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Running Head: USING PYCHOLOGICAL THEORY IN DIGITAL HEALTH EDUCATION 1

Using Psychological Theory to Inform the Development of Digital Health Education

Sara Einhorn

University of North Texas

USING PYCHOLOGICAL THEORY IN DIGITAL HEALTH EDUCATION 2

Abstract

The internet and other digital technologies expanded the delivery of health education across time

zones and geography. However, the practice of digital health education lacks tested theoretical

models for effective ways to leverage technology for learning. Therefore, health educators

should look to psychological theories, including theories of behavior change, as the foundation

for the practice and delivery of digital health education. More specifically, health educators can

look to theories of tailoring, such as Elaboration Likelihood Model (ELM) and Stages of Change

(SOC) as well as theories of motivation, such as Self-Determination Theory (SDT), Social

Cognitive Theory (SCT), and Goal-Setting Theory (GST). Health educators should use these

theories as a basis for the creation and study of methods for designing and delivering effective

digital health education. Future research should aim to understand the mechanisms of how

technology can enhance health education to provide evidence-based digital health education

programs.

Keywords: digital health education, behavior change, stages of change, self-determination

theory, social cognitive theory, goal-setting theory


Using Psychological Theory to Inform the Development of Digital Health Education

After decades of existence, health education programs persist and grow, with more

created every day. However, health education programs only recently began to leverage

technology. According to Berhardt, Chaney, Chaney and Hall (2013), the development and

advancement of technology empowered internet-enabled media, which can potentially transform

health education “by enhancing our ability to implement evidence-based behavior change

strategies in manners that are often far more effective and efficient than were possible in the

past” (p. 1).

As part of the enhancement of health education, or behavior change strategies,

technology now allows health education to reach a wider audience and over a longer period

(Bernhardt et al., 2013). The Internet revolutionized the delivery of health education, from

implementation to evaluation. Along with this revolution comes the need to determine the most

effective and efficient manners in which to deliver health education using these new

technologies. While many questions arise regarding health education and new technologies, not

all of them have answers. Regardless, some insight exists when exploring theories of behavior

change.

Like the more traditional types of health education, technology-based programs should be

based on behavior change theories to result in positive long-term outcomes (Riley et al., 2011).

While some information exists on theory-based technology interventions, research has not

saturated the topic area. Like traditional educators, to combat the lack of research, Morrison

(2015) and Riley et al. (2011) look to psychological theories, including theories of behavior

change, to inform the implementation of digital health education. This paper will explore the use

of tailoring, motivation, social support, and self-regulation to inform digital health education to

motivate students to learn.


Tailoring Digital Health Education to Motivate Students

Tailoring occurs in the larger continuum of mass communication, group targeting, and

individualized targeting (Hawkins, Kreuter, Resnicow, Fishbein & Djikstra, 2007). According to

Hawkins et al. (2007), these three groupings highlight two distinct factors in tailoring:

segmentation, meaning the defined groupings of people, and customization, meaning the degree

to which people perceive materials as personally relevant (see Figure 1). Ideally, to achieve the

most tailored message, the materials would use high customization and segmentation. While both

of these are not always attainable, the rest of this section will look into the use of individually

targeted tailoring.

Figure 1. Continuum of tailoring (p.455)

Individualized targeting falls under the term ‘tailoring’ which refers to the use of

personalized information including known behaviors and characteristics specific to the student

(Bensley et al., 2004; Rimer & Kreuter, 2006; Morrison, 2015). Many studies suggest that

tailoring information positively affects motivation, behavior change, and engagement (Bensley et

al., 2004; Hawkins et al., 2007; Lustria, Cortese, Noar & Glueckauf, 2009). In fact, tailored

materials result in an increased likelihood students read and recall the information in addition to


viewing it as personally relevant (Rimer & Kreuter, 2006; Hawkins et al., 2007; Lustira et al.,

2009; Morrison, 2015).

Additionally, tailored material “enables individualized feedback, commands greater

attention, is processed more intensively, contains less redundant information, and is perceived

more positively by health consumers” (Lustria et al., 2009, p.156). Tailoring information in

health education could engage students on a deeper level because students identify the

information as relevant and individualized. While education via the Internet may not allow for as

much personalization as face-to-face interactions, computers and technology have the unique

ability to use complex tailoring algorithms that can be programmed and executed instantly.

On the other hand, Djikstra (2005) has noted mixed results in past studies of tailoring

educational materials due to the varying types of tailoring. Additionally, certain types of health

education and behavior change may require more effective motivators than tailored material. For

example, Morrison (2015) found that extraneous information (less tailored) may be a positive

factor when offering information that contradicts students’ typical beliefs so that they can

process the contradictions more peripherally. Therefore, health educators must understand the

past experiences and knowledge of the students, which may prove difficult when using only

technology.

The varying information about tailoring health education and the situations to which it

applies generates outstanding questions. How does tailoring truly work? What exactly triggers

these feelings in students? Are these methods of tailoring that are better than others? How can

the ‘digital’ component of digital health education make use of or enhance students’ learning and

motivation?


A Theoretical Basis for Tailoring

Elaboration Likelihood Model (ELM), psychology theory, explains how personalizing

information can persuade people (Petty, Cacciopo & Goldman, 1981). The theory suggests

“information that is perceived to be personally relevant (as in the case of tailored information)

enhances an individual’s motivation to elaborate on the message, and consequently, his/her

receptivity to persuasion efforts” (Lustria, 2009, p.157). According to Dijkstra (2005), ELM does

not specify how cues can make information more personally relevant; it simply proposes that the

perception of personal relevance allows for easier persuasion. Therefore, Djikstra (2005)

suggests self-referent encoding explains the effectiveness of tailoring materials.

Self-referent encoding occurs when a person makes personal connections between the

material presented and his/her own life (Hawkins et al. 2007; Djikstra, 2005; Morrison, 2015).

For example, a graduate of the University of North Texas will find a study of University of North

Texas students more personally relevant than a study of students from the University of

Washington. The reader in this example will likely make the personal connection to the

University of North Texas and thus begin self-referent encoding. In the study by Djikstra (2005),

tailoring the information only slightly in an online smoking cessation intervention led to more

self-referent encoding and, in turn, more quitting behaviors when compared to standard (non-

tailored) materials. Djikstra (2005) concludes personalization and feedback may relate to higher

effectiveness in online health education programs (in this case for smoking cessation).

Further supporting the mechanisms above, a review of digital health education studies

that used tailoring found that effective tailoring occurred via personalization, feedback, and

content matching (Hawkins, et al., 2007; Lustria et al., 2009). Personalization involves the

inclusion of small but identifiable personal details (age, gender etc.). Feedback consists of


individualized recommendations based on information provided by each person. Content

matching provides content based on relevant information determined by individual responses.

These three methods of tailoring serve to make the health information more personally relevant

to persuade students to change behavior and so that students will see themselves in the material

(self-referent encoding).

Three main types of feedback exist (Hawkins et al. 2007, Lustria et al., 2009).

Descriptive feedback simply reports known facts based on student responses, such as stating that

the student reported eating three fruits and vegetables each day. Comparative feedback takes

information reported by the student and compares it to information known about others. For

example, comparative feedback may state that because a student eats three fruits and vegetables

each day, s/he eats about as much as the rest of the population. The third type of feedback,

known as evaluative feedback, makes judgments based on student information, such as reporting

that the student’s fruit and vegetable intake is below the recommended value of 5-9 each day.

To elaborate on how tailoring can provide personalized information, feedback, and

matched content, digital health education can further look to the Transtheoretical Model (TTM)

or Stages of Change (SOC). Health education already makes use of SOC when developing

interventions aimed at behavior change but may not register as a distinct method of tailoring.

According to the SOC, change occurs over a long period instead of in one instant action (Bensley

et al., 2004). Therefore, people exist in five different stages of change, shown in Figure 2.


Figure 2. Stages of change

When tailoring digital health education, programs should account for individual stages of

change to pair personally relevant material with each person. For example, providing a student in

the pre-contemplation stage with materials on how to take specific action to lose weight would

not be an effective method of tailoring. Instead, the student in the pre-contemplation stage could

receive information that demonstrates why s/he should consider making a change. Screening

questions can determine each student’s stage of change to ensure the collection of appropriate

information. If the educational program occurs over an extended period, instructors should ask

these questions multiple times as students can move to a different stage.

The Bottom Line in Tailoring Digital Health Education

Regardless of the exact mechanism of tailoring, Kreuter, Oswalkd, Bull & Clark (2000)

found that matching information relevant to the individual or customizing the material presented

results in increased motivation to change health behavior, even if intentional tailoring using the

mechanisms previously described doesn’t occur. When considering the theories and mechanisms

of tailoring, this result demonstrates that, at the very least, matching personally relevant


information engages the student and invites contemplation of behavior change. However, it

becomes somewhat clear that tailoring information at any level can positively affect health

education.

Transferring health education to the digital realm allows for increased tailoring, despite

decreased personal interaction, which can further motivate students to absorb, contemplate, and

act on the information provided. ELM, self-referent encoding, and SOC provide a theoretical

groundwork on which to tailor materials as they all guide educators on the personalization,

feedback, and content matching necessary to address students in their current situation with their

current views. However, tailoring material in this fashion does not consider other factors such as

preference in media used for delivery, cultural norms, and motivation levels (Kreuter et al.,

2000). While a psychological framework for including all of these factors does not necessarily

exist, health educators should still be careful to consider them. Instead, health educators can look

to psychology to explain theories of motivation to drive sustained behavior change.

Using Theories of Motivation in Digital Health Education

Using technology may also pose a challenge when employing theories about the

motivation for health education. Instructors frequently neglect persistence, retention,

achievement, and satisfaction in motivation in digital learning (Chen & Jang, 2010). Clearly,

behavior change interventions in digital health education need to consider theories of motivation.

As an example, many people experience the difficulty of trying to change behaviors to lose

weight or be more active, especially external instead of internal pressure exists to do so. As

described below, external versus internal factors differ in their ability to motivate students to

learn and change health behaviors.


According to Self-Determination Theory (SDT), the following can enhance student’s

motivation to engage in the health education program and make a change: supporting the

student’s basic human needs to feel as though s/he has chosen to make the change (autonomy),

that s/he is capable of making the change (competence), and that s/he finds connection and

support in making the change (relatedness) (Chen & Jang, 2010; Morrison, 2015). Technology

can enhance the delivery of each of these factors by programming classes appropriately.

Conversely, a person no longer feels motivated and may feel alienated or disjointed if the

program excludes any one of the three elements (Chen & Jang, 2010).

Within SDT, three constructs deal specifically with motivation, as shown in Figure 3

below (Ryan & Deci, 2000; Chen & Jang, 2010). First, intrinsic motivation occurs when a person

completes an activity for enjoyment or a challenge. For example, when someone chooses to run a

race to challenge herself to continue to be healthy after casually running for a few months.

Second, extrinsic motivation occurs when a person completes an activity for a reward or external

pressure. For example, when a man chooses to quit smoking because his wife wants him to quit.

Extrinsic motivation consists of four constructs: external regulation – motivation from an

external reward; introjected regulation – motivation to demonstrate self-worth; identified

regulation – motivation through valuing of the end goal; integrated regulation – motivation

integrated into personal beliefs. Amotivation, the third construct, consists of the lack of

motivation. According to SDT, the types of internal motivation possess the most positive affect

while lacking motivation or obtaining motivation from external factors can lead to negative

consequences.


Figure 3. The self-determination continuum.

The constructs that form SDT relate closely to several theories of online learning, thus

supporting the choice to use SDT as a theoretical starting point for online health education.

According to Chen & Jang (2010), “flexible learning, computer-mediated communication and

social interaction, and challenges for learning technical skills” (p.742) connect to autonomy,

competence, and relatedness. Previous research shows that SDT can predict many learning

outcomes such as performance, diligence, and course approval (Deci & Ryan, 1985). Therefore,

online learning can apply SDT to address and evaluate the rampant attrition in online courses

while serving as the foundation upon which to create effective digital health education.

Additionally, many studies have already established effective ways to motivate students in health

education using SDT.

Educators may start exploring theory as a basis for digital health education, but they also

need to test this theory. As Chen & Jang (2010) note from various studies, motivation differs

depending on the setting in which the learner participates. In other words, students learning in a

traditional environment will be motivated by different factors than those learning in an online

environment. Therefore, researchers need to study and validate the use of SDT in online settings

to glean the most appropriate use for digital health education.


To assess SDT in an online learning environment, Chen & Jang (2010) created a

theoretical model for online learner motivation, shown in Figure 4, which they tested among

students in an online certificate program. Results showed supporting autonomy and competence

positively affected perceived satisfaction of the three basic needs identified by SDT. Positive

needs satisfaction, in turn, positively affected self-determination. Additionally, and contrary to

many studies of SDT, self-determination did not affect learning outcomes but positive support of

autonomy and competency coupled with perceived satisfaction of needs positively affected

learning outcomes.

Figure 4. Chen & Jang (2010) proposed theoretical model for SDT in online learning.

Several implications exist for teachers and instructional designers using SDT for online

education according to Chen & Jang (2010). To support students’ self-determination, teachers

need to provide a meaningful reason to complete each lesson, a relationship that supports choice

and flexibility instead of pressure, and acknowledgment of any negative feelings that arise. This


study also found that not supporting students’ needs had negative effects and that perception of

social support impacted students more than actual support given, further supporting the idea that

student needs’ must be understood to provide successful online education. Furthermore, student

motivation should be understood in the context of SDT instead of on a dichotomous level

because, as described above, different reasons for course enrollment can lead to varying levels of

course completion.

Social Support and Connection

Providing the student with connection and social support may provide the biggest

challenge of the three factors of SDT (Morrison, 2015). According to White and Dorman (2001),

research studied traditional social support groups for several diseases and health education topics

and results showed enhanced quality of life, improved decision making, and empowered

participants. Shifting focus to online support groups, benefits include 24/7 access from anywhere

in the world and anonymity which can reduce biases and increase openness (White & Dorman,

2001). Online support can also reach those who are potentially difficult to reach including those

who may not normally obtain social support in-person despite a desire to do so. Online support is

also much more cost-effect compared to in-person support.

Health educators should consider certain situations when designing technology-based

health education with social support built in. Some health topics, such as those that are more

stigmatized, benefit more from support of weak ties, like strangers on the internet (White &

Dorman, 2001; Morrison, 2015). On the other hand, support from close ties, like family and

friends, benefit stigmatized topics (Morrison, 2015).

Health educators also need to consider the best ways to create behavior change using

social support. One study by Centola (2011) suggests that the more alike a group of people


within a social support network, the more likely health behavior change will occur. Centola

(2011) notes that “although these correlations do not imply causal effects of specific traits, they

do suggest that a minimal level of overlapping characteristics between social contacts may

improve the spread of [health] behaviors through social networks” (p.1271). This minimal

overlapping of characteristics may mimic some of the constructs in tailoring, such as self-

referent encoding, which then pushes students to absorb the material and make a behavior

change.

In their study of patients seeking information about pain management, Kostova, Caiata-

Zufferey, and Schulz (2015) noted that users’ behavior in an online support forum differed based

on experience/skill with pain management, with more practiced users looking for tailored

information instead of information about experiences. The behavior described by Kostova et al.

(2015) further supports the idea tailored information may drive behavior change in online social

support for health education. Furthermore, users accessed information that was relevant to their

specific disease stage and experience, supporting tenets in SOC and tailoring discussed

previously (Kostova et al.,2015).

On the other side, some possible disadvantages exist, such as negative feedback and

proliferation of negative behaviors to explore. Online support groups can leave out those cannot

access the internet which often includes disadvantaged or older students (White & Dorman,

2001). It can also increase inflammatory language and interactions as well as misinformation due

to the anonymous nature of the internet. Many studies do not report any negative effects but,

instead, note that a potential for negative effects. These mixed results point to the need for more

research on the adverse effects and designs to avoid when creating online social support in health

education.


When comparing face-to-face interaction with online social interaction, disadvantages

exist. Many mixed results occur in this arena, namely because comparisons are not the same

across studies and many studies do not utilize a theoretical base. However, face-to-face

interaction is expensive and cannot reach as many people as a widespread social network on the

internet. Therefore, research should explore the particular mechanism for online support. For

example, online social support did not have any effect on smoking cessation in one study

(Newman, Szkodny, Llera, & Przeworski, 2011). Furthermore, according to a study by Newman

et al. (2011), more sustained behavior change (in this case abstinence from smoking, drugs,

alcohol, or gambling) occurred with contact from a therapist. The results described in the two

examples above highlight the potential need for human contact, but again possibly only in certain

situations such as that of addiction.

When it comes to social support in digital health education, only one thing is clear:

researchers need more studies of how theory can inform practice. As an example of this need,

among a review of twelve studies by Laranjo et al. (2015), only five made any mention of a

theoretical basis for including social support. Some studies may include social support just to see

if it helps without giving any thought to the mechanism behind the success or lack thereof. Based

on the studies mentioned, some theories pulled for tailoring as well as SDT apply reasonably in

the realm of social support. Health educators need to reach into this rich psychological research

and determine the best way to apply it to online social support. To further inform online social

support and focusing more on the students’ need for autonomy and competence, health educators

can turn to Social Cognitive Theory and Goal-Setting Theory.


Using Social Cognitive Theory and Goal-Setting Theory in Digital Health Education

Basic Tenets of Social Cognitive Theory

Building upon SDT, Social Cognitive Theory (SCT) can further help motivate students to

learn digital health education. According to Bandura (1998), SCT combines cognitive factors,

environmental factors, and behavioral factors. Environmental factors include social norms and

one’s ability to change his/her environment. Cognitive factors include knowledge, expectations,

and attitudes. Behavioral factors include skills, practice, and self-efficacy, described below.

These factors interact to determine human behavior and behavior change.

Self-efficacy consists of the sentiment that one’s actions will produce the desired result.

Without this sentiment, many people do not feel motivated to act, for example, someone who

feels s/he will never be able to lose weight has no desire to take action to do so. Therefore, health

education needs to empower people to feel as though their actions will result in the desired

outcome by helping students to set appropriate and attainable goals as well as providing them

with what Bandura (1998) calls mastery experiences to help firmly establish self-efficacy.

Feelings of self-efficacy can affect everything from deciding to make a change to gathering the

motivation to do so (Bandura, 1998). Normative influences frame this view of the self in the

context of society.

Normative influences include the social norms that dictate how people should act and

interact (Bandura, 1998). Norms influence behavior by applying social pressures to behavior that

result in a ‘desired’ outcome and socially expected behaviors gain positive reactions. Due to the

negative reactions that non-socially acceptable behaviors gain, students then self-regulate based

on social norms to avoid negative reactions. Students create goals and compare them to societal

norms, judging themselves against this background. Social norms serve as a significant tenet in


SCT because Bandura (19998) argues that these norms help form human behavior through

expectation and attitudes pervaded by social norms.

SCT overlaps with many of the health education theories mentioned above, further

supporting the use of theory to inform digital health education practice. Both self-efficacy and

the ideas of competence and autonomy in SDT propose that students need to feel as though they

have chosen to make a change and maintain the capability to manifest the desired outcome.

However, SCT looks more at how expectation influences behavior change. Additionally, both

theories look toward a social component and feedback in this context. SDT cites relatedness

whereas SCT looks at normative influences. Both constructs assert that interactions with others

influence behavior change.

Goal-Setting Theory and its overlap with SCT

Goal-Setting Theory (GST) can help students develop appropriate goals in health

education. Initial findings on goal setting demonstrated that choosing goals that are difficult yet

reasonable to attain lead to better performance than easy or vague goals (Locke & Latham,

2006). Four constructs lead to successful performance of goals including persistence, directing

attention, motivation, and knowledge. Locke & Latham (2006) also include the construct of self-

efficacy from SCT as well as moderating factors of goal setting which include feedback,

commitment to the goal, complexity of the task, and situational constraints.

Clearly, constructs from GST overlap with SCT. First, self-efficacy holds a central role in

both theories which makes sense because students need to feel as though they can complete the

chosen goal otherwise they will not work toward it. This ties in with moderating factor of

commitment to the goal; if students set attainable goals, they will become more likely to act and

thus increase their commitment. Appropriate goals should be stated clearly and specifically so


students will feel committed to them and as though they can reach them. Much like the theories

explained in tailoring, students like to receive feedback on their journey toward achieving a goal

so they can continually develop and support their self-efficacy throughout the process.

Applying SCT and GST to Digital Health Education

According to Morrison (2015), these theories suggest that “goals will be more effective at

motivating behaviour when they are specific, learning orientated, achievable in the short-term

but sufficiently challenging, and linked to a longer-term, distal goals” (p. 4). Therefore,

technology-based health education should help users choose and set appropriate goals (Morrison,

2015), again building on the idea of autonomy from SDT. Technology can enable the constant

feedback addressing student progress that, according to tailoring theories, allows students to feel

positive about their achievements and ability to continue learning and changing behavior.

According to Anderson-Bill, Winnett, & Wojcik (2011), digital health education

programs must help students develop their self-efficacy by using physically and socially

supportive settings while stimulating positive expectations for change. Consequently, students

can then gain self-regulating skills such as planning, problem-solving, and goal-setting.

While less information exists on the use of SCT and GST in online health education,

some preliminary information exists. In one study by Tortelero et al. (2010), an online sex

education intervention based on SCT and self-regulation was shown to reduce risky sexual

behaviors in adolescents. However, this study does not explore the mechanism by which students

changed, or at least delayed, risky behavior. Another study by Turner-McGrievy et al. (2009)

found that a weight loss intervention delivered via podcast was more effective when based on

SCT as compared to a non-theory-based podcast. These podcasts focused on expectations and

reasons to achieve a healthy weight, modeled how to keep a food diary, increased self-efficacy


behaviors, and increased knowledge about making behavior changes. Due to the limited

intervention, the authors tested an intervention based on SCT that added the ability to self-

monitor and self-regulate and found that participants discover ways to self-regulate outside of the

tools provided, still supporting the use of SCT (Turner-McGrievy & Tate, 2011).

It appears only anecdotal evidence exists for digital health education programs using SCT

and GST. Although health education traditionally uses of both theories, they do not seem to

translate to study in the digital arena. That being said, due to the overlap of constructs and ideas

between SCT, GST and the theories mentioned in previous parts of this paper, SCT and GST

should be studied to determine the most effective way to use them to develop digital health

education. Many questions remain in this arena, highlighting the absence of a formal and

systematic methodology to use theory in digital health education.

Conclusion

The field of health education has been quick to adopt the use of technology, with mixed

results. Health education holds importance for behavior change and disease prevention and

management, supporting a need to discern the effectiveness of digital health education. While

some research exists about digital health education, there is no central methodology for

developing and implementing effective digital health education programs. This lack of central

methodology should drive future health educators to use theory to influence practice and to study

the effectiveness of future interventions to develop a concrete approach.

Many of the psychological theories of behavior change share similar characteristics such

as the need to feel in control and able to make changes that will result in observable outcomes.

Arguably, competence feels much like self-efficacy because both focus on believing one has the

knowledge and experience to attain a goal. The relatedness aspects of SDT closely connect to the

ideas of tailoring including feedback, addressing materials based on where people are in the


SOC, and making the information personally relevant. The fact that all of these theories overlap

with similar constructs further supports their legitimacy as well as the proposition for further

study of their applications in digital health education.

The sections above clearly show psychological theories and theories of behavior change

should inform design and delivery of digital health education. Future research will need to

combine the underlying theories that support tailoring, motivation, social interaction, and goal

setting to build a framework for developing and implementing digital health education. A great

need for research exists since this field is in its early years. This research should aim to answer

questions such as: What are the most effective ways to motivate students to change health

behavior using technology? How can theory inform digital health education in real world

contexts? How do the practical effects of theory change when taken from in-person to digital

contexts?


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