Citation: Mustafa, A.S.; Ali, N.;
Dhillon, J.S.; Alkawsi, G.; Baashar, Y.
User Engagement and Abandonment
of mHealth: A Cross-Sectional
Survey. Healthcare 2022,10, 221.
Academic Editor: Daniele Giansanti
Received: 7 October 2021
Accepted: 14 December 2021
Published: 24 January 2022
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User Engagement and Abandonment of mHealth: A
Abdulsalam Salihu Mustafa 1, * , Nor’ashikin Ali 1, Jaspaljeet Singh Dhillon 2, Gamal Alkawsi 3 ,* and
Yahia Baashar 3
1College of Graduate Studies, Universiti Tenaga Nasional, Kajang 43000, Malaysia; firstname.lastname@example.org
2College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia;
3Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Malaysia;
*Correspondence: email@example.com (A.S.M.); firstname.lastname@example.org (G.A.)
Mobile health (mHealth) apps have great potential to improve health outcomes. Given that
mHealth apps have become ubiquitous, there is limited focus on their abandonment. Data concerning
crucial metrics, including reasons for adoption and discontinued use, are limited. This study aims to
gain broad insights into utilization of mHealth and game-like features promoting user engagement.
We conducted a cross-sectional survey of 209 mHealth users worldwide. The 17-item survey assessed
sociodemographics, as well as the key motivators for mHealth uptake and discontinued use. Our
ﬁndings show that sports and ﬁtness activity tracking were the most common categories of health
apps, with most users engaging with them at least several times a week. Interestingly, the most
downloaded mHealth apps among younger adults include MyFitnessPal, Fitbit, Nike Run Club,
and Samsung Health. Critical drivers of abandonment of mHealth apps were amotivation, loss of
interest, and experimenting with different apps to identify the most suitable tool. Additionally, the
ﬁnancial cost of mHealth apps is crucial, with most participants advocating for free or more affordable
apps. The study ﬁndings suggest that while many individuals utilize mHealth, several factors drive
their abandonment. Moreover, data indicate that mHealth developers need to consider gamiﬁcation
strategies to sustain user commitment, as well as psychological variables, such as intrinsic motivation.
health behaviour; motivation; gamiﬁcation; game elements; mHealth; abandonment;
Mobile health (mHealth) apps are widely used in the health sector to support be-
havioural health outcomes and improve users’ health. mHealth software runs on smart-
phones and other mobile devices to promote wellness and prevent medical conditions
among the general public [
]. These apps can help shift attitudes and behaviours by dis-
seminating, gathering, and analysing health-related data and supporting interventions.
Evidently, mHealth interventions are rapidly growing in popularity. Indeed, 325,000 health
apps were widely accessible on major app stores in 2017, up by nearly 24% since 2016 [
Globally, the mHealth apps market was valued at about USD 28,320 billion in 2018 and
is predicted to reach USD 10,235 billion by 2023 [
]. Popular mHealth apps for tracking
physical activity (PA), step count, food intake, and medication adherence include Fitbit,
Medisafe, Nike+, MyFitnessPal, and Strava.
Several studies highlight the beneﬁts of mHealth [
]. For instance, patients can
self-monitor their progress or set exercise or food intake objectives using health apps.
Additionally, these apps can be utilized for online consultations, medication adherence,
health literacy, and weight management. Although mHealth apps are perceived as ben-
eﬁcial, it is unclear whether they are critical for users to achieve and sustain long-term
Healthcare 2022,10, 221. https://doi.org/10.3390/healthcare10020221 https://www.mdpi.com/journal/healthcare
Healthcare 2022,10, 221 2 of 13
health behaviour. Prior studies mainly focused on the intention to use mHealth apps, with
limited studies on actual usage. As such, intention to use may not be critical in determining
actual usage [
]. Hence, there is need to examine factors that determine actual usage of
mHealth. In addition, research related to adherence and abandonment of mHealth apps is
Gamiﬁcation introduces game-design elements in order to improve a non-game con-
text and make it game-like [
]. To date, gamiﬁcation has been applied in several domains
to make tasks or activities more gratifying, pleasant, and enjoyable. Some popular do-
mains where gamiﬁcation is applied include online learning [
]; healthcare [
]. The gamiﬁcation strategy appears to improve intrinsic motivation by ful-
ﬁlling individuals’ psychological/emotional requirements through the use of game-like
components. Intrinsic motivation refers to performance of an activity solely for the sake of
enjoyment, excitement, and interest [
]. Various game-design features utilized in gamiﬁed
systems include badges, points, leader boards, avatars, challenges, and levels [8,15]. Criti-
cal components of gamiﬁcation strategies are similar to existing health behaviour-change
techniques (BTC). Within behavioural literature, BCTs are the primary change agents that
inspire people to changing their health-related behaviours, such as PA adoption [
may provide suitable strategies to sustaining health behavioural changes.
Similarly, with gamiﬁcation, several mHealth apps leverage game-design elements to
encourage and sustain health-behaviour outcomes. Although past research suggests that
gamiﬁcation can promote health-behavioural outcomes, the results are inconsistent [
Correspondingly, these studies were short-term and failed to provide evidence of long-term
use of or adherence to mHealth.
Despite the promise of mHealth studies on primary motivation for using mHealth
apps, evidence of their adoption and discontinued use are limited. Accordingly, we aim
to identify the critical success factors (drivers) and motivational affordances behind user
uptake, abandonment, and continued usage of mHealth apps. In addressing this limitation,
this study expands on existing literature on the acceptance, adherence to, and abandonment
of mHealth towards improving users’ PA behaviour.
The rest of the paper is organized as follows: Section 2describes the research method-
ology. Section 3presents the results, while Section 4discusses the ﬁndings. Finally, the
conclusion, limitations, and future directions are presented in Section 5.
2. Materials and Methods
2.1. Survey Items
We conducted an online cross-sectional study over three months in 2021. The ques-
tionnaire was adapted to the extant literature [
] and further reﬁned to ensure content
validity. Necessary wording changes and validation were performed to ﬁt the context of
mHealth usage (Table 1). Subsequently, the questionnaire was pre-tested by two experts to
avoid issues with wordings, measurement, and ambiguities.
The survey consisted of ﬁfteen items involving thirteen closed and two open questions.
The survey items were categorized into the following: (1) sociodemographic characteristics,
including gender and age; (2) types of mHealth used and desired features; (3) motivational
affordances; (4) reasons for mHealth app adoption; (5) adherence; and (6) discontinued use.
The web-based survey was administered to participants via Google Forms, and all items
were only available in the English language. The average completion time was six minutes.
Healthcare 2022,10, 221 3 of 13
Table 1. Survey measurement items (adapted from ).
Sociodemographic What is your Gender?
What is your Age?
Types of mHealth used and
Which Smartphone Operating System (OS) do you use?
What kind of health and wellness apps have you downloaded
over the past 12 months?
How many health and ﬁtness apps have you used over the last
What are the most important features for you in the health and
Adoption What is the name of the health and ﬁtness app that you use?
How do you usually know about the health and ﬁtness apps you
install on your phone?
What is the main reason you use health or ﬁtness apps?
How often do you use a health and ﬁtness app in a month?
What is the main source of encouragement that you receive to use
health and ﬁtness apps?
How many hours do you spend each day on your smartphone?
If you could change anything about the ﬁtness apps you are
using, what would it be?
Which of these better explains why you stop using the health and
Using a health and ﬁtness apps motivates me to exercise more.
Using a health and ﬁtness apps motivates me to eat healthier.
Using a health and ﬁtness apps helps me to keep track of
2.2. Survey Participants
The survey focused on users of the Android Play Store and Apple Store. Previous
related literature targeted Android users; however, we targeted users of both platforms
to obtain broader perspectives. The exclusion criteria were non-users of mHealth apps.
Participants were recruited by email, SurveyCircle online platform, and Facebook. A
summary of the study objective and procedure was provided to participants beforehand.
We followed our university’s ethical approval and notiﬁcation processes to protect the
respondents’ well-being and to comply with university standards. Participants voluntarily
agreed to participate, and no incentives were offered; all questions were compulsory.
2.3. Data Collection
The survey was web-based, using a commercially available survey host (Google
Forms https://docs.google.com/forms/, accessed on 1 September 2020). Respondents
who agreed to participate in the study were required to give informed consent online (click
to agree). All of the data for this study were collected over ﬁve months from September
2020 to January 2021. We collected data (17-item survey) from the target group of mHealth
users anonymously, with no speciﬁc personal data saved. After three months, the survey
link was deactivated.
2.4. Data Analysis
The responses were downloaded into a spreadsheet and reviewed for accuracy and
missing values. The data were coded based on themes and analysed accordingly. We
assessed non-response bias by comparing early and late responses in two groups, as
suggested by [
]. As a result, non-response bias does not appear to be an issue in this study.
We categorized the responses into heavy and light users based on extant literature [
Participants who reported using the mHealth apps regularly were deﬁned as “heavy users”.
On the other hand, “light users” indicated using mHealth apps occasionally or irregularly.
The classiﬁcation of mHealth users in this study is given in Table 2.
Healthcare 2022,10, 221 4 of 13
Table 2. Classiﬁcation of mHealth users.
Classiﬁcation of User Usage Frequency
Several times a day
1–2 days a week
Twice a month
Once a month
Did not use in the past month
3.1. Sample Characteristics
A total of 209 responses were included in the study. Speciﬁcally, according to the ﬁnd-
ings, 93 males (44.5%) and 116 females (55.5%) participated in the survey. All participants
were aged between 18 and 65 years, with a mean age of 28.8. The majority of respondents
(75%) were between 18 and 35 years, with the 65-and-older age group having the least
number of respondents (0.5%). In addition, participants were current or former users of
mHealth. Most respondents (35.9%) spent 3–5 h per day, on average, using a smartphone,
followed by 6–10 h per day (26.8%) and 1–2 h per day (16.3%). Of note, only 5.3% of
respondents cited using smartphones regularly.
Given Android’s more considerable market dominance [
], it was expected that
most respondents use Android (53.2%), as compared to Apple iOS (46.9%). The data show
that most users (81.8%) reported using an average of one to three mHealth apps in the past
twelve months. In total, respondents downloaded an average of 128 different mHealth apps.
App descriptions and categories were coded based on the health behaviour targeted by
the app. Furthermore, we identiﬁed ten of the most commonly used health apps (Table 3),
accounting for more than half (59.7%) of the more likely apps to be downloaded. As
indicated, respondents reported using a range of mHealth apps, with MyFitnessPal the
most prevalent (14.8%), followed by Fitbit (7.2%), Nike Run Club (7.2%), and Samsung
Health (7.2%). Moreover, participants also reported using several other unfamiliar health
Table 3. Ten commonly used ﬁtness apps.
App Description No. of Users %
MyFitnessPal Weight loss, calorie counter,
and dieting app 31 14.8
Fitbit Health & Fitness
All-day activity, workouts, and
sleep-tracking app 15 7.2
Nike Run Club & Training
Running app with GPS-guided
run and challenges 15 7.2
Supports healthier lifestyle,
tacks sleep, dieting,
App tracking cycling and
running with GPS and
Leap Fitness Full-body workout app 9 4.3
Life Sum Fitness Personalized dieting, exercise,
and calorie-tracking app 8 3.8
Apple Health Workout, sleep, steps, and
all-day activity-tracking app 7 3.3
Flo Fit Women’s health and ﬁtness
and period-tracker app 7 3.3
Calm App Meditation, sleep, and
relaxation app 6 2.9
Healthcare 2022,10, 221 5 of 13
3.2. Characterizing Popular mHealth Apps
Based on the responses, we considered the most popular mHealth app categories in
the mainstream (Apple and Google Play stores), as illustrated in Figure 1. We observed
that sports and ﬁtness activity tracking (29.7%), wellbeing (19.8%), and weight loss (17.8%)
were the three most popular downloaded app categories. Similarly, regarding features
based on a classiﬁcation of behaviour-change techniques [
], respondents indicated
maintaining or improving physical ﬁtness (34.4%) as the most critical feature. In addition,
respondents also revealed maintaining or losing weight (24.9%) and positively changing or
improving lifestyle (22%) to be signiﬁcant features.
Figure 1. Breakdown of most popular mHealth app categories.
As illustrated in Figure 1, respondents identiﬁed goal setting (74.6%) as an essential
game-like feature. Conversely, activity trackers and step counters are lesser signiﬁcant
features in the mHealth apps. Remarkably, the popularity of the game-like features (points,
badges, rewards, and leader boards) shows an increase in consumer acceptance of gamiﬁed
mHealth apps (30.6%). The increasing recognition of game-like features is shifted from
prior literature [
], which identiﬁed two game features—rewards and sharing—to be
3.3. mHealth App Adoption
In the context of mHealth adoption, we explored positive drivers of mHealth app
download. We discover that respondents most often discover health apps based on popular
recommendations in the app store (Figure 2). However, only a few respondents indicated
learning about health apps from their employers or medical professionals. Surprisingly,
it is expected that health experts signiﬁcantly inﬂuence recommendations of health apps.
Generally, individuals interested in using health apps are expected to seek the advice of
medical experts concerning which particular health app to adopt. Such recommendations
from medical professionals are usually based on knowledge and experience [
consistent with previous ﬁndings [
], health experts have limited inﬂuence on mHealth
app recommendations. Moreover, respondents’ positive drivers for engaging with ﬁtness
apps include sustaining or improving physical ﬁtness levels, maintaining or losing weight,
and improving quality of life.
Healthcare 2022,10, 221 6 of 13
Figure 2. Positive drivers for engaging with mHealth apps.
3.4. mHealth App Use
It is crucial to establish critical factors that promote long-term engagement with
mHealth apps. In this context, participants’ primary reasons for engagement were main-
taining or improving physical ﬁtness levels (34.4%), losing/managing weight (24.9%), and
improving quality of life (22%). Focusing on motivational affordances, we found that
respondents are mainly encouraged to use mHealth apps through social media (16.3%) and
friends (15.3%). In any case, it should be noted that several users explicitly acknowledged
not receiving any encouragement (49.3%). Regarding the frequency of mHealth use, 48.8%
of users engage with health apps daily or several times a week (Figure 3). A considerable
number of users engaged with an mHealth apps in the past month (19.6%).
Figure 3. Health app usage frequency.
3.5. mHealth App Abandonment
Despite the effectiveness of health and ﬁtness apps, a signiﬁcant challenge is lack
of sustained use. A recent study shows that user commitment to mHealth is low, with
about 53% uninstalled within 30 days of download [
]. Figure 4shows participants’ main
reasons for app abandonment. In line with other studies [
], the results show that lack
of interest or declining motivation (31.6%) is one of the critical factors for the abandonment
of mHealth apps. Furthermore, participants highlighted frequently downloading apps
Healthcare 2022,10, 221 7 of 13
before selecting the most acceptable and uninstalling the rest (21.5%). Moreover, lack of
desired features in the app (18.7%), the app not being fun (10%), and not being easy to use
(8.6%) were also identiﬁed as reasons for discontinued use of mHealth apps.
Figure 4. Main reasons for app abandonment by users.
Our results are broadly in line with the literature, which found that the novelty effect
signiﬁcantly inﬂuences user abandonment of health-intervention apps [
users may be drawn to a new app out of curiosity at ﬁrst, they may lose interest in the
app once the novelty effect wears off. As a result, user motivation to use mHealth may
decline over time. Therefore, features that sustain healthy behavioural outcomes ought to
be identiﬁed and implemented in mHealth apps.
Participants were asked what features they would want to see in their existing mHealth
apps. From the responses, inductive thematic analysis was adopted to analyse the data, and
15 themes were extracted based on behaviour-change techniques (BCT) speciﬁed by the
taxonomy of [
]. To assess the implementation of gamiﬁcation techniques (GT), we used
the taxonomy proposed by Hoffmann and colleagues [
]. Table 4illustrates the
identiﬁed based on BCT and GT. Of note, we observed some overlap between BCT and GT.
Table 4. Identiﬁed themes based on BCT and GT Taxonomies.
Themes Identiﬁed Taxonomy
Cost (More affordable or free) -
Less in-app advertising -
Additional food and dieting options BCT
More personalized (customization) BCT, GT
(combination of game elements) GT
Use of leader board GT
Increasing challenges GT
Level of difﬁculty (levels) GT
Social relatedness GT
Improving motivation BCT, GT
Syncing with other devices GT
Improving user experience BCT, GT
Goal-oriented (achievement) BCT, GT
Quality of feedback (feedback) BCT, GT
Healthcare 2022,10, 221 8 of 13
The cost of apps was cited as a signiﬁcant factor among respondents. Interestingly, in
line with prior related literature [
], most participants advocated that mHealth should
be accessible (free) or more affordable. Additionally, limiting of advertisements, more food
or dieting options, and the need for more personalized and goal-oriented features were
also considered desired features for inclusion in mHealth apps. In gamiﬁcation context,
some identiﬁed themes were linked to various motivational affordances (game elements)
in mHealth apps. The motivational affordances identiﬁed in this study include leader
boards, challenges, levels, connection (relatedness), achievement, feedback, prompting, and
tracking. An important factor was that most respondents who use non-gamiﬁed mHealth
apps stated that motivation signiﬁcantly affects their adherence. Hence, introducing game-
like features can increase the prospect of adoption and continued use of ﬁtness apps.
3.6. Fitness App Motivation
Motivation is considered critical to the success of mHealth apps. Prior research on
health and ﬁtness apps demonstrated that lack of motivation (amotivation) was associated
with low adoption of mHealth apps [
]. Accordingly, we examined the role of motivation
in adoption of and adherence to mHealth. However, similar to [
], many users were
motivated to remain physically active. More importantly, more than half (55%) of the
participants perceive that ﬁtness apps motivated them to exercise more, while only 15.3%
hold opposite views. In a similar vein, 41.1% concurred that mHealth apps motivated them
to eat healthier, while 22.9% disagreed. Furthermore, 73.2% supported that health apps
help track their goals, while 8.1% differed.
3.7. Heavy vs. Light Fitness-App Users
As previously mentioned, heavy users indicated using ﬁtness apps daily, while partic-
ipants, who did not use ﬁtness apps within a month, were categorised as light users. This
is illustrated in Table 5, along with the participants’ demographic information. Regarding
mHealth adoption, heavy users reported using a range of mHealth apps, with MyFitness-
Pal, Fitbit, Samsung Health, and Strava being the most common. Equally, light users also
reported using similar apps. Interestingly, some light users were unable to recall the ﬁtness
apps that they installed.
Table 5. Comparison between heavy and light users.
Characteristics Heavy Users n= 51 Light Users n= 41
Male 22 (43%) 21 (51%)
Female 29 (57%) 20 (49%)
18–25 age group 19 12
26–35 age group 20 20
36–46 age group 11 8
Android OS 31 21
Apple iOS 20 20
Popular category of ﬁtness
Sports and ﬁtness
Sports and Fitness
Popular ﬁtness apps MyFitnessPal, Fitbit, Samsung Health,
MyFitnessPal, Samsung Health, Strava, Calm,
and 30-day ﬁtness at home.
Most important app features Personalization, food tracker, game-like
Personalization, game-like features,
Primary motivation for engaging
with ﬁtness apps
To maintain or lose weight.
To maintain or improve my level of
To positively change my lifestyle or
improve my quality of life.
To positively change my lifestyle or improve
my quality of life.
To maintain or lose weight.
To maintain or improve my level of
Healthcare 2022,10, 221 9 of 13
Table 5. Cont.
Characteristics Heavy Users n= 51 Light Users n= 41
Reasons for abandoning
ﬁtness apps -
Does not have the features that I want.
Bored or/and lose motivation.
Does not meet their demand.
As in Table 5, heavy and light users downloaded the same category of ﬁtness apps:
sports and ﬁtness, wellbeing, diet, and weight loss. In the context of motivating features,
both user groups reported personalization, food tracker, game-like features, and feedback
as the most popular. Besides, light users also reported notiﬁcations as a signiﬁcant feature.
Notably, the two user groups considered game-like features (points, badges, rewards, or
leader boards) motivating.
Regarding user motivation, most heavy users agreed that mHealth apps inspire them
to exercise more (68.6%), eat healthier (52.9%), and keep track of their goals (84.3%). In
contrast, 39% of light users disputed that ﬁtness apps encourages them to eat healthier,
while 41% were unsure. Also, nearly half (48.8%) of light users concurred that ﬁtness apps
encouraged them to keep track of their goals. It is worth noting that over half (53.7%) of
light users were unsure whether ﬁtness apps motivated them to exercise more.
Light users identiﬁed four reasons explaining why they abandoned mHealth apps.
Other participants expressed concern regarding the apps not being enjoyable, absence of de-
sired features, boredom, lack of motivation, or downloading several apps and uninstalling
the unsuitable ones.
In the context of features they would like to include in their existing apps, participants
reported that apps should be easier to use, more personalized, accessible (free), have more
daily notiﬁcations, and be more engaging.
Speciﬁcally, participants highlighted ﬁve prominent examples, summarized below:
“More simplified, sometimes there’s too many things going on and too many things to track.”
•“Add personalized experiences and feedback”
“I wish that more of them are free. Or at least less costly. As a student, I don’t really
have the money to buy a subscription”
•“Add notiﬁcations and measure progress”
“The thing that lacks in the existing apps is the motivation, it would be perfect to have
something that will encourage to continue doing exercise and following the milestones
without a break. For now, it is like I use an app for 1, 2, 3 days and then I lose my
motivation to do exercise using app.”
In our study, most app users fall into two groups: 18–25 and 26–35 (75%, female: 97,
male: 60). Similarly, a previous report showed that the most likely mHealth users in the US
are in the 18–34 age group [
]. Notably, as with similar studies [
], older adults have a
lower uptake of health apps than younger individuals. Despite low adoption of mHealth
apps, recent studies focused on tailored mHealth apps for older adults [
]. The increase
in the development of novel mHealth targeting the elderly provides an opportunity to
improve their PA and ﬁtness levels.
Another critical point is that while subscription cost might affect adoption of mHealth
apps, there appears to be a correlation between mHealth pricing and quality. Indeed, more
expensive apps tend to be of higher quality in terms of usability and improved features [
In other words, free apps may have limited functionality. As a result, this suggests a
trade-off between subscription cost and quality of app features.
According to the ﬁndings, the two user groups (heavy and light) indicated down-
loading and using the same category of apps. Additionally, the data suggest that both
groups consider similar app features (personalization, food tracker, game-like features,
Healthcare 2022,10, 221 10 of 13
feedback) as critical drivers for uptake and continued use of mHealth. This similarity
implies that some ﬁtness-app features may only appeal to highly motivated individuals
who are committed and may appeal less to those with lower motivation levels. As such,
app developers need to consider personalizing ﬁtness apps to target users with different
motivation levels. Additionally, it appears that game elements (leaderboard, badges, points,
and challenges) act as motivational affordances, leading to improved app engagement.
More importantly, our data underscores the importance of the novelty effect as a
critical factor inﬂuencing the lack of sustained use after adoption [
]. In a previous
], for instance, respondents reported boredom and loss of motivation as reasons for
abandoning these apps. These stated factors could be due to lack of interest or enjoyment
when using the app after an extended period. As a result, engaging with the apps is
no longer fun, and users may not be intrinsically motivated. Hence, the effectiveness of
mHealth may not be feasible if users become unmotivated. Further study, for example,
utilizing longitudinal designs with a large sample size, is required to effectively examine
the correlation between users’ intrinsic motivation and mHealth adherence. This will also
ensure increased reliability.
Among the most compelling reasons for app abandonment by users is the absence
of features that users require. Another primary reason is lack of motivation to sustaining
using mHealth apps and maintaining behavioural changes. It becomes critical to involve
user input in the design of mHealth apps in order to meet user expectations. The following
suggestions are directed to developers and mHealth service providers.
For developers, our ﬁndings also have important implications. Indeed, one of the most
crucial aspects of app developers’ service offerings is that the app positively impacts health
and well-being. Hence, tailoring mHealth apps to the needs of speciﬁc user groups seems
promising for increasing engagement and preventing a decline in motivation. Nevertheless,
developers need to introduce positive strategies and behaviour-change techniques (BTCs)
to motivate users to engage with their apps for more signiﬁcant health beneﬁts.
mHealth service providers should use the ﬁndings of this study to include consumer
requirements in the app-development process. This would eventually increase consumer
engagement levels. The empirically signiﬁcant factors identiﬁed in this study would
become the user requirements for mHealth apps. Therefore, mHealth service providers
should personalize ﬁtness apps to target users with different types of motivation.
Gamiﬁcation of health apps is a promising approach to counteract the often-decreasing
long-term motivation of health-app users. Given the rapid adoption of gamiﬁcation by
practitioners and researchers in the healthcare domain, to date, there is little knowledge
of the efﬁcacy of mHealth apps in the long term. Although prior studies highlighted the
signiﬁcant impact of motivation on the sustained use of ﬁtness apps [
], it is unclear what
drives adoption of, engagement with, and discontinued use of these apps. In response,
this study seeks to further explore these gaps. The ﬁndings indicated that motivation acts
as a positive driver of ﬁtness-app adherence. That is, highly motivated individuals are
more likely to continue using health apps and sustain their usage. Reﬂecting on this point,
designers may consider effective strategies to sustain behaviour change in individuals
with little or no motivation post-adoption of mHealth apps. We noted, however, that one
of these strategies is gamiﬁcation, essential for promoting continued use. Concerning
this, some respondents indicated that boredom affects their long-term engagement with
mHealth. Remarkably, respondents advocated introducing game-like features in certain
apps to increasing motivation to continue engaging in PA. Thus, the need for further
investigation of more effective technology-based solutions for promoting and sustaining
healthy behaviour becomes critical.
Healthcare 2022,10, 221 11 of 13
There are a few limitations to this study. Regarding the checkpoints, recall bias is
an issue, as study participants considered mHealth usage in the previous six to twelve
months only. Another signiﬁcant limitation is that we failed to determine the theories
applied in the apps because it would have been challenging to ask respondents to self-
identify these theories. Notwithstanding, prior research suggests the need for integrative
theory-based mHealth apps to sustain health-behaviour change [
], primarily studies
with longitudinal designs and those centred on integrative theoretical models. While
this study was open to all nationalities, we did not ask speciﬁc demographic questions
identifying respondents’ nationality, race, or profession. In prospective studies, additional
demographic questions will improve the cross-cultural generalizability of the outcomes,
in particular, sociocultural factors inﬂuencing adherence to mHealth apps. Additionally,
we failed to identify the direct effect of speciﬁc game-design elements on adherence and
Conceptualization, A.S.M., N.A. and J.S.D.; methodology, A.S.M.; val-
idation, A.S.M., N.A. and J.S.D.; analysis, A.S.M.; investigation, A.S.M.; data curation, A.S.M.;
draft preparation, A.S.M., N.A., J.S.D., G.A. and Y.B.; writing—review and editing,
A.S.M., G.A. and Y.B.; supervision, N.A. and J.S.D.; project administration, N.A.; funding acquisition,
N.A. All authors have read and agreed to the published version of the manuscript.
The authors would like to acknowledge Universiti Tenaga Nasional’s ﬁnancial support
under the Bold Research Grant (Project No. RJO10517844/012) and the publication support through
J510050002-IC-6 BOLDREFRESH2025-CENTRE OF EXCELLENCE from the iRMC of Universiti
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Ethics Committee of the college of graduate studies
Informed Consent Statement:
Informed consent was obtained from all subjects involved in
Data Availability Statement:
Data are available from the corresponding author for researchers who
meet the criteria for access the data.
We gratefully acknowledge the contribution of Elizabeth L. Murnane, Dart-
mouth College, for her involvement, exemplary support, and highly constructive comments on
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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