention type & Technology type

Participants

Measures

Key findings & conclusions

SPECIFIC INFORMATION

Relevance to Interaction design (Overall)

Interaction Design findings: Mentioned but

not addressed (e.g. Usability, UX, etc.)

Interaction Design findings (Mentioned

and addressed), e.g., Usability, UX etc.

Miscellaneous related factors

References to the HCI field

Interaction Design Issues in the Development and Assessment of Stress Management Apps: A Scoping Literature Review and Analysis

235

appear to be suitable for the specialized needs of

those populations.

A general conclusion from a review study

(Ptakauskaite et al., 2018) is that stress management

apps typically do not sufficiently support the user in

action taking in order to deal with an issue at hand.

Furthermore, it is concluded that a limited range of

stress management (Christmann et al., 2017) or

gamification (Hoffmann et al., 2017) techniques are

being used in mHealth apps. Some common

functions are user education and data collection

features, e.g., mood updates or sleep tracking

(Michelle et al., 2014). According to Payne et al.

(2016), 96% of reviewed apps were utilizing

behavior tracking. Meditation and mindfulness

techniques were also common, according to Coulon

et al. (2016), with 73% of reviewed apps using them.

App-based interventions appear to be visited more

frequently that web-based ones (Morrison et al.,

2018). However a study by Morrison et al. (2017)

shows that different notification systems make no

difference in user engagement.

Relevance to ID was considered “low” for 13

papers, “high” for 6 papers, and for 8 papers it was

“medium”, “medium- low” or “medium-high”. An

issue, which was commonly identified but rarely

addressed, was data privacy (e.g. Apolinário-Hagen

et al., 2019; Rodriguez-Paras et al., 2017; Smith et

al., 2015). One of the studies attempted to address

the privacy issue (Børøsund et al., 2018) by not

storing user information and by having their app

been recognized as an official hospital intervention.

Wearables and other sensory data (e.g.

geographical data, social media, data mining, etc.)

are often mentioned as holding numerous potential

in mood state identification (Paredes et al., 2014;

Rodriguez-Paras et al., 2017; Serino et al., 2014).

However, the potential is rarely fully utilized. Such

data, are often limited by privacy concerns, but

could also help address another issue, relevant to ID.

That is targeting specific user’s needs via

personalized interventions (Coelhoso et al., 2019).

Another pertinent finding is reported by Coulon et

al. (2016), who found that many apps provide poor

instructions about the use of their functions.

It is worth mentioning that there has not yet been

a consensus regarding what is the right amount of

exposure to stress management apps (Morrison et

al., 2018). In addition, there have not been any

official regulations governing such apps, and ethical

concerns about users’ safety have been expressed

(e.g. Coulon et al., 2016).

Our analysis also indicates that some ID issues

have been successfully approached and tackled, in

existing literature on stress management apps. For

example, the study by Børøsund et al. (2018)

explicitly aims to achieve a user friendly design,

e.g., by providing informative time estimates for

each exercise and provision of visual aids.

Additionally, the issue of user fatigue is addressed

by presenting small text sections and using an easy-

to-follow language. Similar findings regarding

language accessibility have also been analyzed by

Smith et al. (2015) in their systematic review of apps

for managing pain and pain-related stress. Moreover,

it is also recommended (Michelle et al., 2014) that

apps can assist in therapy sessions to support both

the therapist and the user via, e.g., increasing

commitment with mobile CBT (cognitive

behavioural therapy) homework.

Several papers mention gamification as a

strategy that can provide external rewards

(Hoffmann et al., 2017), and it is also emphasized

that apps should aim to provide support for intrinsic

motivation (Ahtinen et al., 2013; Ewais &

Alluhaidan, 2015). Gentle guidance on reflection is

generally considered a good practice, while it is also

pointed out that supporting action , e.g. by providing

convenient daily content, is important (Ahtinen et

al., 2013; Ptakauskaite et al., 2018). Finally, when

biofeedback is discussed and implemented

(Munster-Segev et al., 2017; Uddin et al., 2016), the

importance of convenience and good comfort is

usually mentioned.

A variety of other aspects of stress management

apps are also mentioned in the papers from this

group. Firstly, many stress management apps are

free or cost less than 1$ (Coulon et al., 2016; Payne

et al., 2016). Secondly, since accessibility is

considered a crucial advantage for the apps, it is

noted that including an offline mode, independent of

internet connection, would allow users to have some

support at any time (Hoffmann et al., 2017). Finally,

Børøsund et al. (2018), point to the importance of

the right time to introduce stress management apps

to users (more specifically to ill users), as various

circumstances could play an important role.

3.2 B: Apps for Mental Health

This category includes 1 empirical exploratory study

(Proudfoot et al., 2010) , 2 literature reviews, and 4

papers reporting app reviews. All 7 papers are

categorized as having ”low” or ”medium” relevance

to ID. None of them explicitly mention HCI field,

however one systematic app review (Escoffery et al.,

2018) emphasized the need for different fields to

collaborate regarding mHealth development.

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The empirical study paper used adult volunteers

as sample population, and the rest of the papers

utilized researchers and reviewers to conduct app

assessments. Most app reviews utilized some kind of

assessment scale while 2 papers (Christmann et al.,

2017; Radovic et al., 2016) relied more on the

experience of the reviewers. The studies show high

acceptability of app usage for mental health support.

They further show that very few apps report efficacy

evidence. Aesthetics are found to be generally high,

but engagement and persuasive principles are rarely

utilized. It is noted that the apps, especially in

treating recognized disorders, need to function

according to guidelines appropriate for clinical

practice (e.g. Nicholas et al., 2015).

Several issues, specifically relevant to ID, were

identified in category B papers. To begin with, a

recurrent issue in 4 papers was privacy (Chan et al.,

2017; Nicholas et al., 2015; Proudfoot et al., 2010;

Radovic et al., 2016). Many apps are not transparent

about their usage of the mental health data they

acquire, or their security policies. Users have

recommended the provision of security functions,

like protected accounts, and have overall expressed

major concern about privacy (e.g. Proudfoot et al.,

2010). Furthermore, users appear to appreciate

convenience and ease of use factors (e.g., reminders,

progress feedback, and an offline mode).

There is also need for apps to account for special

populations like youth or adults living with

disorders. It may include, e.g., providing easy to

follow texts, and disease relevant functions and

information (aspects that are commonly neglected)

(Low & Manias, 2019).

Further concerns that need to be addressed are

the description contents and handling of severe

situations. It is reported that apps descriptions can

often be misleading in order to promote

downloading instead of aiming in assisting the

individual in their commitment to the desired

outcome (e.g. Escoffery et al., 2018). The second

concern is related to apps with inadequately

developed AI agents. AI chat bots, as well as some

other app functions, often encounter difficulties

understanding the actual meaning the user is trying

to convey. This is an issue on its own, but it

aggravates when severe cases are identified (e.g.

suicide ideations) but not properly addressed.

Instead, the user is redirected to a different source,

e.g., a hotline, with no further assistance by the chat

agent (Christmann et al., 2017; Nicholas et al.,

2015). At least some level of support should be

provided, considering the user might have chosen

the app assistance in order to avoid such

confrontations.

We also detected a number of ID approaches that

have been used to resolve issues. Some apps use

tracking options both automated ones (e.g. step

tracking) and manual ones (e.g., mood tracking).

The former type helps users in disease management,

and the latter can be effective in progress and

engagement (e.g. Christmann et al., 2017; Geuens et

al., 2016).

Several papers emphasize that bringing in

experts from behavioural sciences is essential when

implementing evidence based approaches, as well as

introducing these approaches to app users, in order

to increase app credibility (e.g. Escoffery et al.,

2018). It has also been shown that end-users’ and

experts’ intervention in app design, results in higher

usefulness and acceptability of the apps (Escoffery

et al., 2018). Lastly, communication with both peers

and healthcare providers could be further increased

with support techniques or digital rewards in order

to avoid user’s isolation (e.g. Escoffery et al., 2018;

Geuens et al., 2016).

3.3 C: Technology for Mental

Wellbeing in General, Not Limited

to Apps

The group comprises 3 papers: 1 literature/

technology review paper and 2 papers reporting

comparative empirical studies of different

technological solutions (Jaques et al., 2017;

Williams et al., 2013). The review paper (Woodward

et al., 2019) presents state-of-the-art in technologies

“beyond mobile apps”, employed for assessing and

improving mental well-being. The topics start with

an overview of technological alternatives to

traditional methods for assessing mental well-being,

as well as existing mobile apps for stress

management. Then proceeds to discuss (a) the use of

tangible interfaces to manage stress, (b) collecting

various types of data from sensors embedded in

wearable technologies (e.g., location, motion,

ambient light and noise, heart rate), as well as from

social media, and applying machine learning

algorithms to the date to sense mental well-being,

and (c) technology-based interventions, such as

those based on virtual and augmented reality,

biofeedback, and real-time haptic feedback. The

paper also identifies a number of challenges,

including privacy and users’ digital skills, and

opportunities, including user feedback, for future

work on technologies for mental well-being.

Interaction Design Issues in the Development and Assessment of Stress Management Apps: A Scoping Literature Review and Analysis

237

The papers reporting empirical comparative

studies deal, respectively, with different mood-

predicting machine learning models and different

form-factors in human-agent interaction. Jaques et

al. (2017) use real-life continuous monitoring data

from physiological sensors, smartphones and self-

reports, as well as weather information, to compare

tomorrow’s mood predictions generated by different

machine learning models. They found that

personalized models outperform generic ones.

Experimental study by Williams et al. (2013)

compares stress-inducing in-car notifications

delivered via a smartphone and an intelligent agent,

implemented as either a static persona or a social

robot. It was found that the participants were less

stressed and more often performed safety

precautions with agents, and they developed a

deeper bond with the robot.

In two of the papers, Williams et al. (2013) and

Woodward et al. (2019) ID issues play an important

role. The comparison of agent form-factors in

Williams et al. (2013) is, essentially, an interaction

design study, even though the paper is not published

in a mainstream HCI/ interaction design outlet. The

main focus of Woodward et al. (2019) is on mental

health, rather than ID per se. However, the paper, as

mentioned, addresses a number of issues, which are

directly relevant to ID. Both papers indicate that the

way a technology is embodied is of key importance

and tangible technologies implemented as physical

objects, such as squeezable devices or social robots,

may have advantages for managing stress over more

abstract, screen-based mobile apps. The need for

attention to privacy and accessibility (Woodward et

al., 2019) is again highlighted (e.g., consider

potentially lower digital competence of older adults),

and point to the enormous potential of using the

wealth of data from various sensors for detecting

users’ subjective states.

When discussing opportunities for future

research, Woodward et al. (2019) mention a focus

group study with people with severe learning and

physical disabilities, which study helped to better

understand potential users’ needs and requirements,

as an example of potential benefits of getting

insights from intended users of a technology.

Williams et al. (2013) argue that developing

contextually aware systems, probably connected to

other apps and services, is a promising approach to

developing intelligent assistants.

3.4 D: Mobile Apps for Health in

General

The group includes 8 papers: 2 literature reviews, 5

technology reviews, and 1 empirical study. Payne et

al. (2015) presents a systematic review of health-

related scientific studies of apps for behavior

intervention. The paper finds that almost all apps

used in the studies were based on specific theories or

evidence-based strategies, they appear to be

effective in achieving behavior change, and self-

monitoring was the most common measure utilized

in the studies. It was also found that for users the

most important features are ease of use, time per use,

and app convenience. Matthews et al. (2016) report

a study in which 2 researchers reviewed and coded

20 articles on mobile apps promoting physical

activity, using the Persuasive Systems Design

model. It was found that many persuasive

technology features were represented in the selected

articles, but system credibility was not significantly

presented.

The technology reviews included the following

papers. Chang et al. (2012) presented twelve apps

(they simulated download pages and reviews) to

online participants and recorded their attitudes

toward the apps. The responses were analyzed using

a UX assessment framework comprising seven

factors. It was found that Ease of Use was

mentioned in regard to all apps, while the Social

Support was missing. Singh et al. (2016) conducted

a review of a set of mobile apps for patients with

chronic illnesses. Independent reviewers were asked

to read apps’ descriptions and assess the usefulness

of the apps. It was found that only a minority of the

apps were considered potentially useful, and about a

fifth of the apps were not updated for at least two

years. In the study by Langrial et al. (2012) four

experts were asked to apply the Persuasive Systems

Design (PSD) model when assessing 12 behavior

change apps. It was found that tailoring was not used

in the evaluated applications, and there was a lack of

features for credibility, social support, and

augmenting human-computer dialogue. Lister et al.

(2014) analyzed the use of gamification in health

apps. Three trained coders assessed 132 apps for 10

effective game elements, 6 core components of

health gamification, and 13 core health behavior

constructs. It was found that while elements of

gamification are widely used in health and fitness

apps, there is a lack of integration and industry

standards. In a study by Pagoto et al. (2013), two

assessors rated 30 commercial weight-loss apps for

20 behavioral strategies derived from DPP (Diabetes

CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications

238

Prevention Program). Behavioral strategies that help

improve motivation, reduce stress, and assist with

problem solving were found to be missing in the

apps.

In the empirical study conducted by Vaghefi &

Tulu (2019) the participants were asked to use apps

for 14 days, and factors affecting long-term use of

the apps were analyzed. It was found that continued

use of the apps can be explained by (a) user’s

persistence in achieving health goals and (b) users’

assessment of app and its capabilities, including

interface design, navigation, notifications, data

collection methods, goal management, knowledge

depth, system rules, actionable recommendations,

and user-system fit.

Of the 8 papers in the group, we found 4 papers

being of “high” relevance to ID, 2 of “medium”

relevance and 2 of “low” relevance. The high

relevance papers (Chang et al., 2012; Langrial et al.,

2012; Payne et al., 2015; Vaghefi & Tulu, 2019)

explicitly address ID issues, such as ease of use,

factors of UX, augmenting human-computer dialog,

interface design, and user-system fit. Medium

relevance papers (Lister et al., 2014; Matthews et al.,

2016) point to potentially relevant issues, such as

persuasive technology features and gamification, but

the discussion of the issues is mostly limited to the

codes produced by app raters, and making

conclusion about the representation of individual

components of the framework, or the coding

scheme, used in the study, in the analyzed apps. Low

relevance papers (Pagoto et al., 2013; Singh et al.,

2016) report studies, in which sets of apps were

reviewed and rated from the point of view of

perceived usefulness or underlying behavioral

strategies, rather than human-technology interaction.

4 DISCUSSION

4.1 Commonly Mentioned ID-Related

Issues and Strategies

Our analysis indicates that a range of issues, directly

related to ID, are discussed in current literature

dealing with the design and evaluation of stress

management/ mHealth apps. Some of the most

commonly mentioned issues are ease of use, user

engagement, and privacy.

Ease of use refers to how effortless or intuitive

it is for the user to navigate an app, as well as learn

and use its functionality. It is a key requirement

since failing to meet it may result in an increased,

rather than reduced, level of stress. Studies show

that most apps have an acceptable level of ease of

use (e.g. Chang et al., 2012; Coulon et al., 2016;

Payne et al., 2015), but there is still room for

improvement (e.g. Proudfoot et al., 2010). In

particular, evidence suggests that ease of use can be

facilitated by providing such design features as

reminders and an offline mode (Low & Manias,

2019), which features are currently not always

provided.

It is also suggested, that to support ease of use,

more automatic data collection options (e.g., step

counters) should be considered, where privacy

allows for it, in order to decrease the manual effort

and input required from the user (Geuens et al.,

2016; Payne et al., 2015; Vaghefi & Tulu, 2019).

User engagement is a crucial success factor for

any technology-based interventions that require a

sustained use of a technology. Studies of stress

management/ mHealth apps show that user

engagement may present a challenge. It was found

that users engage with mobile apps more frequently

compared to, e.g., web-based equivalents (Morrison

et al., 2018). However, overall engagement can be

low (Escoffery et al., 2018; Singh et al., 2016). Ease

of use and accessibility, discussed above, are,

arguably, some of the factors affecting user

engagement, but there are several other factors to

consider, as well. Design and context of the app play

a significant role in engagement (Escoffery et al.,

2018). For example, if users invest time in manual

mood tacking, but do not receive feedback regarding

progress, users’ engagement can be decreased.

Intelligent notifications do not appear to make a

significant difference in engagement (Morrison et

al., 2017).

Two key strategies, which are being widely

explored in current research in order to deal with the

challenge of user engagement, are gamification (e.g.,

Hoffmann et al., 2017) and persuasive technologies.

(e.g., Geuens et al., 2016). These strategies are

employed in both technology reviews, serving as a

framework for evaluation, and design, serving as a

set of principles and guidelines for developing new

technologies.

The very nature of stress management apps

implies that privacy is a critically important issue.

Human-technology interaction in that case is likely

to contain sensitive information, which the user

would not want to share with others. The literature

we analysed indicates that privacy is a prominent

issue for both users and designers of the technology

(e.g., Børøsund et al., 2018; Rodriguez-Paras et al.,

2017; Nicholas et al., 2015; Proudfoot et al., 2010).

Interaction Design Issues in the Development and Assessment of Stress Management Apps: A Scoping Literature Review and Analysis

239

4.2 References to ID/ HCI Literature

While, as mentioned, a significant proportion of

papers in the set we analysed discuss ID-related

issues, most papers do so without a strong and

explicit link to literature in interaction design and

HCI. Only a few papers from the set are published in

interaction design/ HCI journals or conference

proceedings. Some papers do not cite any ID sources

at all. Those that do cite ID/HCI literature, often

provide selected or dated references.

A lack of cross-field collaboration between, on

the one hand, the healthcare-focused majority of

stress management app studies and, on the other

hand, ID/ HCI research, is also apparent in the

discussion of topics, which are relevant to each of

the research fields. In particular, gamification and

persuasive technologies are studied both within and

outside ID/ HCI. We observed that the papers we

analysed mostly refer to gamification and persuasive

technology studies outside ID/ HCI.

The need to involve experts from various fields,

when conducting research and development, related

to stress management/ mHealth technologies, is

mentioned in a number of papers. For instance, it is

suggested that would be useful to involve experts

from other fields, e.g. behavioral scientists, when

trying to increase the level of user engagement

(Woodward et al., 2019). However, in our analysis

we could not find specific calls for more

involvement of experts from ID/ HCI.

4.3 Prospects for Future Work

In our view, there are ample reasons for a closer

involvement of the field of ID in research on stress

management/ mHealth apps. First of all, concepts

and tools, which are developed in ID/ HCI, and

which are currently of limited use in the design and

evaluation of stress management apps, can be

employed to more successfully deal with already

identified ID issues with mHealth technologies.

It would be especially beneficial for app

designers to adopt the user-centered design

approach, which forms the foundation of ID. It is

important to involve end-users in addition to

collecting experts’ opinions on various development

stages of mHealth apps, even more so when they are

intended for special users. This will help developers

stay on track while increase both usefulness and

acceptability of end users (Low & Manias, 2019). It

is also important to include personalized progress

and personalized next steps to direct the individual

user according to their needs (Vaghefi & Tulu, 2019;

Coelhoso et al., 2019).

Second, massive technological transformations,

which are currently taking place, open up significant

new possibilities. For instance, AI have enormous

potential for developing successful applications. AI-

powered chat bots have already been used to target a

more personalized approach in “pocket psychiatry”,

and the approach can be considered promising, even

though there are several practical issues that need to

be addressed before implementation of AI can be

considered complete (Chan et al., 2017). At the same

time, these developments present difficult challenges

to both mHealth and ID/ HCI, which means it would

be in the best interests of both fields to join forces

when addressing these new challenges.

5 CONCLUSIONS

In this paper, we present a scoping literature review

of how ID issues are addressed in current research

on stress management apps. Our analysis shows that

ID issues, especially ease of use, user engagement,

and privacy, have been common concerns in the

design and evaluation of stress management/

mHealth technologies. The analysis also suggests

that dealing with such concerns may greatly benefit

from establishing closer connections between studies

of mHealth and current interaction design/ HCI

research and practice. Such connection would make

it possible to use state-of-the art ID concepts and

methods for dealing with existing ID issues. In

addition, a stronger connection between the fields

would be, arguably, essential for successfully

addressing emerging challenges and opportunities,

such as those related to the increased use of AI in

technology-based health interventions.

Finally, it should be mentioned that our study

represents an initial step toward achieving a detailed

understanding of ID-related issues, challenges, and

opportunities in stress management apps research.

Conducting a scoping, rather than systematic,

literature search allowed us to rapidly identify some

overall patterns and trends, but it needs to be

followed by further, more focused analyses.

ACKNOWLEDGEMENTS

This work was partially supported by the

Wallenberg AI, Autonomous Systems and Software

Program – Humanities and Society (WASP-HS)

CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications

240

funded by the Marianne and Marcus Wallenberg

Foundation and the Marcus and Amalia Wallenberg

Foundation, grant MMW 2019.0220.

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