ArticlePDF Available

Abstract

Background Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users. Objective This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions. Methods An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics. Results The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; P<.001), with an adjusted R2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (P<.001) and ≥60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness. Conclusions This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category.
Original Paper
The Intersection of Persuasive System Design and Personalization
in Mobile Health: Statistical Evaluation
Aleise McGowan1, PhD; Scott Sittig2, RHIA, MHI, PhD; David Bourrie3, PhD; Ryan Benton3, PhD; Sriram Iyengar4,
PhD
1School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS, United States
2University of Louisiana at Lafayette, Lafayette, LA, United States
3University of South Alabama, Mobile, AL, United States
4University of Arizona, Pheonix, AZ, United States
Corresponding Author:
Scott Sittig, RHIA, MHI, PhD
University of Louisiana at Lafayette
PO Box 43565
Lafayette, LA, 70504
United States
Phone: 1 3374826160
Email: scott.sittig@louisiana.edu
Abstract
Background: Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg,
smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous
nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that
persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on
the psychological characteristics of users.
Objective: This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen
perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived
persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by
creating more engaging solutions.
Methods: An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health
motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the
persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based
survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app
screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear
regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics.
Results: The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health
motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive
principles and psychological characteristics lead to greater perceived persuasiveness. The Ftest (ie, ANOVA) for model 1 was
significant (F9,6540=191.806; P<.001), with an adjusted R2of 0.208, indicating that the demographic variables explained 20.8%
of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness
(P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years
(P<.001) and 60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2of 0.403, indicating
that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3%
of the variance in perceived persuasiveness.
Conclusions: This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived
persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and
education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that
varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the
primary persuasive technology category.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 1https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
(JMIR Mhealth Uhealth 2022;10(9):e40576) doi: 10.2196/40576
KEYWORDS
persuasive technology; personalization; psychological characteristics; self-efficacy; health consciousness; health motivation;
personality traits; mobile health; mHealth; mobile phone
Introduction
Background
Given the ubiquitous nature of mobile devices across all
socioeconomic groups, digital health technologies have
demonstrated their efficacy as key components in educating
and treating patients [1]. Mobile health (mHealth) uses mobile
devices to practice medicine and public health. Unlike
clinic-based treatments, where health care data are sparingly
personalized, the ever-present nature of digital health
technologies allows for an extensive and more intimate treatment
plan. Although digital health technologies allow for the real-time
transfer of user data, which allows for more intimate user
interaction, these technologies are met with a unique set of
challenges, such as creating and maintaining engagement [2].
The efficacy of digital health technologies relies strongly on
their ability to continuously engage and re-engage users [3].
The closed-loop engagement process begins with engagement
and continuously moves through disengagement to allow the
patient to re-engage upon disengagement [4,5]. Properly
engaging patients has repeatedly been shown to improve patient
outcomes [2].
However, at the core of engagement using digital health
technologies, there remains a gap in the literature on how to
successfully design these tools based on an individual’s dynamic
psychological makeup. For instance, there remains a need to
learn more about how mHealth treatments work and how to
make them more effective. In particular, research on the impact
of certain intervention features on user engagement is an
important next step in the development of theory and evaluation
to develop a science for user engagement [6]. Although the
positive influence of persuasion on changing an individual’s
attitude and behavior has been established [7,8], researchers
have contended the need for personalized systems that address
individual’s personalities to increase the effectiveness of these
tools [9,10]. One-size-fits-all digital health technologies that
target behavior change to improve the user’s health often fail
because they do not target the psychological traits that drive an
individual’s motivations and behaviors, partly because of the
lack of guidance from intervention designers and data scientists
with numerous options [11]. A dynamic personalized approach
to developing persuasive technologies is imperative, as research
has shown that strategies that may influence change in an
individual with one psychological type may dissuade another
individual with a different psychological type [12].
User engagement is a widely used multifaceted term that extends
beyond a user’s desire to use digital health technologies to the
depth of the user’s investment [13]. Digital health technologies
developers are often tasked with developing tools designed to
engage patients, yet little emphasis has been placed on
understanding what motivates users to engage with digital health
technologies. Developers must move past using a cookie-cutter,
one-size-fits-all solution, and seek to develop digital health
technologies designed to traverse the fluid terrain that navigates
between the expectations of the user and the technological
capabilities of the tool. The fluid nature of goals and user
preferences determined by user characteristics must also be
considered in order to foster various engagement trajectories
with digital health technologies. Synonymous with the
engagement process, the development of digital health
technologies must be dynamic in nature, traversing between
design and redesign guided by use [14]. The unconscious
disregard for the interdependency among technology, human
characteristics, and the socioeconomic environment has been
determined to be one of the factors in digital health technologies
failing to sustain innovations in the health care field [15,16].
Persuasive technology has emerged as a significant contributor
to patient engagement and is used practically in every area of
health and wellness [7,17]. Persuasive technology is an umbrella
term that encompasses any software (eg, mobile apps) or
hardware (eg, smartwatches) designed to influence users to
either perform a preferable behavior once or on a long-term
basis. These modifications must be achieved without the use of
deception, coercion, or inducements [18,19]. By adequately
applying persuasive technology, intervention developers have
the potential to improve patient outcomes by successfully
closing the engagement loop. The modification of user behavior
thrives under personalized care that persuasive technology must
offer. However, absent from the current literature is adequate
information on how app designers are to operationalize
persuasive design principles based on a more user-centric view
[20]. Research is immersed in studies related to the user
experience derived from metrics and quantifications, but there
remains a void in the literature seeking a more intimate view
of the consumer and how they interact with persuasive principles
to help guide design processes. The design process is further
impaired by a lack of understanding of the psychological
characteristics of digital health technology users [21]. Previous
research has focused on the development of theories
concentrated on predicting acceptance or adherence instead of
guiding persuasive technology design principles [22]. This
research is needed to fill the gap in the literature addressing the
user-centric development of persuasive technologies and
developing a better understanding of the psychological
characteristics necessary for the successful engagement of digital
health technology users.
Consumer and Patient Engagement
There is consensus that an implicit level of engagement is
required for digital health technologies to be effective. The
absence of engagement impedes digital health technologies
from attaining their full potential [23]. This emerging stream
of research is built on a somewhat challenging and unstable
foundation, as the authors used various procedures to measure
engagement [24]. With various metrics in play, the ability to
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 2https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
quantify engagement is a daunting and challenging task [24,25].
This ambiguity further exacerbates our efforts to assess effective
engagement.
Digital health technology developers must exercise quantitative
and qualitative methods when designing engaging applications
[26]. Quantitative measures evaluating intensity and breadth of
use are often used to determine the level of consumer
engagement [27]. Such a holistic view is not always feasible
for developers, but the use of tangible metrics (eg, the amount
of screen time of the digital health technology and the number
of likes and shares) can be quantified and used for quantitative
data [3]. For engagement to be meaningful, digital health
technologies must modify user behavior and advance ordinary
experiences into aesthetically pleasing ones [28].
Chapman et al [29] proposed that engagement was dichotomous,
being either less passive or more passive based on the level of
control. More controlled engagement requires information
processing such as critical thinking and reasoning and involves
a less passive state of engagement. Passive engagement requires
less control and is easier to achieve, because the level of effort
and motivation is low. Although easier to achieve and maintain,
passive engagement is less useful in the successful achievement
of established goals that require high levels of cognition [29].
The delivery of appropriately tailored digital health technology
content can increase users’engagement and positively influence
outcomes. This makes it imperative to understand how to design
digital health technologies based on patient and consumer
preferences [30]. Identifying the features of digital health
technologies that stimulate user engagement is crucial for
developing effective tools [21]. One of the key factors in the
development of digital health technologies that enhance
engagement through the aforementioned techniques is persuasive
technology.
Characteristics such as gender, age, and personality affect how
users respond to persuasive technologies, causing a pivot from
one-size-fits-all solutions to a more user-centric approach [12].
Persuasive technologies can adapt to the individualized
characteristics of users, increasing their likelihood of changing
their behavior or attitude [31]. Studies show that persuasive
technologies that personalize content instead of using
one-size-fits-all approaches are more successful in effectively
persuading users [32-34]. One-size-fits-all persuasive
technologies can be enhanced when a user’s individual attitudes
and characteristics are used to influence and personalize the
persuasiveness of the intervention [35].
Although research has shown that individualized persuasive
technology is more effective than persuasive technology
designed from a one-size-fits-all perspective [36-38], developers
often fail to consider the individualized behavior of stakeholders
and how it impacts achieving a target behavior [39]. Digital
health technologies that deviate from compartmentalized
one-size-fits-all approaches offer a medium through which
health care providers can meet the growing demands of users,
preferring a more personalized approach [26,40]. This growing
demand necessitates the ability to understand how to design
digital health technologies that are dynamic enough to
accommodate the differing predispositions of end users [30].
Designers must understand how to tailor digital health
technologies according to individual characteristics to effectively
engage users with these tools. By tailoring digital health
technologies to users’ characteristics, developers can deliver
guidance that is appropriate, relevant, and has a positive impact
on engagement [41]. Disregard for the interconnectedness
between human characteristics and technology is one reason
digital health technologies inevitably become high technology
with little to no impact [42]. Current theories are inept at
informing digital health technology developers on how to
develop and evaluate more adaptive interventions [43,44].
Recognizing the psychological characteristics of end users will
allow developers to systematically approach the integration of
persuasive design components into digital health technologies.
Data-centered persuasive technologies seek to modify user
attitudes or behaviors through users’ behavioral data [45].
Current technology allows intervention designers to dynamically
generate personalized interventions based on a specific user’s
personal characteristics [46]. Dynamic approaches acknowledge
that interventions designed for one user may not necessarily fit
the model required to effectively engage another user. User
characteristics often dictate the most effective persuasive
technique [35]. Persuasive technologies applicable to the health
care domain are more effective when personalized based on the
user’s personal characteristics [47]. Because personalized
persuasive techniques evoke a different response from more
traditional, one-size-fits-all techniques, intervention designers
must shift to a more individualized approach guided by the
individual’s preferences [12].
Personalized interventions that target nuances that drive users’
choices and behaviors are better suited to facilitate effective
engagement than black box, one-size-fits-all solutions [11]. It
has long been established that personalized content is more
effective as it increases user attention, leading to effective
engagement [32]. The application of data collected from
individuals is a more advanced method of persuasion that
increases the probability of success and results in more active
and effective intervention [45]. Determining the key data
elements to collect to enhance perceived persuasiveness is
critical in efforts to improve engagement (both in the short and
long term).
Psychological Characteristics
Self-efficacy
Self-efficacy is loosely defined as an individual’s belief that
they are capable of successfully executing courses of action
required to successfully produce specific behaviors [48]. An
individual’s estimate of self-efficacy varies in 3 dimensions:
magnitude (the individual’s belief in their ability to complete
a task), strength (the individual’s confidence that they are
capable of completing various components or varying levels of
difficulty in a task), and generality (the extent to which an
individual’s self-efficacy transfers from one task to related tasks)
[48,49]. Self-efficacy is regarded as a core premise of human
performance, as demonstrated by its use across multiple domains
including education [50,51], exercise [52], physical activity
[53], career [54], and health care [55].
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 3https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Individuals avoid tasks that they presume to exceed their ability
levels [56]. Situations in which these tasks occur affect an
individual’s evaluation of self-efficacy. Self-efficacy is more
likely to increase when individuals are able to ascribe success,
as opposed to failure, to their individual skill set [56,57]. The
difficulty level of a task also correlates with an individual’s
appraisal of self-efficacy [58]. Tasks deemed difficult to
successfully complete tend to have a negative effect on an
individual’s appraisal of self-efficacy [59]. According to
Bandura [56], individuals will go so far as to be unwilling to
attempt to manage situations where their low self-efficacy
indicates a negative outcome [60].
Hypothesis 1: self-efficacy will positively influence interpreted
mHealth screen perceived persuasiveness.
Health Consciousness
Health consciousness is defined as the measure to which an
individual integrates health concerns into their daily regime
[61-63]. Unlike health motivation (HM), which is external in
nature, health consciousness refers to “how” an individual
achieves a healthy lifestyle [61]. Research has shown that the
higher an individual’s health consciousness, the more likely
they are to adopt a lifestyle grounded in health behaviors such
as fitness and nutritional activities [62,64]. These individuals
are cognizant of their health and therefore influenced to adopt
these healthier behaviors needed to improve or maintain their
health [65].
Studies have shown that health consciousness can positively
influence engagement in health-oriented actions [66]. This
motivation to engage in health-oriented actions has the
propensity to push individuals to become connoisseurs of health
information via media sources such as television [64] and the
internet [67]. Also observed has been the correlation between
the increase in health consciousness and the increase in
preventive health care [61,68]. Individuals with high health
consciousness reportedly seek to develop and preserve a healthy
lifestyle [69].
Hypothesis 2: health consciousness will positively influence
interpreted mHealth screen perceived persuasiveness.
Health Motivation
HM is closely related to health consciousness, as it is one of
the 3 elements that comprise health consciousness [67]. HM is
an individual’s drive to engage in health-related activities to
improve or maintain preventive health behaviors [61,70]. HM
has been found to be a relatively consistent state deeply rooted
in an individual’s psychological composition [61]. Research
has shown that HM serves as the source of an individual’s
desire, adoption, and practice of preventive health behaviors
[61,70]. Motivation has been found to be both competency-based
(whether a person can achieve the goal) and goal-oriented (the
way a task is managed is determined by the individual’s
objective) [71].
It has also been determined that HM can gauge an individual’s
well-being with regard to health behavior–related concerns and
actions [72] and drive consumer engagement in health
maintenance behaviors [70]. HM is directly linked to an
individual’s internal characteristics [61]. Research has
consistently shown that internalized motivation results in more
pronounced adherence to preventive health behaviors such as
weight loss [73,74]. Whether an individual expects to succeed
also plays a key role in their degree of motivation [75].
Hypothesis 3: HM will positively influence interpreted mHealth
screen perceived persuasiveness.
Personality Traits
Personality traits and strategies used to engage users have an
impact on the effective engagement of digital health technology
[76]. Understanding these personality traits is critical for creating
digital health solutions that meet the needs of users. One of the
most commonly used personality models is the Big Five factor
model [77]. The Big Five factor framework was developed by
Goldberg [78] and later validated by Costa and McCrae [78,79].
This model delineated five factors of personality:
1. Openness to experience: the extent to which an individual
requires intellectual stimulation, change, and variety
2. Conscientiousness: the extent to which an individual is
willing to comply with conventional rules, norms, and
standards
3. Extraversion: the extent to which an individual needs
attention and social interaction
4. Agreeableness: the extent to which an individual needs
pleasant and harmonious relationships with others
5. Neuroticism: the extent to which an individual observes
the world as threatening and beyond their control [80]
Each Big Five personality category can be regarded as a
continuum in which individual scores range from high to low
(Figure 1 [77]).
Figure 1. Big Five continuum.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 4https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Frequent time constraints are the drivers for a more succinct
measurement tool [81]. The Mini-International Personality Item
Pool (Mini-IPIP) Scale is a condensed 20-item diagnostic tool
that has been validated in multiple studies [82]. Researchers
have used the Big Five framework to predict user characteristics
across a conglomerate of domains: career [83], relationship
satisfaction and love styles [84], academic performance [85],
preventive health care [86], and more.
Individual personality traits often reflect not only what drives
and motivates people but also what they prefer. The Big Five
personality dimensions describe human behavior in 5
dimensions: openness, conscientiousness, extraversion or
introversion, agreeableness or disagreeableness, and neuroticism.
Individual personality traits should be an antecedent of consumer
engagement with mHealth apps [81].
Hypothesis 4: openness will positively influence interpreted
mHealth screen perceived persuasiveness.
Hypothesis 5: conscientiousness will positively influence
interpreted mHealth screen perceived persuasiveness.
Hypothesis 6: extraversion will positively influence interpreted
mHealth screen perceived persuasiveness.
Hypothesis 7: agreeableness will positively influence interpreted
mHealth screen perceived persuasiveness.
Hypothesis 8: neuroticism will negatively influence interpreted
mHealth screen perceived persuasiveness.
Methods
Ethics Approval
Institutional review board approval was obtained from the
University of South Alabama (application 18-353/1314060-1).
Overview
To examine the factors related to engagement behavior with the
intention to use an mHealth app, a multiple-phase experiment
was conducted in the summer of 2020. This experiment involved
a survey-based design with a series of 25 mHealth app screens
that featured the use of persuasive principles, with a focus on
physical activity. This study used exploratory factor analysis
(EFA) and multiple linear regression to aid designers in the
user-centric development of persuasive technologies. This study
aimed to develop a better understanding of the psychological
characteristics necessary for the successful engagement of digital
health technology users.
Recruitment
Participants were recruited by Qualtrics International, Inc to
use the web-based survey system by XM Research Service [87],
which has been previously used by researchers in a variety of
disciplines [88,89]. Qualtrics reimbursed participants with a
predetermined amount of money arranged between Qualtrics
and participants. Once interested participants were selected by
Qualtrics, they were directed to the informed consent page via
an anonymous link. Upon consenting to participate, they were
directed to a web-based engagement screen survey. Participants
were recruited between July 23, 2020, and August 3, 2020. The
engagement screen survey took an average of 28.08 (19.35 SD)
minutes to complete. There were 273 completed survey
responses; however, 11 (4%) were deleted owing to evident
signs of respondents being “speeders” that completed the survey
in an impossibly quick time or “straight lining” and giving
identical answer choices repeatedly, leaving this study with 262
(95.9%) viable responses.
Screen Development
To examine the perceived persuasiveness of mHealth screens,
25 unique mHealth screens were developed following the
persuasive system design (PSD) categories and principles
developed by Oinas-Kukkonen and Harjumaa [90]. The screens
were all developed with the central theme of improving or
increasing exercise as a use case.
The mHealth screen development process began with the
creation of a wireframe prototype [91]. The prototype was
created on sheets of paper, with each sheet representing one of
the mHealth app screens. The initial step for each prototype was
to document the persuasive system category, design principle,
and targeted implementation as per Oinas-Kukkonen [90]. A
brief description of the details of the screen was then added to
the prototype, followed by the mHealth screen being given a
reference name based on the details in the write-up used
throughout the questionnaire development and analysis process.
Table 1 presents examples of the initial steps. A sketch of the
prototype was then drawn based on the documentation so that
each sheet would represent one of the mHealth screens.
BuildFire [92] was used to develop a digital high-fidelity
prototype for each mobile app screen. These prototypes were
used to support the design goals established in the initial
prototype. Once the prototypes were developed, iPhone XS
Max was used to create still images of the mHealth screens
using the screenshot function. This method was used so that the
image would visually represent what a user would see on their
smartphone. The images were exported from the mobile phone
to a laptop computer via email. Once the prototypes were
exported, 2 experts in the field of persuasive technology
conducted a blind review to validate the mHealth screen,
representing the persuasive technology principle intended by
the author. The expert review panel consisted of a reviewer with
12 years of experience in the field of persuasive technology and
a reviewer with 9 years of experience in the field of persuasive
technology. Each expert created a datasheet with the associated
screen names and listed the PSD principles identified on each
screen.
Following the expert inspection and blind review, a consultation
was held with the expert review panel, where notes and
suggestions were reviewed. The review and modification
processes continued until the developer and reviewers reached
a consensus. The mHealth screens were iteratively evaluated,
modified, and improved following each expert inspection and
blind review. For the initial round, 23 mHealth screens were
developed: Add, Start, Burpee-Squat, Increase, Mountain,
Target, Trophy, Late, Calories, Dinner Chat, Tracker, About
Us, Stories, Leaderboard, Journal, Partners, Ads, Strategy, CDC,
HIPAA, Contact, Before After, and Yoga. During the initial
expert review, the developer and reviewers identified 11
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 5https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
mHealth screens with conflicting persuasive technology
principles that required modification: Target, Dinner Chat,
About Us, Journal, Partners, Strategy, HIPAA, Contact, Before
After, Yoga, and CDC. CDC was dropped following the initial
review because the designed persuasive category was not seen
by either of the 2 reviewers, and the category that was identified
was seen on another screen. The Apple mHealth screen was
created to replace the CDC and submitted with revisions for
round 2. Consensus was reached on the 23 mHealth screens
during the second round. In addition, 3 paper and high-fidelity
prototypes were created for the remaining mHealth screens
(SSL, Avatar, and Recreation) following the aforementioned
methods. Additional mHealth screens were iteratively evaluated,
modified, and improved using expert inspection and blind review
methods used during rounds 1 and 2. The iterative process
resulted in 25 mHealth screens designed for the questionnaire
that were agreed upon through the blind review process, and an
mHealth screen prototype was discarded. The acceptance of the
mHealth screen by round is presented in Table 2.
Table 1. Examples of the initial prototype development steps.
Mock-up nameMock-upTargeted implementationPersuasive system category and
design principle
Primary task support
StartShow literature such as weight loss made simple,
which gives simple steps to get started for losing
weight
Provide simple steps for an activityReduction
Burpee-SquatFitness program with step-by-step workout plan.
Once daily or weekly goals are reached, the next
set of steps are given
Guiding people in a process step by step to
meet a goal
Tunneling
AddUsers can modify the app to reflect their interests
and personality (change color pallet, select what
is displayed on home screen, etc)
The system uses factors relevant to the individ-
ual to motivate the users based on their needs,
interests, personality, and so on
Tailoring
IncreaseIncrease the user’s activity goal based on accom-
plishments or modify dietary plan based on
weight loss
Suggestions, praise, and rewards are given at
appropriate time to motivate users to stay on
track
Personalization
TrackerSummary of daily or weekly activity calculations
and weekly weight summaries
Allows users to follow or monitor their perfor-
mance to ensure that they are staying on track
Self-monitoring
System credibility support
HIPAA
Display information guaranteeing HIPAAa
compliance to reassure users that information
will not be shared with third-party organizations
Apps should appear to be truthful, fair, and
unbiased
Trustworthiness
About UsChat screen showing interaction with person that
resembles a physician or medical professional
Provide content from experts (physicians or
specialists)
Expertise
aHIPAA: Health Insurance Portability and Accountability Act.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 6https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Table 2. Mobile health (mHealth) screen acceptance by round.
Round 3Round 2Round 1Screen name
a
Add
Start
Burpee-Squat
Increase
Mountain
Target
Trophy
Late
Calories
Dinner Chat
Tracker
About Us
Stories
Leaderboard
Journal
Partners
Ads
Strategy
N/A
N/Ac
Dropped
CDCb
HIPAAd
Contact
Before After
Yoga
Replaced CDCN/AApple
N/AN/ASSL
N/AN/AAvatar
N/AN/ARecreation
a: indicates that the mHealth screen was accepted.
bCDC: Centers for Disease Control and Prevention.
cN/A: not applicable.
dHIPAA: Health Insurance Portability and Accountability Act.
The primary task support category aids the user in performing
fundamental tasks by reducing complex tasks into simpler tasks.
The primary task principles include reduction, tunneling,
tailoring, personalization, self-monitoring, simulation, and
rehearsal [90]. Textbox 1 describes the primary task support
design principles [90].
The dialogue support category facilitates human-to-computer
dialogue between the persuasive system and user. The principles
used to provide feedback are praise, rewards, reminders,
suggestions, similarities, liking, and social roles [90]. Textbox
2describes the principles of the dialogue support category [90].
The system credibility category represents how systems can be
made more persuasive by making them more credible. The
principles used to give credibility include trustworthiness,
expertise, surface credibility, real-world feel, authority,
third-party endorsements, and verifiability [90]. Textbox 3
describes the principles of the system credibility category [90].
Principles in the social support category motivate systems
through social influence. Design principles in this category
include social facilitation, social comparison, normative
influence, social learning, cooperation, competition, and
recognition. Textbox 4 shows the principles of social support
[90].
Table 3 depicts the final iteration of testing and includes the
principles per screen and the principle category. Table 4shows
the percentage of screens in the primary persuasive technology
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 7https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
category. Figure 2 shows one of the final mHealth screens
developed. A visual representation of all 25 screens is available
in Multimedia Appendix 1. Of the 25 screens developed, 5
(20%) screens had a primary principle from the primary task
support category, 7 (28%) had a primary principle from the
dialogue support category, 8 (32%) had a primary principle
from the system credibility support category, and 5 (20%) had
a primary principle from the social support category.
Textbox 1. Primary task principles.
Persuasive system category, design principle, and principle description—primary task support
Reduction
Provides simple steps for an activity
Tunneling
Guides people in a process step by step to meet a goal
Tailoring
Uses factors relevant to the individual to motivate the users based on their needs, interests, personality, and so on
Personalization
Suggestions, praise, and rewards are given at appropriate time to motivate users to stay on track
Self-monitoring
Allows users to follow or monitor their performance to ensure they are staying on track
Simulation
Allows the user to observe the cause-and-effect link regarding their behavior
Rehearsal
Allows users to rehearse a behavior
Textbox 2. Dialogue support principles.
Persuasive system category, design principle, and principle description—dialogue support
Praise
Uses images, words, sounds, and so on to praise the user for their behavior
Rewards
Uses web-based rewards, given to the user for performing tasks related to the target behavior
Reminders
Reminds the user of their target behavior
Suggestion
Offers the user suggestions that fit the target behavior
Similarity
Remind users of themselves in some way
Liking
The digital health technology should be visually attractive
Social role
The digital health technology adopts a social role
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 8https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Textbox 3. System credibility principles.
Persuasive system category, design principle, and principle description—system credibility support
Trustworthiness
Apps should appear to be truthful, fair, and unbiased
Expertise
Provide content from sources that are knowledgeable and competent
Surface credibility
Systems should visually appear to be competent and credible
Real-world feel
Systems should highlight the people or organizations that are providing content by providing information about them
Authority
Systems should leverage roles of authority by referring to organizations and people that are seen as authority figures
Third-party endorsements
Systems should provide users with endorsements from third parties that are well known and trusted
Verifiability
Systems should provide ways for users to easily use external sources to verify the accuracy of the content
Textbox 4. Social support principles.
Persuasive system category, design principle, and principle description—social support
Social learning
The digital health technology should target behavior by providing the user with a way to observe other users who are performing the same
target behavior
Social comparison
The digital health technology should motivate the user by allowing them to compare their performance with other users who are performing
the same task
Normative influence
The digital health technology should use normative influence or peer pressure
Social facilitation
The digital health technology should allow users to perceive that other users are using the system to perform the target behavior along with
them
Cooperation
The digital health technology should leverage the users’natural drive to cooperate
Competition
The digital health technology should leverage the users’natural drive to compete with other users
Recognition
The digital health technology should offer users public recognition
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 9https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Table 3. Mobile app screen name with persuasive principles and categories.
Principle 3Principle 2Principle 1 (primary)Screen name
b
PT: tunneling
PTa: tailoring
Add
PT: tunnelingPT: reductionStart
PT: reductionPT: tunnelingBurpee-Squat
DSc: praise
Increase
DS: suggestionPT: rehearsalMountain
PT: personalizationDS: praiseTarget
DS: praiseDS: rewardsTrophy
DS: remindersLate
DS: suggestionCalories
DS: praiseDS: social roleDinner Chat
PT: self-monitoringTracker
SC: authoritySC: trustworthiness
SCd: expertise
About Us
DS: praisePT: simulation
SSe: recognition
Stories
SS: competitionLeaderboard
SC: social facilitationSS: social comparisonSS: social learningJournal
SC: authoritySC: expertiseSC: trustworthinessPartners
SC: surface credibilityAds
SC: expertiseSC: authorityStrategy
SC: authoritySC: expertiseSC: verifiabilityApple
SC: surface credibilitySC: trustworthiness
HIPAAf
SC: real-world feelContact
PT: simulationSC: normative influenceBefore After
SS: social comparisonDS: praiseSS: cooperationYoga
SC: trustworthinessSC: third-party endorsementsSSL
DS: likingDS: similarityAvatar
aPT: primary task support.
bNot available.
cDS: dialogue support.
dSC: system credibility support.
eSS: social support.
fHIPAA: Health Insurance Portability and Accountability Act.
Table 4. Screen category breakdown.
Mobile screens (%)Persuasive technology category
20Primary task support
28Dialogue support
32System credibility support
20Social support
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 10https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Figure 2. Sample mobile health screen developed and accepted during review.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 11https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Figure 5. Big Five continuum.
Measurement Items
New General Self-Efficacy Scale
After completion of the social demographic information, the
participants were asked 8 questions about their self-efficacy
using the New General Self-Efficacy Scale (Multimedia
Appendix 2) by Chen et al [93]. The work by Chen et al [93]
extends the work by Bandura [48,56], which focuses on the
magnitude and strength dimensions of self-efficacy and includes
the generality dimension of self-efficacy. Data on self-efficacy
were collected from participants at baseline and 20 days after
the first survey. The 7-item Likert scale used in this study ranged
from strongly disagree to strongly agree.
Health Consciousness
Participants were then asked to complete 6 questions about their
health consciousness using the Health Consciousness Scale by
Jayanti and Burns [61] (Multimedia Appendix 3), which was
adapted from the original Health Consciousness Scale by Kraft
and Goodell [64]. The development of the health consciousness
scale was facilitated by borrowing items from the literature to
generate items for scales. Multiple items were used to measure
each of the constructs proposed, with purification steps taken
during the development of the scales. The 7-item Likert scale
used in this study ranged from strongly disagree to strongly
agree. Types of health consciousness questions the participants
encountered include “I am interested in information about my
health” and “I read more health-related articles than I did 3 years
ago.”
Health Motivation
Participants were then asked to complete questions about their
HM using the Health Motivation Scale by Jayanti and Burns
[61] (Multimedia Appendix 4). Scale development was
facilitated by borrowing items from the literature, and generating
items for scales was used to develop the HM scale. The scale
development and purification followed well-established
procedures reported in the literature. This section consists of 6
questions using a 7-point Likert scale ranging from strongly
disagree to strongly agree. Participants answered questions
about their HM, such as, “I try to prevent common health
problems before I feel any symptoms” and “I would rather enjoy
life than try to make sure I am not exposing myself to health
risks.”
Personality Traits
Finally, participants were asked to answer personality questions
that generally described them as they were now and not as they
wished to be in the future. The participants completed the
Mini-IPIP Scale by Donnellan et al [82] (Multimedia Appendix
5), which consists of 20 questions focusing on extraversion,
agreeableness, conscientiousness, neuroticism, and intellect.
The stability of the Mini-IPIP Scale was measured at multiple
intervals. The initial study was conducted at intervals of a few
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 12https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
weeks, and the subsequent study was conducted over several
months. The questions were answered using a 7-point Likert
scale ranging from extremely inaccurate to extremely accurate.
Participants rated the accuracy of statements, such as, “Am the
life of the party” and “Am not really interested in others.”
Perceived Persuasiveness
After answering the psychological questions, participants were
asked to complete questions about the perceived persuasiveness
of the individual mHealth screens. The participants completed
the Perceived Persuasiveness Scale by Lehto et al [94]
(Multimedia Appendix 6), which consists of 3 questions using
a 7-point Likert scale ranging from strongly disagree to strongly
agree. During the development of the scale, data examining
perceived persuasiveness were collected from participants at
baseline and 2 and 6 weeks after the intervention. During the
study, participants answered questions, at the screen level, about
the perceived persuasiveness of the mHealth app screens, such
as, “This mobile health screen has an influence on me” and
“This mobile health screen makes me reconsider my overall
health and wellness.”
Results
Exploratory Factor Analysis
EFA was conducted using SPSS to appraise the factor structure
of the survey items. More specifically, principal component
factoring using a Promax rotation was the extraction method
for this analysis [95]. Kaiser normalization (eigenvalue>1) was
used to determine the number of extracted factors. As the
factor-loading cutoff varies in the literature, this research used
a conventional liberal-to-conservative continuum, with all factor
loadings of 0.4 being considered salient for this study, and
cross-loadings >0.2 were considered for elimination [96,97].
The initial iteration of the EFA was conducted on 8 self-efficacy
items, 6 health consciousness items, 6 HM items, 20 Big Five
items, 3 perceived persuasiveness items, 3 intention items, 4
willingness to use items, and 4 marker variable questions (n=62).
A total of 16 items (HM_1, HM_2, Big Five-Conscientiousness
(R) Q8, Big Five-Conscientiousness (R) Q18, all 4 Big Five
Agreeableness items, all 4 Big Five Openness items, and all 4
Big Five Neuroticism items were eliminated owing to
cross-loading issues. In addition, 2 items—Big
Five-Conscientiousness Q3 and Big Five-Conscientiousness
Q13—were eliminated for having correlation coefficients below
the threshold and failing to load properly on other items.
For the final stage, principal component factor analysis of the
remaining 29 items resulted in 6 extracted 6 factors explaining
73.67% of the variance. The factor-loading matrix for the final
solution is presented in Table 5. Hypotheses 4, 5, 7, and 8 were
untestable because of the EFA results. Marker variables were
removed from further statistical analyses after a lack of
correlation was confirmed through EFA analysis.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 13https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Table 5. Final exploratory factor analysis results.
Factor
654321
b
0.736
SEaQ1
0.872SE Q2
0.902SE Q3
0.908SE Q4
0.914SE Q5
0.793SE Q6
0.686SE Q7
0.821SE Q8
0.817
HCcQ1
0.848HC Q2
0.782HC Q3
0.714HC Q4
0.653HC Q5
0.457HC Q6
0.781
HMdQ3
0.847HM Q4
0.878HM Q5
0.728HM Q6
0.973
TF_PPeQ1
0.999TF_PP Q2
0.989TF_PP Q3
0.858
MVf1
0.821MV 2
0.710MV 3
0.830MV 4
0.408
EgQ1
0.624E Q6
0.566E Q11
0.768E Q16
aSE: self-efficacy.
bNot available.
cHC: health consciousness.
dHM: health motivation.
eTF_PP: perceived persuasiveness.
fMV: marker variable.
gE: extraversion.
Statistical Results
Weighted scores were computed for self-efficacy, health
consciousness, HM, extraversion, and perceived persuasiveness,
using the final EFA factor loadings. Table 6 presents the
Cronbach α, mean, SD, and intercorrelation among the variables
included in this study.
Linear regression analysis was performed for weighted variables.
A total of 2 linear regression models were used. Table 7 shows
the regression coefficients. Model 1 included the demographic
control variables of gender, age, and education level as
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 14https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
predictors of perceived persuasiveness. The demographic
variables were dummy coded with “male,” “under 40,and
“less than high school” as the reference category for gender,
age, and education level. The Ftest (ie, ANOVA) for model 1
was significant (F9,6540=191.806; P<.001), with an adjusted R2
of 0.208, indicating that the demographic variables explained
20.8% of the variance in perceived persuasiveness. Gender was
a significant predictor, with women having higher perceived
persuasiveness (B=0.127, SE=0.048; t6540=2.668; P=.008) and
nonbinary individuals having lower perceived persuasiveness
(B=2.856, SE=0.265; t6540=10.767; P<.001) relative to men.
Age was a significant predictor, with individuals aged 40 to 59
age years (B=0.643, SE=0.069; t6540=9.377; P<.001) and
60 years (B=2.116, SE=0.059; t6540=35.752; P<.001) having
lower perceived persuasiveness relative to individuals aged 40
years. Education level was a significant predictor, as individuals
who held associate degrees (B=0.411, SE=0.163; t6540=2.514;
P=.01) and Bachelor’s degrees (B=0.581, SE=0.157;
t6540=3.696; P<.001) tended to have lower perceived
persuasiveness than individuals who had not completed high
school.
In model 2, the theorized effects were added as predictors. The
Ftest for model 2 was significant (F13,6536=341.035; P<.001),
with an adjusted R2of 0.403, indicating that the demographic
variables self-efficacy, health consciousness, HM, and
extraversion together explained 40.3% of the variance in
perceived persuasiveness. Table 7 presents the regression
coefficients for model 2. The nonbinary category of sex
remained a significant predictor; however, the female sex
category was no longer significant in model 2 (B=0.002,
SE=0.042; t6536=0.048; P=.96). Both age categories were
significant predictors in model 2. The associate and Bachelor’s
categories of education level remained significant predictors in
model 2, and the categories of some college (B=0.462,
SE=0.137; t6536=3.378; P<.001) and graduate degree
(B=0.555, SE=0.139; t6536=3.985; P<.001) became significant
in model 2. Self-efficacy was a significant positive predictor
(B=0.263, SE=0.026; t6536=10.174; P<.001), indicating that
individuals with higher self-efficacy tended to have higher
perceived persuasiveness. Health consciousness was a significant
positive predictor (B=0.883, SE=0.022; t6536=40.000; P<.001),
indicating that individuals with higher health consciousness
tended to have higher perceived persuasiveness. HM was a
significant positive predictor (B=0.200, SE=0.017; t6536=11.597;
P<.001), indicating that individuals with higher HM tended to
have higher perceived persuasiveness. Extraversion was a
significant positive predictor (B=0.150, SE=0.026; t6536=5.884;
P<.001), indicating that individuals with higher extraversion
tended to have higher perceived persuasiveness. The results of
significant hypothesis testing are summarized in Table 8.
Table 6. Correlation matrix for weighted variablesa.
654321Cronbach αMean (SD)Variable
b
.833.9394.574 (0.852)Self-efficacy
.724
.239c
.8583.455 (0.994)Health consciousness
.811
0.132c
.067c
.8623.071 (1.205)Health motivation
.605
0.069c
.142c
.263c
.6992.110 (0.816)Extraversion
.987
.159c
.081c
.529c
.283c
.9773.822 (2.047)Perceived persuasiveness
.807
.363c
0.030d
.303c
.391c
.153c
.8403.089 (1.135)Marker variable
aValues on the diagonal are the square roots of the average variance extracted.
bNot available.
cP<.01.
dP<.05.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 15https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Table 7. Results for multiple linear regression modelsa (N=6550).
Model 2b
Model 1a
Variable
Significance
(Pvalue)
ttest (df)B (SE)Significance
(Pvalue)
ttest (df)B (SE)
.980.023 (6537)0.005 (0.202)<.00133.531 (6540)5.406 (0.161)Constant
Control variables
.960.048 (6537)0.002 (0.042).0082.668 (6540)0.127 (0.048)Gender (female)
<.0019.412 (6537)2.239 (0.238)<.00110.767 (6540)2.856 (0.265)Gender (nonbinary)
<.0017.869 (6537)0.477 (0.061)<.0019.377 (6540)0.643 (0.069)Age (40-59 years)
<.00125.816 (6537)1.388 (0.054)<.00135.752 (6540)2.116 (0.059)Age (60 years)
.1201.555 (6537)0.218 (0.140).061.880 (6540)0.302 (0.161)Education (high school
graduate)
<.0013.378 (6537)0.462 (0.137).081.782 (6540)0.279 (0.156)Education (some college, no
degree)
.0062.731 (6537)0.389 (0.142).012.514 (6540)0.411 (0.163)Education (associate degree)
<.0014.542 (6537)0.624 (0.137)<.0013.696 (6540)0.581 (0.157)Education (Bachelor’s de-
gree)
<.0013.985 (6537)0.555 (0.139).710.370 (6540)0.059 (0.159)Education (graduate degree)
Theorized effects
<.00110.174 (6537)0.263 (0.026)
c
Self-efficacy
<.00140.000 (6537)0.883 (0.022)Health consciousness
<.00111.597 (6537)0.200 (0.017)Health motivation
<.0015.884 (6537)0.150 (0.026)Extraversion
aModel 1: R2=0.208
bModel 2: R2=0.403.
c—: indicates that the theorized effects weren't added until model 2.
Table 8. Results of tested hypotheses.
ResultHypothesis
SupportedHypothesis 1: self-efficacy will positively influence interpreted mHealth screen perceived persuasiveness.
SupportedHypothesis 2: health consciousness will positively influence interpreted mHealth screen perceived persuasiveness.
SupportedHypothesis 3: health motivation will positively influence interpreted mHealth screen perceived persuasiveness.
SupportedHypothesis 6: extraversion will positively influence interpreted mHealth screen perceived persuasiveness.
Discussion
Principal Findings
To the best of our knowledge, this study is the first to use a
combination of self-efficacy, health consciousness, HM,
extraversion, gender, age, and education to examine their impact
on the effective engagement of users of digital health
technologies. By integrating psychological characteristics, this
study advances the current understanding of how psychological
characteristics affect the perceived persuasiveness of persuasive
technology. To evaluate this, the researchers examined the
impact of psychological characteristics (self-efficacy, health
consciousness, HM, and Big Five personality traits) on the
perceived persuasiveness of digital health technologies. Using
the PSD framework, this study was designed to evaluate how
these psychological characteristics affect the perceived
persuasiveness of digital health technologies. In addition, the
dynamic intertwining of psychological characteristics that drives
the perceived persuasiveness of the primary PSD technique
categories was illuminated through multiple linear regression
analysis.
Furthermore, this study opens a pathway for designers of digital
health technologies to gain further knowledge on why individual
characteristics must be considered during the design process.
Keizer et al [42] suggested that misalignment between end users
and digital technologies is often a result of developers failing
to consider the end user during the development process.
Although the benefits of personalizing persuasive systems have
been acknowledged, the field is still in its infancy, and there is
very little knowledge on the best way to tailor these technologies
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 16https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
[98,99]. The findings from this study suggest that using a
dynamic, data-centered approach that considers that the end
users’ self-efficacy, health consciousness, HM, extraversion,
age, gender, and education could be a way to increase the
perceived persuasiveness of digital health technologies.
In addition, this research offers developers vital information
pertaining to user-centric development of persuasive digital
health technologies. The information gained can be used by
designers to increase the perceived persuasiveness of digital
health technologies by providing guidance on how to
dynamically use PSD principles based on an individual’s
psychological characteristics and demographic makeup. These
PSD principles can be delivered in various components such as
virtual reality or health care gaming approaches, which can
further establish a stronger connection to an individual’s
psychological characteristics [100].
On the basis of these major findings, the role of self-efficacy
should be considered by persuasive technology designers.
Statistical analysis found self-efficacy to be a significant positive
predictor of perceived persuasiveness. Multiple linear regression
analyses found that health consciousness was a significant
positive predictor of perceived persuasiveness. In addition, the
model found HM to be a significant positive predictor of
perceived persuasiveness. Multiple linear regression analyses
also found extraversion to be a significantly positive predictor
of perceived persuasiveness. These findings are important, as
they shed additional light on which psychological characteristics
influence a user’s perceived persuasiveness. In addition, it helps
validate why one-size-fits-all approaches do not necessarily
work. The findings suggest that individuals with low
self-efficacy and low health consciousness will not necessarily
be influenced (perceived persuasiveness) by the same mHealth
app design as those with higher self-efficacy and health
consciousness levels.
Demographic data, such as age and gender, should also be
considered by developers of digital health technologies. The
findings strongly suggest that the distribution of perceived
persuasiveness shifts from negatively skewed to positively
skewed as an individual ages. In addition, this shift occurs earlier
in women (ie, aged 40-59 years) than in men who do not shift
until the oldest age group (ie, aged 60 years). The perceived
persuasiveness by age group and gender is available in
Multimedia Appendix 7. This was an interesting and unexpected
finding, and additional research is required. Potentially, these
findings can represent the aging process for which health
consciousness, for example, has increased owing to typical
chronic diseases that manifest as individuals age.
Future Research and Limitations
Despite the theoretical and practical contributions of this study,
there are limitations to the generalizability of the findings.
Further examination of the demographic data showed that only
7.3% (19/262) of participants were between the ages of 18 and
29 years. Additional research should be conducted that focuses
on the younger population, aged 18 to 29 years.
The research only examined extraversion due to multicollinearity
issues with other items from the Big Five personality traits.
Sleep et al [101] found that longer measures contain
considerably more variance than shorter, more condensed
measures. Further studies should use a more extensive Big Five
personality test such as the Neo Personality Inventory [102]
rather than the Mini-IPIP Scale [82].
The Adult Hope Scale by Snyder et al [103] was also dropped
from the study owing to multicollinearity issues with the New
General Self-Efficacy Scale by Chen et al [93]. It was observed
that all the self-efficacy constructs and adult hope constructs
were cross-loading; therefore, adult hope was eliminated because
self-efficacy is regarded as a core premise of human
performance across multiple domains, and adult hope
measurements conceptually and operationally function
synonymously as self-efficacy [104].
Multicollinearity issues were also identified among perceived
persuasiveness, intention, and willingness to use; therefore, the
intention and willingness to use constructs were eliminated from
the model because perceived persuasiveness was studied across
multiple domains, and perceived persuasiveness was more
pursuant to this study.
A key limitation of this study is the use of static screens. A fully
developed app will allow researchers to evaluate the engagement
of digital health tools. Running these studies in tandem will
allow researchers to evaluate engagement on both sides to see
if higher perceived persuasiveness leads to higher engagement.
Conclusions
This study aimed to examine how users’ psychological
characteristics influence the perceived persuasiveness of digital
health technologies. This research contributes to advancing the
field of data-driven, user-centric development of persuasive
technologies by investigating the intertwining of users’
psychological characteristics and the perceived persuasiveness
of digital health technologies. This work opens a new research
avenue by examining the role of psychological characteristics
in interpreting the perceived persuasiveness of mHealth screens.
The use of dynamic data-driven capabilities is important for
advancing perceived persuasiveness, which has the potential to
engage users of digital health technologies successfully.
This work also describes the roles that psychological
characteristics play in interpreting mHealth screen perceived
persuasiveness. Evidence has shown that self-efficacy, health
consciousness, HM, extraversion, gender, age, and education
significantly influence the perceived persuasiveness of digital
health technologies. Moreover, this study showed that varying
combinations of psychological characteristics and demographic
variables affected the perceived persuasiveness of the primary
persuasive technology category. Incorporating these
psychological characteristics and demographic variables should
allow digital health technology developers to overcome the gap
stemming from one-size-fits-all approaches.
On the basis of the findings of this research, mHealth app
researchers and developers should design apps that dynamically
interact with users using psychological characteristics and
demographics to drive the persuasive techniques presented to
the user. This process should include a pre-enrollment
assessment, for which the user’s psychological characteristics
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 17https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
are evaluated before deployment of the mHealth app. This would
allow for the right persuasive techniques to be deployed in an
attempt to better engage the user, which can potentially lead to
more favorable behavior. Moving from a “one-size-fits-all” to
a personalized persuasive approach has the potential to create
long-term engagement, which has plagued mHealth researchers
and developers.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Developed mobile health screens.
[PDF File (Adobe PDF File), 1205 KB-Multimedia Appendix 1]
Multimedia Appendix 2
New General Self-Efficacy Scale.
[DOCX File , 16 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Health Consciousness Scale.
[DOCX File , 16 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Health Motivation Scale.
[DOCX File , 16 KB-Multimedia Appendix 4]
Multimedia Appendix 5
Big Five Mini-International Personality Item Pool Scale.
[DOCX File , 17 KB-Multimedia Appendix 5]
Multimedia Appendix 6
Perceived Persuasiveness Scale.
[DOCX File , 15 KB-Multimedia Appendix 6]
Multimedia Appendix 7
Perceived persuasiveness by age and gender.
[PDF File (Adobe PDF File), 128 KB-Multimedia Appendix 7]
References
1. Matthews J, Win KT, Oinas-Kukkonen H, Freeman M. Persuasive technology in mobile applications promoting physical
activity: a systematic review. J Med Syst 2016 Mar;40(3):72. [doi: 10.1007/s10916-015-0425-x] [Medline: 26748792]
2. Birnbaum F, Lewis D, Rosen RK, Ranney ML. Patient engagement and the design of digital health. Acad Emerg Med 2015
Jun;22(6):754-756 [FREE Full text] [doi: 10.1111/acem.12692] [Medline: 25997375]
3. O'Brien H. A holistic approach to measuring user engagement. In: Filimowicz M, Tzankova V, editors. New Directions in
Third Wave Human-Computer Interaction: Volume 2 - Methodologies. Cham, Switzerland: Springer; 2018:81-102.
4. Taki S, Lymer S, Russell CG, Campbell K, Laws R, Ong KL, et al. Assessing user engagement of an mHealth intervention:
development and implementation of the growing healthy app engagement index. JMIR Mhealth Uhealth 2017 Jun 29;5(6):e89
[FREE Full text] [doi: 10.2196/mhealth.7236] [Medline: 28663164]
5. O'Brien HL, Toms EG. What is user engagement? A conceptual framework for defining user engagement with technology.
J Am Soc Inf Sci 2008 Apr;59(6):938-955. [doi: 10.1002/asi.20801]
6. Vandelanotte C, Müller AM, Short CE, Hingle M, Nathan N, Williams SL, et al. Past, present, and future of eHealth and
mHealth research to improve physical activity and dietary behaviors. J Nutr Educ Behav 2016 Mar;48(3):219-28.e1. [doi:
10.1016/j.jneb.2015.12.006] [Medline: 26965100]
7. Orji R, Moffatt K. Persuasive technology for health and wellness: state-of-the-art and emerging trends. Health Informatics
J 2018 Mar;24(1):66-91 [FREE Full text] [doi: 10.1177/1460458216650979] [Medline: 27245673]
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 18https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
8. Silva M, Graham F, Levack W, Hay-Smith J. Persuasive technology and behaviour change in parent-focused eHealth
interventions supporting child health: a scoping review protocol. N Z J Physiother 2019 Mar 31;47(1):36-48 [FREE Full
text] [doi: 10.15619/NZJP/47.1.05]
9. Wall HJ, Campbell CC, Kaye LK, Levy A, Bhullar N. Personality profiles and persuasion: an exploratory study investigating
the role of the Big-5, type D personality and the Dark Triad on susceptibility to persuasion. Pers Individ Dif 2019
Mar;139:69-76. [doi: 10.1016/j.paid.2018.11.003]
10. Kaptein M, Markopoulos P, de Ruyter B, Aarts E. Personalizing persuasive technologies: explicit and implicit personalization
using persuasion profiles. Int J Human Comput Stud 2015 May;77:38-51. [doi: 10.1016/j.ijhcs.2015.01.004]
11. Engl E, Smittenaar P, Sgaier SK. Identifying population segments for effective intervention design and targeting using
unsupervised machine learning: an end-to-end guide. Gates Open Res 2019 Oct 21;3:1503 [FREE Full text] [doi:
10.12688/gatesopenres.13029.2] [Medline: 31701090]
12. Abdullahi AM, Oyibo K, Orji R, Kawu AA. The influence of age, gender, and cognitive ability on the susceptibility to
persuasive strategies. Information 2019 Nov 15;10(11):352. [doi: 10.3390/info10110352]
13. O'Brien HL, Arguello J, Capra R. An empirical study of interest, task complexity, and search behaviour on user engagement.
Inf Process Manag 2020 May;57(3):102226. [doi: 10.1016/j.ipm.2020.102226]
14. Goldkuhl G. From ensemble view to ensemble artefact – an inquiry on conceptualisations of the IT artefact. Syst Sign
Action 2013;7(1):49-72.
15. van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework
to improve the uptake and impact of eHealth technologies. J Med Internet Res 2011 Dec 05;13(4):e111 [FREE Full text]
[doi: 10.2196/jmir.1672] [Medline: 22155738]
16. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and evaluating digital interventions to promote behavior
change in health and health care: recommendations resulting from an international workshop. J Med Internet Res 2017 Jun
29;19(6):e232 [FREE Full text] [doi: 10.2196/jmir.7126] [Medline: 28663162]
17. Karekla M, Kasinopoulos O, Neto DD, Ebert DD, Van Daele T, Nordgreen T, et al. Best practices and recommendations
for digital interventions to improve engagement and adherence in chronic illness sufferers. Eur Psychol 2019 Jan;24(1):49-67.
[doi: 10.1027/1016-9040/a000349]
18. Orji R, Oyibo K, Lomotey RK, Orji FA. Socially-driven persuasive health intervention design: competition, social comparison,
and cooperation. Health Informatics J 2019 Dec;25(4):1451-1484 [FREE Full text] [doi: 10.1177/1460458218766570]
[Medline: 29801426]
19. Iyengar MS, Florez-Arango JF, Garcia CA. GuideView: a system for developing structured, multimodal, multi-platform
persuasive applications. In: Proceedings of the 4th International Conference on Persuasive Technology. 2009 Presented at:
Persuasive '09; April 26-29, 2009; Claremont, CA, USA p. 1-5. [doi: 10.1145/1541948.1541990]
20. Thomson C, Nash J, Maeder A. Persuasive design for behaviour change apps: issues for designers. In: Proceedings of the
Annual Conference of the South African Institute of Computer Scientists and Information Technologists. 2016 Presented
at: SAICSIT '16; September 26-28, 2016; Johannesburg, South Africa p. 1-10. [doi: 10.1145/2987491.2987535]
21. Tuman M, Moyer A. Health intentions and behaviors of health app owners: a cross-sectional study. Psychol Health Med
2019 Aug;24(7):819-826. [doi: 10.1080/13548506.2019.1576911] [Medline: 30729803]
22. Al-Ramahi M, El-Gayar O, Liu J. Discovering design principles for persuasive systems: a grounded theory and text mining
approach. In: Proceedings of the 49th Hawaii International Conference on System Sciences. 2016 Presented at: HICSS '16;
January 5-8, 2016; Koloa, HI, USA p. A-83. [doi: 10.1109/hicss.2016.387]
23. Alkhaldi G, Hamilton FL, Lau R, Webster R, Michie S, Murray E. The effectiveness of prompts to promote engagement
with digital interventions: a systematic review. J Med Internet Res 2016 Jan 08;18(1):e6 [FREE Full text] [doi:
10.2196/jmir.4790] [Medline: 26747176]
24. Holdener M, Gut A, Angerer A. Applicability of the user engagement scale to mobile health: a survey-based quantitative
study. JMIR Mhealth Uhealth 2020 Jan 03;8(1):e13244 [FREE Full text] [doi: 10.2196/13244] [Medline: 31899454]
25. Zagalo N. From experience to engagement. In: Zagalo N, editor. Engagement Design: Designing for Interaction Motivations.
Cham, Switzerland: Springer; 2020:11-30.
26. Sahin C. Rules of engagement in mobile health: what does mobile health bring to research and theory? Contemp Nurse
2018;54(4-5):374-387. [doi: 10.1080/10376178.2018.1448290] [Medline: 29502472]
27. Helsper EJ, Eynon R. Distinct skill pathways to digital engagement. Eur J Commun 2013 Sep 16;28(6):696-713. [doi:
10.1177/0267323113499113]
28. Salehzadeh Niksirat K, Sarcar S, Sun H, Law EL, Clemmensen T, Bardzell J, et al. Approaching engagement towards
human-engaged computing. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems.
2018 Presented at: CHI EA '18; April 21-26, 2018; Montreal, Canada p. 1-4. [doi: 10.1145/3170427.3185364]
29. Chapman P, Selvarajah S, Webster J. Engagement in multimedia training systems. In: Proceedings of the 32nd Annual
Hawaii International Conference on Systems Sciences. 1999 Presented at: HICSS '99; January 5-8, 1999; Maui, HI, USA.
[doi: 10.1109/hicss.1999.772808]
30. Tarute A, Nikou S, Gatautis R. Mobile application driven consumer engagement. Telemat Inform 2017 Jul;34(4):145-156.
[doi: 10.1016/j.tele.2017.01.006]
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 19https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
31. Wiafe I. The role of U-FADE in selecting persuasive system features. In: Khosrow-Pour M, editor. Encyclopedia of
Information Science and Technology. 4th edition. Hershey, PA, USA: IGI Global; 2018:7785-7795.
32. Gena C, Grillo P, Lieto A, Mattutino C, Vernero F. When personalization is not an option: an in-the-wild study on persuasive
news recommendation. Information 2019 Sep 26;10(10):300. [doi: 10.3390/info10100300]
33. Orji R, Mandryk RL, Vassileva J. Gender, age, and responsiveness to Cialdini’s persuasion strategies. In: Proceedings of
the 10th International Conference on Persuasive Technology. 2015 Presented at: PERSUASIVE '15; June 3-5, 2015;
Chicago, IL, USA p. 147-159. [doi: 10.1007/978-3-319-20306-5_14]
34. Orji R, Vassileva J, Mandryk RL. Modeling the efficacy of persuasive strategies for different gamer types in serious games
for health. User Model User Adap Interact 2014 Jul 14;24(5):453-498. [doi: 10.1007/s11257-014-9149-8]
35. Berkovsky S, Freyne J, Oinas-Kukkonen H. Influencing individually: fusing personalization and persuasion. ACM Trans
Interact Intell Syst 2012 Jun 01;2(2):1-8. [doi: 10.1145/2209310.2209312]
36. Orji FA, Oyibo K, Orji R, Greer J, Vassileva J. Personalization of persuasive technology in higher education. In: Proceedings
of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 2019 Presented at: UMAP '19; June 9-12,
2019; Larnaca, Cyprus p. 336-340. [doi: 10.1145/3320435.3320478]
37. Ruijten PA. The similarity-attraction paradigm in persuasive technology: effects of system and user personality on evaluations
and persuasiveness of an interactive system. Behav Inf Technol 2021;40(8):734-746. [doi: 10.1080/0144929x.2020.1723701]
38. Orji RO, Vassileva J, Mandryk RL. Modeling gender differences in healthy eating determinants for persuasive intervention
design. In: Proceedings of the 8th International Conference on Persuasive Technology. 2013 Presented at: PERSUASIVE
'13; April 3-5, 2013; Sydney, Australia p. 161-173. [doi: 10.1007/978-3-642-37157-8_20]
39. Taype GE, Calani MC. Extending persuasive system design frameworks: an exploratory study. In: Proceedings of Information
Technology and Systems. 2020 Presented at: ICITS '20; February 5-7, 2020; Bogota, Colombia p. 35-45. [doi:
10.1007/978-3-030-40690-5_4]
40. Almunawar MN, Anshari M, Younis MZ. Incorporating customer empowerment in mobile health. Health Policy Technol
2015 Dec;4(4):312-319. [doi: 10.1016/j.hlpt.2015.08.008]
41. Yardley L, Choudhury T, Patrick K, Michie S. Current issues and future directions for research into digital behavior change
interventions. Am J Prev Med 2016 Nov;51(5):814-815. [doi: 10.1016/j.amepre.2016.07.019] [Medline: 27745680]
42. Keizer J, Beerlage-de Jong N, al Naiemi N, van Gemert-Pijnen LJ. Persuading from the start: participatory development
of sustainable persuasive data-driven technologies in healthcare. In: Proceedings of the 15th International Conference on
Persuasive Technology. 2020 Presented at: PERSUASIVE '20; April 20–23, 2020; Aalborg, Denmark p. 113-125. [doi:
10.1007/978-3-030-45712-9_9]
43. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile
interventions: are our theories up to the task? Transl Behav Med 2011 Mar;1(1):53-71 [FREE Full text] [doi:
10.1007/s13142-011-0021-7] [Medline: 21796270]
44. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time Adaptive Interventions
(JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med
2018 May 18;52(6):446-462 [FREE Full text] [doi: 10.1007/s12160-016-9830-8] [Medline: 27663578]
45. Shin Y, Kim J. Data-centered persuasion: nudging user's prosocial behavior and designing social innovation. Comput
Human Behav 2018 Mar;80:168-178. [doi: 10.1016/j.chb.2017.11.009]
46. Dalecke S, Karlsen R. Designing dynamic and personalized nudges. In: Proceedings of the 10th International Conference
on Web Intelligence, Mining and Semantics. 2020 Presented at: WIMS '20; June 30-July 3, 2020; Biarritz, France p. 139-148.
[doi: 10.1145/3405962.3405975]
47. Abdullahi AM, Orji R, Kawu AA. Gender, age and subjective well-being: towards personalized persuasive health
interventions. Information 2019 Sep 27;10(10):301. [doi: 10.3390/info10100301]
48. Bandura A. Self-Efficacy: The Exercise of Control. New York, NY, USA: W. H. Freeman; 1997.
49. Bong M. Generality of academic self-efficacy judgments: evidence of hierarchical relations. J Educ Psychol 1997
Dec;89(4):696-709. [doi: 10.1037/0022-0663.89.4.696]
50. van Dinther M, Dochy F, Segers M, Braeken J. The construct validity and predictive validity of a self-efficacy measure for
student teachers in competence-based education. Stud Educ Eval 2013 Sep;39(3):169-179. [doi:
10.1016/j.stueduc.2013.05.001]
51. Bulfone G, Badolamenti S, Biagioli V, Maurici M, Macale L, Sili A, et al. Nursing students' academic self-efficacy: a
longitudinal analysis of academic self-efficacy changes and predictive variables over time. J Adv Nurs 2021
May;77(5):2353-2362. [doi: 10.1111/jan.14771] [Medline: 33559919]
52. Simonavice EM, Wiggins MS. Exercise barriers, self-efficacy, and stages of change. Percept Mot Skills 2008
Dec;107(3):946-950. [doi: 10.2466/pms.107.3.946-950] [Medline: 19235423]
53. Koring M, Richert J, Lippke S, Parschau L, Reuter T, Schwarzer R. Synergistic effects of planning and self-efficacy on
physical activity. Health Educ Behav 2012 Apr;39(2):152-158. [doi: 10.1177/1090198111417621] [Medline: 22167316]
54. Falco LD, Summers JJ. Improving career decision self-efficacy and STEM self-efficacy in high school girls: evaluation of
an intervention. J Career Dev 2017 Jul 24;46(1):62-76. [doi: 10.1177/0894845317721651]
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 20https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
55. Messina R, Rucci P, Sturt J, Mancini T, Fantini MP. Assessing self-efficacy in type 2 diabetes management: validation of
the Italian version of the Diabetes Management Self-Efficacy Scale (IT-DMSES). Health Qual Life Outcomes 2018 Apr
23;16(1):71 [FREE Full text] [doi: 10.1186/s12955-018-0901-3] [Medline: 29685153]
56. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977 Mar;84(2):191-215. [doi:
10.1037//0033-295x.84.2.191] [Medline: 847061]
57. Medrano LA, Flores-Kanter E, Moretti L, Pereno GL. Effects of induction of positive and negative emotional states on
academic self-efficacy beliefs in college students. Psicología Educativa 2016 Dec;22(2):135-141. [doi:
10.1016/j.pse.2015.03.003]
58. Buckworth J. Promoting self-efficacy for healthy behaviors. ACSM'S Health Fitness J 2017;21(5):40-42. [doi:
10.1249/fit.0000000000000318]
59. Lindenmeier J. Promoting volunteerism: effects of self-efficacy, advertisement-induced emotional arousal, perceived costs
of volunteering, and message framing. Voluntas 2008 Mar 15;19(1):43-65. [doi: 10.1007/s11266-008-9054-z]
60. Wigal JK, Creer TL, Kotses H. The COPD self-efficacy scale. Chest 1991 May;99(5):1193-1196. [doi:
10.1378/chest.99.5.1193] [Medline: 2019177]
61. Jayanti RK, Burns AC. The antecedents of preventive health care behavior: an empirical study. J Acad Market Sci 1998
Jan 01;26(1):6. [doi: 10.1177/0092070398261002]
62. Yan M, Filieri R, Raguseo E, Gorton M. Mobile apps for healthy living: factors influencing continuance intention for health
apps. Technol Forecast Soc Change 2021 May;166(C):120644. [doi: 10.1016/j.techfore.2021.120644]
63. Chen MF, Lin NP. Incorporation of health consciousness into the technology readiness and acceptance model to predict
app download and usage intentions. Internet Res 2018 Apr 04;28(2):351-373. [doi: 10.1108/intr-03-2017-0099]
64. Kraft FB, Goodell PW. Identifying the health conscious consumer. J Health Care Mark 1993;13(3):18-25. [Medline:
10129812]
65. Barauskaite D, Gineikiene J, Fennis BM, Auruskeviciene V, Yamaguchi M, Kondo N. Eating healthy to impress: how
conspicuous consumption, perceived self-control motivation, and descriptive normative influence determine functional
food choices. Appetite 2018 Dec 01;131:59-67. [doi: 10.1016/j.appet.2018.08.015] [Medline: 30114492]
66. Parashar S, Mungra Y, Sood G. Health consciousness as an enabler for exploratory buying behavior among consumers.
SCMS J Indian Manag 2019;16(2):87-102.
67. Ahadzadeh AS, Pahlevan Sharif S, Sim Ong F. Online health information seeking among women: the moderating role of
health consciousness. Online Inf Rev 2018 Feb 12;42(1):58-72. [doi: 10.1108/oir-02-2016-0066]
68. Donalds C, Osei-Bryson KM. Cybersecurity compliance behavior: exploring the influences of individual decision style
and other antecedents. Int J Inf Manag 2020 Apr;51(C):102056. [doi: 10.1016/j.ijinfomgt.2019.102056]
69. Park J, Ahn J, Yoo WS. The effects of price and health consciousness and satisfaction on the medical tourism experience.
J Healthc Manag 2017;62(6):405-417. [doi: 10.1097/JHM-D-16-00016] [Medline: 29135765]
70. Tanner EC, Vann RJ, Kizilova E. Consumer-level perceived access to health services and its effects on vulnerability and
health outcomes. J Public Policy Market 2020 Feb 27;39(2):240-255. [doi: 10.1177/0743915620903299]
71. Toste JR, Didion L, Peng P, Filderman MJ, McClelland AM. A meta-analytic review of the relations between motivation
and reading achievement for K–12 students. Rev Educ Res 2020 May 13;90(3):420-456. [doi: 10.3102/0034654320919352]
72. Dehghani M, Kim KJ, Dangelico RM. Will smartwatches last? Factors contributing to intention to keep using smart wearable
technology. Telemat Inform 2018 May;35(2):480-490 [FREE Full text] [doi: 10.1016/j.tele.2018.01.007]
73. Ferron JC, Elbogen EB, Swanson JW, Swartz MS, McHugo GJ. A conceptually based scale to measure consumers’treatment
motivation. Res Soc Work Pract 2010 Jan 05;21(1):98-105. [doi: 10.1177/1049731509357629]
74. Ryan RM, Deci EL. Intrinsic and extrinsic motivation from a self-determination theory perspective: definitions, theory,
practices, and future directions. Contemp Educ Psychol 2020 Apr;61:101860. [doi: 10.1016/j.cedpsych.2020.101860]
75. Muenks K, Wigfield A, Eccles JS. I can do this! The development and calibration of children’s expectations for success
and competence beliefs. Dev Rev 2018 Jun;48:24-39. [doi: 10.1016/j.dr.2018.04.001]
76. Wagner 3rd B, Liu E, Shaw SD, Iakovlev G, Zhou L, Harrington C, et al. e wrapper: operationalizing engagement strategies
in mHealth. Proc ACM Int Conf Ubiquitous Comput 2017 Sep;2017:790-798 [FREE Full text] [doi:
10.1145/3123024.3125612] [Medline: 29362728]
77. Roccas S, Sagiv L, Schwartz SH, Knafo A. The Big Five personality factors and personal values. Pers Soc Psychol Bull
2002 Jun 1;28(6):789-801. [doi: 10.1177/0146167202289008]
78. Goldberg LR. An alternative "description of personality": the big-five factor structure. J Pers Soc Psychol 1990
Dec;59(6):1216-1229. [doi: 10.1037//0022-3514.59.6.1216] [Medline: 2283588]
79. Costa Jr PT, McCrae RR. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI)
Professional Manual. Odessa, FL, USA: Psychological Assessment Resources; 1992.
80. Borghans L, Duckworth AL, Heckman JJ, ter Weel B. The economics and psychology of personality traits. J Human
Resources 2008;43(4):972-1059. [doi: 10.1353/jhr.2008.0017]
81. Gosling SD, Rentfrow PJ, Swann Jr WB. A very brief measure of the Big-Five personality domains. J Res Pers 2003
Dec;37(6):504-528. [doi: 10.1016/S0092-6566(03)00046-1]
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 21https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
82. Donnellan MB, Oswald FL, Baird BM, Lucas RE. The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors
of personality. Psychol Assess 2006 Jun;18(2):192-203. [doi: 10.1037/1040-3590.18.2.192] [Medline: 16768595]
83. Wilmot MP, Wanberg CR, Kammeyer-Mueller JD, Ones DS. Extraversion advantages at work: a quantitative review and
synthesis of the meta-analytic evidence. J Appl Psychol 2019 Dec;104(12):1447-1470. [doi: 10.1037/apl0000415] [Medline:
31120263]
84. White JK, Hendrick SS, Hendrick C. Big five personality variables and relationship constructs. Pers Individ Dif 2004
Nov;37(7):1519-1530. [doi: 10.1016/j.paid.2004.02.019]
85. Stajkovic AD, Bandura A, Locke EA, Lee D, Sergent K. Test of three conceptual models of influence of the big five
personality traits and self-efficacy on academic performance: a meta-analytic path-analysis. Pers Individ Dif 2018
Jan;120:238-245. [doi: 10.1016/j.paid.2017.08.014]
86. Nolan A, McCrory C, Moore P. Personality and preventive healthcare utilisation: evidence from the Irish Longitudinal
Study on Ageing. Prev Med 2019 Mar;120:107-112. [doi: 10.1016/j.ypmed.2018.12.029] [Medline: 30660708]
87. Qualtrics. Provo, UT, USA: Qualtrics; 2019. URL: https://www.qualtrics.com/au/ [accessed 2019-07-18]
88. Pangbourne K, Bennett S, Baker A. Persuasion profiles to promote pedestrianism: effective targeting of active travel
messages. Travel Behav Soc 2020 Jul;20:300-312. [doi: 10.1016/j.tbs.2020.04.004]
89. Kniffin KM, Bogan VL, Just DR. "Big men" in the office: the gender-specific influence of weight upon persuasiveness.
PLoS One 2019 Nov 11;14(11):e0222761 [FREE Full text] [doi: 10.1371/journal.pone.0222761] [Medline: 31710625]
90. Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features. Commun
Assoc Inf Syst 2009 May 1;24:28. [doi: 10.17705/1CAIS.02428]
91. Rosenzweig E. Successful User Experience: Strategies and Roadmaps. Amsterdam, The Netherlands: Elsevier; 2015.
92. Buildfire Corporation. 2019. URL: https://buildfire.com/ [accessed 2018-10-23]
93. Chen G, Gully SM, Eden D. Validation of a new general self-efficacy scale. Organ Res Method 2001 Jan 1;4(1):62-83.
[doi: 10.1177/109442810141004]
94. Lehto T, Oinas-Kukkonen H, Drozd F. Factors affecting perceived persuasiveness of a behavior change support system.
In: Proceedings of the 33rd International Conference on Information Systems. 2012 Presented at: ICIS '12; December 16-19,
2012; Orlando, FL, USA p. 1-15 URL: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.959.
1225&rep=rep1&type=pdf
95. Williams B, Onsman A, Brown T. Exploratory factor analysis: a five-step guide for novices. Australas J Paramed 2010
Aug 02;8(3):990399. [doi: 10.33151/ajp.8.3.93]
96. Knekta E, Runyon C, Eddy S. One size doesn't fit all: using factor analysis to gather validity evidence when using surveys
in your research. CBE Life Sci Educ 2019 Mar;18(1):rm1 [FREE Full text] [doi: 10.1187/cbe.18-04-0064] [Medline:
30821600]
97. Matsunaga M. How to factor-analyze your data right: do’s, don’ts, and how-to’s. Int J Psychol Res 2010 Jun 30;3(1):97-110.
[doi: 10.21500/20112084.854]
98. Orji R, Kaptein M, Ham J, Oyibo K, Nwokeji J. Personalizing persuasive technologies: a road map to the future. In:
Proceedings of the 13th International Conference on Persuasive Technology. 2018 Presented at: PERSUASIVE '18; April
18-19, 2018; Waterloo, Canada p. 1-4. [doi: 10.1145/3213586.3225246]
99. Oyibo K, Orji R, Ham J, Nwokeji J, Ciocarlan A. Personalizing persuasive technologies: personalization for wellbeing. In:
Personalizing Persuasive Technology Workshop. 2019 Presented at: PPT '19; April 9-11, 2019; Limassol, Cyprus. [doi:
10.13140/RG.2.2.26605.00485]
100. Horsham C, Dutton-Regester K, Antrobus J, Goldston A, Price H, Ford H, et al. A virtual reality game to change sun
protection behavior and prevent cancer: user-centered design approach. JMIR Serious Games 2021 Mar 25;9(1):e24652
[FREE Full text] [doi: 10.2196/24652] [Medline: 33764308]
101. Sleep CE, Lynam DR, Miller JD. A comparison of the validity of very brief measures of the Big Five/Five-Factor Model
of personality. Assessment 2021 Apr;28(3):739-758. [doi: 10.1177/1073191120939160] [Medline: 32762351]
102. McCrae RR, Costa Jr PT, Martin TA. The NEO-PI-3: a more readable revised NEO Personality Inventory. J Pers Assess
2005 Jun;84(3):261-270. [doi: 10.1207/s15327752jpa8403_05] [Medline: 15907162]
103. Snyder CR, Harris C, Anderson JR, Holleran SA, Irving LM, Sigmon ST. Adult Hope Scale (AHS). J Pers Soc Psychol
1991;60:570-585. [doi: 10.1037/t00088-000]
104. Zhou M, Kam CC. Hope and general self-efficacy: two measures of the same construct? J Psychol 2016 Jul
03;150(5):543-559. [doi: 10.1080/00223980.2015.1113495] [Medline: 26761605]
Abbreviations
EFA: exploratory factor analysis
HM: health motivation
mHealth: mobile health
Mini-IPIP: Mini-International Personality Item Pool
PSD: persuasive system design
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 22https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
Edited by L Buis; submitted 27.06.22; peer-reviewed by I Schiering, P Knapp, M Alrige; comments to author 21.07.22; revised version
received 04.08.22; accepted 10.08.22; published 14.09.22
Please cite as:
McGowan A, Sittig S, Bourrie D, Benton R, Iyengar S
The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation
JMIR Mhealth Uhealth 2022;10(9):e40576
URL: https://mhealth.jmir.org/2022/9/e40576
doi: 10.2196/40576
PMID:
©Aleise McGowan, Scott Sittig, David Bourrie, Ryan Benton, Sriram Iyengar. Originally published in JMIR mHealth and uHealth
(https://mhealth.jmir.org), 14.09.2022. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete
bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license
information must be included.
JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 9 | e40576 | p. 23https://mhealth.jmir.org/2022/9/e40576 (page number not for citation purposes)
McGowan et alJMIR MHEALTH AND UHEALTH
XSL
FO
RenderX
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Aim: To analyse any changes seen in the academic self-efficacy of nursing students during the three years of their academic education as well as the associated predictive factors. Design: A longitudinal study design was applied. Methods: The sample included 220 students who attended a large university in central Italy. The students' academic self-efficacy was measured using the Academic Nurses' Self-Efficacy Scale. Data were collected annually from 2014/2015 to 2017/2018 at the beginning of the first year (T0), at the end of the first year (T1), at the end of the second year (T2) and at the end of the third year (T3). A repeated measure univariate analysis of variance (ANOVA) was conducted to detect any possible changes in the students' academic self-efficacy scores over the four measurement points. To identify the factors that are predictive of academic self-efficacy, a linear regression model was used. Results: Overall, the students' academic self-efficacy did not change significantly over the three-year period of their education. Both sex (female) and age (24-50 years) during T0-T2 significantly predicted changes in the students' academic self-efficacy over time. Moreover female students started with lower academic self-efficacy scores than male students, although their academic self-efficacy increased over time, while the male students' academic self-efficacy actually decreased over time. In addition, students with a scientific background reported higher academic self-efficacy than other students. Conclusions: Although the students' academic self-efficacy did not change over time, from a theoretical perspective, academic self-efficacy can be developed using a number of strategies such as a well-organised tutorial during the clinical learning phase and feedback or encouragement. Impact: Academic staff should monitor nursing students' academic self-efficacy over time, particularly in the case of male and younger students, students with a partner and students with a humanities background during the first 2 years of the course.
Research
Full-text available
There is growing evidence that persuasive technologies are more likely to be effective in motivating behavioral change if personalized to the target users. This has led to an increasing interest in the field of personalised persuasive technologies (PPT) among researchers and practitioners in the academia and industry, respectively. For example, each of the last two PPT workshops in 2017 and 2018 attracted over 40 participants from over 10 different countries, alongside 12 peer-reviewed paper presentations and one keynote presentation. In the 2019 PPT workshop, we hope to build on the success of the previous years' workshops by focusing on the challenges and opportunities identified during the past workshops in the field of PPT and the subdomain of wellbeing. Thus, the 2019 PPT workshop aims to bring together different groups of researchers and practitioners from the academia and industry with a common interest of advancing the field of PPT in general and wellbeing in particular. Potential participants in the workshop are invited to present their work, share their ideas and experiences, discuss key challenges facing the field, and identify and deliberate on new opportunities that have the potential of moving the field forward. While the theme of this year's workshop is "Personalization for Wellbeing," the 2019 PPT workshop will cover many areas of personalization and tailoring, which include, but not limited to, user models on personalization, computational models on personalization, design and evaluation methods, and personalized persuasive technologies. We welcome submissions and ideas from the domains of persuasive technology and Human-Computer Interaction (HCI) in general, including but not limited to health, wellbeing, sustainability, education, entertainment, games, marketing, eCommerce, social media, safety and security. Successful workshop papers will be archived online and made accessible to the general public.
Conference Paper
Full-text available
The success of persuasive systems in changing people’s attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four commonly employed persuasive strategies (Reward, Competition, Social Comparison, and Social Learning) in PT design. We aim to use our findings to provide design guidelines for personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more likely to be susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Hence, in choosing persuasive strategies to motivate students’ learning and success in the education domain, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.
Article
Full-text available
This study recommends novel strategies for tailoring messages to encourage walking, for use in travel planning, Mobility as a Service platforms and other apps which promote sustainable transport behaviour. We suggest strategies based on individual demographic and psychosocial factors derived from the findings of a study of the persuasiveness of different arguments to encourage walking. 402 participants from across the UK were recruited to evaluate 16 pro-walking arguments systematically varied by type of argumentation used, and the values to which they appealed. We explored interactions between these argument features and participants’ personality, travel attitude, age and recent transport mode usage. We report several interesting findings, including that the types of argumentation used, participants’ travel attitude, and their previous transport uses all had no effect on the perceived persuasiveness of messages. Factors which did have an effect on the perceived persuasiveness of messages included the age and personality of the participants and the value to which the message appealed. We also found several complex interactions between these factors, such as that those higher in agreeableness tended to rate arguments emphasising environmental benefits as more persuasive, and that younger participants tended to rate arguments appealing to the health benefits and convenience of walking as less persuasive.
Article
Full-text available
Self-determination theory (SDT) is a broad framework for understanding factors that facilitate or undermine intrinsic motivation, autonomous extrinsic motivation, and psychological wellness, all issues of direct relevance to educational settings. We review research from SDT showing that both intrinsic motivation and well-internalized (and thus autonomous) forms of extrinsic motivation predict an array of positive outcomes across varied educational levels and cultural contexts and are enhanced by supports for students’ basic psychological needs for autonomy, competence, and relatedness. Findings also show a dynamic link between teacher and student motivation, as teachers are themselves impacted and constrained by controlling mandates, institutional pressures, and leadership styles. Ironically, despite substantial evidence for the importance of psychological need satisfactions in learning contexts, many current educational policies and practices around the globe remain anchored in traditional motivational models that fail to support students’ and teachers’ needs, a knowledge versus policy gap we should aspire to close.
Article
Background Public health sun safety campaigns introduced during the 1980s have successfully reduced skin cancer rates in Australia. Despite this success, high rates of sunburn continue to be reported by youth and young adults. As such, new strategies to reinforce sun protection approaches in this demographic are needed. Objective This study aims to develop a virtual reality (VR) game containing preventive skin cancer messaging and to assess the safety and satisfaction of the design based on end user feedback. Methods Using a two-phase design approach, we created a prototype VR game that immersed the player inside the human body while being confronted with growing cancer cells. The first design phase involved defining the problem, identifying stakeholders, choosing the technology platform, brainstorming, and designing esthetic elements. In the second design phase, we tested the prototype VR experience with stakeholders and end users in focus groups and interviews, with feedback incorporated into refining and improving the design. Results The focus groups and interviews were conducted with 18 participants. Qualitative feedback indicated high levels of satisfaction, with all participants reporting the VR game as engaging. A total of 11% (2/8) of participants reported a side effect of feeling nauseous during the experience. The end user feedback identified game improvements, suggesting an extended multistage experience with visual transitions to other environments and interactions involving cancer causation. The implementation of the VR game identified challenges in sharing VR equipment and hygiene issues. Conclusions This study presents key findings highlighting the design and implementation approaches for a VR health intervention primarily aimed at improving sun protection behaviors. This design approach can be applied to other health prevention programs in the future.
Article
The desire to maintain a healthy lifestyle is growing amongst consumers globally as well as the adoption of health apps. Prior research investigates what affects adoption of a health app, but few studies consider Continuance Intention (CI) for mobile health apps. Drawing on the Information Systems Continuance Model and integrating social (i.e. subjective norms) and psychological factors (i.e. flow experience, health consciousness, behavioral change techniques), we develop a framework testing the factors influencing users’ CI for health apps. The model is validated using PLS analysis and data from 397 health app users from China. The study finds that perceived usefulness, perceived ease of use, flow experience and behavioral change techniques are significant predictors of CI, and satisfaction mediates these effects. Health consciousness positively moderates the effect between perceived usefulness and satisfaction and negatively moderates the effect between perceived ease of use and satisfaction. Lessons for app developers, marketers and health practitioners are drawn.
Article
Personality is of great lay, clinical, and research interest with important functional implications. The field has largely settled on five- or six-factor models as being largely sufficient for descriptive purposes, at least in W.E.I.R.D settings and, as such, numerous measures have been created of varying length and breadth. For a number of reasons, however, super-short forms have come to be quite popular in research endeavors with a number created in the past 20 years. The goal of the present study was to compare the time with completion and general psychometric properties of these measures, as well as examine their convergence with one another and with longer measures in an online community sample ( N = 494). Generally, the psychometric properties of the measures varied considerably in terms of internal consistency and convergence with one another. The brief measures demonstrated mostly adequately convergence with longer measures. Despite this convergence, longer measures were found to contain considerably more variance that was not accounted for by brief measures. We consider the advantages and disadvantages of these measures and suggest that longer measures be prioritized whenever possible.
Article
User engagement has become an important outcome measure in interactive information retrieval (IIR) research, as commercial (e.g., search engines and e-commerce companies) and educational (e.g., libraries) enterprises focus on capturing and retaining customers. User engagement pertains to the kind of investment – emotional, cognitive, behavioural – the user is willing to make in an application. While research has shown how characteristics of users (e.g., individual differences and preferences) and the systems and content with which they interact influence engagement, less is understood about how the tasks people perform using digital applications affect their engagement. Drawing upon a wealth of literature in IIR, this study examined the effects of task on search engagement in a within-subjects Amazon Mechanical Turk (MTurk) experiment. Participants completed six search tasks on different task topics using task versions that included or excluded items and dimensions in the task descriptions. Items refer to things being compared (alternatives) and dimensions correspond to attributes by which items may differ. The task topics were meant to influence user interest in the task, and the versions were intended to manipulate the task doer's degree of certainty as they planned and performed the task, with the expectation that these factors would affect their self-reported engagement. We captured self-reported task perceptions (e.g., complexity, difficulty, interest) and logged search behaviours (e.g., querying, bookmarking) to both validate our manipulations and to understand how these variables related to engagement. Using multi-level modelling (MLM) we discovered that task topic affected user engagement, whereas task version had limited effects. However, participants’ perceptions of the tasks as interesting, difficult, and so on affected their engagement. Through the self-report and behavioural data, we observed that effort (more search engine results page exploration, greater perceived task difficulty) had a negative effect on engagement, while bookmarking pages and the ability to understand the task and how to complete it was associated with positive engagement. These results have implications for designing search tasks, deciphering the relationship between user experience and task complexity in IIR experiments, and aligning self-reports and search behaviours in evaluating online search engagement.