ArticlePDF Available

Abstract and Figures

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
Content may be subject to copyright.
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
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
User Engagement and Abandonment of mHealth: A
Cross-Sectional Survey
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;
2College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia;
3Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Malaysia;
*Correspondence: (A.S.M.); (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;
continued use
1. Introduction
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.
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
also limited.
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 [
]; and
tourism [
]. 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 [
]. BCTs
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 [19]).
Items Measurements
Sociodemographic What is your Gender?
What is your Age?
Types of mHealth used and
desired features
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
12 months?
What are the most important features for you in the health and
fitness app?
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?
Discontinued use
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
fitness apps?
Motivational affordances
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
my goals.
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, 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
Heavy user
Several times a day
1–2 days a week
Light user
Twice a month
Once a month
Did not use in the past month
3. Results
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
apps (n= 21). Table 3presents the ten commonly used mHealth apps in terms of user base.
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
Samsung Health
Supports healthier lifestyle,
tacks sleep, dieting,
and exercise
15 7.2
App tracking cycling and
running with GPS and
social networking
12 5.7
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
less motivating.
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 [
]. However,
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 [
]. While
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
15 themes
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
Game-like features
(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
apps downloaded
Sports and fitness
Weight loss
Sports and Fitness
Weight loss
Popular fitness apps MyFitnessPal, Fitbit, Samsung Health,
and Strava.
MyFitnessPal, Samsung Health, Strava, Calm,
and 30-day fitness at home.
Most important app features Personalization, food tracker, game-like
features, feedback
Personalization, game-like features,
notifications, feedback
Primary motivation for engaging
with fitness apps
To maintain or lose weight.
To maintain or improve my level of
physical fitness.
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
physical fitness.
Healthcare 2022,10, 221 9 of 13
Table 5. Cont.
Characteristics Heavy Users n= 51 Light Users n= 41
Reasons for abandoning
fitness apps -
Not enjoyable.
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.”
4. Discussion
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
study [
], 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.
5. Conclusions
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
health-behaviour outcomes.
Author Contributions:
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
Tenaga Nasional.
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
(June 2021).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in
the study.
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
the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Marston, H.R.; Hall, A.K. Gamification: Applications for health promotion and health information technology engagement. In
Handbook of Research on Holistic Perspectives in Gamification for Clinical Practice Hershey; Novak, D., Tulu, B., Brendryen, H., Eds.; IGI
Global: Hershey, PA, USA, 2015; pp. 78–104. [CrossRef]
Aslam, A.S.; van Luenen, S.; Aslam, S.; van Bodegom, D.; Chavannes, N.H. A systematic review on the use of mHealth to increase
physical activity in older people. Clin. eHealth 2020,3, 31–39. [CrossRef]
Xie, Z.; Nacioglu, A.; Or, C. Prevalence, Demographic Correlates, and Perceived Impacts of Mobile Health App Use Amongst
Chinese Adults: Cross-Sectional Survey Study. JMIR mHealth uHealth 2018,6, e103. [CrossRef] [PubMed]
Lin, Y.; Lou, M. Effects of mHealth-based interventions on health literacy and related factors: A systematic review. J. Nurs. Manag.
2020,29, 385–394. [CrossRef]
Rowland, S.P.; Fitzgerald, J.E.; Holme, T.; Powell, J.; McGregor, A. What is the clinical value of mHealth for patients?
NPJ Digit. Med. 2020,3, 4. [CrossRef]
Liang, X.; Wang, Q.; Yang, X.; Cao, J.; Chen, J.; Mo, X.; Huang, J.; Wang, L.; Gu, D. Effect of mobile phone intervention for diabetes
on glycaemic control: A meta-analysis. Diabet. Med. 2010,28, 455–463. [CrossRef]
Yoganathan, D.; Kajanan, S. What Drives Fitness Apps Usage? An Empirical Evaluation. In IFIP Advances in Information and
Communication Technology; Springer: Berlin/Heidelberg, Germany, 2014; Volume 429, pp. 179–196. [CrossRef]
Deterding, S.; Dixon, D.; Khaled, R.; Nacke, L. From Game Design Elements to Gamefulness: Defining ‘gamification’. In
Proceedings of the International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland,
28–30 September 2011; pp. 9–15. [CrossRef]
Sailer, M.; Sailer, M. Gamification of in-class activities in flipped classroom lectures. Br. J. Educ. Technol.
,52, 75–90. [CrossRef]
Healthcare 2022,10, 221 12 of 13
Vanduhe, H.F.; Nat, V.Z.; Hasan, M. Continuance Intentions to Use Gamification for Training in Higher Education: Integrating
the Technology Acceptance Model (TAM), Social Motivation, and Task Technology Fit (TTF). IEEE Access
,8, 21473–21484.
Available online: (accessed on 1 May 2021). [CrossRef]
Patel, M.S.; Small, D.S.; Harrison, J.D.; Hilbert, V.; Fortunato, M.P.; Oon, A.L.; Rareshide, C.A.L.; Volpp, K.G. Effect of Behaviorally
Designed Gamification with Social Incentives on Lifestyle Modification among Adults with Uncontrolled Diabetes: A Randomized
Clinical Trial. JAMA Netw. Open 2021,4, e2110255. [CrossRef] [PubMed]
Corepal, R.; Best, P.; O’Neill, R.; Tully, M.A.; Edwards, M.; Jago, R.; Miller, S.J.; Kee, F.; Hunter, R.F. Exploring the use of a gamified
intervention for encouraging physical activity in adolescents: A qualitative longitudinal study in Northern Ireland. BMJ Open
2018,8, e019663. [CrossRef]
Lee, A.M.; Chavez, S.; Bian, J.; Thompson, L.A.; Gurka, M.J.; Williamson, V.G.; Modave, F. Efficacy and Effectiveness of Mobile
Health Technologies for Facilitating Physical Activity in Adolescents: Scoping Review. JMIR mHealth uHealth
,7, e11847.
14. Swiatczak, M.D. Towards a neo-configurational theory of intrinsic motivation. Motiv. Emot. 2021,45, 769–789. [CrossRef]
Bai, S.; Hew, K.F.; Huang, B. Does gamification improve student learning outcome? Evidence from a meta-analysis and synthesis
of qualitative data in educational contexts. Educ. Res. Rev. 2020,30, 100322. [CrossRef]
Priesterroth, L.; Grammes, J.; Holtz, K.; Reinwarth, A.; Kubiak, T. Gamification and Behavior Change Techniques in Diabetes
Self-Management Apps. J. Diabetes Sci. Technol. 2019,13, 954–958. [CrossRef] [PubMed]
Mora-Gonzalez, J.; Pérez-López, I.J.; Esteban-Cornejo, I.; Delgado-Fernández, M. A Gamification-Based Intervention Program
that Encourages Physical Activity Improves Cardiorespiratory Fitness of College Students: ‘The Matrix Refvolution Program’.
Int. J. Environ. Res. Public Health 2020,17, 877. [CrossRef]
Johnson, D.; Deterding, S.; Kuhn, K.-A.; Staneva, A.; Stoyanov, S.; Hides, L. Gamification for health and wellbeing: A systematic
review of the literature. Internet Interv. 2016,6, 89–106. [CrossRef] [PubMed]
Murnane, E.L.; Huffaker, D.; Kossinets, G. Mobile health apps: Adoption, adherence, and abandonment. In Proceedings of the
UbiComp/ISWC’15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous
Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September
2015; pp. 261–264. [CrossRef]
Huang, G.; Ren, Y. Linking technological functions of fitness mobile apps with continuance usage among Chinese users:
Moderating role of exercise self-efficacy. Comput. Hum. Behav. 2020,103, 151–160. [CrossRef]
21. Armstrong, J.S.; Overton, T.S. Estimating Nonresponse Bias in Mail Surveys. J. Mark. Res. 1977,14, 396. [CrossRef]
Naimark, J.S.; Madar, Z.; Shahar, D.R.; Pojednic, R.; Janwantanakul, P. The Impact of a Web-Based App (eBalance) in Promoting
Healthy Lifestyles: Randomized Controlled Trial. J. Med. Internet Res. 2015,17, e56. [CrossRef]
Deng, T.; Kanthawala, S.; Meng, J.; Peng, W.; Kononova, A.; Hao, Q.; Zhang, Q.; David, P. Measuring smartphone usage and task
switching with log tracking and self-reports. Mob. Media Commun. 2019,7, 3–23. [CrossRef]
Sharma, S.; Kumar, R.; Krishna, C.R. A survey on analysis and detection of Android ransomware. Concurr. Comput. Pract. Exp.
2021,33, e6272. [CrossRef]
Samdal, G.B.; Eide, G.E.; Barth, T.; Williams, G.; Meland, E. Effective behaviour change techniques for physical activity and
healthy eating in overweight and obese adults; systematic review and meta-regression analyses. Int. J. Behav. Nutr. Phys. Act.
2017,14, 42. [CrossRef] [PubMed]
Michie, S.; Richardson, M.; Johnston, M.; Abraham, C.; Francis, J.; Hardeman, W.; Eccles, M.P.; Cane, J.; Wood, C.E. The Behavior
Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the
Reporting of Behavior Change Interventions. Ann. Behav. Med. 2013,46, 81–95. [CrossRef] [PubMed]
Munson, S.; Consolvo, S. Exploring Goal-setting, Rewards, Self-monitoring, and Sharing to Motivate Physical Activity. In
Proceedings of the 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth)
and Workshops, San Diego, CA, USA, 21–24 May 2012; pp. 25–32. [CrossRef]
Ali, N.; Tretiakov, A.; Whiddett, D.; Hunter, I. Knowledge management systems success in healthcare: Leadership matters.
Int. J. Med. Inform. 2016,97, 331–340. [CrossRef] [PubMed]
Shani, R.; Omer, F. The Uninstall Threat: 2020 App Uninstall Benchmarks; AppsFlyer Ltd: Berlin, Germany, 2021; Available online: (accessed on 20 August 2021).
Attig, C.; Franke, T. I Track, therefore I walk–Exploring the motivational costs of wearing activity trackers in actual users.
Int. J. Hum. Comput. Stud. 2019,127, 211–224. [CrossRef]
Krebs, P.; Duncan, D.T. Health App Use among US Mobile Phone Owners: A National Survey. JMIR mHealth uHealth
,3, e101.
Schmidt-Kraepelin, M.; Thiebes, S.; Stepanovic, S.; Mettler, T.; Sunyaev, A. Gamification in Health Behavior Change Support
Systems A Synthesis of Unintended Side Effects. In Proceedings of the 14th International Conference on Wirtschaftsinformatik,
Siegen, Germany, 24–27 February 2019; pp. 1032–1046.
Hoffmann, A.; Christmann, A.C.; Bleser, G. Gamification in Stress Management Apps: A Critical App Review. JMIR Serious Games
2017,5, e13. [CrossRef]
König, L.M.; Attig, C.; Franke, T.; Renner, B. Barriers to and Facilitators for Using Nutrition Apps: Systematic Review and
Conceptual Framework. JMIR mHealth uHealth 2021,9, e20037. [CrossRef]
Healthcare 2022,10, 221 13 of 13
Peng, W.; Kanthawala, S.; Yuan, S.; Hussain, S.A. A qualitative study of user perceptions of mobile health apps.
BMC Public Health
2016,16, 1158. [CrossRef]
36. Mikulic, M. mHealth–Statistics & Facts; Statista: London, UK, 2020.
Askari, M.; Klaver, N.S.; van Gestel, T.J.; van de Klundert, J. Intention to use Medical Apps Among Older Adults in the
Netherlands: Cross-Sectional Study. J. Med. Internet Res. 2020,22, e18080. [CrossRef]
LaMonica, H.M.; Roberts, A.E.; Davenport, T.A.; Hickie, I.B. Evaluation of the Usability and Acceptability of the InnoWell
Platform as Rated by Older Adults: Survey Study. JMIR Aging 2021,4, e25928. [CrossRef] [PubMed]
Kirkscey, R. mHealth Apps for Older Adults: A Method for Development and User Experience Design Evaluation.
J. Tech. Writ. Commun. 2021,51, 199–217. [CrossRef]
Herrmann, L.K.; Kim, J. The fitness of apps: A theory-based examination of mobile fitness app usage over 5 months. mHealth
2017,3, 2. [CrossRef] [PubMed]
Tsay, C.H.; Kofinas, A.K.; Trivedi, S.K.; Yang, Y. Overcoming the novelty effect in online gamified learning systems: An empirical
evaluation of student engagement and performance. J. Comput. Assist. Learn. 2019,36, 128–146. [CrossRef]
Honary, M.; Bell, B.T.; Clinch, S.; Wild, S.E.; McNaney, R. Understanding the Role of Healthy Eating and Fitness Mobile Apps in
the Formation of Maladaptive Eating and Exercise Behaviors in Young People. JMIR mHealth uHealth
,7, e14239. [CrossRef]
Mustafa, A.S.; Ali, N.; Dhillon, J.S. A Systematic Review of the Integration of Motivational and Behavioural Theories in Game-
Based Health Interventions. In Innovative Systems for Intelligent Health Informatics; Springer: Cham, Switzerland, 2021; Volume 7.
... Typically, this is conducted by incorporating game aspects and ideas from games into other contexts. Using game mechanics significantly boosts user engagement and increases productivity [8]. ...
... among college students [32]. • It can counteract the often-decreasing long-term motivation of health-app users [8]. • Gamified fitness apps can predict user preference according to user data [33] The findings from the analysis demonstrate that gamification can positively impact some aspects of physical activity, motivation [21], engagement, social interaction [23], health activities, user experiences, and the effectiveness of rehabilitation techniques. ...
... Therefore, there is a need to improve safety from data theft significantly. • Usability improvements: Fixing technical issues and bugs to improve the usability of gamified fitness apps and make them more engaging [8]. ...
Full-text available
Gamification features to motivate individuals to exercise have become a trend in the fitness sector that is gaining popularity. It is based on the idea that adding fun and competitive components to workout routines will inspire people to achieve their fitness objectives and maintain a healthy lifestyle. This research study attempts to analyze the literature that explores this concept of gamification in detail, and create a picture of how its implementation has changed fitness and healthy habits. This research incorporated the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach as its research methodology. Search strategy used a set of inclusion-exclusion criteria that helped us examine through hundreds of articles identified in the Web of Science and SCOPUS databases. After exclusive and inclusion criteria, 48 articles were selected to be reviewed in detail. Results have indicated that gamification strategy is a supporting factor to overcome the difficulties of executing exercises. Also, to improve the willingness towards fitness regimens.
... Physical activity is also proven to be very effective in avoiding incident hypertension for adult with prehypertensive and normal blood pressure level [37]. Apart from the benefits of physical activity in management of diabetes and hypertension, fitness apps were found in [38] to be most preferred mHealth app, with the attendant advantage of longest periods of long-term use by patients. ...
... To justify choice of cost-free apps for review, in [38], cost of mHealth apps was held by users as a critical driver of abandonment of mHealth apps. Furthermore, the risk of having hypertension, diabetes, and comorbidity were found to be remarkable in the unemployed demographic [99]. ...
Full-text available
Mobile health (mHealth) systems are sipping into more and more healthcare functions with self-management being the foremost modus operandi. However, there has been challenges. This study explores challenges with mHealth self-management of diabetes and hypertension, two of the most comorbid chronic diseases. Existing literature present the challenges in fragments, certain subsets of the challenges at a time. Nevertheless, feedback from patient/users in extant literature depict very variegated concerns that are also interdependent. This work pursues provision of an encyclopedic, but not redundant, view of the challenges with mHealth systems for self-management of diabetes and hypertension. Furthermore, the work identifies machine learning (ML) and self-management approaches as potential drivers of potency of diabetes and hypertension mobile health systems. The nexus between ML and diabetes and hypertension mHealth systems was found to be under-explored. For ML contributions to management of diabetes, we found that machine learning has been applied most to diabetes prediction followed by diagnosis, with therapy in distant third. For diabetes therapy research, only physical and dietary therapy were emphasized in reviewed literature. The four most considered performance metrics were accuracy, ROC-AUC, sensitivity, and specificity. Random forest was the best performing algorithm across all metrics, for all purposes covered in the literature. For hypertension, in descending order, hypertension prediction, prediction of risk factors, and prediction of prehypertension were most considered areas of hypertension management witnessing application of machine learning. SVM averaged best ML algorithm in accuracy and sensitivity, while random forest averaged best performing in specificity and ROC-AUC.
... Accordingly, it is critical to establish various game-design features and distinguish them so that future research can identify which combination of game features contributes to sustaining user motivation, enjoyment, and health-related benefits. To identify suitable gamification design elements, we carried out a prior study [58] that identified five predominant game components among gamified mHealth users: challenges, progress bars, leaderboards, levels, and feedback. ...
... The study adopted perceived autonomy, competence, and relatedness measures from SDT [31]. The social comparison construct was adopted from ScT [25,32], whereas levels, progress bars, feedback, challenges, and leaderboards were from the gamification literature and a preliminary study [58,76]. Other measures from prior literature include perceived benefits [54], perceived hedonic gratification [77], fitness app quality [78], and facilitating conditions [25]. ...
Full-text available
Mobile health (mHealth) apps are designed to support health behavior outcomes and improve well-being. The existing body of literature confirms mHealth’s overall efficacy in promoting physical activity; however, more research on its utility in sustaining user engagement is needed. Understanding the determinants of an individual’s willingness to continue using mHealth is vital to improving the intervention’s success. This study developed a unified model and survey instrument adapted from extant literature while introducing new constructs to predict the sustained use of gamified mHealth. A pilot study was conducted to validate the survey instrument using 48 gamified fitness app users in Malaysia. The survey instrument was tested following rigorous guidelines for quantitative research in the information system context. According to the findings, the reliabilities of most measurement items met the criterion, and those items were retained. Overall, this paper contributes by integrating social comparison theory and the self-determination theory for sustaining user engagement with gamified mHealth through an extrinsic and intrinsic motivation perspective.
... The perceived usefulness of OnTrack to Health features in improving self-care and decreasing rehospitalization rates may account for long-term patient engagement with the app. Recent investigators showed that patients usually discontinue the use of an mHealth app within 30 days of installing the app [30,31]. The lack of perceived usefulness of app features to achieve targeted behavior change was the main reason associated with discontinuing use [11,30,31]. ...
... Recent investigators showed that patients usually discontinue the use of an mHealth app within 30 days of installing the app [30,31]. The lack of perceived usefulness of app features to achieve targeted behavior change was the main reason associated with discontinuing use [11,30,31]. Considering that all the patients in our study who used OnTrack to Health for a duration of 12 to 36.3 months (long-term users) perceived the app as a useful tool for improving self-care, the perceived usefulness of the app might be associated with sustained engagement in the long-term user. ...
Full-text available
Background Publicly available patient-focused mobile health (mHealth) apps are being increasingly integrated into routine heart failure (HF)–related self-care. However, there is a dearth of research on patients’ experiences using mHealth apps for self-care in real-world settings. Objective The purpose of this study was to explore patients’ experiences using a commercially available mHealth app, OnTrack to Health, for HF self-care in a real-world setting. Methods Patient satisfaction, measured with a 5-point Likert scale, and an open-ended survey were used to gather data from 23 patients with HF who were provided the OnTrack to Health app as a part of routine HF management. A content analysis of patients’ responses was conducted with the qualitative software Atlas.ti (version 8; ATLAS.ti Scientific Software Development GmbH). ResultsPatients (median age 64, IQR 57-71 years; 17/23, 74% male) used OnTrack to Health for a median 164 (IQR 51-640) days before the survey. All patients reported excellent experiences related to app use and would recommend the app to other patients with HF. Five themes emerged from the responses to the open-ended questions: (1) features that enhanced self-care of HF (medication tracker, graphic performance feedback and automated alerts, secured messaging features, and HF self-care education); (2) perceived benefits (provided assurance of safety, improved HF self-care, and decreased hospitalization rates); (3) challenges with using apps for self-care (giving up previous self-care strategies); (4) facilitators (perceived ease of use and availability of technical support); and (5) suggested improvements (streamlining data entry, integration of apps with an electronic medical record, and personalization of app features). Conclusions Patients were satisfied with using OnTrack to Health for self-care. They perceived the features of the app as valuable tools for improving self-care ability and decreasing hospitalization rates. The development of apps in collaboration with end users is essential to ensure high-quality patient experiences related to app use for self-care.
... Design features that increase user engagement are components, such as personalization, reinforcement, communication, navigation, credibility, message presentation, and interface aesthetics (Wei et al., 2020). A recent review showed that one of the primary reasons for the abandonment of mHealth apps is the absence of features that users require, and to sustain engagement, it is recommended to integrate game-like elements to satisfy their psychological and emotional needs to improve intrinsic motivation (Mustafa et al., 2022). ...
Full-text available
Despite the growing number of mHealth apps for tracking and helping users to form and sustain health habits, most apps are not evidence-based and are not evaluated by the users to uncover potential issues and determine effectiveness. To fill this gap, we used a mixed methods approach to evaluate a mood-self-tracking app called Feeling Moodie. Data was collected from 34 participants [age range: 18-55 years old, with 15/34 (44%) being between the ages 26 and 35 years old; sex: 17 males and 17 females] who used the app for 15 days and completed a questionnaire about their experience followed by an interview with 18 participants to uncover more qualitative insights. Results showed a positive range for attractiveness, perspicuity, efficiency, dependability, and stimulation, but not for novelty which suggests that Feeling Moodie can be improved by increasing the level of creativity to further captivate the user's interest. Furthermore, interviews revealed that while some participants expressed doing mood check-ins felt like a "chore," others reported that at first, they had to use it intentionally, but after a while, it became a "rhythm," pulling them to the experience. Based on the insights, we offer practical guidelines for increasing the level of interactivity and gradually guiding the user by using a variety of features to help them to form good habits. The results obtained in this work can inform designers on how to design more personalized apps and increase the possibility that the app will be adopted. The article contributes to a better understanding of the emotional and technological implications for designing and improving the quality of mood-tracking apps.
... The finding affirms that sustained use of mHealth apps in self-managing chronic diseases is generally low among older persons [32]. This may be due to boredom, app fatigue, and lack of motivation as people become more familiar with the features of the app [33]. Although the pattern of app usage beyond the 6-month period remains unclear in this study, it is possible that long-term use might still be low, which is consistent with the findings in previous studies of high abandonment rates after the initial phase of using mHealth apps [34,35]. ...
Full-text available
Background Although mobile health application (mHealth app) programs have effectively promoted disease self-management behaviors in the last decade, usage rates have tended to fall over time. Objective We used a case management approach led by a nurse and supported by a health-social partnership team with the aim of sustaining app usage among community-dwelling older adults and evaluated the outcome differences (i.e, self-efficacy, levels of depression, and total health service usages) between those who continued to use the app. Methods This was a 3-arm randomized controlled trial. A total of 221 older adults with hypertension, diabetes, or chronic pain were randomized into 3 groups: mHealth (n=71), mHealth with interactivity (mHealth+I; n=74), and the control (n=76). The mHealth application was given to the mHealth and mHealth+I groups. The mHealth+I group also received 8 proactive calls in 3 months from a nurse to encourage use of the app. The control group received no interventions. Data were collected at preintervention (T1), postintervention (T2), and at 3 months’ postintervention (T3) to ascertain the sustained effect. ResultsA total of 37.8% of mHealth+I and 18.3% of mHealth group participants continued using the mHealth app at least twice per week until the end of the sixth month. The difference in app usage across the 2 groups between T2 and T3 was significant (χ21=6.81, P=.009). Improvements in self-efficacy (β=4.30, 95% CI 0.25-8.35, P=.04) and depression levels (β=–1.98, 95% CI –3.78 to –0.19, P=.03) from T1 to T3 were observed in the mHealth group participants who continued using the app. Although self-efficacy and depression scores improved from T1 to T2 in the mHealth+I group, the mean values decreased at T3. Health service usage decreased for all groups from T1 to T2 (β=–1.38, 95% CI –1.98 to –0.78, P
... Few intervention studies report on reasons for dropping out. However, those that have, as well as studies investigating why users abandon commercial health apps, suggest several causes, including boredom, the loss of novelty, the lack of time, high data entry burden, the loss of motivation, and the lack of personalized feedback [56,57]. These reasons for attrition are echoed in the themes identified in study 2, highlighting the importance of regular and varied intervention content to hold users' interest. ...
Full-text available
Background: In pregnancy, eating well, keeping active, and avoiding excessive weight gain are associated with better maternal and fetal health outcomes. Dietary and physical activity (PA) interventions can be effective in changing behaviors and managing weight gain. The comparatively lower cost and greater accessibility of digital interventions make them an attractive alternative to in-person interventions. Baby Buddy is a free pregnancy and parenting app from the charity Best Beginnings. Designed to support parents, improve health outcomes, and reduce inequalities, the app is actively used within the UK National Health Service. It offers an ideal platform for delivering and evaluating a new prenatal dietary and PA intervention. Objective: The aim of this study was to create a theory-based intervention within Baby Buddy to empower, encourage, and support expectant parents to develop healthier dietary and PA habits for pregnancy and parenthood. Methods: The intervention's development process was guided by the Behavior Change Wheel, with the person-based approach used to create and test its design. Three stages of qualitative research with pregnant and recently pregnant parents guided the intervention design. Study 1 (n=30), comprising 4 web-based focus groups and 12 telephone interviews, gauged response to the rudimentary concept and generated ideas for its development. Results were analyzed thematically. At this stage, the guiding principles for the intervention development were established, and regular team meetings ensured that the intervention design remained aligned with Best Beginnings' objectives, evidence-based approach, and feasibility criteria. Study 2 (n=29), comprising web-based individual and couple interviews, explored design ideas using wireframes and scripts and generated iterative feedback on the intervention content, branding, and tone. A table of changes analysis tracked design amendments. Study 3 (n=19) tested an app prototype using think-aloud interviews with current Baby Buddy users. A patient and public involvement and engagement activity (n=18) and other expert contributors (n=14) provided ad hoc input into the research process and design development. Results: Study 1 confirmed the appeal and relevance of the intervention concept and its novel approach of including partners. The identified themes underpinned the development of the intervention design. Iterative feedback from study 2, in conjunction with patient and public involvement and engagement and expert contributor input, helped refine the intervention design and ensure its relevance and appeal to a diverse target user group. Study 3 highlighted functionality, content, and design issues with the app prototype and identified ways of improving the user experience. Conclusions: This study illustrates the value of combining a theoretical method for intervention development with the person-based approach to create a theory-based intervention that is also user-friendly, appealing, and engaging for its target audience. Further research is needed to evaluate the effectiveness of the intervention in improving diet, PA, and weight management in pregnancy.
... 11 The critical driver of reduced usage frequency or abandonment of mHealth apps is a loss of motivation and interest. 12 Maintaining a high level of user motivation and interest tends to be more likely to increase the frequency of use of mHealth app. Therefore, app developers need to consider effective strategies to engage users in the habit of using mHealth apps and to maintain user motivation and interest to enhance health behaviors. ...
Full-text available
With the development of mobile communication technology, persuasive technology is widely used in mobile health. Using personalized persuasive strategies in mobile health education (MHE) apps can effectively improve users' health literacy and health behaviors. The transtheoretical model explains the process of user behavior change. Different usage frequency of the app reflects changes in user behavior. However, few studies have examined how the perceived importance of persuasive strategies among older adults changes with increasing use frequency. In this study, we analyzed the sensitivity of 111 older adults in China to persuasive strategies in MHE apps. Thirteen persuasive strategies were selected for this study. A repeated measure analysis of variance (RM-ANOVA) was used to demonstrate the influence of gender, health information attention and frequency of use on the sensitivity of perceived persuasive strategies among older adults. The results revealed older adults with a high usage frequency of health apps were more receptive to persuasive strategies, especially in social comparison strategy. This result may help developers consider factors such as the frequency of use by older users when designing personalized persuasive strategies for MHE apps.
Using gamification techniques to intervene in health behaviors is a promising study. The effect of gamification varies considerably between different users. Therefore, our purpose is to understand the effect of user type and health beliefs on the effect of gamification systems. Physical activity applications are studied as an example in this paper. Firstly, we designed storyboards to explain six gamification elements commonly used in the area of encouraging physical activity. Secondly, we conducted an online study (N = 133). We counted subjects’ user type and health belief by using the modified Hexad User Type Scale and HBM scale. Then we measured the perceived persuasiveness of the six gamification elements using the Perceived Persuasiveness Scale. Finally, the results of the correlation test showed a potential influence of health beliefs on the perception of gamification strategies by different users. The novelty of this study is the combination of the health beliefs with the hexad user type model which developed for gamification systems. This complements the existing correlation between Hexad user types and gamification strategies. Our findings may help system developers to select appropriate gamification strategies for different types of users to achieve better health behavior interventions.KeywordsGamificationHealth BehaviorHexad User TypeM-healthPersonalization
It has become clear that managing and maintaining state-of-the-art healthcare facilities is more important than ever before. Healthcare consists of many different institutions and supports personnel ranging from laboratories of universities, public and private hospitals, research centers of health institutions, public health institutes, institutions, and organizations engaged in research and development in the field of medicine. For all these entities to be successful, there needs to be coordination among the bodies and quality must be kept at a very high level. The Handbook of Research on Quality and Competitiveness in the Healthcare Services Sector considers the current state of the healthcare services sector and examines future directions. Covering topics such as quality excellence models, accreditation, and e-health, this major reference work is an essential resource for economists, healthcare specialists, government officials, consultants, business leaders and executives, healthcare professionals, IT managers, students and educators of higher education, researchers, and academicians. Coverage: The many academic areas covered in this publication include, but are not limited to: Accreditation E-Health Finance Healthcare Insurance Medical Technologies Mobile Devices Public Spending Quality Excellence Models Wearable Devices
Full-text available
This research seeks to improve our understanding of how intrinsic motivation is instantiated. Three motivation theories, flow theory, self-determination theory, and empowerment theory, have informed our understanding of the foundations of intrinsic motivation at work. Taken jointly, they suggest six causal factors for intrinsic motivation: (1) perceived competence, (2) perceived challenge, (3) perceived autonomy, (4) perceived impact, (5) perceived social relatedness, and (6) perceived meaningfulness. Integrating different theoretical perspectives, I employ a case-based configurational approach and conduct coincidence analyses on survey data from a German public utility to analyse the nuanced interplay of these six causal factors for intrinsic motivation. My data show that high perceived meaningfulness or high perceived autonomy is sufficient for high perceived intrinsic motivation and at least one of the two conditions must be present. Further, my findings reveal a common cause structure in which perceived impact is not a causal factor for intrinsic motivation but an additional outcome factor. Subsequent analyses shed light on possible roles of the remaining proposed causal factors by drawing a tentative causal chain structure. The results of this study enhance our understanding of the causal complexity underlying the formation of intrinsic motivation.
Full-text available
Background: Nutrition apps are effective in changing eating behavior and diet-related health risk factors. However, while they may curb growing overweight and obesity rates, widespread adoption is yet to be achieved. Hence, profound knowledge regarding factors motivating and hindering (long-term) nutrition app use is crucial for developing design guidelines aimed at supporting uptake and prolonged use of nutrition apps. Objective: In this systematic review, we synthesized the literature on barriers to and facilitators for nutrition app use across disciplines including empirical qualitative and quantitative studies with current users, ex-users, and nonusers of nutrition apps. Methods: A systematic literature search including 6 databases (PubMed, Web of Science, PsychINFO, PSYNDEX, PsycArticles, and SPORTDiscus) as well as backward and forward citation search was conducted. Search strategy, inclusion and exclusion criteria, and the planned data extraction process were preregistered. All empirical qualitative and quantitative studies published in German or English were eligible for inclusion if they examined adolescents (aged 13-18) or adults who were either current users, ex-users, and nonusers of nutrition apps. Based on qualitative content analysis, extracted individual barriers and facilitators were grouped into categories. Results: A total of 28 publications were identified as eligible. A framework with a 3-level hierarchy was designed which grouped 328 individual barriers and facilitators into 23 subcategories, 12 categories, and 4 clusters that focus on either the individual user (goal setting and goal striving, motivation, routines, lack of awareness of knowledge), different aspects of the app and the smartphone (features, usability of the app or food database, technical issues, data security, accuracy/trustworthiness, costs), positive and negative outcomes of nutrition app use, or interactions between the user and their social environment. Conclusions: The resulting conceptual framework underlines a pronounced diversity of reasons for (not) using nutrition apps, indicating that there is no "one-size-fits-all" approach for uptake and prolonged use of nutrition apps. Hence, tailoring nutrition apps to needs of specific user groups seems promising for increasing engagement.
Full-text available
Importance Gamification is increasingly being used to promote healthy behaviors. However, it has not been well tested among patients with chronic conditions and over longer durations. Objective To test the effectiveness of behaviorally designed gamification interventions to enhance support, collaboration, or competition to promote physical activity and weight loss among adults with uncontrolled type 2 diabetes. Design, Setting, and Participants A 4-arm randomized clinical trial with a 1-year intervention was conducted from January 23, 2017, to January 27, 2020, with remotely monitored intervention. Analyses were conducted between February 10 and October 6, 2020. Participants included 361 adults with type 2 diabetes with hemoglobin A1c levels greater than or equal to 8% and body mass index greater than or equal to 25. Interventions All participants received a wearable device, smart weight scale, and laboratory testing. Participants in the control group received feedback from their devices but no other interventions. Participants in the gamification arms conducted goal setting and were entered into a 1-year game designed using insights from behavioral economics with points and levels for achieving step goals and weight loss targets. The game varied by trial arm to promote either support, collaboration, or competition. Main Outcomes and Measures Co-primary outcomes included daily step count, weight, and hemoglobin A1c level. Secondary outcome was low-density lipoprotein cholesterol level. Intention-to-treat analysis was used. Results Participants had a mean (SD) age of 52.5 (10.1) years; hemoglobin A1c level, 9.6% (1.6%); daily steps, 4632 (2523); weight, 107.4 kg (20.8 kg); and body mass index, 37.1 (6.6). Of the 361 participants, 202 (56.0%) were women, 143 (39.6%) were White, and 185 (51.2%) were Black; with 87 (24.1%) randomized to control; 92 (25.4%) randomized to gamification with support and intervention; 95 (26.3%) randomized to gamification with collaboration; and 87 (24.1%) randomized to gamification with competition. Compared with the control group over 1 year, there was a significant increase in mean daily steps from baseline among participants receiving gamification with support (adjusted difference relative to control group, 503 steps; 95% CI, 103 to 903 steps; P = .01) and competition (606 steps; 95% CI, 201 to 1011 steps; P = .003) but not collaboration (280 steps; 95% CI, −115 to 674 steps; P = .16). All trial arms had significant reductions in weight and hemoglobin A1c levels from baseline, but there were no significant differences between any of the intervention arms and the control arm. There was only 1 adverse event reported that may have been related to the trial (arthritic knee pain). Conclusions and Relevance Among adults with uncontrolled type 2 diabetes, a behaviorally designed gamification intervention in this randomized clinical trial significantly increased physical activity over a 1-year period when designed to enhance either support or competition but not collaboration. No differences between intervention and control groups were found for other outcomes. Trial Registration Identifier: NCT02961192
Full-text available
M-Health interventions designed for healthcare can potentially increase participation and behaviour outcomes. However, interventions need to incorporate a theoretical perspective of behavioural change to enhance their perceived efficacy. Although behavioural outcome theories have gained interest in the health and fitness literature, the implementation of theoretical integration remains largely under-studied. Therefore, we reviewed the efficacy of behavioural gamified interventions based on integrated theories in various contexts, such as healthcare and fitness. Studies were included if an integrated theoretical intervention was implemented to change behaviour in specific contexts. The review aims to uncover the effectiveness of integrated theory in predicting behaviour outcome in interventions. Our findings reveal that in 39 studies, Self Determination Theory (n = 19) and Theory of Planned Behaviour (n = 16) outnumbered other theories in integrated models. Overall, 77% of studies showed evidence that integrated theoretical-based behaviour change interventions can be successful for a short time, with only a few studies that tested these interventions’ long term effects. We discuss the implication of our findings, and also propose potential future directions.
Full-text available
Background As the global population ages, there is increased interest in developing strategies to promote health and well-being in later life, thus enabling continued productivity, social engagement, and independence. As older adults use technologies with greater frequency, proficiency, and confidence, health information technologies (HITs) now hold considerable potential as a means to enable broader access to tools and services for the purposes of screening, treatment, monitoring, and ongoing maintenance of health for this group. The InnoWell Platform is a digital tool co-designed with lived experience to facilitate better outcomes by enabling access to a comprehensive multidimensional assessment, the results of which are provided in real time to enable consumers to make informed decisions about clinical and nonclinical care options independently or in collaboration with a health professional. Objective This study aims to evaluate the usability and acceptability of a prototype of the InnoWell Platform, co-designed and configured with and for older adults, using self-report surveys. Methods Participants were adults 50 years and older who were invited to engage with the InnoWell Platform naturalistically (ie, at their own discretion) for a period of 90 days. In addition, they completed short web-based surveys at baseline regarding their background, health, and mental well-being. After 90 days, participants were asked to complete the System Usability Scale to evaluate the usability and acceptability of the prototyped InnoWell Platform, with the aim of informing the iterative redesign and development of this digital tool before implementation within a health service setting. Results A total of 19 participants consented to participate in the study; however, only the data from the 16 participants (mean age 62.8 years, SD 7.5; range 50-72) who completed at least part of the survey at 90 days were included in the analyses. Participants generally reported low levels of psychological distress and good mental well-being. In relation to the InnoWell Platform, the usability scores were suboptimal. Although the InnoWell Platform was noted to be easy to use, participants had difficulty identifying the relevance of the tool for their personal circumstances. Ease of use, the comprehensive nature of the assessment tools, and the ability to track progress over time were favored features of the InnoWell Platform, whereas the need for greater personalization and improved mobile functionality were cited as areas for improvement. Conclusions HITs such as the InnoWell Platform have tremendous potential to improve access to cost-effective and low-intensity interventions at scale to improve and maintain mental health and well-being in later life. However, to promote adoption of and continued engagement with such tools, it is essential that these HITs are personalized and relevant for older adult end users, accounting for differences in background, clinical profiles, and levels of need.
Full-text available
Background: The increasing health service demand driven by the aging of the global population calls for the development of modes of health service delivery which are less human resource intensive. eHealth in general, and medical apps in particular, are expected to play an important role in this development. While evidence shows mobile medical applications might be effective in improving elderly care, self-management, self-efficacy, health-related behavior and medication adherence, little is known about the elderly's intention to use these technologies when needed, or the factors influencing this intention. Objective: The objective of this study was to investigate the relationship of technology acceptance factors and intention to use mobile medical applications among community dwelling elderly. Methods: Data have been collected using questionnaires. The factors selected from literature have been validated using Cronbach Alpha and tested for significance using logistic regressions. Results: Almost half (49.7%) of the included elderly reported no intention to use medical apps. Adjusted logistic regression analysis per factor showed that the factors Attitude Towards Use (OR 8.50), Perceived Usefulness (OR 5.25), Perceived Ease of Use (OR 4.22), Service Availability (OR 3.46), Sense of Control (OR 3.40), Self-Perceived Effectiveness (OR 2.69), Facilities (OR 2.45), Personal Innovativeness (OR 2.08), Social Relationships (OR 1.79), Subjective Norm (OR 1.48) and Feelings of Anxiety (OR 0.62) significantly influence the intention to use mobile medical applications in the elderly, whereas the factor Finance (OR 0.98) did not. When considered together, a controlled multivariate logistic regression yielded high explained variances of 0.542 (Cox & Snell R2) and 0.728 (Nagelkerke R2). Conclusions: The high odds ratios and explained variance indicate that the factors associated with intention to use medical applications are largely understood and the most important factors are identified. Experimental controlled further research to advance evidence of causality of the relationship between the factors, intention to use, and ultimately actual use, form an important next step to advance the evidence base. For this purpose - and for the meantime - our evidence suggests policies designed at improving Attitude Towards Use appear most effective, followed by policies addressing Perceived Usefulness, Perceived Ease of Use, Service Availability and Sense of Control. Clinicaltrial:
Full-text available
Physical activity (PA) is important for maintaining good physical health. WHO recommends 150 minutes of PA per week to the older population but many older people do not meet this recommendation. The increasing use of mobile technology among elderly provides an opportunity to increase PA. This systematic review was aimed at the usability, acceptability and effectiveness of mHealth (including smartphone, mobile phone, tablet apps, mobile text messages) to increase PA in older people above the age of 55. A literature search related to mHealth, PA and older people was conducted in PubMed, Embase, Web of science and COCHRANE library. The search generated 829 articles, after the screening of articles and reference lists, ten studies were included in the review. Included studies were diverse in the aspects of study design, intervention mode, duration, frequency of reminders and assessment measures. The results of this review indicated that mHealth interventions with motivational back up may be usable, acceptable and beneficial for the maintenance and improvement of PA in the short term. However, the findings are inconclusive about the difference in effectiveness between simple (mobile text message) and complex mHealth interventions (app monitoring with sensors), the optimal frequency for activity reminders and on the long term effectiveness of mHealth.
Smart‐phones have become a necessity for users due to their abundance of services such as global positioning system, Wi‐Fi, voice/video calls, SMS, camera, and so forth. It contains personal information of users including photos, documents, messages, and videos. Android‐based smart‐phones enriched with many applications (commonly known as apps) fascinates users to use this ubiquitous technology up to a full extent. With open architecture and 73% of market share, Android is the most popular mobile operating system (OS) among developers. At the same time, the increasing popularity of Android OS woos attackers or cyber‐criminals to exploit its vulnerabilities. The attackers write malicious code to harm the device and grab users' sensitive information. For example, ransomware (a form of malware) demands ransom from victims to liberate the ceased material for illegal financial gain. The existing survey papers cover the analysis and detection of generic Android malware. The focus of this survey paper is to present an in‐depth threat scenario of Android ransomware. This article not only provides a comprehensive survey on analysis and detection methods for Android ransomware since its beginning (2015) till date (2020); but also presents observations and suggestions for researchers and practitioners to carry out further research.
This study details a method for mHealth app development and user experience design (UX) evaluation, which generates a comprehensive list of stakeholder-users, acknowledges UX barriers, advocates multiple methods, and argues that developers should address the UX needs of each stakeholder-user in a complex health-care system. A case study of a research project on an mHealth app for women who are considering prevention of or treatment for osteoporosis assists to elaborate and define the method. To find any measure of success, a fully functional app for older users should be integrated into the entire health-care system.
Aims: To evaluate the effects of mobile health (mHealth)-based interventions on health literacy and related factors. Background: Few reviews exist on the effects of mHealth-based interventions on the improvement and changes in health literacy and related factors. Evaluation: A systematic review was conducted using the Mixed Methods Appraisal Tool to evaluate the quality of articles. Results: Outcome variables included eHealth literacy, mHealth literacy and health literacy. Two studies showed that health literacy was significantly enhanced after mHealth application use, particularly among those with low education and health literacy levels. Two articles reported that health information seeking and health information appraisal improved after mHealth-based interventions, thereby increasing health literacy levels. In one article, no significant relationship was found between health literacy levels and mHealth literacy. Conclusion: mHealth can enhance health literacy; furthermore, mobile applications effectively improve patient health literacy. However, measurement tools used for evaluating health literacy indicators are inconsistent, with the concept and components of these tools being not specifically designed for evaluating health literacy indicators. Implications for nursing management: To successfully and effectively overcome health problems in diverse clinical settings, the theory-based mHealth services should be adopted while considering their intensity, frequency, duration and credibility.