Incorporating Cognitive Training with Elderly People’s Everyday

Use of Smartphones

Hasanin Mohammed Salman

, Anna Prand-Stritzko

, Barnabé Petit

, Bastien Pujol

, Lara Janssen

Maria Masip Figueras

and Way Kiat Bong

Department of Information Technology, Catholic University in Erbil, Kurdistan Region, Iraq

Department of Computer Science, OsloMet – Oslo Metropolitan University, Pilestredet 35, Oslo, Norway

Keywords: Cognitive Training, Mobile Applications, Elderly People, Older Adults, Everyday Use.

Abstract: The ageing population is growing rapidly, and the risk of cognitive decline among this populace is alarming.

Dementia patients represent one of the most significant groups among individuals experiencing cognitive

decline. Despite having pharmacological medications available to support dementia patients, non-

pharmacological interventions can be a better approach, considering the cost and undesired side effects.

Mobile cognitive training applications (apps) have been introduced to elderly individuals, but adoption rates

have not been high. One possible reason for this can be that they do not perceive such apps as useful. These

apps are standalone apps that do not take users' real-life tasks into consideration. Hence, in this study, we aim

to integrate cognitive training exercises with elderly’s everyday use of smartphones. First, we gathered user

requirements by conducting literature review, app research and an interview with an elderly person. We then

conducted four iterations of prototyping and evaluations, where feedback from participants in the evaluations

was used to improve the prototypes developed in next iteration. Through the entire process, we generated

some lessons learned that are applicable for designing such apps. Future work includes further development

of this app to make it fully functional, and testing it in a longitudinal study.

1 INTRODUCTION

The demographic change has led to a growing

number of the ageing populace. As indicated by the

World Health Organization (WHO), by 2050, the

world's elderly population aged 65 years and above

will be doubled, with an estimated total of 2 billion

people (WHO, 2023). This growth in numbers is

accompanied by a significant increase in the number

of people with dementia, with age being the strongest

recognized risk factor for this disease (WHO, 2017).

Dementia encompasses a set of symptoms related to

a decline in memory, reasoning, and other thinking

skills (WHO, 2017). Various types of dementia exist,

but Alzheimer's Disease (AD) accounts for 60 to 80%

of cases overall (Prince et al., 2016).

Although it is not a type of dementia, mild

cognitive impairment (MCI) is a syndrome deemed a

precursor to dementia (Jongsiriyanyong &

Limpawattana, 2018; Petersen et al., 2018). MCI is a

https://orcid.org/0000-0002-3394-779X

https://orcid.org/0000-0003-3714-123X

cognitive decline that is more extensive than expected

in normal ageing but less severe than dementia

(Jongsiriyanyong & Limpawattana, 2018; Langa &

Levine, 2014). Clinically, healthy elderly individuals

could have some progressive decline in terms of their

cognitive functions, such as processing speed abilities

and memory. The further extensive decline in

cognitive domains could be related to MCI. Having

MCI is an alarming indicator of dementia and

indicates a high risk of development of dementia and

subsequently to more severe AD (Gauthier et al.,

2006; Petersen et al., 2018).

There are pharmacological medications available

to support dementia patients. Nevertheless, the effect

of the available pharmacological medications is not

curative, but they may aid in alleviating symptoms of

dementia (Groot et al., 2016; Smith & Argentina,

2020). Additionally, these medications have

undesirable side effects on patients; thus, finding

Salman, H., Prand-Stritzko, A., Petit, B., Pujol, B., Janssen, L., Figueras, M. and Bong, W.

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones.

DOI: 10.5220/0012625700003699

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2024), pages 73-83

ISBN: 978-989-758-700-9; ISSN: 2184-4984

alternative approaches for controlling the progression

of dementia is crucial (Zhang et al., 2021).

Non-pharmacological interventions, such as

cognitive interventions, could provide a healthier and

less costly alternative to pharmacological

interventions. The adoption of appropriate cognitive

training, also known as brain training or mind training

(Zhang et al., 2021), can activate the brain

compensation mechanisms of the elderly and keep

their cognitive level high (Fabbri et al., 2019; Zhang

et al., 2018). Various research has provided evidence

regarding the effectiveness of cognitive training

exercises in improving elderly’s cognitive

performance and slowing the progression of dementia

(Husseini et al., 2016) and AD(Bonnechère et al.,

2021; Fabbri et al., 2019). Cognitive training

exercises stimulate multiple cognitive domains which

include memory, reasoning, and information

processing speed (Bonnechère et al., 2021; Zhang et

al., 2018). Examples of cognitive training exercises

include jigsaw puzzles, Sudokus, and crosswords. A

good support tool for cognitive training exercises in

aging are smartphone applications (apps)

(Bonnechère et al., 2021). Hereinafter in this article,

“cognitive training apps” refers to smartphone apps

that provide cognitive training exercises for elderly

populace.

An app's success is determined by its engagement

among the intended users, and one of the main factors

influencing app engagement is its usefulness.

(Thorpe et al., 2016). Usefulness concerns whether

the system provides functionalities/features that align

with and fulfill users’ typical "real-life" tasks using

that system (Thorpe et al., 2016; Yingta et al., 2021).

A real-life task is a task that matches an activity or

action that would occur in real-life use. For example,

common real-life tasks concerning smartphone

functions are related to management of messages

(sending/receiving), calls (making/receiving), and

photos (taking/sharing) (Petrovcic et al., 2018).

Thereby, to implement a successful system, from the

perspective of usefulness, designers should consider

the users’ real-life tasks and design a system that can

empower them to perform these tasks.

Regardless of the potential benefits of the

cognitive training apps as an alternative for

pharmacological medications, there is a challenge

with the usefulness of these available apps; they often

are not designed to integrate practising cognitive

training exercises with performing typical real-life

tasks using smartphones (Bonnechère et al., 2021;

Thorpe et al., 2016). For example, in some of the

current apps, when performing a cognitive training

exercise involving puzzles, the user's task is to

rearrange the tiles back to their original shape. Even

though this exercise stimulates various cognitive

skills, the user is unable to associate the completion

of the exercise with a typical smartphone function,

such as making a call, or an app that is regularly used

on a smartphone, like YouTube.

Elderly people from various demographic

backgrounds possess different ways of using a

smartphone. In Norway, elderly people do use

smartphones regularly; 93% of elderly individuals

aged 65 to 74, and 75% of those between 75 and 79

years old use phones with Internet (SSB, 2023).

Accordingly, our objective is to propose a design

concept to fabricate an app that incorporates

practising cognitive training exercises with the

elderly everyday use of smartphones. To the best of

our knowledge, no study has yet attempted to

integrate cognitive training exercises with regular use

of smartphones among the elderly individuals.

2 METHODOLOGY

In this study, we adopted a user-centered design

(UCD) (Karat, 1996) approach to ensure that the

needs and preferences of elderly people were

considered throughout the entire process. According

to MacKenzie (2012), two requirements must be met

to apply the experiential results to individuals who

were not tested: (1) the participants must be members

of the targeted population; and (2) a sufficient number

of participants must be tested. In light of this, a total

of nine elderly, which is a valid sample size for a

problem discovery study, participated in the study

(Macefield, 2009).

Participants were recruited through convenience

sampling, which means they were invited to

participate for the study due to their easy accessibility

(Sedgwick, 2013). The criteria for inclusion were

being above the age of 65 (according to the definition

of an elderly person by WHO (2023), and the

retirement age in Norway (NAV, 2023)), being

cognitively healthy and using a smartphone. The app

was targeted to be used by elderly individuals for

cognitive training purposes with a focus on

prevention rather than treatment.

Prior to participating in the study, participants

were first briefed about the project. They were then

asked to give their consent by signing a consent form.

All participants were met physically in person at their

homes, except for one (he preferred to meet

somewhere else), in order to create the most natural

setting for using the app. The entire process took

around an hour for each participant.

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

2.1 User Requirements Gathering

Using literature search and app research, relevant

studies and existing mobile apps focusing on cognitive

training were identified. Three main areas were

carefully considered during this stage, i.e., types of

cognitive exercise, the elderly’s everyday use of smart-

phones, and user engagement which includes rewards.

Developed on the findings from literature search

and app research, we conducted an interview with an

elderly individual using a semi-structured interview

guide. During this interview, since we were interested

in integrating elderly individuals’ use of smartphones

with training cognitive exercises, we first asked

questions about the interviewee’s smartphone usage.

Questions such as “What are your favourite apps”,

“How often do you use social media apps/ messenger

services/ internet browser?” and “Have you ever used

any cognitive training app?” were asked. We then

presented her designs of some existing cognitive

training apps and sought her opinions about them.

2.2 Iterations of Prototyping and

Evaluation

Based on the user requirements, we created the first

prototype. As UCD requires an iterative process, we

conducted a total of four iterations of prototyping and

evaluation. For evaluations, usability testing was

conducted with two elderly individuals in each

iteration. Similar to the interview in user requirement

gathering, all participants were first asked about their

smartphone usage. They were then presented with the

prototypes and instructed to use them.

During the testing, we encouraged the participants

to think aloud. We observed the participants’

interaction with the prototypes. To gain a better

understanding of how we could provide a better user

experience of our prototypes, questions concerning

the design choice were asked. Examples of these

questions include: “What do you think about the

design/ colours/ layout/ size of icons?”, “What do you

think about getting rewards after doing the

exercises?”, “What do you think about the recap of

your performances?” and “What kind of skill/ ability

would you like to work on?”.

After every testing, the results were analysed to

identify improvements required to make for the next

iteration’s prototype. All prototypes were created

using Figma.

3 RESULTS

Table 1 summarizes the participants’ profiles, which

include demographic background, smartphone usage,

and their favourite type of cognitive training exercise

Table 1: Participants’ profile.

Age Gender Smartphone

use

Most used apps

Often play

games on

smart

hone

Age-

decline

Favourite type

of cognitive

trainin

exercise

Experience with

cognitive

trainin

exercise

P1 70 F Daily Facebook,

YouTube

No No N/A* No

P2 68 M Daily Instant

messaging

(

)

, Faceboo

Yes No Problem-solving

with numbers

P3 70 M Two to

three times

a wee

Vipps

(payment),

Games

No No Problem-solving

with numbers

P4 83 F Daily IM, Facebook No Vision Focus training

using sorting

P5 76 F Daily IM, Facebook Yes No Focus training

using sorting

P6 71 M Daily Spotify No Vision Problem-solving

with numbers

Yes

P7 69 F Daily News, Weather No No Focus training

usin

sortin

P8 72 M Daily IM, Facebook No Vision Problem-solving

with numbers

P9 67 F Daily YouTube,

social media

No No Focus training

usin

sortin

*Participated in user-requirement study, hence did not evaluate different types of cognitive training exercise.

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones

revealed after conducting evaluations with them.

They used their smartphone every day, except P3. He

only used it two to three times a week because he

preferred to use a laptop. In terms of experience of

using a cognitive training app before, P6 was the only

participant who had experience. He played an app

named Lumosity app before. Despite not having

experience with a cognitive training app, the other

participants were aware of the existence of such app.

P1 even expressed her willingness to try. In each

iteration, all participants tried out two different types of

cognitive training exercises. Although the participants

were able to identify their favourite type of exercise,

both types received almost similar responses.

3.1 User Requirement Gathering

After conducting literature review and app research,

we chose the Skillz app (TriangleLabs, 2018) and

Peak (PopReach, 2023) as both claimed to have a

suitable design for an elderly user. Peak was also used

in a study assessing the effectiveness of cognitive

mobile games in “real-life” use among elderly people

(Bonnechère et al., 2021), which means elderly used

this app independently and without specific

guidelines on training frequency. Not only that Peak

was used in “real-life” scenarios, Bonnechère et al.

(2021) also reported individuals of all ages

demonstrated improved cognitive performance, such

as increased scores and processing speed, after

training with the games in Peak.

In terms of types of exercises, we identified two

main categories, i.e., problem-solving and focus

training. The former involves exercises that challenge

users to find solutions, such as arranging numbers in

ascending or descending order. The latter refers to

exercises that require users to utilize their focus and

concentration skills. For instance, identifying and

sorting the same object.

We had P1 tested both Skillz and Peak. One major

finding is that the menu and instructions did not appear

intuitive to her. She was confused most of the time

when attempting to do the exercise. Both apps had

similar exercises, but with different designs. We took

advantage of this opportunity to compare both designs

and asked her to choose her preference. For instance,

in the focus training type of exercise when users had to

choose and sort images (Fig. 1), P1 preferred the design

from Peak, because it was more intuitive for her to

click on the arrows right beside the image.

In terms of incorporating cognitive training app

into everyday use of smartphones, P1 expressed the

need to have a “skip” button. The suggestions to

perform cognitive training exercises should always

come after she has completed her intended actions.

However, this also depends on the original app she

was using. If it was a “less important” app like

YouTube, she would not mind being suggested to

perform cognitive training exercises even before she

started watching anything on YouTube. Regarding

user engagement, she was interested in having

rewards to motivate her. Additionally, feedback to

show the progress after each exercise was also

appreciated. Other than that, she also provided

feedback on the colours and shapes of elements for

the exercise design, such as having simple colours

(not overly striking and colourful).

Figure 1: Focus training exercise (Skillz on the left and

Peak on the right).

3.2 First Iteration

In the prototype for the first iteration, we first

attempted to implement the feature of incorporating

elderly’s everyday use of smartphones. We

incorporated the cognitive training app with a text

message app and YouTube. As illustrated in Fig. 2,

from left to right: the user completed sending a text

message, was then asked to perform a cognitive

training exercise, and instructed to do a problem-

solving exercise that required tapping the numbers on

the screen in ascending order.

Based on the findings from user requirements

gathering, we created two prototypes: one for

problem-solving with number sorting (Fig 2) and one

for focus training, where users needed to categorize

objects. We improved the design by providing clearer

and shorter instructions to the users. We also included

a skip button for their convenience. Lastly, when they

have completed the exercise, the app will display the

status of exercise completion and their scores. The

colours used in the app were kept simple.

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Figure 2: Incorporating cognitive training exercise in text

message app.

P2 and P3 participated in the usability testing in

this iteration. Both had experience in playing games

on smartphones. Overall, they liked the design of the

prototype. The colours and sizes of elements in the

exercises were deemed suitable. In terms of usability,

both of them completed the exercises without much

issue, except for the fact that they did not notice the

“start” button (see Fig. 2, rightmost) and they clicked

on the object instead of the arrow (same design as in

Skillz app in Fig 1).

Regarding rewards, both expressed an interest in

receiving vouchers as rewards, and they wanted more

animations when receiving rewards. For P3, he would

be willing to do more cognitive training exercises if

they were recommended to him and recognized as

beneficial for his health. Both P2 and P3 agreed with

P1 regarding the appropriate timing for suggesting to

perform cognitive training exercises, i.e., after they

have completed the intended tasks.

3.3 Second Iteration

To address the usability issue of not noticing the

“start” button, the instruction “press start to play” was

added (see Fig. 3, leftmost). Animations were also

incorporated around the score (see Fig. 3, middle). To

avoid the confusion of clicking on arrows instead of

exercise objects, we removed the arrows (see Fig.3,

rightmost). Furthermore, we developed a new

exercise that required users to pair two words with the

same or similar meaning. In addition, we also added

higher difficulty levels for the same set of exercises.

P4 and P5 were involved in the usability testing

during this iteration. Same as in the previous iteration,

both testers had issues understanding where and when

to start the exercise. They overlooked the “start”

button. The removal of arrows in the prototype of this

iteration resulted in better usability. However, P4

Figure 3: Changes made to improve the prototype in second

iteration.

faced a slight issue with the colours not effectively

differentiating between right and wrong answers

when the answers were revealed. Unlike the other

earlier participants, both P4 and P5 did not find

monetary rewards like vouchers motivating. For P4,

she was happy with seeing words like

“Congratulations!” when she completed the exercise,

while for P5, it would be more engaging for her if a

doctor recommended these exercises.

3.4 Third Iteration

Therefore, we made the “start” button bigger by

removing the “skip” button on the instruction page

and kept it exclusively on the page when users were

offered to perform cognitive training exercise) (see

Fig. 4, leftmost). The bigger “start” button had an

animation so that users could know better where and

when to start the exercise. To skip the exercise, users

were required to click on the cross button, located in

the top right corner of the pop-up box.

We also implemented an improved design that

emphasized on informing users when getting the

correct answers (see Fig. 4, middle). In the previous

iterations, users had to end the exercise when they

made a mistake. In this iteration, we introduced a

design that provided them the option to either end the

exercise, or make a reattempt (see Fig.4, rightmost).

Lastly, as illustrated in Fig 5, we implemented

additional new designs for receiving rewards based

on all the feedback we had received from previous

iterations. By presenting these new designs to the new

testers, we hoped to gain further insights on rewards

or results that could enhance user engagement. These

new designs included displaying scores, presenting

playcoins that can be converted to monetary

vouchers, showing attained levels, comparing results

to historical data, and displaying newly earned

badges.

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones

Figure 4: Changes made to improve the prototype in third

iteration.

Figure 5: Designs for rewards.

In this iteration, P6 and P7 took part in the

usability testing. Despite having implemented all the

changes to make the exercise starting point clearer,

both testers still had issues understanding where to

start. They mistakenly thought they could begin right

away at the instruction page. Other than that, they

managed to complete the exercise without

encountering many usability issues. Regarding

rewards, both liked most of the proposed designs,

with no specific preferences. Unlike P5, P6 preferred

if the advice, or general information about performing

a cognitive training was given by a researcher or an

expert, instead from a doctor.

3.5 Fourth Iteration

In the final iteration, as illustrated in Fig. 6, we

modified the instruction page by completely

removing the “start” button from that page. When

users were asked to play the exercise, they were

offered either to skip or agree to do that. If they would

like to skip the exercise, they would click on the

“skip” button, or the cross button in the top right

corner. On the instruction page, the exercise started

automatically after instructions were displayed. This

Figure 6: The page that prompts users to perform a

cognitive training exercise (left) and the instruction page

(right).

was the only major change in this iteration, and testers

P8 and P9 had no issue understanding this design. We

purposely asked both of them to skip the exercise as

part of the testing tasks, and they managed to

complete this task by clicking on the “skip” button.

Overall, both testers were able to interact with the

cognitive training app without experiencing any

usability issues. They liked the app and were positive

to try it when it was fully developed.

4 DISCUSSION

In this study, we adopted a UCD approach, gathered

user requirements through literature review and app

research, and conducted four iterations of prototyping

and evaluations. Throughout the process, we

Box 1: Lessons learned.

1. To engage elderly individuals, user interface design

is more important than types of cognitive training

exercises.

2. Make the interactions in exercises natural and

relatable to the real world.

3. Emphasize sensitive/important user interface

elements to enhance their visibility.

4. Ensure logical flow in the app.

5. “Assuring” interactions do not always work for

elderly individuals as they might trust the

interactions to be simpler.

6. Keep elderly individuals motivated throughout the

entire process, i.e., before, during and after

performing cognitive training exercises.

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

continuously improved the prototype and identified

essential elements for designing an app that integrates

elderly’s everyday smartphone use with cognitive

training. The results appear promising, as all

participants expressed openness to using this app.

Based on our findings, we have generated some

lessons learned for designing a cognitive training app

for elderly individuals. This list of lessons learned is

presented on Box 1.

4.1 Drivers of User Engagement

The four iterations of prototyping and evaluation

involved two different types of cognitive training

exercises: problem-solving and focus training.

Participants’ feedback revealed that exercise

preference was not based on the type of exercise itself

but rather on how the exercise was designed,

specifically, the user interface (UI) design. Despite

the participants' ability to pinpoint their preferred

type of exercise, both types obtained nearly

indistinguishable responses from all participants.

Thus, the users’ engagement with exercises is

dependent on the presence of a user-friendly design.

This finding is in good agreement with the findings of

Jamieson et al. (2022) and Lilholt et al. (2015), which

indicated that the users, including elderly individuals

(Zhu et al., 2022), engage with exercises that have an

intuitive and easy-to-understand UI design.

From the findings, we are able to identify two

drivers essential for promoting their engagement with

the cognitive exercise app. These drivers are based on

two design principles, i.e., “match between system

and real world”, and “recognition rather than recall”.

According to the relevant evaluation studies (Kaur &

Chen, 2022; Lilholt et al., 2015), these two design

principles play a significant role in determining the

user’s acceptance of the examined health systems.

According to Nielsen (1994), the “match between

system and real world” design principle suggests that

the design should utilize language and concepts that

are familiar to users in their daily life routines. In the

second iteration, the exercise was modified by

removing arrows (refer to Fig. 3, rightmost) as it was

deemed more natural for participants to click on an

item rather than an arrow. In addition, the

participants’ feedback highlighted the importance of

following this principle to improve user

experience

and overall engagement. Research shows that users

preferred a clear connection between the exercises in

the app and their potential positive impacts, as well as

actual contributions to their mental well-being. In this

study, participants expressed the opinion that they

would perceive cognitive training exercises app more

engaging if the recommendations, including the

potential benefits, were provided by someone who

typically offers such guidance in the real world, such

as a physician, researcher, or other expert.

Currently, we have this design by incorporating

reward feedback from experts. To further leverage

this valuable insight, future app design should

consider integrating a feature that enables for the

seamless communication of expert recommendations

directly within the app interface. This could involve

incorporating brief messages, notifications, or even

interactive content from authoritative figures,

providing users with personalized insights and

guidance. Consequently, it would enhance the overall

user experience and foster greater user engagement

with the cognitive training app.

The second principle is “recognition rather than

recall”. Nielsen (1994) describes this principle as

minimizing the user's need to remember information

by ensuring UI elements are visible. In line with this

principle, during the early iterations (first and second)

participants faced some usability-related challenges,

such as recognizing the “start” button, and

distinguishing colours between correct and wrong

answers. In the later iterations (third and fourth),

redesigning the start button and subtle elements with

more noticeable sizes and colours, along with

supporting animations, effectively addressed these

recognition issues in the initial designs. This redesign

concept aligns with the recommendations by

Inostroza et al. (2016), who emphasize the

importance of prominently placing sensitive/

important UI elements to reduce the users’ cognitive

load and improve their ability to recognize those

elements in smartphone UI.

Implementing the principles of "match between

system and real world" and "recognition rather than

recall" provides an enhanced user experience by

mitigating cognitive load, which in turn improves

user engagement with mobile apps. The findings of

Salman et al. (2018) and Petrovčič et al. (2018),

which demonstrate the positive impact of

implementing these two principles on elderly user

engagement with smartphones, are in good agreement

with our interpretation of the findings. First, applying

the "match between system and real world" principle

helps users feel more comfortable and self-assured

when using an app that replicates their perception of

the real world. This leads to faster learning curves and

a greater chance of sustained user engagement with

mobile apps. Second, "recognition rather than recall"

makes the mobile app more intuitive and user-

friendly by reducing the requirement for users to

remember particular UI elements or information. This

approach promotes a smooth user experience, which

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones

lowers frustration and encourages continued

engagement. Both outcomes are particularly more

important to achieve, especially when considering

elderly individuals who are using an unfamiliar app;

they are more susceptible to demotivation, making it

crucial to ensure their user experience (Bong & Chen,

2015).

In conformance with the above discussion, app

designers should take into consideration the

requirements and needs of elderly users when

designing an engaging mobile app. However, meeting

requirements that satisfy all elderly individuals can be

a challenging task. For instance, some participants

preferred simple colours, while others favoured active

animations. Similarly, there were different opinions

on how to represent rewards, whether through

playcoins or without them. These challenges are to be

expected when conducting research involving elderly

individuals using a mobile health app (Liu et al.,

2021; Matthew-Maich et al., 2016).

4.2 Other Design Elements

One usability issue that we encountered in multiple

iterations was related to the “start” button. Most

participants overlooked the “start” button and had the

expectation that the exercise would begin right away.

One possible explanation is that our initial design

may not have provided a clear logical flow. The

participants did not expect that they had to manually

start the exercise after agreeing to perform cognitive

training exercises. Such an “assuring” interaction

might not be necessary for elderly individuals. This

can be seen as an “over-trusting” behaviour, which is

consistent with that of Zhong et al. (2020) who

reported a similar behaviour among their participants

when interacting with the “start” button on a

microwave. In their study, participants demonstrated

a tendency of "over-trusting" the functionality of the

"start" button on the microwave, mistakenly

assuming that pressing it directly would be sufficient

for operation. They were not anticipating the need for

any other "additional" interactions. Additionally,

there may be too many texts being displayed in the

same screen (see Fig. 3, leftmost).

When it comes to skipping performing cognitive

training exercises, we observed that both P8 and P9

chose the “skip” button instead of the cross symbol in

the top-right corner. The design featuring both

buttons was only implemented in the fourth iteration.

Therefore, we were only able to test them once with

two participants. We acknowledged that this lesson

learned is based on a very small sample size. Besides

addressing the usability issue with the “start” button,

we implemented this design to provide users multiple

options for declining the offer to perform cognitive

training exercises, by incorporating the universal

design principle “flexibility in use” (Story et al.,

1998), as well as Nielsen (1994)’s heuristic

“flexibility and efficiency of use”. It was not

surprising that P8 and P9 preferred the "skip" button

over the cross symbol, as we were aware that the

visibility of the cross button was comparatively

lower. Nonetheless, studies have also demonstrated

that elderly individuals have a better understanding of

buttons with text rather than those with symbols or

images (Bong & Chen, 2015; Gatsou et al., 2011).

In addition, we have also identified some suitable

approaches for integrating cognitive training

exercises with elderly’s everyday use of smartphones.

All participants in our study would prefer to receive

the prompts to perform cognitive training exercises

after they have completed the intended tasks on their

smartphones. However, participants who spend more

time on entertainment activities such as watching

YouTube, playing games, and browsing Facebook,

expressed no objection to receiving the suggestions

even before starting to use those apps. This can be due

to elderly perceive performing cognitive training

exercises on smartphones more important than

engaging in entertainment activities. When

investigating the acceptance of technology among

elderly people in Hong Kong, Chen and Chan (2014)

identified other factors that influenced elderly

people’s technology acceptance such as cognitive

ability and self-reported health conditions. This

suggests that elderly people might perceive the use of

technology for health-related purposes to be more

important than engaging in entertaining activities.

However, one important thing is that the option to

decline the offers to perform cognitive training

exercises can never be overlooked, as the offers shall

serve as reminders that encourage them to perform

cognitive training exercises regularly.

5 CONCLUSIONS

In this study, we aim to propose a design concept for

creating a mobile app that primarily targets the

elderly population; this app would encourage them to

perform cognitive training exercises when they are

using common smartphone functions. Through four

iterations of prototyping and evaluations, we have

gained valuable insights on designing an effective

cognitive training app for elderly individuals. These

findings have significant implications for researchers

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

and mobile app designers, as they can benefit from

the lessons learned generated in this study.

This study is at its initial stages of inquiry, where

we have gathered user requirements and proposed the

design concept for creating this mobile app.

Therefore, one of the main limitations is that we have

yet to evaluate whether this app can effectively

encourage elderly smartphone users to perform

cognitive training exercises over a longer period of

time. Another essential element for this app that we

did not manage to cover in this study, is about how

often these suggestions asking elderly smartphone

users to perform cognitive training exercises should

be prompted. Although elderly individuals can

choose to skip when being asked, it is important to

ensure the frequency of prompting suggestions does

not irritate them.

Research bias is a recognized challenge, and in

this study, it may be particularly noticeable. The first

interview for user requirement gathering and all

evaluations except with P8 and P9 were conducted by

a group of students enrolled in the European Project

Semester course. Although this observation is

subjective, they reported that Norwegian participants

who evaluated the prototypes with them were very

polite and friendly. They might have provided biased

answers to favour the students. We were able to

identify usability issues by observing how the

participants interacted with the prototype. However,

in terms of design elements such as colours and

rewards after completing an exercise, there was

nothing for us to observe.

Lastly, other limitations of this study include a

small sample size and relatively homogeneous

participants, predominantly representing the ethnic

Norwegian population. Despite our awareness of the

rule of thumb for conducting usability evaluations,

we had to consider factors such as feasibility concerns

(both in terms of recruiting participants and

conducting evaluations with them) and this study

being at its initial stage of inquiry (Caine, 2016).

Therefore, we made the decision to have two

participants in each iteration for this UCD approach.

As part of our future research plan, we intend to

conduct further testing and evaluation of the most

recent version of the prototype. This forthcoming

evaluation will engage a minimum of 15 participants,

a sample size deemed adequate for generating

statistically significant findings, in multiple rounds of

app testing iterations. The execution of such a

prospective investigation will serve to mitigate a

limitation identified in the current study, namely the

insufficient availability of participants for engaging

in iterative evaluations of prototype versions.

Subsequent versions of the app, informed by the

outcomes of this planned study, hold promise for

enhancing our ability to reach a comprehensive and

statistically substantiated determination regarding the

extent to which revised prototype versions address

participants’ concerns and ultimately improve their

user experience.

We hope to further develop the mobile app and

have it fully functional. Future studies should

consider conducting a longitudinal study to

investigate how elderly individuals engage with and

benefit from the app over an extended period. A

larger sample size should also be targeted. In

addition, we should engage researchers with expertise

in brain function, cognitive psychology, or related

fields to collaborate. This multidisciplinary approach

will provide valuable insights concerning the app’s

effectiveness in training elderly individuals’

cognitive abilities.

Building on this multidisciplinary collaboration,

we aim to have the app used and tested by patients

with MCI. Although the app is not specifically

designed for them, engaging in cognitive training

exercises could provide some level of cognitive

stimulation, potentially helping to slow the

progression of cognitive decline. Importantly, it

should be noted that the design, i.e., usability and

functionality, of such an app for individuals with MCI

would likely be different. Therefore, it will require

evaluations with different settings through further

research and user testing. Last but not least, by

utilizing advancements in machine learning and

artificial intelligence, we see potential in

incorporating a personalised system in the app. This

would adapt the UI and the game logic/mechanism to

better suit specific groups of users and/or patients.

ACKNOWLEDGEMENTS

We would like to thank all participants for taking the

time to participate in this study, allowing us to

conduct interviews and usability testing, and

graciously hosting us in their homes.

REFERENCES

Bong, W. K., & Chen, W. (2015). Mobile instant messaging

for the elderly. Procedia Computer Science, 67, 28-37.

Bonnechère, B., Klass, M., Langley, C., & Sahakian, B. J.

(2021). Brain training using cognitive apps can improve

cognitive performance and processing speed in older

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones

adults. Scientific Reports, 11(1), 12313.

https://doi.org/10.1038/s41598-021-91867-z

Caine, K. (2016). Local standards for sample size at CHI.

Proceedings of the 2016 CHI conference on human

factors in computing systems,

Chen, K., & Chan, A. H. S. (2014). Gerontechnology

acceptance by elderly Hong Kong Chinese: a senior

technology acceptance model (STAM). Ergonomics,

57(5), 635-652.

Fabbri, L., Mosca, I. E., Gerli, F., Martini, L., Pancani, S.,

Lucidi, G., Savazzi, F., Baglio, F., Vannetti, F., &

Macchi, C. (2019). The Games for Older Adults Active

Life (GOAL) project for people with mild cognitive

impairment and vascular cognitive impairment: a study

protocol for a randomized controlled trial. Frontiers in

neurology, 9, 1040.

Gatsou, C., Politis, A., & Zevgolis, D. (2011). Text vs

visual metaphor in mobile interfaces for novice user

interaction. Information Services & Use, 31, 271-279.

https://doi.org/10.3233/ISU-2012-0657

Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C.,

Ritchie, K., Broich, K., Belleville, S., Brodaty, H.,

Bennett, D., & Chertkow, H. (2006). Mild cognitive

impairment. The lancet, 367(9518), 1262-1270.

Groot, C., Hooghiemstra, A. M., Raijmakers, P. G., van

Berckel, B. N., Scheltens, P., Scherder, E. J., van der

Flier, W. M., & Ossenkoppele, R. (2016). The effect of

physical activity on cognitive function in patients with

dementia: a meta-analysis of randomized control trials.

Ageing research reviews, 25, 13-23.

Husseini, F., Damirchi, A., & Babaei, P. (2016). Effect of

brain training on cognitive performance in elderly

women diagnosed with mild cognitive impairment.

Caspian Journal of Neurological Sciences, 2(4), 25-31.

Inostroza, R., Rusu, C., Roncagliolo, S., Rusu, V., &

Collazos, C. A. (2016). Developing SMASH: A set of

SMArtphone's uSability Heuristics. Computer

Standards & Interfaces, 43, 40-52.

Jamieson, M., Cullen, B., Lennon, M., Brewster, S., &

Evans, J. (2022). Designing ApplTree: usable

scheduling software for people with cognitive

impairments. Disability and Rehabilitation: Assistive

Technology, 17(3), 338-348.

Jongsiriyanyong, S., & Limpawattana, P. (2018). Mild

cognitive impairment in clinical practice: a review

article. American Journal of Alzheimer's Disease &

Other Dementias®, 33(8), 500-507.

Karat, J. (1996). User centered design: quality or quackery?

interactions, 3(4), 18-20.

Kaur, A., & Chen, W. (2022). The Usability of Training

Apps for Older Adults–A Heuristic Evaluation.

International Conference on Human-Computer

Interaction,

Langa, K. M., & Levine, D. A. (2014). The diagnosis and

management of mild cognitive impairment: a clinical

review. Jama

, 312(23), 2551-2561.

Lilholt, P. H., Jensen, M. H., & Hejlesen, O. K. (2015).

Heuristic evaluation of a telehealth system from the

Danish TeleCare North Trial. International journal of

medical informatics, 84(5), 319-326.

Liu, N., Yin, J., Tan, S. S.-L., Ngiam, K. Y., & Teo, H. H.

(2021). Mobile health applications for older adults: a

systematic review of interface and persuasive feature

design. Journal of the American Medical Informatics

Association, 28(11), 2483-2501.

Macefield, R. (2009). How to specify the participant group

size for usability studies: a practitioner's guide. Journal

of usability studies, 5(1), 34-45.

MacKenzie, I. S. (2012). Human-computer interaction: An

empirical research perspective.

Matthew-Maich, N., Harris, L., Ploeg, J., Markle-Reid, M.,

Valaitis, R., Ibrahim, S., Gafni, A., & Isaacs, S. (2016).

Designing, implementing, and evaluating mobile health

technologies for managing chronic conditions in older

adults: a scoping review. JMIR mHealth and uHealth,

4(2), e5127.

NAV. (2023). Alderspensjon. https://www.nav.no/

alderspensjon

Nielsen, J. (1994). 10 Usability heuristics for user interface

design. In.

Petersen, R. C., Lopez, O., Armstrong, M. J., Getchius, T.

S., Ganguli, M., Gloss, D., Gronseth, G. S., Marson, D.,

Pringsheim, T., & Day, G. S. (2018). Practice guideline

update summary: Mild cognitive impairment: Report of

the Guideline Development, Dissemination, and

Implementation Subcommittee of the American

Academy of Neurology. Neurology, 90(3), 126-135.

Petrovčič, A., Rogelj, A., & Dolničar, V. (2018). Smart but

not adapted enough: Heuristic evaluation of

smartphone launchers with an adapted interface and

assistive technologies for older adults. Computers in

Human Behavior, 79, 123-136.

Petrovcic, A., Šetinc, M., Burnik, T., & Dolnicar, V.

(2018). A comparison of the usability of a standard and

an age-friendly smartphone launcher: experimental

evidence from usability testing with older adults.

International Journal of Rehabilitation Research,

41(4), 337-342.

PopReach. (2023). Peak - Brain Training on the App Store.

https://apps.apple.com/us/app/peak-brain-

training/id806223188

Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M.,

& Karagiannidou, M. (2016). World Alzheimer report

2016: improving healthcare for people living with

dementia: coverage, quality and costs now and in the

future.

Salman, H. M., Ahmad, W. F. W., & Sulaiman, S. (2018).

Usability evaluation of the smartphone user interface in

supporting elderly users from experts’ perspective. Ieee

Access, 6, 22578-22591.

Sedgwick, P. (2013). Convenience sampling. Bmj, 347.

Smith, L., & Argentina, V. (2020). The usability of physical

activity and cognitive training applications in people

with mild cognitive impairment. Research in

Gerontological Nursing, 13(2), 64-72.

SSB. (2023). Bruk av IKT i husholdningene

https://www.ssb.no/statbank/table/12349/

Story, M. F., Mueller, J. L., & Mace, R. L. (1998). The

universal design file: Designing for people of all ages

and abilities.

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

Thorpe, J. R., Rønn‐Andersen, K. V., Bień, P., Özkil, A. G.,

Forchhammer, B. H., & Maier, A. M. (2016). Pervasive

assistive technology for people with dementia: a UCD

case. Healthcare technology letters, 3(4), 297-302.

TriangleLabs. (2018). Skillz - Brain Games on the App

Store. https://apps.apple.com/us/app/skillz-brain-

games/id1332466209

WHO. (2017). Global action plan on the public health

response to dementia 2017–2025.

WHO. (2023). Ageing and health. https://www.who.int/

news-room/fact-sheets/detail/ageing-and-health

Yingta, N., Nocera, J. A., Brew, O., & Rehman, I. U.

(2021). A systematic review of usefulness design goals

of occupational mobile health apps for healthcare

workers. Human-Computer Interaction–INTERACT

2021: 18th IFIP TC 13 International Conference, Bari,

Italy, August 30–September 3, 2021, Proceedings, Part

V 18,

Zhang, P., Wu, D., Shang, Y., Ren, W., Liang, J., Wang, L.,

& Li, C. (2021). Initial performance predicts

improvements in computerized cognitive training:

Evidence from a selective attention task. PsyCh

Journal, 10(5), 742-750.

Zhang, R., Li, F., & Li, Y. (2018). Design of a rehabilitation

training system for older adults with mild cognitive

impairment. 2018 11th International Symposium on

Computational Intelligence and Design (ISCID),

Zhong, Y., Harada, E. T., Tanaka, S., & Ankyu, E. (2020).

Usability Study of Electronic Product with Healthy

Older Adults Based on Product Semantic. HCI

International 2020–Late Breaking Posters: 22nd

International Conference, HCII 2020, Copenhagen,

Denmark, July 19–24, 2020, Proceedings, Part II 22,

Zhu, D., Jing, Y., Huang, R., Gao, Y., Liu, Y., Zou, Z., &

Liu, W. (2022). Designing a Mobile Application for

Working Memory Training through Understanding the

Psychological and Physiological Characteristics of

Older Adults. Sustainability, 14(21), 14152.

Incorporating Cognitive Training with Elderly People’s Everyday Use of Smartphones